Chimerica and Expected Return of Us Stocks

CHIMERICA AND EXPECTED RETURN OF US STOCKS

Jasmine Z. Yu† ‡ EDHEC Business School Nice, France

November 2017

* I thank Professor Abraham Lioui and Professor Rene Gracia, both are professors at the Finance, Law and Accounting Department of EDHEC Business School for their dissertation advice. I also thank Professor Stefano Marmi of Scuola Normale Superiore (SNS) for factor data made available on his website and Tracy Wu from China Stock Market & Accounting Research (CSMAR) for annual report information. Jasmine Z. Yu is PhD in Finance Candidate at EDHEC Business School. The author’s correspondence is, email: [email protected]. Address: EDHEC Business School, 393, Promenade des Anglais BP3116 06202 Nice cedex 3 – France. Phone: + 1 646 338 8638.

CHIMERICA AND EXPECTED RETURN OF US STOCKS

Abstract

This paper develops a scorecard to proxy for “Chimerica” and to measure the anomalous return associated with US firms’ overseas expansion in Greater China. Based upon ‘Chimerica’, the symbiotic relation between the People’s Republic of China and the United States of America, this paper uses a variety of approaches, some of which return-based such as regression analysis and Fama MacBeth estimation while others, fundamental- based such as Claus/Thomas earnings analysis, and illustrates little success in proving that the US stock market has actually rewarded companies for doing business in China. The Chimerica premium was about 0.43% per annum from 2006 to 2010. Sector portfolios based on forward-looking analyst forecasts could deliver more dramatic Chimerica premium at 1-2%. However, results are mixed and not always consistent across approaches, especially after advanced econometric modelling and testing. Down-market protection seems to stand out though, for the higher Chimerica-scored companies and proves the benefit for global diversification of revenue streams in periods of market stress.

Keywords: Chimerica, multi-factor model, abnormal earnings, and Fama-Macbeth method, model misspecification, model specification test

1 Introduction

The holy grail in investment management is all about consistently outperforming the capitalization-weighted market benchmarks. Practitioners have long been implementing investment strategies grounded in academic research. But active management is a zero- sum game, i.e. aggregate outperformance and underperformance cross out. If everyone is reading the same award-winning academic research and following the same investment strategies, why not everyone outperforms? The most renowned academic research in finding cross sectional predictability in stock returns probably belongs to Fama and French (1992) who found the three factors, market, size and book-value-to-market (B/M) capture the variation in average stock returns. Cahart (1997) subsequently discovered the price momentum factor while Fama and French (2015) illuminated firm profitability and investment pattern are factors that also drive stock returns. Other firm-characteristics-based and market-based factors, include liquidity (Acharya and Pedersen, 2005, and Ibbotson 2013) have been found by academic researchers. And now these factors have inundated the field of smart beta investing. Composite trading strategies —going long high-expected-return stocks and short low-expected-return stocks are devised to take advantage of some of the findings. However, results have been mixed. Lewellen (2015) found that many of the factors had turned out to be insignificant predictors of return after testing 15 characteristic-based factors. Other researchers (Hou et al., 2017) used a large data library of 447 anomalies to test and only found 286 (64%) anomalies insignificant at the 5% level. Imposing the t- cutoﬀ of three raises the number of insigniﬁcance to 380 (85%). The explanations that researcher gave oftentimes have to do with increased ease of trading and improved market micro-structure. For example, Chordier et al. (2014) found that increased liquidity and trading activity associated with attenuation of prominent equity return anomalies due to increased arbitrage. Average returns based on prominent anomalies halved after

decimalization. Policies that stimulate liquidity and reduce trading cost improve capital market efficiency. My paper belongs to the strand of research that strives to identify anomalies or factors that predict returns. Unlike other pure firm-based characteristics, the anomaly I try to explain has a macro bent and is inspired by a set of profound geopolitical and socioeconomic influences that are the most prevalent in the last decade. The influences that gave rise to the factor will undoubtedly rise and fall with the changes in the macro environment, such as government policies leading to prosperity, war or peace. That’s what makes investing so interesting because it’s dynamic – what worked in the past may or may not work in the future. Of course, any new anomaly found should be subject to rigorous econometric testing held to the highest standard. Chimerica is a new word coined by historian Niall Ferguson and economist Moritz Schularick (2007) In the article ‘Chimerica” and the Global Asset Market Boom published in International Finance, they described the following relationship - cheap exports by Chinese companies to America allow the Chinese to accumulate large currency reserves that are channelled into purchasing U.S. government securities, which have kept nominal and real long-term interest rates artificially low in the United States. At the same time, China’s provision of cheap labour and the United States’ spending in foreign direct investment have allowed American companies to reinvent themselves by selling to Chinese consumers (Ferguson 2008). This paper seeks to prove that the US equity market recognizes the Chimerica factor1 but does not necessarily always price those American companies possessing higher China exposure with higher stock price. Lower expected returns provide insurance against bad times. In the investment community, there has long been the notion that in order to escape from the slow growth of the developed markets and to benefit from emerging markets’ phenomenal growth, an investor may not need to chase the very volatile emerging markets stocks but should consider instead select exposure to global companies

1 Chimerica factor and China exposure are used synonymously in this paper.

who are well positioned to capture labour participation and rising consumer spending in developing countries and should expect to be handsomely rewarded by the capital markets (Sterling, 2010). Indeed, rather than confining our thinking and research to nation-state boundaries, nowadays we need to consider the implications of globalization, a new but ubiquitous macroeconomic phenomenon which has existed since the beginning of the 1990’s. The emergence of the developing economies has posed the challenging question whether they have made developed equities more attractive or instead they have crowded out and diverted limited capital for investment. Therefore, the input for analysis needs to be expanded beyond the traditional standalone national statistics. The case study in this paper is “Chimerica” – the two countries which some believe are becoming more like a single economic entity. Their unique relationship has become the axis of the world economy – everything from interest rates to inflation to the global imbalances in trade and finances (Karabell, 2009). In more recent years, Ferguson (2015) argued that the 2008 Global Financial crisis didn’t lead to a Sino-American divorce, despite mutual accusations of monetary manipulation. Instead, like any couple who spend long enough in each other’s company, the Chimericans grew ever more alike. China took steps to make its economy less state-led and export-driven, and more market-led and consumption- driven. China’s economy became dangerously reliant on easy money and mounting debt, and prone to bubbles, beginning with urban real estate. Given the ‘Chimerica’ macro picture, I strive to examine its impact at the micro firm level and see if this macro factor has translated into being an anomaly that global investors can exploit. I selected 100 largest US companies that were perceived to be the most “global” to test the concept of Chimerica. I analyzed these companies’ historical returns in the period between 2006 and 2010 and researched their annual reports in order to understand at the firm level, how these companies may have interacted with China. In order to implement these methodologies, I develop a “scientific” scorecard approach to consistently evaluate a US company’s extent to which their business is

exposed to Greater China, i.e. the mainland, Hong Kong, Macau and Taiwan. The scorecard uses a point system that considers the Chinese elements reflected in the American company’s publicly available financial disclosures, for example, revenue, cost, SG&A expense, operating and investing cash flows, etc. These US companies are then ranked in accordance to their overall scores. Inspired by factor and style investing, I tried to approximate the Chimerca factor by subtracting returns of the high Chimerica scored company from returns of the low Chimerica scored companies. Then I used a range of techniques that includes basic characteristic sorting, regression analysis, Fama Macbetch estimation to advanced econometrics for statistical testing. Besides historical returns, I also focused on company fundamentals and made good use of a large quantity of stock analyst forecasts to arrive at a forward-looking view in understanding the efficacy of the Chimerica factor. Since the identification and articulation of major investment styles by William Sharpe (1978), investment style analysis has gone beyond the realms of active vs. passive management and techniques of market timing vs. security selection. Investment Style Analysis (Ibbostson and Kim, 2012) and Claus/Thomas (CT hereafter, 2001) are used to test Chimerica as an anomaly, in addition to the well-known market, size and value factors (Fama and French, 1992, 2008). Pioneered by William F. Shapre (Fama and Macbeth, 1973) , benchmark style cross-sectional analysis is historical return-based. It primarily utilizes portfolio sorts and ordinary least squares and general least squares regressions which focus on portfolio characteristics. Claus/Thomas approach is fundamental-based utilizing analyst earnings and growth estimates. This approach projects individual stock return using current market price and analysts’ quarterly earnings forecasts which are arrived from company-specific qualitative and quantitative variables known today. Consistent results drawn from combining returns-based and fundamental-based methodologies are intending to make the conclusion in this paper more robust. Fama Macbeth procedure ignores the estimation errors in betas, advanced econometrics around extensions to the traditional beta pricing models are applied,

including allowing for serial correlation in the underlying market factors and adjusting for small sample bias (Shanken, 1992), using t-statistic that incorporates asymptotic distributions under model misspecification (Jagannnathan and Wang, 1998), comparison of the OLS and the GLS estimators, though GLS is often more precise but also biased (Shanken, Zhou, 2007), and employing the sample cross-sectional regression (CSR) R2 as a measure of model performance versus the typical adjusted R2 (Kan, Robotti and Shanken, 2013). Also, a t-statistic of 3.0 is being applied as Chimerica is observed purely on an empirical basis and thus requires a higher hurdle to clear (Harvey & Liu, 2016). Harvey’s and Liu’s method of purging out systematic factor risks and allowing for the possibility of time-series and cross-sectional dependence is also followed. The technique accommodates a wide range of test statistics and can be used for both asset pricing tests based on portfolio sorts as well as tests using individual asset returns. I have found that the premium associated with the Chimerica factor was around 43 bps per annum on a historical basis through back-testing. The Chimerica factor could potentially enhance return. And this is positive. However, the more China-exposed American companies tended to withstand down markets better. There’s a varying degree of success in statistical testing of the Chimerica factor, generally with sorting technique outperforming regression techniques and forward-looking quartile and sector portfolios having demonstrated success. On a fundamental forward-looking basis, the impact of Chimerica was felt strongest in the sector portfolio construction, with an average 1-2% premium on an annual basis. The rest of the paper is organized as follows. Section 2 Scorecard and Data introduces how I build the scorecard, stock universe, time period chosen and the key results revealed by the scorecard. Section 3.1 focuses on return differences of the US stocks ranked into four quartiles based on size, style and Chimerica scores. Equal- weighted quartile and half portfolios are formed to analyze risk and return characteristics of Chimerica scorecard, size and value/growth styles. It is revealing that stocks of highly scored companies tended to do well during the 2007-2008 US stock market downturn in

the midst of the Global Financial Crisis. Section 3.2 Chimerica factor is formally identified by having returns of highly scored (i.e. first quartile) stocks subtracted from returns of lowly scored (4th quartile) stocks. This factor is then used as an independent variable in the CAPM and Fama-french 3-factor frameworks to test for statistical significance. Returns of 10 Chimerica-scored portfolios and 10 industry/sector portfolios are regressed against Fama-French three factors and the Chimerica factor. Section 3.3 Fama Macbeth Estimation is used to test if Chimerica drives portfolio returns and also attempts to price how much return we would expect to receive for a particular beta exposure to the Chimerica factor. Various advanced and innovative econometrics methods are applied to illuminate the efficacy of the Chimerica factor. Section 3.4 Systematic factor returns as expressed in Fama-French three factors are purged from Chimerica so the study the Chimerica factor as a standalone factor. Portfolios are purged from traditional factors and regressed on Chimerica or Chimerica itself purged from other factors. Section 4 Thomas/Claus methodology produces expected returns by stock analyst. It also tests the significance of the long/short portfolio forecast returns and long/short GICS sector portfolio forecast returns. Section 5 Conclusion summarizes research findings and highlights direction for future research relating to the subject.

2 Scorecard and Data

The sample for this research is the 100 largest US companies in terms of market capitalization, i.e. constituents of the S&P 100 index (Bloomberg Ticker: OEX Index). This seems to be an appropriate sample representing the largest American enterprises that are likely to have the willingness and resources to do business aboard. Their disclosures and corporate governance are among the highest quality and at the same time, their stocks are also the most liquid and widely held. Index constituents from June 2004 to June 2010 were picked up and six years of data were compiled for the analysis of this paper.

The starting point was to develop a systematic approach to measure how a US stock is exposed to China. I define China exposure as US company generating revenue through sales activities in China, and/or generating profit through investing activities in China, and/or just having prospecting activities in China. It includes activities primarily on Chinese mainland but due to increasing economic integration, it also includes Taiwan and Hong Kong in the analysis of certain companies depending where these companies’ China headquarters and activities are based. A scorecard containing 15 elements of operational and strategic significance is devised to measure the exposure at the company level. Take Intel Corporation (ticker: INTC) in 2010 as an example. Intel was scored on the below elements with 1 or 0 to represent binary yes/no answers except for sales estimates which are in percentage terms. Intel’s total score in 2010 was 14. Disclose Sales % of Growth Source Ticker S&D Sales/ R&D Manufacture Estimate Total Market Material Profitability INTC 1 1 1 1 1 1 1 1

> 500 Chinese >5 AR Total Hire Invest Location Sub/JV Cont'd Employees Yrs Disclosure >3 Score INTC 1 1 1 1 1 0 1 14

Scorecard Definition: 1 Sales and distribution. Company has this sales and distribution activity in China

2 Company financial statements disclose sales and/or profit from China

3 Company management and/or investment analysts estimate company sales in China (in percentage)

4 Disclosed or estimated sales in China as a percentage of total

5 Company management and/or investment analysts assert that China is the firm's growth opportunity 6 Company performs research and development activity in China

7 Company has manufacturing activity in China

8 Company sources materials locally for their manufacturing activities

9 Company hires locally in China

10 Company currently employs over 500 people in China

11 Company management and/or investment analysts disclose and/or estimate investment amount in China 12 Company has operated in China for over 5 years

13 Company has country wide operations or representative offices in China

14 Company has subsidiaries and/or joint ventures in China

15 Company’s annual report mentioned China at least three times

In terms of the time periods chosen for the scorecard, Karabell (2009) points out that Chimerica finally came into formal existence in 2004 after China’s accession into the WTO in 2001. Since it took firm’s long-term planning and execution to break into China’s market, the scorecard captures gradual developments and is not assumed to change drastically year over year. Six annual scorecards were created. They reflect constituent companies within the S&P 100 index and their cumulative efforts to build out their Chinese operations. The time span chosen (2004-2010) includes the 2008 Global Financial Crisis when stock markets across the world tumbled and the hypothesis of diversified global revenue streams providing stability was put to test. Similar to Investment Style Analysis (Ibbotson and Kim, 2012), the methodology of the study consisted of a two-part algorithm for the selection (prior) year and the performance (current) year. For each selection year (June 30, 2004-June 30, 2009), I examined the top 100 US stocks in the S&P 100 Index by market capitalization in beginning of June. The scorecard is also created in accordance to the company annual report disclosure published before June 30 of each year. In each of the performance years (June 30, 2004 – June 30, 2010), the portfolios selected were equally weight at the beginning of each year and passively held. Delisting of any kind (e.g. liquidations, mergers) caused the position to be liquidated and held as cash for the remainder of the performance year. I recorded returns at the end of the performance year for each selection-year portfolio so that the portfolios were “identifiable before the fact”. The rationale for equally-weighted portfolio rather than market capitalization- weighted portfolio is two-fold. First, to follow the exact same method in Investment Style

Analysis set out by Ibbotson and Kim (2012). And second, to avoid the outcome of having mega-cap stocks dominate the analysis. The biggest stock in the S&P 100 index was 41 times larger than the 100th stock in 2010 in terms of market capitalization while the total market capitalization of the top-decile companies in the S&P 100 index was 19 times larger than that of the bottom-decile companies. The scorecard indicates that generally the larger the company size, the higher the score. In fact, when two companies are scored the same on the 15 elements, the larger of its size, the higher is its rank. The top quartile most China exposed companies are on average twice bigger than the bottom quartile companies with the least China exposure. Intuitively, the bigger the US firm, the harder for them to find domestic growth opportunities and the more pressure to expand globally. The clear exception in the top quartile was Avon Products Inc. which has had a very successful Chinese franchise since its entry in China in the 1990’s. Though not one of the largest companies by market cap, both its overall average and the median scores increased by 15-16% from 2007 to 2010 reflecting increased financial disclosure, market intelligence and sell-side research coverage available over time and the significance of these companies’ China activities. [Table 1 about here.] Table 1 shows American companies’ exposure to China has increased over the time period covered in the study. In 2010, 88 of the S&P 100 companies or 91% of the market capitalization have China exposure, compared to 77 companies or 86% of the market capitalization three years ago. Direct revenue from China has also increased across all sectors since 2007, ranging from 2% to 47% of total revenues. The technology sector has the most China exposure in terms of market capitalization. This in part reflects the outsourcing practices of many American multinationals as well as China’s status as the world’s factory where the Chinese workers assemble popular consumer electronics that are sold across the developed world (e.g. Apple Inc.). The consumers and industrial sectors contain the largest number of firms that do business in China with the highest China related revenue. These sectors have benefited from increasing Chinese consumer

spending and the vast infrastructure projects that contributed to China’s phenomenal GDP growth. The least number of companies are in the heavily regulated sectors, such as financial services (e.g. regional banks) and utilities. While these companies may be more inward-looking, this is also due to Chinese government’s unwillingness to open up these sectors for foreign competition. Largely impacted by the 2008 Global Financial Crisis, the financial sector saw a major drop of 3% in market capitalization in 2010 while all other sectors’ China exposure has increased over time in market capitalization terms. Overall, the market capitalization of financial services firms in 2010 has still not recovered to the pre-crisis level.

3 In Search of Chimerica Factor The objective of this section is to isolate systematic factors such as market, size and style from the Chimerica factor and test for factor effectiveness of the Chimerica factor on a standalone basis. It strives to answer the question if the Chimerica factor produces the effect of enhancing or reducing corss-section of stock returns.

3.1 Chimerica vs. Size and Style

I start with comparing return differences in the time period chosen. Stocks are ranked into 4 quartiles based on size, style (value/growth) and Chimerica scores. Equal-weighted portfolios are formed for each quartile. This is similar to the approach used in the Ibbotson and Kim paper (2012). [Table 2 about here.] Table 2 reports the annualized arithmetic mean, geometric mean and standard deviation of returns for each equal-weighted quartile portfolio with respect to size, style and Chimerica score. While most research in academic finance is done on arithmetic returns, the geometric returns are reflective of realistic investor experience over time.

For example, when a stock declines by 50% in one year, it takes more than 50% return in the next year to break even. Whereas arithmetic returns would be zero in this case. The annualized geometric mean is the compound annual return realized by the portfolios over the period, which, unlike arithmetic mean, is not diminished by the variability of returns. For the time period chose (June 30 2004 – June 30 2010), size effect was the most prominent, i.e. returns were in a uniformly descending order. Small-size 4th quartile companies outperformed large-size 1st quartile companies by 4.70%. In terms of style effect, returns of quartile portfolios were less consistent, however, the bottom half of growth stocks outperformed the top half of value stocks by 0.63%. The most growthy 4th quartile companies outperformed the most value 1st quartile companies by 3.11%. That growth stocks outperformed value stocks in those six years chosen including the 2008- 2009 financial crisis is a not a surprise. Because returns of bank stocks, a major “value” sector substantially declined in the period chosen, though the value factor tends to outperform the growth factor over much longer periods of time. In terms of Chimerica score, returns of quartile portfolios were also not in a uniform sequential order. It’s worth noting that the lowly Chimerica scored 4th quartile companies outperformed highly scored 1st quartile companies by 0.43% on an empirical basis before statistical test for significance. Geometric returns in the table are shown as reference but not analyzed, primarily because both the index constituents and the Chimerica scoring change year over year, disallowing continuously compounding of the same stocks. To summarize, smaller capitalization, growthier stocks and stocks with less exposure to China are shown to have delivered higher returns in those six years. The reason why US growth stocks outperformed value stocks is because the period chosen has incorporated 2008-2009, the Global Financial Crisis when bank stocks which are normally categorized as value stocks underperformed. As a parallel comparison, Chinese growth stocks also outperformed value stocks during this period of time. Another interesting observation is that lowly-scored companies tended to have higher volatility than highly-scored companies. High Chimerica means more diversified global business

exposure, and hence, the realized returns associated with these companies were less volatile. [Table 3 about here.] Table 3 examines calendar year stock performance based on Chimerica scores and put stocks into high- to low-scored quartiles. Chimerica premium has been most of the time positive. Lowly scored companies in quartile 4 outperformed highly scored companies in quartile 1 most predominantly in 2004 by 18.07% and to a less extend, in 2006 by 3.50%. Both years registered “banner” returns of double digits. Chimerica premium in most of the calendar years was negative. However, stocks of high Chimerica- scored companies in quarter 1 did particularly well ex post in crisis years 2007 and 2008 outperforming the lowly scored companies in quartile 4 by 2.25% and 6.82% respectively. Through those six years chosen, the top half of high Chimerica-scored companies actually underperformed the bottom half of low Chimerica-scored companies by 2.76%, though the highly-scored companies in the 1st quartile outperformed the lowly-scored 4th quartile companies by 0.43%. This seems to be consistent with the concept that overall lower expected returns provide insurance against bad times. Fundamentally, stocks with more diversified global revenue streams, e.g. Chimerica in this case, weathered the storm of 2007-2008 better as the Financial Crisis had a larger impact on US domestic oriented stocks (i.e. companies with low Chimerica scores). Take calendar years of 2007 and 2008 as examples when these stocks delivered negative double-digit returns. In 2007, high Chimerica-scored first quartile companies outperformed the second quartile companies, second-quartile companies outperformed third quartile companies and third quartile companies outperformed thosed in the fourth quartile. I observed the same results in calendar year 2008. Among the many probable explanations, one could argue without testing for statistical significance that the diversification benefit from overseas revenue and profit streams of the first quartile companies with high Chimerca scores indeed seems to stand out in times of acute crisis. Of course, these observations were empirical and are subject to statistical significance tests.

