The Triumph of Contrarian Investing : Crowds, Manias, and Beating The

Crowds, Manias, and Beating the Market by Going Against the Grain

Ned Davis

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Foreword vii Acknowledgments xiii 1 Introduction 1 2 Scientific Studies on Crowd Psychology 19 3 Brief History of Manias and Panics 25 4 Headlines and Cover Stories 35 5 Indicators of Crowd Psychology 47 6 Postscript 61 Addendum 65 References 171 Index 173

V This page intentionally left blank. FOREWORD

N 1971, ABOUT the time I first knew Ned Davis and began sharing market timing ideas with him, I launched a stock market letter, The Zweig Forecast, which I wrote for 27 years, before coming to my senses and taking some things off of my plate. My market forecasts used a variety of indicators, including monetary, tape, and valuation. But the group, which I emphasized the most, included the sentiment indicators. To that end, I wrote a booklet, IInvestor Expectations (a fancy moniker for “investor sentiment”), which I sent to every new sub scriber to the market letter. The introduction to that booklet is reprinted below. The booklet also included numerous articles that I had written for Barron’s, primarily on sentiment indicators. I was fortunate that with one glaring exception, each article made a correct forecast of the market, beginning with one on options volume as an indicator of sentiment back in November 1970. It read bullish, and the market obligingly shot straight up. That was followed by my invention of the putscalls ratio in a Barron’s article in the spring of 1971. I warned then of excessive optimism and the risk of a decline. That was followed by a severe intermediate correction, which lasted about 7 months. In those da ys, puts and calls were traded only over the counter through rather secretive options dealers. But I had gotten options data back to 1945 from the SEC, while finishing my Ph.D. dissertation in finance at Michigan State. I might have been the only one with the data at that time .. . and I found it a wonderful source of “investor sentiment.” So, obviously, while Ned and I were strong believers in using investor sentiment, I not only had a theory, but also had a couple of righton publicly made forecasts on my resume. These were follo wed by more than 15 other Barron’s “forecasting” articles o ver the y ears, with only one turkey in the group (if you e ver see the article on floor traders shorts, please burn it!). And most of these articles featured “sentiment” indicators, some of which I invented (the total oddlot short ratio), or just improved upon (public shorts ratio). Anyhow, the Barron’s articles helped to promote the Zweig Forecast, which over the y ears was ranked first in riskadjusted return in the Hulbert ratings among all the services. And in turn, that helped to launch my suc cessful money management business, which bolted ahead in spades after both Ned and I called the 1987 market crash—primarily with the aid of sentiment indicators. So I’m not just touting some theor y in order to help Ned sell his book. Rather, I’m attempt ing, in a very brief way, to demonstrate that sentiment indicators have a lot of value. And in both my case and Ned’s, it helped us to build extremely successful stock market businesses based not on theory, but on results. As the old saw goes: “The proof is in the pudding.”

VII

A SPECIAL REPORT—INVESTOR EXPECTATIONS: WHY THEY ARE THE KEY TO STOCK MARKET TRENDS

Economic factors, particularly monetary variables and interest rates, certainly influence the longterm values of common stocks, but it is Investor Expectations that exert the most dynamic impact on stock prices. If one were both able to measure the magnitude of Investor Expectations and to properly interpret them, major stock market movements could be anticipated within a rea sonable degree of error.

WHAT ARE INVESTOR EXPECTATIONS?

Investor Expectations are simply the collective opinions of various groups of stock market participants . . . either investors or speculators. For convenience, such opinions may be expressed in terms of their relative degree of optimism or pessimism. Normally, it is optimal to segregate the marketplace into reasonably homogeneous groups of investors in order to obtain measurements of various types of sentiment. Such g roupings, for example, might include oddlot investors, short sellers, exchange members, mutual fund investors, foreign investors, etc. . . . By obtaining many such samples of investor attitudes, one can decrease the probability of deriving a misleading reading of market expectations in the aggregate. In addition, it has been found that the expectations of some investor groupings are more reliable than those of others, and that the expectations of some g roups are meaningful only under specified market conditions. Thus, as Investor Expectations are broken down into greater numbers of subclassifications, their predictive capacity is enhanced, as is the opportunity to corroborate one reading with another.

THE PREDICTIVE THEORY OF INVESTOR EXPECTATIONS

A forerunner to the theory of Investor Expectations was eloquently presented in 1962 by Pro fessor Paul Cootner, then of M.I.T. Cootner hypothesized that stock market prices conform to a random walk within reflecting barriers. The reflecting barriers theory works as follows: There exist two broad categories of stock market participants: “professionals” and “nonpro fessionals.” Professionals constitute a distinct minority and do not necessarily include all or even many of those who make their living in Wall Street. Professionals can obtain fairly reliable funda mental research at a very low marginal cost, and because they are relatively knowledgeable about intrinsic values of stocks, they have a fair idea as to the course of future stock prices. On the other hand, the vast majority of investors are nonprofessionals (e.g., oddlotters, the “public,” etc.) who have poor access to research and very naïve ideas about intrinsic values. So, when they make their investment decisions, the nonprofessionals on the average are as likely to be wrong as not. Hence, when nonprofessionals are dominating market activity, prices will wander randomly about some central value. FOREWORD IX

Professionals, however, are aware of intrinsic values, and they carefully watch the random movements in prices created by the nonprofessionals. When these random movements cause prices to wander sufficiently far from intrinsic values (or to one of the hypothesized “reflecting barri ers”), the professionals will then step in to profit on the differences between prices and values. For example, suppose Stock XYZ shown in the figure is valued intrinsically by professionals at $50 per share. As long as the random prices generated by nonprofessionals do not deviate greatly from $50 in the short run (say between the barriers of $45 to $55), the professionals will do nothing. (Note, in the long run the barriers will shift upward or downward as intrinsic value changes). But, suppose that nonprofessionals become overly bullish for some reason and push prices to the $55 barrier. Now, the difference between prices and values is “large,” and thus it behooves the professionals to sell or even to sell short. Similarly, if nonprofessionals become excessively bearish and sink prices down to the $45 barrier, the stock would be rated “underval ued” by the professionals, prompting them to enter the market as buyers and to push prices back up again. Observe, that whenever the nonprofessionals become excessively enthusiastic or unduly pes simistic about prices and drive them to a reflecting barrier, the professionals enter the market and push prices away from the barrier in exactly the opposite direction from that which the nonprofes sionals anticipate! The general theory of in vestor expectations thus de velops: WHENEVER NONPROFES SIONAL INVESTORS BECOME “SIGNIFICANTLY” ONESIDED IN THEIR EXPECTATIONS ABOUT THE FUTURE COURSE OF STOCK PRICES, THE MARKET WILL MOVE IN THE DIRECTION OPPOSITE TO THAT WHICH IS ANTICIPATED BY THE MASSES!

RANDOM WALKS WITHIN REFLECTING BARRIERS

Cootner verified his “reflecting barriers” hypothesis by means of some sophisticated statisti cal techniques, but he left open the question as to how (or even if) investors might actually profit from the theory. Hypothetically, profits could be made in one of two ways: either by following the professional investors or by going the opposite way of the nonprofessionals. Unfortunately, the first alternative offers limited hope. Professionals, because they are rela tively bright, are smart enough to cover their tracks until it is usually too late for one to follow them profitably. In addition, academic research has demonstrated that the number of true profes sionals . . . that is those who can consistently anticipate prices with accuracy . . . are unbelievably few in number, certainly far fewer than the number of investors normally considered “profession als;” thus, their activities are rarely visible. One exception is the trading activity of Corporate Insiders (officers, directors, and very large stockholders). But all is not lost. Given the nonprofessionals’ propensity to err, given their relatively vast numbers, and given the fact that there is abundant statistical data available with which to measure their expectations, one theoretically can achieve above normal profits by engaging in the reverse activity visàvis that of the nonprofessionals . . . at least at those moments when their consensus X FOREWORD

Random walks within reflecting barriers. RANDOM WALKS WITHIN REFLECTING BARRIERS Courtesy of Marty Zweig, Investor Expectations.

Price Stock XYZ Professionals sell while nonprofessionals are extremely bullish $55 Upper Barrier

$50 Intrinsic Value Random price fluctuations created by nonprofessionals $45 Lower Barrier Professionals buy while nonprofessionals are extremely bearish

Time is historically “extreme.” All the forecaster needs to do is: (1) develop a sound measurement sys tem of nonprofessional opinion; (2) establish stable parameters which signify when that opinion is signif icantly “extreme”; (3) maintain the emotional stability to act in diametrically opposite fashion to that of the masses. Step (1) is fairly easy to achieve. Step (2) is extraordinarily difficult, although years of care ful research have uncovered useful parameters. But even where the first two steps have been suc cessful, most investors fail at Step (3). It is difficult to part company with the “crowd” . . . to buy when nearly everyone is bearish and things “look” bleakest . .. or to sell when the masses are ram pantly bullish and the economy appears strong. Yet, this is precisely what must be done in order to produce superior returns. After all, if one goes along with the majority of investors all of the time, he is doomed to duplicate the mediocre performance of the crowd.

WHY DOES THE INVESTOR EXPECTATIONS THEORY WORK?

Someone is bound to be skeptical. Why should the market drop when the masses are extremely bullish, or why should it rise when nearly everyone is bearish? The answer is simple. Suppose the overwhelming numbers of investors (call them nonprofessionals) become rampantly bullish on the market. The logical extension of highly bullish expectations results in the purchase of stocks right up to the respective financial limits of the masses. At the very moment when the masses become most bullish, they will be very nearly fully invested! They won’t have the financial capacity to do more buying. Who then is left to create demand? Certainly not the minority of investors we call professionals. It is that group which recognizes overvaluations, and presumably FOREWORD XI has been the supplier of stock to the nonprofessionals during the time that both prices and the opti mism of the masses were rising. Thus, when the crowd has become extraordinarily bullish, a dearth of demand exists. The non professionals are loaded with stocks and are cashpoor, while the professionals are liquid, but in no frame of mind to buy. Demand is saturated, and even minor increases in supply will cause stock prices to tumble. At this point, prices are a strong bet to go but down! Similarly, when the masses of nonprofessionals become heavily bearish, they panic and sell out. Supply soon evaporates and prices have strong odds to rise!

Martin Zweig Managing Director ZweigDiMenna Associates L.L.C. This page intentionally left blank. Acknowledgments

I would like to thank my associates who helped me with this book including Ed Clissold and Sam Burns. I also want to thank my e xecutive assistant Darlene Andronaco for typing, Andrea JustinianoBlake for layout and design, Nancy Grab for compliance issues, and Lee Ann Tillis for helping to manage the process. We were aided by some initial work from interns Brody Davis (who also helped edit), Evan Davis, and Bradley Wilson. Tim Hayes, Julie Font, and Karen Tuttle also helped edit. In the first chapter I tried to list a number of people and sources that have influenced my think ing regarding contrary opinion and the madness of crowds.

XIII

THE ROAD NOT TAKEN Two roads diverged in a wood, and I— I took the one less traveled by, And that has made all the difference. —Robert Frost

S A HIGH SCHOOL STUDENT,I FIRST READ Robert Frost’s famous poem “The Road Not Taken” transcribed on a poster in a store, and I immediately bought it to hang on my wall. Since that day, something in my nature has driven me to be wary of the crowd and take “the road less traveled” both in the stock market and in my personal life. In life, forging one’s own path is important, as it teaches the lessons of individu ality and independence. (For those interested, this book includes a “postscript” on why I believeA one should also consider “the road less traveled” in one’s personal life.) Furthermore, in investing, taking the road less traveled can provide the key to understanding the stock market and profiting from it. In this book we will e xplore crowd psychology and how it manifests itself in the stock market and determine what we should do to remain openminded. We will also examine studies on how crowds tend to control us and how crowd psychology has historically led to massive manias and busts. In addition we will focus on media cover stories that capture the popular social mood and on objective quantitative indicators that teach us when crowd psychology is at an extreme and warn us to use contrary opinion and take the road less traveled.

MORE BAD NEWS ON FORECASTING

(and are forecasts thus useless?)

My book Being Right or Making Money (published in 2000) begins by saying, “BAD NEWS ABOUT FORECASTING (Being Right),” and it states, “I’ve yet to find anyone who could con sistently and reliably forecast an uncertain future.” If it were really possible to forecast consis tently and reliably, contrary opinion (the road less traveled) would not be so important. So I thought that it might be of interest to see how “the best and the brightest” have measured up in forecasting the stock market in the period since Being Right or Making Money was published. Fig ures 11 and 12 (from InvesTech Research Market Analyst on December 20, 2002) show the pre dictions of the panelists on the wellknown Louis Rukeyser’s Wall Street program for both 2001 and 2002, and Figures 13, 14, and 15 present the predictions of the top Wall Street strategists in Barron’s magazine. As can be seen, the forecasting record was dismal. Not a single panelist had a prediction as low as the actual close for 2001 or 2002. Even Barron’s says the strategists “missed by a mile.”

FIGURE 11 Wall Street Week with Louis FIGURE 12 Wall Street Week with Louis Rukeyser, 2001 panel predictions. Rukeyser, 2002 panel predictions.

Wall Street Week With Louis Rukeyser Wall Street Week With Louis Rukeyser 2001 Panel Predictions 2002 Panel Predictions DJIA NASDAQ DJIA NASDAQ Panelist Close Close Panelist Close Close Ralph Acampora 11,400 3,200 Ralph Acampora 11,200 2,400 Laszlo Birinyi 13,050 3,450 Laszlo Birinyi 11,050 2,200 Ed Brown 11,735 2,810 Ed Brown 11,100 2,220 Frank Cappiello 12,100 3,800 Frank Cappiello 12,100 2,810 Elizabeth Dater 11,740 2,734 Elizabeth Dater 10,235 2,404 Alison Deans 11,400 3,450 Alison Deans 10,000 2,100 Harvey Eisen 12,300 3,100 Harvey Eisen 11,500 2,450 Mary Farrell 12,500 4,600 Mary Farrell 13,750 2,650 Tom Gallagher 12,000 2,700 Tom Gallagher 11,050 2,130 Francis Gannon 12,250 3,400 Francis Gannon 11,150 2,150 Kim Goodwin 12,400 3,000 Kim Goodwin 10,800 2,260 Louis Holland 12,504 2,842 Louis Holland 10,947 1,987 Michael Holland 12,375 3,010 Michael Holland 12,345 2,579 John Kim 12,100 3,000 John Kim 10,750 2,100 Gretchen Lash 11,800 2,500 Gretchen Lash 10,950 2,280 Barbara Marcin 12,000 3,000 Barbara Marcin 11,500 2,400 Roger McNamee 12,000 3,200 Roger McNamee 11,900 2,240 Brian Rogers 11,900 2,250 Brian Rogers 11,250 2,250 Nick Sargen 11,400 2,900 Nick Sargen 10,750 2,000 Liz Ann Sonders 13,000 3,500 Liz Ann Sonders 12,400 2,680 Robert Stovall 12,675 3,675 Robert Stovall 11,600 2,146 Martin Zweig 13,170 2,570 Martin Zweig 10,500 1,700 Average Forecast 12,173 3,122 Average Forecast 11,310 2,279 Actual Close 10,021 1,950 Actual Close 8,342 1,336 FIGURE 13 Barron’s strategists’ forecasts, 2000. 2000 S&P 500 Profit 30-Yr Strategist Firm DJIA Growth T-Bond Marshall Acuf f Salomon Smith Barney 12,200 8-9% 6.5% Byron Wien Mor gan Stanley Dean Witter 12,500 10.0% 7.0% Abby Joseph Cohen Goldman Sachs 12,300 8.0% 6.7% Thomas Galvin Donaldson Lufkin & Jenrette 13,000 13.0% 6.0% Edward Kerschner PaineW ebber 12,500 10.0% 6.0% Jeffrey Applegate Lehman Brothers 12,750 14.0% 6.5% Greg Smith Prudential Securities 13,000 18.0% 6.5% Richard Bernstein Merrill L ynch 11,200 19.0% 6-6.25% Douglas Cliggott J.P. Mor gan 10,200 19.0% 6.8% Elizabeth Mackay Bear Stearns 12,600 10.0% 6.0% Stuart Freeman A.G. Edwards 13,000 10.0% 6.0% Average Forecast 12,295 12.7% 6.4% Actual Close 10,787 3.8%* 5.5% *Earnings estimates vary between reported actual and various measures of operating profi ts. We used reported profi ts for "actual close" earnings estimates for 2002. From Barron's published on 01/03/2000, 1/1/2001, and 12/31/2001. FIGURE 14 Barron’s strategists’ forecasts, 2001. 2001 S&P 500 Profi t 10-Yr Strategist Firm DJIA S&P 500 Growth T-Bond Marshall Acuff Salomon Smith Barney 11,800 1,500 6.0% 5.5% Jeffrey Applegate Lehman Brothers 13,000 1,675 7.0% 4.8% Richard Bernstein Merrill Lynch 11,000 1,365 0-5% 4.75-4.90% Douglas Cliggott J.P. Morgan 11,000 1,400 0.0% 5.5% Abby Joseph Cohen Goldman Sachs 13,000 1,650 7-8% 5.5% Stuart Freeman A.G. Edwards 12,500 1,700 8.0% 5.3% Thomas Galvin Credit Suisse First Boston 12,650 1,600 9.0% 5.0% Edward Kerschner UBS Warburg N/A 1,715 0-5% 4.75-5.0% Elizabeth Mackay Bear Stearns 13,200 1,650 7.0% 5.6% Thomas McManus Banc of America Securities 11,500 1,525 4.5% 5.4% Greg Smith Prudential Securities 12,000 1,450 6.0% 5.5% Byron Wien gan Mor Stanley Dean Witter 12,000 1,500 10.0% 5.5% Average Forecast 12,150 1,561 5.8% 5.3% Actual Close 10,021 1,148 -50.6%* 5.1% *Earnings estimates vary between reported actual and various measures of operating profi ts. We used reported profi ts for "actual close" earnings estimates for 2002. From Barron's published on 01/03/2000, 1/1/2001, and 12/31/2001. FIGURE 15 Barron’s strategists’ forecasts, 2002. 2002 S&P 500 Profi t 10-Yr Strategist Firm DJIA S&P 500 Growth T-Bond Edward Kerschner UBS Warburg N/A 1,570 4.0% 5.0% Stuart Freeman A.G. Edwards 12,000 1,350 15.0% 5.8% Abby Joseph Cohen Goldman Sachs 11,850 1,363 14.0% 4.3% Edward Yardeni Deutsche Banc 11,500 1,300 15.0% 4.5% Jeffrey Applegate Lehman Brothers 11,500 1,350 13.0% 5.1% Thomas Galvin Credit Suisse First Boston 11,400 1,375 9.0% 5.0% Richard Bernstein Merrill Lynch 10,000 1,200 8.0% 5.3% Tobias Levkovich Salomon Smith Barney 10,800 1,350 1.6% 5.2% Thomas McManus Banc of America Securities 10,400 1,200 0.0% 5.8% Steve Galbraith Morgan Stanley 11,050 1,225 7.0% 5.3% Douglas Cliggott J.P. Morgan 8,500 950 -5.0% 4.8% Average Forecast 10,900 1,294 7.4% 5.1% Actual Close 8,342880 14.7%* 3.8% *Earnings estimates vary between reported actual and various measures of operating profi ts. We used reported profi ts for "actual close" earnings estimates for 2002. From Barron's published on 01/03/2000, 1/1/2001, and 12/31/2001. 4 THE TRIUMPH OF CONTRARIAN INVESTING

Not to be outdone, the December 27, 1999, BusinessWeek, in its Fearless Forecast issue, pub lished an article titled “Will the Bull Outrun Predictions Again?” that chastises the 55 stock mar ket gurus to “stop underestimating the bull market’s strength and resilience.” The article ends by saying, “The odds are good that the consensus—and even some of the biggest bulls—will prove too bearish.” The experts advised, on average, 69 percent in stocks, 24 percent in bonds, and 6 per cent in cash. Their predictions for 2000 are shown in Figure 16. Note that the forecasts for 2000 and 2001 for the S&P 500 were within 1 point of each other. Fiftytwo of the fiftyfive experts (95 percent) who forecasted the S&P 500 for 2000 were too optimistic.

FIGURE 16 BusinessWeek market forecast survey.

BUSINESSWEEK MARKET FORECAST SURVEY

DJIA SP500 NASDAQ RUSSELL 2000

Actual 1999 Close 11,497 1,469 4,069 2000 BW Survey 12,154 1,559 3,805 Actual 2000 Close 10,787 1,320 2,471 2001 BW Survey 12,015 1,558 3,583 Actual 2001 Close 10,021 1,148 1,950 489 2002 BW Survey 11,090 1,292 2,236 520 Actual 2002 Close 8,342 880 1,336 383

In the December 25, 2000, BusinessWeek, an article titled “Why the Experts Are Upbeat” sur veyed 38 “gurus” and found, for the consensus, an expected close of 12,015 on the Dow for 2001, 1558 on the S&P 500, and 3583 on the NASDAQ. They advised, on average, 66 percent in stocks, 26 percent in bonds, and just 8 percent in cash. All but one of those who were surveyed overesti mated where the Dow, S&P, and NASDAQ would be at yearend. On December 31, 2001, BusinessWeek again did its sur vey “of the smartest players on Wall Street,” and this time it used 54 “experts, ” whose consensus predicted 11,090 for the Dow, 1292 for the S&P 500, and 2236 for the N ASDAQ. They advised 70 percent in stocks, 20 percent in bonds, and 9 percent in cash. Not a single forecaster predicted an S&P 500 as low as 880 for the 2002 close. And it wasn’t just the experts who got 2002 wrong. The collage shown in Figure 17, courtesy of the Elliott Wave Financial Forecast (January 3, 2003), echoes the high crowd opti mism across the country at the start of 2002. Furthermore it wasn’t just investment experts and Wall Street media who were confident at the top of the bubble and hopeful during the bear market. The stock market is simply one of the best indi cators of the overall social mood. Look at the chart in Figure 18 on consumer confidence and you can see that, at extremes, crowd psychology is so powerful that nearly everybody gets caught up in it. I first published this chart on March 8, 2000, two days before the alltime peak in the NASDAQ. INTRODUCTION 5

FIGURE 17 2002 forecasts collage. © Elliott Wave Theorist International, Inc., January 2003. 2002 Forecasts Business Bounces Back —U.S. News & World Report 1/8/02 Stop Whining! 2002–Bring It On Gloomy Forecasters are forgetting history

—Barrons 1/14/02 For Double-digit earnings growth and W

0 “There's no way prices can

benign inflation environment will fuel all a 20% gain in the S&P 500 to 1375 by go down significantly. In

ecasters other words, stay long, buy S

year-end. treet —Brokerage House Strategy, 12/17/01 more and don't even think about going short.” — —A

analysts 11,50 The stock resurgence is tlant here to stay, say the bulls The Case for a “Super-V” a

Journal —BusinessWeek 12/31/01 smel BusinessWeek Three stimulating economic factors pin

hopes

Constitution, DJIA may come together for 2002 Q&A: Still a True Believer in Dow 36,000 —Barron’s, 12/31/01 lar —December 31, 2001 “It's hard to be bearish when

The Outlook for Stocks the Fed is doing everything for ecovery

For investors, 2002 should Business Press: Forecasts the economy except dropping consumer be better than 2001. of recovery spring forth $100 bills out of airplanes.” 12/29/01 —N.Y. Times, 1/2/02 —Atlanta Journal Constitution 1/8/02 —Money Manager, 1/1/02 After Two Years of Suffering, Investors Hope for a Rebound —Wall Street Journal, 1/2/02

The table shown in Figure 19, put together by Bianco Research on January 3, 2003, reports the results of a Wall Street Journal survey of leading economists regarding their forecast of the level and direction of interest rates 6 months forward. Currently, 55 economists are surveyed in this semiannual report. What the table shows is that fully 71 percent of the time (30 out of 42) the consensus of economists could not even forecast the dir ection of rates, either up or down, for 6 months forward. This record is so much worse than the probable outcome of a series of coin tosses that it argues that the tools that economists use are fatally in error. Finally, InvesTech Research Market Analyst also featured the following forecast from Fortune in 1981 and 1983: 1981–1982 Recession (July ’81–Nov. ’82) A new batch of statistics from the Commerce Department, some dramatically revised from earlier estimates, demonstrate beyond reasonable doubt that a recession has begun. It will, however, be one of the mildest of the postwar period. Fortune—December 14, 1981 It was the longest of the postwar period, spanning a year and a half, or nearly twice as long as the postwar average. Unemployment reached double digits for the first time in 40 years. Though 6 THE TRIUMPH OF CONTRARIAN INVESTING

Monthly Data 2/28/1967 - 1/31/2003 (Log Scale) Dow Jones Industrial Average 14296 Jan 2000 14296 11666 DJIA Gain/Annum When: Jun 1998 11666 9521 (2/28/1969 - 12/31/2002) 9521 7769 Consumer Gain/ % 7769 6340 Confidence is: Annum of Time 6340 5174 Above 113 0.2 22.4 5174 4222 * Between 66 and 113 5.8 65.6 4222 66 and Below 26.3 12.0 Sep 1987 3446 Feb 1989 3446 2812 2812 2295 Feb 1992 2295 1873 Jan 1991 1873 1528 1528 Oct 1968 Dec 1972 1247 1247 1018 1018 831 831 678 Oct 1982 678 553 May 1980 553 12/31/2002 = 8341.6 451 Dec 1974 451 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

145 145 140 140 135 135 130 Extreme Optimism = Bearish for Stocks 130 125 125 120 120 115 115 110 110 105 105 100 100 95 95 90 90 85 85 80 80 75 75 70 70 65 65 60 60 55 55 50 50 45 45 40 Extreme Pessimism = Bullish for Stocks 1/31/2003 = 79.0 40

(HOT00308) Consumer Confidence (Conference Board)

the decline in GNP was only 2.6%, it came after an aborted recovery from the 1980 recession — prompting many to dub the slump the worst of the postwar period. Fortune—July 11, 1983 CLEARLY THERE HAS GOT TO BE A BETTER WAY TO INVEST THAN FOLLOWING THE FORECASTING CROWD!

And there is. Instead of arguing, as my book Being Right or Making Money did, that one should not forecast, this book will ironically argue that orecasting can be useful because it allowsf one to go contrary to a strong bullish or bearish crowd psychology and make money doing so.This INTRODUCTION 7

FIGURE 19 The Wall Street Journal forecasting survey. Bianco Research, LLC.

The Wall Street JournalForecasting Survey Long-Term Interest Rate Forecasts for the Next 6 Months Yield Absolute Diff. Was Yield When Forecast Forecasted Actual between Forecast Date of Survey for 6 mos. Change in Change Forecast & Direction Survey Published Forward Yield in Yield Actual Correct? Jan-8213.45% 13.05% -0.40% 0.47% 0.87% Jul-8213.92% 13.27% -0.65% -3.51% 2.86% Yes Jan-8310.41% 10.07% -0.34% 0.57% 0.91% Jul-8310.98% 10.54% -0.44% 0.89% 1.33% Jan-8411.87% 11.39% -0.48% 1.77% 2.25% Jul-8413.64% 13.78% 0.14% -2.11% 2.25% Jan-8511.53% 11.56% 0.03% -1.09% 1.12% Jul-8510.44% 10.50% 0.06% -1.17% 1.23% Jan-86 9.27% 9.42% 0.15% -1.99% 2.14% Jul-86 7.28% 7.41% 0.13% 0.21% 0.08% Yes Jan-87 7.49% 7.05% -0.44% 1.01% 1.45% Jul-87 8.50% 8.45% -0.05% 0.48% 0.53% Jan-88 8.98% 8.65% -0.33% -0.13% 0.20% Yes Jul-88 8.85% 9.36% 0.51% 0.14% 0.37% Yes Jan-89 8.99% 9.25% 0.26% -0.95% 1.21% Jul-89 8.04% 8.12% 0.08% -0.07% 0.15% Jan-90 7.97% 7.62% -0.35% 0.43% 0.78% Jul-90 8.40% 8.16% -0.24% -0.16% 0.08% Yes Jan-91 8.24% 7.65% -0.59% 0.17% 0.76% Jul-91 8.41% 8.22% -0.19% -1.02% 0.83% Yes Jan-92 7.39% 7.30% -0.09% 0.39% 0.48% Jul-92 7.78% 7.61% -0.17% -0.39% 0.22% Yes Jan-93 7.39% 7.44% 0.05% -0.72% 0.77% Jul-93 6.67% 6.83% 0.16% -0.33% 0.49% Jan-94 6.34% 6.26% -0.08% 1.27% 1.35% Jul-94 7.61% 7.30% -0.31% 0.26% 0.57% Jan-95 7.87% 7.94% 0.07% -1.23% 1.30% Jul-95 6.64% 6.60% -0.04% -0.70% 0.66% Yes Jan-96 5.94% 6.00% 0.06% 0.95% 0.89% Yes Jul-96 6.89% 6.86% -0.03% -0.25% 0.22% Yes Jan-97 6.64% 6.52% -0.12% 0.14% 0.26% Jul-97 6.78% 6.79% 0.01% -0.86% 0.87% Jan-98 5.92% 6.02% 0.10% -0.28% 0.38% Jul-98 5.64% 5.72% 0.08% -0.55% 0.63% Jan-99 5.09% 5.04% -0.05% 0.89% 0.94% Jul-99 5.98% 5.83% -0.15% 0.50% 0.65% Jan-00 6.48% 6.38% -0.10% -0.58% 0.48% Yes Jul-00 5.90% 6.01% 0.11% -0.40% 0.51% Jan-01 5.50% 5.35% -0.15% 0.30%* 0.45% Jul-01 5.40% 5.30% -0.10% -0.38% 0.28% Yes Jan-02 5.02% 5.06% 0.04% -0.22% 0.26% Jul-02 4.80% 5.20% 0.40% -0.98% 1.38% Jan-03 3.82% 4.42% 0.60% ???? ???? Difference between forecast and actual (average of all periods) 0.84% Batting average 0.286 Starting with the July 2001 survey, the benchmark interest rate changed from the 30-year Treasury bond to the 10-year Treasury note. * The actual change for January 2001 reﬂ ects the change of the 30-year bond. 8 THE TRIUMPH OF CONTRARIAN INVESTING supposition agrees with Bernard Baruch’s idea, originally stated in 1932: “Without due recogni tion of crowd thinking our theories of economics leave much to be desired” (Mackay, 1989).

CROWD PSYCHOLOGY AND THE THEORY OF CONTRARY OPINION

A crusty Vermont libertarian, the late Humphrey B. Neill (1992), originally formulated the theory of “contrary opinion” more than 60 years ago. He wrote under the title of “The Rumina tor,” and originally all he tried to do was discover the prevalent market opinions and meditate (“ruminate”) over their possible failings. Later the iconoclast Neill hosted gatherings in New Hampshire and then Vermont (very “far from the beaten path”) called the Contrary Opinion Forum, at which I have been honored to speak several times. More and more over the years I saw Neill come to believe, as I do, that mass psychology is of primary importance in market move ments. Here is what Neill wrote in the foreword of his book, The Art of Contrary Thinking: The art of contrar y thinking may be stated simply: Thrust your thoughts out of the rut. In a word, be a nonconformist when using your mind. Sameness of thinking is a natural attribute. So you must expect to practice a little in order to get into the habit of throwing your mind into directions which are opposite to the obvious. Obvious thinking—or thinking the same way in which everyone else is thinking—commonly leads to wrong judgments and wrong conclusions. Let me give you an easily remembered epigram to sum up this thought: When everyone thinks alike, everyone is likely to be wrong. Neill goes on to remind us, though, that people are not necessarily wrong in all of the choices they make in their everyday lives. Individuals, when they stop to think things through, may make perfectly reasonable decisions. It is when something occurs that has wide emotional appeal that the “crowd instinct” can take over due to people following their emotions, and their behavior then becomes different from how they would behave on their own. Early in my career as an investment analyst, I was struck by how often the market seemed to be illogical and irrational in regard to the economic fundamentals. So “contrary opinion” analysis fascinated me. Yet I was not sold on it until I watched a series of incredible calls on the stock mar ket by the late analyst Edson Gould, whom I met at Neill’s Contrary Opinion Forum. In studying Gould’s methods, I came across an essay he wrote. It would be fair to say that this essay expressed a force behind the market that changed and focused my attention in a different direction, toward psychology. In his essay “My Most Important Discovery,” Gould relates how he realized psychol ogy to be a driving force behind the stock market: I read a book, The Crowd, written in the late nineteenth century by a French social scientist, Gus tave Le Bon. It was a study of the popular mind based largely upon the experience of crowds in the French Revolution. Here was the essential ingredient, the missing link, for which I had been search ing. An apparently irrational stock market became comprehensible. Order emerged from chaos. Effect was finally linked to cause. I came to the initial realization, since reinforced, that the action of the stock market is nothing more nor less than a manifestation of mass crowd psychology in action. INTRODUCTION 9

Following Neill’s and Gould’s research, I have found in my own research that the market action is largely a result of mass psychology. Consequently, I have attempted to incorporate quan titative indicators of crowd psychology into our broader, multifactor market timing models. Examples of these indicators are discussed in Chapter 5.

EXPLANATION OF WHY CONTRARY OPINION WORKS

Thus, if you want to try to catch major market turning points, you can start with contrary opin ion—wait for majority opinion to reach an extreme and then assume the opposite position. At turning points, contrary sentiment indicators are nearly always right. Almost by definition, a top in the market is the point of maximum optimism, and a bottom in the market is the point of maxi mum pessimism. To better understand how contrary opinion operates, think of money as financial liquidity, and think of an extreme in liquidity as the direct opposite of an extreme in psychology. If people decided that the Dow Industrials would rise by 25 percent, for instance, they would rush out and buy stocks. Everyone would become fully invested, the market would be overbought, nobody would be left to buy, and the market wouldn’t be able to go any higher. When optimism is extreme, liquidity is low. On the other hand, if everyone were pessimistic and thought that the Dow would drop by 25 percent, the weak and nervous stockholders would sell, the market would be sold out, and nobody would be left to sell. In this case, the market couldn’t go down any more. Whereas increasing opti mism and confidence produces falling liquidity, rising pessimism and fear results in rising liquid ity. Figure 110 illustrates the key relationship between psychology and liquidity. My favorite way to describe this inverse relationship is to compare liquidity to a car’s shock absorbers. As you drive down the road, you will inevitably encounter some potholes—some ran dom, unpredictable, negative events. If your car has good shocks (abundant liquidity), you will be able to continue merrily along your journey after encountering a pothole. But if your car has poor shocks (no liquidity), you may crash. Another way of looking at contrary opinion is to compare stockholders to nuts in a tree. An investor once wrote me and asked, “How do you get nuts out of a nut tree?” The answer, he said, is through a nutshaking machine, which would be hooked to the nut tree. The machine would rat tle the tree, and the nuts would drop until all the nuts had fallen out. In other words, when there is enough fear in the mark et, all the weak holders are shaken out, and there is no selling left to be done. “Have the nuts been shaken out,” the contrarian asks, “or are all the speculative traders fully invested?” In terms of shaking nervous holders out of the market, see Figure 111, which my company put out on September 11, 2001, the day of the terrible terrorist attacks. Note that in most cases, the shortterm panic actually cleaned the market out for significant rallies. The figure shows that the DJIA dropped by a median of 5 percent during crisis events but rallied afterward. The implication of this is that after an initial ne gative reaction to a tragic event, a recovery can be expected. Of course, the list is subjective, and even the reaction dates are subject to interpretation in some cases. 10 THE TRIUMPH OF CONTRARIAN INVESTING

FIGURE 110 Stock mutual funds cashassets ratio versus S&P. Source: Copyright © 2003, Standard & Poor’s, a division of The McGrawHill Companies, Inc. © 2003 Ned Davis Research, Inc. All rights reserved.