The next question we ask is whether the Chimerica effect is distinct from the existing well-established size and style effects. We constructed double-quartile portfolios that combined Chimerica score with each of the size and style quartile portfolios. In other words, is investing in Chimerica stocks equivalent to investing in small stocks or growth stocks? [Table 4 about here.] Table 4 reports the annual arithmetic mean return, geometic mean return and standard deviation of returns, as well as the average number of stocks, for each intersection portfolios. The arithmetic mean return is the simple average of returns while the geometric mean return is the time-weighted or compounded annual return that is generally lower than the arithmetic mean return.The strongest Chimerica effect resides with small stocks and followed by big stocks, with the premiums of the Chimerica factor at 3.17% and 1.43% respectively while big and smaller stocks did not exhibit positive Chimerica premiums. Across the smaller stock quartile, big stock quartile and bigger stock quartile, the top half of high Chimerica-scored stocks returned less than the bottom half of low Chimerica-scored stocks. Only in the small stock quartile, the bottom half of low Chimerica-scored stocks returned more than the top half of high Chimerica-scored stocks, indicating Chimerica premium. The Chimerica effect does not present uniformity across all size quartiles, though the Chimerica effect is found to be so high and negative with big stocks. The conclusion from Table 4 seems to be that even though the size premium was observed in the time period chosen (Table 2), the Chimerica premium did not coexist with the size premium and probably operated independently. Only two out of four size quartiles (Small and Bigger) existed Chimerica premiums. Certainly, statistical significance has not been tested here. [Table 5 about here.] Similarly, to address the question of how the Chimerica effect differs from style of value or growth, we constructed equally weighted double-sorted portfolios on Chimerica scores and the earnings-to-price ratio (E/P). Table 5 reports the annual return results for

the 16 value/growth and Chimerica portfolios. Except for the high value quartile portfolio where lowly-scored stocks in the 4th quartile outperformed highly-scored stocks in the 1st quartile by 2.53%, all other style portfolios, mid value, mid growth and high growth showed opposite results. The value premium is strongly negative, apart for the mid high scores. There is also the lack of consistency across the style quartile portfolio in testing the Chimerica scores. Chimerica effect seems to be the least plausible with style portfolios. From the results of tables 4 and 5, it seems to be possible to justify Chimerica differs from each of the established size and style factors for the time period chosen. We do not observe much synergistic effect when combining low Chimerica scores with other factors. It is unclear that Chimerica can be mixed with the higher-performing small-size or growth portfolios for the time period tested. Therefore, Chimerica seems to warrant more analysis, independent of investment style analysis.

3.2 Chimerica as a Factor

This section focuses on expressing Chimerica as a factor, i.e. a series of long-short portfolio returns. I constructed monthly returns of a long-short portfolio in which the returns of the bottom quartile portfolio (lowest Chimerica scored companies) were subtracted from the returns of the top quartile portfolio (highest Chimerica scored companies). This series represents the Chimerica factor as the dependent variable and then is regressed on market, size and style factors. The market, size and value/growth style factors were defined by Fama-French and extracted from the Dartmouth University website (http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html). It’s important to note that the definition of these factors do not correspond to those on Tables 2, 3, 4 and 5 as the universes of stocks, time periods chosen and the groupings are different, though sorting is the basic tool being employed across all tables. [Table 6 about here.]

It’s worth pointing out that the means of these factors, size, style and Chimerica in Panel A of Table 6 do not match those in Table 2. First, the prior table specifically reports on the universe of 100 largest US stocks during the period from June 2004 to June 2010 whereas Table 6 uses Fama-French factor data as input from the Dartmouth University database. These Fama-French factors are value-weighted, as opposed to equally-weighted factors reported on Table 2. Second, the data in the Table 2 uses June- June 12-month annualized returns as input while Table 6 is based on monthly returns. Due to dividend compounding, Chimerica-scored quartile portfolio returns slightly varied with the mean of the Chimerica factor at 9 bps in Table 6 vs. 43 bps on Table 2. Panel A of table 6 reports that the means of all Fama-French factor returns and the Chimerica factor returns were positive. Other than the market factor and stlye factors, returns of size and Chimerica factors were positively skewed. The variation of factor returns, as measured by standard deviation is the highest for market return (3.07%), followed by style (2.71%), size (2.35%) and Chimerica (2.11%). The minimum of all factor returns is also the lowest for the market factor, which was over two to three times as big as other factors’ minimums. The Global Financial Crisis strongly explained this large negative factor returns of market and style (Value). On the maximum factor return side, market and style factor returns were again the largest, reflecting the strong rebound experienced by the same factors post crisis. Panel B shows the average of 10 Chimerica decile portfolios that has arithmetic return of 0.12% per annum. Though the returns of the 10 portfolios are not in a uniform ascending order, we do observe that higher scored Chimerica portfolios generally underperform less scored portfolios, with a 0.10% of premium for the average of Portfolio 6-10 minus the average of Portfolio 1-5. Interesting to note that higher returns are associated with higher volatility. However, returns for Portfolio 6-10 are all negatively skewed while returns for Portfolio 1-5 are less negatively skewed. Panel C shows the returns for the 10 industry sector portfolios. All sectors had positive mean returns. The return dispersion is narrow within a range of 1.31%, with the energy sector returning the highest 1.37% and the Telecommunication Communication sector the lowest 0.06%.

Returns of most industry sector portfolios are negative skewed. Consumer discretionary, financials and materials sectors are exceptions though their positive skewness is only in the narrow range of 0.07-1.14%. The materials sector experiences the highest drawdown at -29.98% while it also enjoys the highest draw-up at +54.54%. Next, as a further quest for the anomalies, I use the CAPM framework, regressing

Chimerica long-short factor return (Rit) on the excess returns of the US market (RMt).

Monthly and arithmetic returns are used in the creation of the Chimerica long-short factor. I follow Fama-French’s definition of “the market” where they use all NYSE, Amex and Nasdaq firms. And market return is value-weighted. Risk free rate (Rft) is obtained from Kenneth R. French’s website.

푅, = α+ 훽 푅 − 푅 + 휖 (1)

In the standard Fama-French three-factor model, the long-short Chimerica factor is regressed on the long market portfolio and the long-short size and value portfolios. Kenneth French’s website also provides factor returns.

푅, = α+ 훽 푅 − 푅 + 푠푆푀퐵 +ℎ퐻푀퐿 + 휖 (2)

[Table 7 about here.] Table 7 presents the time-series regression analyses, with Chimerica factor as the explained variable and using the individual Fama-French three factor, market, size and style and combined three factors as independent variables. On the one-factor regressions, none of the market, size or style factor explain the Chimerica factor. The adjusted r-squares are very low at 0.00%, -0.01% and -0.01% respectively. Similarly, none of the t-stats for the market, size and style factors has reached the statistically significant level. Using Fama-French three factors combined, the adjusted r-square of the regression

does not improve at all. Alpha is negative and statistically insignificant. The beta coefficient for the market factor is negative while the beta coefficients for size and style factors are positive. Results from this table seem to indicate that Chimerica is indeed an anomaly, that neither the individual Fama-French three factors or the Fama-French three- factor model can explain it well. [Table 8 about here.] Table 8 presents results of cross section analyses on the portfolio sorts. Panel A are 10 Chimerica portfolios sorted by Chimerica score, using market, size, style as explainable factors and Chimerica-sorted portfolio returns as explained factor. The purpose is the assess if the three factors explain majority of the return variation. Any left alpha is something that Chimerica can potentially explain. The alphas in 6 out of 10 portfolios are positive. The adjusted r-squares are all very high ranging from 79-89% and the t-stats for alphas in 9 portfolios are insignificant, except for Portfolio 5. This indicates the general effectiveness of the Fama-French three-factor model in explaining the Chimerica anomaly in a portfolio setting. Here the market is defined as the 10 portfolios, as opposed to the broad market such as the S&P 500 index. T-statistics for the market factor in particular are very high in excess of 17.0 for all ten portfolios, surpassing the new standard of t-statistic greater than 3 for a higher hurdle to clear for market prices of risk (Harvey and Liu, 2016). Panel B shows cross section results of the ten industry sector portfolios. Again, the Fama-French 3-factor model is meaningful in explaining most of the industry sector portfolios, with the minimum adjusted R2 at 29% for the telecommunications sector portfolio while the maximum at 88% for the industrials portfolio. The industrials sector portfolios show statistically meaningful alpha. The market factor also dominates the other two factors in explaining industry/sector portfolio results across the board. Results from both Table 7 and Table 8 seem to conclude that the Chimerica factor in general cannot be fully explained by the Fama French individual factors and combined

model both on a standalone basis and in portfolio settings. On a historical basis, there were more successes with certain sector or scored portfolios.

3.3 Fama Macbeth Estimation

Fama Macbeth factor premium estimation is a two stage analysis (Fama and Macbeth, 1973). The first stage involves a set of regressions equal in number to the number of assets being tested. Stage two is a set of regressions equal in number to the number of time periods. According to Hsu (2007), the first stage regressions are a set of time series regressions of each asset’s returns on the factors, and Stage 2 tells us the premium awarded to each factor exposure.

푅, = 훼 + 훽, 푅 − 푅 + 훽,푆푀퐵 + 훽,퐻푀퐿 +

훽,퐿푀퐻 + 휖,

푅, = 훼 + 훽, 푅 − 푅 + 훽,푆푀퐵 + 훽,퐻푀퐿 +

훽,퐿푀퐻 + 휖,

푅, = 훼 + 훽, 푅 − 푅 + 훽,푆푀퐵 + 훽,퐻푀퐿 +

훽,퐿푀퐻 + 휖, …

푅, = 훼 + 훽, 푅 − 푅 + 훽,푆푀퐵 + 훽,퐻푀퐿 +

훽,퐿푀퐻 + 휖, Excess returns of each stock or portfolio across the time period chosen are used as dependent variables. Independent variables as defined in Section 3.2 are market excess return, size (small minus big), style (high minus low) and Chimerica (low score minus high score).

Expressed in matrix form, the regression for Rn would look as follows:

푅 = 퐹훽 + 휖 (4)

Where Rn is a n × 1 vector of excess returns, F is a 1 × (m + 1) matrix of factors where all elements in the first column are 1, βn is a (m+ 1) × 1 vector of factor loadings where all elements in the first row are the intercept αn, and ɛn is a n × 1 vector of error terms. n is the sample size of asset returns and T is the number of periods. Upon completing (4), we know how each stock’s or portfolio’s return is affected by each factor. To calculate factor premiums for each factor, we run a set of cross sectional regression. 훽, the unobservable factor loading is the estimated for 훽 of each asset for each factor, i.e. market, size, style and Chimerica that are empirically estimated factor loadings. 푅, = 훼 + 훾, 훽, + 훾,훽, + 훾,훽, + 훾,훽, + 푒 푅, = 훼 + 훾, 훽, + 훾,훽, + 훾,훽, + 훾,훽, + 푒 … 푅, = 훼 + 훾, 훽, + 훾,훽, + 훾,훽, + 훾,훽, + 푒

Expressed in matrix form, the regressions for Rt 푅 =β 훾 (5)

Where Rt is an n × 1 vector of average asset returns, 훽 is an n × (m + 1) vector of factor loadings where all elements in the first column are 1, and ɤis an (m+ 1) × 1 vector of factor premia where all elements in the first row are the intercept .

T-statistics on 훾 terms are used to gauge if 훾 estimated is statistically significant and different from zerio. This is the gamma estimated in the second stage. 훾 푗 (6) ϒ,푗/√

∂ϒ, Is the standard deviation of 훾, term.

Statistical inference with Fama Macbeth method is typically conducted under the assumption that the models are correctly specified, i.e., expected returns are exactly linear in asset betas. This can be a problem in practice since all models are, at best, approximations of reality and are likely to be subject to a certain degree of misspecification. As mentioned in the Introduction Section, various econometric methods (Shanken, 1992, Jagannathan and Wang, 1998 and Shanken, Kan, Robotti, 2013) and subsequent statistics are further analysed on the cross section results. [Table 9 about here.] First, on the Chimerica-scored portfolios purged from one, three and four factors, the initial observation is on the adjusted R2 s of Ordinary Least Square method for one- factor (market), three-factor (market, size and style) and four-factor (including Chimerica) models have improved with the additional factor inclusion from 23%, 27% to 35%. The addition of the Chimerica factor increased explanatory power by 8%. The loadings of these factors in market price of risk and covariance are positive for the market factor and Chimerica factor while negative for size and style. None of the t-statistics is significant though. The set of t-tests include Fama MacBeth test (tFM, 1973), Shanken test (ts, 1992),

Jagannathan and Wang test (tjw, 1998) and the Kan, Robotti and Shenken test (tkrs, 2013). The Shanken test aims at correcting errors in variables, the Jagannathan and Wang test assumes correctly specified models and the Kan, Robotti and Shenken test accounts for model misspecification. On the market prices of risk (ϒs) and covariance risk (λs), Table 9 shows that the Fama-Macbeth t statistic (tFM) and Shanken t statistic (ts) for the ϒs in 1- factor, 3-factor and 4-factor models are exactly the same. Yet, when correcting for the error in variables and model misspecification, this is no longer the case. The t-stats for covariance risk, λs are different across all tests. One can reject the null when testing the cross sectional adjusted R2 is equal to 1, as the small p-values indicate statistical significance. This is consistent with the very large standard error of the sample R2 in excess of 1.0 for one-factor, three-factor and four-factor models respectively. The pricing errors are also high. The statistic csrt, which is an approximate F-test of model specification

shows 0.19, 0.13 and 0.12 for one-factor, 3-facotr and 4-factor models. These results seem to indicate one-factor, three-factor and 4-factor models don’t explain well enough excess returns of the 10 Chimerica-scored portfolios under the Ordinary Least Squared function. It seems to indicate that there is no Chimerica in a statistically significant way for this set of US stocks sorted in 10 portfolios ranked by Chimerica scores. [Table 10 about here.] Table 10 shows results of the same tests under General Least Square methodology. The GLS function eliminates heteroskedasticity in the error term and thus represents a higher statistical hurdle to clear whereas the OLS function assumes normal distributed residuals We see less dramatic enhancement with the inclusion of the Chimerica factor in the 4-factor model as the adjusted R2 has increased from less than 1% for the one-factor model, to 28.8% in the Fama-French three factor model and eventually 29.0% in the 4-factor model. The factor loadings for the market factor are positive whereas they are negative for size, style and Chimerica factors. Again, none of the t- statistics is significant, except for tFM associated with the style factor. The p-value for cross section of adjusted R2 equal to 1 is small so we can reject the null. Standard error, csrt and p-value for model misspecification are relatively low. Even though these results again indicate that one-factor, three-factor and 4-factor models don’t explain excess returns of the 10 Chimerica-scored portfolios well, the model seems to be better specified under GLS when compared to those under the OLS function. [Table 11 about here.] [Table 12 about here.] Tables 11 and 12 summarize the results for the 10 sector/industry portfolios, repeating the same tests for the same 1-factor, 3-factor and 4-factor models in OLS and GLS settings. The Chimerica factor has more explanatory power in explaining this set of returns. The adjusted R2 increases from 45%, 48% to 57% in OLS and 3%, 4% and 5% in GLS for 1-factor, 3-factor and 4-factor models respectively.

Looking at market prices of risk (ϒs) and covariance risk (λs) for the 10 sector/industry portfolios, one can see the factor loadings for market price of risk and covariance are negative for all factors, except the market factor. This means that Chimerica is detractive in explaining excess returns. Again, none of the t-stats are statistically significant across both the OLS and GLS functions and across market prices of risk and covariances. The p-values are relatively small so one can reject the nulls that the cross section of adjust R2 is equal to 1. Standard errors are higher under OLS, nonetheless they are not small under GLS either. The csrt’s are relatively small for both OLS and GLS, ranging from 0.15 for the one-factor model, 0.14 for the three-factor model and 0.10 for the 4-factor model. The p-values for model misspecification in OLS are in the range of 0.28 to 0.41. What we can conclude from these two tables is that one-factor, three-factor and four-factor models do not explain the 10 sector/industry portfolios. These are similar results drawn from table 9 and table 10. Chimerica does not exist in 10 sector/industry portfolios. [Table 13 about here.] In quest for the efficacy of the Chimerica factor, I run cross section regression of excess returns on combining the 10 Chimerica score-sorted portfolios and 10 sector/industry portfolios. Tables 13 shows the results under the OLS function. The adjusted R2 continues to increase within the inclusion of additional factors. It goes up from 39% in the one-factor model, to 42% in the three-factor model to 43% in the four- factor model. Under OLS, factor loadings are negative for size, style and Chimerica factors while positive for the market factor. The t-stats for all factors across the board continue to be low and do not reach the statistical significance level. Low p-values allow us to reject the nulls that the adjusted R2 is equal to 1 and high p-values allow us to accept the nulls that the adjust R2 is equal 0. Standard errors, csrt’s and p-values for model misspecification are high. Again, one-factor, three-factor and four-factor models do not explain the excess returns of the combined 10 Chimerica-scored portfolios and 10 sector/industry portfolios.

[Table 14 about here.] As expected, the adjusted R2 under GLS has substantially increased with the incorporation of Chimerica factor from less than 1%, 6.8% to 7.1% under 1-factor, 3-factor and 4-factor models. None of the factors have significant t-statistics. The factor loadings for market and Chimerica factors are positive while they are negative for size and style factors. The p-values are small so to reject the nulls that the cross section adjusted R2 is equal to 1. Standard errors, csrt’s and p-values for model misspecifications are smaller under GLS and hence, indicate a better model fit under GLS. But they have not reached the statistical significance level to indicate that the one-factor, three-factor and four- factor models can explain the excess returns of the combined 10 Chimerica-scored portfolios and 10 sector/industry portfolios. These statistics from Table 13 and 14 seem to again indicate that there’s no Chimerica in these combined portfolios.

3.4 Chimerica and the Cross Section

Since the Chimerica factor is developed purely based on empirical experience based on Furgerson and Schularik (2006), rather than derived from First Principles, it is subject to the “purging” exercise, in which the underlying systematic risk exposure to Chimerica is separated to ascertain that it is meaningful on a standalone basis. The intuitive explanation could be that investors want unadulterated domestic US exposure that is distinct from Chinese macroeconomic risk and thus reward the least Chinese exposed stocks, the highest return. Another plausible explanation could be domestic US dollar- based investors who are dominating the US stock market like to chase faster growth American companies, of which are less Chinese exposed. Then the question becomes about avoiding any confounding effects. The tools in this section are portfolios (10 chimerica-scored portfolios and 10 industry/sector portfolios) purged from three systematic factors, namely the market, size and style (value vs. growth) factors. [Table 15 about here.]

Next, I run cross-sectional regressions of (purged) excess returns on 10 Chimerica-scored portfolios purged from traditional Fama-French three factors and regress on Chimerica or Chimerika itself purged from factors. Tables 15 shows the results under the OLS function from a cross sectional analysis when the test assets are the 10 Chimerica-scored portfolios. The loadings (both covariances and betas) are computed over the whole sample and used at each single period to compute the market prices of risk according to Fama-Macbeth (1973). Both the Chimerica-scored portfolios and the factors have been purged from three Fama-French factors. The sign has been positive for both Chimerica and Chimerica New factors and the t-statistics have not

2 been meaningful across all tests such as tFM, tS, tJW, and tKRS. The adjusted R s are small, at 6.6% for both factors. One can reject the null as suggested by the low p-value when testing that the cross sectional adjusted R2 is equal to one. In the same token, one accepts the null that this sample metric is equal to 0. The standard error of the sample R2s are small at 15.2% and 14.8% respectively for both factors while csrt is small for Chimerica factor (0.23) but bigger for Chimerica New factor (0.40). The p-values for model misspecification are at 0.14 and 0.01 respectively. It’s disappointing that these results are inconclusive for the Chimerica and Chimerica new factors. [Table 16 about here.] Table 16 reports results of GLS function performed on the cross-sectional regressions of (purged) excess returns on 10 Chimerica-scored portfolios purged from traditional Fama-French three factors and regress on Chimerica or Chimerika itself purged from factors. The R2 is so small that it’s tempting to throw away these results and models right away. The signs of the factor loadings are positive for Chimerica and negative for Chimerica new factor after purge. But unfortunately, none of the t-stats is meaningful. [Table 17 about here.] Next, I run the cross-sectional regressions of purged excess returns on 10 industry/sector portfolios purged from traditional Fama-French three factors and

regress on Chimerica and Chimerica itself purged from factors. Table 17 reports results from the OLS function. The signs for both Chimerica and Chimerica new are negative, indicating value addition of the factor. The adjust R2’s are much lower less than 1% when compared to those derived from the 10 Chimerica-scored portfolios. However, none of the t-stats has reached the statistical significance level, even though p-value for adjust R2 equal to 1, standard errors, csrt’s and p-values for model misspecification have been low. [Table 18 about here.] Table 18 reports the results from GLS function performed on the cross-sectional regressions of purged excess returns on 10 industry/sector portfolios purged from the traditional Fama-French three factors and regress on Chimerica and Chimerica itself purged from factors. The signs for loadings of market price of risk and covariance have turned positive, indicating how unstable the factors and the models are when switching from the OLS function to the GLS function. The adjusted R2 is close to zero and none of the t-statistics is significant. There’s not much again in this set of analysis. [Table 19 about here.] Finally, I run cross sectional analysis on the expanded opportunity set, i.e. combined 10 Chimerica-scored portfolios and 10 sector/industry portfolios purged from traditional Fama-French three factors and regress on Chimerica and Chimerica itself purged from other factors. Table 19 reports very low adjust R2s of less than 1%. The signs for loadings are positive. All t-stats for both Chimerica and Chimerica New are insignificantThe p-values for cross sectional adjusted R2 equal to 1 is small so we can reject the null. The csrt’s are big. Again, the results did not lead to much research finding. [Table 20 about here.] Tables 20 reports results from the GLS function performed on the cross section of the expanded opportunity set, i.e. combined 10 Chimerica-scored portfolios and 10 sector/industry portfolios purged from traditional Fama-French three factors and

regress on Chimerica or Chimerka itself purged from other factors. Results from this table have a bit more different from those in the prior tables. First of all, the adjusted R2s are negative, indicating the inability of Chimerica factor and new Chimerica factor after purge to explain combined 10 Chimerica-scored portfolios and 10 inustry/sector portfolios results. Second, the signs for the loadings of market price of risk and covariance have turned positive. Third, none of the t-stats is meaningful. Fourth, the p- value is very small so one can reject the null that the cross section adjusted R2 equal to 1. Finally, csrt’s are of decent size while standard errors are relatively large and p-values for model misspecification are small. In summary, Chimerica is an anomaly not explained by the traditional CAPM or Fama-French three-factor models. However, in portfolio settings, both the time series and cross-sectional analysis on the purged and unpurged basis have not led to the finding of much explanatory power of the Chimerica factor in explaining excess returns of 10 Chimerica-scored portfolios, 10 sector/industry portfolios or the combined 10 Chimerica- scored portfolios and 10 sector/industry portfolios. There’s not a single meaningful t- statistics. This means that the Chimerica effect could have existed on an empirical basis but it’s very difficult if not impossible to put it to rigorous scientific tests.

4.0 Chimerica Factor in Fundamental Analysis

After testing if Chimerica has been priced by realized return factor variables of the market, I ask if the Chimerica factor also shows up in the future expected returns forecasted based on company fundamentals.

4.1 Claus/Thomas Analysis

This paper attempts to use a second methodology to prove that the return differential between the highest exposure stocks and the lowest exposure stocks is meaningful and

recurring so that the anomaly can be employed in practical money management. Claus/Thomas model is used by utilizing analysts’ earnings forecasts. Essentially, the expected return of a stock in the Claus/Thomas model is the equivalent IRR that equates today’s price to analysts’ earnings estimates. Different from the traditional discounted cash flow model and dividend growth model, Claus/Thomas model does not heavily rely on the terminal earnings growth rate provided by equity analysts, which tends to be overly optimistic about earning growth beyond year 5, though their near-term earnings forecast can be rather unbiased. Claus/Thomas model makes good use of the historical book value of the company and their abnormal earnings above the cost of equity. The expected return of a stock, k is derived from the equation below.

푎푒 푎푒 푎푒 푎푒 푎푒 푃 = 푏푣 + + + + + (1 + 푘) (1 + 푘) (1 + 푘) (1 + 푘) (1 + 푘)

푎푒(1+ 푔) + (푘 − 푔)(1 + 푘 )

Where

푎푒 = 푒 − 푘(푏푣) = expected abnormal earnings for year t, or forecast accounting earnings less a charge for the cost of equity, 푘 = expected rate of return on the market portfolio, derived from the abnormal earnings model. k is firm specific and shows up in both denominator and numerator

푒 = earnings forecast for year t,

푏푣 = expected book (or accounting) value of equity at the end of year t. and

푔 = terminal year abnormal earnings growth rate assumed to be equal to expected inflation, which is the difference between the risk-free rate (10-year Treasury) and the yield of 10-year inflation-linked government bonds

4.2 Equity Premium for S&P 100 Index [Figure 1 about here.] Figure 1 shows the expected returns over risk free rate by Sector/Industry as of March 30, 2007 and March 18, 2010. All sectors are expected to generate positive excess returns in both years, with 2010’s forecast expected returns well in excess of 8% across all sectors. 2010 witnessed the rapid rebound of stock prices post Global Financial Crisis and was one of the banner years in terms of the US stock market return. The sector that declined the most during 2008-2009 was financial services companies. This sector was indeed expected to generate the most excess return, in excess of 10%. This speaks to the powerfulness of the Claus/Thomas model, because we all know now that banks, insurance companies and asset management companies actually did perform the best in 2010. In contrast, the excess returns across all sectors were significantly lower in 2007, all hovering between 1-4%. This again was accurate as the summer of 2007 saw the decline of quant-managed hedge funds that started the subsequent market decline into 2008. Appendix 3contains stock by stock forecast for the S&P 100 companies via Claus/Thomas methodology. Expected returns for each of the S&P 100 stocks for the one year after March 2007 and March 2010 are calculated respectively. These two dates are selected to correspond to the 2010 scorecard and the 2007 scorecard. Analysts’ earnings estimates and company book values are extracted from I/B/E/S (the Institutional Brokers’ Estimate System). To estimate individual expected stock return, the actual fiscal year-end book value of the company disclosed prior to the release of earnings estimate is used. For each company, I/B/E/S provides a number of analyst forecasts. The median of the forecasts is used for each of the next five fiscal years to calculate future abnormal- earnings and future book values. In the few cases where fiscal year 4 and 5 earnings forecasts are not available, these are estimated by extrapolation based on the growth rates derived from fiscal year 2 and 3. As a part of the terminal value calculation, expected inflation is estimated in accordance with the Claus/Thomas model by subtracting the 10-year

Treasury yield from the yield of 10-year inflation-linked government bonds to proxy the expected inflation. To examine the validity and practical relevance of the Claus/Thomas approach, let’s first have a quick look at the derived expected return for the S&P 100 index, which is the weighted average of individual S&P 100 company returns, according to the index’s value-weighted methodology. The reason that I focus on value-weighted average of expected returns here, rather than equally weighted is to be consistent with the S&P 100 index construction. And the equity risk premium of the index is the difference between the stock expected return and the 3-month T-bill return. The equity risk premium in 2010 for the S&P 100 index is calculated to be at 9.4% while the equity risk premium in 2007 at 2.9%. This huge gap of equity premium (6.5%) in the two selected years is indicative of how cheaply US equity in general had been priced post the 2008 financial crisis relative to its history and relative to bonds. In other words, in looking back and based on valuation, an intelligent investor should have sold US equity in 2007 but should have bought it in 2010.

4.3 Long/Short Portfolios and Testing

Using the same scorecards as in the ex-post analysis, long/short portfolios are created by going long the lowest ranked Chimerica stocks while shorting the highest ranked. Due to the fact that multiple analysts following the same company submit various forecasts, a statistical test that has all analysts input (rather than the median analyst forecast in 3.2) is necessary to determine the significance of forecast return for the long/short portfolios. First, hypothesis test concerning differences between means is used to prove the significance of the result of the long/short portfolio. In other words, the average return of the long side of the portfolio minus the average return of the short side of the portfolio should be statistically significantly from zero assuming normal distribution of returns. The hypotheses are therefore:

퐻 = 휇 − 휇 =0

퐻 = 휇 − 휇 ≠0

Using monthly returns, the test statistic for a test of the difference between two population means based on analyst earnings estimates is

푥 − 푥 − 휇 − 휇 t= s s + n n

푛 − 1) s + (푛 − 1s s = 푛 + 푛 −2

s = W s s = W s , , , ,

ST is variance of analyst forecasts for each stock in the top portfolio that contains high Chimerica-scored stocks

SB is variance of analyst forecasts for each stock in the bottom portfolio that contains low Chimerica-scored stocks

WT is the stock’s weight in the top portfolio

WB is the stock’s weight in the bottom portfolio

S1 is sum of squared errors relating to the top portfolio

S2 is sum of squared errors relating to the bottom portfolio

[Table 21 about here.] Table 21 uses as input over 2000 analyst forecasts extracted from I/B/E/S database for the S&P 100 companies during the second quarter of 2010. Fiscal year 1, 2, 3, 4, 5 earnings forecasts, as well as long term growth estimates are used as input for the

Claus/Thomas model to estimate forward-looking expected return given the current stock price, accounting book value and analyst forecasts. Four kinds of equal-weighted and capitalization-weighted portfolios are formed. Half portfolio goes long on the top 50 Chimerica-scored companies while shorting the bottom 50. Quartile portfolio, quintile portfolio and decile portfolio are formed accordingly. Of the eight portfolios presented in the table, except for the EW quartile portfolio, all others have statistically meaningful results at 95% confidence level. Across all instances of half, quartile, quintile and decile portfolios, half of them indicates that stocks in the top portfolios with the most US exposure outperformed stocks in the bottom portfolios with the least US exposure. The other half of the portfolios show opposite results. Therefore, on a forward-looking basis, the efficacy of the Chimerica factor is a glass half-full and half- empty. [Table 22 about here.] Table 22 contains the sector long/short portfolios. 100 S&P companies are segmented into industry sectors to further observe the anomaly. It seems that Chimerica factor is especially significant for the consumer discretionary sector, with both equal- weighted and value-weighted showing statistically significant premium of 3.44% and 7.15% respectively. The materials and utilities sectors also show positive premium associated with the Chimerica factor, however their t-statistics are insignificant. The rest of the seven industry/sector portfolios do not show positive Chimerica premiums. Results of equal-weighted consumer discretionary sector, financials sector, telecommunications sector portfolios and value-weighted financial sector portfolio have negative Chimerica premiums that are also statistically significant.

5 Conclusion

Wall Street Journal Columnist, Bret Stephens (2010) wrote - “Our time” is supposed to be one of China’s unstoppable rise and America’s inevitable decline. As for the US, Americans understand that innovation is inherently serendipitous. One might argue that American serendipity is also the product of a persevering spirit to discover, explore and conquer new markets and capture new business opportunities overseas. This paper using the Chimerica phenomenon, strives to replicate the factor exhibited by US stocks in a measurable quantitative approach (i.e. scorecard) in order to understand if Chimerica can be exploited by investors to make profit by focusing on buying stocks that do business in China. Through the various approaches discussed in the paper, some of them return- based such as regression and Fama MacBeth estimation while others, fundamental-based such as Claus/Thomas earnings analysis, this paper illustrates very limited success in proving that the US stock market has actually rewarded such American serendipity. Results are not always consistent across approaches. Down-market protection seems to stand out though, for the higher Chimerica-scored companies and proves the benefit for global diversification of revenue streams. One of the explanations could be that these American companies analyzed are truly global entities that pay attention not only to China but also every other significant growth markets out there. Therefore, this paper and its associated scorecard can be extended to measure all non US exposure, as opposed to Greater China exposure only. Limitation of this paper includes a relatively small sample size (100 US companies) and short time period (6 years). It would be much better if the research could expand onto a larger sample size, say the S&P 500 companies. When sorting portfolios, it would be good to also add value-weighted quartile portfolios to prove robustness. It would also be helpful to implement the same methodologies on the 100 largest local Chinese companies and see if their returns have been enhanced by exposing to America. This “full picture” would probably make the global capital markets integration topic more interesting.

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Table 1: Summary Statistics of S&P 100’s China Exposure, 2004 - 2010 The table shows by GICS (Global Industry Classification Standards) sector, the percentage of market capitalization with China exposure, Chinese revenue exposure as a percentage of total revenue, number companies with China exposure and total number of companies in the sector by June 2004 and June 2010 respectively. China exposure is defined in accordance to the scorecard methodology. The sector level aggregate information is the sum of individual companies within the sector. The revenue information is highlighted as it contains more impactful information.

% of Market Capitalization with Chinese Revenue Exposure as % GICS Sectors China Exposure of Total Revenue 2010 2004 2010 2004 IT / Telecom 21% 19% 2 - 47% 0 - 34% Consumers 17% 16% 1 - 7% Not Disclosed Healthcare 14% 11% 2 - 5% < 1% Industrials 12% 13% 2 - 23% 4-21% Energy/Materials 12% 12% 0 - 4% Not Disclosed Financials 12% 15% Not Disclosed < 2% Utilities 3% 0% Not Disclosed Not Applicable Total S&P 100 91% 86% Number of Companies with Total Number of Companies in GICS Sectors China Exposure the Sector 2010 2004 2010 2004 IT / Telecom 12 16 13 17 Consumers 24 18 25 19 Healthcare 10 12 10 14 Industrials 16 13 18 16 Energy/Materials 12 8 14 13 Financials 12 10 15 20 Utilities 2 0 5 1 Total S&P 100 88 77 100 100

Table 2: Cross-Sectional Style Returns, June 2004 – June 2010 This table presents arithmetic mean returns, geometric mean returns and the standard deviations associated with the equal-weighted quartile portfolios, as well as the universe. Portfolios are sorted by size, style and Chimerica scores provided by the scorecard. Annual returns are extracted from Bloomberg from June 2004 to June 2010. In the event that companies with the same Chimerica scores reside intra-quartile, aggregate returns of these companies are used to proportionate the right amount of returns split across quartiles.

Cross Section Result Q1 Q2 Q3 Q4 Q1-Q4 Size(Q1=Micro;Q4=Large) Arithmetric Mean(%) 5.61 5.05 1.79 0.91 4.70 Geometric Mean(%) 1.71 3.59 0.19 -0.88 Standard Deviation (%) 28.66 18.21 19.05 15.03

Value(Q1=Value;Q4=Growth) Arithmetric Mean(%) 0.78 4.65 3.17 3.89 -3.11 Geometric Mean (%) -3.32 3.13 2.28 2.09 Standard Deviation (%) 29.57 18.59 14.38 19.87

Chimerica Score(Q1=High;Q4=Low) Arithmetric Mean(%) 2.73 1.65 3.98 3.16 -0.43 Geometric Mean (%) 1.69 0.91 2.39 1.48 Standard Deviation (%) 15.62 13.14 19.08 19.44

Universe Aggregate Arithmetric Mean(%) 3.34 Geometric Mean (%) 1.54 Standard Deviation (%) 20.11

Table 3: Annual Performance Based on Chimerica (June 2004 – June 2010) This table shows calendar-year returns for the equal-weighted quartile portfolios sorted by Chimerica scores. Scores are generated by the Chimerica Scorecard and annual returns from June 2004 to June 2010 are extracted from Bloomberg. In the event that companies with the same Chimerica scores reside intra-quartile, aggregate returns of these companies are used to proportionate the right amount of returns split across quartiles. It is observed that during down market years 2007 and 2008, higher US exposed stocks (top half) outperformed lower US exposed stocks (bottom half), thus good to be owned in down markets.

Q1 Q2 Q3 Q4 Q1 - Q4 Top - Bottom Half Chimerica Score 2004 1.42 2.05 10.51 19.49 -18.07 (Q1=High;Q4=Low) 2005 10.13 6.15 16.61 5.37 4.76 2006 18.81 16.64 24.53 22.31 -3.50 2007 -9.38 -11.67 -12.58 -11.63 2.25 2008 -21.10 -16.13 -25.68 -27.92 6.82 2009 16.51 12.83 10.49 11.33 5.18

Average 2.73 1.65 3.98 3.16 -0.43 -2.76

Table 4: Size and Chimerica Score Quartile Portfolio, June 2004 – June 2010 This table presents arithmetic mean returns, geometric mean returns and the standard deviations associated with the double-sorted equal-weighted quartile portfolios. Portfolios are sorted by size and Chimerica scores provided by the scorecard. Annual returns are extracted from Bloomberg from June 2004 to June 2010. In the event that companies with the same Chimerica scores reside intra- quartile, aggregate returns of these companies are used to proportionate the right amount of returns split across quartiles. Hence, the number of stocks in each of the quartile portfolio varies and the numbers of stocks in the quartile portfolios do not add up to 25.

Quartile High Score Mid-High Score Mid-Low Score Low Score Low - High Smaller Stocks Arithmetic Mean (%) 7.23 4.05 -3.88 7.19 -0.04 Geometric Mean 4.53 1.25 -6.92 3.19 -1.34 Standard Deviation 24.96 24.54 24.52 27.67 Average no. of Stocks 8 8 8 7

Small Stocks Arithmetic Mean (%) 2.02 3.69 4.09 5.19 3.17 Geometric Mean 0.30 2.89 2.79 4.21 3.91 Standard Deviation 20.84 13.86 16.85 15.01 Average no. of Stocks 8 9 11 8

Big Stocks Arithmetic Mean (%) 2.42 2.93 1.08 -1.48 -3.90 Geometric Mean 1.24 1.91 -0.15 -2.96 -4.20 Standard Deviation 16.35 15.61 16.81 18.12 Average no. of Stocks 8 8 9 7

Bigger Stocks Arithmetic Mean (%) -1.08 2.09 -1.30 0.35 1.43 Geometric Mean -1.65 1.70 -2.00 -0.82 0.83 Standard Deviation 11.86 9.84 12.56 16.14 Average no. of Stocks 9 9 8 8

Table 5: Value/Growth and Chimerica Score Quartile Portfolios, June 2004 – June 2010 This table presents arithmetic mean returns, geometric mean returns and the standard deviations associated with the double-sorted equal-weighted quartile portfolios. Portfolios are sorted by style and Chimerica scores provided by the scorecard. Annual returns are extracted from Bloomberg from June 2004 to June 2010. In the event that companies with the same Chimerica scores reside intra- quartile, aggregate returns of these companies are used to proportionate the right amount of returns split across quartiles. Hence, the number of stocks in each of the quartile portfolio varies and the numbers of stocks in the quartile portfolios do not add up to 25.

Quartile High Score Mid-High Score Mid-Low Score Low Score Low - High High Value (high E/P) Arithmetic Mean (%) -2.04 4.62 -0.44 0.49 2.53 Geometric Mean -4.52 2.37 -3.52 -3.28 1.24 Standard Deviation 23.46 23.65 25.45 29.57 Average no. of Stocks 8 10 8 7

Midvalue Arithmetic Mean (%) 6.09 0.43 1.00 5.61 -0.48 Geometric Mean 4.57 -1.11 0.00 4.50 -0.07 Standard Deviation 19.14 18.40 15.20 16.07 Average no. of Stocks 7 8 9 8

Midgrowth Arithmetic Mean (%) 2.16 2.85 2.40 0.55 -1.61 Geometric Mean 1.40 2.60 1.89 -0.56 -1.96 Standard Deviation 13.13 7.90 10.94 15.67 Average no. of Stocks 8 10 9 8

High Growth (low E/P) Arithmetic Mean (%) 5.17 1.18 4.38 1.75 -3.42 Geometric Mean 3.51 0.65 2.39 -0.58 -4.09 Standard Deviation 21.44 11.04 20.48 21.41 Average no. of Stocks 8 9 8 8

Table 6: Factor Summary Statistics Using monthly returns, this table presents the summary statistics of Fama-French three factors (market, size and style) and Chimerica factors. Panel A shows aggregate results from June 2004 to June 2010. Panel B shows ten portfolios sorted by Chimerica score with P1 containing stocks with the highest Chimerica scores. Panel C shows ten industry sector portfolios according to the stock’s Global Industry Classification Standards (GICS) assigned by Bloomberg.

Panel A: All Factors

Market SMB HML Chimerica mean 0.12 0.26 0.35 0.09 median 0.57 -0.01 0.34 -0.10 std 3.07 2.35 2.71 2.11 skewness -0.66 0.31 -0.66 0.43 kurtozis 5.90 2.69 5.39 3.64 min -11.21 -4.29 -9.86 -4.90 max 9.71 6.11 7.57 6.47

Panel B: 10 Chimerica Decile Portfolios (Monthly Returns)

Avg P 1-5 Minus P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 Average Ave P6-10 mean -0.05 0.18 0.29 0.24 -0.32 0.13 0.24 0.12 0.02 0.33 0.12 -0.10 median 0.41 0.19 0.70 0.15 -0.17 0.46 0.13 0.25 0.51 0.57 std 3.49 2.04 3.46 2.45 4.08 3.89 4.71 4.17 2.17 2.89 skewness -0.93 -0.59 0.22 -0.18 -0.62 -0.57 -0.58 -0.62 -0.33 -0.34 kurtozis 5.19 4.76 7.53 4.15 7.98 5.77 7.51 5.57 3.81 3.32 min -13.47 -7.27 -11.51 -6.21 -15.79 -13.06 -16.85 -15.71 -5.44 -6.79 max 6.56 4.89 14.52 7.45 13.18 11.40 17.74 11.58 5.86 7.96

Panel C: 10 Industry Sector Portfolios (Monthly Returns)

Consumer Consumer Health Information Telecom Discretionary Staples Energy Financials Care Industrials Technology Materials Services Utilities mean 0.31 0.29 1.37 0.09 0.17 0.86 0.10 0.38 0.06 0.22 median 1.29 0.46 2.40 1.02 0.52 1.67 1.39 -0.19 1.19 0.98 std 6.85 3.59 7.48 9.06 4.62 5.74 5.61 10.94 6.29 4.64 skewness 0.32 -0.74 -0.83 0.07 -0.56 -0.76 -0.48 1.14 -0.75 -0.88 kurtozis 9.21 4.73 4.94 6.61 4.17 4.71 3.32 10.89 3.69 4.64 min -24.92 -12.63 -25.89 -27.73 -14.75 -17.24 -15.18 -29.98 -19.16 -15.43 max 30.91 7.98 18.73 30.51 10.39 16.76 13.89 54.54 12.20 12.53

Table 7: Chimerica and Traditional Systemic Factor Regressions, June 2004 – June 2010 This table presents results of time-series regression analyses, with Chimerica factor as the explained variable and using the individual Fama-French three factor, market, size and style and combined three factors as independent variables. Intercepts, coefficient, t- statistics and adjusted r-squares are reported as followed. The Chimerica factor is constructed by using monthly returns of a long- short portfolio in which the returns of the bottom quartile portfolio (lowest Chimerica scored companies) were subtracted from the returns of the top quartile portfolio (highest Chimerica scored companies)

1 Factor 2 α tα β tβ adj. R Market 0.10 0.41 -0.11 -1.30 0.01 SMB 0.11 0.45 -0.09 -0.80 -0.01 HML 0.07 0.30 0.04 0.45 -0.01

3 Factor α Market SMB HML adj. R2

0.09 -0.12 -0.06 0.10 0.00 t Stats (-0.2439) (-1.3871) (0.9711) (0.2937)

Table 8: Cross Section with Portfolio Sorts, June 2004 – June 2010 This table attempts to use the Fama-French three factor model to explain Chimerica and presents results of cross section analyses for the 10 portfolios sorted by Chimerica score and the 10 industry sector portfolios. P1 contains stocks with the highest Chimerica scores. Panel A: 10 Chimerica Decile Portfolios

β tβ α tα Market SMB HML Market SMB HML adj. R2 P1 -0.21 -1.24 1.01 0.17 -0.02 17.10 2.28 -0.24 0.84 P2 0.14 1.70 0.65 -0.06 -0.05 23.20 -1.62 -1.51 0.89 P3 0.16 1.16 1.06 -0.06 0.04 21.63 -0.92 0.70 0.88 P4 0.19 1.42 0.74 -0.05 -0.05 15.80 -0.92 -1.01 0.79 P5 -0.47 -2.75 1.24 -0.09 0.08 20.72 -1.25 1.14 0.88 P6 0.04 0.17 1.17 0.03 -0.15 15.78 0.28 -1.87 0.79 P7 0.02 0.10 1.39 -0.03 0.19 20.81 -0.35 2.56 0.89 P8 -0.04 -0.22 1.24 0.14 -0.05 18.73 1.70 -0.69 0.85 P9 -0.06 -0.61 0.64 -0.02 0.03 17.59 -0.38 0.78 0.84 P10 0.24 1.72 0.87 -0.03 -0.02 17.94 -0.46 -0.28 0.84

Panel B: 10 Industry Sector Portfolios

β tβ α tα Market SMB HML Market SMB HML adj. R2 Consumer Discretionary -0.03 -0.09 1.88 0.24 0.17 14.14 1.45 1.12 0.78 Consumer Staples 0.20 0.86 1.00 -0.14 0.03 12.52 -1.38 0.35 0.71 Energy 1.22 1.90 1.77 0.02 -0.18 7.89 0.06 -0.71 0.48 Financials -0.38 -0.97 2.57 -0.20 0.61 18.77 -1.18 4.03 0.87 Health Care 0.17 0.44 1.17 -0.10 -0.33 8.75 -0.59 -2.18 0.52 Industrials 0.66 2.77 1.75 -0.12 0.08 21.04 -1.16 0.92 0.88 Information Technology 0.01 0.04 1.72 0.31 -0.56 18.66 2.70 -5.48 0.85 Materials -0.18 -0.32 3.02 0.12 0.50 15.11 0.47 2.26 0.81 Telecommunication Services 0.16 0.26 1.25 -0.26 -0.52 5.65 -0.93 -2.09 0.29 Utilities 0.27 0.68 1.14 -0.44 -0.20 8.15 -2.55 -1.31 0.48

Table 9: Cross Section on 10 Chimerica Portfolios with Market, Size, Style and Chimerica as Factors (OLS) Test assets are monthly excess return of the ten Chimerica portfolios sorted by Chimerica Score and purged from four factors of Market, Size, Style and Chimerica. This table reports the results from cross-sectional regressions using Ordinary Least Squares method. A full period two-pass regression is used, first in the time series to estimate betas and the in the cross section to estimate both the market prices of betas (ϒs) and covariances risks (λs). Estimates are reported along with t-statistics. The t-statistics reported correspond to the Fama MacBeth t-stat (tFM), the correction for errors in variables as suggested by Shanken (1992) (ts), the correction proposed by Jagannathan and Wang (1998) (tJW) and lastly that proposed by Kan, Robotti, and Shanken (2013) (tKRS) to account for model misspecification. Also reported are the sample cross sectional R2s values along with specification tests based on Kan, Robotti and Shanken (2013): se(R2) is the standard error of the sample R2 under the assumption that 0 < ᵨ2 <1; the p-value of a generalized version of Shanken’s CSRT model specification test is reported, an approximate F-test of model specification.