Standard & Poor's 500 Stock Index Monthly Data 12/31/1965 - 1/31/2003 (Log Scale) 1426 1426 1205 S&P 500 Gain/Annum When: 1205 1018 Gain/ % 1018 861 Cash/Assets(%): Annum of Time 861 727 Above 9.5 20.1 19.8 727 615 Between 6.9 and 9.5 6.1 45.4 615 520 * 6.9 and Below -0.9 34.8 520 439 439 371 371 314 314 265 265 224 224 189 189 160 160 135 NDR uses the following ICI categories 135 to compute the cash/assets ratio: 114 114 97 Aggressive Growth Sector 97 82 GrowthIncome - Equity 82 Growth & Income 69 69 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1/31/2003 = 4.4%

12 12 Excessive Cash Extreme Pessimism Bullish 11 11

10 10

9 9

8 8

7 7

6 6

5 5 Bearish Low Cash Extreme Optimism 4 4

(S430) Stock Mutual Funds Cash/Assets Ratio Source: Investment Company Institute

The impact of contrary opinion can also be illustrated by comparing the market to a theater. If someone yelled “fire” in a theater full to the rafters with people, panic would break out and peo ple would get crushed. But if someone yelled “fire” in a theater with very few people, the people would get up and walk out in an orderly manner. In looking at any market, it is important to deter mine the degree to which the market is a crowded theater or an empty one. What makes contrary opinion really valuable is that it opens your mind and keeps you from being swept up in the crowd—keeps you from being part of the herd shown in Figure 112. With an open mind you can say to yourself, “I know the majority is right, and I know the world is going to hell in a handbasket, but what if the minority is right? What if there is a silver lining in the cloud out there?” Contrary opinion allows you to be flexible, enabling you to turn your emotions inside out and to act when you need to act. INTRODUCTION 11

CRISIS EVENTS, DJIA DECLINES AND SUBSEQUENT PERFORMANCE

DJIA Percentage Gain Date Range Days After Reaction Dates Event Reaction Dates % Gain/Loss 22 63 126 Fall of France 05/09/1940 - 06/22/1940 -17.1 -0.5 8.4 7.0 Pearl Harbor 12/07/1941 - 12/10/1941 -6.5 3.8 -2.9 -9.6 Truman Upset Victory 11/02/1948 - 11/10/1948 -4.9 1.6 3.5 1.9 KoreanWar 06/23/1950 - 07/13/1950 -12.0 9.1 15.3 19.2 Eisenhower Heart Attack 09/23/1955 - 09/26/1955 -6.5 0.0 6.6 11.7 Sputnik 10/03/1957 - 10/22/1957 -9.9 5.5 6.7 7.2 Cuban Missile Crisis 10/19/1962 - 10/27/1962 1.1 12.1 17.1 24.2 JFK Assassination 11/21/1963 - 11/22/1963 -2.9 7.2 12.4 15.1 U.S. Bombs Cambodia 04/29/1970 - 05/26/1970 -14.4 9.9 20.3 20.7 Kent State Shootings 05/04/1970 - 05/14/1970 -4.2 0.4 3.8 13.5 Arab Oil Embargo 10/18/1973 - 12/05/1973 -17.9 9.3 10.2 7.2 Nixon Resigns 08/09/1974 - 08/29/1974 -15.5 -7.9 -5.7 12.5 U.S.S.R. in Afghanistan 12/24/1979 - 01/03/1980 -2.2 6.7 -4.0 6.8 Hunt Silver Crisis 02/13/1980 - 03/27/1980 -15.9 6.7 16.2 25.8 Falkland Islands War 04/01/1982 - 05/07/1982 4.3 -8.5 -9.8 20.8 U.S. Invades Grenada 10/24/1983 - 11/07/1983 -2.7 3.9 -2.8 -3.2 U.S. Bombs Libya 04/15/1986 - 04/21/1986 2.6 -4.3 -4.1 -1.0 Financial Panic '87 10/02/1987 - 10/19/1987 -34.2 11.5 11.4 15.0 Invasion of Panama 12/15/1989 - 12/20/1989 -1.9 -2.7 0.3 8.0 Gulf War Ultimatum 12/24/1990 - 01/16/1991 -4.3 17.0 19.8 18.7 Gorbachev Coup 08/16/1991 - 08/19/1991 -2.4 4.4 1.6 11.3 ERM U.K. Currency Crisis 09/14/1992 - 10/16/1992 -6.0 0.6 3.2 9.2 World Trade Center Bombing 02/26/1993 - 02/27/1993 -0.5 2.4 5.1 8.5 Russia Mexico Orange County 10/11/1994 - 12/20/1994 -2.8 2.7 8.4 20.7 Oklahoma City Bombing 04/19/1995 - 04/20/1995 0.6 3.9 9.7 12.9 Asian Stock Market Crisis 10/07/1997 - 10/27/1997 -12.4 8.8 10.5 25.0 U.S. Embassy Bombings Africa 08/07/1998 - 08/10/1998 -0.3 -11.2 4.7 6.5 Russian LTCM Crisis 08/18/1998 - 10/08/1998 -11.3 15.1 24.7 33.7 Mean -7.1 3.8 6.8 12.5 Median -4.6 3.9 6.7 12.1

Days = Market Days T_900 9/11/2001

WINNERS WHO USE CONTRARY OPINION

In his book How to Be Rich, J. Paul Getty (1983), the “richest man in the world” at the time, wrote in his first chapter, entitled “How I Made My First Billion,” In business, as in politics, it is never easy to go against the beliefs and attitudes held by the majority. The businessman who moves counter to the tide of prevailing opinion must expect to be obstructed, derided and damned. So it was with me when, in the depths of the U.S. economic slump of the 1930s, I resolved to make largescale purchases and build a selfcontained oil business. My friends and acquaintances—to say nothing of my competitors—felt my buying spree would prove to be a fatal mistake. 12 THE TRIUMPH OF CONTRARIAN INVESTING

FIGURE 112 Stampede. Diane Schmidt.

In 1962, Getty again bought stocks against the following headlines: “Black Monday Panic on Wall Street—Investors Lose Billions As Market Breaks—Nation Fears New 1929 Debacle.” Getty said to a puzzled correspondent, “I’d be foolish not to buy.” “Most seasoned investors are doubt less doing much the same thing,” he said; and feeling like a schoolmaster conducting a shor t course in the first principles of investment, he continued by saying, “They’re snapping up the fine stock bargains available as a result of the emotionally inspired selling wave.” In Charles Mackay’s 1989 book, Extraordinary Popular Delusions and the Madness of Crowds, the legendary Bernard Baruch says in the foreword that “all economic movements by their very nature, are motivated by crowd psychology. . .. Without due recognition of crowd think ing (which often seems crowdmadness) our theories of economics leave much to be desired.” But listen to how he sees crowd thinking: “Schiller’s dictum: Anyone taken as an individual, is tolera bly sensible and reasonable—as a member of a crowd, he at once becomes a blockhead.” The following, written by Baruch, in October 1932, prescribes a “potent incantation” to use against crowd thinking: I have always thought that if, in the lamentable era of the “New Economics,” culminating in 1929, even in the very presence of dizzily spiraling prices, we had all continuously repeated, “two and two still make four,” much of the evil might have been averted. Similarly, even in the general moment of gloom in which this foreword is written, when many begin to wonder if declines will never halt, the appropriate abracadabra may be: They always did. Peter Lynch in Beating the Street written in 1993 says, “Over the past three decades, the stock market has come to be dominated by a herd of professional investors. Contrary to popular INTRODUCTION 13 belief, this makes it easier for the amateur investor. You can beat the market by ignoring the herd.” Marty Zweig, in his 1986 book, Winning on Wall Street, has this important qualification regarding the use of contrary opinion: The idea is that if you use contrary opinion, you should go against the majority. But that’s an oversimplification and certainly not true in the middle of a bull market. Just because 51% of the crowd is bullish and 49% bearish is no reason the market cannot go higher. In fact, it probably will advance at that point. The time to be wary of crowd psychology is when the crowd gets extraordi narily onesided. In the book Market Wizards (1989) by Jack Schwager, Paul Tudor Jones stated “I learned that even though markets look their very best when they are setting new highs, that is often the best time to sell. He [Eli Tullis] instilled in me the idea that, to some extent, to be a good trader, you have to be a contrarian.” In the book The Warren Buffett Way (Hagstrom, 1994), fundamentalist value investor Warren Buffett is quoted as saying he has “long felt that the only value of stock forecasters is to make for tune tellers look good.” Buffett is also quoted as saying: The most common cause of low prices is pessimism—sometimes pervasive, sometimes spe cific to a company or industry. We want to do business in such an environment, not because we like pessimism but because we like the prices it produces. It’s optimism that is the enemy of the rational buyer . . . we simply attempt to be fearful when others are greedy and to be greedy only when oth ers are fearful. Legendary investor Leon Levy (2002) says in his book, The Minds of Wall Street, “the only course in which I ever got an A+ was abnormal psychology. What better preparation could there be to tackle the role of psycholo gy in markets?” He quotes the great British economist and philosopher John Maynard Keynes as saying, “Markets can remain irrational longer than you can remain solvent.” Levy asks, “Why should the mark ets be any more perfect than the very human emotions and calculations that drive it? Investors overreact and so do markets. Investors get swept up in moods and so do markets. And this interplay creates investment opportunities.” Finally, in the 2001 book Stock Market Wizards by Jack Schwager (published near the end of the bull market), when successful trader Steve Cohen was asked if he had any feelings about how the current longrunning bull market will end, he responded, “It’s going to end badly: it always ends badly. Everybody in the world is talking stocks now. Everybody wants to be a trader. To me that is the sign of something ending, not something beginning. You can’t have everybody on one side of the fence. The world doesn’t work that way.”

MY PERSONAL EXPERIENCE WITH CONVENTIONAL WISDOM

When I came into the investment business, the conventional wisdom of nearly everyone was as follows: “I’ve never met a rich technician.” “You can’t make money shortterm trading.” 14 THE TRIUMPH OF CONTRARIAN INVESTING

“Eightyfive percent of people who trade options and futures lose money.” “Worrying all the time about risks will paralyze you from capturing the rewards from stocks.” And so on. I do not claim to be the world’s greatest investor, and I’ve been much too conservative to hit a lot of home runs. Nevertheless, I believed in what I was doing. While my techniques are not for everyone, they were right for my psyche. So I mostly use technical analysis (with heavy doses of contrary opinion); I am a very shortterm trader; I make big use of options and futures; and I constantly worry about risks. And taking the road less traveled has worked for me. Since I started Ned Davis Research in 1980, I’ve never had a losing year on my investments. I am not trying to tout my own investment techniques. The real point of sharing my personal experience is that you have to find what works for you and follow your own dream.

MAKING OUR OWN REALITY

I’ve often wondered about the psychological forces behind why the crowd and popular fore casts are so often wrong. Earlier I offered one theory for the stock market, which is that crowd psychology and liquidity (potential demand) are inversely related. Looking further, I have become fascinated with the concept that we all create our own realities. This is a difficult concept to grasp, so I will try to explain it and give a few examples. An important truth I’ve learned is that people will view reality according to the way they want to perceive it or believe it should be. This was illustrated to me during a human relations class I took. I read that a longtime warden from New York’s infamous Sing Sing prison said, “Few of the criminals in Sing Sing regard themselves as bad men. They are just as human as you and I. So they rationalize, they explain. . . . Most of them attempt by a for m of reasoning, fallacious or logical, to justify their antisocial acts even to themselves. . . . the desperate men and women behind prison walls don’t blame themselves for anything.” Rationalization is a powerful coping mechanism. Another good example is people w ho seemingly must gamble. Despite the fact that casinos make hundreds of millions of dollars every year, I’ve almost never met a gambler who claimed to have been a loser. The gamblers will look you straight in the eye when they tell you that. It is my belief that the pain of losing is so great they actually forget the losses. Denial is a powerful defense mechanism. Yet another illustration: In listening to the sexual harassment testimony given during the Clarence Thomas confirmation hearing, I found that it was impossible for me to discern who was telling the truth and who was lying, but clearly I knew that one of them had to be lying. That is, until I heard a wise psychiatrist say that she thought both of them were telling the truth. At least it was the truth as far as each of them saw it. Illusion or delusion is a powerful psychological force. Some time ago I read a fascinating magazine interview with actor Ralph Fiennes who played the evil Nazi Amon Goeth in Schindler’s List:

Q—Was there an emotional residue from the experience of playing a character he views as obscene and sick? INTRODUCTION 15

A—After a long pause he answers softly, “I think there was a price to pay for this one. When you’re investigating behavior that is so negative, so intensely for three months, then you feel sort of peculiar because you might have at moments enjoyed it and at the same time you feel slightly soiled by it. . . . It’s not a rational thing, but it’s an instinctive thing. . . . If you’re playing a role, you are immersing yourself in thinking about that character—how he moves, how he thinks. In the end he becomes an extension of your own self. You like him.It just throws up all kinds of question marks about acting, about human behavior, about how evil is probably a lot closer to the surface than we like to think. A person’s mind can sometimes get badly twisted under intense emotional pressure. Then there’s the O. J. Simpson case. Was he guilty? After the innocent verdict, 36 percent of whites said he was innocent compared with 73 percent of AfricanAmericans. William Raspberry (1994), the black Pulitzer Prizewinning journalist, said, “How can that be? Are white people, less invested in Simpson’s fate, being objective while blacks are being emotional? Have we come to the point where color is of such importance as to override every other consideration, to render us, black and white, incapable of a shared reality?” My favorite example of imagination distor ting reality is watching basketball games. Almost always the vast majority of home fans at a game will swear that the referees favored the opposing team (many even proclaim the other team has paid off the refs) even though their home team won the game, and even though objective statistics show that if there is a bias, the calls in an average game favor the home team. Crowd psychology is contagious and can influence even what we see with our own eyes. One’s perception equals one’s reality (see Figure 113). Finally, the latest example of “making one’s own reality” came after 9/11. Very quickly after the 9/11 attack, the U.S. government identified the 19 hijackers personally and individually by the Arab countries from which they came, by what they did in the United States, and by the fact that they were all linked to Osama bin Laden’s AlQaeda network. With the hard evidence presented, almost the entire U.S. population pulled together behind President Bush in his determination to wipe out the AlQaeda terrorist organization. Figure 114 shows that 90 percent of the U.S. peo ple polled gave President Bush their approval, the highest approval rating ever (also note the stock market usually does best when Presidential approval is low—contrary opinion at work). Five months after the attack, after nearly all the “hard evidence” was already made public, Gallup also polled a statistically significant 9924 Muslims of nine Arab countries. According to CNN, 61 per cent said “they did not believe Arab groups carried out the September 11 th terrorist attack.” Fur thermore, only 11 percent of these Muslims had a favorable view of President Bush, while 58 percent had unfavorable opinions. Of those surveyed, 77 percent said any U.S. military action in Afghanistan was morally unjustified compared with 9 percent who said it was justified. In case anyone feels it was a lack of free press in some of those countries that led to these antiU.S. views, the story of French author Thierry Meyssan (2002) refutes that thought. Meyssan wrote a book entitled 9/11, The Big Lie seeking to prove that the September 11 acts of terrorism were commit ted not by Arab terrorists but by U.S. special services. Meyssan believes the attacks were organ ized by “ultrarightist” highranking officials. In any case, Meyssan achieved great popularity, and his book was on the top of the bestseller list in France for many months. 16 THE TRIUMPH OF CONTRARIAN INVESTING

Psychiatrist

NDR University

WALL STREET

2003

I know most people feel they have a good grasp of reality and that it is all these other people who are “lost in space.” Ho wever, even among my siblings, when we talk about our parents and how we were raised, I sometimes get the feeling that our realities are so different I can’t believe we had the same parents. So what I hope this section will show is that people see and hear mostly what they want to believe or what the group (crowd) to which they belong believes. The bottom line is that people often create their own realities based upon things that may have happened to them as far back as their v ery early years of life. We are all subject to that condition. We are human. This means what feels right, easy, and obvious in your gut is quite often wrong.

THE NED DAVIS RESEARCH RESPONSE TO ALL THIS

Given (1) all the evidence in this book which shows the crowd is almost always wrong at extremes, (2) the pressures toward, and the ease of, being swept up by the crowd, and (3) the pos INTRODUCTION 17

Weekly Data 8/21/1959 - 1/24/2003 (Log Scale) Dow Jones Industrial Average 10801 10801 9195 Dow Jones Gain/Annum When: 9195 7827 7827 Gallup Poll Presidential Gain/ % 6662 Approval Rating is: Annum of Time 6662 5671 Above 65 2.2 19.8 5671 4828 *Between 50 and 65 4.5 44.3 4828 4110 50 and Below 9.9 36.0 4110 3498 3498 2978 2978 2535 2535 2158 2158 1837 1837 1564 1564 1331 1331 1133 1133 964 964 821 821 699 699 595 595

1960 1965 1970 1975 1980 1985 1990 1995 2000

Kennedy Johnson Nixon Ford Carter Reagan Bush89 Clinton Bush (2) 90 88 88 84 83 84 80 79 80 76 75 76 73 72 71 72 68 67 68 68 68 64 64 60 60 56 56 52 52 48 49 48 44 44 43 40 40 36 36 35 35 32 32 31 28 28 29 28 Source: Gallup Poll, www.gallup.com 24 24 Latest Reading = 1/24/2003 = 60% 24

(S0510) Gallup Poll Presidential Approval Rating sibility that under stress, our own realities could become badly distor ted, a clear study of crowd psychology and objective indicators to measure investor sentiment is critical. Such a study can open one’s mind to thinking about other possibilities, keep us from getting swept up by mob fever, and, in many cases, encourage us to take the road less tra veled. Since such a study can lead to increased profits, I have written this book to help both your investing and, possibly, your thought process in life. Also, it is because of the importance of crowd sentiment in the mark et that Ned Davis Research (NDR) builds so many indicators of crowd psychology; they can allow us to pass judgment devoid of emotionalism. This page intentionally left blank. CHAPTER2 SCIENTIFIC STUDIES ON CROWD PSYCHOLOGY

HE HISTORY OF FINANCIAL MARKETS IS RIFE with examples of irrational behavior by investors. But why do thousands of rational individuals, as a group, continue to make irrational decisions? At least part of the ans wer can be found in social psycholo gy. Social scientists have conducted several experiments that indicate that it is human nature to be heavily influenced by crowds. Most of the experiments were not tested in the conteTxt of investor psychology. Instead, they show that people are influenced by crowds in many aspects of their lives, and investing is just one of them. The studies show that people make decisions based on their emotions, and their emotions are partially created by their surroundings. The stock market is nothing more than a reflection of investors’ aggregate emotions, or as we like to say at Ned Davis Research, the stock market is the manifestation of group psychology in motion.

ANCHORING TO THE CROWD

One of the earliest studies on group conformity was conducted by Muzafer Sherif in 1935. As described by James McConnell (1980) in Understanding Human Behavior, Sherif ’s experiment consisted of students watching a pinpoint of light in a dark room. The light was stationary, but to the human eye it appeared to move (the phenomenon is known as the autokinetic effect). Sherif asked the students to estimate how far the light moved. For the first part of the experiment students made their decisions in private. Their answers varied widely, from a fraction of an inch to over a foot. For the second part of the experiment Sherif put the students into groups and had them answer in front of each other. Sherif found that ans wers anchored on the first response. In other

Copyright © 2004 The McGrawHill Companies. Click here for terms of use. 20 THE TRIUMPH OF CONTRARIAN INVESTING words, each group created its own average, which was highly influenced by the answer given by the first respondent. Since the autokinetic effect is an illusion and is perceived differently by each person, there is no inherent reason someone would need to change his or her answer based on someone else’s answer. Nevertheless, the subjects of the experiment felt compelled to conform to the crowd. If one views the Sherif study in the context of the stock market being a reflection of investor sentiment, the connection to group psychology is obvious. Stock prices are determined by the aggregate opinion of investors, and the aggregate opinion is influenced by the voices heard by the most people. Twentyfourhour news channels interview professional investors providing their opinions. Even those who do not watch financial news are exposed to commercials from financial services companies. Magazines, newspapers, and the Internet are full of opinions from analysts and money managers. Investors would have to try not to be exposed to the financial media in order to avoid them, and Sherif ’s findings indicate that investors are influenced by the exposure.

GROUP PRESSURE

Years after Sherif ’s experiment, psychologist Solomon Asch added a layer of complexity to group cohesion research. His study (McConnell, 1980) consisted of showing students an 8inch line on a piece of paper. He told the students to study the line and then placed it out of sight. On another piece of paper he showed them three lines: one 8, one 8 3/4, and one 10 inches long. Asch then asked the students to tell him which of the three lines matched the line on the first piece of paper. When asked individually, students answered correctly 99 percent of the time. Asch then altered the experiment. He had the students answer in groups. Each group included “stooges” who purposely answered incorrectly. (The students did not know the other participants were stooges.) When the stooges said that the 8 3/4inch line was the correct line, about twothirds of the subjects agreed. Even when the stooges said the 10inch line matched the line on the first piece of paper, approximately onethird of the students agreed. Just as interesting as the fact that people succumbed to group pressures were their reasons for yielding. About half of the students who answered incorrectly admitted that the pressure from the other members of the group overwhelmed them. They thought there was a trick they did not see, or they felt pressured not to go against the group. The other half actually believed they were giv ing the correct answer. They did not realize they were being led astray. Since virtually all the stu dents answered correctly in private, this clearly illustrates human nature’s ability to be influenced by group pressures. Given the complex nature of the financial markets, the stock market can be compared to the 8 3/4inch line in that the “correct” answer is not always obvious. Manias always appear obvious in hindsight. During the height of the stock market bubble, however, investors could be influenced, either knowingly or unknowingly, into believing a mania is not a mania. The recent NASDAQ Bubble is a case in point. Three years after the fact, it appears obvious the market was priced to perfection. At the time, however, the fact that everyone (at least it seemed like everyone) was get SCIENTIFIC STUDIES ON CROWD PSYCHOLOGY 21 ting rich on technology and Internet stocks made it easier to overlook overvaluations, oversupply, and overconfidence. The previous examples illustrate that people are influenced by others when they are physically present. Most investors, however, are not on trading floors. They make their decisions in their office or at home. University of Texas professors Robert Blake and Henry Helson recognized and addressed this issue. Through their use of tape recorders, they proved that the stooges do not even have to be physically present to influence the subjects of the experiment.

CROWD PSYCHOLOGY AND INVESTING

Behavioral finance, the academic term for the study of investor sentiment, has long been con sidered to be on the outskirts of finance. However, the NASDAQ Bubble has given new credence to the field, so much credence that Daniel Kahneman was awarded the 2002 Nobel Prize in eco nomics for his study of individual investor behavior with the late Amos Tversky. In his book Beyond Greed and Fear, Hersh Shefrin (2000) illustrates how Kahneman and Tversky pioneered the ideas of regret theory and loss aversion, which imply that people are sometimes motivated by minimizing their regrets rather than maximizing their wealth. While minimizing regret and maxi mizing wealth are typically compatible, occasionally they come into conflict. Kahneman and Tversky used this simple experiment to illustrate their point: You are faced with two choices. Option A is a certain loss of $7500. Option B is a 75 percent chance of a loss of $10,000 and a 25 percent chance of breaking even. The expected payoffs are the same for both scenarios: Option A: $7500*100% = $7500 Option B: $10,000*75% + $0*25% =$7500 Yet Kahneman and Tversky found that most people choose option B. Why? Because most people want to avoid taking the loss. They found that a loss has to be about 2 1/2 times as large as a gain for the two to offset. While the first experiment studied minimizing regret, Kahneman and Tversky’s second survey studied maximizing wealth. The second scenario also presents two options: Option A is a sure gain of $2400. Option B is a 25 percent chance of a $10,000 gain and a 75 percent chance of breaking even. Despite the fact that option B has a higher expected payoff than A, most people chose the certain $2400 gain over the unlikely possibility of a $10,000 windfall. Option A: $2400*100% = $2400 Option B: $10,000*25% + $0*75% = $2500 This experiment illustrates that people are lossaverse when looking at gains. Most people will take a slightly lower payoff if it is guaranteed over a larger, but uncertain, gain. The two surveys together indicate that people react differently when they are maximizing wealth versus minimiz ing regret. The study also illustrates that investors are influenced by how information is presented to them. 22 THE TRIUMPH OF CONTRARIAN INVESTING

Hersh Shefrin and Meir Statman tested regret theory in the context of avoiding blame. Their study (Shefrin, 2000) consisted of a scenario with three investors who decided to buy certificates of deposit. Investor A had been invested in stocks, but he decided, through his own research, to switch to CDs. Investor B also switched to CDs from stocks, but he did so based on the advice of his financial adviser. Investor C had already owned CDs, so he just rolled over his CDs when they expired. Shefrin and Statman asked students which investor would be the most upset if stocks out performed CDs over the subsequent few months. About 70 percent said A, 12 percent said B, 0 percent said C, and 18 percent said no one. Investors B and C have good “excuses” they can tell themselves. B can blame his financial adviser, and C can say he always invests in CDs anyway. Investor A, however, has no one to blame but himself. One of the more common corollaries to Kahneman and Tversky’s theories is the impact of crowd psychology on loss aversion. If investors are trying to minimize their regret, then going along with the crowd can be the path of least resistance. If an investor follows the crowd and is wrong, then at least he was not the only person to make the mistake. If an investor goes against the crowd and is wrong, then he must deal with not only the financial loss but also the psychological loss of watching other investors enjoy their windfall. Even if the investor believes the crowd is wrong, he may decide that the benefit of “safety in numbers” is greater than the potential pain of being wrong. This type of cro wd psychology could hav e pla yed a role in the NASDAQ Bubble. The NASDAQ soared 85.6 percent in 1999, but as shown in Figure 21, 10 stocks accounted for 45.8 percent of the index’s gains. The equalweighted Value Line Composite and NYSE Advance/Decline line peaked in 1998, further demonstrating that most stocks were not participat ing in the final stages of the bull mark et. The narrow rally meant that almost everyone was invested in the same select names. As a result, the risks of going against the crowd were extraor dinary. If an investor was underweight Technology and Internet stocks and they continued to soar, then he was guaranteed to under perform his peers. Conversely, if he followed the crowd and the market declined, then he was no worse off than anyone else. Social psychologists have shown that it is human nature to be influenced by our surroundings. Why should investing be any different? History suggests that it isn’t. SCIENTIFIC STUDIES ON CROWD PSYCHOLOGY 23

Top Contributors as a % of NASDAQ Composite Index (12/31/1998 - 12/31/1999)

QCOM

SUNW

ORCL

INTC CSCO YHOO 4.4% 4.0%

5.7% ICGE 3.6% 9.0% DELL 2.9% AMGN 1.8% 1.7% MSFT

1.6% 11.1%

Remaining NASDAQ stocks

Top 10 Contributors Contribution = Accounted For 45.8% Market Cap Gains of NASDAQ Composite Gains NASDAQ Composite Market Cap Gains Notes: 1) Common stocks only (excludes preferred, mutual funds) 2) Static, beginning-period cap weight used (BOTC1) 3) Price change only, no dividends included This page intentionally left blank. CHAPTER3 BRIEF HISTORY OF MANIAS AND PANICS

HE PREVIOUS CHAPTER DESCRIBED PSYCHOLOGICAL studies illustrating how people are influenced by their surroundings when making decisions, including investment choices. The stock market is nothing more than an aggregate account of the opinions of millions of individual investors. Since the market is composed of the opinions of investors and since those opinions are influenced by their surroundings, the ebb and flow of the stock markTet reflects changes in crowd psychology. Therefore, almost by definition the peak in the mar ket is the point of maximum optimism, and the trough is the point of maximum pessimism. The exact level of the peaks and troughs, however, varies in each cycle (see Chapter 5 for investor psychology indicators). Upon occasion, crowd psychology completely diverges from macroeconomic and companyspecific fundamentals, resulting in manias and panics. In order to demonstrate this point, this chapter provides several examples of extremes in crowd psychology. Backgrounds, names, and geographies may differ, but the common link between all the stories is that the pressure of the crowd pushed investors to temporarily ignore the underlying economic environment.

MISSISSIPPI SCHEME

In 1841 Charles Mackay (reprinted in 1989) published a book entitled Extraordinary Popular Delusions and the Madness of Cr owds, in w hich he explores different speculative episodes throughout history during which crowd psychology caused the rise and fall of economic systems. Mackay opens his book by retelling the story of the famous “Mississippi Scheme” engineered by the mad financial wizard John Law.

The death of King Louis XIV left France in financial disarray. The opulent king had accrued a phenomenal national debt, taxes were crippling, and sentiment was overwhelmingly pessimistic. Enter Law, a brilliant young man with a plan to right France. Law proposed that France grant him control of the Mississippi Company, an organization that had a monopoly over trade with the MississippiLouisiana area. Once he took control, he set up a national bank, which in turn inher ited France’s national debt. Speculators quickly bought bank bonds that were backed by stock in the Mississippi Company. Rampant buying pushed stock prices through the roof, and as people saw their friends change from pauper to millionaire virtually overnight, they ran to invest in the Mississippi Company. The French government, thrilled with the wealth with which Law was seemingly infecting France and blind to the terrible consequences of his system, poured money into the bank, sponsoring several similar banks around France. The French people were overcome with avarice and rushed to invest further. The future looked beautiful, and France’s growing wealth seemed unstoppable. One of many problems with Law’s system, aside from the obvious problem that bank money was backed only with paper rather than some kind of gold or silver, was the Mississippi Company itself. The company actually served no function at all. France was not trading with nor developing the Mississippi region. The unbridled mass of buying the company stock was the only thing push ing the stock higher, rather than the success of the company itself. The Mississippi Scheme soon reached the height of its popularity, at which point virtually all France was invested. According to Robert Menschel (2002) in Markets, Mobs, & Mayhem, the price of Mississippi shares rose approximately 6200 percent over a 13month period. With no one left to invest, the price began to drop. As people rushed to sell, the market collapsed. Perhaps if only the citizens had been invested, France could ha ve survived the crash. However, with the French government so intimately entwined with Law’s system, the whole economic system fell apart. Several days into the crash, 15 people were trampled to death trying to get money from the bank. According to Menschel, share prices tumbled 99 percent over the 13 months following the peak. The government and the coun try fell into an economic despair from which they did not recover for almost 100 years.

SOUTH SEA BUBBLE

One might be inclined to blame the conditions in France preceding the Mississippi Scheme for why Law’s system could so thoroughly wreck the French economy. The people were destitute, left in financial ruin from the extravagant spending of Louis XIV, and starving for an easy way to right their problems. Certainly that was an environment rich for speculative opportunities. However, France’s rise and fall was not a fluke. The pattern has been repeated throughout history. Even while England was watching France rise and fall, it was building a stock system of its own. The English historical speculative disaster called the “South Sea Bubble” began in the 1700s. England was in a state of prosperity at this time; independent businesses flourished, and the econ omy was soaring. The solid state of the country would prove to be England’s saving grace after the crash. BRIEF HISTORY OF MANIAS AND PANICS 27

The South Sea Company was the section of England’s trading companies that dealt with the South Sea region of the empire. Inspired by Law’s Mississippi Scheme, the South Sea Company offered to take over England’s national debt for the sole purpose of alerting the English public to the existence of the company. The ploy worked, and the company began selling off stock: £1 of stock for every £1 of debt it had taken. The stock’s popularity was immediate; share prices rose more than 200 points in less than 2 months. However, the company did not stop there. The direc tors realized that they needed a new way to increase the cashflow into the company. They offered stockholders a loan—for every £100 the public had invested, investors could receive up to £250 in loans. The gimmick worked so well that the South Sea Company compounded the loan process three times, and the company’s stock price eventually rose as much as 1000 percent over an 18 month period according to Menschel (2002) in Markets, Mobs, & Mayhem. England was swept up in financial success. Everyone was an investor, and new companies offering stock opportunities sprang up. These new companies, inspired by the South Sea Company, ultimately hurt the original organization by taking business away from the South Sea. Angered by a slowing in the number of investors, the directors of the South Sea Company appealed to Parliament, which passed a series of Bubble Acts forbidding the existence of all companies without a government charter. The Bub ble Acts proved to be the pin that burst the South Sea Bubble. Unable to tell which companies were legitimate and which were fabrications, investors sold quickly as confidence plummeted, and South Sea shares tumbled appro ximately 84 percent in just 6 months. Perhaps if the South Sea Company had developed trade in the South Seas, it could have prevented its downfall. However, in reality, the South Sea Company was identical to the Mississippi Company—neither actually did anything except sell stock. The buying alone pushed prices higher rather than actual company suc cess. The South Sea Company, facing bankruptcy, appealed to the Bank of England, begging the bank to buy large blocks of South Sea stock. The Bank of England refused the proposal, and the South Sea Company collapsed. The situation in England differed from that in France because in France the speculative com pany actually controlled the bank system. Therefore, when the Mississippi Company fell apart, the entire economic system in France was overturned. In England, the Bank of England refused to get involved with the South Sea Compan y, so after its downfall, the Bank of England still remained somewhat intact. Also, England had a much sturdier economic base before the speculation, help ing the British to salvage some of their original economy.

RUSSIA’S MMM MANIA

After reading those two stories, one might now concede that crowd psychology played a part in the financial ruin of England and France in the 1700s. Investors bought without considering the validity of either the company in which they were investing or the consequences of a bubble that would inevitably collapse. They simply followed the masses, blinded by riches. There were wise men w ho spoke out against the speculation; however, the very few who spoke publicly were regarded as fools, were ridiculed, and were ignored. Despite these facts, one might still argue that 28 THE TRIUMPH OF CONTRARIAN INVESTING these examples happened almost 300 years ago, and this could never happen in modern society. In fact, an almost identical situation occurred less than a decade ago in Russia. In his book Contrarian Investment Strategies: The Next Generation, author David Dreman (1998) explores the rise and fall of the MMM company in Russia. The year 1994 saw a Russia open for the first time to free enterprise and the world of capitalism. A brilliant and ambitious cap italist named Sergei Mavrodi opened MMM early in 1994. He advertised well, spending millions of dollars on TV and newspaper ads. Shares were first issued in February at $1 a share, a price that rose to $65 by midJuly. MMM estimated that, at the height of its popularity, it had between 5 mil lion and 10 million shareholders. However, in mid1994, the Russian government began investi gating MMM only to discover there was actually no business at all behind MMM, making MMM an even more audacious scheme than that in England or France, where there was at least a sem blance of a company. The government investigated MMM, and when it revealed its findings, the stock price collapsed, falling from $60 to $0.46. People trampled each other in the streets trying to redeem their worthless stock. Mavrodi, however, was far from finished. He claimed that MMM was getting ready to release new products that would be instant successes, and he offered to redeem the shares of the neediest citizens at $50 a share. The mobs turned around overnight, as enormous lines formed to buy more shares of MMM stock. Mavrodi was incarcerated for tax eva sion, but, taking advantage of a Russian law allowing members of Parliament to avoid prosecution for almost all ille gal activities, he ran for Parliament in a district with a large number of MMM investors. His platform promised to use the government to rescue MMM. Mavrodi won, and now free from prosecution, he admitted to the public that he had no intention of saving MMM. Unlike England and France, the fall of MMM did not bring down the Russian economy. There were simply too many citizens not invested in MMM. However, millions of people lost everything they had as a result of obediently following the crowd, blind to the inherent risks.