2 Market Prices of Betas (ϒs) Covariances (λs) adj. R 2 OLS Market SMB HML Chimerica Market SMB HML Chimerica adj. R Mkt Mkt,Smb,Hml Mkt,Smb,Hml,Chim ϓ 0.1005 0.0108 0.2261 Sample R 2 0.2261 0.2745 0.3538 t FM 0.2792 0.2792 p(ᵨ2 = 1) 0.0000 0.0000 0.0000 t s 0.2792 0.2789 p(ᵨ2 = 0) 0.7812 0.9086 0.8957 2 t JW 0.2792 0.2725 se(R ) 1.2672 1.2034 1.0711

t KRS 0.2777 0.2711 csrt 0.1871 0.1253 0.1149 ϓ 0.1074 -0.4426 -0.4823 0.0480 -0.0837 -0.0739 0.2745 pval of model spec. 0.2499 0.3575 0.3151

t FM 0.2982 -0.5939 -0.6972 0.8320 -0.5860 -0.7116

t s 0.2982 -0.5749 -0.6767 0.7978 -0.5632 -0.6832

t JW 0.2974 -0.5661 -0.6753 0.8124 -0.5610 -0.6986

t KRS 0.2959 -0.3567 -0.4177 0.5968 -0.3633 -0.4240 ϓ 0.1112 -0.3723 -0.6419 0.2488 0.0618 -0.0635 -0.1066 0.0715 0.3538

t FM 0.3090 -0.4912 -0.9666 0.8525 1.0984 -0.4325 -1.0715 1.0399

t s 0.3090 -0.4680 -0.9258 0.8385 1.0317 -0.4088 -1.0068 0.9775

t JW 0.3084 -0.4753 -0.9102 0.8379 1.0242 -0.4227 -0.9909 1.0858 t KRS 0.3077 -0.3243 -0.5584 0.8566 0.7785 -0.3011 -0.6080 1.0997

Table 10: Cross Section on 10 Chimerica Portfolios with Market, Size, Style and Chimerica as Factors (GLS) Test assets are excess return of the ten Chimerica portfolios sorted by Chimerica Score and purged from four factors of Market, Size, Style and Chimerica. This table reports the results from cross-sectional regressions using General Least Squares method when there is a certain degree of correlation between the residuals in a regression model. A full period two-pass regression is used, first in the time series to estimate betas and the in the cross section to estimate both the market prices of betas (ϒs) and covariances risks (λs). Estimates are reported along with t-statistics. The t-statistics reported correspond to the Fama MacBeth t-stat (tFM), the correction for errors in variables as suggested by Shanken (1992) (ts), the correction proposed by Jagannathan and Wang (1998) (tJW) and lastly that proposed by Kan, Robotti, and Shanken (2013) (tKRS) to account for model misspecification. Also reported are the sample cross sectional R2s values along with specification tests based on Kan, Robotti and Shanken (2013): se(R2) is the standard error of the sample R2 under the assumption that 0 < ᵨ2 <1; the p-value of a generalized version of Shanken’s CSRT model specification test is reported, an approximate F-test of model specification.

2 Market Prices of Betas (ϒs) Covariances (λs) adj. R 2 GLS Market SMB HML Chimerica Market SMB HML Chimerica adj. R Mkt Mkt,Smb,Hml Mkt,Smb,Hml,Chim ϓ 0.1176 0.0126 0.0064 Sample R 2 0.0064 0.2880 0.2902 t FM 0.3270 0.3270 p(ᵨ2 = 1) 0.0000 0.0000 0.0000 t s 0.3270 0.3265 p(ᵨ2 = 0) 0.7437 0.3318 0.4484 2 t JW 0.3270 0.3179 se(R ) 0.0395 0.3402 0.3414

t KRS 0.3270 0.3179 csrt 0.1870 0.1023 0.1004 ϓ 0.1176 -0.2911 -1.2038 0.0755 -0.0451 -0.1884 0.2880 pval of model spec. 0.0000 0.0000 0.0000

t FM 0.3270 -0.5200 -2.0590 1.5062 -0.4178 -2.1230

t s 0.3270 -0.4769 -1.8992 1.3312 -0.3735 -1.8539

t JW 0.3270 -0.5023 -1.9847 1.2798 -0.3999 -1.9818

t KRS 0.3270 -0.3883 -1.6929 1.2256 -0.3296 -1.6723 ϓ 0.1176 -0.2872 -1.2131 -0.0633 0.0772 -0.0435 -0.1909 0.0115 0.2902

t FM 0.3270 -0.5127 -2.0677 -0.2456 1.5165 -0.4016 -2.1274 0.1904

t s 0.3270 -0.4696 -1.9047 -0.2430 1.3381 -0.3585 -1.8549 0.1701

t JW 0.3270 -0.4931 -1.9809 -0.2418 1.3099 -0.3796 -1.9575 0.1864 t KRS 0.3270 -0.3829 -1.6899 -0.2336 1.2537 -0.3162 -1.6708 0.1851

Table 11: Cross Section on 10 Sector/Industry Portfolios with Market, Size, Style and Chimerica as Factors (OLS) Test assets are excess return of the ten Sector/Industry portfolios and purged from four factors of Market, Size, Style and Chimerica. Table 11 reports the results from cross-sectional regressions using Ordinary Least Squares method. A full period two-pass regression is used, first in the time series to estimate betas and the in the cross section to estimate both the market prices of betas (ϒs) and covariances risks (λs). Estimates are reported along with t-statistics. The t-statistics reported correspond to the Fama MacBeth t-stat (tFM), the correction for errors in variables as suggested by Shanken (1992) (ts), the correction proposed by Jagannathan and Wang (1998) (tJW) and lastly that proposed by Kan, Robotti, and Shanken (2013) (tKRS) to account for model misspecification. Also reported are the sample cross sectional R2s values along with specification tests based on Kan, Robotti and Shanken (2013): se(R2) is the standard error of the sample R2 under the assumption that 0 < ᵨ2 <1; the p-value of a generalized version of Shanken’s CSRT model specification test is reported, an approximate F-test of model specification.

2 Market Prices of Betas (ϒs) Covariances (λs) adj. R 2 OLS Market SMB HML Chimerica Market SMB HML Chimerica adj. R Mkt Mkt,Smb,Hml Mkt,Smb,Hml,Chim ϓ 0.1995 0.0214 0.4471 Sample R 2 0.4471 0.4771 0.5685 t FM 0.5476 0.5476 p(ᵨ2 = 1) 0.0000 0.0000 0.0000 t s 0.5475 0.5453 p(ᵨ2 = 0) 0.5754 0.7180 0.7857 2 t JW 0.5520 0.5279 se(R ) 0.9074 0.8576 0.6740

t KRS 0.5601 0.5349 csrt 0.1511 0.1427 0.1002 ϓ 0.2095 -0.0411 -0.1903 0.0357 -0.0121 -0.0380 0.4771 pval of model spec. 0.4060 0.2728 0.4007

t FM 0.5751 -0.0622 -0.3444 0.6735 -0.0963 -0.4637

t s 0.5750 -0.0618 -0.3426 0.6663 -0.0956 -0.4596

t JW 0.5759 -0.0629 -0.3417 0.6164 -0.0986 -0.4518

t KRS 0.5827 -0.0430 -0.3614 0.6234 -0.0663 -0.4708 ϓ 0.1826 -0.8615 -0.3602 -1.1401 0.0323 -0.1870 -0.0268 -0.2717 0.5685

t FM 0.5021 -0.9617 -0.6284 -1.2472 0.6079 -1.0297 -0.3269 -1.2165

t s 0.4996 -0.8012 -0.5434 -1.0354 0.4978 -0.8404 -0.2680 -0.9910

t JW 0.5012 -0.8064 -0.5288 -1.0689 0.3948 -0.8518 -0.2644 -1.0137 t KRS 0.4902 -0.5469 -0.5422 -0.7050 0.3927 -0.5606 -0.2648 -0.6606

Table 12: Cross Section on 10 Sector/Industry Portfolios with Market, Size, Style and Chimerica as Factors (GLS)

Test assets are excess return of the ten Sector/Industry portfolios and purged from four factors of Market, Size, Style and Chimerica. This table reports the results from cross-sectional regressions using General Least Squares method when there is a certain degree of correlation between the residuals in a regression model. A full period two-pass regression is used, first in the time series to estimate betas and the in the cross section to estimate both the market prices of betas (ϒs) and covariances risks (λs). Estimates are reported along with t-statistics. The t-statistics reported correspond to the Fama MacBeth t-stat (tFM), the correction for errors in variables as suggested by Shanken (1992) (ts), the correction proposed by Jagannathan and Wang (1998) (tJW) and lastly that proposed by Kan, 2 Robotti, and Shanken (2013) (tKRS) to account for model misspecification. Also reported are the sample cross sectional R s values along with specification tests based on Kan, Robotti and Shanken (2013): se(R2) is the standard error of the sample R2 under the assumption that 0 < ᵨ2 <1; the p-value of a generalized version of Shanken’s CSRT model specification test is reported, an approximate F-test of model specification.

2 Market Prices of Betas (ϒs) Covariances (λs) adj. R 2 GLS Market SMB HML Chimerica Market SMB HML Chimerica adj. R Mkt Mkt,Smb,Hml Mkt,Smb,Hml,Chim ϓ 0.2146 0.0230 0.0280 Sample R 2 0.0280 0.0395 0.0516 t FM 0.5941 0.5941 p(ᵨ2 = 1) 0.0000 0.0000 0.0000 t s 0.5940 0.5912 p(ᵨ2 = 0) 0.5526 0.9498 0.9816 2 t JW 0.5941 0.5626 se(R ) 0.0955 0.1203 0.1660

t KRS 0.5938 0.5623 csrt 0.1516 0.1483 0.1411 ϓ 0.2132 -0.0995 0.1574 0.0231 -0.0281 0.0166 0.0395 pval of model spec. 0.0000 0.0000 0.0000

t FM 0.5903 -0.1663 0.3303 0.4635 -0.2463 0.2369

t s 0.5903 -0.1657 0.3294 0.4604 -0.2449 0.2356

t JW 0.5902 -0.1688 0.3344 0.4412 -0.2493 0.2323

t KRS 0.5891 -0.1144 0.2682 0.4089 -0.1708 0.1988 ϓ 0.2133 -0.2238 0.1185 -0.2445 0.0216 -0.0557 0.0177 -0.0583 0.0516

t FM 0.5904 -0.3303 0.2435 -0.3958 0.4315 -0.4147 0.2525 -0.3904

t s 0.5903 -0.3258 0.2412 -0.3904 0.4240 -0.4074 0.2483 -0.3837

t JW 0.5923 -0.3300 0.2408 -0.3834 0.3946 -0.4081 0.2521 -0.3707 t KRS 0.5910 -0.1863 0.1821 -0.2469 0.3764 -0.2292 0.2193 -0.2346

Table 13: Cross Section on 10 Chimerica Portfolios and 10 Sector/Industry Portfolios (OLS) This table shows results from cross-sectional regressions of excess returns on ten Chimerica portfolios sorted by Chimerica score and 10 Sector/Industry portfolios. This table reports the results from cross-sectional regressions using Ordinary Least Squares method. A full period two-pass regression is used, first in the time series to estimate betas and the in the cross section to estimate both the market prices of betas (ϒs) and covariances risks (λs). Estimates are reported along with t-statistics. The t-statistics reported correspond to the Fama MacBeth t-stat (tFM), the correction for errors in variables as suggested by Shanken (1992) (ts), the correction proposed by Jagannathan and Wang (1998) (tJW) and lastly that proposed by Kan, Robotti, and Shanken (2013) (tKRS) to account for model misspecification. Also reported are the sample cross sectional R2s values along with specification tests based on Kan, Robotti and Shanken (2013): se(R2) is the standard error of the sample R2 under the assumption that 0 < ᵨ2 <1; the p-value of a generalized version of Shanken’s CSRT model specification test is reported, an approximate F-test of model specification.

2 Market Prices of Betas (ϒs) Covariances (λs) adj. R 2 OLS Market SMB HML Chimerica Market SMB HML Chimerica adj. R Mkt Mkt,Smb,Hml Mkt,Smb,Hml,Chim ϓ 0.1759 0.0189 0.3944 Sample R 2 0.3944 0.4210 0.4306 t FM 0.4854 0.4854 p(ᵨ2 = 1) 0.0000 0.0000 0.0000 t s 0.4853 0.4838 p(ᵨ2 = 0) 0.6224 0.7495 0.7948 2 t JW 0.4879 0.4687 se(R ) 0.9835 0.9395 0.9093

t KRS 0.4924 0.4726 csrt 0.3244 0.3108 0.3098 ϓ 0.1833 -0.1029 -0.1838 0.0341 -0.0235 -0.0351 0.4210 pval of model spec. 0.5794 0.5045 0.4459

t FM 0.5059 -0.1712 -0.3394 0.6594 -0.2052 -0.4368

t s 0.5059 -0.1702 -0.3377 0.6525 -0.2036 -0.4330

t JW 0.5056 -0.1739 -0.3346 0.6128 -0.2123 -0.4242

t KRS 0.5088 -0.1145 -0.3511 0.6165 -0.1371 -0.4412 ϓ 0.1789 -0.2567 -0.2117 -0.2491 0.0331 -0.0565 -0.0322 -0.0533 0.4306

t FM 0.4941 -0.4370 -0.3947 -0.6772 0.6363 -0.4985 -0.3970 -0.6030

t s 0.4939 -0.4303 -0.3896 -0.6698 0.6221 -0.4878 -0.3887 -0.5897

t JW 0.4934 -0.4352 -0.3834 -0.6609 0.5626 -0.4975 -0.3841 -0.5806 t KRS 0.4914 -0.2364 -0.4020 -0.3836 0.5629 -0.2625 -0.3930 -0.3196

Table 14: Cross Section on 10 Chimerica Portfolios and 10 Sector/Industry Portfolios (GLS) This table shows results from cross-sectional regressions of excess returns on ten Chimerica portfolios sorted by Chimerica score and 10 Sector/Industry portfolios. This table reports the results from cross-sectional regressions using General Least Squares method when there is a certain degree of correlation between the residuals in a regression model. A full period two-pass regression is used, first in the time series to estimate betas and the in the cross section to estimate both the market prices of betas (ϒs) and covariances risks (λs). Estimates are reported along with t-statistics. The t-statistics reported correspond to the Fama MacBeth t-stat (tFM), the correction for errors in variables as suggested by Shanken (1992) (ts), the correction proposed by Jagannathan and Wang (1998) (tJW) and lastly that proposed by Kan, Robotti, and Shanken (2013) (tKRS) to account for model misspecification. Also reported are the sample cross sectional R2s values along with specification tests based on Kan, Robotti and Shanken (2013): se(R2) is the standard error of the sample R2 under the assumption that 0 < ᵨ2 <1; the p-value of a generalized version of Shanken’s CSRT model specification test is reported, an approximate F-test of model specification.

2 Market Prices of Betas (ϒs) Covariances (λs) adj. R 2 GLS Market SMB HML Chimerica Market SMB HML Chimerica adj. R Mkt Mkt,Smb,Hml Mkt,Smb,Hml,Chim ϓ 0.1176 0.0126 0.0031 Sample R 2 0.0031 0.0678 0.0716 t FM 0.3270 0.3270 p(ᵨ2 = 1) 0.0000 0.0000 0.0000 t s 0.3270 0.3265 p(ᵨ2 = 0) 0.7437 0.6296 0.7464 2 t JW 0.3270 0.3179 se(R ) 0.0190 0.1111 0.1118

t KRS 0.3270 0.3179 csrt 0.3246 0.2945 0.2930 ϓ 0.1176 -0.5439 -0.3201 0.0455 -0.1060 -0.0478 0.0678 pval of model spec. 0.0000 0.0000 0.0000

t FM 0.3270 -1.2690 -0.7992 1.0122 -1.2899 -0.8056

t s 0.3270 -1.2408 -0.7878 0.9684 -1.2291 -0.7725

t JW 0.3270 -1.3107 -0.7797 0.9461 -1.3067 -0.7943

t KRS 0.3270 -0.9556 -0.6487 0.9608 -0.9928 -0.6719 ϓ 0.1176 -0.5391 -0.3319 0.0797 0.0485 -0.1035 -0.0518 0.0217 0.0716

t FM 0.3270 -1.2572 -0.8259 0.3144 1.0610 -1.2552 -0.8579 0.3627

t s 0.3270 -1.2284 -0.8137 0.3137 1.0135 -1.1956 -0.8215 0.3486

t JW 0.3270 -1.2985 -0.8029 0.3132 1.0137 -1.2681 -0.8309 0.3614 t KRS 0.3270 -0.9493 -0.6628 0.3068 1.0301 -0.9699 -0.6964 0.3467

Table 15: Chimerica Factor in the Cross Section – Pricing 10 Chimerica Scored Portfolios (OLS) This table shows results from cross-sectional regressions of (purged) excess returns on 10 Chimerica-socred portfolios purged from traditional Fama-French three factors and regress on Chimerica or Chimerika itself purged from factors. Table reports the results from cross-sectional regressions using Ordinary Least Squares method. A full period two-pass regression is used, first in the time series to estimate betas and the in the cross section to estimate both the market prices of betas (ϒs) and covariances risks (λs). Estimates are reported along with t-statistics. The t-statistics reported correspond to the Fama MacBeth t-stat (tFM), the correction for errors in variables as suggested by Shanken (1992) (ts), the correction proposed by Jagannathan and Wang (1998) (tJW) and lastly that proposed 2 by Kan, Robotti, and Shanken (2013) (tKRS) to account for model misspecification. Also reported are the sample cross sectional R s values along with specification tests based on Kan, Robotti and Shanken (2013): se(R2) is the standard error of the sample R2 under the assumption that 0 < ᵨ2 <1; the p-value of a generalized version of Shanken’s CSRT model specification test is reported, an approximate F-test of model specification.

Market Prices of Betas (ϒs) Covariances (λs) adj. R2 2 OLS Chimerica Chimerica New Chimerica Chimerica New adj. R Chimerica Chimerica New ϓ 0.2539 0.0581 0.0658 Sample R 2 0.0658 0.0658 t FM 0.8450 0.8450 p(ᵨ2 = 1) 0.0000 0.0000 t s 0.8429 0.8347 p(ᵨ2 = 0) 0.4280 0.4144 2 t JW 0.8325 0.8453 se(R ) 0.1521 0.1480

t KRS 0.7927 0.8127 csrt 0.2264 0.3969 ϓ 0.2435 0.0581 0.0658 pval of model spec. 0.1392 0.0083

t FM 0.8450 0.8450

t s 0.8432 0.8350

t JW 0.8337 0.8486 t KRS 0.8162 0.8307

Table 16: Chimerica Factor in the Cross Section – Pricing 10 Chimerica Scored Portfolios (GLS) This table shows results from cross-sectional regressions of (purged) excess returns on 10 Chimerica-socred portfolios purged from traditional Fama-French three factors and regress on Chimerica or Chimerika itself purged from factors. Table reports the results from cross-sectional regressions using General Least Squares method when there is a certain degree of correlation between the residuals in a regression model. A full period two-pass regression is used, first in the time series to estimate betas and the in the cross section to estimate both the market prices of betas (ϒs) and covariances risks (λs). Estimates are reported along with t-statistics. The t-statistics reported correspond to the Fama MacBeth t-stat (tFM), the correction for errors in variables as suggested by Shanken (1992) (ts), the correction proposed by Jagannathan and Wang (1998) (tJW) and lastly that proposed by Kan, Robotti, and Shanken (2013) (tKRS) to account for model misspecification. Also reported are the sample cross sectional R2s values along with specification tests based on Kan, Robotti and Shanken (2013): se(R2) is the standard error of the sample R2 under the assumption that 0 < ᵨ2 <1; the p-value of a generalized version of Shanken’s CSRT model specification test is reported, an approximate F-test of model specification.

Market Prices of Betas (ϒs) Covariances (λs) adj. R2 2 GLS Chimerica Chimerica New Chimerica Chimerica New adj. R Chimerica Chimerica New ϓ 0.0855 0.0196 1.22E-01 Sample R 2 1.22E-01 -2.92E-01

t FM 0.3342 0.4204 p(ᵨ2 = 1) 0.00E+00 0.00E+00 t s 0.3130 0.3270 p(ᵨ2 = 0) 0.1630 1.0000 2 t JW 0.3336 0.4274 se(R ) 0.0887 0.1850

t KRS 0.3034 0.4031 csrt 0.2428 0.2415 ϓ -0.0712 -0.0170 -2.92E-01 pval of model spec. 0.0000 0.0000

t FM -0.2155 -0.2272

t s -0.2362 -0.2549

t JW -0.2151 -0.2242 t KRS -0.1900 -0.2070

Table 17: Chimerica Factor in the Cross Section – Pricing 10 Sector/Industry Portfolios (OLS) This table shows results from cross-sectional regressions of (purged) excess returns on 10 Sector/Industry portfolios purged from traditional Fama-French three factors and regress on Chimerica or Chimerika itself purged from factors. Table reports the results from cross-sectional regressions using Ordinary Least Squares method. A full period two-pass regression is used, first in the time series to estimate betas and the in the cross section to estimate both the market prices of betas (ϒs) and covariances risks (λs). Estimates are reported along with t-statistics. The t-statistics reported correspond to the Fama MacBeth t-stat (tFM), the correction for errors in variables as suggested by Shanken (1992) (ts), the correction proposed by Jagannathan and Wang (1998) (tJW) and lastly that proposed 2 by Kan, Robotti, and Shanken (2013) (tKRS) to account for model misspecification. Also reported are the sample cross sectional R s values along with specification tests based on Kan, Robotti and Shanken (2013): se(R2) is the standard error of the sample R2 under the assumption that 0 < ᵨ2 <1; the p-value of a generalized version of Shanken’s CSRT model specification test is reported, an approximate F-test of model specification.

Market Prices of Betas (ϒs) Covariances (λs) adj. R2 2 OLS Chimerica Chimerica New Chimerica Chimerica New adj. R Chimerica Chimerica New ϓ -0.095 -0.022 0.002 Sample R 2 0.002 0.002 t FM -0.128 -0.128 p(ᵨ2 = 1) 0.000 0.000 t s -0.128 -0.128 p(ᵨ2 = 0) 0.937 0.935 2 t JW -0.128 -0.129 se(R ) 0.048 0.047

t KRS -0.079 -0.080 csrt 0.237 0.236 ϓ -0.091 -0.022 0.002 pval of model spec. 0.118 0.119

t FM -0.128 -0.128

t s -0.128 -0.128

t JW -0.128 -0.128 t KRS -0.081 -0.082

Table 18: Chimerica Factor in the Cross Section – Pricing 10 Sector/Industry Portfolios (GLS) This table shows results from cross-sectional regressions of (purged) excess returns on 10 Sector/Industry portfolios purged from traditional Fama-French three factors and regress on Chimerica or Chimerika itself purged from factors. Table reports the results from cross-sectional regressions using General Least Squares method when there is a certain degree of correlation between the residuals in a regression model. A full period two-pass regression is used, first in the time series to estimate betas and the in the cross section to estimate both the market prices of betas (ϒs) and covariances risks (λs). Estimates are reported along with t-statistics. The t-statistics reported correspond to the Fama MacBeth t-stat (tFM), the correction for errors in variables as suggested by Shanken (1992) (ts), the correction proposed by Jagannathan and Wang (1998) (tJW) and lastly that proposed by Kan, Robotti, and Shanken (2013) (tKRS) to account for model misspecification. Also reported are the sample cross sectional R2s values along with specification tests based on Kan, Robotti and Shanken (2013): se(R2) is the standard error of the sample R2 under the assumption that 0 < ᵨ2 <1; the p-value of a generalized version of Shanken’s CSRT model specification test is reported, an approximate F-test of model specification.

Market Prices of Betas (ϒs) Covariances (λs) adj. R2 2 GLS Chimerica Chimerica New Chimerica Chimerica New adj. R Chimerica Chimerica New ϓ 0.138 0.032 0.003 Sample R 2 0.003 0.003 t FM 0.247 0.247 p(ᵨ2 = 1) 0.000 0.000 t s 0.246 0.246 p(ᵨ2 = 0) 0.865 0.859 2 t JW 0.246 0.247 se(R ) 0.032 0.031

t KRS 0.170 0.171 csrt 0.228 0.229 ϓ 0.132 0.032 0.003 pval of model spec. 0.000 0.000

t FM 0.247 0.247

t s 0.247 0.246

t JW 0.245 0.246 t KRS 0.177 0.177

Table 19: Chimerica Factor in the Cross Section – Pricing Combined 10 Chimerica Portfolios and 10 Sector Portfolios (OLS) This table shows results from cross-sectional regressions of (purged) excess returns on combined 10 Chimerica-socred portfolios and 10 sector/industry portfolios purged from traditional Fama-French three factors and regress on Chimerica or Chimerika itself purged from factors. Table reports the results from cross-sectional regressions using Ordinary Least Squares method. A full period two-pass regression is used, first in the time series to estimate betas and the in the cross section to estimate both the market prices of betas (ϒs) and covariances risks (λs). Estimates are reported along with t-statistics. The t-statistics reported correspond to the Fama MacBeth t-stat (tFM), the correction for errors in variables as suggested by Shanken (1992) (ts), the correction proposed by Jagannathan and Wang (1998) (tJW) and lastly that proposed by Kan, Robotti, and Shanken (2013) (tKRS) to account for model misspecification. Also reported are the sample cross sectional R2s values along with specification tests based on Kan, Robotti and Shanken (2013): se(R2) is the standard error of the sample R2 under the assumption that 0 < ᵨ2 <1; the p-value of a generalized version of Shanken’s CSRT model specification test is reported, an approximate F-test of model specification.

Market Prices of Betas (ϒs) Covariances (λs) adj. R2 2 OLS Chimerica Chimerica New Chimerica Chimerica New adj. R Chimerica Chimerica New ϓ 0.066 0.015 0.001 Sample R 2 0.001 0.001

t FM 0.146 0.146 p(ᵨ2 = 1) 0.000 0.000 t s 0.145 0.145 p(ᵨ2 = 0) 0.920 0.921 2 t JW 0.145 0.145 se(R ) 0.030 0.030

t KRS 0.100 0.100 csrt 0.472 0.915 ϓ 0.063 0.015 0.001 pval of model spec. 0.212 0.004

t FM 0.146 0.146

t s 0.145 0.145

t JW 0.145 0.146 t KRS 0.100 0.100

Table 20: Chimerica Factor in the Cross Section – Pricing Combined 10 Chimerica Portfolios and 10 Sector Portfolios (GLS) This table shows results from cross-sectional regressions of (purged) excess returns on combined 10 Chimerica-socred portfolios and 10 sector/industry portfolios purged from traditional Fama-French three factors and regress on Chimerica or Chimerika itself purged from factors. Table reports the results from cross-sectional regressions using General Least Squares method when there is a certain degree of correlation between the residuals in a regression model. A full period two-pass regression is used, first in the time series to estimate betas and the in the cross section to estimate both the market prices of betas (ϒs) and covariances risks (λs). Estimates are reported along with t-statistics. The t-statistics reported correspond to the Fama MacBeth t-stat (tFM), the correction for errors in variables as suggested by Shanken (1992) (ts), the correction proposed by Jagannathan and Wang (1998) (tJW) and lastly that proposed 2 by Kan, Robotti, and Shanken (2013) (tKRS) to account for model misspecification. Also reported are the sample cross sectional R s values along with specification tests based on Kan, Robotti and Shanken (2013): se(R2) is the standard error of the sample R2 under the assumption that 0 < ᵨ2 <1; the p-value of a generalized version of Shanken’s CSRT model specification test is reported, an approximate F-test of model specification. Market Prices of Betas (ϒs) Covariances (λs) adj. R2 2 GLS Chimerica Chimerica New Chimerica Chimerica New adj. R Chimerica Chimerica New ϓ 0.005 0.001 -0.124 Sample R 2 -0.124 -0.079 t FM 0.018 0.012 p(ᵨ2 = 1) 0.000 0.000 t s 0.014 0.012 p(ᵨ2 = 0) 1.000 1.000 2 t JW 0.017 0.012 se(R ) 0.042 0.215

t KRS 0.013 0.010 csrt NA NA ϓ 0.554 0.132 -0.079 pval of model spec. NA NA

t FM 0.978 1.456

t s 1.086 1.325

t JW 0.905 1.287 t KRS 0.760 0.898

Table 21: Summary of Long-Short Portfolio Returns and Testing Statistics This table is based on over 2000 analyst forecasts extracted from I/B/E/S database for the S&P 100 companies during the second quarter of 2010. Fiscal year 1, 2, 3, 4, 5 earnings forecasts, as well as long term growth estimates are used as input for the Claus/Thomas model to estimate one-year forward-looking expected return. Four kinds of equal-weighted and capitalization-weighted portfolios are formed. Half portfolio goes long on the top 50 Chimerica-scored companies while shorting the bottom 50. Quartile portfolio, quintile portfolio and decile portfolio are formed in accordance.

Forward 12-month Expected Return as of June 30,2010 and Sorted by Chimerica Score EW Average Return VW Average Return S&P 100 Top Bottom Top - Bottom Top Bottom Top - Bottom Universe Return Variance Return Variance Return t-Stat Return Variance Return Variance Return t-Stat

Half 12.11 0.34 12.61 0.33 -0.50 -4.33 11.74 0.57 13.16 0.64 -1.42 -9.04 Quartile 12.58 0.76 12.29 0.63 0.29 1.21 11.61 1.47 12.53 1.48 -0.92 -2.69 Quintile 12.80 0.87 12.01 0.90 0.79 2.65 11.07 0.48 12.45 2.03 -1.38 -3.88 Decile 14.32 2.92 12.45 2.63 1.87 2.51 11.79 0.82 9.10 0.91 2.69 6.45

Table 22: Long-Short GICS Sector Portfolio Returns and Testing Statistics Using the same scorecards as in the ex-post analysis, for each GICS (Global Industry Classification Standard) sector, long/short portfolios are created by going long the highest 50% ranked Chimerica stocks while shorting the lowest 50% ranked. Statistical test concerning the difference in means reveals significance of the results. Again, the forecasts of earnings per share, growth rate, standard deviation of analyst forecasts and book value per share are extracted from the I/B/E/S database for the period of April to June 2010.

Forward 12-month Expected Return as of June 30,2010 and Sorted by Chimerica Score and GICS Sector Number % of S&P 100 # of Analyst EW Average Return VW Average Return of Firms Market Cap Forecasts Top Half Bottom Half Top - Bottom Top Half Bottom Half Top - Bottom Return Variance Return Variance Return t-Stat Return Variance Return Variance Return t-Stat S&P 100 Index 100 100% 2,031

By GICS Sector Energy 10 12% 220 15.42 6.00 14.09 8.59 1.33 0.78 15.17 14.05 15.19 10.18 -0.01 -0.01 Materials 5 2% 61 17.19 16.84 13.14 9.72 4.05 1.36 16.54 14.93 12.23 4.80 4.31 1.68 Industrials 12 9% 192 12.78 3.68 13.43 6.60 -0.65 -0.50 12.18 3.83 12.77 4.40 -0.59 -0.50 Consumer Discretionary 11 8% 237 11.26 1.13 9.93 1.09 1.33 2.18 10.63 0.82 9.50 0.95 1.13 2.07 Consumer Staples 15 14% 219 9.18 0.23 10.31 0.62 -1.13 -3.44 8.10 0.56 10.86 0.62 -2.75 -7.15 Health Care 10 12% 245 11.10 0.57 11.68 0.63 -0.58 -1.19 11.44 0.86 10.79 0.32 0.65 1.33 Financials 16 16% 265 17.40 2.26 14.41 4.28 2.99 3.30 19.17 7.14 13.50 6.59 5.67 4.33 Information Technology 14 21% 476 11.53 0.94 11.27 0.51 0.26 0.57 10.71 0.64 11.17 0.57 -0.46 -1.10 Telecom Services 3 4% 68 11.08 7.70 5.76 1.45 5.32 2.49 11.21 9.24 8.71 7.50 2.50 0.86 Utilities 4 1% 48 8.21 1.43 9.18 2.46 -0.97 -0.69 8.24 1.34 9.36 2.90 -1.12 -0.77

Average 1.19 0.93

Figure 1 Expected Return (over Risk Free Rate) by Sector/Industry

This figure shows the expected returns over risk-free rate by sector/industry based on analysts’ forecasts as of 3/18/2010 and 3/30/2007. Analysts’ earnings estimates and company book values are extracted from I/B/E/S (the Institutional Brokers’ Estimate System). To estimate individual expected stock return, the actual fiscal year-end book value of the company disclosed prior to the release of earnings estimate is used. For each company, I/B/E/S provides a number of analyst forecasts. The median of the forecasts is used for each of the next five fiscal years to calculate future abnormal- earnings and future book values. In the few cases where fiscal year 4 and 5 earnings forecasts are not available, these are estimated by extrapolation based on the growth rates derived from fiscal year 2 and 3. As a part of the terminal value calculation, expected inflation is estimated in accordance with the Claus/Thomas model by subtracting the 10-year US Treasury bond yield from yield of the 10-year inflation-linked government bonds. Appendix 3 on the following pages indicate the expected returns for each of the largest 100 stocks as of March 18, 2010 and March 30, 2007. The aggregate expected return for the 100 largest stocks of the S&P 100 index is the market-cap weighted average of individual company returns. And the equity risk premium is the difference between the stock expected returns weighted according to market capitalization and the 3-month T- Bill return. 3-month T-Bill rate on March 30, 2007 was 5.07% whereas 3-month T-Bill rate on March 18, 2010 was 0.16%.

Expected Returns by Sector 12.00%

10.00%

8.00%

6.00%

4.00%

2.00%

0.00%

3/18/2010 3/30/2007

Appendix 1 Scorecard Construction

The objective is to rank companies from the highest to the lowest Chimerica exposure. A scorecard is devised using both qualitative and quantitative elements. For example, China sales as a percentage of total company sales are in numeric terms while binary yes/no answers use 1 or 0 to represent. For each of the S&P 100 companies, detailed bottom-up analysis entails studying 10-K filings, analyst research reports, company websites and Google searches which contain key evidence for input to the scorecard. The 2010 scorecard uses all 15 elements while the 2007 scorecard uses the first 14 elements due to data availability. Scorecard Elements Sales and distribution. If yes, company has this sales and distribution activity 1 S&D in China, value = 1; otherwise, value =0. Disclose Company financial statements disclose sales and/or profit from China due to 2 sales/profit its significance. If yes, value =1; otherwise, value = 0. Sales Company management and/or investment analysts estimate company sales 3 Estimate in China. If yes, value =1; otherwise, value = 0. Disclosed or estimated sales in China as a percentage of total sales. If not 4 % of Total available, value = 0. Growth Company management and/or investment analysts assert that China is the 5 market firm's growth opportunity. If yes, value =1; otherwise, value = 0. Company performs research and development activity in China. If yes, value 6 R&D =1; otherwise, value = 0. Company has manufacturing activity in China. If yes, value =1; otherwise, 7 Manufacture value = 0. Source Company sources materials locally in China for their manufacturing activities. 8 Materials If yes, value =1; otherwise, value = 0. 9 Hire Company hires locally in China. If yes, value =1; otherwise, value = 0. >500 CN Company currently employs over 500 locals in China. If yes, value =1; 10 Employees otherwise, value = 0. Company management and/or investment analysts disclose and/or estimate 11 Investment investment amount in China. If yes, value =1; otherwise, value = 0. >5 Years of Company has operated in China for over 5 years. If yes, value =1; otherwise, 12 Operation value = 0. Company has nationwide operations in China, value =1; company has only 13 Locations representative offices or no office in China, value = 0. Company has subsidiaries and/or joint ventures in China. If yes, value =1; 14 Subs/JVs otherwise, value = 0. AR Disclosure > Company’s annual report mentions China no less than 3 times If yes, 15 or = 3 value=1; otherwise, value =0.

Appendix 2 Chimerica Scorecard

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Disclose % of Total AR Sales/Prf/As Sales S&D Sales Growth Mkt R&D, Design Manfact Source Mat Hire > 500 CN EEs Invest >5 Yrs Location Sub/JV Disclosure > Total sets/strateg Estimate >5% or = 3 y in China 2004 MMM UN Equity 3M Co 1 1 1 1 1 1 1 1 1 1 1 11 2005 MMM UN Equity 3M Co 1 1 1 1 1 1 1 1 1 1 1 1 12 2006 MMM UN Equity 3M Co 1 1 1 1 1 1 1 1 1 1 1 1 12 2007 MMM UN Equity 3M Co 1 1 1 1 1 1 1 1 1 1 1 1 12 2008 MMM UN Equity 3M Co 1 1 1 1 1 1 1 1 1 1 1 1 12 2009 MMM UN Equity 3M Co 1 1 1 1 1 1 1 1 1 1 1 1 12 2006 ABT UN Equity Abbott Laboratories 1 1 1 1 1 1 1 1 1 9 2007 ABT UN Equity Abbott Laboratories 1 1 1 1 1 1 1 1 1 9 2008 ABT UN Equity Abbott Laboratories 1 1 1 1 1 1 1 1 1 9 2009 ABT UN Equity Abbott Laboratories 1 1 1 1 1 1 1 1 1 9 2004 AES UN Equity AES Corp/VA 1 1 1 1 1 1 1 1 1 1 1 11 2005 AES UN Equity AES Corp/VA 1 1 1 1 1 1 1 1 1 1 1 11 2006 AES UN Equity AES Corp/VA 1 1 1 1 1 1 1 1 1 1 1 11 2007 AES UN Equity AES Corp/VA 1 1 1 1 1 1 1 1 1 1 1 11 2008 AES UN Equity AES Corp/VA 1 1 1 1 1 1 1 1 1 1 1 1 12 2004 LU UN Equity Alcatel-Lucent USA Inc 1 1 1 1 1 1 1 1 1 1 1 1 1 1 14 2005 LU UN Equity Alcatel-Lucent USA Inc 1 1 1 1 1 1 1 1 1 1 1 1 1 1 14 2004 AA UN Equity Alcoa Inc 1 1 1 1 1 1 1 1 1 1 10 2005 AA UN Equity Alcoa Inc 1 1 1 1 1 1 1 1 1 1 10 2006 AA UN Equity Alcoa Inc 1 1 1 1 1 1 1 1 1 1 1 11 2007 AA UN Equity Alcoa Inc 1 1 1 1 1 1 1 1 1 1 1 11 2008 AA UN Equity Alcoa Inc 1 1 1 1 1 1 1 1 1 1 1 11 2009 AA UN Equity Alcoa Inc 1 1 1 1 1 1 1 1 1 1 1 1 12 2004ATI UN Equity Allegheny Technologies Inc 1 1 1 1 1 1 1 1 1 1 1 1 12 2005ATI UN Equity Allegheny Technologies Inc 1 1 0 1 1 1 1 1 1 1 1 1 1 12 2006ATI UN Equity Allegheny Technologies Inc 1 1 0 1 1 1 1 1 1 1 1 1 1 12 2007ATI UN Equity Allegheny Technologies Inc 1 1 0 1 1 1 1 1 1 1 1 1 1 12 2004 ALL UN Equity Allstate Corp/The 1 1 2 2005 ALL UN Equity Allstate Corp/The 1 1 2 2006 ALL UN Equity Allstate Corp/The 1 1 2 2007 ALL UN Equity Allstate Corp/The 1 1 2 2008 ALL UN Equity Allstate Corp/The 1 1 2 2009 ALL UN Equity Allstate Corp/The 1 1 2 2004 MO UN Equity Altria Group Inc 1 1 1 1 1 1 1 1 1 1 10 2005 MO UN Equity Altria Group Inc 1 1 1 1 1 1 1 1 1 1 10 2006 MO UN Equity Altria Group Inc 1 1 1 1 1 1 1 1 1 1 10 2007 MO UN Equity Altria Group Inc 1 1 1 1 1 1 1 1 1 1 10 2008 MO UN Equity Altria Group Inc 1 1 1 1 1 1 1 1 1 1 1 11 2009 MO UN Equity Altria Group Inc 1 1 1 1 1 1 1 1 1 1 10 2009 AMZN UW Equity Amazon.com Inc 1 1 1 1 1 1 1 7 2004 AEP UN Equity American Electric Power Co Inc 0 2005 AEP UN Equity American Electric Power Co Inc 0 2006 AEP UN Equity American Electric Power Co Inc 0 2007 AEP UN Equity American Electric Power Co Inc 0 2008 AEP UN Equity American Electric Power Co Inc 0 2009 AEP UN Equity American Electric Power Co Inc 0 2004 AXP UN Equity American Express Co 1 1 1 1 1 1 6 2005 AXP UN Equity American Express Co 1 1 1 1 1 1 6 2006 AXP UN Equity American Express Co 1 1 1 1 1 1 6 2007 AXP UN Equity American Express Co 1 1 1 1 1 1 6 2008 AXP UN Equity American Express Co 1 1 1 1 1 1 6 2009 AXP UN Equity American Express Co 1 1 1 1 1 1 6 2004 AIG UN Equity American International Group Inc 1 1 1 1 1 1 1 7 2005 AIG UN Equity American International Group Inc 1 1 1 1 1 1 1 7 2006 AIG UN Equity American International Group Inc 1 1 1 1 1 1 1 7 2007 AIG UN Equity American International Group Inc 1 1 1 1 1 1 1 1 8