CRASH OF 1929

It would be easy to dismiss these historic examples. After all, France and England suf fered their economic collapses over 200 years ago, and Russia’s lightningquick rise and fall could sim ply be attributed to a response of a country experiencing capitalism for the first time. Those of us in developed economies can find reasons why the same forces could not overtake our markets and economies. Better infor mation flow from corporations, detailed research from brokerage firms, government oversight to protect investors, and stabilizing tactics by monetary policy boards should ensure that investors in developed economies do not fall prey to the psychology of the masses. Nevertheless, the U.S. financial markets are not immune to the effect of group psychology, and like other markets, the U.S. stock market has been engulfed in manias and panics. Perhaps the most notorious bubble led to the crash of 1929. While there are numerous reasons and explana tions for the 1920s’ bubble and subsequent Great Depression, the role of investor psychology can not be ignored. As America entered the Roaring Twenties, the end of the Great War in Europe and BRIEF HISTORY OF MANIAS AND PANICS 29 excitement over the mass commoditization of new technologies such as the radio, the telephone, electrification, the automobile, and the airplane produced a wave of optimism. The impact these technologies made on society is arguably larger than the productivity gains via computers and the Internet in the late twentieth century. According to Robert Sobel (1965) in The Big Board: A His tory of the New York Stock Market, as the decade advanced, the optimism began to detach itself from reality. Installment buying made automobiles affordable to millions of Americans who oth erwise could not pay for them. While clearly a positive for the auto industry, investors responded by pumping up General Motors’ stock price to $222 from $146 over a 2month period in early 1926 after GM CEO John J. Raskob said that his company’s stock price was 100 points too low. Charles Lindbergh’s flight across the Atlantic spurred imaginations about the possibilities of the airline industry. Investors, caught up in the speculation, pushed Wright Aeronautical, the company that built The Spirit of St. Louis, to $245 from $25 in the 19 months after the flight. While some worried that the speculation would result in a panic similar to the one in 1907, the general con sensus, according to Sobel, was that a crash was not possible, “because of strong leadership on the Street, enlightened Federal Reserve policies, the strong economic structure of the nation, and the ability to profit from lessons learned from the past.” While the Federal Reserve worried about the speculation, it initially refused to raise interest rates because doing so would damage the fragile recovery in Europe and strain the English gold standard. Finally, in February 1929 the Fed tried to prick the bubble by warning banks against bor rowing from the Fed to make margin loans. In direct defiance of the Fed, Charles A. Mitchell, the head of National City Bank and director of the New York Federal Reserve Bank, promised to loan $25 million to investors who were unable to obtain funds from the Fed. The move produced a new wave of optimism that Wall Street leaders would not let the market decline. By early September, with the Dow Jones Industrial Average up 30 percent over the previous 3 months, public infatuation with the stock market reached saturation. The downward momentum built throughout October and climaxed on October 28 and 29 with the DJIA tumbling 12.8 percent and 11.7 percent, respectively. Wall Street leaders attempted to stymie the crash and buoy investor sentiment with staged public buying, but the decline was too severe for even the titans of Wall Street to prevent. Too many individuals and too many corporations had tied their fortunes to the stock market for the crash not to impact the econom y. Capital market conditions did not permit companies to issue stock or borrow, which set off a downward spiral of slower growth, rising unemployment, decreasing demand, lower investment, slower growth, etc. The DJIA finally hit its nadir in July 1932, 89 percent below its peak, and did not recover to precrash levels until 1954. Earnings for the S&P 500 did not surpass 1929 levels until the postWorld War II boom in 1948. The following quote by Alan Greenspan, published in the June 25, 1999, issue of InvesTech Research, summarizes the boom and bust of the 1920s and 1930s from the perspective of mone tary policy as well as investor sentiment:

. . . the excess credit which the Fed pumped into the economy (in the 1920s) spilled over into the stock market—triggering a fantastic speculative boom. Belatedly, Federal Reserve officials 30 THE TRIUMPH OF CONTRARIAN INVESTING

attempted to sop up excess reserves and finally succeeded in breaking the boom. But it was too late! By 1929 the speculative imbalances had become so overwhelming that the attempt precipi tated a sharp retrenching and a consequent demoralizing of business confidence.

Greenspan made this comment in 1966, more than 30 years before he found himself fighting the next great bubble in the stock market.

NASDAQ BUBBLE

The crash of 1929 left an indelible impression on the psyche of U.S. investors. Surely the les sons of the crash would prevent investors from being swept into another bubble. In the late 1990s, however, investors, business leaders, and the economy fell victim to another crowd mania. The surges in markets around the globe and billions of dollars in international inflows into the U.S. stock market suggest the phenomenon w as global in nature; however, the NASDAQ Composite was the widely accepted epicenter. Se veral positive fundamental factors in the previous two decades laid the foundation for the boom. Demographic trends broadened the appeal of investing to baby boomers; the secular decline in interest rates increased the relative attractiveness of stocks; corporations restructured to become more competitive in the global economy; the fall of Communism in Eastern Europe spurred the popularity of Americanstyle capitalism to record heights. Perhaps most importantly, the late 1980s and 1990s witnessed a technology and produc tivity boom not seen since the 1920s. Computers became an inte gral part of American life and , soon after that, an important part of life all over the world. Millions of families bought computers, as the Internet became the main way to research information and stay in touch with friends and family. As far as the economy was concerned, computers carried an even larger and more vital implication: They increased productivity. Computers did not need a salary and worked many times faster than any human; thus, increased computer usage cut expenses and raised profits. Brilliant and courageous entrepreneurs believed that dotcom companies would soon replace brick and mortar buildings filled with actual workers, and as a result, dotcom companies like Amazon.com, eBay, Monster.com, and E*Trade became investor favorites. In her book Ride the Wave, Sherry Cooper (2001) points out that from a valuation standpoint, “New Economy” stocks created a problem. How does one value a company whose assets are human capital and concepts? Industrial companies use real assets. Accounting rules provide a rel atively simple way to measure these assets. The assets are placed on the balance sheet and depre ciated over time. However, the costs of acquiring New Economy assets, such as brand equity, are expensed during the period the money is spent, leaving no assets on the balance sheet. In addition, very few Internet companies produced profits according to generally accepted accounting princi ples, and so investors valued companies based on pricetopro forma earnings, pricetosales, or even pricetoconcept ratios. The new valuation metrics provided companies with incentives to spend lavishly to build brand recognition. Cooper relates the tale of one dotcom CEO, Michael Budowski, who was in charge of a company called OurBeginnings. The company had only 12 BRIEF HISTORY OF MANIAS AND PANICS 31 employees and had revenues of about $1 million. However, it paid over $4 million for three com mercials during the 2000 Super Bowl, and as a result, it had to pay an additional $1 million to upgrade its technology to handle the overwhelming influx of business. Like the manias in France, England, and Russia, the riches made by a few early investors encouraged others to find their fortunes. The success of new Internet companies such as Ama zon.com and eBay led investors to seek the next hot company. Remarkable firstday gains by IPOs such as VA Software (+698 percent) and Theglobe.com (+606 percent) turned companies with a few million in revenues into companies with multibillion dollar market capitalizations. Cisco Sys tems, the maker of the switches and routers that enable the Internet to function, became the poster child of the New Economy. As the table in Figure 31 shows, Cisco’s market cap, just above $541 billion, was approximately the same as the combined market caps of 25 bluechip stocks, but its earnings and revenue were only a fraction of those of the “Old Economy” stocks. A March 24, 2000 Chart of the Day (a onepage report focusing on a specific topic) featuring the table, states, “. . . it is clear that the valuation on Cisco (and many other Tech/Net stocks) has already antici pated years and years of revenue and sales.” Twentyfourhour news channels such as CNBC, CNNfn, and Bloomberg News furthered the public’s interest through their continuous coverage and constant reminders of the market’s ascent. Investment websites such as TheStreet.com and Motley Fool provided investors with realtime

INVESTMENT OPTION A INVESTMENT OPTION B

03/23/2000 1999 1999 03/23/2000 1999 1999 Ticker Company Name Market Cap Revenues Earnings Ticker Company Name Market Cap Revenues Earnings

F Ford Motor Co 53.75 162.56 7.22 CSCO Cisco Sys Inc 541.27 15.00 2.54 TX Texaco Inc 27.70 35.06 1.15 MER Merrill Lynch & Co 39.56 34.88 2.58 DD Du Pont (E I) De Nemours & Co 57.36 26.94 7.68 AET Aetna Inc 8.24 26.45 0.69 IP International Paper Co 16.08 24.58 0.18 SLE Sara Lee Corp 16.42 20.15 1.17 RTN.B Raytheon Co - Class B 6.34 20.04 0.40 CAT Caterpillar Inc 14.19 19.70 0.95 AMR AMR Corp 4.71 19.13 0.99 FDX Fedex Corp 11.53 17.37 0.63 MMM Minnesota Mining & Mfg Co 35.03 15.66 1.76 MCD McDonalds Corp 47.64 13.26 1.95 ADM Archer-Daniels-Midland Co 6.52 13.21 0.19 GT Goodyear Tire & Rubber Co 3.67 12.88 0.24 JPM Morgan (JP) & Co Inc 23.80 11.82 2.02 BUD Anheuser Busch Cos Inc 28.14 11.70 1.40 LLY Lilly (Eli) & Co 70.20 9.91 2.72 SPLS Staples Inc 9.74 8.84 0.33 FOX FOX Entertainment Group Inc 18.74 7.94 0.18 ED Consolidated Edison Hldg Inc 7.02 7.49 0.69 AAPL Apple Computer Inc 22.73 6.77 0.63 MYG Maytag Corp 2.66 4.32 0.33 HLT Hilton Hotels Corp 2.81 2.33 0.17 DJ Dow Jones & Co Inc 6.66 2.00 0.27

Total 541.24 535.00 36.52 Total 541.27 15.00 2.54 Price / Sales 1.01 Price / Sales 36.08 Price / Earnings 14.82 Price / Earnings 213.10

NOTE: All numbers in $ Billions 32 THE TRIUMPH OF CONTRARIAN INVESTING information. Online trading enabled investors to act on this new information and trade faster and cheaper than ever before. Armed with the belief that this time was different, investors flocked to the market, pouring billions of dollars into Technology and Internet stocks. Nowhere was the mania more evident than in the NASDAQ Composite. The index surged 40 percent in 1998 and 86 percent in 1999, including a 48 percent surge in the fourth quarter. Since the index is capitalizationweighted, the statistics reflected appreciation in only the largest com panies. Despite the remarkable rallies in Technology and Internet stocks, most companies were enduring a stealth bear market. Figure 32 shows the NASDAQ Composite in the top clip, the NASDAQ Advance/Decline line in the second clip, and the number of NASDAQ stocks making new 52week highs and lows in the bottom clip. The Advance/Decline line is a cumulative total of the difference between the number of stocks that rise and fall each day. As Figure 32 shows, the NASDAQ Composite Advance/Decline line was in a downtrend in the late 1990s even as the price line continued to make record highs. Likewise, the NYSE A/D line peaked in April 1998. Further illustrating the narrow advance, the number of stocks making new 52week highs peaked at 500 on July 16, 1997. Despite numerous new highs in the NASDAQ over the next 33 months, the num ber of stocks hitting new highs became entrenched in a downtrend. The mania continued into early

NASDAQ Composite Index vs Breadth Indicators Daily Data 1/02/1997 - 2/19/2003 5048.62 5145 NASDAQ Composite 4665 2/19/2003 = 1334.32 4274.67 Log Scale Right 4229 3833 3475

3164.55 3150 2864.48 2856 2589 2490.11 2313.85 2347 2291.86 2014.25 2059.38 2128 1929 1745.85 1749 1716.24 1638.80 1487.94 1585 1388.06 1499.53 1437 1419.12 1423.19 1303 1201.00 1181 456.86 1114.11 440.36 420 435.17 432.22 390.95 390 NASDAQ Daily Advance/Decline Line 376.76 2/19/2003 = 200.49 360 345.09 Scale Left 330 300 309.10 270 261.88 245.30 240 249.44 232.77 212.36 210 500 464 428 200.39 289 231 266 248 183 137 166 165 169 225 93 100 25 0 -25 -100 151 203 182 -225 282 263 -400 459 479 434 564 527 569 -625 NASDAQ New Highs & New Lows 788 737 -900 Scale Right -1225 1326 J MM J S NJMM JSNJMMJ S NJMM J SNJM MJ SNJ MM J SNJ (BOTC2) 1997 1998 1999 2000 2001 2002 2003 BRIEF HISTORY OF MANIAS AND PANICS 33

2000. Through March 10 the NASDAQ Composite surged 24 percent to a record high of 5048. But as the Fed took away liquidity by reducing the money supply and raising shortterm interest rates, the market began to lose steam. By April 11, just 1 month after hitting its peak, the NASDAQ had given back all its gains for the year. Once the momentum turned negative, there was no going back. Just as investor optimism and greed fed the market’s surge, pessimism and fear spurred the decline. The NASDAQ finished year 2000 down 39 percent. The index fell another 21 percent in 2001, and by October 9, 2002, the index had fallen to 1114, a level not seen since August 1996 and 78 percent below its record high. The stock market decline denied companies’ access to muchneeded capital. Without the capital, companies burned through cash quickly. Not only did startup companies such as Pets.com go bankrupt, but more established companies such as Lucent Technologies and Global Crossing faced major difficulties. By late 2002 it became apparent that the new metrics used to value Technology and Internet stocks enabled corporations to mislead investors. Companies such as Enron, WorldCom, and Adelphia Communications had hidden debt, capitalized operating expenses, and overstated revenue, respectively. AOL Time Warner posted a $98.7 billion loss in 2002 as it wrote down the value of assets stemming from America Online’s $103.5 billion acqui sition of Time Warner in January 2001. Investors worried about missing the gains that the rest of the crowd enjoyed, and so they ignored concerns about accounting practices until after the mar ket crashed. Economists have coined the terms endowment effect and status quo bias to explain, at least partially, why investors may have ignored the signs of trouble and even continued to hold NAS DAQ stocks after inconsistencies became apparent. As explained by Richard Thaler (1992) in The Winner’s Curse, the endowment effect asserts that people are generally willing to demand more to sell something they already own or inherit than to buy something they do not own. Pioneered by Richard Zeckhauser and William Samuelson, the status quo bias states that when faced with two or more opportunities, people tend to choose the option that favors the current situation. In the case of the NASDAQ Bubble (and other bubbles as well), the endowment effect and the sta tus quo bias indicate that investors who owned Technology and Internet stocks were inclined to ignore concerns over valuation and accounting methods. As a result, they were less likely to sell until the evidence was overwhelming, at which point stock prices had already dropped consider ably. There are several other examples of manias, such as tulip bulbs in the Netherlands in the 1600s, railroads in the United States and United Kingdom in the 1800s, gold in the United States in the late 1970s and early 1980s, and stocks and real estate in Japan in the 1980s. Figure 33, first created by NDR’s Larry Winer for a Chart of the Day on January 27, 2000, shows four such bub bles: the Dow Jones Industrial Average from 1924 to 1932, U.S. gold prices from 1979 to 1980, the Nikkei 225 from 1983 to 1992, and the NASDAQ Composite from 1994 to 2003. The text of the Chart of the Day relates the performance of the NASDAQ Composite to the previous three bubbles, stating, “As Alan Greenspan has noted, nobody knows how high is up in a bubble and, in fact, one only knows for sure that it is a bubble several months after the top. But what is clear is that most of these bubbles do not end gently. In fact, the aftermath is violent and often retraces a 34 THE TRIUMPH OF CONTRARIAN INVESTING

Historical Market Bubbles

5000 4900 4800 GOLD NASDAQ Composite 4700 1979-1980 1994-2003 4600 4500 4400 4300 4200 4100 4000 3900 3800 3700 DJIA 3600 1924-1932 3500 3400 3300 3200 3100 3000 2900 2800 2700 2600 2500 2400 2300 2200 2100 NIKKEI 2000 1900 1983-1992 1800 1700 1600 1500 1400 1300 1200 1100 1000 900 800 700 SDMJ S D MJ SDM JSDM JSDMJ S D MJ SDM JSD MJ S D

(COD00127) 1995 1996 1997 1998 1999 2000 2001 2002 2003 goodly portion of the bubble rise.” It does not matter if the bubble occurred 300 years ago or 3 years ago, or if it occurred in Europe, Japan, or America, or if it happened in an emerging market or a developed market—the actions of the crowd are the same. These few examples show that over different time periods, disparate cultures, and various de grees of technological innovation, the powerful forces of crowd psychology have, given the right conditions, repeatedly given rise to manias and their inevitable crashes. CHAPTER4 HEADLINES AND COVER STORIES

OME OF THE BEST ILLUSTRATIONS OF EXTREMES in crowd psychology have been pro vided by magazine and newspaper cover stories. The headlines for these stories, geared to attract attention on the newsstand (and sell magazines to the crowd wrapped up in the headline story), make great contrary opinion indicators and can help identify times to “beware of the crowd at extremes.” SThe covers of weekly news and business magazines have often served as notable contrary indicators for stock prices. Paul Montgomery, an astute analyst and a friend of NDR, in the early 1980s went back and studied Time magazine covers since the 1920s. He found that for about 30 days after a bullish or bearish Time cover, the market’s performance was usually consistent with the cover. In fact, if you had invested in the stock market for the 30day period following the cover stories, your investment would have gained at a rate of 30 percent per annum. While the covers have been right for those trailing 30 days, he found that they have been wrong over the subsequent 11month periods more than 80 percent of the time, thus showing that cover stories have tended to occur near points of maximum momentum on the upside or downside. By acting contrary to the magazine covers after 30 days, you would have beaten the equivalent buyandhold return by about five times over the next 11 months. The same tendencies have generally held true for Time magazine and other major media sources since Montgomery’s original findings.

HEADLINES SURROUNDING THE 1929 CRASH

Looking back at 1929, one of the most tumultuous times in U.S. stock market history, we can see how newspaper headlines reflected the mood of the day and mostly acted as contrarian indi

Copyright © 2004 The McGrawHill Companies. Click here for terms of use. 36 THE TRIUMPH OF CONTRARIAN INVESTING cators. As shown in Figure 41, the headlines in the “newspaper of record,” the New York Times, prior to and at the start of the market’s massive decline reflected optimism and a lack of concern about a possible bubble in stock prices or the possibility of a major drop. One of the leading stock market pundits in 1929 was Yale economist Irving Fisher, who is referenced in the October 16 Times headline, saying that stocks were “permanently high.” He was a wellknown bull at the time and made many of the same arguments justifying the high stock prices of the late 1920s that were echoed 70 years later during the late 1990s bubble period. Other commentators featured in the headlines described the market’s rise into mid1929 as “justified” and proclaimed that stock prices were likely to “stay at high levels for years to come.” The Wall Street Journal also reflected the bullish enthusiasm just before the peak, and on August 23, 1929, wrote: “According to the Dow theory, this development [the Dow’s rise] reestablishes the major upward trend. Reassurance on this score gave fresh stimulus to bullish enthusiasm, and a long list of representative stocks surged upward to new highs. . . . The outlook for the fall months seems brighter than at any time.” The Dow’s closing high occurred 10 days later on September 3, and was not surpassed for another 25 years.

Daily Data 1/02/1929 - 8/01/1930 (Log Scale) Dow Jones Industrial Average 1929-1930

381 Headlines From The New York Times 381 July 3, 1929 October 16, 1929 369 SEES STOCK RISE JUSTIFIED 369 Moody's Says Returns Are In Line With Industrial Activity FISHER SEES STOCKS PERMANENTLY HIGH

357 October 13, 1929 October 22, 1929 357 STOCK PRICES WILL STAY AT HIGH LEVEL FOR YEARS WASHINGTON VIEWS SITUATION AS SOUND 346 TO COME, SAYS OHIO ECONOMIST Officials Hold That Decline In Stocks Will Not 346 Disturb Business Materially

335 October 24, 1929 335 PRICES OF STOCKS CRASH IN HEAVY LIQUIDATION

325 October 25, 1929 325 WORST STOCK CRASH STEMMED BY BANKS; 315 12,894,650-SHARE DAY SWAMPS MARKET; 315 LEADERS CONFER, FIND CONDITIONS SOUND 305 305 WALL STREET OPTIMISTIC AFTER STORMY DAY 296 296

287 287

278 278

269 269

261 261

253 253

245 245

237 237

230 October 26, 1929 230 BANKERS PLEDGE CONTINUED SUPPORT 223 HOOVER SAYS BUSINESS BASIS IS SOUND 223 October 30, 1929 216 STOCKS COLLAPSE IN 16,410,030-SHARE DAY, 216 BUT RALLY AT CLOSE CHEERS BROKERS; 209 BANKERS OPTIMISTIC, TO CONTINUE AID 209

203 203

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOVDEC JAN FEB MAR APR MAY JUN JUL AUG

(BOTC3) 1929 1930 HEADLINES AND COVER STORIES 37

Contrary to the optimistic outlooks featured in the news, after an initial decline and short rally, in a period of approximately 1 month between October 10 and November 13, 1929, the Dow Industrials suffered an enormous 44 percent loss before starting to recover. And even during and after the market crash, the headlines indicated that bankers, business leaders, and President Hoover were still optimistic and saw conditions as “sound.” Even the day after the infamous Black Thursday crash on October 24, the New York Times headlines said, “Leaders Confer, Find Condi tions Sound” and “Wall Street Optimistic after Stormy Day.” Such public reassurances from busi ness and political leaders were likely made at least partly with the goal of preventing further panic and pessimism, but they would have been cold comfort to any investor who followed the headlines and held onto his stocks. Despite the massive decline in late 1929, the Dow went on to fall another 79 percent from the initial crash low of November 13, 1929, to its ultimate low on July 8, 1932, as the U.S. economy fell into the Great Depression.

COVER STORIES: 1960s–1970s

Turning to more recent times, between 1966 and 1982 stock prices endured another secular bear market like the one that began in 1929, and that saw significant volatility but little or no net gains over the 16year period. That generally bearish period, however, included several substantial cyclical rallies. Between 1966 and 1982, the Dow Jones Industrial Average saw five rallies and four declines of greater than 30 percent. At or near many of those peaks and troughs were cover stories that, in hindsight, would have been good contrary indicators, as shown in Figure 42. For example, on November 2, 1968, BusinessWeek featured a story titled “The Boom That Just Won’t Stop.” The boom did stop, at least in the stock market. One month later, on December 3, 1968, the DJIA peaked at 985. The market proceeded to fall 18.6 percent over the next 7 months on its way to a 35.9 percent total decline over 18 months. In May 1970, with the Dow in the midst of its deepest bear market in percentage terms since World War II and the longest in duration in over a decade, a May 2, 1970, Economist story natu rally asked the question, “When Will the Selling Stop?” The article goes on to state that “in the last analysis, there is no floor in equity prices, except that at such a low level that it becomes ludicrous to dwell on it. . . . Once stock prices have begun to roll there is liable to be nothing that will imme diately stop the rot.” True to Paul Montgomery’s analysis, the cover story was correct over the immediate term, with the market tumbling 11.7 percent over the next 3 weeks. The Newsweek of May 25, 1970, also captures the building gloom among investors. The cover is a collage of dismal economic pictures: people selling apples as they come off an unemployment line, houses missing rooftops, stock prices falling off the bottom of a chart, and kettles represent ing prices and wages about to explode. Three weeks after the Economist story and one day after the publishing date of the Newsweek cover, the market bottomed on May 26 around 631 and pro ceeded to climb over the 950 level by April 1971, and in 1972 it burst through the 1000 level for the first time in six years. 38 THE TRIUMPH OF CONTRARIAN INVESTING

Weekly Data 1/05/1968 - 12/26/1980 (Log Scale) Dow Jones Industrial Average

1043 "The Boom That Just "Not A Bear "1200 on the Dow" "Seven Bulls, One Bear" 1043 Won't Stop" Among Them" Barron's Barron's 1020 1020 BusinessWeek Barron's January 8, 1973 January 10, 1977 997 November 2, 1968 January 1, 1973 997 975 975 954 954 933 933 913 913 893 893 873 873 854 854 835 835 817 817 799 799

781 "Subdued - But Bullish" 781 764 Barron's 764 December 31, 1973 "The Death of Equities" 747 747 BusinessWeek 731 August 13, 1979 731 "When Will The 715 715 Selling Stop?" 699 Economist 699 May 2, 1970 684 684 669 669 "The Big Bad Bear" 654 Newsweek "Recession's Greetings" 654 Time 640 September 9, 1974 640 December 9, 1974 "RECESSION" 626 626 Newsweek 612 May 25, 1970 612 "How Bad a Slump?" 598 Newsweek 598 585 December 2, 1974 585

MJSD M JSDMJ SD MJ SDM JSDMJ SD M JSDMJ SD MJ SDM JSDMJ SD M JSD MJ SD (BOTC4) 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980

At that point, Barron’s came out with a classic headline on January 1, 1973, which read “Not a Bear among Them.” After bringing the country’s best and brightest market professionals to New York for an allday session on the outlook for the next year, Barron’s reported that its yearend panel of experts was “bullish on Wall Street, business, the market. Security firms, if not NYSE— will flourish no matter what changes come.” In the next week, Barron’s headline “1200 on the Dow” was deemed “a modest expectation for 1973” by the panel. “With earnings of $74 and a multiple of 18, the Industrials could top 1300.” The Dow stood at 1050, and at least 1200 was expected by the best and the brightest of the market’s experts. Contrarily, the market slid downhill to a calendaryear low of 788 in early December and continued further down to 577 the following year. What happened that the experts didn’t know? There were some random, unpredictable, neg ative events that occurred. Watergate exploded, and there was a war in the Middle East, which resulted in OPEC pushing up the price of oil. Alone, these events may not have had a prolonged effect on the market. But combined with the low liquidity from all the positive market opinions, they proved to be more than the market could handle. As if someone had yelled “fire” in a crowded theater, the people rushed to get out of stocks, and a lot of people got hurt. Headlines continued to HEADLINES AND COVER STORIES 39 flash the sentiment of “Wall Street” throughout the decline of FIGURE 43 “Recession’s the 1973–1974 bear market. In December 1973, the Barron’s Greetings.” yearend panel was “Subdued—But Bullish,” while the Dow Getty Images. was reeling from a drop of more than 20 percent over the first 11 months of that year. Following this once again bullish out look, the market did not oblige and continued downward throughout 1974. On September 9, 1974, with the Dow down around the 650 level, Newsweek came out with an angry bear on the cover. The NYSE pillars were crumbling on either side of the bear, and a sign showed Wall Street as a oneway street head ing straight down. The title was “The Big Bad Bear.” Stocks did decline for another month into October, consistent with Paul Montgomery’s findings stated earlier. The market then rebounded 15 percent from the October low of 584 through early November, before dropping to the ultimate bear market low on December 6, 1974. Sure enough, the December 2, 1974, Newsweek cover blared the question “How Bad a Slump?” with a picture of Uncle Sam riding a car down a steep declining price line. Time, as Figure 43 shows, also shared in the gloom the following week, on December 9, with a sickly thin and worn Santa Claus under the holiday message “Recession’s Greetings.” From that Decem ber 6 low the market rose 76 percent in the ensuing bull market.

THE 1980s–EARLY 1990s

The same message continues throughout the subsequent mark et cycles showing the media’s need to sell headlines with the focus on public opinion. The figures in this section show the tim ing of some of these contrarianfriendly headlines. As shown in Figure 44, the market’s plummet in 1987 was no exception to the cover story hype both before and after the significant event. A perfect example was the October 1987 issue of Fortune which showed a smiling Greenspan under the headline “Why Greenspan Is Bullish.” Note that while the cover date of October 26 was 1 week after the actual market crash, monthly maga zines reach newsstands and mailboxes several weeks ahead of their cover date. Just after the crash in October, Time flashed in big type on its November 2, 1987, issue, “The Crash—After a wild week on Wall Street, the world is different.” This sensational cover, shown in Figure 45, certainly set out to capture the doom and gloom sentiment of the day. As it turned out, the Dow Jones Indus trial Average had bottomed on October 19 and, after a brief rise and fall into early December, con tinued on a bull market run gaining 72.5 percent through July 1990. Not only did the stock market enjoy an extraordinary run during the late 1980s and early 40 THE TRIUMPH OF CONTRARIAN INVESTING

Daily Data 4/01/1987 - 12/31/1991 (Log Scale) Dow Jones Industrial Average

3184 "The New Face of Recession" 3184 3130 BusinessWeek 3130 3078 "The Selling of America - December 24, 1990 3078 3026 Foreign Investors 3026 2976 Buy, Buy, Buy" 2976 2926 Time 2926 2877 September 14, 1987 2877 2829 2829 2781 2781 2735 2735 "Why Greenspan is Bullish" 2689 2689 Fortune 2644 2644 October 26, 1987 2600 2600 2556 2556 2513 2513 2471 2471 2430 2430 2389 2389 2349 2349 2310 2310 2271 2271 2233 "High Anxiety" 2233 2196 Time 2196 2159 October 15, 1990 2159 2123 2123 2087 2087 2052 "Is The 2052 Party 2018 2018 Over?" 1984 1984 Newsweek "The Crash - After a wild week on Wall 1951 October 27, 1951 Street, the world is different" 1918 1987 1918 1886 Time 1886 1855 November 2, 1987 1855 1824 1824 "How to Ride Out the Bear Market" "How Bad?" 1793 U. S. News & World Report 1793 BusinessWeek 1763 November 9, 1987 1763 1734 November 2, 1987 1734

M JSNJMM J S N J MM J SNJM MJ SNJ MM J S N (BOTC5) 1988 1989 1990 1991

1990s, but the economy was enjoying unprecedented success as well. The United States was in the midst of the longest expansion since the buildup to the Vietnam War. However, the bull market and expansion came to an end in 1990. The Dow Jones Industrial Average peaked in July just shy of 3000, and the economy slipped into recession the same month. By October 11, 1990, the Dow had declined 21.2 percent. Concerns over the stock market and economy were reflected in the Time October 15, 1990, cover “High Anxiety.” The magazine features a picture of a man hanging from a clock several stories above a busy city street. The subtitle reads, “Looming Recession, govern ment Paralysis, and the threat of War are giving Americans a case of the jitters.” While the cover may have accurately reflected the mood of investors, it is often this type of environment in which bull markets begin. On October 11, 1990, the Dow Jones Industrial Average began a 24.1 percent rally through February 15, 1991, on its way to an 8year bull market, the longest since at least 1900. On December 24 of the same year, BusinessWeek featured a cover titled “The New Face of Recession,” depicting uncertainty over the steep losses in whitecollar jobs. A subtitle asks the question, “How long will it last?” According to the National Bureau of Economic Research, the HEADLINES AND COVER STORIES 41

FIGURE 45 “The Crash.” relatively mild recession would end just 3 months later. As for Getty Images. the stock market, the Dow Jones Industrial Average did endure a brief, 2week, 6.3 percent correction before starting an 18.8 percent surge through midFebruary. A year after the cover story, the DJIA was up 16.4 percent and well into the historic bull market.

THE LATE 1990s–EARLY 2000s

Looking to the more recent market swings of the mid and late 1990s, cover stories can still be found reflecting the exag gerated sentiment of the times as shown in Figure 46. At the beginning of 1994, the Federal Reserve began a series of interest rate increases aimed at limiting potential inflation, and it had the effect of causing a sharp decline of 9 to 10 percent in the major stock indices in February and March of that year. With interest rates rising and stock prices trading near their lowest levels in nearly a year, sentiment had become very bearish. So it is not surprising that Newsweek’s April 11, 1994, cover featured a grizzly bear tearing through newspaper stock tables with the headline “How to Survive in a Scary Market.” But as it turned out, buying when the market looked scariest would have been profitable since the S&P 500 made its low on April 4, 1994, and has never been as low since. A year later, the market was up 15 percent and in the midst of a recordsetting bull market. After seeing the Dow surge over 150 percent from the April 1994 lows, the April 27, 1998, Newsweek cover showed a cartoon of a bull wearing a wedding dress, with the words “Like It or Not, You’re Married to the Market.” While the story inside may list the potential risks and rewards of the previous and future market action, this cover shows the exuberant feeling of the crowd and the desire by everyone for the wealthproducing rise to continue. Sure enough, the Dow topped on July 17, 1998, and proceeded into the first bear market period in an extremely long time. After the bear market bottomed on August 31, 1998, there were expected feelings of worry and fear that this was a sign of the future. Newsweek’s cover for October 12, 1998, reads “The Crash of ’99?” with a subtitle of “It doesn’t have to happen—but here’s why it might.” To the con trarian, this was a bullish sign. Contrary to all the gloom in the fall of 1998, the major indices embarked on a dramatic surge from the 1998 lows to their alltime peaks in early 2000, and optimism again pervaded. A widely noted (and subsequently derided) example of excessive optimism prior to the market’s peak in early 2000 was the cover of the September 1999 issue of The Atlantic Monthly titled “DOW 36,000: The Right Price for Stocks” (see Figure 47). The article and a book of the same name made the case that the stock mark et was severely undervalued and that the Dow, then trading around 10,000, would be fairly valued at 36,000. The authors’ reasoning was determined to be 42 THE TRIUMPH OF CONTRARIAN INVESTING

Weekly Data 1/08/1993 - 12/27/2002 (Log Scale) Dow Jones Industrial Average

12695 "Looking Beyond The Bear" 12695 12246 Time 12246 11813 March 26, 2001 11813 11395 11395 10992 "Like It Or Not, You're 10992 10603 Married to the Market" 10603 10228 Newsweek 10228 April 27, 1998 9866 9866 9517 9517 9181 "Wall Street: Is The Party 9181 8856 Over?" 8856 8543 BusinessWeek 8543 8241 April 17, 2000 8241 7949 7949 7668 7668 7397 7397 7135 7135 6883 "The Crash of '99?" 6883 6639 Newsweek 6639 6404 October 12, 1998 6404 6178 6178 5959 5959 5749 5749 5545 5545 5349 5349 5160 5160 4977 4977 4801 4801 4632 4632 4468 4468 4310 4310 4157 4157 4010 4010 3868 3868 3732 3732 3600 3600 3472 "How to Survive in a Scary Market" 3472 3349 Newsweek 3349 3231 April 11, 1994 3231

MJ SD MJ SDM JSDMJSDM JSDMJ SD MJ SDM JSDMJ SDM JSD (BOTC6) 1994 1995 1996 1997 1998 1999 2000 2001 2002 based on incor rect financial formulas and on the controversial assumption that stocks are less risky than bonds. So ev en though few investors are likely to have really believed that the Dow would suddenly triple in value, the ar ticle’s appearance on the cover of a generalinterest literary magazine was a clear example of how far optimism about the market had spread at that time. It also reflects how even analysis based on quantitative data and financial theories can be distorted by excessive optimism. After the market’s initial decline from the March peaks in the NASDAQ and S&P 500, Busi nessWeek’s April 17, 2000, cover asked “Wall Street: Is The Party Over?” The subheading answered the question and reflected the general lack of concern about the market’s high valuations and recent correction: “Hightech stocks are undergoing a muchneeded correction. But relax, the overall market probably won’t tank. What we’re seeing looks more like a healthy flight to quality.” This outlook turned out to be another contrarian sign, since after a short summer rally, the broad market did in fact continue to “tank,” led by a continued collapse in technologyrelated stocks. And about a y ear later, Time magazine had a growling grizzly bear (accessorized in Wall Street style with a briefcase, cell phone, tie, and hat—see Figure 48) on its March 26, 2001, cover with the words “Looking be yond the Bear. Yes, it’s scary out there, but a recession isn’t a sure HEADLINES AND COVER STORIES 43

Figure 47 “Dow 36,000.” thing. Here’s why.” As fate would have it, the National Bureau Christoph Niemann (artist); Atlantic of Economic Research, the official arbiter of recessions and Monthly, September 1999. expansions, would later declare the first recession in over a decade to have in fact begun that very month of March 2001. And Time’s reassurance about “looking beyond the bear” was also a contrarian sign since the market was soon falling again even before the terrorist attacks of that September. Perhaps an even more dramatic illustration of the ability of magazine covers to “call the top” is again found in Time magazine, but in this case for a particular company or indus try’s stock prices. The editors of Time bestow the title of Per son of the Year on the individual who most influenced the events of that year. Reflecting the dominance of the Internet on the stock market and economy, the 1999 Person of the Year was none other than Jeff Bezos, CEO of Amazon.com, the leading Internetbased retailer. The subtitle reads, “E Commerce is changing the way the world shops.” The choice of Bezos reflects how the optimism over the Internet had per vaded not only Wall Street but Main Street as well. Despite the fact that the company had never been profitable, Amazon.com was an investor favorite and bellwether for the technology sector. Amazon soared 42.2 FIGURE 48 “Looking beyond percent in 1999 to bring its total appreciation since its Ma y the Bear.” 1997 IPO to over 5300 percent. But once again, by the time the Getty Images. story of Amazon’s growth was big enough for Time to make its CEO Person of the Year, Amazon’s stock had seen its peak: The alltime record high for Amazon.com occurred on December 10, 1999, just about the time the magazine hit the newsstands, and by September 2001 had fallen as much as 94 percent from that level. Other Internetrelated companies saw similar declines in their stock prices as the Internet bubble burst and reality set in. In summar y, media covers serve the contrarian well as both a reflection of extreme sentiment and a purveyor of the same. Observing this extreme sentiment and realizing its effect on liquidity only helped clarify when the turning points would occur. So while media headlines and cover stories can not be used alone as the basis for investment decisions, they can provide a valuable realtime picture of popular sentiment and can serve as anecdotal support for other measures of stock market psychology. 44 THE TRIUMPH OF CONTRARIAN INVESTING