2008 AIG UN Equity American International Group Inc 1 1 1 1 1 1 1 1 8 2004 AMGN UQ Equity Amgen Inc 1 1 1 3 2005 AMGN UQ Equity Amgen Inc 1 1 1 3 2006 AMGN UQ Equity Amgen Inc 1 1 1 3 2007 AMGN UW Equity Amgen Inc 1 1 1 3 2008 AMGN UW Equity Amgen Inc 1 1 1 1 4 2009 AMGN UW Equity Amgen Inc 1 1 1 3 2004 3393199Q UN Equity Anheuser-Busch Cos Inc 1 1 1 1 1 1 1 1 1 1 1 11 2005 3393199Q UN Equity Anheuser-Busch Cos Inc 1 1 1 1 1 1 1 1 1 1 1 11 2006 3393199Q UN Equity Anheuser-Busch Cos Inc 1 1 1 1 1 1 1 1 1 1 1 11 2007 3393199Q UN Equity Anheuser-Busch Cos Inc 1 1 1 1 1 1 1 1 1 1 1 11 2007AAPL UW EquityApple Inc 1 1 1 1 1 1 1 7 2008AAPL UW EquityApple Inc 1 1 1 1 1 1 1 7 2009AAPL UW EquityApple Inc 1 1 1 1 1 1 1 7 2004 653707Q UN Equity AT&T Corp 0 2004 T UN Equity AT&T Inc 0 2005 T UN Equity AT&T Inc 0 2006 T UN Equity AT&T Inc 0 2007 T UN Equity AT&T Inc 0 2008 T UN Equity AT&T Inc 0 2009 T UN Equity AT&T Inc 0 2004AVP UN Equity Avon Products Inc 1 1 1 1 1 1 1 1 1 1 1 1 12 2005AVP UN Equity Avon Products Inc 1 1 1 1 1 1 1 1 1 1 1 1 12 2006AVP UN Equity Avon Products Inc 1 1 1 1 1 1 1 1 1 1 1 1 12 2007AVP UN Equity Avon Products Inc 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 14 2008AVP UN Equity Avon Products Inc 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 14 2009AVP UN Equity Avon Products Inc 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 14 2004 BHI UN Equity Baker Hughes Inc 1 1 1 3 2005 BHI UN Equity Baker Hughes Inc 1 1 0 1 1 4 2006 BHI UN Equity Baker Hughes Inc 1 1 0 1 1 4 2007 BHI UN Equity Baker Hughes Inc 1 1 0 1 1 1 5 2008 BHI UN Equity Baker Hughes Inc 1 1 1 1 4 2009 BHI UN Equity Baker Hughes Inc 1 1 1 1 4 2004 BAC UN Equity Bank of America Corp 1 1 1 1 1 1 6 2005 BAC UN Equity Bank of America Corp 1 1 1 1 1 1 1 1 8 2006 BAC UN Equity Bank of America Corp 1 1 1 1 1 1 1 1 1 1 10 2007 BAC UN Equity Bank of America Corp 1 1 1 1 1 1 1 1 1 1 10 2008 BAC UN Equity Bank of America Corp 1 1 1 1 1 1 1 1 1 1 10 2009 BAC UN Equity Bank of America Corp 1 1 1 1 1 1 1 1 1 1 10 2008 BK UN Equity Bank of New York Mellon Corp/The 1 1 2009 BK UN Equity Bank of New York Mellon Corp/The 1 1 2004 BAX UN Equity Baxter International Inc 1 1 1 1 1 1 1 1 1 1 1 11 2005 BAX UN Equity Baxter International Inc 1 1 1 1 1 1 1 1 1 1 1 11 2006 BAX UN Equity Baxter International Inc 1 1 1 1 1 1 1 1 1 1 1 11 2007BAX UN Equity Baxter International Inc 1 1 1 1 1 1 1 1 1 1 1 1 12 2008BAX UN Equity Baxter International Inc 1 1 1 1 1 1 1 1 1 1 1 1 12 2009BAX UN Equity Baxter International Inc 1 1 1 1 1 1 1 1 1 1 1 1 12 2004 BDK UN Equity Black & Decker Corp/The 1 1 1 1 1 1 1 1 1 1 10 2005 BDK UN Equity Black & Decker Corp/The 1 1 1 1 1 1 1 1 1 1 1 11 2006 BDK UN Equity Black & Decker Corp/The 1 1 1 1 1 1 1 1 1 1 1 11 2004BA UN Equity Boeing Co/The 1 1 1 0 1 1 1 1 1 1 1 1 1 1 13 2005BA UN Equity Boeing Co/The 1 1 1 0 1 1 1 1 1 1 1 1 1 1 13 2006BA UN Equity Boeing Co/The 1 1 1 0 1 1 1 1 1 1 1 1 1 1 13 2007BA UN Equity Boeing Co/The 1 1 1 0 1 1 1 1 1 1 1 1 1 12 2008BA UN Equity Boeing Co/The 1 1 1 0 1 1 1 1 1 1 1 1 1 1 13 2009BA UN Equity Boeing Co/The 1 1 1 0 1 1 1 1 1 1 1 1 1 12 2004 BMY UN Equity Bristol-Myers Squibb Co 1 1 1 1 1 1 1 1 1 1 1 1 12 2005 BMY UN Equity Bristol-Myers Squibb Co 1 1 1 1 1 1 1 1 1 1 1 1 12 2006 BMY UN Equity Bristol-Myers Squibb Co 1 1 1 1 1 1 1 1 1 1 1 1 12 2007 BMY UN Equity Bristol-Myers Squibb Co 1 1 1 1 1 1 1 1 1 1 1 1 12 2008 BMY UN Equity Bristol-Myers Squibb Co 1 1 1 1 1 1 1 1 1 1 1 1 12 2009 BMY UN Equity Bristol-Myers Squibb Co 1 1 1 1 1 1 1 1 1 1 1 1 12 66

2004 BNI UN Equity Burlington Northern Santa Fe LLC 0 2005 BNI UN Equity Burlington Northern Santa Fe LLC 0 2006 BNI UN Equity Burlington Northern Santa Fe LLC 0 2007 BNI UN Equity Burlington Northern Santa Fe LLC 0 2008 BNI UN Equity Burlington Northern Santa Fe LLC 0 2004 HET UN Equity Caesars Entertainment Corp/Old 0 2005 HET UN Equity Caesars Entertainment Corp/Old 0 2006 HET UN Equity Caesars Entertainment Corp/Old 0 2004 CPB UN Equity Campbell Soup Co 1 1 1 1 4 2005 CPB UN Equity Campbell Soup Co 1 1 1 1 4 2006 CPB UN Equity Campbell Soup Co 1 1 1 1 4 2007 CPB UN Equity Campbell Soup Co 1 1 1 1 4 2008 CPB UN Equity Campbell Soup Co 1 1 1 1 1 5 2009 CPB UN Equity Campbell Soup Co 1 1 1 1 1 1 6 2007 COF UN Equity Capital One Financial Corp 0 2008 COF UN Equity Capital One Financial Corp 0 2009 COF UN Equity Capital One Financial Corp 0 2006 CAT UN Equity Caterpillar Inc 1 1 1 1 1 1 1 1 1 1 1 1 12 2007 CAT UN Equity Caterpillar Inc 1 1 1 1 1 1 1 1 1 1 1 1 12 2008 CAT UN Equity Caterpillar Inc 1 1 1 1 1 1 1 1 1 1 1 1 1 13 2009 CAT UN Equity Caterpillar Inc 1 1 1 1 1 1 1 1 1 1 1 1 12 2004 CBS UN Equity CBS Corp 1 1 1 3 2005 CBS UN Equity CBS Corp 1 1 1 3 2006 CBS UN Equity CBS Corp 1 1 1 3 2007 CBS UN Equity CBS Corp 1 1 1 1 4 2008 CBS UN Equity CBS Corp 1 1 1 1 4 2006CVX UN Equity Chevron Corp 1 1 1 1 1 1 1 1 1 1 1 1 1 13 2007CVX UN Equity Chevron Corp 1 1 1 1 1 1 1 1 1 1 1 1 1 13 2008CVX UN Equity Chevron Corp 1 1 1 1 1 1 1 1 1 1 1 1 1 13 2009CVX UN Equity Chevron Corp 1 1 1 1 1 1 1 1 1 1 1 1 1 13 2004 CI UN Equity Cigna Corp 1 1 1 1 1 1 6 2005 CI UN Equity Cigna Corp 1 1 1 1 1 1 6 2006 CI UN Equity Cigna Corp 1 1 1 1 1 1 6 2007 CI UN Equity Cigna Corp 1 1 1 1 1 1 6 2008 CI UN Equity Cigna Corp 1 1 1 1 1 1 6 2004 CSCO UQ Equity Cisco Systems Inc 1 1 1 1 1 1 1 1 1 9 2005 CSCO UQ Equity Cisco Systems Inc 1 1 1 1 1 1 1 1 1 9 2006 CSCO UQ Equity Cisco Systems Inc 1 1 1 1 1 1 1 1 1 9 2007 CSCO UW Equity Cisco Systems Inc 1 1 1 1 1 1 1 1 1 1 10 2008 CSCO UW Equity Cisco Systems Inc 1 1 1 1 1 1 1 1 1 1 10 2009 CSCO UW Equity Cisco Systems Inc 1 1 1 1 1 1 1 1 1 1 10 2004 C UN Equity Citigroup Inc 1 1 1 1 1 1 1 1 8 2005 C UN Equity Citigroup Inc 1 1 1 1 1 1 1 1 8 2006C UN Equity Citigroup Inc 1 1 1 1 1 1 1 1 1 9 2007 C UN Equity Citigroup Inc 1 1 1 1 1 1 1 7 2008 C UN Equity Citigroup Inc 1 1 1 1 1 1 6 2009 C UN Equity Citigroup Inc 1 1 1 1 1 1 6 2004 2968900Q UN Equity Clear Channel Communications Inc 1 1 1 1 1 1 1 1 8 2005 2968900Q UN Equity Clear Channel Communications Inc 1 1 1 1 1 1 1 1 8 2006 2968900Q UN Equity Clear Channel Communications Inc 1 1 1 1 1 1 1 1 8 2007 2968900Q UN Equity Clear Channel Communications Inc 1 1 1 1 1 1 1 7 2004KO UN Equity Coca-Cola Co/The 1 1 1 1 1 1 1 1 1 1 10 2005KO UN Equity Coca-Cola Co/The 1 1 1 1 1 1 1 1 1 1 1 1 12 2006KO UN Equity Coca-Cola Co/The 1 1 1 1 1 1 1 1 1 1 1 1 12 2007KO UN Equity Coca-Cola Co/The 1 1 1 1 1 1 1 1 1 1 1 1 12 2008KO UN Equity Coca-Cola Co/The 1 1 1 1 1 1 1 1 1 1 1 11 2009KO UN Equity Coca-Cola Co/The 1 1 1 1 1 1 1 1 1 1 1 1 12 2004CL UN Equity Colgate-Palmolive Co 1 1 1 1 1 1 1 1 1 1 10 2005CL UN Equity Colgate-Palmolive Co 1 1 1 1 1 1 1 1 1 1 10 2006CL UN Equity Colgate-Palmolive Co 1 1 1 1 1 1 1 1 1 1 10 2007CL UN Equity Colgate-Palmolive Co 1 1 1 1 1 1 1 1 1 9 2008CL UN Equity Colgate-Palmolive Co 1 1 1 1 1 1 1 1 1 1 1 11 67

2009CL UN Equity Colgate-Palmolive Co 1 1 1 1 1 1 1 1 1 1 10 2005 CMCSA UQ Equity Comcast Corp 1 1 2006 CMCSA UQ Equity Comcast Corp 1 1 2007 CMCSA UW Equity Comcast Corp 1 1 2008 CMCSA UW Equity Comcast Corp 1 1 2009 CMCSA UW Equity Comcast Corp 1 1 2004 CSC UN Equity Computer Sciences Corp 1 1 1 1 1 1 1 7 2005 CSC UN Equity Computer Sciences Corp 1 1 1 1 1 1 1 7 2006 CSC UN Equity Computer Sciences Corp 1 1 1 1 1 1 1 7 2007 CSC UN Equity Computer Sciences Corp 1 1 1 1 1 1 1 7 2007COP UN Equity ConocoPhillips 1 1 1 1 1 1 1 1 1 1 1 1 12 2008COP UN Equity ConocoPhillips 1 1 1 1 1 1 1 1 1 1 1 1 12 2009COP UN Equity ConocoPhillips 1 1 1 1 1 1 1 1 1 1 1 1 12 2009 COST UW Equity Costco Wholesale Corp 1 1 1 1 1 1 6 2008COV UN Equity Covidien PLC 1 1 1 1 1 1 1 1 8 2007 CVS UN Equity CVS Caremark Corp 0 2008 CVS UN Equity CVS Caremark Corp 0 2009 CVS UN Equity CVS Caremark Corp 0 2005 DELL UQ Equity Dell Inc 1 1 1 1 1 1 1 1 1 1 1 1 12 2006 DELL UQ Equity Dell Inc 1 1 1 1 1 1 1 1 1 1 1 1 12 2007 DELL UW Equity Dell Inc 1 1 1 1 1 1 1 1 1 1 1 1 12 2008 DELL UW Equity Dell Inc 1 1 1 1 1 1 1 1 1 1 1 1 12 2009 DELL UW Equity Dell Inc 1 1 1 1 1 1 1 1 1 1 1 1 1 13 2004 DALRQ UN Equity Delta Air Lines Inc/Old 1 1 2 2005 DALRQ UN Equity Delta Air Lines Inc/Old 1 1 2009 DVN UN Equity Devon Energy Corp 1 1 1 1 1 1 1 7 2004 DOW UN Equity Dow Chemical Co/The 1 1 1 1 1 1 1 1 1 9 2005 DOW UN Equity Dow Chemical Co/The 1 1 1 1 1 1 1 1 1 9 2006 DOW UN Equity Dow Chemical Co/The 1 1 1 1 1 1 1 1 8 2007 DOW UN Equity Dow Chemical Co/The 1 1 1 1 1 1 1 1 1 9 2008 DOW UN Equity Dow Chemical Co/The 1 1 1 1 1 1 1 1 1 1 1 11 2009 DOW UN Equity Dow Chemical Co/The 1 1 1 1 1 1 1 1 1 1 1 11 2004 EKDKQ UN Equity Eastman Kodak Co 1 1 1 1 1 1 1 1 1 1 1 1 12 2005 EKDKQ UN Equity Eastman Kodak Co 1 1 0 1 1 1 1 1 1 1 1 1 1 1 13 2006 EKDKQ UN Equity Eastman Kodak Co 1 1 0 1 1 1 1 1 1 1 1 1 1 1 13 2004 DD UN Equity EI du Pont de Nemours & Co 1 1 0 1 1 1 1 1 1 1 1 1 1 12 2005 DD UN Equity EI du Pont de Nemours & Co 1 1 0 1 1 1 1 1 1 1 1 1 1 12 2006DD UN Equity EI du Pont de Nemours & Co 1 1 0 1 1 1 1 1 1 1 1 1 1 1 13 2007DD UN Equity EI du Pont de Nemours & Co 1 1 0 1 1 1 1 1 1 1 1 1 1 1 13 2008DD UN Equity EI du Pont de Nemours & Co 1 1 1 1 1 1 1 1 1 1 1 1 1 1 14 2009DD UN Equity EI du Pont de Nemours & Co 1 1 1 1 1 1 1 1 1 1 1 1 1 1 14 2004 EP UN Equity El Paso LLC 1 1 1 1 1 1 1 1 1 1 1 11 2005 EP UN Equity El Paso LLC 1 1 1 1 1 1 1 1 1 1 1 11 2006 EP UN Equity El Paso LLC 0 2007 EP UN Equity El Paso LLC 0 2008 EP UN Equity El Paso LLC 0 2004 EMC UN Equity EMC Corp/MA 1 1 1 1 4 2005 EMC UN Equity EMC Corp/MA 1 1 1 1 1 5 2006 EMC UN Equity EMC Corp/MA 1 1 1 1 1 5 2007 EMC UN Equity EMC Corp/MA 0 2008 EMC UN Equity EMC Corp/MA 0 2009 EMC UN Equity EMC Corp/MA 0 2004 ETR UN Equity Entergy Corp 0 2005 ETR UN Equity Entergy Corp 0 2006 ETR UN Equity Entergy Corp 0 2007 ETR UN Equity Entergy Corp 0 2008 ETR UN Equity Entergy Corp 0 2009 ETR UN Equity Entergy Corp 0 2004 EXC UN Equity Exelon Corp 0 2005 EXC UN Equity Exelon Corp 0 2006 EXC UN Equity Exelon Corp 0 2007 EXC UN Equity Exelon Corp 0 68

2008 EXC UN Equity Exelon Corp 0 2009 EXC UN Equity Exelon Corp 0 2004 XOM UN Equity Exxon Mobil Corp 1 1 1 1 1 5 2005 XOM UN Equity Exxon Mobil Corp 1 1 1 1 1 5 2006 XOM UN Equity Exxon Mobil Corp 1 1 1 1 1 5 2007 XOM UN Equity Exxon Mobil Corp 1 1 1 1 1 5 2008 XOM UN Equity Exxon Mobil Corp 1 1 1 1 1 1 1 7 2009 XOM UN Equity Exxon Mobil Corp 1 1 1 1 1 1 1 7 2004 FDX UN Equity FedEx Corp 1 1 1 1 1 1 1 1 8 2005 FDX UN Equity FedEx Corp 1 1 1 1 1 1 1 1 8 2006 FDX UN Equity FedEx Corp 1 1 1 1 1 1 1 1 8 2007 FDX UN Equity FedEx Corp 1 1 1 1 1 1 1 1 8 2008 FDX UN Equity FedEx Corp 1 1 1 1 1 1 1 1 8 2009 FDX UN Equity FedEx Corp 1 1 1 1 1 1 1 1 8 2004 F UN Equity Ford Motor Co 1 1 1 1 1 1 1 1 1 1 1 11 2005 F UN Equity Ford Motor Co 1 1 1 1 1 1 1 1 1 1 1 11 2006 F UN Equity Ford Motor Co 1 1 1 1 1 1 1 1 1 1 1 11 2007 F UN Equity Ford Motor Co 1 1 1 1 1 1 1 1 1 1 1 11 2008 F UN Equity Ford Motor Co 1 1 1 1 1 1 1 1 1 1 1 11 2009 F UN Equity Ford Motor Co 1 1 1 1 1 1 1 1 1 1 1 11 2004 GD UN Equity General Dynamics Corp 0 2005 GD UN Equity General Dynamics Corp 0 2006 GD UN Equity General Dynamics Corp 0 2007 GD UN Equity General Dynamics Corp 0 2008 GD UN Equity General Dynamics Corp 0 2009 GD UN Equity General Dynamics Corp 0 2004 GE UN Equity General Electric Co 1 1 2005 GE UN Equity General Electric Co 1 1 2006 GE UN Equity General Electric Co 1 1 2007 GE UN Equity General Electric Co 1 1 2008 GE UN Equity General Electric Co 1 1 2009 GE UN Equity General Electric Co 1 1 2009 GILD UW Equity Gilead Sciences Inc 1 1 2004 1028411Q UN Equity Gillette Co/The 1 1 1 1 1 1 1 1 1 1 10 2004 GS UN Equity Goldman Sachs Group Inc/The 1 1 1 1 1 1 6 2005 GS UN Equity Goldman Sachs Group Inc/The 1 1 1 1 1 1 6 2006 GS UN Equity Goldman Sachs Group Inc/The 1 1 1 1 1 1 6 2007 GS UN Equity Goldman Sachs Group Inc/The 1 1 1 1 1 1 6 2008 GS UN Equity Goldman Sachs Group Inc/The 1 1 1 1 1 1 1 7 2009 GS UN Equity Goldman Sachs Group Inc/The 1 1 1 1 1 1 1 7 2007 GOOGL UW Equity Google Inc 1 1 1 3 2008 GOOGL UW Equity Google Inc 1 1 1 3 2009 GOOGL UW Equity Google Inc 1 1 1 1 4 2004 HAL UN Equity Halliburton Co 1 1 1 1 4 2005 HAL UN Equity Halliburton Co 1 1 1 1 4 2006 HAL UN Equity Halliburton Co 1 1 1 1 4 2007 HAL UN Equity Halliburton Co 1 1 1 1 4 2008 HAL UN Equity Halliburton Co 1 1 1 1 4 2009 HAL UN Equity Halliburton Co 1 1 1 3 2004 HIG UN Equity Hartford Financial Services Group Inc/Th 1 1 1 3 2005 HIG UN Equity Hartford Financial Services Group Inc/Th 1 1 1 3 2006 HIG UN Equity Hartford Financial Services Group Inc/Th 1 1 1 3 2007 HIG UN Equity Hartford Financial Services Group Inc/Th 1 1 1 3 2008 HIG UN Equity Hartford Financial Services Group Inc/Th 1 1 1 3 2004 3605818Q UN Equity HCA Inc/DE 0 2005 3605818Q UN Equity HCA Inc/DE 0 2004 HPQ UN Equity Hewlett-Packard Co 1 1 1 1 1 1 1 1 1 1 1 1 12 2005 HPQ UN Equity Hewlett-Packard Co 1 1 1 1 1 1 1 1 1 1 1 1 12 2006 HPQ UN Equity Hewlett-Packard Co 1 1 1 1 1 1 1 1 1 1 1 1 12 2007 HPQ UN Equity Hewlett-Packard Co 1 1 1 1 1 1 1 1 1 1 1 1 12 2008 HPQ UN Equity Hewlett-Packard Co 1 1 1 1 1 1 1 1 1 1 1 1 12 2009 HPQ UN Equity Hewlett-Packard Co 1 1 1 1 1 1 1 1 1 1 1 1 12 69