POLITICALLY MOTIVATED QUOTES

Just as the media headlines abound with reflections of public hype, speech makers also pro vide their version of extreme sentiment. Often this is the result of their research on the feelings of the public they serve. Douglas Casey (1995) claims, in Media, Mania & the Markets, that during Clinton’s first presidential campaign, the candidate received 120 pages of faxes per day summa rizing what the media were saying about the political candidates and the election. All this infor mation was used to help his team of speechwriters determine what should be said and promoted. While reflecting public sentiment, public speakers have the ability to influence crowd opinion with their words as well. Either way, the stronger the sentiment, the greater the absorption. In the 1890s, Gustave Le Bon wrote in his book The Crowd: A Study of the Popular Mind(repub lished in 1982), “Given to exaggeration in its feeling, a crowd is only impressed by excessive sentiments. An ora tor wishing to move a crowd must make an abusive use of violent affirmations.” This follows the contrary opinion theory that if the crowd’s opinion is too strong, investors should be prepared to look the other way. Looking back to the early part of the twentieth century, Calvin Coolidge provided his oppor tunity to share the extreme optimism of the day. On December 4, 1928, his State of the Union address included these words: “No Congress of the United States ever assembled, on surveying the state of the union, has met with a more pleasing prospect than that which appears at the present time. In the domestic field there is tranquility and contentment . . . and the highest record of years of prosperity.” As you can see in Figure 49, he was speaking before the biggest period of eco nomic despair in the country’s modern history. Perhaps his words both reflected and inspired the people’s extreme optimism that led to the topping of the market. Another example occurred during the tumultuous time of the Vietnam War. Lyndon Johnson had to deal with the economics of war on top of all the other factors. His State of the Union address at the beginning of 1966 came at a time when the market was peaking and recession had not been seen since 1961. He spoke of the state of national prosperity with the words “I can report to you tonight what y ou have seen for yourselves already—in e very city and countryside. This Nation is flourishing.” Following that note of optimism, the market peaked on February 9, 1966, before heading into a recession later in the same year. The peak level of 995 was not reached again until November 1972. In the period between, the market rallied from the 1966 lows around 744 to a top (lower than the 1966 peak) in December 1968. Following Richard Nixon’s 1968 election, in January 1969, President Johnson, in his farewell State of the Union address, showed a renewed positive senti ment, as he had 3 years earlier. He stated, “I think all Americans know that our prosperity is broad and it is deep, and it has brought record profits, the highest in our history, and record wages.” Once again, the politicians and the crowd were too enthusiastic about the future, creating an extreme in sentiment that ended the market’s climb. The Dow Industrials dropped by over 35 percent over the next year and a half. Just prior to Richard Nixon’s January 1973 State of the Union address, the Dow Industrials had a spectacular recovery from the May 1970 low of 631 to climb over the 1000 mark for the first HEADLINES AND COVER STORIES 45

Weekly Data 1/03/1920 - 2/21/2003 (Log Scale) Presidential Sentiment 10912 William J. Clinton (1/27/2000): 10912 9445 "... the state of our Union is the 9445 strongest it has ever been." 8176 8176 7078 7078 6127 6127 5304 Dow Jones Industrial Average 5304 4591 Richard M. Nixon (1/1973): 4591 3974 "The basic state of the Union is sound, and full of promise." 3974 3440 Richard M. Nixon (1/30/1974): 3440 2978 "There will be no recession in the United States of America." 2978 2578 2578 2232 2232 Lyndon B. Johnson (1/14/1969): 1932 "...our prosperity is broad and it is deep..." 1932 1672 1672 1448 Lyndon B. Johnson (1/12/1966): 1448 1253 "...This Nation is flourishing." 1253 1085 1085 939 939 813 813 Calvin Coolidge (12/4/1928): 704 "Tranquility, contentment, 704 609 and prosperity" 609 527 Ronald Reagan (1/25/1983): 527 456 "...the state of our Union is strong, 456 395 Gerald R. Ford (1/15/1975): but our economy is troubled." 395 342 "... and I must say to you that the 342 296 state of the Union is not good." 296 256 256 222 222 192 192 166 166 144 144 125 125 108 108 93 93 81 Franklin D. Roosevelt (4/7/1932 & 3/4/1933): 81 70 "These unhappy times call for the building of plans... 70 61 that put their faith once more in the forgotten man 61 52 at the bottom of the economic pyramid." 52 45 "... the only thing we have to fear is fear itself..." 2/21/2003 = 8018.11 45

(BOTC7) 1920 1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 time ever. With the wind behind his sails, the President proclaimed, “The basic state of the Union today is sound , and full of promise. We enter 1973 economically strong, militarily secure, and most important of all, at peace after a long and trying war.” This was a grand display of optimism for all to hear. As is now evident, the 1973–1974 bear market ensued from that month onward, cul minating in a drop of 45 percent from the January 1973 highs. Ignoring the results of the previous year, and perhaps telling his constituents what they wanted to hear, in his January 1974 State of the Union address, Nixon promised, “There will be no reces sion in the United States of America.” Little did he know, the recession had already begun, and it became evident in the months to come. By the end of the year, the market was hitting its lows. The GDP growth rate (yeartoyear percentage change) dropped almost 3 percent from the last quarter of 1973 to the first quarter of 1974. After Gerald Ford took over for Nixon, he presented a classic contrarian quote when he deliv ered his address in January 1975. He ended his opening remarks with “and I must say to you that the State of the Union is not good.” From that point of maximum pessimism, with the President of the United States reflecting the financial woes of the people, the market roared into a new bull market. 46 THE TRIUMPH OF CONTRARIAN INVESTING

A similar note of economic pessimism can be found in Ronald Reagan’s address in January 1983 when he said “the state of our Union is strong, but our economy is troubled.” While the econ omy certainly did appear troubled at the time, the stock market had made its major low in the pre ceding August of 1982 and was in the early stages of a new secular bull market phase. So by the time the economic worry was great enough to make it into the President’s speeches, all the nerv ous investors had sold, and the market was already looking ahead to the next phase of the cycle. Finally, during his last State of the Union address, President Clinton spoke on January 27, 2000, and showed his enthusiasm with the positive statement, “the state of our Union is the strongest it has ever been.” Surely, he was sharing the positive feelings expressed by the previous year’s low unemployment rate, high investor inflows, and surging stock market indices. What he was not referencing was the dangerous lack of liquidity from all those inflows and high consumer debt. The Dow’s alltime peak occurred on January 14, 2000, and since then the index has fallen as much as 37 percent from that peak. These are just a handful of examples, all taken from the words of U.S. presidents. More can be found throughout history, from various leaders and “experts” who are followed by the masses. The important thing to understand is that when listening to someone speak, try to determine the moti vation for the comments, see how it fits into the picture of public sentiment, and look for possible contrary outcomes for the future. Once again, beware of the crowd at extremes, for they will cre ate the turning points that can be most profitable. CHAPTER5 INDICATORS OF CROWD PSYCHOLOGY

ECAUSE CROWD SENTIMENT IS SUCH A POWERFUL force on stock prices, it is natural to look for ways to quantify and analyze sentiment to use it as a tool for making invest ment decisions. This is par ticularly true when w e realize that in many cases, price movements cannot be satisfactorily explained by changes in underlying economic fun damentals. Much of the observed volatility in stock prices, particularly in the short rBun, must therefore be attributed to changes in investor perceptions or psychology. So tracking investor psychology on an ongoing basis is one of our primary goals at Ned Davis Research, and we have developed various methods of monitoring sentiment and using it for investment decision making. In fact, one of the longstanding tenets of NDR’s research efforts is “beware of the crowd at extremes,” and the indicators in this chapter are examples of the quantitative methods we use to identify when the crowd may be at an extreme. But it quickly becomes apparent that accurately tracking and quantifying the perceptions and emotions of thousands or millions of investors over time is a tall order. Even if precise measure ments of crowd sentiment were available, we would still be faced with the more daunting problem of how do we know for sure when sentiment has gone “too far” and will reverse? While govern ment agencies and investment firms impose certain regulations on activities like short selling, program trading, or borrowing money to buy stocks, there are few true constraints on how bullish or bearish investors can become. This is evident by looking at the history of manias, bubbles, and crashes discussed earlier in this book. So if poll data or other data sho w that, say, 70 percent of investors are bullish and 30 percent are bearish, how do we know if that represents excessive bull ishness and signals an imminent market reversal or if investors will become even more bullish and reach 80 or 90 percent bulls before a reversal occurs?

The answer is that we cannot know for certain at a given moment if we have reached a senti ment extreme. We can only know for certain in hindsight. But we have found that it is not neces sary to know the exact moment of a sentiment peak as it occurs. If we wait for readings that have indicated extremes in the past, and then see signs of a reversal occurring, we can still make money by taking a contrary position. The indicators discussed in this chapter show some of the ways we incorporate sentiment data into our market outlook.

INDICATORS BASED ON SURVEYS AND MARKET DATA

Figure 51 shows a relatively straightforward example of tracking sentiment and the market’s tendency to perform contrary to the majority opinion. The line plotted in the top section of the chart is the weekly closing value of the S&P 500 Index. The solid line plotted in the bottom sec tion is based on a weekly survey of members of the American Association of Individual Investors (AAII), who are asked to indicate their opinion about the direction of the stock market over the

FIGURE 51 S&P 500 versus American Association of Individual Investors. Source: Copyright © 2003, Standard & Poor’s, a division of The McGrawHill Companies, Inc. © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 7/31/1987 - 2/21/2003 (Log Scale) Standard & Poor's 500 Stock Index 1451 1451 1313 S&P 500 Gain/Annum When: 1313 1187 (7/31/1987 - 2/21/2003) 1187 1074 1074 AAII Gain/ % 971 Survey: Annum of Time 971 878 Above 61 0.0 40.4 878 795 795 Between 44 and 61 8.2 45.1 719 719 *44 and Below 20.6 14.5 650 650 588 588 532 532 481 481 435 435 393 393 356 356 322 322 291 Investors are asked for their opinion on 291 263 the direction of the stock market over 263 238 the next six months. 238

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

80 Two-Week Front-Weighted Smoothing Excessive Optimism 80 76 2/21/2003 = 29.9 76 72 72 68 68 64 64 60 60 56 56 52 52 48 48 44 44 40 40 36 36 32 32 28 28 24 Extreme Pessimism Bulls 2/21/2003 = 21.0 24 Bears 2/21/2003 = 57.9

(S508) American Association of Individual Investors Bulls/(Bulls + Bears) Source: American Assoc. of Individual Investors, Chicago, IL INDICATORS OF CROWD PSYCHOLOGY 49 next 6 months. Responses are classified as bullish (optimistic), neutral, or bearish (pessimistic). For this indicator, we take the percentage of bullish responses and divide by the sum of the bull ish plus the bearish responses to get the percentage of all those with a definite opinion who are bullish each week (the neutral responses are not included). The line shown in the Figure is a 2 week average of the weekly calculation to reduce the indicator’s volatility. Our historical analysis indicates that readings below 44 percent bulls reflect extreme pes simism among individual investors, while readings above 61 percent reflect potentially excessive optimism. The box in the upper lefthand corner of the figure shows the S&P 500’s historical per formance based on the indicator’s reading at any given time. When the AAII bullish percentage has been below 44 percent, the S&P has confounded the bearish majority and risen at a rate well above average. But when the indicator has been above 61 percent, the S&P has frustrated the bull ish majority and shown almost no net gain on average. And when the indicator has been between those two extremes, the market has shown an annualized gain in line with the market’s longrun average return. Thus we can see in Figure 51 quantifiable evidence of how the market tends to perform contrary to the crowd when too many people are on the same side of the fence. There are similar data based on the market outlook expressed by professional market newslet ter writers, which are determined each week by Investors Intelligence, Inc. Investors Intelligence classifies the advice given by a wide variety of stock market advisory services as bullish, bearish, or basically bullish but expecting a “correction” or nearterm decline and thus on the fence. As we did with Figure 51, we construct an indicator by taking the percentage of bulls and dividing by the sum of the bulls plus the bears (again, ignoring the “correction” or neutral opinions). But as noted above, it can be difficult to find levels that consistently indicate extreme bullish ness or bearishness over the longerterm history of these data. We find that the levels that repre sent extremes in advisory sentiment can shift over time, and that these shifts can be explained to a large degree by considering the fundamental backdrop reflected in the monetary (interest rate) conditions of the U.S. economy. So when monetary conditions are favorable for stocks (i.e., when shortterm interest rates are falling), there is fundamental justification for people being bullish, and we require more extreme bullish readings and less extreme bearish readings to generate a con trary signal. Conversely, when interest rate conditions are unfavorable for stocks (rising rates), it does not take as much bullishness to signify extreme optimism, and more bearishness is required to signal a pessimistic extreme. In Figure 52, we use the current trend in shortterm bond prices to alter the levels that signify excessive bullishness or bearishness dynamically. That is, when rates are falling, excessive bull ishness is indicated by readings of more than 81 percent bulls, and extreme pessimism is indicated by readings below 51 percent. When rates are rising, it only requires a reading of 58 percent bulls to indicate an extreme in optimism, while a reading of 31 percent is needed for a pessimistic extreme. The results shown in the figure indicate that the hypothetical historical results of a strat egy of buying when extreme pessimism is signaled (“B” arrows in the figure) and selling stocks and switching to cash (commercial paper) when extreme optimism is signaled (“S” arrows) would have seen no losing trades, and the strategy’s return would be significantly higher than the buy andhold return of the S&P 500 over the period shown. And by being safely in cash for a signifi 50 THE TRIUMPH OF CONTRARIAN INVESTING

FIGURE 52 S&P versus bulls/ (bulls + bears)—dynamic brackets. Source: Copyright © 2003, Standard & Poor’s, a division of The McGrawHill Companies, Inc. © 2003 Ned Davis Research, Inc. All rights reserved.

) Standard & Poor's 500 Stock Index Weekly Data 7/19/1968 - 2/21/2003 (Log Scale 1700 S 1700 Profitable Long Trades: 100% S 1448 Gain/Annum: 14.3% 1448 1234 Buy-Hold Gain/Annum: 6.4% 1234 1051 1051 Latest Signal 7/19/2002 = 847.76 S 895 S= Switch into Commercial Paper 895 763 763 650 B B 650 S B 554 (Monetary Conditions Based on 26-Week Rate of Change 554 in Short-Term Government Bonds) S 472 S 472 402 S 402 Monetary Positive 342 S B 342 292 Buy When Bulls/(Bulls + Bears) < 51 292 Sell When Bulls/(Bulls + Bears) > 81 249 B 249 212 S B 212 180 S B 180 B 154 S S S 154 S S S S 131 S S S S B 131 112 112 95 Monetary Negative 95 B 81 Buy When Bulls/Bears(Bulls + Bears) < 31 81 B B 69 B B B B B Sell When Bulls/Bears(Bulls + Bears) > 58 69 B B 59 B 59 B 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

90 90 Bearish 85 85 80 80 75 75 70 70 65 65 60 60 55 55 50 50 45 45 40 40 35 35 30 30 25 25 Bullish 2/21/2003 = 55.2%

(S502) Bulls/(Bulls+Bears) - Dynamic Brackets Source: Investors Intelligence cant part of the time, an investor’s overall risk level would be lower than it would be if it were exposed to stocks all the time. There are other indicators based not on surveys but on the actual flows of money into various financial instruments that indicate whether investors are becoming more bullish or bearish. An example of an indicator based on what people are actually doing with their money (rather than what they say) is shown in Figure 53. It is based on the amount of money invested in certain Rydex mutual funds that are designed to track, either directly (long) or inversely (short), certain market indices like the S&P 500 and NASDAQ100. We combine the assets held in all the funds designed to be “long” the market (the “bull” funds), some of which use leverage to magnify returns, and divide by the total assets held in both bull and bear funds (bear funds provide returns equivalent to being short the underlying index, and some also use leverage). This again shows us the percentage of assets invested in bullish funds relative to bullish plus bearish funds. As shown in the box in the figure, when Rydex fund investors have more than 82.5 percent of their total assets in bull funds, it indicates excessive optimism, and the S&P 500 has declined substantially on average under those conditions. When bull fund assets make up less than 51 percent of the INDICATORS OF CROWD PSYCHOLOGY 51

FIGURE 53 S&P 500 versus Rydex bull funds/Rydex bull + bear funds. Source: Copyright © 2003, Standard & Poor’s, a division of The McGrawHill Companies, Inc. © 2003 Ned Davis Research, Inc. All rights reserved.

Daily Data 1/13/1994 - 2/21/2003 (Log Scale) Standard & Poor's 500 Stock Index 1478 1478 1384 S&P 500 Gain/Annum When: 1384 1297 1297 1215 Gain/ % 1215 Indicator in Bottom Clip: Annum of Time 1138 1138 1066 Above 82.5 -19.0 19.9 1066 999 Between 51 & 82.5 1.5 53.8 999 936 *51 and Below 45.2 26.3 Rydex Nova Fund seeks returns corresponding 936 to 150% of the S&P 500 performance 877 Rydex Ursa Fund seeks returns inversely 877 822 correlated with the S&P 500 performance 822 770 Rydex OTC Fund seeks returns corresponding 770 to the NASDAQ 100 Index 721 Rydex Arktos Fund seeks returns inversely 721 676 correlated with the NASDAQ 100 (Sept. 1998 inception) 676 633 Rydex Titan Fund seeks returns corresponding 633 to 200% of the S&P 500 performance 593 Rydex Tempest Fund seeks returns inversely corresponding 593 555 to 200% of the S&P 500 performance 555 520 Rydex Velocity Fund returns corresponding 520 488 to 200% of the NASDAQ 100 performance 488 Rydex Venture Fund seeks returns inversely corresponding 457 to 200% of the NASDAQ 100 performance 457 MJ SDM JSDMJ SD MJ SDM JSD MJ SD M JSDMJ SD MJ SD 1995 1996 1997 1998 1999 2000 2001 2002 2003 95 95 90 5-Day Smoothing Excessive Speculative 90 85 Optimism 85 80 80 75 75 70 70 65 65 60 60 55 55 50 50 45 45 40 Extreme Speculative 40 35 Pessimism 35 30 30 Bull Funds = Rydex Nova + Titan + Velocity + OTC Assets 25 25 Bear Funds = Rydex URSA + Arktos + Tempest + Venture 20 20 15 Data Courtesy of: 15 10 Rydex Funds, www.rydexfunds.com 2/21/2003 = 30.2% 10

(S0572A) Rydex Bull Funds / Rydex Bull + Bear Funds total, signaling extreme bearishness, the S&P has shown much better than average gains. While the amount of historical data is limited, indicators like this help tell us what investors are doing rather than what they are saying, and confirm that it generally pays to move contrary to the posi tion of the crowd. Another way of looking at what traders are actually doing with their money is to track the positions of futures traders who buy and sell contracts on the benchmark S&P 500 stock index. As part of its regulation of U.S. futures markets, the Commodity Futures Trading Commission (CFTC) collects data weekly from U.S. futures brokers on the positions of different categories of traders. It divides traders into categories based on the type of trading they do and the size of their positions. The first distinction is between commercial traders and noncommercial traders, with commercial traders being those who regularly deal in the asset underlying a futures contract (in this case the stocks in the S&P 500 Index) and who register as such with the CFTC. These traders are also called “hedgers” since they often are using futures to hedge or offset exposure to the underlying asset. Noncommercial traders are all other traders, and are generally assumed to be 52 THE TRIUMPH OF CONTRARIAN INVESTING taking positions in futures on a speculative basis. Noncommercial traders are also divided into large and small speculators based on the size of the positions they hold. Because the speculative trader historically has tended to be more influenced by sentiment and is most often on the wrong side of the market at extremes, NDR has used the data on noncommer cial futures traders to construct an indicator of their current positions (shown in the bottom clip of Figure 54). The indicator represents the net aggregate position (sum of the number of contracts held long minus those held short) of noncommercial traders as a percentage of the highest and low est net position over the last 78 weeks (18 months). Thus it shows speculators’ positions relative to the range of their recent history. Heavy net long positions relative to recent history (high indicator readings) indicate high optimism among speculators in the S&P 500; heavy net short positions (low readings) reflect high pessimism. The results in the figure’s box show that readings below 40 percent (high pessimism) have been associated with the S&P 500 rising strongly, while readings above 60 percent (high optimism) have been associated with the S&P 500 losing ground on aver age. So again we find that when speculators in the S&P 500 (“the crowd”) are leaning heavily to

FIGURE 54 S&P 500 Index futures versus Speculator COT Index. Perpetual Contract®. Perpetual Contract® is a registered trademark of CSI® (Commodity Systems, Inc.) Source: Copyright © 2003, Standard & Poor’s, a division of The McGrawHill Companies, Inc. © 2003 Ned Davis Research, Inc. All rights reserved.

S&P 500 Index Futures vs Speculator COT Index Weekly Data 9/07/1984 - 2/14/2003 (Log Scale) 1472 1472 1317 S&P 500 Gain/Annum When: S&P 500 Index Futures 1317 1178 (9/07/1984 - 2/14/2003) (Perpetual Contract) 1178 1054 1054 COT Speculator Gain/ % 942 Index is: Annum of Time 942 843 Above 60% -0.9 42.3 843 754 754 * Between 40% and 60% 12.2 26.2 675 675 603 Below 40% 21.3 31.5 603 540 540 483 Commitments of Traders (COT) data is gathered weekly on Tuesdays 483 432 but reported by the CFTC only every other Friday 432 386 386 346 346 309 309 277 277 247 247 221 Source: Commodity System, Inc. (CSI) 221 198 www.csidata.com 2/14/2003 = 836.18 198 177 177 100 Speculator COT Index = 100 95 Net Positions of Noncommercial Speculators 95 Traders as a Percentage of 90 Bullish 90 85 78-Week (1.5 Year) Range 85 80 (Six-week smoothing) 80 75 75 70 70 65 65 60 60 55 55 50 50 45 45 40 40 35 35 30 30 25 25 20 Speculators 20 15 Bearish 15 10 10 5 Source: Pinnacle Data Corp. 5 www.pinnacledata.com 2/14/2003 = 41.2

(S561A) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 INDICATORS OF CROWD PSYCHOLOGY 53 one side (with commercial traders holding the opposite positions, since every futures contract must represent a long and a short), the market has tended to move contrary to the crowd’s position. Lest it seem that references to “the crowd” or “the public” are meant to include only nonpro fessional investors and exclude Wall Street professionals, Figure 55 shows an indicator reflecting the bullishness or bearishness of Wall Street market strategists. High readings in the data in the figure’s lower clip indicate that market strategists at major Wall Street investment firms are bull ish and recommending higher allocations to stocks. Low readings reflect caution by the strategists and lower recommended stock allocations. Despite the fact that these are experienced professional stock market analysts, when they are excessively bullish as a group, the market has tended to per form poorly, while extreme pessimism among the strategists has tended to occur near market lows and be associated with betterthanaverage returns in the S&P 500. So even supposedly savvy pro fessionals, who are paid handsomely to advise their firms’clients about the stock market, are often wrong when they become excessively bullish or bearish as a group. Figure 56 shows a composite shortterm sentiment indicator that NDR has constructed (the NDR crowd sentiment poll) that combines the readings of seven different sentiment indicators like

FIGURE 55 S&P 500 stock index versus Wall Street strategist sentiment. Source: Copyright © 2003, Standard & Poor’s, a division of The McGrawHill Companies, Inc. © 2003 Ned Davis Research, Inc. All rights reserved.

Standard & Poor's 500 Stock Index Weekly Data 8/30/1985 - 2/18/2003 (Log Scale) 1445 1445 1292 1292 1156 Sentiment Data in bottom clip is proprietary. 1156 1034 Components of historical values contain NDR estimates. 1034 925 925 827 827 740 740 662 662 592 592 529 529 473 473 423 423 379 379 339 339 303 303 271 271 242 242 217 2/18/2003 = 851.17 217 194 Current week uses latest daily S&P close; all prior weeks use weekly closing price. 194

1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 70 S&P 500 -6.1% GPA in this zone (43.8% of the time) 70 68 68 66 66 64 64 62 Extreme Optimism 62 60 60 58 58 56 56 54 54 52 52 50 50 48 Extreme Pessimism 48 46 46 44 44 S&P 500 30.1% GPA in this zone (19.0% of the time) 2/18/2003 = 62%

(S0576) Wall Street Strategist Sentiment 54 THE TRIUMPH OF CONTRARIAN INVESTING the ones described above. By incorporating measures of sentiment based on different types of investors or traders (e.g., newsletter writers, individual investors, option traders), we can get a more comprehensive picture of market sentiment. Like the other indicators, the one plotted in the lower clip represents the percentage of investors who are bullish at any given time, while the upper clip plots the S&P 500 Index. And while the available data history is again relatively limited, we have used these data to identify peaks and troughs in sentiment to give us a better idea of where sentiment peaks and troughs are likely to occur in the future. The arrows shown in the figure are not “buy” or “sell” trading signals that could have been followed in real time; they are placed in the figure only in hindsight to indicate the points of extreme optimism and pessimism at past market turning points. By doing this we see, for instance, that the average reading at optimistic extremes has been 66.9 percent bulls, while the average reading at pessimistic extremes has been 47.1 percent bulls. So we can use these levels as a frame of reference for assessing sentiment going forward. The box in the figure also indicates the S&P 500’s annualized returns based on the level of the indicator. Read

FIGURE 56 S&P 500 Composite Index versus NDR crowd sentiment poll. Source: Copyright © 2003, Standard & Poor’s, a division of The McGrawHill Companies, Inc. © 2003 Ned Davis Research, Inc. All rights reserved.

Daily Data 12/01/1995 - 2/18/2003 S&P 500 Composite Index (updated weekly onWednesday mornings) 1560 Extremes Generated when Sentiment Reading: 1560 1500 Rises above 61.5% = Extreme Optimism 1500 1440 Declines below 55.5% = Extreme Pessimism 1440 1380 Sentiment must reverse by 10 percentage 1380 1320 points to signal an extreme in addition to 1320 1260 the above extreme levels being reached. 1260 1200 Arrows represent extremes in optimism and 1200 1140 pessimism. They do not represent buy and 1140 sell signals and can only be known for 1080 certain (and added to the chart) 1080 1020 in hindsight. 1020 960 960 900 S&P500 Gain/Annum When: 900

840 NDR Crowd Gain/ % 840 780 Average Value Of Indicator At: Sentiment Poll is: Annum of Time 780 720 Optimistic Extremes (down arrows)= 66.9 Above 61.5 -6.6 25.6 720 Pessimistic Extremes (up arrows)= 47.1 Between 50 and 61.5 5.1 52.1 660 Average Spread Between Extremes = 19.8 660 600 *50 and Below 18.8 22.3 600

J M MJ SNJ MM J SNJM MJ SNJMM J S NJMM JSNJMM J SN J MM J SNJ 1996 1997 1998 1999 2000 2001 2002 2003

71.1 71.8 72 70.4 70.5 72 69.6 Extreme Optimism (Bearish) 70 68.8 69.2 70 68 67.2 67.366.8 66.8 67.2 67.1 68 66.7 66.4 66.1 66 65.1 64.8 65.3 66 64 63.0 63.1 64 62 62.0 61.9 62 60 59.4 60 58 58 56 56 55.3 53.5 54 54.7 54.5 54 52 52 50 51.7 51.6 51.7 51.8 51.8 51.5 50 48 48.7 48.7 48 47.9 46 46.5 46 46.0 45.7 44 45.2 44.6 44 42 42.3 42.4 42 40 40.4 40 38 39.0 38 36 37.6 36 34 Extreme Pessimism (Bullish) 34 33.5 2/18/2003 = 35.1 33.9

(S574) NDR Crowd Sentiment Poll INDICATORS OF CROWD PSYCHOLOGY 55 ings below 50 percent have been associated with higher average returns in the S&P 500, while readings above 61.5 percent have been associated with negative S&P returns. For an even more dramatic example of how following the crowd can be dangerous, Figure 5 7 shows the historical record of what would have happened if an investor had bought the S&P 500 at the points of extreme optimism and reversed position (sold short) at the points of extreme pes simism. In every case, the market moved contrary to the crowd, and such a strategy would have resulted in huge losses. Again, the peaks and troughs in sentiment were determined in hindsight, so it would have been impossible to actually follow such a trading strategy (or its profitable reverse) in reality. But using the parameters and perspective drawn from this hypothetical demon stration can provide us with a powerful tool for judging the crowd’s psychology and the market’s likely direction going forward.

NONTRADITIONAL SENTIMENT INDICATORS

One of the ways that Ned Davis Research differs from other analysts is that we often use indi cators that are not traditionally considered sentiment indicators to gauge investor psychology, par ticularly for longerterm perspective. For example, the priceearnings ratio of the S&P 500 (see Figure 58) is generally considered a valuation indicator , but we frequently use valuation as a measure of investor expectations and sentiment. Any measure that reflects what investors are will ing to pay for stocks relative to their current underlying sales, earnings, dividends, or other fun damental factors can be useful for assessing the mood of investors. That is, an investor willing to pay 20 times a company’s yearly earnings can be assumed to be more optimistic about the com pany’s prospects than an investor who is only willing to pay 10 times earnings. So when investors as a group are willing to pay very high prices for each dollar of underlying revenue, earnings, div idends, or assets, it implies high expectations and high optimism. When in vestors will only buy stocks at low valuations, it reflects low expectations and pessimism. And the same principle used with other sentiment indicators can be applied: When investor sentiment or expectations become extreme, the market tends to react by moving in the opposite direction. While valuation measures can be very useful in gauging sentiment and expectations, they tend to be longer term in nature and thus can be more difficult to use for tactical market timing than the indicators discussed earlier in this chapter. For more intermediateterm indicators, we often use relative valuation measures, typically comparing stock valuations with bond yields to see if investors have become excessively optimistic or pessimistic about either of those two competing asset classes. Figure 59 shows an indicator representing the ratio of the yield on the 10year Trea sury note to the earnings yield of the S&P 500 Index (the earnings yield is the reciprocal of the P/E ratio shown in Figure 58). High readings indicate investors are becoming excessively opti mistic about stocks relative to bonds, while low readings indicate excessive enthusiasm for bonds over stocks. Again, we find that extreme readings in this ratio are a warning and that the asset that has attracted the most enthusiasm has tended to underperform the other as investors reallocate into the undervalued asset. 56 THE TRIUMPH OF CONTRARIAN INVESTING

NDR CROWD SENTIMENT POLL (S574) EXTREME EXTREME POINT CROWD CROWD DATE PESSIMISM OPTIMISM S&P 500 P/L RIGHT WRONG 01/15/96 51.732 600 -62 x 02/12/96 66.748 662 -25 x 04/12/96 45.241 637 -32 x 05/17/96 63.026 669 -38 x 07/29/96 42.258 631 -126 x 11/29/96 65.101 757 -36 x 12/16/96 51.556 721 -56 x 01/20/97 68.753 777 -19 x 040/4/97 39.008 758 -194 x 07/30/97 71.140 952 -33 x 09/15/97 51.726 920 -63 x 10/07/97 67.153 983 -77 x 11/12/97 44.570 906 -78 x 12/05/97 64.805 984 -27 x 01/26/98 51.800 957 -122 x 03/16/98 71.791 1079 -2 x 06/15/98 47.853 1077 -107 x 07/16/98 70.440 1184 -210 x 09/04/98 33.536 974 -214 x 11/23/98 69.614 1188 -47 x 12/14/98 54.665 1141 -134 x 01/08/99 70.528 1275 -50 x 03/02/99 51.762 1226 -124 x 04/13/99 67.262 1350 -44 x 04/20/99 55.281 1306 -61 x 05/13/99 66.787 1368 -74 x 06/14/99 48.672 1294 -125 x 07/16/99 65.275 1419 -165 x 10/18/99 42.412 1254 -196 x 01/13/00 69.209 1450 -96 x 02/24/00 51.473 1353 -174 x 03/24/00 66.828 1528 -146 x 05/25/00 45.997 1382 -129 x 07/17/00 66.361 1511 -91 x 07/28/00 54.507 1420 -101 x 09/01/00 67.152 1521 -147 x 10/13/00 48.721 1374 -58 x 11/06/00 62.020 1432 -117 x 12/26/00 45.653 1315 -59 x 01/30/01 61.922 1374 -223 x 03/16/01 40.412 1151 -158 x 05/22/01 67.121 1309 -343 x 09/21/01 37.581 966 -207 x 01/04/02 66.079 1173 -76 x 02/08/02 46.501 1096 -68 x 03/08/02 63.082 1164 -327 x 02/20/03 33.857? 837 ? x? AVERAGE 47.08 66.88 TOTAL -5059 INDICATORS OF CROWD PSYCHOLOGY 57

FIGURE 58 S&P 500 stock index versus S&P 500 priceearnings ratio. Source: Copyright © 2003, Standard & Poor’s, a division of The McGrawHill Companies, Inc. © 2003 Ned Davis Research, Inc. All rights reserved.