2004 HSH UN Equity Hillshire Brands Co/The 1 1 1 1 1 1 1 1 1 9 2005 HSH UN Equity Hillshire Brands Co/The 1 1 1 1 1 1 1 1 1 9 2006 HSH UN Equity Hillshire Brands Co/The 1 1 1 1 1 1 1 1 1 9 2007 HSH UN Equity Hillshire Brands Co/The 1 1 1 1 1 1 1 1 1 9 2008 HSH UN Equity Hillshire Brands Co/The 1 1 1 1 1 1 1 1 1 9 2009 HSH UN Equity Hillshire Brands Co/The 1 1 1 1 1 1 1 1 1 9 2004 HNZ UN Equity HJ Heinz Co 1 1 1 1 1 1 1 1 1 9 2005 HNZ UN Equity HJ Heinz Co 1 1 1 1 1 1 1 1 1 9 2006 HNZ UN Equity HJ Heinz Co 1 1 1 1 1 1 1 1 1 9 2007 HNZ UN Equity HJ Heinz Co 1 1 1 1 1 1 1 1 1 9 2008 HNZ UN Equity HJ Heinz Co 1 1 1 1 1 1 1 1 1 9 2009 HNZ UN Equity HJ Heinz Co 1 1 1 1 1 1 1 1 1 9 2004 HD UN Equity Home Depot Inc/The 1 1 1 3 2005 HD UN Equity Home Depot Inc/The 1 1 1 3 2006 HD UN Equity Home Depot Inc/The 1 1 1 1 4 2007 HD UN Equity Home Depot Inc/The 1 1 1 1 1 1 1 1 8 2008 HD UN Equity Home Depot Inc/The 1 1 1 1 1 1 1 1 1 1 10 2009 HD UN Equity Home Depot Inc/The 1 1 1 1 1 1 1 1 1 1 10 2004 HON UN Equity Honeywell International Inc 1 1 2 2005 HON UN Equity Honeywell International Inc 1 1 1 3 2006 HON UN Equity Honeywell International Inc 1 1 1 1 4 2007 HON UN Equity Honeywell International Inc 1 1 1 1 1 1 6 2008HON UN Equity Honeywell International Inc 1 1 1 1 1 1 1 1 8 2009HON UN Equity Honeywell International Inc 1 1 1 1 1 1 1 1 8 2004INTC UQ Equity Intel Corp 1 1 1 1 1 1 1 1 1 1 1 1 1 13 2005INTC UQ Equity Intel Corp 1 1 1 1 1 1 1 1 1 1 1 1 1 1 14 2006INTC UQ Equity Intel Corp 1 1 1 1 1 1 1 1 1 1 1 1 1 1 14 2007 INTC UW Equity Intel Corp 1 1 1 1 1 1 1 1 1 1 1 1 1 1 14 2008 INTC UW Equity Intel Corp 1 1 1 1 1 1 1 1 1 1 1 1 1 1 14 2009 INTC UW Equity Intel Corp 1 1 1 1 1 1 1 1 1 1 1 1 1 1 14 2004 IBM UN Equity International Business Machines Corp 1 1 1 1 1 1 1 1 1 1 1 11 2005 IBM UN Equity International Business Machines Corp 1 1 1 1 1 1 1 1 1 1 1 11 2006 IBM UN Equity International Business Machines Corp 1 1 1 1 1 1 1 1 1 1 1 11 2007 IBM UN Equity International Business Machines Corp 1 1 1 1 1 1 1 1 1 1 1 11 2008 IBM UN Equity International Business Machines Corp 1 1 1 1 1 1 1 1 1 1 1 11 2009 IBM UN Equity International Business Machines Corp 1 1 1 1 1 1 1 1 1 1 1 11 2004 IP UN Equity International Paper Co 1 1 1 1 1 1 1 1 1 1 1 11 2005 IP UN Equity International Paper Co 1 1 1 1 1 1 1 1 1 1 1 11 2006 IP UN Equity International Paper Co 1 1 1 1 1 1 1 1 1 1 1 11 2007 IP UN Equity International Paper Co 1 1 1 1 1 1 1 1 1 1 1 11 2008 IP UN Equity International Paper Co 1 1 1 1 1 1 1 1 1 1 1 11 2004JNJ UN Equity Johnson & Johnson 1 1 1 1 1 1 1 1 1 9 2005JNJ UN Equity Johnson & Johnson 1 1 1 1 1 1 1 1 1 9 2006JNJ UN Equity Johnson & Johnson 1 1 1 1 1 1 1 1 1 1 10 2007JNJ UN Equity Johnson & Johnson 1 1 1 1 1 1 1 1 1 1 10 2008JNJ UN Equity Johnson & Johnson 1 1 1 1 1 1 1 1 1 1 10 2009JNJ UN Equity Johnson & Johnson 1 1 1 1 1 1 1 1 1 1 10 2004 JPM UN Equity JPMorgan Chase & Co 1 1 1 1 1 1 1 7 2005 JPM UN Equity JPMorgan Chase & Co 1 1 1 1 1 1 1 7 2006 JPM UN Equity JPMorgan Chase & Co 1 1 1 1 1 1 1 7 2007 JPM UN Equity JPMorgan Chase & Co 1 1 1 1 1 1 1 7 2008 JPM UN Equity JPMorgan Chase & Co 1 1 1 1 1 1 1 7 2009 JPM UN Equity JPMorgan Chase & Co 1 1 1 1 1 1 1 7 2004 LB UN Equity L Brands Inc 1 1 1 1 4 2005 LB UN Equity L Brands Inc 1 1 1 1 4 2006 LB UN Equity L Brands Inc 1 1 1 1 4 2007 LB UN Equity L Brands Inc 1 1 1 1 1 5 2004 LEHMQ UN Equity Lehman Brothers Holdings Inc 1 1 1 1 1 1 6 2005 LEHMQ UN Equity Lehman Brothers Holdings Inc 1 1 1 1 1 1 6 2006 LEHMQ UN Equity Lehman Brothers Holdings Inc 1 1 1 1 1 1 6 2007 LEHMQ UN Equity Lehman Brothers Holdings Inc 1 1 1 1 1 1 1 7 2008 LEHMQ UN Equity Lehman Brothers Holdings Inc 1 1 1 1 1 1 1 7 70

2009 LMT UN Equity Lockheed Martin Corp 1 1 2009 LOW UN Equity Lowe's Cos Inc 1 1 2009 MA UN Equity MasterCard Inc 1 1 1 1 1 1 1 7 2004 987200Q UN Equity May Department Stores Co/The 1 1 2004 MCD UN Equity McDonald's Corp 1 1 1 1 1 1 1 1 1 1 1 11 2005 MCD UN Equity McDonald's Corp 1 1 1 1 1 1 1 1 1 1 1 11 2006 MCD UN Equity McDonald's Corp 1 1 1 1 1 1 1 1 1 1 1 11 2007 MCD UN Equity McDonald's Corp 1 1 1 1 1 1 1 1 1 1 1 11 2008 MCD UN Equity McDonald's Corp 1 1 1 1 1 1 1 1 1 1 1 11 2009 MCD UN Equity McDonald's Corp 1 1 1 1 1 1 1 1 1 1 1 11 2004 MEDI UQ Equity Medimmune LLC 0 2005 MEDI UQ Equity Medimmune LLC 0 2006 MEDI UQ Equity Medimmune LLC 0 2004 MDT UN Equity Medtronic Inc 1 1 1 1 1 5 2005 MDT UN Equity Medtronic Inc 1 1 1 1 1 5 2006 MDT UN Equity Medtronic Inc 1 1 1 1 1 5 2007 MDT UN Equity Medtronic Inc 1 1 1 1 1 5 2008 MDT UN Equity Medtronic Inc 1 1 1 1 1 5 2009 MDT UN Equity Medtronic Inc 1 1 1 1 1 1 1 7 2004 MRK UN Equity Merck & Co Inc 1 1 1 1 1 1 1 1 1 1 1 11 2005 MRK UN Equity Merck & Co Inc 1 1 1 1 1 1 1 1 1 1 1 11 2006 MRK UN Equity Merck & Co Inc 1 1 1 1 1 1 1 1 1 1 1 1 12 2007 MRK UN Equity Merck & Co Inc 1 1 1 1 1 1 1 1 1 1 1 11 2008 MRK UN Equity Merck & Co Inc 1 1 1 1 1 1 1 1 1 1 1 11 2009 MRK UN Equity Merck & Co Inc 1 1 1 1 1 1 1 1 1 1 1 11 2004 MER UN Equity Merrill Lynch & Co Inc 1 1 1 1 1 1 1 7 2005 MER UN Equity Merrill Lynch & Co Inc 1 1 1 1 1 1 1 7 2006 MER UN Equity Merrill Lynch & Co Inc 1 1 1 1 1 1 1 7 2007 MER UN Equity Merrill Lynch & Co Inc 1 1 1 1 1 1 1 1 8 2009 MET UN Equity MetLife Inc 1 1 1 1 1 1 1 1 8 2004 MSFT UQ Equity Microsoft Corp 1 1 1 1 1 5 2005 MSFT UQ Equity Microsoft Corp 1 1 1 1 1 1 6 2006 MSFT UQ Equity Microsoft Corp 1 1 1 1 1 1 6 2007 MSFT UW Equity Microsoft Corp 1 1 1 1 1 1 1 1 8 2008 MSFT UW Equity Microsoft Corp 1 1 1 1 1 1 1 7 2009 MSFT UW Equity Microsoft Corp 1 1 1 1 1 1 1 1 8 2007 MDLZ UN Equity Mondelez International Inc 1 1 1 1 1 1 1 1 1 1 1 11 2008 MDLZ UN Equity Mondelez International Inc 1 1 1 1 1 1 1 1 1 1 10 2009 MDLZ UN Equity Mondelez International Inc 1 1 1 1 1 1 1 1 1 1 10 2009 MON UN Equity Monsanto Co 1 1 2004 MS UN Equity Morgan Stanley 1 1 1 1 1 1 6 2005 MS UN Equity Morgan Stanley 1 1 1 1 1 1 1 7 2006 MS UN Equity Morgan Stanley 1 1 1 1 1 1 1 7 2007 MS UN Equity Morgan Stanley 1 1 1 1 1 1 1 7 2008 MS UN Equity Morgan Stanley 1 1 1 1 1 1 1 7 2009 MS UN Equity Morgan Stanley 1 1 1 1 1 1 1 7 2004 MTLQQ UN Equity Motors Liquidation Co 1 1 0 1 1 1 1 1 1 1 1 1 1 12 2005 MTLQQ UN Equity Motors Liquidation Co 1 1 1 1 1 1 1 1 1 1 1 1 1 13 2006 MTLQQ UN Equity Motors Liquidation Co 1 1 1 1 1 1 1 1 1 1 1 1 1 13 2007 MTLQQ UN Equity Motors Liquidation Co 1 1 1 1 1 1 1 1 1 1 1 1 1 13 2008 MTLQQ UN Equity Motors Liquidation Co 1 1 1 1 1 1 1 1 1 1 1 1 1 13 2008NOV UN Equity National Oilwell Varco Inc 1 1 1 1 1 1 1 1 1 1 1 1 12 2009NOV UN Equity National Oilwell Varco Inc 1 1 1 1 1 1 1 1 1 1 1 1 12 2004 0203524D UN Equity National Semiconductor Corp 1 1 1 1 1 1 1 1 1 1 10 2005 0203524D UN Equity National Semiconductor Corp 1 1 1 1 1 1 1 1 1 1 1 1 12 2006 0203524D UN Equity National Semiconductor Corp 1 1 1 1 1 1 1 1 1 1 1 1 1 13 2007 0203524D UN Equity National Semiconductor Corp 1 1 1 1 1 1 1 1 1 1 1 1 1 13 2004 NXTL UQ Equity Nextel Communications Inc 0 2009NKE UN Equity NIKE Inc 1 1 1 1 1 1 1 1 1 1 1 1 12 2004 NSC UN Equity Norfolk Southern Corp 0 2005 NSC UN Equity Norfolk Southern Corp 0 2006 NSC UN Equity Norfolk Southern Corp 1 1 2 71

2007 NSC UN Equity Norfolk Southern Corp 1 1 2008 NSC UN Equity Norfolk Southern Corp 1 1 2009 NSC UN Equity Norfolk Southern Corp 1 1 2008 NYX UN Equity NYSE Euronext 1 1 1 1 1 5 2009 NYX UN Equity NYSE Euronext 1 1 1 1 4 2009OXY UN Equity Occidental Petroleum Corp 0 2004 OMX UN Equity OfficeMax Inc 1 1 2 2005 OMX UN Equity OfficeMax Inc 1 1 2 2006 OMX UN Equity OfficeMax Inc 1 1 2 2004 ORCL UQ Equity Oracle Corp 1 1 1 1 1 1 1 1 1 9 2005 ORCL UQ Equity Oracle Corp 1 1 1 1 1 1 1 1 1 9 2006 ORCL UQ Equity Oracle Corp 1 1 1 1 1 1 1 1 1 9 2007 ORCL UW Equity Oracle Corp 1 1 1 1 1 1 1 1 1 9 2008 ORCL UW Equity Oracle Corp 1 1 1 1 1 1 1 1 1 9 2009 ORCL UW Equity Oracle Corp 1 1 1 1 1 1 1 1 1 9 2004PEP UN Equity PepsiCo Inc 1 1 1 1 1 1 1 1 1 1 1 11 2005PEP UN Equity PepsiCo Inc 1 1 1 1 1 1 1 1 1 1 1 11 2006PEP UN Equity PepsiCo Inc 1 1 1 1 1 1 1 1 1 1 1 11 2007PEP UN Equity PepsiCo Inc 1 1 1 1 1 1 1 1 1 1 1 11 2008PEP UN Equity PepsiCo Inc 1 1 1 1 1 1 1 1 1 1 1 11 2009PEP UN Equity PepsiCo Inc 1 1 1 1 1 1 1 1 1 1 1 11 2004PFE UN Equity Pfizer Inc 1 1 1 1 1 1 1 1 1 1 1 11 2005PFE UN Equity Pfizer Inc 1 1 1 1 1 1 1 1 1 1 1 11 2006PFE UN Equity Pfizer Inc 1 1 1 1 1 1 1 1 1 1 1 11 2007PFE UN Equity Pfizer Inc 1 1 1 1 1 1 1 1 1 1 1 11 2008PFE UN Equity Pfizer Inc 1 1 1 1 1 1 1 1 1 1 1 11 2009PFE UN Equity Pfizer Inc 1 1 1 1 1 1 1 1 1 1 1 11 2008 PM UN Equity Philip Morris International Inc 1 1 2 2009 PM UN Equity Philip Morris International Inc 1 1 2004 PG UN Equity Procter & Gamble Co/The 1 1 1 1 1 1 1 1 1 1 10 2005 PG UN Equity Procter & Gamble Co/The 1 1 1 1 1 1 1 1 1 1 1 11 2006 PG UN Equity Procter & Gamble Co/The 1 1 1 1 1 1 1 1 1 1 1 11 2007 PG UN Equity Procter & Gamble Co/The 1 1 1 1 1 1 1 1 1 1 1 11 2008 PG UN Equity Procter & Gamble Co/The 1 1 1 1 1 1 1 1 1 1 1 11 2009 PG UN Equity Procter & Gamble Co/The 1 1 1 1 1 1 1 1 1 1 1 11 2009 QCOM UW Equity QUALCOMM Inc 1 1 1 1 1 1 1 1 1 1 1 1 1 1 14 2004 RSH UN Equity RadioShack Corp 1 1 2 2005 RSH UN Equity RadioShack Corp 1 1 2 2006 RSH UN Equity RadioShack Corp 1 1 1 3 2004 RTN UN Equity Raytheon Co 1 1 1 1 4 2005 RTN UN Equity Raytheon Co 1 1 1 1 4 2006 RTN UN Equity Raytheon Co 1 1 1 1 4 2007 RTN UN Equity Raytheon Co 1 1 1 1 4 2008 RTN UN Equity Raytheon Co 1 1 1 1 1 5 2009 RTN UN Equity Raytheon Co 1 1 1 1 1 5 2007 RF UN Equity Regions Financial Corp 0 2008 RF UN Equity Regions Financial Corp 0 2009 RF UN Equity Regions Financial Corp 0 2004ROK UN Equity Rockwell Automation Inc 1 1 1 1 1 1 1 1 1 1 1 1 1 13 2005ROK UN Equity Rockwell Automation Inc 1 1 1 1 1 1 1 1 1 1 1 1 1 13 2006ROK UN Equity Rockwell Automation Inc 1 1 1 1 1 1 1 1 1 1 1 1 1 13 2007ROK UN Equity Rockwell Automation Inc 1 1 1 1 1 1 1 1 1 1 1 1 1 13 2004 SLB UN Equity Schlumberger Ltd 1 1 1 3 2005 SLB UN Equity Schlumberger Ltd 1 1 1 3 2006 SLB UN Equity Schlumberger Ltd 1 1 1 1 4 2007 SLB UN Equity Schlumberger Ltd 1 1 1 1 4 2008 SLB UN Equity Schlumberger Ltd 1 1 1 1 4 2009 SLB UN Equity Schlumberger Ltd 1 1 1 1 4 2004 605555Q UN Equity Sears Roebuck and Co 1 1 2004 SO UN Equity Southern Co/The 0 2005 SO UN Equity Southern Co/The 0 2006 SO UN Equity Southern Co/The 1 1 72

2007 SO UN Equity Southern Co/The 0 2008 SO UN Equity Southern Co/The 0 2009 SO UN Equity Southern Co/The 0 2006 0848680D UN Equity Sprint Communications Inc 0 2007 0848680D UN Equity Sprint Communications Inc 0 2008 0848680D UN Equity Sprint Communications Inc 0 2009 0848680D UN Equity Sprint Communications Inc 0 2006 TGT UN Equity Target Corp 1 1 2007 TGT UN Equity Target Corp 1 1 2008 TGT UN Equity Target Corp 1 1 2009 TGT UN Equity Target Corp 1 1 2004TXN UN Equity Texas Instruments Inc 1 1 1 1 1 1 1 1 1 1 1 11 2005TXN UN Equity Texas Instruments Inc 1 1 1 1 1 1 1 1 1 1 1 11 2006TXN UN Equity Texas Instruments Inc 1 1 1 1 1 1 1 1 1 1 1 11 2007TXN UN Equity Texas Instruments Inc 1 1 1 1 1 1 1 1 1 1 1 11 2008TXN UN Equity Texas Instruments Inc 1 1 1 1 1 1 1 1 1 1 1 11 2009TXN UN Equity Texas Instruments Inc 1 1 1 1 1 1 1 1 1 1 1 11 2004 TWX UN Equity Time Warner Inc 1 1 1 1 1 5 2005 TWX UN Equity Time Warner Inc 1 1 1 1 1 5 2006 TWX UN Equity Time Warner Inc 1 1 1 1 1 5 2007 TWX UN Equity Time Warner Inc 1 1 1 1 4 2008 TWX UN Equity Time Warner Inc 1 1 1 1 1 1 6 2009 TWX UN Equity Time Warner Inc 1 1 1 1 1 1 6 2004 TOY UN Equity Toys R US Inc/Old 1 1 1 1 1 1 1 1 1 9 2004 TYC UN Equity Tyco International Ltd 1 1 1 1 1 1 1 1 8 2005 TYC UN Equity Tyco International Ltd 1 1 1 1 1 1 1 1 8 2006 TYC UN Equity Tyco International Ltd 1 1 1 1 1 1 1 1 8 2007 TYC UN Equity Tyco International Ltd 1 1 1 1 1 1 1 1 8 2008 TYC UN Equity Tyco International Ltd 1 1 1 1 1 1 1 1 1 9 2004 UIS UN Equity Unisys Corp 0 2005 UIS UN Equity Unisys Corp 0 2006 UIS UN Equity Unisys Corp 0 2006 UPS UN Equity United Parcel Service Inc 1 1 1 1 1 1 1 1 8 2007 UPS UN Equity United Parcel Service Inc 1 1 1 1 1 1 1 1 8 2008 UPS UN Equity United Parcel Service Inc 1 1 1 1 1 1 1 1 8 2009 UPS UN Equity United Parcel Service Inc 1 1 1 1 1 1 1 1 8 2004 UTX UN Equity United Technologies Corp 1 1 1 1 1 1 1 1 1 1 1 11 2005 UTX UN Equity United Technologies Corp 1 1 1 1 1 1 1 1 1 1 1 11 2006 UTX UN Equity United Technologies Corp 1 1 1 1 1 1 1 1 1 1 1 11 2007 UTX UN Equity United Technologies Corp 1 1 1 1 1 1 1 1 1 1 1 11 2008 UTX UN Equity United Technologies Corp 1 1 1 1 1 1 1 1 1 1 1 11 2009 UTX UN Equity United Technologies Corp 1 1 1 1 1 1 1 1 1 1 1 11 2008 UNH UN Equity UnitedHealth Group Inc 0 2009 UNH UN Equity UnitedHealth Group Inc 0 2004 USB UN Equity US Bancorp/MN 0 2005 USB UN Equity US Bancorp/MN 0 2006 USB UN Equity US Bancorp/MN 0 2007 USB UN Equity US Bancorp/MN 0 2008 USB UN Equity US Bancorp/MN 0 2009 USB UN Equity US Bancorp/MN 0 2004 VZ UN Equity Verizon Communications Inc 1 1 2 2005 VZ UN Equity Verizon Communications Inc 1 1 2 2006 VZ UN Equity Verizon Communications Inc 1 1 2 2007 VZ UN Equity Verizon Communications Inc 1 1 2 2008 VZ UN Equity Verizon Communications Inc 1 1 2 2009 VZ UN Equity Verizon Communications Inc 1 1 2 2007 0966288D UN Equity Wachovia Corp 0 2009 WAG UN Equity Walgreen Co 1 1 2004 WMT UN Equity Wal-Mart Stores Inc 1 1 1 1 1 1 1 1 1 1 1 1 12 2005 WMT UN Equity Wal-Mart Stores Inc 1 1 1 1 1 1 1 1 1 1 1 1 12 2006 WMT UN Equity Wal-Mart Stores Inc 1 1 1 1 1 1 1 1 1 1 1 1 12 2007 WMT UN Equity Wal-Mart Stores Inc 1 1 1 1 1 1 1 1 1 1 1 1 12