Monthly Data 3/31/1926 - 1/31/2003 (Log Scale) Standard & Poor's 500 Stock Index 1860 1860 Median S&P 500 % After Reaching 76.9 Year Average S 1280 Extremes 1280 PE of 15.6 x 12-Month Earnings 881 881 Months P/E P/E All () S 606 Later > 20.2 < 9.3Periods S 606 417 3 -2.5 5.8 2.3 S&P 500 Monthly Close 417 287 6 -7.3 5.0 4.4 () 287 198 9 -1.8 11.3 6.7 S&P 500 12-Month 198 136 12 -1.4 13.2 8.9 S Reported Earnings 136

94 24 0.8 27.5 15.6 Dec 1989 22.87 94 B 64 S Dec 1990 21.34 64 B Dec 1991 15.91 44 S B Dec 1992 19.09 44 Dec 1993 21.89 31 S 31 Dec 1994 30.60 21 S Dec 1995 33.96 21 14 Dec 1996 38.73 14 B Dec 1997 39.72 10 Earnings based on estimate for latest Dec 1998 37.70 10 7 B quarter until actual earnings released Dec 1999 48.17 7 B Dec 2000 50.00 5 B Dec 2001 24.69 5 3 Dec 2002 28.31 (E) 3 Dec 2003 39.12 (E) 2 B Latest 12-Month Earnings 12/31/2002 = $28.31 (Estimate) 2 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 43.8 43.8 38.8 38.8 34.4 25-Year Average = 18.23 34.4 30.5 50-Year Average = 16.74 30.5 27.0 1/31/2003 = 30.23 27.0 23.9 23.9 21.2 Expensive 21.2 18.8 18.8 16.7 Norm (---) 16.7 14.8 14.8 13.1 13.1 11.6 11.6 10.3 10.3 9.1 9.1 8.1 8.1 7.1 Bargains 7.1 6.3 6.3

(S660) S&P 500 Price/Earnings Ratio

An even longerterm perspective on investor psychology and expectations can be seen by monitoring the percentage of all financial assets held in stocks. Investors’ decisions about what fraction of their investable assets to put into stocks reflect their longterm expectations and risk assessments. High stock holdings indicate high optimism about the longterm returns for stocks, while low readings indicate low expectations and potentiall y excessive bearishness. Again, we look for extremes in perceptions and sentiment to be followed by reversals contrary to the major ity. Figure 510 shows the percentage of household financial assets held in stocks over time, based on data collected by the Federal Reserve. Because this percentage is based on the market values of stocks and other assets, changes in this percentage can be caused either by changes in stock prices relative to other assets (bonds, etc.) or by investors shifting money between stocks and other assets. To get perspective on current investor sentiment, we can compare the current percentage of household assets held in stocks with the longterm norms shown in the figure and with the extreme readings found at past major market peaks and troughs. Extreme readings tell us to be on alert for potentially significant longterm reversals in the stock market. 58 THE TRIUMPH OF CONTRARIAN INVESTING

FIGURE 59 S&P 500 total return/Lehman Bros. longterm bond total return. Source: Copyright © 2003, Standard & Poor’s, a division of The McGrawHill Companies, Inc. © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/02/1981 - 2/21/2003 (Log Scale) Standard & Poor's 500 Total Return / Lehman Brothers Long-Term Bond Total Return 244 244 228 S&P 500/Bond Ratio Gain/Annum When: 228 213 213 200 Bond Yield/ Gain/ % 200 Earnings Yield: Annum of Time 187 187 175 Above 1.45 -13.1 30.4 175 164 Between 1.2 and 1.45 0.8 45.4 164 153 *1.2 and Below 18.1 24.2 153 144 144 135 135 126 126 118 118 110 110 103 103 97 97 91 91 85 85 79 79 74 2/21/2003 = 102 74 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2.41 2.41 2.27 2.27 2.13 Favors Bonds vs. Stocks 2.13 2.00 2.00 1.88 1.88 1.77 1.77 1.67 1.67 1.57 1.57 1.47 1.47 1.38 1.38 1.30 1.30 1.22 1.22 1.15 1.15 1.08 Favors Stocks vs. Bonds 1.08 1.02 1.02 0.96 2/21/2003 = 1.09 0.96

(AA45) 10-Year Treasury Yields / Standard & Poor's 500 Earnings Yields

Finally, a more anecdotal but intuitive way of tracking the enthusiasm of individual investors for stocks on a longerterm basis is to monitor the number of active investment clubs in the United States. Based on data from the National Association of Investors Corporation, the number of domestic investment clubs can be an indication of the public’s interest in the stock market. During periods of drawnout market weakness, like 1981–1982, public investors have little need for an investment club since they’re not even exposed to the market (note in Figure 511 the low invest ment club totals during that period). But when the market enters prolonged periods of strength, as was the case after the 1990 bottom, public investors find themselves plunging into the stock mar ket but needing help in deciding which stocks to buy. As with the explosive interest in mutual fund buying during the 1990s, the rise in the number of investment clubs reflected widespread public demand for stocks. While public demand helps power the market upward, it’s important to recognize that once extremely high levels have been reached, the implication is that the market is very overbought— and vulnerable as a result. When public demand is extremely weak, the implication is that the worst news has already been seen, that the market is very oversold, and that a new advance lies ahead. INDICATORS OF CROWD PSYCHOLOGY 59

Quarterly Data 3/31/1952 - 9/30/2002 Stocks as a Percentage of Financial Assets - Households & Personal Trusts 35 34.4% (4th Qtr 1968) 35 (Includes Equity Mutual Funds) Billions of Dollars 33.5% 34 Latest Quarter Based on Incomplete Data (1st Qtr 2000) 34 33 33 32 32 31 31 30 30 29 29 Equities = $5675.1 28 ______= 19.5% 28 26.3% Total Finl Assets = $29110.6 27 (4th Qtr 1972) 27 26 26 25 25 24 24 23 23

22 50-Year Mean = 21.3% 22 21 21 20 20 25-Year Mean = 17.7% 19 19 18 18 17 17 16 16 9.4% 15 (2nd Qtr 1982) 15 14 14 13 13 12 12 11 11 10 10

(S482) 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000

In conclusion, it appears that most investors and analysts would agree that it is all but impos sible to satisfactorily explain the movements in stock (or other asset) prices based strictly on changes in underlying economic fundamentals. Because stock prices are determined in an open market by humans making decisions based on incomplete information, it is inevitable that the emotional and psychological tendencies and biases that affect all humans will be reflected in the behavior of stock prices. And there is ample evidence both within and outside the history of finan cial markets that people behave differently when they are a part of a group or crowd than they would if acting alone. Consequently, at Ned Davis Research we have focused considerable effort over the years on developing ways to track the psychological state and crowd behavior of investors as a means of improving our ability to avoid our own psychological biases and the welldocumented pitfalls of following the crowd. The indicators discussed in this chapter are examples of the quantitative data that we have found useful in objectively analyzing the sentiment of investors as a group. And by being willing to view indicators that are not normally considered “sentiment” indicators from the perspective of looking for signs of extremes in investor psychology, we can construct a more diverse array of tools to use in market analysis. Those interested in further details about stock mar 60 THE TRIUMPH OF CONTRARIAN INVESTING

Dow Jones Industrial Average Yearly High-Low-Close Yearly Data 12/31/1956 - 12/31/2003

10949 9535 8304 7232 6298 Dow Jones Industrial Average 5485 updated through 2/14/2003 37050 Log Scale Left 4777 36075 4160 35100 3623 34125 3156 33150 32175 2748 31200 2393 30225 2084 29250 1815 28275 1581 * 27300 1377 26325 1199 25350 1044 24375 910 23400 792 22425 690 21450 601 20475 523 19500 456 18525 Number of U.S. Investment Clubs 17550 Scale Right 16575 15600 Data for 2003 Reflects Number* of Clubs as of 1/31/2003 14625 (# = 28075) 13650 12675 Source: National Association of Investors Corp. 11700 10725 9750 8775 7800 6825 5850 4875 3900 2925

(S487) 1956 1958 1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 ket sentiment indicators should also refer to the information in Chapters 6 and 7 of The Research Driven Investor, a comprehensive guide to investment timing indicators written by NDR’s Global Equity Strategist Tim Hayes (2000). So while economic fundamentals tend to be reflected in financial asset prices eventually, we believe the magnitude of the nonfundamentally driven changes in stock prices o ver time makes analysis of investor psychology and crowd behavior essential for risk management and return enhancement. CHAPTER6 POSTSCRIPT

HIS POSTSCRIPT HAS NOTHING TO DO WITH investing. It is rather my “twocents worth” opinion on taking the “road less traveled” in one’s personal life. I first started thinking about all this as a freshman in high school when I heard a speech about “the grave dangers of conformity.” Since that time, I have watched too many teenagers and college students f all victim to conformity as they attempted to Tingratiate themselves with the “in crowd”—the popular group. I saw their passion for “being accepted” and “fitting in” lead to crowd madness, including binge drinking, taking drugs, cheat ing on tests, becoming anorexic, or really destroying their lives by reacting to crowd rejection with suicides, depression, or even Columbinetype anger. It made me see how good, rational individu als were really changed by peer pressure, which is incredibly powerful in making the individual conform to the group. It is not just individual lives being destroyed that bothers me. I had a quote in my introductory chapter about “how evil is probably a lot closer to the surface than we like to think.” I believe that people have free will to do good or evil. But the most unspeakable evil throughout history has come when evil infects a mob (or a dictator leading a mob), sometimes e ven in the name of reli gion. So when I went to college, I was determined to be an individual rather than a member of a popular fraternity or group. I was attracted by thoughts from diverse writers that celebrated the individual—from the “right” by libertarians like Ayn Rand, who extolled rugged individualism and freedom, saying “civilization is the process of setting man free from men” (The Fountainhead, quoted in Bartlett, 1992) and from the “left” by Jean Paul Sartre, who in Huis Clos (No Exit) stated that “Hell is seeing y ourself through other people’s eyes” (Bartlett, 1992). Of course, that gives others control over who you are. I want to be respectful of others’ feelings, and I want to listen to criticism because there may be something I can learn. But I decided that the one person I really had to live with 24 hours every day was myself, so I mostly try to see myself through my eyes.

Later I remember being stirred by a Paul Anka song that Frank Sinatra made famous. It says, I’ve lived a life that’s full, I traveled each and every highway, and more, much more than this, I did it my way. In my business endeavors I have also been mindful of these words by J. Paul Getty in 1983 (the Bill Gates and Warren Buffett of my youth—note also his book was originally published by Play boy) that I read as I started my career: The resourceful and aggressive man who wants to get rich will find the field wide open, pro vided he is willing to heed and act upon his imagination, relying on his own abilities and judgment rather than conforming to patterns and practices established by others. The nonconformist—the leader and originator—has an excellent chance to make his fortune in the business world. He can wear a green toga instead of a grayflannel suit, drink yak’s milk rather than martinis, drive a Kibitka instead of a Cadillac and vote the straight Vegetarian Ticket— and none of it will make the slightest difference. Ability and achievement are bona fides no one dares question, no matter how unconventional the man who presents them. It has always been my contention that an individual who can be relied upon to be himself and to be honest unto himself can be relied upon in every other way. I want to summarize my thoughts thus far by quoting Ralph Waldo Emerson: Society everywhere is in conspiracy against the manhood of every one of its members. . . . The virtue in most request is conformity. Selfreliance is its aversion. It loves not realities and creators, but names and customs. Whoso would be a man must be a nonconformist. It is easy in the world to live after the world’s opinion; it is easy in solitude to live after our own; but the great man is he who in the midst of the crowd keeps with perfect sweetness the inde pendence of solitude. A foolish consistency is the hobgoblin of little minds, adored by little statesmen and philoso phers and divines. With consistency a great soul has simply nothing to do. Nothing can bring you peace but yourself. (Self Reliance, quoted in Bartlett, 1992). After 9/11 my focus changed somewhat from my own goals and philosophy to a profound thankfulness that I grew up in a nation that, in my opinion, allows freedom and individualism to flourish. Yes, we are a democracy where, by definition, the majority (crowd) rules. And that in itself is a historic gift, but only when guided by laws and principles to protect the individual. So the fathers and framers of this country inserted into the Constitution a series of checks and bal ances to make certain that we could not have tyranny either from the masses (crowd) or from the rulers. Note that the Declaration of Independence first talks of individuals being endowed by their creator with certain unalienable rights—“that among these are life, liberty and the pursuit of hap piness” and it is only after that it states that “to secure these rights, governments are instituted.” Then nine of the first ten Amendments to the Constitution, which compose the bulk of the Bill of Rights, are all about protecting individuals’ freedom to be different with freedom of religion, POSTSCRIPT 63 freedom of speech, the freedom of the press, the freedom to assemble and protest, etc. All of the first nine amendments were designed to protect individual liberties from being trampled by the crowd. It is my view that this country is not great because of the incredible multitude of goods and services we have been able to produce in a free enterprise system, nor do I believe this is a great country because we are the main superpower with unbelievable military might. Rather, I think this is a great country because the Constitution, and especially the Bill of Rights, set up a framework where we spiritually nourished those free individuals who wanted to take the road less traveled. And these contrarians, nonconformists, creative originators, etc., brought forth inventors from Thomas Jefferson, Thomas Edison, Henry Ford, Ben Franklin, George Washington Carver, Wilbur and Orville Wright, Eli Whitney, Isaac Singer, Albert Einstein, Robert Fulton, Robert Jarvik, Samuel Morse, Alexander Graham Bell, Jonas Salk, Bill Gates, etc., who have created inventions, wiped out disease, and by their spirit and individualism helped the human condition as much as any other people. Lord Acton said in 1887 that “power tends to corrupt and absolute power corrupts absolutely” (letter to Bishop Mandell Creighton, quoted in Bartlett, 1992). I agree with this observation, and I tend to distrust any entity (person, government, business, etc.) that might try to wield absolute power. So I am grateful for the extensive system of restraints on absolute power that we have—and the resulting freedom that it affords us as citizens. Thus, we have three branches of government— executive, legislative, and judicial—that share power. This is a country that celebrates individual freedom and achievement, and there is no doubt in my mind that the Bill of Rights and the separation of power have nourished an independent entre preneurial spirit to explore the road less tra veled. That is the very reason we have had so many achievements that have benefited the entire world. Sometimes taking the road less traveled is lonely, and you must be willing to ignore a lot of people pressuring you to take the popular road and conform to the majority. But in my stock mar ket career, in my country, and in my life—for me the road less traveled makes all the difference. The final word on following kings or crowds goes to Rudyard Kipling (from the poem “A Charm,” quoted in Bartlett, 1992): If you can talk with crowds and keep your virtue, Or walk with Kings—nor lose the common touch. Yours is the Earth and everything that’s in it. And—which is more—you’ll be a Man, my son! This page intentionally left blank. Addendum This page intentionally left blank. SENTIMENT ON INDIVIDUAL STOCKS

HE PRECEDING CHAPTERS OF THIShave primarily focused on sentiment and BOOK psychology as they relate to the overall stock market or other categories of assets. And while sentiment indicators for the overall market are critical, many investors own only particular stocks rather than the entire market. Thus there is a need for an analysis of sentiment indicators for individual stocks. Like the valuationoriented sentiment indi cators for the markT et discussed in Chapter 5, at NDR we find that we can use indicators based on the fundamental variables of an individual company as measures of crowd sentiment and psy chology toward that stock. Such indicators are typically based on a comparison of a company’s current stock price to some measure of the compan y’s underlying value, such as its assets, earn ings, revenues, or dividends. The charts shown in the figures on the following pages represent our analysis of a number of representative stocks using fundamental variables to assess investor sen timent. Regardless of which of the various variables is chosen, though, the same principle applies: When investors are willing to pay a high price for a company’s assets, earnings, etc., they are most likely very optimistic about the company’s future and potentially overconfident, and investors who will pay only a low price for a stock relative to its fundamentals are likely to be relatively pes simistic and potentially overly fearful. So we can see what the “crowd” is thinking about a stock’s prospects by looking at how much investors are willing to pay for each dollar of underlying assets, earnings, etc. When we see crowd sentiment toward a stock reaching extreme levels, we look for an opportunity to take a contrary position to that of the crowd. Differences among companies and industries can cause the range of values that constitutes “high” or “low” valuations, and thus extreme crowd sentiment, to vary significantly among stocks

Copyright © 2004 The McGrawHill Companies. Click here for terms of use. 68 THE TRIUMPH OF CONTRARIAN INVESTING and industries. So we look at a stock’s valuation measure in the context of its own historical range, and make the determination of valuationoptimism being high or low based on the historical rela tionship of each stock’s current valuation to its own longterm norms. One challenge we face, though, is determining which of the innumerable pieces of data on a company’s underlying fundamentals to use as the basis for analyzing market sentiment for a par ticular stock. Because there is such a wide variety of companies and industries represented in the stock market, one particular fundamental variable (such as earnings) may not be appropriate or the most useful for all companies. A given variable may be more relevant than another for particular companies or industries due to variations in accounting practices, corporate structures, regulatory influences, or simply what investors or analysts have chosen to focus on. Consequently, we have analyzed each of the stocks shown on the following pages by testing seven different fundamental variables to see which one has been most useful in identifying extremes in investor sentiment for each stock. Those variables include trailing fourquarter per share values for: 1. Cash dividends 2. Sales—total topline revenues 3. Earnings—bottomline net income according to generally accepted accounting principles 4. Cashflow—earnings adjusted for noncash accounting figures such as depreciation 5. A 20quarter average of earnings 6. A 20quarter average of cashflow 7. Book value (shareholder’s equity)—corporate assets minus liabilities We included the 20quarter (5year) averages of earnings and cashflow as variables in order to smooth out the earnings and cashflow (both of which can be negative) for certain companies that have had very volatile earnings and would otherwise be difficult to analyze. For each variable, we also tested a range of threshold values to determine what level of the variable’s reading has best indicated an extreme relative to the stock’s historical norms. For exam ple, if we are considering sales as the fundamental variable, we use the ratio of the stock’s price per share to sales per share and test a range of different pricesales ratios to see what levels would have best signaled excessive optimism and pessimism historically. We might find, for instance, that a ratio of 1.5 times sales has indicated e xcessive euphoria by investors, while a ratio of 0.7 times sales has indicated excessive pessimism. The same process is used to test each of the seven variables listed above, and the best results from that process are used in the charts shown in the figures. Each stock charted in the figures on the following pages thus has dashed lines plotted along side the historical prices in the bottom section of the figure that reflect the levels we have found to have indicated excessive bullishness or bearishness among investors in that stock, based on one of the seven fundamental variables. By using these levels as a frame of reference, we can get a clearer view of the impact of crowd psychology on individual stock prices. And we can use these charts going forward to tell us when sentiment toward a particular stock is likely to be ADDENDUM: SENTIMENT ON INDIVIDUAL STOCKS 69 excessively bullish or bearish and thus when we should be watching for an opportunity to buy (if investors are overly fearful) or sell (if investors are overly optimistic). Readers can use the param eters shown in the figures in the following pages and apply them to the most current fundamen tal data to gauge investor sentiment for those stocks in the future. It should be kept in mind, however, that all of the analysis shown on the following pages was developed with the benefit of hindsight, and therefore the results shown are hypothetical. Their primary purpose is to give a “bigpicture” perspective on crowd sentiment on individual stocks, rather than to serve as the basis for a trading system. Nonetheless, the parameters shown in the figures that follow can be used as guides for assess ing the stocks going forward. For example, if a figure for a particular stock shows that 1.5 times sales represents excessive optimism and 0.7 times sales represents excessive pessimism, one could simply look up the company’s most recent fourquarter sales per share figure (generally available at no cost from various financial websites) and calculate the current pricesales ratio and see if it falls outside the range of 0.7 to 1.5. And while the readings generated through this type of valuationbased analysis are very use ful, trying to use them by themselves can be difficult. Stocks can be undervalued and out of favor, or overvalued and excessively popular, for frustratingly long periods of time in some cases. This means that buying as soon as a stock first falls to a level indicating excessive pessimism can mean having to hold through further declines or sideways periods before sentiment starts to reverse and a new sustainable uptrend takes hold. The reverse is true when selling stocks that show excessive optimism. Also note that in many cases a stock may have spent perhaps 60 percent of the time in the optimism or pessimism zones (i.e., outside the two dashed lines in the figure), and so the zones are not meant to be targets to act on themselves, but rather buy or sell “alerts.” In addition, while it is generally the case that the further back in time a stock’s historical data go, the more valuable the valuationsentiment perspective is, companies can in some cases change their underlying busi ness or behavior enough to make comparisons with the past less rele vant. For these reasons, investors should incorporate other indicators based on a stock’s price trend and relative strength to finetune entry and exit points. By using technical (price momentum–based) indicators alongside sentimentbased indicators, investors can impr ove their odds of outperforming and significantly reduce downside risk. One example of such a trend indicator is shown in the top section of each figure, which is a 39week moving average of the weekly closing price. When the price is above the moving average, the trend is positive; and when the moving average itself is also rising, the uptrend is considered stronger. So, for example, if we see a stock showing excessive bearish sen timent (in the lower section of the figure), we would watch for the price to then cross above its 39 week moving average (in the figure’s upper section) to identify a lowrisk entry point. Potential sale candidates would be identified using the reverse process: Watch for stocks showing excessive optimism and then falling below the moving average. Regardless of any quantitative readings, though, further research is always advised before taking any action based on the information in these figures. In conclusion, the f igures shown in this addendum present examples of how the impact of crowd psychology can be monitored for individual stocks using a combination of the stock’s price 70 THE TRIUMPH OF CONTRARIAN INVESTING and fundamental variables related to the company. This sort of sentiment analysis is most effec tively used in conjunction with price trend indicators to help finetune the timing of buys and sells. The same principle of crowd psychology that applies to asset classes can be applied to individual stocks: Beware of the crowd at extremes, and look for opportunities to take positions contrary to the crowd after it reaches an extreme in sentiment. FIGURE A1 3M Company (MMM). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) 3M Company (MMM : NYSE) 133.2 133.2 116.1 116.1 101.2 101.2 88.2 88.2 76.9 76.9 67.0 67.0 58.4 58.4 50.9 50.9 44.4 44.4 38.7 38.7 33.7 33.7 29.4 29.4 25.6 25.6 22.3 22.3 19.4 19.4 16.9 16.9 14.8 14.8

71 12.9 12.9 11.2 11.2 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 133.2 133.2 116.1 116.1 101.2 Price/20Q Cash Flow GPA Time Sentiment 101.2 88.2 * Above 15.9-13.6 17.6Overconfident 88.2 76.9 From 11 to 15.99. 4 43.9 Reasonable 76.9 11 and Below 27.4 38.4 Overly fearful 67.0 67.0 58.4 58.4 50.9 50.9 44.4 44.4 38.7 38.7 33.7 33.7 29.4 29.4 25.6 25.6 22.3 22.3 19.4 19.4 16.9 16.9 14.8 14.8 12.9 12.9 11.2 11.2 FIGURE A2 Abbott Laboratories (ABT). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Abbott Laboratories (ABT : NYSE) 51.8 51.8 41.9 41.9 33.8 33.8 27.3 27.3 22.1 22.1 17.8 17.8 14.4 14.4 11.6 11.6 9.4 9.4 7.6 7.6 6.1 6.1 5.0 5.0 4.0 4.0 3.2 3.2 2.6 2.6 2.1 2.1 1.7 1.7

72 1.4 1.4 1.1 1.1 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 51.8 51.8 41.9 41.9 33.8 Price/20Q Earnings GPA Time Sentiment 33.8 27.3 Above 31.1-10.4 30.4Overconfident 27.3 22.1 * From 25.4to 31.18.2 30.9 Reasonable 22.1 25.4 and Below 49.5 38.6 Overly fearful 17.8 17.8 14.4 14.4 11.6 11.6 9.4 9.4 7.6 7.6 6.1 6.1 5.0 5.0 4.0 4.0 3.2 3.2 2.6 2.6 2.1 2.1 1.7 1.7 1.4 1.4 1.1 1.1 FIGURE A3 Advanced Micro Devices, Inc. (AMD). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Advanced Micro Devices, Inc. (AMD : NYSE) 42.2 42.2 35.5 35.5 29.9 29.9 25.1 25.1 21.1 21.1 17.8 17.8 14.9 14.9 12.6 12.6 10.6 10.6 8.9 8.9 7.5 7.5 6.3 6.3 5.3 5.3 4.4 4.4 3.7 3.7 3.1 3.1 2.6 2.6

73 2.2 2.2 1.9 1.9 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 42.2 42.2 35.5 Price / Sales GPA Time Sentiment 35.5 29.8 Above 1.5 -23.6 39.0Overconfident 29.8 25.1 * From 0.9to1.5 9.5 40.2 Reasonable 25.1 21.1 0.9 and Below 71.1 20.8 Overly fearful 21.1 17.7 17.7 14.9 14.9 12.5 12.5 10.5 10.5 8.9 8.9 7.5 7.5 6.3 6.3 5.3 5.3 4.4 4.4 3.7 3.7 3.1 3.1 2.6 2.6 2.2 2.2 1.9 1.9 FIGURE A4 Affiliated Computer Services, Inc. (ACS). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 9/30/1994 - 8/15/2003 (Log Scale) Affiliated Computer Services, Inc. (ACS : NYSE) 52.8 52.8 46.2 46.2 40.5 40.5 35.5 35.5 31.1 31.1 27.2 27.2 23.8 23.8 20.9 20.9 18.3 18.3 16.0 16.0 14.0 14.0 12.3 12.3 10.8 10.8 9.4 9.4 8.3 8.3 7.2 7.2 6.3 6.3

74 5.6 5.6 4.9 4.9 NJM MJSNJ MM J SN J M MJ SNJ MM J SNJ M MJ SNJ MM JSNJMM JSNJMM JSNJMM J 1995 1996 1997 1998 1999 2000 2001 2002 2003 54.2 54.2 47.4 47.4 41.5 Price/Cash Flow GPA Time Sentiment 41.5 36.3 Above 16.7-14.8 35.7Overconfident 36.3 31.7 From 14.4to 16.7 -9.9 26.3 Reasonable 31.7 * 14.4 and Below 140.7 38.0 Overly fearful 27.7 27.7 24.3 24.3 21.2 21.2 18.6 18.6 16.2 16.2 14.2 14.2 12.4 12.4 10.9 10.9 9.5 9.5 8.3 8.3 7.3 7.3 6.4 6.4 5.6 5.6 4.9 4.9 FIGURE A5 Air Products and Chemicals, Inc. (APD). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Air Products and Chemicals, Inc. (APD : NYSE) 48.1 48.1 41.3 41.3 35.4 35.4 30.4 30.4 26.1 26.1 22.3 22.3 19.2 19.2 16.4 16.4 14.1 14.1 12.1 12.1 10.4 10.4 8.9 8.9 7.6 7.6 6.5 6.5 5.6 5.6 4.8 4.8 4.1 4.1

75 3.5 3.5 3.0 3.0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 48.1 48.1 41.3 41.3 35.4 Price/Dividends GPA Time Sentiment 35.4 30.4 * Above 50.8-16.1 39.0Overconfident 30.4 26.1 From 46.3to 50.87.5 22.7 Reasonable 26.1 46.3 and Below 48.4 38.4 Overly fearful 22.3 22.3 19.2 19.2 16.4 16.4 14.1 14.1 12.1 12.1 10.4 10.4 8.9 8.9 7.6 7.6 6.5 6.5 5.6 5.6 4.8 4.8 4.1 4.1 3.5 3.5 3.0 3.0 FIGURE A6 AlbertoCulver Company (ACV). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 7/31/1981 - 8/15/2003 (Log Scale) Alberto-Culver Company (ACV : NYSE) 50.8 50.8 40.5 40.5 32.3 32.3 25.8 25.8 20.6 20.6 16.4 16.4 13.1 13.1 10.5 10.5 8.4 8.4 6.7 6.7 5.3 5.3 4.3 4.3 3.4 3.4 2.7 2.7 2.2 2.2 1.7 1.7 1.4 1.4

76 1.1 1.1 0.9 0.9 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1982 1983 1984 1985 1986 1987 1988 1989 1990 50.8 50.8 40.5 40.5 32.3 Price/Earnings GPA Time Sentiment 32.3 25.8 * Above 20.9-18.9 24.7Overconfident 25.8 20.6 From 16.8to 20.9 20.1 40.0 Reasonable 20.6 16.8 and Below 57.7 35.3 Overly fearful 16.4 16.4 13.1 13.1 10.5 10.5 8.4 8.4 6.7 6.7 5.3 5.3 4.3 4.3 3.4 3.4 2.7 2.7 2.2 2.2 1.7 1.7 1.4 1.4 1.1 1.1 0.9 0.9 FIGURE A7 Alcan, Inc. (AL). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Alcan, Inc. (AL : NYSE) 45.2 45.2 40.8 40.8 36.8 36.8 33.2 33.2 30.0 30.0 27.0 27.0 24.4 24.4 22.0 22.0 19.9 19.9 17.9 17.9 16.2 16.2 14.6 14.6 13.2 13.2 11.9 11.9 10.7 10.7 9.7 9.7 8.7 8.7

77 7.9 7.9 7.1 7.1 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 45.2 45.2 40.8 40.8 36.8 Price/Book Value GPA Time Sentiment 36.8 33.2 * Above 1.3-21.8 29.2Overconfident 33.2 30.0 From 1to1.3 12.8 44.9 Reasonable 30.0 1 and Below 30.7 25.9 Overly fearful 27.0 27.0 24.4 24.4 22.0 22.0 19.9 19.9 17.9 17.9 16.2 16.2 14.6 14.6 13.2 13.2 11.9 11.9 10.7 10.7 9.7 9.7 8.7 8.7 7.9 7.9 7.1 7.1 FIGURE A8 Alcoa, Inc. (AA). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Alcoa, Inc. (AA : NYSE) 41.7 41.7 35.9 35.9 30.9 30.9 26.5 26.5 22.8 22.8 19.6 19.6 16.9 16.9 14.5 14.5 12.5 12.5 10.8 10.8 9.3 9.3 8.0 8.0 6.8 6.8 5.9 5.9 5.1 5.1 4.4 4.4 3.7 3.7

78 3.2 3.2 2.8 2.8 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 41.7 41.7 35.9 35.9 30.9 Price / Sales GPA Time Sentiment 30.9 26.5 * Above 0.906 -13.7 24.0Overconfident 26.5 22.8 From 0.585 to 0.906 10.1 37.6 Reasonable 22.8 0.585 and Below 25.6 38.5 Overly fearful 19.6 19.6 16.9 16.9 14.5 14.5 12.5 12.5 10.8 10.8 9.3 9.3 8.0 8.0 6.8 6.8 5.9 5.9 5.1 5.1 4.4 4.4 3.7 3.7 3.2 3.2 2.8 2.8 FIGURE A9 Altria Group, Inc. (MO). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Altria Group, Inc. (MO : NYSE) 52.5 52.5 42.7 42.7 34.7 34.7 28.3 28.3 23.0 23.0 18.7 18.7 15.2 15.2 12.4 12.4 10.1 10.1 8.2 8.2 6.7 6.7 5.4 5.4 4.4 4.4 3.6 3.6 2.9 2.9 2.4 2.4 1.9 1.9

79 1.6 1.6 1.3 1.3 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 65.6 65.6 52.7 52.7 42.4 Price/Cash Flow GPA Time Sentiment 42.4 34.0 Above 11.9-18.7 16.4Overconfident 34.0 27.3 From 8.4to 11.9 10.6 43.3 Reasonable 27.3 *8.4 and Below 38.6 40.3 Overly fearful 22.0 22.0 17.7 17.7 14.2 14.2 11.4 11.4 9.2 9.2 7.4 7.4 5.9 5.9 4.8 4.8 3.8 3.8 3.1 3.1 2.5 2.5 2.0 2.0 1.6 1.6 1.3 1.3 FIGURE A10 American Express Company (AXP). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) American Express Company (AXP : NYSE) 55.3 55.3 45.9 45.9 38.0 38.0 31.5 31.5 26.1 26.1 21.6 21.6 17.9 17.9 14.8 14.8 12.3 12.3 10.2 10.2 8.4 8.4 7.0 7.0 5.8 5.8 4.8 4.8 4.0 4.0 3.3 3.3 2.7 2.7

80 2.3 2.3 1.9 1.9 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 55.3 55.3 45.9 45.9 38.0 38.0 31.5 31.5 26.1 26.1 21.6 21.6 17.9 17.9 14.8 14.8 12.3 12.3 10.2 10.2 8.4 8.4 7.0 7.0 5.8 5.8 4.8 4.8 4.0 Price/20Q Earnings GPATime Sentiment 4.0 3.3 * Above 20.7 -6.8 28.7Overconfident 3.3 2.7 From 11.8to 20.7 18.3 45.9 Reasonable 2.7 11.8 and Below 32.2 25.4 Overly fearful 2.3 2.3 1.9 1.9 FIGURE A11 Amgen, Inc. (AMGN). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 6/17/1983 - 8/15/2003 (Log Scale) Amgen, Inc. (AMGN : NASDAQ) 60.1 60.1 35.0 35.0 20.4 20.4 11.8 11.8 6.9 6.9 4.0 4.0 2.3 2.3 1.4 1.4 0.8 0.8 0.5 0.5 0.3 0.3 0.2 0.2 81 0.1 0.1 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1984 1985 1986 1987 1988 1989 1990 1991 76.04 76.04 46.30 Price/Sales GPA Time Sentiment 46.30 28.20 Above 17.1-50.7 15.5Overconfident 28.20 17.17 *From 9.3 to 17.1 34.3 44.9 Reasonable 17.17 10.46 9.3 and Below 83.0 39.6 Overly fearful 10.46 6.37 6.37 3.88 3.88 2.36 2.36 1.44 1.44 0.88 0.88 0.53 0.53 0.32 0.32 0.20 0.20 0.12 0.12 0.07 0.07 0.04 0.04 0.03 0.03 0.02 0.02 0.01 0.01 FIGURE A12 Analog Devices, Inc. (ADI). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Analog Devices, Inc. (ADI : NYSE) 87.1 87.1 64.2 64.2 47.3 47.3 34.9 34.9 25.7 25.7 18.9 18.9 13.9 13.9 10.3 10.3 7.6 7.6 5.6 5.6 4.1 4.1 3.0 3.0 2.2 2.2 1.6 1.6 1.2 1.2 0.9 0.9

82 0.7 0.7 0.5 0.5 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 87.1 87.1 64.2 64.2 47.3 Price/Cash Flow GPA Time Sentiment 47.3 34.9 * Above 17.1-16.9 35.3Overconfident 34.9 From 12.6to 17.1 39.4 28.0 Reasonable 25.7 12.6 and Below 50.6 36.7 Overly fearful 25.7 18.9 18.9 14.0 14.0 10.3 10.3 7.6 7.6 5.6 5.6 4.1 4.1 3.0 3.0 2.2 2.2 1.6 1.6 1.2 1.2 0.9 0.9 0.7 0.7 0.5 0.5 FIGURE A13 AnheuserBusch Companies, Inc. (BUD). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Anheuser-Busch Companies, Inc. (BUD : NYSE) 48.4 48.4 38.8 38.8 31.0 31.0 24.8 24.8 19.9 19.9 15.9 15.9 12.8 12.8 10.2 10.2 8.2 8.2 6.5 6.5 5.2 5.2 4.2 4.2 3.4 3.4 2.7 2.7 2.2 2.2 1.7 1.7 1.4 1.4

83 1.1 1.1 0.9 0.9 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 48.4 48.4 38.8 38.8 31.0 Price/Dividends GPA Time Sentiment 31.0 24.8 * Above 63.6-18.4 16.1Overconfident 24.8 19.9 From 42.5to 63.6 24.2 45.3 Reasonable 19.9 42.5 and Below 31.3 38.6 Overly fearful 15.9 15.9 12.8 12.8 10.2 10.2 8.2 8.2 6.5 6.5 5.2 5.2 4.2 4.2 3.4 3.4 2.7 2.7 2.2 2.2 1.7 1.7 1.4 1.4 1.1 1.1 0.9 0.9 FIGURE A14 ArcherDanielsMidland Company (ADM). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Archer-Daniels-Midland Company (ADM : NYSE) 18.3 18.3 15.9 15.9 13.8 13.8 12.0 12.0 10.4 10.4 9.0 9.0 7.8 7.8 6.8 6.8 5.9 5.9 5.1 5.1 4.4 4.4 3.8 3.8 3.3 3.3 2.9 2.9 2.5 2.5 2.2 2.2 1.9 1.9

84 1.6 1.6 1.4 1.4 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 18.2 18.2 15.6 15.6 13.4 Price/Cash Flow GPA Time Sentiment 13.4 11.5 Above 9.3-19.4 38.5Overconfident 11.5 * From 7.5to 9.3 6.7 39.9 Reasonable 9.8 7.5 and Below 88.9 21.6 Overly fearful 9.8 8.4 8.4 7.2 7.2 6.2 6.2 5.3 5.3 4.5 4.5 3.9 3.9 3.3 3.3 2.9 2.9 2.5 2.5 2.1 2.1 1.8 1.8 1.5 1.5 1.3 1.3 FIGURE A15 Bank of America Corporation (BAC). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Bank Of America Corporation (BAC : NYSE) 79.4 79.4 65.7 65.7 54.4 54.4 45.0 45.0 37.3 37.3 30.9 30.9 25.5 25.5 21.1 21.1 17.5 17.5 14.5 14.5 12.0 12.0 9.9 9.9 8.2 8.2 6.8 6.8 5.6 5.6 4.7 4.7 3.9 3.9

85 3.2 3.2 2.6 2.6 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 79.4 79.4 65.7 65.7 54.4 Price/Dividends GPA Time Sentiment 54.4 45.0 * Above 30.8 -8.0 34.6Overconfident 45.0 37.3 From 25.6to 30.88.7 33.0 Reasonable 37.3 25.6 and Below 52.4 32.4 Overly fearful 30.9 30.9 25.5 25.5 21.1 21.1 17.5 17.5 14.5 14.5 12.0 12.0 9.9 9.9 8.2 8.2 6.8 6.8 5.6 5.6 4.7 4.7 3.9 3.9 3.2 3.2 2.6 2.6 FIGURE A16 Bank One Corporation (ONE). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Bank One Corporation (ONE : NYSE) 58.8 58.8 48.6 48.6 40.3 40.3 33.3 33.3 27.6 27.6 22.8 22.8 18.9 18.9 15.6 15.6 12.9 12.9 10.7 10.7 8.9 8.9 7.3 7.3 6.1 6.1 5.0 5.0 4.2 4.2 3.4 3.4 2.9 2.9

86 2.4 2.4 2.0 2.0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 58.8 58.8 48.6 Price/Book Value GPA Time Sentiment 48.6 * Above 2-13.0 37.4Overconfident 40.3 From 1.8 to 2 -2.8 21.7 Reasonable 40.3 33.3 1.8 and Below 53.3 40.9 Overly fearful 33.3 27.6 27.6 22.8 22.8 18.9 18.9 15.6 15.6 12.9 12.9 10.7 10.7 8.9 8.9 7.3 7.3 6.1 6.1 5.0 5.0 4.2 4.2 3.4 3.4 2.9 2.9 2.4 2.4 2.0 2.0 FIGURE A17 Baxter International, Inc. (BAX). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Baxter International, Inc. (BAX : NYSE) 55.2 55.2 47.4 47.4 40.8 40.8 35.0 35.0 30.1 30.1 25.9 25.9 22.2 22.2 19.1 19.1 16.4 16.4 14.1 14.1 12.1 12.1 10.4 10.4 9.0 9.0 7.7 7.7 6.6 6.6 5.7 5.7 4.9 4.9

87 4.2 4.2 3.6 3.6 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 54.4 54.4 45.5 45.5 38.0 Price/Dividends GPA Time Sentiment 38.0 31.8 * Above 48.6 -9.7 37.0Overconfident 31.8 26.5 From 34.5to 48.66.6 30.3 Reasonable 26.5 34.5 and Below 34.2 32.7 Overly fearful 22.2 22.2 18.5 18.5 15.5 15.5 12.9 12.9 10.8 10.8 9.0 9.0 7.6 7.6 6.3 6.3 5.3 5.3 4.4 4.4 3.7 3.7 3.1 3.1 2.6 2.6 2.1 2.1 FIGURE A18 Boeing Company, The (BA). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Boeing Company, The (BA : NYSE) 63.8 63.8 53.0 53.0 44.0 44.0 36.6 36.6 30.4 30.4 25.3 25.3 21.0 21.0 17.4 17.4 14.5 14.5 12.0 12.0 10.0 10.0 8.3 8.3 6.9 6.9 5.7 5.7 4.8 4.8 4.0 4.0 3.3 3.3

88 2.7 2.7 2.3 2.3 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 63.8 63.8 53.0 53.0 44.0 Price/20Q Cash Flow GPA Time Sentiment 44.0 36.6 Above 13.2-19.1 28.3Overconfident 36.6 30.4 * From 7.8to 13.2 12.5 43.5 Reasonable 30.4 7.8 and Below 35.1 28.2 Overly fearful 25.3 25.3 21.0 21.0 17.4 17.4 14.5 14.5 12.0 12.0 10.0 10.0 8.3 8.3 6.9 6.9 5.7 5.7 4.8 4.8 4.0 4.0 3.3 3.3 2.7 2.7 2.3 2.3 FIGURE A19 Boise Cascade Corporation (BCC). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Boise Cascade Corporation (BCC : NYSE) 48.6 48.6 45.0 45.0 41.6 41.6 38.5 38.5 35.6 35.6 33.0 33.0 30.5 30.5 28.2 28.2 26.1 26.1 24.1 24.1 22.3 22.3 20.7 20.7 19.1 19.1 17.7 17.7 16.4 16.4 15.1 15.1 14.0 14.0

89 13.0 13.0 12.0 12.0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 48.6 48.6 45.0 45.0 41.6 41.6 38.5 38.5 35.6 35.6 33.0 33.0 30.5 30.5 28.2 28.2 26.1 26.1 24.1 24.1 22.3 22.3 20.7 20.7 19.1 19.1 17.7 17.7 16.4 Price/Sales GPATime Sentiment 16.4 15.1 Above 0.329 -13.3 39.3Overconfident 15.1 14.0 From 0.256 to 0.329 -2.9 30.0 Reasonable 14.0 13.0 * 0.256 and Below 28.2 30.8 Overly fearful 13.0 12.0 12.0 FIGURE A20 Campbell Soup Company (CPB). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Campbell Soup Company (CPB : NYSE) 53.7 53.7 44.1 44.1 36.2 36.2 29.8 29.8 24.5 24.5 20.1 20.1 16.5 16.5 13.6 13.6 11.2 11.2 9.2 9.2 7.5 7.5 6.2 6.2 5.1 5.1 4.2 4.2 3.4 3.4 2.8 2.8 2.3 2.3

90 1.9 1.9 1.6 1.6 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 53.7 53.7 44.1 44.1 36.2 Price/Dividends GPA Time Sentiment 36.2 29.8 Above 46.7 -9.7 28.0Overconfident 29.8 24.5 * From 34.9to 46.7 10.7 32.7 Reasonable 24.5 34.9 and Below 30.9 39.3 Overly fearful 20.1 20.1 16.5 16.5 13.6 13.6 11.2 11.2 9.2 9.2 7.5 7.5 6.2 6.2 5.1 5.1 4.2 4.2 3.4 3.4 2.8 2.8 2.3 2.3 1.9 1.9 1.6 1.6 FIGURE A21 Chubb Corporation (CB). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Chubb Corporation (CB : NYSE) 82.4 82.4 68.9 68.9 57.6 57.6 48.2 48.2 40.3 40.3 33.7 33.7 28.2 28.2 23.6 23.6 19.7 19.7 16.5 16.5 13.8 13.8 11.5 11.5 9.6 9.6 8.1 8.1 6.7 6.7 5.6 5.6 4.7 4.7

91 3.9 3.9 3.3 3.3 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 82.4 82.4 68.9 68.9 57.6 Price/20Q Earnings GPA Time Sentiment 57.6 48.2 * Above 17.6-14.4 29.1Overconfident 48.2 40.3 From 14.5to 17.6 16.1 32.7 Reasonable 40.3 14.5 and Below 34.4 38.3 Overly fearful 33.7 33.7 28.2 28.2 23.6 23.6 19.7 19.7 16.5 16.5 13.8 13.8 11.5 11.5 9.6 9.6 8.1 8.1 6.7 6.7 5.6 5.6 4.7 4.7 3.9 3.9 3.3 3.3 FIGURE A22 Cisco Systems, Inc. (CSCO). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 2/16/1990 - 8/15/2003 (Log Scale) Cisco Systems, Inc. (CSCO : NASDAQ) 59.7 59.7 32.9 32.9 18.1 18.1 10.0 10.0 5.5 5.5 3.0 3.0 1.7 1.7 0.9 0.9 0.5 0.5 0.3 0.3 0.2 0.2 92 0.1 0.1

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

59.7 59.7

32.9 Price/Sales GPA Time Sentiment 32.9 Above 12 13.6 20.5Overconfident 18.1 * From 6.1to12 46.0 58.5 Reasonable 18.1 6.1 and Below 109.2 21.0 Overly fearful 10.0 10.0 5.5 5.5 3.0 3.0 1.7 1.7 0.9 0.9 0.5 0.5 0.3 0.3 0.2 0.2 0.1 0.1 FIGURE A23 Citigroup, Inc. (C). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/30/1987 - 8/15/2003 (Log Scale) Citigroup, Inc. (C : NYSE) 48.9 48.9 40.4 40.4 33.4 33.4 27.6 27.6 22.9 22.9 18.9 18.9 15.6 15.6 12.9 12.9 10.7 10.7 8.9 8.9 7.3 7.3 6.1 6.1 5.0 5.0 4.1 4.1 3.4 3.4 2.8 2.8 2.3 2.3

93 1.9 1.9 1.6 1.6

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

54.9 54.9 44.5 44.5 36.0 Price/Earnings GPA Time Sentiment 36.0 29.2 Above 21.4-36.2 15.1Overconfident 29.2 23.6 * From 15.5to 21.4 14.3 46.5 Reasonable 23.6 15.5 and Below 41.9 38.4 Overly fearful 19.1 19.1 15.5 15.5 12.6 12.6 10.2 10.2 8.2 8.2 6.7 6.7 5.4 5.4 4.4 4.4 3.5 3.5 2.9 2.9 2.3 2.3 1.9 1.9 1.5 1.5 1.2 1.2 FIGURE A24 Comcast Corporation—Class A (CMCSA). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Comcast Corporation -Class A (CMCSA : NASDAQ) 46.2 46.2 34.8 34.8 26.3 26.3 19.8 19.8 14.9 14.9 11.3 11.3 8.5 8.5 6.4 6.4 4.8 4.8 3.6 3.6 2.7 2.7 2.1 2.1 1.6 1.6 1.2 1.2 0.9 0.9 0.7 0.7 0.5 0.5 94 0.4 0.4 0.3 0.3 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 46.2 46.2 34.8 34.8 26.3 Price/Sales GPA Time Sentiment 26.3 19.8 Above 4-22.3 20.0Overconfident 19.8 14.9 * From 2.5to4 17.2 45.8 Reasonable 14.9 2.5 and Below 61.7 34.1 Overly fearful 11.3 11.3 8.5 8.5 6.4 6.4 4.8 4.8 3.6 3.6 2.7 2.7 2.1 2.1 1.6 1.6 1.2 1.2 0.9 0.9 0.7 0.7 0.5 0.5 0.4 0.4 0.3 0.3 FIGURE A25 Computer Sciences Corporation (CSC). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 5/09/2003 (Log Scale) Computer Sciences Corporation (CSC : NYSE) 85.2 85.2 68.9 68.9 55.7 55.7 45.1 45.1 36.4 36.4 29.5 29.5 23.8 23.8 19.3 19.3 15.6 15.6 12.6 12.6 10.2 10.2 8.2 8.2 6.7 6.7 5.4 5.4 4.4 4.4 3.5 3.5 2.9 2.9

95 2.3 2.3 1.9 1.9 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 85.2 85.2 68.9 68.9 55.7 Price/Earnings GPA Time Sentiment 55.7 45.1 Above 28.6-15.1 23.3Overconfident 45.1 36.4 From 14.8to 28.67.8 46.2 Reasonable 36.4 * 14.8 and Below 41.8 30.5 Overly fearful 29.5 29.5 23.8 23.8 19.3 19.3 15.6 15.6 12.6 12.6 10.2 10.2 8.2 8.2 6.7 6.7 5.4 5.4 4.4 4.4 3.5 3.5 2.9 2.9 2.3 2.3 1.9 1.9 FIGURE A26 ConAgra Foods, Inc. (CAG). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) ConAgra Foods, Inc. (CAG : NYSE) 33.3 33.3 26.5 26.5 21.1 21.1 16.8 16.8 13.4 13.4 10.7 10.7 8.5 8.5 6.8 6.8 5.4 5.4 4.3 4.3 3.4 3.4 2.7 2.7 2.2 2.2 1.7 1.7 1.4 1.4 1.1 1.1 0.9 0.9 96 0.7 0.7 0.6 0.6 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 33.3 33.3 26.5 26.5 21.1 Price/Cash Flow GPA Time Sentiment 21.1 16.8 Above 11.8-22.2 18.5Overconfident 16.8 13.4 * From 8.9to 11.84.4 46.0 Reasonable 13.4 8.9 and Below 64.9 35.5 Overly fearful 10.7 10.7 8.5 8.5 6.8 6.8 5.4 5.4 4.3 4.3 3.4 3.4 2.7 2.7 2.2 2.2 1.7 1.7 1.4 1.4 1.1 1.1 0.9 0.9 0.7 0.7 0.6 0.6 FIGURE A27 Diagnostic Products Corporation (DP). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 6/11/1982 - 8/15/2003 (Log Scale) Diagnostic Products Corporation (DP : NYSE) 47.5 47.5 39.3 39.3 32.5 32.5 26.9 26.9 22.3 22.3 18.4 18.4 15.3 15.3 12.6 12.6 10.5 10.5 8.7 8.7 7.2 7.2 5.9 5.9 4.9 4.9 4.1 4.1 3.4 3.4 2.8 2.8 2.3 2.3

97 1.9 1.9 1.6 1.6 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1983 1984 1985 1986 1987 1988 1989 1990 1991 59.6 59.6 48.5 Price/Cash Flow GPA Time Sentiment 48.5 39.4 Above 20.9-17.3 27.4Overconfident 39.4 32.1 From 16.2to 20.9 25.1 31.9 Reasonable 32.1 26.1 * 16.2 and Below 34.3 40.6 Overly fearful 26.1 21.2 21.2 17.3 17.3 14.0 14.0 11.4 11.4 9.3 9.3 7.6 7.6 6.1 6.1 5.0 5.0 4.1 4.1 3.3 3.3 2.7 2.7 2.2 2.2 1.8 1.8 1.4 1.4 FIGURE A28 Dow Chemical Company, The (DOW). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Dow Chemical Company, The (DOW : NYSE) 43.8 43.8 38.6 38.6 33.9 33.9 29.9 29.9 26.3 26.3 23.1 23.1 20.4 20.4 17.9 17.9 15.8 15.8 13.9 13.9 12.2 12.2 10.8 10.8 9.5 9.5 8.3 8.3 7.3 7.3 6.5 6.5 5.7 5.7

98 5.0 5.0 4.4 4.4 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 43.8 43.8 38.6 Price/20Q Cash Flow GPA Time Sentiment 38.6 * Above 8.6-10.5 37.4Overconfident 33.9 From 5.4 to 8.6 15.3 36.2Reasonable 33.9 29.9 5.4 and Below 24.0 26.4 Overly fearful 29.9 26.3 26.3 23.1 23.1 20.4 20.4 17.9 17.9 15.8 15.8 13.9 13.9 12.2 12.2 10.8 10.8 9.5 9.5 8.3 8.3 7.3 7.3 6.5 6.5 5.7 5.7 5.0 5.0 4.4 4.4 FIGURE A29 EMC Corporation (EMC). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 4/04/1986 - 8/15/2003 (Log Scale) EMC Corporation (EMC : NYSE) 71.4 71.4

36.0 36.0

18.2 18.2

9.2 9.2

4.6 4.6

2.3 2.3

1.2 1.2

0.6 0.6

0.3 0.3

0.2 0.2 99 0.1 0.1

1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

71.4 71.4

36.0 Price/Sales GPA Time Sentiment 36.0 Above 4.7 -2.5 31.6Overconfident 18.2 * From 2.7to 4.7 24.1 31.1 Reasonable 18.2 2.7 and Below 57.9 37.3 Overly fearful 9.2 9.2

4.6 4.6

2.3 2.3

1.2 1.2

0.6 0.6

0.3 0.3

0.2 0.2

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Eastman Kodak Company (EK : NYSE) 88.5 88.5 80.2 80.2 72.7 72.7 65.9 65.9 59.7 59.7 54.2 54.2 49.1 49.1 44.5 44.5 40.4 40.4 36.6 36.6 33.2 33.2 30.1 30.1 27.3 27.3 24.7 24.7 22.4 22.4 20.3 20.3 18.4 18.4 100 16.7 16.7 15.1 15.1 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 88.5 88.5 80.2 Price/Dividends GPA Time Sentiment 80.2 Above 21.4-11.2 38.6Overconfident 72.7 From 16 to 21.4 -4.0 40.2Reasonable 72.7 65.9 *16 and Below 48.5 21.1 Overly fearful 65.9 59.7 59.7 54.2 54.2 49.1 49.1 44.5 44.5 40.4 40.4 36.6 36.6 33.2 33.2 30.1 30.1 27.3 27.3 24.7 24.7 22.4 22.4 20.3 20.3 18.4 18.4 16.7 16.7 15.1 15.1 FIGURE A31 Emerson Electric Company (EMR). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Emerson Electric Company (EMR : NYSE) 73.2 73.2 63.1 63.1 54.3 54.3 46.8 46.8 40.3 40.3 34.8 34.8 29.9 29.9 25.8 25.8 22.2 22.2 19.2 19.2 16.5 16.5 14.2 14.2 12.3 12.3 10.6 10.6 9.1 9.1 7.8 7.8 6.8 6.8 101 5.8 5.8 5.0 5.0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 73.2 73.2 63.1 63.1 54.3 Price/Dividends GPA Time Sentiment 54.3 46.8 Above 41.2 -8.8 24.4Overconfident 46.8 40.3 * From 30.9to 41.20.3 39.8 Reasonable 40.3 30.9 and Below 38.0 35.9 Overly fearful 34.8 34.8 29.9 29.9 25.8 25.8 22.2 22.2 19.2 19.2 16.5 16.5 14.2 14.2 12.3 12.3 10.6 10.6 9.1 9.1 7.8 7.8 6.8 6.8 5.8 5.8 5.0 5.0 FIGURE A32 FactSet Research Systems, Inc. (FDS). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 6/28/1996 - 8/15/2003 (Log Scale) FactSet Research Systems, Inc. (FDS : NYSE) 44.4 44.4 39.6 39.6 35.3 35.3 31.5 31.5 28.2 28.2 25.1 25.1 22.4 22.4 20.0 20.0 17.9 17.9 16.0 16.0 14.2 14.2 12.7 12.7 11.3 11.3 10.1 10.1 9.0 9.0 8.1 8.1 7.2 7.2 102 6.4 6.4 5.7 5.7 JSNJMM JSNJ MM JSN J MMJ SNJ M MJSNJ MM J SN J M MJ SN J MM J 1997 1998 1999 2000 2001 2002 2003 50.3 50.3 44.6 44.6 39.5 Price/Sales GPA Time Sentiment 39.5 35.0 Above 8-57.9 17.5Overconfident 35.0 31.0 * From 5to8 41.0 45.9 Reasonable 31.0 5 and Below 107.6 36.6 Overly fearful 27.5 27.5 24.4 24.4 21.6 21.6 19.2 19.2 17.0 17.0 15.1 15.1 13.3 13.3 11.8 11.8 10.5 10.5 9.3 9.3 8.2 8.2 7.3 7.3 6.5 6.5 5.7 5.7 FIGURE A33 First Data Corporation (FDC). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 4/10/1992 - 8/15/2003 (Log Scale) First Data Corporation (FDC : NYSE) 41.3 41.3 36.9 36.9 33.0 33.0 29.5 29.5 26.4 26.4 23.6 23.6 21.1 21.1 18.9 18.9 16.9 16.9 15.1 15.1 13.5 13.5 12.1 12.1 10.8 10.8 9.7 9.7 8.6 8.6 7.7 7.7 6.9 6.9 103 6.2 6.2 5.5 5.5 JSD M JSD MJ S D MJ SDM JSD MJ S D M JSD MJ S D MJ SDM JSD MJS D M J 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 41.3 41.3 36.9 36.9 33.0 Price/Sales GPA Time Sentiment 33.0 29.5 * Above 3.7-15.4 38.4Overconfident 29.5 26.4 From 3.3to 3.7 18.3 27.6 Reasonable 26.4 3.3 and Below 75.7 34.0 Overly fearful 23.6 23.6 21.1 21.1 18.9 18.9 16.9 16.9 15.1 15.1 13.5 13.5 12.1 12.1 10.8 10.8 9.7 9.7 8.6 8.6 7.7 7.7 6.9 6.9 6.2 6.2 5.5 5.5 FIGURE A34 Ford Motor Company (F). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Ford Motor Company (F : NYSE) 56.1 56.1 45.2 45.2 36.4 36.4 29.3 29.3 23.6 23.6 19.0 19.0 15.3 15.3 12.3 12.3 9.9 9.9 8.0 8.0 6.5 6.5 5.2 5.2 4.2 4.2 3.4 3.4 2.7 2.7 2.2 2.2 1.8 1.8 104 1.4 1.4 1.1 1.1 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 56.1 56.1 45.2 Price/Book Value GPA Time Sentiment 45.2 * Above 2.1-21.3 24.1Overconfident 36.4 From 1to 2.1 16.8 47.0Reasonable 36.4 29.3 1 and Below 19.5 28.9 Overly fearful 29.3 23.6 23.6 19.0 19.0 15.3 15.3 12.3 12.3 9.9 9.9 8.0 8.0 6.5 6.5 5.2 5.2 4.2 4.2 3.4 3.4 2.7 2.7 2.2 2.2 1.8 1.8 1.4 1.4 1.1 1.1 FIGURE A35 Forest Laboratories, Inc. (FRX). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Forest Laboratories, Inc. (FRX : NYSE) 43.6 43.6 24.7 24.7 14.0 14.0 8.0 8.0 4.5 4.5 2.6 2.6 1.5 1.5 0.8 0.8 0.5 0.5 0.3 0.3 0.2 0.2 105 0.1 0.1 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 46.8 46.8 30.4 30.4 Price/Sales GPA Time Sentiment 19.7 Above 7.8 -2.7 22.3Overconfident 19.7 12.8 * From 5.7to 7.8 23.2 37.8 Reasonable 12.8 5.7 and Below 57.9 39.9 Overly fearful 8.3 8.3 5.4 5.4 3.5 3.5 2.3 2.3 1.5 1.5 1.0 1.0 0.6 0.6 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 FIGURE A36 General Dynamics Corporation (GD). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) General Dynamics Corporation (GD : NYSE) 101.8 101.8 86.2 86.2 73.0 73.0 61.8 61.8 52.3 52.3 44.3 44.3 37.5 37.5 31.7 31.7 26.9 26.9 22.7 22.7 19.2 19.2 16.3 16.3 13.8 13.8 11.7 11.7 9.9 9.9 8.4 8.4 7.1 7.1 106 6.0 6.0 5.1 5.1 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 101.9 101.9 86.3 86.3 73.1 Price/Dividends GPA Time Sentiment 73.1 62.0 * Above 60.9-13.1 34.2Overconfident 62.0 52.5 From 47.2to 60.9 21.5 34.1 Reasonable 52.5 47.2 and Below 27.3 31.6 Overly fearful 44.5 44.5 37.7 37.7 32.0 32.0 27.1 27.1 22.9 22.9 19.4 19.4 16.5 16.5 14.0 14.0 11.8 11.8 10.0 10.0 8.5 8.5 7.2 7.2 6.1 6.1 5.2 5.2 FIGURE A37 General Electric Company (GE). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) General Electric Company (GE : NYSE) 53.5 53.5 42.7 42.7 34.1 34.1 27.2 27.2 21.7 21.7 17.4 17.4 13.9 13.9 11.1 11.1 8.8 8.8 7.0 7.0 5.6 5.6 4.5 4.5 3.6 3.6 2.9 2.9 2.3 2.3 1.8 1.8 1.5 1.5 107 1.2 1.2 0.9 0.9 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 53.5 53.5 42.7 42.7 34.1 Price/Dividends GPA Time Sentiment 34.1 27.2 Above 60.6 -1.1 18.6Overconfident 27.2 21.7 * From 33 to 60.66.4 43.0 Reasonable 21.7 33 and Below 34.9 38.4 Overly fearful 17.4 17.4 13.9 13.9 11.1 11.1 8.8 8.8 7.0 7.0 5.6 5.6 4.5 4.5 3.6 3.6 2.9 2.9 2.3 2.3 1.8 1.8 1.5 1.5 1.2 1.2 0.9 0.9 FIGURE A38 General Motors Corporation (GM). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) General Motors Corporation (GM : NYSE) 88.7 88.7 79.6 79.6 71.5 71.5 64.2 64.2 57.6 57.6 51.7 51.7 46.4 46.4 41.6 41.6 37.4 37.4 33.5 33.5 30.1 30.1 27.0 27.0 24.2 24.2 21.8 21.8 19.5 19.5 17.5 17.5 15.7 15.7 108 14.1 14.1 12.7 12.7 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 88.7 88.7 79.6 79.6 71.5 Price / Sales GPA Time Sentiment 71.5 64.2 Above 0.217 -18.5 20.6Overconfident 64.2 57.6 From 0.157 to 0.217 0.6 56.5 Reasonable 57.6 * 0.157 and Below 34.2 22.9 Overly fearful 51.7 51.7 46.4 46.4 41.6 41.6 37.4 37.4 33.5 33.5 30.1 30.1 27.0 27.0 24.2 24.2 21.8 21.8 19.5 19.5 17.5 17.5 15.7 15.7 14.1 14.1 12.7 12.7 FIGURE A39 Genuine Parts Company (GPC). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Genuine Parts Company (GPC : NYSE) 35.4 35.4 31.1 31.1 27.3 27.3 24.0 24.0 21.1 21.1 18.6 18.6 16.3 16.3 14.3 14.3 12.6 12.6 11.1 11.1 9.7 9.7 8.6 8.6 7.5 7.5 6.6 6.6 5.8 5.8 5.1 5.1 109 4.5 4.5 4.0 4.0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 36.6 36.6 32.3 32.3 28.5 28.5 25.2 25.2 22.3 22.3 19.7 19.7 17.4 17.4 15.4 15.4 13.6 13.6 12.0 12.0 10.6 10.6 9.4 9.4

8.3 Price/Dividends GPA Time Sentiment 8.3 7.3 Above 33 -11.5 33.9Overconfident 7.3 6.5 From 30 to 33 13.8 27.7 Reasonable 6.5 5.7 *30 and Below 24.9 38.4 Overly fearful 5.7 5.1 5.1 4.5 4.5 3.9 3.9 FIGURE A40 GeorgiaPacific Corporation (GP). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Georgia-Pacific Corporation (GP : NYSE) 49.6 49.6 43.5 43.5 38.2 38.2 33.5 33.5 29.4 29.4 25.8 25.8 22.7 22.7 19.9 19.9 17.5 17.5 15.3 15.3 13.5 13.5 11.8 11.8 10.4 10.4 9.1 9.1 8.0 8.0 7.0 7.0 6.2 6.2 110 5.4 5.4 4.7 4.7 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 60.4 60.4 52.4 52.4 45.5 Price/Cash Flow GPA Time Sentiment 45.5 39.5 * Above 5-35.6 19.9Overconfident 39.5 34.3 From 3.5to5 9.0 43.3 Reasonable 34.3 3.5 and Below 26.9 36.9 Overly fearful 29.8 29.8 25.9 25.9 22.4 22.4 19.5 19.5 16.9 16.9 14.7 14.7 12.8 12.8 11.1 11.1 9.6 9.6 8.3 8.3 7.2 7.2 6.3 6.3 5.5 5.5 4.7 4.7 FIGURE A41 Gillette Company, The (G). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Gillette Company, The (G : NYSE) 56.1 56.1 43.6 43.6 33.9 33.9 26.4 26.4 20.5 20.5 16.0 16.0 12.4 12.4 9.6 9.6 7.5 7.5 5.8 5.8 4.5 4.5 3.5 3.5 2.7 2.7 2.1 2.1 1.7 1.7 1.3 1.3 1.0 1.0 111 0.8 0.8 0.6 0.6 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 56.1 56.1 43.6 43.6 33.9 Price/20Q Cash Flow GPA Time Sentiment 33.9 26.4 Above 23.1 -7.5 35.7Overconfident 26.4 20.5 * From 19 to 23.1 32.1 26.0 Reasonable 20.5 19 and Below 33.9 38.3 Overly fearful 16.0 16.0 12.4 12.4 9.6 9.6 7.5 7.5 5.8 5.8 4.5 4.5 3.5 3.5 2.7 2.7 2.1 2.1 1.7 1.7 1.3 1.3 1.0 1.0 0.8 0.8 0.6 0.6 FIGURE A42 GlaxoSmithKline plc (GSK). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 8/01/1980 - 8/15/2003 (Log Scale) GlaxoSmithKline plc (GSK : NYSE) 66.2 66.2 51.2 51.2 39.6 39.6 30.7 30.7 23.7 23.7 18.3 18.3 14.2 14.2 11.0 11.0 8.5 8.5 6.6 6.6 5.1 5.1 3.9 3.9 3.0 3.0 2.4 2.4 1.8 1.8 1.4 1.4 1.1 1.1 112 0.8 0.8 0.7 0.7 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 71.2 71.2 54.8 Price /Earnings GPA Time Sentiment 54.8 Above 32.4-34.3 15.1Overconfident 42.3 From 18.5to 32.4 25.6 50.3Reasonable 42.3 32.6 * 18.5 and Below 44.1 34.6 Overly fearful 32.6 25.1 25.1 19.3 19.3 14.9 14.9 11.5 11.5 8.8 8.8 6.8 6.8 5.3 5.3 4.0 4.0 3.1 3.1 2.4 2.4 1.9 1.9 1.4 1.4 1.1 1.1 0.8 0.8 0.7 0.7 FIGURE A43 H.J. Heinz Company (HNZ). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) H.J. Heinz Company (HNZ : NYSE) 49.6 49.6 41.3 41.3 34.3 34.3 28.5 28.5 23.7 23.7 19.7 19.7 16.4 16.4 13.7 13.7 11.4 11.4 9.4 9.4 7.8 7.8 6.5 6.5 5.4 5.4 4.5 4.5 3.8 3.8 3.1 3.1 2.6 2.6 113 2.2 2.2 1.8 1.8 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 49.6 49.6 41.3 Price/Sales GPA Time Sentiment 41.3 34.3 * Above 1.3 -9.8 40.9Overconfident 34.3 28.5 From 1.2 to 1.3 16.4 15.0 Reasonable 28.5 23.7 1.2 and Below 37.2 44.1 Overly fearful 23.7 19.7 19.7 16.4 16.4 13.7 13.7 11.4 11.4 9.4 9.4 7.8 7.8 6.5 6.5 5.4 5.4 4.5 4.5 3.8 3.8 3.1 3.1 2.6 2.6 2.2 2.2 1.8 1.8 FIGURE A44 HON Industries, Inc. (HNI). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 8/01/1980 - 8/15/2003 (Log Scale) HON Industries, Inc. (HNI : NYSE) 33.7 33.7 28.4 28.4 23.9 23.9 20.2 20.2 17.0 17.0 14.3 14.3 12.1 12.1 10.2 10.2 8.6 8.6 7.2 7.2 6.1 6.1 5.1 5.1 4.3 4.3 3.7 3.7 3.1 3.1 2.6 2.6 2.2 2.2 114 1.8 1.8 1.6 1.6 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 33.7 33.7 28.4 28.4 23.9 Price/Earnings GPA Time Sentiment 23.9 20.2 * Above 18.8-15.5 26.5Overconfident 20.2 17.0 From 15 to 18.8 14.6 33.8 Reasonable 17.0 15 and Below 36.9 39.7 Overly fearful 14.3 14.3 12.1 12.1 10.2 10.2 8.6 8.6 7.2 7.2 6.1 6.1 5.1 5.1 4.3 4.3 3.7 3.7 3.1 3.1 2.6 2.6 2.2 2.2 1.8 1.8 1.6 1.6 FIGURE A45 Halliburton Company (HAL). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Halliburton Company (HAL : NYSE) 58.9 58.9 52.9 52.9 47.5 47.5 42.7 42.7 38.3 38.3 34.4 34.4 30.9 30.9 27.8 27.8 24.9 24.9 22.4 22.4 20.1 20.1 18.1 18.1 16.2 16.2 14.6 14.6 13.1 13.1 11.7 11.7 10.5 10.5 115 9.5 9.5 8.5 8.5 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 58.9 58.9 52.9 52.9 47.5 Price / Sales GPA Time Sentiment 47.5 42.7 Above 0.825 -18.1 34.9Overconfident 42.7 38.3 * From 0.544 to 0.825 5.3 45.3 Reasonable 38.3 0.544 and Below 30.8 19.8 Overly fearful 34.4 34.4 30.9 30.9 27.8 27.8 24.9 24.9 22.4 22.4 20.1 20.1 18.1 18.1 16.2 16.2 14.6 14.6 13.1 13.1 11.7 11.7 10.5 10.5 9.5 9.5 8.5 8.5 FIGURE A46 Harrah’s Entertainment, Inc. (HET). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 2/09/1990 - 8/15/2003 (Log Scale) Harrah's Entertainment, Inc. (HET : NYSE) 46.5 46.5 39.2 39.2 33.1 33.1 27.9 27.9 23.5 23.5 19.9 19.9 16.8 16.8 14.1 14.1 11.9 11.9 10.1 10.1 8.5 8.5 7.2 7.2 6.0 6.0 5.1 5.1 4.3 4.3 3.6 3.6 3.1 3.1 116 2.6 2.6 2.2 2.2

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

46.5 46.5 39.2 39.2 33.1 33.1 27.9 27.9 23.5 23.5 19.9 19.9 16.8 16.8 14.1 14.1 11.9 11.9 10.1 10.1 8.5 8.5 7.2 7.2 6.0 6.0 5.1 5.1 4.3 4.3 Price/Sales GPATime Sentiment 3.6 Above 1.3 -8.2 36.3Overconfident 3.6 3.1 * From 1to 1.3 14.1 28.3 Reasonable 3.1 2.6 1 and Below 46.1 35.4 Overly fearful 2.6 2.2 2.2 FIGURE A47 Harsco Corporation (HSC). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 8/01/1980 - 8/15/2003 (Log Scale) Harsco Corporation (HSC : NYSE) 43.9 43.9 38.9 38.9 34.5 34.5 30.6 30.6 27.1 27.1 24.0 24.0 21.3 21.3 18.8 18.8 16.7 16.7 14.8 14.8 13.1 13.1 11.6 11.6 10.3 10.3 9.1 9.1 8.1 8.1 7.2 7.2 6.4 6.4 117 5.6 5.6 5.0 5.0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 43.9 43.9 38.9 38.9 34.5 Price/Book Value GPA Time Sentiment 34.5 30.6 * Above 2-14.6 26.7Overconfident 30.6 27.1 From 1.6to2 5.4 33.7 Reasonable 27.1 1.6 and Below 31.8 39.6 Overly fearful 24.0 24.0 21.3 21.3 18.8 18.8 16.7 16.7 14.8 14.8 13.1 13.1 11.6 11.6 10.3 10.3 9.1 9.1 8.1 8.1 7.2 7.2 6.4 6.4 5.6 5.6 5.0 5.0 FIGURE A48 HewlettPackard Company (HPQ). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Hewlett-Packard Company (HPQ : NYSE) 60.2 60.2 48.6 48.6 39.2 39.2 31.7 31.7 25.6 25.6 20.6 20.6 16.7 16.7 13.4 13.4 10.9 10.9 8.8 8.8 7.1 7.1 5.7 5.7 4.6 4.6 3.7 3.7 3.0 3.0 2.4 2.4 2.0 2.0 118 1.6 1.6 1.3 1.3 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 60.2 60.2 48.7 48.7 39.3 Price/Cash Flow GPA Time Sentiment 39.3 31.8 * Above 11.4-22.2 40.7Overconfident 31.8 25.7 From 9.5to 11.4 47.3 24.4 Reasonable 25.7 9.5 and Below 42.3 35.0 Overly fearful 20.8 20.8 16.8 16.8 13.6 13.6 11.0 11.0 8.9 8.9 7.2 7.2 5.8 5.8 4.7 4.7 3.8 3.8 3.1 3.1 2.5 2.5 2.0 2.0 1.6 1.6 1.3 1.3 FIGURE A49 Hilton Hotels Corporation (HLT). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Hilton Hotels Corporation (HLT : NYSE) 21.8 21.8 19.2 19.2 16.9 16.9 14.9 14.9 13.1 13.1 11.6 11.6 10.2 10.2 9.0 9.0 7.9 7.9 7.0 7.0 6.1 6.1 5.4 5.4 4.8 4.8 4.2 4.2 3.7 3.7 3.3 3.3 2.9 2.9 119 2.5 2.5 2.2 2.2 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 21.8 21.8 19.2 19.2 16.9 Price/Cash Flow GPA Time Sentiment 16.9 14.9 * Above 8-17.3 39.0Overconfident 14.9 13.1 From 6.9to8 10.7 23.6 Reasonable 13.1 6.9 and Below 39.5 37.3 Overly fearful 11.6 11.6 10.2 10.2 9.0 9.0 7.9 7.9 7.0 7.0 6.1 6.1 5.4 5.4 4.8 4.8 4.2 4.2 3.7 3.7 3.3 3.3 2.9 2.9 2.5 2.5 2.2 2.2 FIGURE A50 Home Depot, Inc., The (HD). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 12/27/1985 - 8/15/2003 (Log Scale) Home Depot, Inc., The (HD : NYSE) 58.1 58.1 41.4 41.4 29.5 29.5 21.0 21.0 14.9 14.9 10.6 10.6 7.6 7.6 5.4 5.4 3.8 3.8 2.7 2.7 1.9 1.9 1.4 1.4 1.0 1.0 0.7 0.7 0.5 0.5 120 0.4 0.4 0.3 0.3