2008 WMT UN Equity Wal-Mart Stores Inc 1 1 1 1 1 1 1 1 1 1 1 1 12 2009 WMT UN Equity Wal-Mart Stores Inc 1 1 1 1 1 1 1 1 1 1 1 1 12 2004 DIS UN Equity Walt Disney Co/The 1 1 1 1 1 1 1 7 2005 DIS UN Equity Walt Disney Co/The 1 1 1 1 1 1 1 7 2006 DIS UN Equity Walt Disney Co/The 1 1 1 1 1 1 1 1 8 2007 DIS UN Equity Walt Disney Co/The 1 1 1 1 1 1 1 1 8 2008 DIS UN Equity Walt Disney Co/The 1 1 1 1 1 1 1 1 8 2009 DIS UN Equity Walt Disney Co/The 1 1 1 1 1 1 1 1 8 2004 WFC UN Equity Wells Fargo & Co 0 2005 WFC UN Equity Wells Fargo & Co 0 2006 WFC UN Equity Wells Fargo & Co 0 2007 WFC UN Equity Wells Fargo & Co 0 2008 WFC UN Equity Wells Fargo & Co 0 2009 WFC UN Equity Wells Fargo & Co 0 2004 WY UN Equity Weyerhaeuser Co 0 2005 WY UN Equity Weyerhaeuser Co 0 2006 WY UN Equity Weyerhaeuser Co 1 1 2007 WY UN Equity Weyerhaeuser Co 1 1 1 1 4 2008 WY UN Equity Weyerhaeuser Co 1 1 1 1 1 1 1 1 8 2009 WY UN Equity Weyerhaeuser Co 1 1 1 1 1 1 1 1 1 1 10 2004 WMB UN Equity Williams Cos Inc/The 0 2005 WMB UN Equity Williams Cos Inc/The 0 2006 WMB UN Equity Williams Cos Inc/The 0 2007 WMB UN Equity Williams Cos Inc/The 0 2008 WMB UN Equity Williams Cos Inc/The 0 2009 WMB UN Equity Williams Cos Inc/The 0 2004 XRX UN Equity Xerox Corp 1 1 1 1 1 1 1 1 1 9 2005 XRX UN Equity Xerox Corp 1 1 1 1 1 1 1 1 1 9 2006 XRX UN Equity Xerox Corp 1 1 1 1 1 1 1 1 1 9 2007 XRX UN Equity Xerox Corp 1 1 1 1 1 1 1 1 1 9 2008 XRX UN Equity Xerox Corp 1 1 1 1 1 1 1 1 1 9 2009 XRX UN Equity Xerox Corp 1 1 1 1 1 1 1 1 1 9

Appendix 3: Claus/Thomas Expected Return Forecast Analysts’ earnings estimates and company book values are extracted from I/B/E/S (the Institutional Brokers’ Estimate System). To estimate individual expected stock return, the actual fiscal year-end book value of the company disclosed prior to the release of earnings estimate is used. For each company, I/B/E/S provides a number of analyst forecasts. The median of the forecasts is used for each of the next five fiscal years to calculate future abnormal- earnings and future book values. In the few cases where fiscal year 4 and 5 earnings forecasts are not available, these are estimated by extrapolation based on the growth rates derived from fiscal year 2 and 3. As a part of the terminal value calculation, expected inflation is estimated by subtracting the 10-year Treasury yield from yield of 10- year inflation-linked government bonds to approximate inflation over the next 10 years. The tables on the following pages indicate the expected returns for each of the S&P 100 stocks as of March 18, 2010 and as of March 30, 2007. These two dates are selected to correspond to the 2010 scorecard and the 2007 scorecard. The expected return for the S&P 100 index is the weighted average of individual S&P 100 company returns. And the equity risk premium of the index is the difference between the stock expected return and the 3-month T-bill return.

As of March 18, 2010

Expected Return for S&P 100 Constituents Expected 3-month T- Equity Risk Rank by Index Name Ticker Return Bill Premium Scorecard Weight 3/18/2010 3/18/2010 3/18/2010 1 Intel Corp INTC 1.84 10.24% 0.16% 10.08% 2 Dow Chemical Co/The DOW 0.49 13.54% 0.16% 13.38% 3 EI du Pont de Nemours DD 0.54 9.33% 0.16% 9.17% 4 Cisco Systems Inc CSCO 2.11 9.14% 0.16% 8.98% 5 Caterpillar Inc CAT 0.61 11.48% 0.16% 11.32% 6 Xerox Corp XRX 0.20 15.17% 0.16% 15.01% 7 Baxter International I BAX 0.40 9.66% 0.16% 9.50% 8 Coca-Cola Co/The KO 1.96 7.98% 0.16% 7.82% 9 Pfizer Inc PFE 1.95 11.29% 0.16% 11.13% 10 Ford Motor Co F 0.62 15.35% 0.16% 15.19% 11 Alcoa Inc AA 0.19 9.12% 0.16% 8.96% 12 Boeing Co/The BA 0.78 8.28% 0.16% 8.12% 13 NIKE Inc NKE 0.45 7.16% 0.16% 7.00% 14 General Electric Co GE 2.71 8.89% 0.16% 8.73% 15 Avon Products Inc AVP 0.20 9.44% 0.16% 9.28% 16 Procter & Gamble Co/Th PG 2.91 7.37% 0.16% 7.21% 17 International Business IBM 2.69 9.95% 0.16% 9.79% 18 Abbott Laboratories ABT 1.19 9.30% 0.16% 9.14% 19 QUALCOMM Inc QCOM 0.95 8.36% 0.16% 8.20% 20 Dell Inc DELL 0.37 9.25% 0.16% 9.09% 21 McDonald's Corp MCD 1.21 8.35% 0.16% 8.19% 22 Wal-Mart Stores Inc WMT 1.77 9.14% 0.16% 8.98% 23 Merck & Co Inc (Schering-Plough) MRK 1.75 10.34% 0.16% 10.18% 24 PepsiCo Inc/NC PEP 1.69 9.29% 0.16% 9.13% 25 EMC Corp/Massachusetts EMC 0.62 7.31% 0.16% 7.15% 26 Honeywell Internationa HON 0.51 11.37% 0.16% 11.21% 27 HJ Heinz Co HNZ 0.23 7.34% 0.16% 7.18% 28 Exxon Mobil Corp XOM 4.73 9.30% 0.16% 9.14% 29 ConocoPhillips COP 1.29 11.00% 0.16% 10.84% 30 Microsoft Corp MSFT 3.09 9.54% 0.16% 9.38% 31 Hewlett-Packard Co HPQ 1.77 8.89% 0.16% 8.73% 32 Oracle Corp ORCL 1.41 9.03% 0.16% 8.87% 33 Schlumberger Ltd SLB 1.15 7.29% 0.16% 7.13% 34 3M Co MMM 0.89 8.40% 0.16% 8.24% 35 Kraft Foods Inc KFT 0.83 9.21% 0.16% 9.05% 36 Colgate-Palmolive Co CL 0.64 8.12% 0.16% 7.96% 37 Time Warner Inc TWX 0.59 9.53% 0.16% 9.37% 38 Morgan Stanley MS 0.58 11.47% 0.16% 11.31% 39 News Corp NWSA 0.49 9.10% 0.16% 8.94% 40 Sara Lee Corp SLE 0.16 9.11% 0.16% 8.95% 41 Campbell Soup Co CPB 0.11 8.50% 0.16% 8.34% 42 Weyerhaeuser Co WY 0.14 7.04% 0.16% 6.88% 43 Chevron Corp CVX 2.41 11.25% 0.16% 11.09% 44 Philip Morris Internat PM 1.37 9.55% 0.16% 9.39% 45 Walt Disney Co/The DIS 1.07 8.97% 0.16% 8.81% 46 United Parcel Service UPS 0.98 8.12% 0.16% 7.96% 47 Bristol-Myers Squibb C BMY 0.68 7.85% 0.16% 7.69% 48 United Technologies Co UTX 0.99 9.09% 0.16% 8.93% 49 Johnson & Johnson JNJ 2.61 9.12% 0.16% 8.96% 50 Goldman Sachs Group In GS 1.14 12.71% 0.16% 12.55%

Expected Return for S&P 100 Constituents - Cont'd Expected 3-month T- Equity Risk Rank by Index Name Ticker Return Bill Premium Scorecard Weight 3/18/2010 3/18/2010 3/18/2010 51 Amazon.com Inc AMZN 0.68 6.49% 0.16% 6.33% 52 MetLife Inc MET 0.54 15.50% 0.16% 15.34% 53 FedEx Corp FDX 0.41 7.85% 0.16% 7.69% 54 Apple Inc AAPL 3.68 6.41% 0.16% 6.25% 55 Bank of America Corp BAC 2.51 14.05% 0.16% 13.89% 56 Citigroup Inc C 1.39 13.30% 0.16% 13.14% 57 American Express Co AXP 0.78 9.81% 0.16% 9.65% 58 Texas Instruments Inc TXN 0.49 10.65% 0.16% 10.49% 59 Monsanto Co MON 0.45 9.59% 0.16% 9.43% 60 Halliburton Co HAL 0.35 9.68% 0.16% 9.52% 61 Google Inc GOOG 1.91 8.08% 0.16% 7.92% 62 Medtronic Inc MDT 0.68 9.19% 0.16% 9.03% 63 UnitedHealth Group Inc UNH 0.58 10.25% 0.16% 10.09% 64 Mastercard Inc MA 0.32 9.86% 0.16% 9.70% 65 AT&T Inc T 2.43 9.78% 0.16% 9.62% 66 Home Depot Inc HD 0.90 9.01% 0.16% 8.85% 67 National Oilwell Varco NOV 0.25 8.69% 0.16% 8.53% 68 Freeport-McMoRan Coppe FCX 0.49 7.98% 0.16% 7.82% 69 JPMorgan Chase & Co JPM 2.46 11.68% 0.16% 11.52% 70 Bank of New York Mello BK 0.51 10.56% 0.16% 10.40% 71 Verizon Communications VZ 1.30 8.97% 0.16% 8.81% 72 Devon Energy Corp DVN 0.49 11.67% 0.16% 11.51% 73 Baker Hughes Inc BHI 0.30 8.68% 0.16% 8.52% 74 Amgen Inc AMGN 0.86 10.73% 0.16% 10.57% 75 Target Corp TGT 0.65 10.11% 0.16% 9.95% 76 Gilead Sciences Inc GILD 0.49 10.37% 0.16% 10.21% 77 Costco Wholesale Corp COST 0.41 6.81% 0.16% 6.65% 78 Berkshire Hathaway Inc BRK/B 1.99 5.50% 0.16% 5.34% 79 CVS Caremark Corp CVS 0.72 10.65% 0.16% 10.49% 80 Lowe's Cos Inc LOW 0.57 9.29% 0.16% 9.13% 81 Walgreen Co WAG 0.48 9.09% 0.16% 8.93% 82 Lockheed Martin Corp LMT 0.40 12.79% 0.16% 12.63% 83 General Dynamics Corp GD 0.40 10.44% 0.16% 10.28% 84 Raytheon Co RTN 0.32 8.01% 0.16% 7.85% 85 American Electric Powe AEP 0.25 7.70% 0.16% 7.54% 86 NYSE Euronext NYX 0.12 13.01% 0.16% 12.85% 87 Wells Fargo & Co WFC 2.36 11.01% 0.16% 10.85% 88 Occidental Petroleum C OXY 1.10 9.68% 0.16% 9.52% 89 Comcast Corp CMCSA 0.83 12.00% 0.16% 11.84% 90 US Bancorp USB 0.74 11.21% 0.16% 11.05% 91 Altria Group Inc MO 0.68 10.68% 0.16% 10.52% 92 Southern Co SO 0.44 8.58% 0.16% 8.42% 93 Exelon Corp EXC 0.43 5.99% 0.16% 5.83% 94 Norfolk Southern Corp NSC 0.34 10.11% 0.16% 9.95% 95 Capital One Financial COF 0.30 10.57% 0.16% 10.41% 96 Allstate Corp/The ALL 0.26 14.03% 0.16% 13.87% 97 Sprint Nextel Corp S 0.23 4.61% 0.16% 4.45% 98 Entergy Corp ETR 0.23 8.03% 0.16% 7.87% 99 Williams Cos Inc/The WMB 0.19 6.31% 0.16% 6.15% 100 Regions Financial Corp RF 0.14 14.43% 0.16% 14.27%

As of March 30, 2007

Expected Return for S&P 100 Constituents Expected 3-month T- Equity Risk Rank by Index Name Ticker Return Bill Premium Scorecard Weight 3/30/2007 3/30/2007 3/30/2007 1 Motorola Inc MOT 0.48 8.75% 5.03% 3.72% 2 EI du Pont de Nemours & Co DD 0.55 6.59% 5.03% 1.56% 3 Boeing Co/The BA 0.89 8.08% 5.03% 3.05% 4 General Electric Co GE 4.63 7.45% 5.03% 2.42% 5 Microsoft Corp MSFT 2.92 7.77% 5.03% 2.74% 6 Eli Lilly & Co LLY 0.65 6.89% 5.03% 1.86% 7 Pfizer Inc PFE 2.11 7.23% 5.03% 2.20% 8 Dow Chemical Co/The DOW 0.50 5.01% 5.03% -0.02% 9 Intel Corp INTC 1.62 7.64% 5.03% 2.61% 10 QUALCOMM Inc QCOM 0.85 6.35% 5.03% 1.32% 11 Corning Inc GLW 0.47 8.07% 5.03% 3.04% 12 Johnson & Johnson JNJ 2.10 7.24% 5.03% 2.21% 13 International Business Machines Corp IBM 1.69 8.63% 5.03% 3.60% 14 PepsiCo Inc/NC PEP 1.24 7.03% 5.03% 2.00% 15 Abbott Laboratories ABT 0.97 8.28% 5.03% 3.25% 16 Oracle Corp ORCL 0.92 7.38% 5.03% 2.35% 17 Wyeth WYE 0.91 6.69% 5.03% 1.66% 18 Dell Inc DELL 0.76 10.89% 5.03% 5.86% 19 3M Co MMM 0.73 7.48% 5.03% 2.45% 20 Bristol-Myers Squibb Co BMY 0.73 7.38% 5.03% 2.35% 21 Caterpillar Inc CAT 0.59 11.87% 5.03% 6.84% 22 Procter & Gamble Co/The PG 2.26 6.18% 5.03% 1.15% 23 Cisco Systems Inc CSCO 1.99 7.78% 5.03% 2.75% 24 Altria Group Inc MOT 1.73 5.43% 5.03% 0.40% 25 ConocoPhillips COP 1.51 9.91% 5.03% 4.88% 26 Hewlett-Packard Co HQP 1.37 8.03% 5.03% 3.00% 27 Coca-Cola Co/The KO 1.24 7.01% 5.03% 1.98% 28 Schlumberger Ltd SLB 1.18 9.15% 5.03% 4.12% 29 McDonald's Corp MCD 0.71 6.65% 5.03% 1.62% 30 Kraft Foods Inc KFT 0.66 5.29% 5.03% 0.26% 31 News Corp NWSA 0.58 5.46% 5.03% 0.43% 32 Schering-Plough Corp USA SPG 0.53 6.81% 5.03% 1.78% 33 Anheuser-Busch Cos Inc BUD 0.47 8.26% 5.03% 3.23% 34 EMC Corp/Massachusetts EMC 0.45 5.96% 5.03% 0.93% 35 Emerson Electric Co EMR 0.44 6.90% 5.03% 1.87% 36 Baxter International Inc BAX 0.43 7.58% 5.03% 2.55% 37 Colgate-Palmolive Co CL 0.39 7.95% 5.03% 2.92% 38 Bank of America Corp BAC 2.55 11.17% 5.03% 6.14% 39 Wal-Mart Stores Inc WMT 1.37 7.60% 5.03% 2.57% 40 Honeywell International Inc HON 0.52 7.61% 5.03% 2.58% 41 Alcoa Inc AA 0.41 7.99% 5.03% 2.96% 42 Citigroup Inc C 2.98 11.19% 5.03% 6.16% 43 American International Group Inc AIG 2.14 11.87% 5.03% 6.84% 44 JPMorgan Chase & Co JPM 1.94 7.42% 5.03% 2.39% 45 Merck & Co Inc MRK 1.27 7.53% 5.03% 2.50% 46 Prudential Financial Inc PRU 0.53 8.12% 5.03% 3.09% 47 FedEx Corp FDX 0.40 6.89% 5.03% 1.86% 48 Halliburton Co HAL 0.37 9.94% 5.03% 4.91% 49 Exxon Mobil Corp XOM 5.55 10.52% 5.03% 5.49% 50 AT&T Inc Ticker 3.01 5.98% 5.03% 0.95%

Expected Return for S&P 100 Constituents - Cont'd Expected 3-month T- Equity Risk Rank by Index Name Ticker Return Bill Premium Scorecard Weight 3/30/2007 3/30/2007 3/30/2007 51 Chevron Corp CVX 2.13 9.19% 5.03% 4.16% 52 Goldman Sachs Group Inc/The GS 1.04 6.54% 5.03% 1.51% 53 Home Depot Inc HD 0.91 8.90% 5.03% 3.87% 54 United Technologies Corp UTX 0.83 7.60% 5.03% 2.57% 55 Walt Disney Co/The DIS 0.80 7.98% 5.03% 2.95% 56 Texas Instruments Inc TXN 0.63 7.91% 5.03% 2.88% 57 Monsanto Co MON 0.43 5.96% 5.03% 0.93% 58 Devon Energy Corp DVN 0.41 6.87% 5.03% 1.84% 59 Google Inc GOOG 1.34 7.56% 5.03% 2.53% 60 Apple Inc APPL 1.24 7.27% 5.03% 2.24% 61 Time Warner Inc TWX 0.94 6.41% 5.03% 1.38% 62 United Parcel Service Inc UPS 0.91 7.29% 5.03% 2.26% 63 Merrill Lynch & Co Inc MER 0.86 7.95% 5.03% 2.92% 64 American Express Co AXP 0.85 8.74% 5.03% 3.71% 65 Medtronic Inc MDT 0.70 6.83% 5.03% 1.80% 66 MetLife Inc MET 0.56 7.40% 5.03% 2.37% 67 eBay Inc EBAY 0.43 6.59% 5.03% 1.56% 68 Lehman Brothers Holdings Inc LEHMQ 0.47 8.23% 5.03% 3.20% 69 Yahoo! Inc YHOO 0.39 4.58% 5.03% -0.45% 70 Target Corp TGT 0.64 7.80% 5.03% 2.77% 71 Wachovia Corp WB 1.15 7.40% 5.03% 2.37% 72 Amgen Inc AMGN 0.75 9.28% 5.03% 4.25% 73 Sprint Nextel Corp S 0.70 4.27% 5.03% -0.76% 74 CVS Caremark Corp CVS 0.66 7.67% 5.03% 2.64% 75 Lowe's Cos Inc LOW 0.54 8.69% 5.03% 3.66% 76 Walgreen Co WAG 0.51 7.09% 5.03% 2.06% 77 Gilead Sciences Inc GILD 0.43 4.40% 5.03% -0.63% 78 Verizon Communications Inc VZ 1.41 7.12% 5.03% 2.09% 79 Wells Fargo & Co WFC 1.38 8.18% 5.03% 3.15% 80 Comcast Corp CMCSA 1.03 5.51% 5.03% 0.48% 81 UnitedHealth Group Inc UNH 0.81 8.15% 5.03% 3.12% 82 Federal National Mortgage Association FNMA 0.75 6.59% 5.03% 1.56% 83 US Bancorp USB 0.67 7.57% 5.03% 2.54% 84 WellPoint Inc WLP 0.58 7.22% 5.03% 2.19% 85 Exelon Corp EXC 0.57 8.62% 5.03% 3.59% 86 Occidental Petroleum Corp OXY 0.57 5.09% 5.03% 0.06% 87 Marathon Oil Corp MRO 0.48 4.36% 5.03% -0.67% 88 Valero Energy Corp VLO 0.48 6.96% 5.03% 1.93% 89 Federal Home Loan Mortgage Corp FMCC 0.47 8.80% 5.03% 3.77% 90 Washington Mutual Inc WAMUQ 0.45 3.34% 5.03% -1.69% 91 Allstate Corp/The ALL 0.44 9.28% 5.03% 4.25% 92 Travelers Cos Inc/The TRV 0.42 6.87% 5.03% 1.84% 93 Lockheed Martin Corp LMT 0.39 7.29% 5.03% 2.26% 94 Capital One Financial Corp COF 0.38 8.41% 5.03% 3.38% 95 General Dynamics Corp GD 0.37 6.34% 5.03% 1.31% 96 Hartford Financial Services Group Inc HIG 0.37 7.51% 5.03% 2.48% 97 Union Pacific Corp UNP 0.37 5.33% 5.03% 0.30% 98 Freeport-McMoRan Copper & Gold Inc FCX 0.36 7.43% 5.03% 2.40% 99 Energy Future Holdings Corp TXU 0.36 8.62% 5.03% 3.59% 100 SunTrust Banks Inc STI 0.36 6.53% 5.03% 1.50%