1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

97.3 97.3 69.8 69.8 50.1 Price/20Q Cash Flow GPA Time Sentiment 50.1 36.0 Above 82.3 -2.2 14.7Overconfident 36.0 25.8 From 50.4to 82.3 19.3 45.7 Reasonable 25.8 * 50.4 and Below 62.9 39.6 Overly fearful 18.5 18.5 13.3 13.3 9.5 9.5 6.8 6.8 4.9 4.9 3.5 3.5 2.5 2.5 1.8 1.8 1.3 1.3 0.9 0.9 0.7 0.7 0.5 0.5 0.3 0.3 0.2 0.2 FIGURE A51 Honda Motor Co., Ltd. (HMC). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 8/01/1980 - 8/15/2003 (Log Scale) Honda Motor Co., Ltd. (HMC : NYSE) 21.6 21.6 18.2 18.2 15.4 15.4 13.0 13.0 11.0 11.0 9.2 9.2 7.8 7.8 6.6 6.6 5.6 5.6 4.7 4.7 4.0 4.0 3.3 3.3 2.8 2.8 2.4 2.4 2.0 2.0 1.7 1.7 1.4 1.4 121 1.2 1.2 1.0 1.0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 21.6 21.6 18.2 Price /Dividends GPA Time Sentiment 18.2 15.4 * Above 142.1 -9.8 36.7Overconfident 15.4 13.0 From 110.1 to 142.1 4.5 31.9 Reasonable 13.0 11.0 110.1 and Below 63.6 31.4 Overly fearful 11.0 9.2 9.2 7.8 7.8 6.6 6.6 5.6 5.6 4.7 4.7 4.0 4.0 3.3 3.3 2.8 2.8 2.4 2.4 2.0 2.0 1.7 1.7 1.4 1.4 1.2 1.2 1.0 1.0 FIGURE A52 Illinois Tool Works, Inc. (ITW). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Illinois Tool Works, Inc. (ITW : NYSE) 72.2 72.2 57.8 57.8 46.3 46.3 37.0 37.0 29.6 29.6 23.7 23.7 19.0 19.0 15.2 15.2 12.1 12.1 9.7 9.7 7.8 7.8 6.2 6.2 5.0 5.0 4.0 4.0 3.2 3.2 2.6 2.6 2.0 2.0 122 1.6 1.6 1.3 1.3 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 72.2 72.2 57.8 57.8 46.3 Price/Dividends GPA Time Sentiment 46.3 37.0 Above 81.8 -6.1 35.5Overconfident 37.0 29.6 * From 71.5to 81.8 19.1 24.6 Reasonable 29.6 71.5 and Below 42.1 39.9 Overly fearful 23.7 23.7 19.0 19.0 15.2 15.2 12.1 12.1 9.7 9.7 7.8 7.8 6.2 6.2 5.0 5.0 4.0 4.0 3.2 3.2 2.6 2.6 2.0 2.0 1.6 1.6 1.3 1.3 FIGURE A53 IngersollRand Company Limited (IR). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Ingersoll-Rand Company Limited (IR : NYSE) 65.0 65.0 56.3 56.3 48.7 48.7 42.1 42.1 36.5 36.5 31.6 31.6 27.3 27.3 23.6 23.6 20.5 20.5 17.7 17.7 15.3 15.3 13.3 13.3 11.5 11.5 9.9 9.9 8.6 8.6 7.4 7.4 6.4 6.4 123 5.6 5.6 4.8 4.8 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 68.0 68.0 64.0 64.0 60.0 Price/Cash Flow GPA Time Sentiment 60.0 56.0 * Above 11.4-12.1 27.4Overconfident 56.0 From 8.9to 11.46.0 32.6 Reasonable 52.0 8.9 and Below 30.6 40.0 Overly fearful 52.0 48.0 48.0 44.0 44.0 40.0 40.0 36.0 36.0 32.0 32.0 28.0 28.0 24.0 24.0 20.0 20.0 16.0 16.0 12.0 12.0 8.0 8.0 4.0 4.0 0.0 0.0 FIGURE A54 Integrated Device Technology, Inc. (IDTI). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 2/17/1984 - 8/15/2003 (Log Scale) Integrated Device Technology, Inc. (IDTI : NASDAQ) 86.1 86.1 69.2 69.2 55.6 55.6 44.7 44.7 35.9 35.9 28.8 28.8 23.2 23.2 18.6 18.6 15.0 15.0 12.0 12.0 9.7 9.7 7.8 7.8 6.2 6.2 5.0 5.0 4.0 4.0 3.2 3.2 2.6 2.6 124 2.1 2.1 1.7 1.7 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1985 1986 1987 1988 1989 1990 1991 1992 83.8 83.8 63.8 63.8 48.6 Price/Sales GPA Time Sentiment 48.6 37.0 Above 4-50.0 21.1Overconfident 37.0 28.2 * From 1.6to4 21.3 42.8 Reasonable 28.2 1.6 and Below 39.1 36.1 Overly fearful 21.5 21.5 16.4 16.4 12.5 12.5 9.5 9.5 7.2 7.2 5.5 5.5 4.2 4.2 3.2 3.2 2.4 2.4 1.9 1.9 1.4 1.4 1.1 1.1 0.8 0.8 0.6 0.6 FIGURE A55 Intel Corporation (INTC). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Intel Corporation (INTC : NASDAQ) 63.3 63.3 46.2 46.2 33.8 33.8 24.7 24.7 18.0 18.0 13.2 13.2 9.6 9.6 7.0 7.0 5.1 5.1 3.8 3.8 2.7 2.7 2.0 2.0 1.5 1.5 1.1 1.1 0.8 0.8 0.6 0.6 0.4 0.4 125 0.3 0.3 0.2 0.2 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 61.7 61.7 42.8 42.8 29.6 Price/20Q Cash Flow GPA Time Sentiment 29.6 Above 33 -25.1 16.8Overconfident 20.6 From 17.1to33 11.7 56.5 Reasonable 20.6 14.2 * 17.1 and Below 87.9 26.7 Overly fearful 14.2 9.9 9.9 6.8 6.8 4.7 4.7 3.3 3.3 2.3 2.3 1.6 1.6 1.1 1.1 0.8 0.8 0.5 0.5 0.4 0.4 0.3 0.3 0.2 0.2 FIGURE A56 International Business Machines Corporation (IBM). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) International Business Machines Corporation (IBM : NYSE) 128.2 128.2 111.5 111.5 96.9 96.9 84.3 84.3 73.3 73.3 63.8 63.8 55.4 55.4 48.2 48.2 41.9 41.9 36.5 36.5 31.7 31.7 27.6 27.6 24.0 24.0 20.9 20.9 18.1 18.1 15.8 15.8 13.7 13.7 126 11.9 11.9 10.4 10.4 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 127.8 127.8 110.5 110.5 95.6 Price/Sales GPA Time Sentiment 95.6 82.6 Above 1.9-13.0 18.0Overconfident 82.6 71.4 * From 0.9to 1.9 13.1 59.3 Reasonable 71.4 0.9 and Below 10.0 22.6 Overly fearful 61.7 61.7 53.3 53.3 46.0 46.0 39.7 39.7 34.2 34.2 29.5 29.5 25.4 25.4 21.9 21.9 18.8 18.8 16.2 16.2 13.9 13.9 11.9 11.9 10.1 10.1 8.6 8.6 FIGURE A57 International Paper Company (IP). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) International Paper Company (IP : NYSE) 55.7 55.7 50.0 50.0 44.8 44.8 40.2 40.2 36.1 36.1 32.3 32.3 29.0 29.0 26.0 26.0 23.3 23.3 20.9 20.9 18.8 18.8 16.8 16.8 15.1 15.1 13.5 13.5 12.1 12.1 10.9 10.9 9.8 9.8 127 8.8 8.8 7.9 7.9 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 55.7 55.7 50.0 50.0 44.8 Price / Sales GPA Time Sentiment 44.8 40.2 * Above 0.633 -13.5 35.8Overconfident 40.2 36.1 From 0.562 to 0.633 -5.6 26.0 Reasonable 36.1 0.562 and Below 40.9 38.1 Overly fearful 32.3 32.3 29.0 29.0 26.0 26.0 23.3 23.3 20.9 20.9 18.8 18.8 16.8 16.8 15.1 15.1 13.5 13.5 12.1 12.1 10.9 10.9 9.8 9.8 8.8 8.8 7.9 7.9 FIGURE A58 Intuit, Inc. (INTU). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 3/12/1993 - 8/15/2003 (Log Scale) Intuit, Inc. (INTU : NASDAQ) 75.1 75.1 63.9 63.9 54.4 54.4 46.3 46.3 39.4 39.4 33.5 33.5 28.5 28.5 24.3 24.3 20.7 20.7 17.6 17.6 15.0 15.0 12.7 12.7 10.8 10.8 9.2 9.2 7.8 7.8 6.7 6.7 5.7 5.7 128 4.8 4.8 4.1 4.1 J SDM JSD MJ S D MJSDMJ S D MJ SDM JSD MJ S D MJ SDMJS D MJ 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 75.1 75.1 63.9 63.9 54.4 Price/Sales GPA Time Sentiment 54.4 46.3 Above 7.1-70.7 17.7Overconfident 46.3 39.4 * From 3.9to 7.1 68.1 50.3 Reasonable 39.4 3.9 and Below 73.6 32.0 Overly fearful 33.5 33.5 28.5 28.5 24.3 24.3 20.7 20.7 17.6 17.6 15.0 15.0 12.7 12.7 10.8 10.8 9.2 9.2 7.8 7.8 6.7 6.7 5.7 5.7 4.8 4.8 4.1 4.1 FIGURE A59 Jabil Circuit, Inc. (JBL). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 4/30/1993 - 8/15/2003 (Log Scale) Jabil Circuit, Inc. (JBL : NYSE) 57.8 57.8 44.3 44.3 34.1 34.1 26.1 26.1 20.1 20.1 15.4 15.4 11.8 11.8 9.1 9.1 7.0 7.0 5.4 5.4 4.1 4.1 3.2 3.2 2.4 2.4 1.9 1.9 1.4 1.4 1.1 1.1 0.8 0.8 129 0.6 0.6 0.5 0.5 JSD MJ S D M JSD MJS D MJ SDM JSD MJ S D M JSD MJ S D MJSDM J 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 56.3 56.3 41.1 41.1 30.0 30.0 21.9 21.9 16.0 16.0 11.7 11.7 8.5 8.5 6.2 6.2 4.6 4.6 3.3 3.3 2.4 2.4 1.8 1.8 1.3 Price/Earnings GPA Time Sentiment 1.3 0.9 * Above 68 -16.1 23.9Overconfident 0.9 From 26.4to68 17.6 40.4 Reasonable 0.7 26.4 and Below 128.2 35.7 Overly fearful 0.7 0.5 0.5 0.4 0.4 0.3 0.3 0.2 0.2 FIGURE A60 JeffersonPilot Corporation (JP). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Jefferson-Pilot Corporation (JP : NYSE) 48.2 48.2 40.4 40.4 33.8 33.8 28.3 28.3 23.7 23.7 19.9 19.9 16.7 16.7 14.0 14.0 11.7 11.7 9.8 9.8 8.2 8.2 6.9 6.9 5.8 5.8 4.8 4.8 4.0 4.0 3.4 3.4 2.8 2.8 130 2.4 2.4 2.0 2.0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 48.2 48.2 40.4 40.4 33.8 Price/Earnings GPA Time Sentiment 33.8 28.3 * Above 13.8 -12.0 23.6Overconfident 28.3 23.7 From 11.2to 13.8 11.7 36.6 Reasonable 23.7 11.2 and Below 31.5 39.8 Overly fearful 19.9 19.9 16.7 16.7 14.0 14.0 11.7 11.7 9.8 9.8 8.2 8.2 6.9 6.9 5.8 5.8 4.8 4.8 4.0 4.0 3.4 3.4 2.8 2.8 2.4 2.4 2.0 2.0 FIGURE A61 Johnson Controls, Inc. (JCI). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Johnson Controls, Inc. (JCI : NYSE) 89.7 89.7 76.0 76.0 64.4 64.4 54.6 54.6 46.2 46.2 39.2 39.2 33.2 33.2 28.1 28.1 23.8 23.8 20.2 20.2 17.1 17.1 14.5 14.5 12.3 12.3 10.4 10.4 8.8 8.8 7.5 7.5 6.3 6.3 131 5.4 5.4 4.5 4.5 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 89.5 89.5 75.5 75.5 63.7 Price/20Q Cash Flow GPA Time Sentiment 63.7 53.7 * Above 8.2-15.0 35.1Overconfident 53.7 45.3 From 7.3to 8.2 19.8 27.2 Reasonable 45.3 7.3 and Below 36.1 37.7 Overly fearful 38.2 38.2 32.3 32.3 27.2 27.2 23.0 23.0 19.4 19.4 16.3 16.3 13.8 13.8 11.6 11.6 9.8 9.8 8.3 8.3 7.0 7.0 5.9 5.9 5.0 5.0 4.2 4.2 FIGURE A62 Johnson and Johnson (JNJ). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Johnson and Johnson (JNJ : NYSE) 58.6 58.6 47.6 47.6 38.6 38.6 31.4 31.4 25.5 25.5 20.7 20.7 16.8 16.8 13.7 13.7 11.1 11.1 9.0 9.0 7.3 7.3 5.9 5.9 4.8 4.8 3.9 3.9 3.2 3.2 2.6 2.6 2.1 2.1 132 1.7 1.7 1.4 1.4 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 58.9 58.9 47.8 47.8 38.8 Price/Dividends GPA Time Sentiment 38.8 31.5 Above 68 -6.5 26.6Overconfident 31.5 25.6 * From 46.9to68 12.1 36.2 Reasonable 25.6 46.9 and Below 38.9 37.3 Overly fearful 20.8 20.8 16.9 16.9 13.7 13.7 11.1 11.1 9.0 9.0 7.3 7.3 6.0 6.0 4.8 4.8 3.9 3.9 3.2 3.2 2.6 2.6 2.1 2.1 1.7 1.7 1.4 1.4 FIGURE A63 Jones Apparel Group, Inc. (JNY). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 5/17/1991 - 8/15/2003 (Log Scale) Jones Apparel Group, Inc. (JNY : NYSE) 43.5 43.5 37.8 37.8 32.9 32.9 28.6 28.6 24.8 24.8 21.6 21.6 18.8 18.8 16.3 16.3 14.2 14.2 12.3 12.3 10.7 10.7 9.3 9.3 8.1 8.1 7.0 7.0 6.1 6.1 5.3 5.3 4.6 4.6 133 4.0 4.0 3.5 3.5 JSD MJ SDMJ SDMJ SDM JSD MJS D MJ SDMJ SDMJ SDM JSD MJ S D MJSDMJ 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 49.2 49.2 42.5 42.5 36.7 Price/Earnings GPA Time Sentiment 36.7 31.7 Above 18.2-15.7 38.1Overconfident 31.7 27.3 From 15.5to 18.2 -9.7 22.6 Reasonable 27.3 * 15.5 and Below 96.1 39.3 Overly fearful 23.6 23.6 20.4 20.4 17.6 17.6 15.2 15.2 13.1 13.1 11.3 11.3 9.8 9.8 8.4 8.4 7.3 7.3 6.3 6.3 5.4 5.4 4.7 4.7 4.0 4.0 3.5 3.5 FIGURE A64 KLATencor Corporation (KLAC). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 10/10/1980 - 8/15/2003 (Log Scale) KLA-Tencor Corporation (KLAC : NASDAQ) 85.1 85.1 64.7 64.7 49.3 49.3 37.5 37.5 28.5 28.5 21.7 21.7 16.5 16.5 12.6 12.6 9.6 9.6 7.3 7.3 5.5 5.5 4.2 4.2 3.2 3.2 2.4 2.4 1.9 1.9 1.4 1.4 1.1 1.1 134 0.8 0.8 0.6 0.6 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 85.0 85.0 64.5 64.5 49.0 Price/Sales GPA Time Sentiment 49.0 37.2 * Above 5.7-18.3 19.6Overconfident 37.2 28.2 From 3.1to 5.7 14.4 40.4 Reasonable 28.2 3.1 and Below 44.5 39.9 Overly fearful 21.4 21.4 16.3 16.3 12.3 12.3 9.4 9.4 7.1 7.1 5.4 5.4 4.1 4.1 3.1 3.1 2.4 2.4 1.8 1.8 1.4 1.4 1.0 1.0 0.8 0.8 0.6 0.6 FIGURE A65 Kellogg Company (K). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Kellogg Company (K : NYSE) 45.6 45.6 38.3 38.3 32.1 32.1 27.0 27.0 22.6 22.6 19.0 19.0 16.0 16.0 13.4 13.4 11.3 11.3 9.5 9.5 7.9 7.9 6.7 6.7 5.6 5.6 4.7 4.7 3.9 3.9 3.3 3.3 2.8 2.8 135 2.3 2.3 2.0 2.0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 45.6 45.6 38.3 38.3 32.1 32.1 27.0 27.0 22.6 22.6 19.0 19.0 16.0 16.0 13.4 13.4 11.3 11.3 9.5 9.5 7.9 7.9 6.7 Price/Sales GPATime Sentiment 6.7 Above 2.3 -15.1 16.0Overconfident 5.6 From 1.7to2.3 6.2 42.7 Reasonable 5.6 4.7 *1.7 and Below 32.2 41.2 Overly fearful 4.7 3.9 3.9 3.3 3.3 2.8 2.8 2.3 2.3 2.0 2.0 FIGURE A66 KnightRidder, Inc. (KRI). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Knight-Ridder, Inc. (KRI : NYSE) 65.6 65.6 56.7 56.7 49.1 49.1 42.4 42.4 36.7 36.7 31.8 31.8 27.5 27.5 23.8 23.8 20.6 20.6 17.8 17.8 15.4 15.4 13.3 13.3 11.5 11.5 10.0 10.0 8.6 8.6 7.5 7.5 6.5 6.5 136 5.6 5.6 4.8 4.8 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 70.5 70.5 60.8 60.8 52.3 Price/20Q Cash Flow GPA Time Sentiment 52.3 45.1 Above 12.6 -6.5 38.5Overconfident 45.1 38.9 * From 10.4to 12.6 10.6 40.4 Reasonable 38.9 10.4 and Below 50.0 21.1 Overly fearful 33.5 33.5 28.8 28.8 24.9 24.9 21.4 21.4 18.5 18.5 15.9 15.9 13.7 13.7 11.8 11.8 10.2 10.2 8.8 8.8 7.5 7.5 6.5 6.5 5.6 5.6 4.8 4.8 FIGURE A67 Lehman Brothers Holding, Inc. (LEH). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 5/06/1994 - 8/15/2003 (Log Scale) Lehman Brothers Holding, Inc. (LEH : NYSE) 76.8 76.8 67.2 67.2 58.8 58.8 51.5 51.5 45.1 45.1 39.5 39.5 34.5 34.5 30.2 30.2 26.5 26.5 23.2 23.2 20.3 20.3 17.7 17.7 15.5 15.5 13.6 13.6 11.9 11.9 10.4 10.4 9.1 9.1 137 8.0 8.0 7.0 7.0 JSNJMM JSNJMM JSNJMM JSN J MM J SNJ M MJ SNJ MM J SNJ M MJ SN J MM JSNJMM J 1995 1996 1997 1998 1999 2000 2001 2002 2003 76.8 76.8 67.2 67.2 58.8 Price/Cash Flow GPA Time Sentiment 58.8 51.5 * Above 10 -20.2 29.4Overconfident 51.5 45.1 From 8.3to10-10.8 27.9 Reasonable 45.1 8.3 and Below 122.5 42.6 Overly fearful 39.5 39.5 34.5 34.5 30.2 30.2 26.5 26.5 23.2 23.2 20.3 20.3 17.7 17.7 15.5 15.5 13.6 13.6 11.9 11.9 10.4 10.4 9.1 9.1 8.0 8.0 7.0 7.0 FIGURE A68 Limited Brands, Inc. (LTD). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Limited Brands, Inc. (LTD : NYSE) 22.1 22.1 14.8 14.8 9.8 9.8 6.6 6.6 4.4 4.4 2.9 2.9 2.0 2.0 1.3 1.3 0.9 0.9 0.6 0.6 0.4 0.4 0.3 0.3

138 0.2 0.2 0.1 0.1 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 29.3 29.3 19.7 19.7 13.2 13.2 8.9 8.9 6.0 6.0 4.0 4.0 2.7 2.7 1.8 1.8 1.2 1.2 0.8 0.8 0.6 Price / Sales GPATime Sentiment 0.6 Above 1.5 -2.7 17.8Overconfident 0.4 * From 0.9to1.5 6.9 40.0 Reasonable 0.4 0.3 0.9 and Below 55.7 42.1 Overly fearful 0.3 0.2 0.2 0.1 0.1 FIGURE A69 Lowe’s Companies, Inc. (LOW). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Lowe's Companies, Inc. (LOW : NYSE) 42.5 42.5 31.9 31.9 24.0 24.0 18.1 18.1 13.6 13.6 10.2 10.2 7.7 7.7 5.8 5.8 4.4 4.4 3.3 3.3 2.5 2.5 1.9 1.9 1.4 1.4 1.0 1.0 0.8 0.8 0.6 0.6 139 0.4 0.4 0.3 0.3 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 42.5 42.5 31.9 31.9 24.0 Price/20Q Cash Flow GPA Time Sentiment 24.0 18.1 * Above 28.5-12.2 22.9Overconfident 18.1 From 16.7to 28.5 33.6 39.4 Reasonable 13.6 16.7 and Below 35.3 37.7 Overly fearful 13.6 10.2 10.2 7.7 7.7 5.8 5.8 4.4 4.4 3.3 3.3 2.5 2.5 1.9 1.9 1.4 1.4 1.0 1.0 0.8 0.8 0.6 0.6 0.4 0.4 0.3 0.3 FIGURE A70 May Department Stores Company (MAY). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) May Department Stores Company (MAY : NYSE) 42.8 42.8 35.9 35.9 30.1 30.1 25.3 25.3 21.3 21.3 17.8 17.8 15.0 15.0 12.6 12.6 10.6 10.6 8.9 8.9 7.4 7.4 6.3 6.3 5.3 5.3 4.4 4.4 3.7 3.7 3.1 3.1 2.6 2.6 140 2.2 2.2 1.8 1.8 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 42.8 42.8 35.9 Price/Dividends GPA Time Sentiment 35.9 Above 38.5-13.4 24.4Overconfident 30.1 From 28.3to 38.5 2.5 35.7 Reasonable 30.1 25.3 * 28.3 and Below 38.6 39.8 Overly fearful 25.3 21.3 21.3 17.8 17.8 15.0 15.0 12.6 12.6 10.6 10.6 8.9 8.9 7.4 7.4 6.3 6.3 5.3 5.3 4.4 4.4 3.7 3.7 3.1 3.1 2.6 2.6 2.2 2.2 1.8 1.8 FIGURE A71 McDonald’s Corporation (MCD). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) McDonald's Corporation (MCD : NYSE) 43.5 43.5 35.1 35.1 28.3 28.3 22.8 22.8 18.4 18.4 14.9 14.9 12.0 12.0 9.7 9.7 7.8 7.8 6.3 6.3 5.1 5.1 4.1 4.1 3.3 3.3 2.7 2.7 2.2 2.2 1.7 1.7 1.4 1.4 141 1.1 1.1 0.9 0.9 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 43.5 43.5 35.1 35.1 28.3 Price/Earnings GPA Time Sentiment 28.3 22.8 * Above 21 -6.1 24.8Overconfident 22.8 18.4 From 15.2to21 14.5 40.3 Reasonable 18.4 15.2 and Below 29.8 34.9 Overly fearful 14.9 14.9 12.0 12.0 9.7 9.7 7.8 7.8 6.3 6.3 5.1 5.1 4.1 4.1 3.3 3.3 2.7 2.7 2.2 2.2 1.7 1.7 1.4 1.4 1.1 1.1 0.9 0.9 FIGURE A72 McGrawHill Companies, Inc. (MHP). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) McGraw-Hill Companies, Inc. (MHP : NYSE) 63.3 63.3 53.5 53.5 45.3 45.3 38.3 38.3 32.4 32.4 27.4 27.4 23.2 23.2 19.6 19.6 16.6 16.6 14.0 14.0 11.9 11.9 10.0 10.0 8.5 8.5 7.2 7.2 6.1 6.1 5.1 5.1 4.4 4.4 142 3.7 3.7 3.1 3.1 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 63.3 63.3 53.5 53.5 45.3 Price / Sales GPA Time Sentiment 45.3 38.3 * Above 2 -3.2 30.8Overconfident 38.3 32.4 From 1.5to2 3.7 33.8 Reasonable 32.4 1.5 and Below 40.5 35.4 Overly fearful 27.4 27.4 23.2 23.2 19.6 19.6 16.6 16.6 14.0 14.0 11.9 11.9 10.0 10.0 8.5 8.5 7.2 7.2 6.1 6.1 5.1 5.1 4.4 4.4 3.7 3.7 3.1 3.1 FIGURE A73 MedImmune, Inc. (MEDI). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 5/10/1991 - 8/15/2003 (Log Scale) MedImmune, Inc. (MEDI : NASDAQ) 72.9 72.9 55.7 55.7 42.6 42.6 32.6 32.6 24.9 24.9 19.0 19.0 14.5 14.5 11.1 11.1 8.5 8.5 6.5 6.5 5.0 5.0 3.8 3.8 2.9 2.9 2.2 2.2 1.7 1.7 1.3 1.3 1.0 1.0 143 0.8 0.8 0.6 0.6 JSD MJ SDMJ S D MJ SDM JSD MJS D MJ SDMJ S D MJ SDM JSD MJ S D MJSDMJ 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 78.0 78.0 59.4 59.4 45.2 Price/Sales GPA Time Sentiment 45.2 34.4 Above 22.2-54.5 22.8Overconfident 34.4 26.2 From 10.3to 22.2 64.4 40.8 Reasonable 26.2 * 10.3 and Below 78.7 36.4 Overly fearful 20.0 20.0 15.2 15.2 11.6 11.6 8.8 8.8 6.7 6.7 5.1 5.1 3.9 3.9 3.0 3.0 2.3 2.3 1.7 1.7 1.3 1.3 1.0 1.0 0.8 0.8 0.6 0.6 FIGURE A74 Merck and Company, Inc. (MRK). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Merck and Company, Inc. (MRK : NYSE) 84.0 84.0 67.6 67.6 54.4 54.4 43.7 43.7 35.2 35.2 28.3 28.3 22.8 22.8 18.3 18.3 14.7 14.7 11.8 11.8 9.5 9.5 7.7 7.7 6.2 6.2 5.0 5.0 4.0 4.0 3.2 3.2 2.6 2.6 144 2.1 2.1 1.7 1.7 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 88.5 88.5 71.0 71.0 56.9 Price/Cash Flow GPA Time Sentiment 56.9 45.7 Above 24.9-11.1 15.0Overconfident 45.7 36.6 From 14.8to 24.9 18.1 50.2 Reasonable 36.6 * 14.8 and Below 23.3 34.8 Overly fearful 29.4 29.4 23.6 23.6 18.9 18.9 15.2 15.2 12.2 12.2 9.8 9.8 7.8 7.8 6.3 6.3 5.0 5.0 4.0 4.0 3.2 3.2 2.6 2.6 2.1 2.1 1.7 1.7 FIGURE A75 Merrill Lynch and Company (MER). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Merrill Lynch and Company (MER : NYSE) 66.5 66.5 52.9 52.9 42.2 42.2 33.6 33.6 26.7 26.7 21.3 21.3 17.0 17.0 13.5 13.5 10.8 10.8 8.6 8.6 6.8 6.8 5.4 5.4 4.3 4.3 3.4 3.4 2.7 2.7 2.2 2.2 1.7 1.7 145 1.4 1.4 1.1 1.1 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 66.5 66.5 52.9 52.9 42.2 Price / Sales GPA Time Sentiment 42.2 33.6 * Above 0.809 -15.7 21.1Overconfident 33.6 26.7 From 0.46 to 0.809 14.1 39.3 Reasonable 26.7 0.46 and Below 44.2 39.6 Overly fearful 21.3 21.3 17.0 17.0 13.5 13.5 10.8 10.8 8.6 8.6 6.8 6.8 5.4 5.4 4.3 4.3 3.4 3.4 2.7 2.7 2.2 2.2 1.7 1.7 1.4 1.4 1.1 1.1 FIGURE A76 Microsoft Corporation (MSFT). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 3/14/1986 - 8/15/2003 (Log Scale) Microsoft Corporation (MSFT : NASDAQ) 46.6 46.6 29.0 29.0 18.1 18.1 11.3 11.3 7.0 7.0 4.4 4.4 2.7 2.7 1.7 1.7 1.1 1.1 0.7 0.7 0.4 0.4 0.3 0.3

146 0.2 0.2 0.1 0.1

1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

46.6 46.6 29.0 29.0 Price/Sales GPA Time Sentiment 18.1 Above 7.8 -6.2 19.7Overconfident 18.1 * From 4.5to 7.8 32.7 43.9 Reasonable 11.3 4.5 and Below 76.0 36.4 Overly fearful 11.3 7.0 7.0 4.4 4.4 2.7 2.7 1.7 1.7 1.1 1.1 0.7 0.7 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 FIGURE A77 Motorola, Inc. (MOT). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Motorola, Inc. (MOT : NYSE) 52.6 52.6 42.6 42.6 34.5 34.5 28.0 28.0 22.7 22.7 18.4 18.4 14.9 14.9 12.1 12.1 9.8 9.8 7.9 7.9 6.4 6.4 5.2 5.2 4.2 4.2 3.4 3.4 2.8 2.8 2.2 2.2 1.8 1.8 147 1.5 1.5 1.2 1.2 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 52.6 52.6 42.6 42.6 34.5 Price/20Q Cash Flow GPA Time Sentiment 34.5 28.0 * Above 11.8-18.5 40.6Overconfident 28.0 22.7 From 9.8to 11.8 -1.4 23.5 Reasonable 22.7 9.8 and Below 60.9 35.8 Overly fearful 18.4 18.4 14.9 14.9 12.1 12.1 9.8 9.8 7.9 7.9 6.4 6.4 5.2 5.2 4.2 4.2 3.4 3.4 2.8 2.8 2.2 2.2 1.8 1.8 1.5 1.5 1.2 1.2 FIGURE A78 National Semiconductor Corporation (NSM). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) National Semiconductor Corporation (NSM : NYSE) 71.1 71.1 59.9 59.9 50.5 50.5 42.5 42.5 35.8 35.8 30.2 30.2 25.4 25.4 21.4 21.4 18.0 18.0 15.2 15.2 12.8 12.8 10.8 10.8 9.1 9.1 7.6 7.6 6.4 6.4 5.4 5.4 4.6 4.6 148 3.8 3.8 3.2 3.2 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 71.1 71.1 59.9 59.9 50.5 Price/20Q Cash Flow GPA Time Sentiment 50.5 42.5 * Above 15.3-30.7 23.2Overconfident 42.5 35.8 From 8.1to 15.3 10.2 40.3 Reasonable 35.8 8.1 and Below 30.1 36.5 Overly fearful 30.2 30.2 25.4 25.4 21.4 21.4 18.0 18.0 15.2 15.2 12.8 12.8 10.8 10.8 9.1 9.1 7.6 7.6 6.4 6.4 5.4 5.4 4.6 4.6 3.8 3.8 3.2 3.2 FIGURE A79 Newell Rubbermaid, Inc. (NWL). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Newell Rubbermaid, Inc. (NWL : NYSE) 48.0 48.0 37.1 37.1 28.8 28.8 22.3 22.3 17.3 17.3 13.4 13.4 10.3 10.3 8.0 8.0 6.2 6.2 4.8 4.8 3.7 3.7 2.9 2.9 2.2 2.2 1.7 1.7 1.3 1.3 1.0 1.0 0.8 0.8 149 0.6 0.6 0.5 0.5 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 48.0 48.0 37.1 37.1 28.8 Price/Cash Flow GPA Time Sentiment 28.8 22.3 Above 14.3-17.7 26.7Overconfident 22.3 17.3 * From 9.3 to 14.38.3 38.4 Reasonable 17.3 9.3 and Below 67.0 34.9 Overly fearful 13.4 13.4 10.3 10.3 8.0 8.0 6.2 6.2 4.8 4.8 3.7 3.7 2.9 2.9 2.2 2.2 1.7 1.7 1.3 1.3 1.0 1.0 0.8 0.8 0.6 0.6 0.5 0.5 FIGURE A80 Oracle Corporation (ORCL). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 3/14/1986 - 8/15/2003 (Log Scale) Oracle Corporation (ORCL : NASDAQ) 34.8 34.8 19.1 19.1 10.5 10.5 5.8 5.8 3.2 3.2 1.7 1.7 1.0 1.0 0.5 0.5 0.3 0.3 0.2 0.2 0.1 0.1 150 0.0 0.0

1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

34.8 34.8

19.1 Price/Sales GPA Time Sentiment 19.1 * Above 6.2-23.1 31.8Overconfident 10.5 From 4.7to 6.2 34.0 30.1 Reasonable 10.5 4.7 and Below 115.7 38.1 Overly fearful 5.8 5.8 3.2 3.2 1.7 1.7 1.0 1.0 0.5 0.5 0.3 0.3 0.2 0.2 0.1 0.1 0.0 0.0 FIGURE A81 PPG Industries, Inc. (PPG). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) PPG Industries, Inc. (PPG : NYSE) 69.7 69.7 58.8 58.8 49.7 49.7 41.9 41.9 35.4 35.4 29.9 29.9 25.2 25.2 21.3 21.3 18.0 18.0 15.2 15.2 12.8 12.8 10.8 10.8 9.1 9.1 7.7 7.7 6.5 6.5 5.5 5.5 4.6 4.6 151 3.9 3.9 3.3 3.3 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 69.7 69.7 58.8 58.8 49.7 Price/Dividends GPA Time Sentiment 49.7 41.9 Above 34.4-13.8 35.8Overconfident 41.9 35.4 * From 29 to 34.4 13.0 26.9 Reasonable 35.4 29 and Below 43.9 37.3 Overly fearful 29.9 29.9 25.2 25.2 21.3 21.3 18.0 18.0 15.2 15.2 12.8 12.8 10.8 10.8 9.1 9.1 7.7 7.7 6.5 6.5 5.5 5.5 4.6 4.6 3.9 3.9 3.3 3.3 FIGURE A82 PepsiCo, Inc. (PEP). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) PepsiCo, Inc. (PEP : NYSE) 47.8 47.8 38.7 38.7 31.3 31.3 25.3 25.3 20.4 20.4 16.5 16.5 13.4 13.4 10.8 10.8 8.7 8.7 7.1 7.1 5.7 5.7 4.6 4.6 3.7 3.7 3.0 3.0 2.4 2.4 2.0 2.0 1.6 1.6 152 1.3 1.3 1.0 1.0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 49.8 49.8 40.1 40.1 32.4 Price/Earnings GPA Time Sentiment 32.4 26.1 Above 27 -5.3 22.1Overconfident 26.1 21.1 * From 16.4to27 13.4 41.5 Reasonable 21.1 16.4 and Below 35.8 36.4 Overly fearful 17.0 17.0 13.7 13.7 11.1 11.1 8.9 8.9 7.2 7.2 5.8 5.8 4.7 4.7 3.8 3.8 3.1 3.1 2.5 2.5 2.0 2.0 1.6 1.6 1.3 1.3 1.0 1.0 FIGURE A83 Progressive Corporation, The (PGR). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Progressive Corporation, The (PGR : NYSE) 63.9 63.9 46.2 46.2 33.5 33.5 24.2 24.2 17.6 17.6 12.7 12.7 9.2 9.2 6.7 6.7 4.8 4.8 3.5 3.5 2.5 2.5 1.8 1.8 1.3 1.3 1.0 1.0 0.7 0.7 0.5 0.5 0.4 0.4 153 0.3 0.3 0.2 0.2 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 63.9 63.9 46.2 46.2 33.5 Price/Sales GPA Time Sentiment 33.5 24.2 Above 1.5-10.5 14.3Overconfident 24.2 17.6 * From 0.9to 1.5 17.1 51.1 Reasonable 17.6 0.9 and Below 66.1 34.6 Overly fearful 12.7 12.7 9.2 9.2 6.7 6.7 4.8 4.8 3.5 3.5 2.5 2.5 1.8 1.8 1.3 1.3 1.0 1.0 0.7 0.7 0.5 0.5 0.4 0.4 0.3 0.3 0.2 0.2 FIGURE A84 RadioShack Corporation (RSH). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) RadioShack Corporation (RSH : NYSE) 69.2 69.2 56.0 56.0 45.3 45.3 36.6 36.6 29.6 29.6 23.9 23.9 19.4 19.4 15.7 15.7 12.7 12.7 10.2 10.2 8.3 8.3 6.7 6.7 5.4 5.4 4.4 4.4 3.5 3.5 2.9 2.9 2.3 2.3 154 1.9 1.9 1.5 1.5 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 69.5 69.5 56.8 56.8 46.4 Price/Sales GPA Time Sentiment 46.4 37.9 Above 1.1 -6.9 36.0Overconfident 37.9 30.9 * From 0.8to 1.1 15.3 28.8 Reasonable 30.9 0.8 and Below 32.0 35.2 Overly fearful 25.3 25.3 20.6 20.6 16.8 16.8 13.8 13.8 11.2 11.2 9.2 9.2 7.5 7.5 6.1 6.1 5.0 5.0 4.1 4.1 3.3 3.3 2.7 2.7 2.2 2.2 1.8 1.8 FIGURE A85 Rohm and Haas Company (ROH). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Rohm and Haas Company (ROH : NYSE) 43.0 43.0 36.1 36.1 30.3 30.3 25.4 25.4 21.3 21.3 17.9 17.9 15.0 15.0 12.6 12.6 10.6 10.6 8.9 8.9 7.4 7.4 6.2 6.2 5.2 5.2 4.4 4.4 3.7 3.7 3.1 3.1 2.6 2.6 155 2.2 2.2 1.8 1.8 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 43.0 43.0 36.1 Price/Sales GPA Time Sentiment 36.1 * Above 1.2-16.0 33.2Overconfident 30.3 From 1to1.2 5.7 25.3 Reasonable 30.3 25.4 1 and Below 45.5 41.5 Overly fearful 25.4 21.3 21.3 17.9 17.9 15.0 15.0 12.6 12.6 10.6 10.6 8.9 8.9 7.4 7.4 6.2 6.2 5.2 5.2 4.4 4.4 3.7 3.7 3.1 3.1 2.6 2.6 2.2 2.2 1.8 1.8 FIGURE A86 Sara Lee Corporation (SLE). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Sara Lee Corporation (SLE : NYSE) 28.3 28.3 22.9 22.9 18.5 18.5 15.0 15.0 12.1 12.1 9.8 9.8 8.0 8.0 6.4 6.4 5.2 5.2 4.2 4.2 3.4 3.4 2.8 2.8 2.2 2.2 1.8 1.8 1.5 1.5 1.2 1.2 1.0 1.0 156 0.8 0.8 0.6 0.6 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 28.3 28.3 22.9 22.9 18.5 Price/20Q Cash Flow GPA Time Sentiment 18.5 15.0 Above 15.1 -9.8 24.7Overconfident 15.0 12.1 From 11.1to 15.19.6 42.1 Reasonable 12.1 * 11.1 and Below 44.2 33.2 Overly fearful 9.8 9.8 8.0 8.0 6.4 6.4 5.2 5.2 4.2 4.2 3.4 3.4 2.8 2.8 2.2 2.2 1.8 1.8 1.5 1.5 1.2 1.2 1.0 1.0 0.8 0.8 0.6 0.6 FIGURE A87 ScheringPlough Corporation (SGP). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Schering-Plough Corporation (SGP : NYSE) 53.3 53.3 42.1 42.1 33.4 33.4 26.4 26.4 20.9 20.9 16.5 16.5 13.1 13.1 10.4 10.4 8.2 8.2 6.5 6.5 5.1 5.1 4.1 4.1 3.2 3.2 2.5 2.5 2.0 2.0 1.6 1.6 1.3 1.3 157 1.0 1.0 0.8 0.8 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 53.3 53.3 42.1 42.1 33.4 Price/Earnings GPA Time Sentiment 33.4 26.4 * Above 21.6 -9.4 26.1Overconfident 26.4 20.9 From 17.2to 21.6 19.6 33.9 Reasonable 20.9 17.2 and Below 23.9 40.0 Overly fearful 16.5 16.5 13.1 13.1 10.4 10.4 8.2 8.2 6.5 6.5 5.1 5.1 4.1 4.1 3.2 3.2 2.5 2.5 2.0 2.0 1.6 1.6 1.3 1.3 1.0 1.0 0.8 0.8 FIGURE A88 Schlumberger Limited (SLB). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Schlumberger Limited (SLB : NYSE) 85.9 85.9 77.6 77.6 70.2 70.2 63.5 63.5 57.4 57.4 51.9 51.9 46.9 46.9 42.4 42.4 38.4 38.4 34.7 34.7 31.4 31.4 28.4 28.4 25.7 25.7 23.2 23.2 21.0 21.0 19.0 19.0 17.2 17.2 158 15.5 15.5 14.0 14.0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 85.4 85.4 76.5 76.5 68.4 Price/Dividends GPA Time Sentiment 68.4 Above 67 -15.1 29.3Overconfident 61.3 * From 47.7to675.1 33.5 Reasonable 61.3 54.8 47.7 and Below 19.3 37.2 Overly fearful 54.8 49.1 49.1 43.9 43.9 39.3 39.3 35.2 35.2 31.5 31.5 28.2 28.2 25.3 25.3 22.6 22.6 20.2 20.2 18.1 18.1 16.2 16.2 14.5 14.5 13.0 13.0 11.6 11.6 FIGURE A89 St. Paul Companies, Inc., The (SPC). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) St. Paul Companies, Inc., The (SPC : NYSE) 52.5 52.5 45.4 45.4 39.3 39.3 34.1 34.1 29.5 29.5 25.5 25.5 22.1 22.1 19.1 19.1 16.6 16.6 14.4 14.4 12.4 12.4 10.8 10.8 9.3 9.3 8.1 8.1 7.0 7.0 6.0 6.0 5.2 5.2 159 4.5 4.5 3.9 3.9 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 52.5 52.5 45.4 45.4 39.3 Price/Dividends GPA Time Sentiment 39.3 34.1 Above 31.8-19.3 27.4Overconfident 34.1 29.5 * From 25.9to 31.8 14.7 33.7 Reasonable 29.5 25.9 and Below 27.1 39.0 Overly fearful 25.5 25.5 22.1 22.1 19.1 19.1 16.6 16.6 14.4 14.4 12.4 12.4 10.8 10.8 9.3 9.3 8.1 8.1 7.0 7.0 6.0 6.0 5.2 5.2 4.5 4.5 3.9 3.9 FIGURE A90 Sysco Corporation (SYY). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Sysco Corporation (SYY : NYSE) 28.3 28.3 21.5 21.5 16.4 16.4 12.5 12.5 9.5 9.5 7.2 7.2 5.5 5.5 4.2 4.2 3.2 3.2 2.4 2.4 1.9 1.9 1.4 1.4 1.1 1.1 0.8 0.8 0.6 0.6 0.5 0.5 0.4 0.4 160 0.3 0.3 0.2 0.2 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 28.3 28.3 21.5 21.5 16.4 Price/Cash Flow GPA Time Sentiment 16.4 12.5 * Above 17.3 -8.4 23.4Overconfident 12.5 9.5 From 13.4to 17.3 11.3 40.0 Reasonable 9.5 13.4 and Below 65.6 36.6 Overly fearful 7.2 7.2 5.5 5.5 4.2 4.2 3.2 3.2 2.4 2.4 1.9 1.9 1.4 1.4 1.1 1.1 0.8 0.8 0.6 0.6 0.5 0.5 0.4 0.4 0.3 0.3 0.2 0.2 FIGURE A91 Target Corporation (TGT). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Target Corporation (TGT : NYSE) 40.3 40.3 32.4 32.4 26.0 26.0 20.8 20.8 16.7 16.7 13.4 13.4 10.8 10.8 8.6 8.6 6.9 6.9 5.6 5.6 4.5 4.5 3.6 3.6 2.9 2.9 2.3 2.3 1.8 1.8 1.5 1.5 1.2 1.2 161 1.0 1.0 0.8 0.8 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 40.3 40.3 32.4 32.4 26.0 Price/Cash Flow GPA Time Sentiment 26.0 20.8 Above 12 -23.0 17.9Overconfident 20.8 16.7 * From 7.1to12 27.0 44.1 Reasonable 16.7 7.1 and Below 30.3 38.0 Overly fearful 13.4 13.4 10.8 10.8 8.6 8.6 6.9 6.9 5.6 5.6 4.5 4.5 3.6 3.6 2.9 2.9 2.3 2.3 1.8 1.8 1.5 1.5 1.2 1.2 1.0 1.0 0.8 0.8 FIGURE A92 Union Pacific Corporation (UNP). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Union Pacific Corporation (UNP : NYSE) 68.2 68.2 61.3 61.3 55.1 55.1 49.5 49.5 44.5 44.5 40.0 40.0 36.0 36.0 32.3 32.3 29.1 29.1 26.1 26.1 23.5 23.5 21.1 21.1 19.0 19.0 17.1 17.1 15.3 15.3 13.8 13.8 12.4 12.4 162 11.1 11.1 10.0 10.0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 68.2 68.2 61.3 61.3 55.1 Price/Book Value GPA Time Sentiment 55.1 49.5 Above 1.6-23.2 25.0Overconfident 49.5 44.5 * From 1.2to 1.6 14.5 41.1 Reasonable 44.5 1.2 and Below 26.1 33.8 Overly fearful 40.0 40.0 36.0 36.0 32.3 32.3 29.1 29.1 26.1 26.1 23.5 23.5 21.1 21.1 19.0 19.0 17.1 17.1 15.3 15.3 13.8 13.8 12.4 12.4 11.1 11.1 10.0 10.0 FIGURE A93 Verizon Communications, Inc. (VZ). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 4/19/1984 - 8/15/2003 (Log Scale) Verizon Communications, Inc. (VZ : NYSE) 63.3 63.3 56.5 56.5 50.5 50.5 45.1 45.1 40.3 40.3 36.0 36.0 32.2 32.2 28.8 28.8 25.7 25.7 23.0 23.0 20.5 20.5 18.3 18.3 16.4 16.4 14.6 14.6 13.1 13.1 11.7 11.7 10.4 10.4 163 9.3 9.3 8.3 8.3 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1985 1986 1987 1988 1989 1990 1991 1992 63.3 63.3 56.5 56.5 50.5 Price/Dividends GPA Time Sentiment 50.5 45.1 * Above 22.2 -9.8 41.2Overconfident 45.1 40.3 From 19.8to 22.22.3 20.2 Reasonable 40.3 19.8 and Below 34.0 38.6 Overly fearful 36.0 36.0 32.2 32.2 28.8 28.8 25.7 25.7 23.0 23.0 20.5 20.5 18.3 18.3 16.4 16.4 14.6 14.6 13.1 13.1 11.7 11.7 10.4 10.4 9.3 9.3 8.3 8.3 FIGURE A94 Viacom, Inc.—Class B (VIA.B). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 6/01/1990 - 8/15/2003 (Log Scale) Viacom, Inc. -Class B (VIA.B : NYSE) 70.0 70.0 61.9 61.9 54.6 54.6 48.2 48.2 42.6 42.6 37.6 37.6 33.2 33.2 29.3 29.3 25.9 25.9 22.9 22.9 20.2 20.2 17.9 17.9 15.8 15.8 13.9 13.9 12.3 12.3 10.9 10.9 9.6 9.6 164 8.5 8.5 7.5 7.5 J SDMJS D MJ SDM JSD MJ S D MJ SDMJS D MJ SDM JSD MJ S D MJ SDMJ S D MJSDM J 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 70.0 70.0 61.9 Price/20Q Cash Flow GPA Time Sentiment 61.9 Above 32.3 -24.7 22.5Overconfident 54.6 *From 22.7to 32.39.3 41.2 Reasonable 54.6 48.2 22.7 and Below 36.3 36.3 Overly fearful 48.2 42.6 42.6 37.6 37.6 33.2 33.2 29.3 29.3 25.9 25.9 22.9 22.9 20.2 20.2 17.9 17.9 15.8 15.8 13.9 13.9 12.3 12.3 10.9 10.9 9.6 9.6 8.5 8.5 7.5 7.5 FIGURE A95 W.W. Grainger, Inc. (GWW). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) W.W. Grainger, Inc. (GWW : NYSE) 53.8 53.8 45.9 45.9 39.2 39.2 33.5 33.5 28.6 28.6 24.4 24.4 20.9 20.9 17.8 17.8 15.2 15.2 13.0 13.0 11.1 11.1 9.5 9.5 8.1 8.1 6.9 6.9 5.9 5.9 5.0 5.0 4.3 4.3 165 3.7 3.7 3.1 3.1 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 53.8 53.8 45.9 45.9 39.2 Price/Earnings GPA Time Sentiment 39.2 33.5 Above 20.4-20.1 27.1Overconfident 33.5 28.6 * From 16.9to 20.4 17.4 34.8 Reasonable 28.6 16.9 and Below 32.2 38.1 Overly fearful 24.4 24.4 20.9 20.9 17.8 17.8 15.2 15.2 13.0 13.0 11.1 11.1 9.5 9.5 8.1 8.1 6.9 6.9 5.9 5.9 5.0 5.0 4.3 4.3 3.7 3.7 3.1 3.1 FIGURE A96 Wachovia Corporation (WB). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Wachovia Corporation (WB : NYSE) 59.4 59.4 48.8 48.8 40.0 40.0 32.9 32.9 27.0 27.0 22.1 22.1 18.2 18.2 14.9 14.9 12.2 12.2 10.1 10.1 8.3 8.3 6.8 6.8 5.6 5.6 4.6 4.6 3.7 3.7 3.1 3.1 2.5 2.5 166 2.1 2.1 1.7 1.7 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 59.4 59.4 48.8 Price/20QEarnings GPA Time Sentiment 48.8 40.0 * Above 16.7 -4.7 19.9Overconfident 40.0 32.9 From 10.7to 16.78.4 43.8 Reasonable 32.9 27.0 10.7 and Below 30.7 36.3 Overly fearful 27.0 22.1 22.1 18.2 18.2 14.9 14.9 12.2 12.2 10.1 10.1 8.3 8.3 6.8 6.8 5.6 5.6 4.6 4.6 3.7 3.7 3.1 3.1 2.5 2.5 2.1 2.1 1.7 1.7 FIGURE A97 WalMart Stores, Inc. (WMT). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Wal-Mart Stores, Inc. (WMT : NYSE) 56.5 56.5 37.4 37.4 24.7 24.7 16.3 16.3 10.8 10.8 7.1 7.1 4.7 4.7 3.1 3.1 2.1 2.1 1.4 1.4 0.9 0.9 0.6 0.6 0.4 0.4 0.3 0.3

167 0.2 0.2 0.1 0.1 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 81.1 81.1 53.8 53.8 35.7 Price/Dividends GPA Time Sentiment 35.7 Above 276.1 -16.0 16.8Overconfident 23.7 From 189.1 to 276.1 25.3 42.7 Reasonable 23.7 15.7 * 189.1 and Below 60.0 40.5 Overly fearful 15.7 10.4 10.4 6.9 6.9 4.6 4.6 3.0 3.0 2.0 2.0 1.3 1.3 0.9 0.9 0.6 0.6 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 FIGURE A98 Walt Disney Company, The (DIS). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Walt Disney Company, The (DIS : NYSE) 39.2 39.2 31.5 31.5 25.4 25.4 20.5 20.5 16.5 16.5 13.3 13.3 10.7 10.7 8.6 8.6 6.9 6.9 5.6 5.6 4.5 4.5 3.6 3.6 2.9 2.9 2.3 2.3 1.9 1.9 1.5 1.5 1.2 1.2 168 1.0 1.0 0.8 0.8 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 44.4 44.4 35.7 35.7 28.7 Price/Cash Flow GPA Time Sentiment 28.7 23.1 * Above 13.8-20.7 25.4Overconfident 23.1 18.6 From 10.7to 13.8 11.1 39.3 Reasonable 18.6 10.7 and Below 54.2 35.3 Overly fearful 14.9 14.9 12.0 12.0 9.7 9.7 7.8 7.8 6.2 6.2 5.0 5.0 4.0 4.0 3.2 3.2 2.6 2.6 2.1 2.1 1.7 1.7 1.4 1.4 1.1 1.1 0.9 0.9 FIGURE A99 Weyerhaeuser Company (WY). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) Weyerhaeuser Company (WY : NYSE) 69.2 69.2 63.7 63.7 58.7 58.7 54.0 54.0 49.7 49.7 45.8 45.8 42.2 42.2 38.8 38.8 35.7 35.7 32.9 32.9 30.3 30.3 27.9 27.9 25.7 25.7 23.6 23.6 21.8 21.8 20.0 20.0 18.5 18.5 169 17.0 17.0 15.6 15.6 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 69.2 69.2 63.7 63.7 58.7 Price/Dividends GPA Time Sentiment 58.7 54.0 * Above 35.3-39.1 16.7Overconfident 54.0 49.7 From 26.7to 35.3 10.0 45.8 Reasonable 49.7 45.8 26.7 and Below 24.5 37.5 Overly fearful 45.8 42.2 42.2 38.8 38.8 35.7 35.7 32.9 32.9 30.3 30.3 27.9 27.9 25.7 25.7 23.6 23.6 21.8 21.8 20.0 20.0 18.5 18.5 17.0 17.0 15.6 15.6 FIGURE A100 William Wrigley Jr. Company (WWY). © 2003 Ned Davis Research, Inc. All rights reserved.

Weekly Data 1/04/1980 - 8/15/2003 (Log Scale) William Wrigley Jr. Company (WWY : NYSE) 52.1 52.1 42.3 42.3 34.4 34.4 27.9 27.9 22.7 22.7 18.4 18.4 14.9 14.9 12.1 12.1 9.9 9.9 8.0 8.0 6.5 6.5 5.3 5.3 4.3 4.3 3.5 3.5 2.8 2.8 2.3 2.3 1.9 1.9 170 1.5 1.5 1.2 1.2 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1981 1982 1983 1984 1985 1986 1987 1988 1989 52.7 52.7 42.8 42.8 34.7 Price/20Q Earnings GPA Time Sentiment 34.7 28.2 Above 39.3 -7.5 18.8Overconfident 28.2 22.9 * From 28.6to 39.39.5 45.9 Reasonable 22.9 28.6 and Below 41.4 35.2 Overly fearful 18.5 18.5 15.1 15.1 12.2 12.2 9.9 9.9 8.0 8.0 6.5 6.5 5.3 5.3 4.3 4.3 3.5 3.5 2.8 2.8 2.3 2.3 1.9 1.9 1.5 1.5 1.2 1.2 REFERENCES

Bartlett, John. 1992. Bartlett’s Familiar Quotations. Boston: Little, Brown and Company. Casey, Douglas. 1995. Media, Mania & the Markets. Baltimore, Md.: Agora Financial Place of Publishing. Cooper, Sherry. 2001. Ride the Wave: Taking Control in a Turbulent Financial Age. Upper Saddle River, NJ: Prentice Hall. Dreman, David. 1998. Contrarian Investment Strategies: The Next Generation. New York: Simon & Schuster. Getty, J. Paul. 1983. How to Be Rich. New York: The Berkley Publishing Group. Hagstrom, Robert G., Jr. 1994. The Warren Buffett Way: Investment Strategies of the World’s Greatest Investor. New York: John Wiley & Sons. Hayes, Timothy. 2000. The Research Driven Investor: How to Use Information, Data and Analy sis for Investment Success. New York: McGrawHill. Le Bon, Gustave. 1982. The Cr owd: A Study of the Popular Mind. Atlanta, Ga.: Cherokee Pub lishing Company. Levy, Leon. 2002. The Minds of Wall Street. New York: Public Affairs. Lynch, Peter, and John Rothchild. 1993. Beating The Street. New York: Simon & Schuster. Mackay, Charles. 1989. Extraordinary Popular Delusions and the Madness of Crowds. New York: Barnes & Noble Books. McConnell, James. 1980. Understanding Human Behavior. New York: Holt, Rinehart and Win ston. Menschel, Robert. 2002. Markets, Mobs & Mayhem: A Modern Look at the Madness of Crowds. New York: John Wiley & Sons. Meyssan, Thierry. 2002. 9/11, the Big Lie. New York: USA Books. Neill, Humphrey B. 1992. The Art of Contrary Thinking: It Pays to Be Contrary! Caldwell, Idaho: Caxton Printers.

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Raspberry, William. 1994. Distinguished Commentary. Schwager, Jack D. 1989. Market Wizards. New York: New York Institute of Finance/Simon & Schuster. Schwager, Jack D. 2001. Stock Market Wizards. New York: HarperCollins. Shefrin, Hersh. 2000. Beyond Greed and Fear. Boston: President and Fellows of Harvard College. Sobel, Robert. 1965. The Big Board: A History of the New York Stock Market. New York: Free Press. Thaler, Richard. 1992. The Winner’s Curse. New York: Free Press. Zweig, Martin. 1986. Winning on Wall Street. New York: Warner Books. INDEX

Abbott Laboratories, 72 Being Right or Making Money (Davis), 2, 6–8 Acton, Lord, 63 Bell, Alexander Graham, 63 Adelphia Communications, 33 Beyond Greed and Fear (Shefrin), 21 Advanced Micro Devices, Inc., 73 Bezos, Jeff, 43 Affiliated Computer Services, Inc., 74 Bianco Research, 5, 7 Air Products and Chemicals, Inc., 75 Big Board, The (Sobel), 29 Airline industry, 29 Bill of Rights, 62–63 AlQaeda network, 15 Blake, Robert, 21 AlbertoCulver Company, 76 Blame avoidance, 22 Alcan, Inc., 77 Bloomberg News, 31 Alcoa, Inc., 78 Boeing Company, The, 88 Altria Group, Inc., 79 Boise Cascade Corporation, 89 Amazon.com, 31, 43 Bubble Acts (England), 27 American Association of Individual Investors (AAII), 48–49 Budowski, Michael, 30–31 American Express Company, 80 Buffett, Warren, 13, 62 Amgen, Inc., 81 Bush, George W., 15 Analog Devices, Inc., 82 Business Week: AnheuserBusch Companies, Inc., 83 cover stories, 37, 40–41, 42 AOL Time Warner, 33 forecasts, 4 ArcherDanielsMidland Company, 84 Art of Contrary Thinking, The (Neill), 8 Campbell Soup Company, 90 Asch, Solomon, 20 Carver, George Washington, 63 Atlantic Monthly, cover stories, 41–42 Casey, Douglas, 44 Autokinetic effect, 19–20 Chubb Corporation, 91 Automobile industry, 29 Cisco Systems, Inc., 31, 92 Citigroup, Inc., 93 Bank of America Corporation, 85 Clinton, Bill, 44, 46 Bank One Corporation, 86 CNBC, 31 Barron’s: CNNfn, 31 cover stories, 38–39 Cohen, Steve, 13 strategist forecasts, 2, 3 Comcast Corporation Class A, 94 Zweig and, xi Commercial paper prices, 4950 Bartlett, John, 61–62, 63 Commercial traders, 5152 Baruch, Bernard, 8, 12 Commodity Futures Trading Commission (CFTC), 5152 Baxter International, Inc., 87 Computer industry, 3034 Beating the Street (Lynch), 12–13 Computer Sciences Corporation, 95 Behavioral finance, 21–23 ConAgra Foods, Inc., 96

173

Conference Board, The, 6 FactSet Research Systems, Inc., 102 Constitution, U.S., 62–63 Federal Reserve Board, The, 29–30, 41 Contrarian Investment Strategies (Dreman), 28 Financial assets, stocks as percentage of total personal, 57, 59 Contrary opinion (see Theory of contrary opinion) First Data Corporation, 103 Contrary Opinion Forum, 8 Fisher, Irving, 36 Coolidge, Calvin, 44 Ford, Gerald, 45–46 Cooper, Sherry, 30–31 Ford, Henry, 63 Cootner, Paul, viii–x Ford Motor Company, 104 Cover stories (see Headlines and cover stories) Forecasting: Crash of 1929, 33–37 by Barron’s strategists, 2, 3 headlines surrounding, 35–37 by BusinessWeek survey, 4 nature of, 28–30 by Fortune, 5–6 Creighton, Mandell, 63 problems of, 2–8 Crowd, The (Le Bon), 8, 44 by Wall Street Journal survey, 5, 7 Crowd psychology, 19–23, 47–60 by Wall Street Week panelists, 2–3 anchoring the crowd, 19–20 Forest Laboratories, Inc., 105 behavioral finance, 21–23 Fortune: cover stories and, 37–43 cover stories, 39 group pressure, 20–21 forecasts, 5–6 headlines and, 35–37 Fountainhead, The (Rand), 61 indicators based on surveys and market data, 48–55 France, Mississippi Scheme, 25–26 Le Bon and, 8, 44 Franklin, Ben, 63 market responses to crisis, 9–11 Frost, Robert, 1 politically motivated quotes and, 44–46 Fulton, Robert, 63 successful users of, 11–13 Futures contracts, 51–52 (See also Manias and panics) Gallup poll presidential approval ratings, 17 Gates, Bill, 62, 63 Declaration of Independence, 62 General Dynamics Corporation, 106 Diagnostic Products Corporation, 97 General Electric Company, 107 Dow Chemical Company, The, 98 General Motors Corporation, 29, 108 Dow Jones Industrial Average (DJIA): Genuine Parts Company, 109 consumer confidence versus, 6 GeorgiaPacific Corporation, 110 crash of 1929 and, 29 Getty, J. Paul, 11–12, 62 crisis events and, 9, 11 Gillette Company, The, 111 headlines from 1960s and 1970s, 37, 38 GlaxoSmithKline plc, 112 headlines from 1980s and 1990s, 39–41 Global Crossing, 33 headlines from 1990s to late 2002, 41–43 Gold mania (1979–1980), 33–34 mania of 1924–1932, 29, 33–34, 36–37 Gould, Edson, 8, 9 presidential performance versus, 15, 17, 44–46 Grainger (W. W.), Inc., 165 Dreman, David, 28 Great Depression, 28–30 Great War, 28–29 Eastman Kodak Company, 100 Greenspan, Alan, 29–30, 33, 39 eBay, 31 Group conformity research, 19–21 Economist, cover stories, 37 Group pressure, 20–21 Edison, Thomas, 63 Einstein, Albert, 63 H. J. Heinz Company, 113 Elliott Wave Financial Forecast, 4 Halliburton Company, 115 EMC Corporation, 99 Harrah’s Entertainment, Inc., 116 Emerson, Ralph Waldo, 62 Harsco Corporation, 117 Emerson Electric Company, 101 Hayes, Tim, 59–60 Endowment effect, 33 Headlines and cover stories: England: 1960s1970s, 37–39 Bubble Acts, 27 1980searly 1990s, 39–41 gold mania (1979–1980), 33–34 late 1990searly 2000s, 41–43 South Sea Bubble, 26–27 surrounding the Crash of 1929, 35–37 Enron, 33 Hedgers, 51–52 Extraordinary Popular Delusions and the Madness of Crowds Heinz (H. J.) Company, 113 (Mackay), 12, 25–26 Helson, Henry, 21 INDEX 175

HewlettPackard Company, 118 Lindbergh, Charles, 29 Hilton Hotels Corporation, 119 Liquidity: Home Depot, Inc., The, 120 versus market indicators, 9, 10 HON Industries, Inc., 114 theory of contrary opinion and, 9–11 Honda Motor Co., Ltd., 121 Loss aversion, 21–22 Hoover, Herbert, 37 Louis XIV, King of France, 25–26 How to Be Rich (Getty), 11–12 Lowe’s Companies, Inc., 139 Huis Clos (No Exit) (Sartre), 61 Lucent Technologies, 33 Hulbert ratings, vii Lynch, Peter, 12–13

Illinois Tool Works, Inc., 122 McConnell, James, 19–20 IngersollRand Company Limited, 123 McDonald’s Corporation, 141 Integrated Device Technology, Inc., 124 McGrawHill Companies, Inc., 142 Intel Corporation, 125 Mackay, Charles, 12, 25–26 International Business Machines Corporation, 126 Manias and panics, 25–34 International Paper Company, 127 Crash of 1929, 28–30, 35–37 Internet companies, 30–34, 43 Dow Jones Industrial Average (1924–1932), 33–34 Intrinsic values, viii–ix gold (1979–1980), 33–34 Intuit, Inc., 128 Mississippi Scheme, 25–26 Investech Research Market Analyst, 2, 5, 29–30 NASDAQ Bubble, 20–23, 30–34, 42 Investment clubs, 58, 60 Nikkei 225 (1983–1991), 33–34 Investor expectations theory: Russian MMM mania, 27–28 nature of, viii South Sea Bubble, 26–27 predictive theory of, viii–ix tulip bulb mania, 33 reasons for effectiveness of, x–xi Market crash of 1987, xi reflecting barriers theory and, viii–x Market indicators (see Dow Jones Industrial Average [DJIA]; (See also Theory of contrary opinion) NASDAQ Composite Index; S&P 500) Investor Expectations (Zweig), vii Market Wizards (Schwager), 13 Investor sentiment, vii Markets, Mobs, & Mayhem (Menschel), 26, 27 crowd psychology and, 20–23 Mavrodi, Sergei, 28 group conformity research and, 19–21 May Department Stores Company, 140 on individual stocks, 67–70 Media, Mania & the Markets (Bezos), 44 nontraditional, 55–60 MedImmune, Inc., 143 (See also Theory of contrary opinion) Menschel, Robert, 26, 27 Investors Intelligence, Inc., 49 Merck and Company, Inc., 144 Merrill Lynch and Company, 145 Jabil Circuit, Inc., 129 Meyssan, Thierry, 15 Japan, Nikkei 225 (1983–1992), 33–34 Microsoft Corporation, 146 Jarvik, Robert, 63 Minds of Wall Street, The (Levy), 13 Jefferson, Thomas, 63 Mississippi Company, 26 JeffersonPilot Corporation, 130 Mississippi Scheme, 25–26 Johnson, Lyndon, 44 Mitchell, Charles A., 29 Johnson and Johnson, 132 MMM company (Russia), 28 Johnson Controls, Inc., 131 MMM Mania, 27–28 Jones, Paul Tudor, 13 Monetary conditions, 49 Jones Apparel Group, Inc., 133 Montgomery, Paul, 35, 37, 39 Morse, Samuel, 63 Kahneman, Daniel, 21 Motley Fool, 31–32 Kellogg Company, 135 Motorola, Inc., 147 Keynes, John Maynard, 13 Kipling, Rudyard, 63 NASDAQ Composite Index: KLATencor Corporation, 134 Advance/Decline Line, 32 KnightRidder, Inc., 136 NASDAQ Bubble, 20–23, 30–34, 42 National Association of Investors Corporation, 58 Law, John, 25–26 National Bureau of Economic Research (NBER), 40–41, 42–43 Le Bon, Gustave, 8, 44 National City Bank, 29 Lehman Brothers Holding, Inc., 137 National Semiconductor Corporation, 148 Levy, Leon, 13 Ned Davis Research, 14, 16–17, 47, 52–56, 59–60 Limited Brands, Inc., 138 Neill, Humphrey B., 8–9 176 INDEX

Net aggregate position, 52 Shortterm bond prices, 49–50 Netherlands, tulip bulb mania, 33 Simpson, O. J., 15 New Economy stocks, 20–23, 30–34, 42 Singer, Isaac, 63 New York Federal Reserve Bank, 29 Sobel, Robert, 29 New York Times, headlines and the Crash of 1929, 36, 37 South Sea Bubble, 26–27 Newell Rubbermaid, Inc., 149 South Sea Company, 27 Newsweek, cover stories, 37, 39, 41 S&P 500: Nikkei 225 bubble (1983–1992), 33–34 versus 10year Treasury notes, 55–58 9/11, The Big Lie (Meyssan), 15 versus American Association of Individual Investors, Nixon, Richard, 44–45 48–49 Noncommercial traders, 51–52 versus bullishbearish indicators, 49–50 Nonprofessional investors, reflecting barriers theory and, viii–x earnings yield, 55–57 NYSE Advance/Decline line, 22 versus Ned Davis Research crowd sentiment poll, 53–54 versus Rydex mutual funds, 50–51 Old Economy stocks, 31 versus S&P priceearnings ratio, 55–57 Oracle Corporation, 150 versus Speculator COT Index, 51–52 Osama bin Laden, 15 versus stock mutual fund cashassets ratio, 9, 10 OurBeginnings, 30–31 versus Wall Street strategist sentiment, 53 Speculator COT Index, 51–52 PepsiCo, Inc., 152 State of the Union addresses, 44–46 Pets.com, 33 Statman, Meir, 22 PPG Industries, Inc., 151 Status quo bias, 33 Priceearnings ratio (P/E), 55–57 Stock market indicators (see Dow Jones Industrial Average Professional investors, reflecting barriers theory and, viii–x [DJIA]; NASDAQ Composite Index; S&P 500) Progressive Corporation, The, 153 Stock Market Wizards (Schwager), 13 Putscalls ratio, vii Sysco Corporation, 160

Quotations, politically motivated, 44–46 Target Corporation, 161 Technical analysis, 14 RadioShack Corporation, 154 Terrorism: Railroad industry, 33 AlQaeda network, 15 Rand, Ayn, 61 attacks of September 11, 2001, 9–10, 15 Random walk within reflecting barriers, viii–x Thaler, Richard, 33 Raskob, John J., 29 Theglobe.com, 31 Raspberry, William, 15 Theory of contrary opinion: Reagan, Ronald, 45–46 applications outside of markets, 13–14, 61–63 Reflecting barriers theory, viii–x creating own reality and, 14–15 Regret theory, 21–22 crowd psychology and, 8–9 (See also Crowd psychology) Research Driven Investor, The (Hayes), 59–60 explanation of, 9–11 Ride the Wave (Cooper), 30–31 Robert Frost and, 1 Roaring Twenties, 28–29 liquidity and, 9–11 Rohm and Haas Company, 155 Ned Davis Research and, 14, 16–17 Rukeyser, Louis, 2 successful users of, 11–13 Russia, MMM Mania, 27–28 TheStreet.com, 31–32 Rydex mutual funds, 50–51 Thomas, Clarence, 14 3M Company, 71 St. Paul Companies, Inc., The, 159 Time magazine, cover stories, 35–36, 39, 40, 42–43 Salk, Jonas, 63 Tulip bulb mania, 33 Samuelson, William, 33 Tversky, Amos, 21 Sara Lee Corporation, 156 Sartre, Jean Paul, 61 Understanding Human Behavior (Sherif and McConnell), ScheringPlough Corporation, 157 19–20 Schindler’s List (movie), 14–15 Union Pacific Corporation, 162 Schlumberger Limited, 158 United Kingdom, gold mania (1979–1980), 33–34 Schwager, Jack, 13 United States: Sentiment indicators (see Investor sentiment) Constitution, 62–63 September 11, 2001 terrorist attacks, 9–10, 15 Federal Reserve Board, The, 29–30, 41 Shefrin, Hersch, 21–22 gold mania (1979–1980), 33–34 Sherif, Muzafer, 19–20 Great Depression, 28–30 INDEX 177

United States (Cont.): Wall Street Journal (Cont.): NASDAQ Bubble, 20–23, 30–34, 42 surveys, 5, 7 stock market crash of 1929, 28–30, 33–37 Wall Street Week, 2 stock market crash of 1987, vii Walt Disney Company, The, 168 stock market indicators (see Dow Jones Industrial Average Warren Buffett Way, The (Buffett), 13 [DJIA]; NASDAQ Composite Index; S&P 500) Weyerhaeuser Company, 169 terrorist attacks of September 11, 2001, 9–10, 15 Whitney, Eli, 63 William Wrigley Jr. Company, 170 VA Software, 31 Winer, Larry, 33–34 Valuation indicators, 55–57 Winner’s Curse, The (Thaler), 33 Value Line Composite, 22 Winning on Wall Street (Zweig), 13 Verizon Communications, Inc., 163 World War II, 37 Viacom, Inc. Class B, 164 WorldCom, 33 Vietnam War, 40, 44 Wright, Orville, 63 Wright, Wilbur, 63 W. W. Grainger, Inc., 165 Wright Aeronautical, 29 Wachovia Corporation, 166 WalMart Stores, Inc., 167 Zeckhauser, Richard, 33 Wall Street Journal: Zweig, Martin, vii–xv, 13 headlines and the Crash of 1929, 36 Zweig Forecast, The (newsletter), vii ABOUT THE AUTHOR

Louis Rukeyser has called Ned Davis, his guest on both Wall $treet Week and Louis Rukeyser’s Wall Street, “one of the greatest market historians of the past century.” A 58yearold Phi Beta Kappa graduate of the University of North Carolina at Chapel Hill, Ned has been professionally involved in the stock market since 1966. The institutionally oriented Ned Davis Research, based in Venice, Florida, produces nine dif ferent timing services and has built one of the largest independent research firms on Wall Street. Ned be gan Ned Davis Research in 1980 after 12 years as partner and Director of Technical Research at J. C. Bradford & Company. The research is marketed by Davis, Mendel and Regen stein, Inc. (NASD), of Atlanta, Georgia, of which Ned is Chairman of the Board of Directors. The focus of Ned’s work is timing models and indicators. These computerderived timing tools iden tify buying and selling junctures in such diverse markets as the Dow Industrials, the S&P stock index futures, the bond market, currencies, and gold, as well as 100 industry groups, individual stocks, inflation, the economy, and foreign stock markets. All Ned Davis Research publications are fully illustrated by the rich graphics that have become their hallmark. Ned’s approach, in general, differs from those of other technical analysts in that his models employ a broad spectrum of timing tools: price action, volume, investor psychology, val uation fundamentals, economic strain or ease, the bondstock relationship, flow of funds studies, Federal Reserve policy, and monetary tools. In total, his research is best characterized as an objec tive, disciplined approach to investing that focuses on managing risk, staying in harmony with the primary trend, and avoiding major disasters.