TRADING BIOPHARMACEUTICAL STOCKS AFTER CATASTROPHIC ONE-DAY DECLINES
A THESIS
Presented to
The Faculty of the Department of Economics and Business
The Colorado College
In Partial Fulfillment of the Requirements for the Degree
Bachelor of Arts
By
Daniel Elliott Ward
December 2012 TRADING BIOPHARMACEUTICAL STOCKS AFTER CATASTROPIC ONE-DAY DECLINES
Daniel Elliott Ward
December 2012
Mathematical Economics
Abstract
This thesis analyzes volatility of small capitalization biopharmaceutical stocks after significant one-day price drops. Stock performances after one-day declines of ten percent or greater for companies in the NASDAQ Biotechnology Index were gathered from 2011-2012 to test for evidence of market overreaction. While no substantial evidence was found for overreaction, long-term performance suggested that traders underreact during the initial stock drop, with underreaction most prevalent in stocks seeing an initial one-day drop of at least twenty percent. Overreaction only appeared present when companies saw a stock drop due to negative pipeline results.
KEYWORDS: (Efficient Market Hypothesis, Overreaction Hypothesis, Underreaction, Biopharmaceuticals, Stock Market Analysis)
ON MY HONOR, I HAVE NEITHER GIVEN NOR RECEIVED UNAUTHORIZED AID ON THIS THESIS
TABLE OF CONTENTS
ABSTRACT ii 1 INTRODUCTION...... 1
2 LITERATURE REVIEW...... 3 2.1 History…………………………...... 3 2.2 The Efficient Market Hypothesis...... 5 2.2.1 Size Effect...... 6 2.2.2 January Effect...... 7 2.2.3 Post Earnings Announcement Drift ...... 7 2.2.4 Stock Splits and Reverse Splits ...... 8 2.2.5 IPOs...... 9 2.3 Support for the Overreaction Hypothesis...... 10 2.4 Challenges to the Overreaction Hypothesis...... 13 2.5 Stock Selection...... 16 2.5.1 Technical Analysis...... 16 2.5.2 Fundamental Analysis...... 17 2.6 Investing in Biopharmaceutical Stocks...... 18 2.7 Summary...... 21
3 THEORY...... 23
4 METHODOLOGY...... 25 4.1 Data Selection……………………...... 25 4.1 Method……………………...... 27
5 EMPIRICAL RESULTS...... 32
6 CONCLUSION...... 47 SOURCES CONSULTED...... 50
LIST OF TABLES
5.1 Summary Statistics…………………………………………………………... 34
5.2 Two-Day Abnormal Returns: Testing for Significance…………………….. 34
5.3 Six-Month Abnormal Returns: Testing for Significance…………………… 37
5.4 Testing for Difference in Means: News Form and Drop Size……………… 38
5.5 Summary Statistics with Removal of Industry Crash……………………….. 41
5.6 Two-Day Abnormal Returns with Removal of Industry Crash…...….……... 41
5.7 Six-Month Abnormal Returns with Removal of Industry Crash…...……….. 44
5.8 Testing for Difference in Means with Industry Crash Removed……………. 44
5.9 Bootstrapped Skew-Adjusted T-Test for Six-Month Abnormal Returns……. 45
LIST OF FIGURES
1.1 KERX Stock Performance…………………..………..………………………. 1
1.2 ECYT Stock Performance…………………...………..………………………. 1
1.3 CHTP Stock Performance…………………...………..………………………. 2
5.1 Distribution of Six-Month Abnormal Returns………..………………………. 35
5.2 Two-Day Abnormal Returns………………………………………………….. 40
5.3 Six-Month Abnormal Returns……….………………………………………... 43
ACKNOWLEDGEMENTS
I would like to thank Professor Jim Parco for always being available and agreeing to work with me on an independent study concerning stock market volatility, which allowed me to put forth the best final product possible.
I would also like to thank my parents for their unwavering support during my thesis and throughout my entire academic career
CHAPTER I
INTRODUCTION
On April 2nd, 2012, the stock of Keryx Biopharmaceuticals (KERX) dropped sixty-five percent due to poor results from a Phase 3 clinical trial for Perifosine, a compound geared at treating colorectal cancer. In the next six months, KERX stock saw a fifty-six percent gain fueled by anticipation for trial results for the phosphate binder
Zerenex. On December 13th, 2011, the stock of biopharmaceutical company Endocyte
(ECYT) dropped sixty-four percent in response to poor Phase 2 results for diagnostic imaging agent EC20, yet saw over one-hundred percent appreciation over the next six months.
Figure 1.1 Figure 1.2
KERX STOCK PERFORMANCE ECYT STOCK PERFORMANCE
Source: Stockcharts.com Source: Stockcharts.com
1 Catastrophic one-day stock collapses are not uncommon in the biopharma industry, yet stocks do not always experience significant appreciation after plummeting from bad news. After receiving numerous objections to its New Drug Application from the FDA,
Chelsea Therapeutics stock (CHTP) dropped thirty-eight percent on February 13th, 2012 and saw its stock decline another sixty-seven percent from its February 13th closing price over the next six months.
FIGURE 1.3
CHTP STOCK PERFORMANCE
Source: Stockcharts.com
Typical financial metrics used for valuation are irrelevant as most small capitalization biopharmaceutical companies have little or no revenue. Investors place huge bets for or against a company based on the perceived chance a drug will continue to progress through the FDA process, which causes biopharma stocks to see the biggest price gyrations in the market and makes them high-risk/high-reward investments. This high volatility offers opportunities for enormous profits for well-timed trades. This begs the question: Can a trader implement a strategy to achieve consistent abnormal profits from taking a position after these large price drops? This paper begins to answer this question by investigating whether the market overreacts on these single-day sharp price declines.
2
CHAPTER II
LITERATURE REVIEW
History
Equity trading in the United States began in 1790 with the establishment of the
Board of Brokers in the city of Philadelphia (Vacca, 2006). The New York Stock
Exchange did not emerge until 1817 when a constitution established the “New York
Stock & Exchange Board” (Terrell, 2010). Trading and investing in stocks became steadily more popular with a drastic increase in stock ownership occurring throughout the
20th century. In a 1952 survey, the New York Stock Exchange (NYSE) reported that more than six million Americans owned stock, a number that rose sharply to a total of more than fifty-one million Americans by 1991 (NYSE Euronext, 2012). Improvements in equity trading have facilitated this dramatic increase in American stock ownership. In
1976, the NYSE began allowing odd lots (transactions with less than 100 shares), which made it easier for people with less money to invest in companies with higher share prices like Google or Apple (NYSE Euronext, 2012). By 2001, the NYSE eliminated fractional trading and moved to a decimal trading system, which increased liquidity and further improved trading on the exchange (NYSE Euronext, 2012).
These improvements have not alleviated some of the factors that have historically scared a number of prospective investors away from the exchanges. One of the major factors that has caused people to avoid equities has been the market crashes, with the
3 worst occurring between 1929 and 1932 when the Dow Jones Industrial Average (DJIA) dropped 89% (NYSE Euronext, 2012). Ten years ago, the crash termed the “dot-com bubble” was triggered by the overvaluation of technology companies with little or no earnings. Another crash occurred eight years later with the DJIA losing over half its value in a seventeen-month period (Google Finance, 2012). Technology has exacerbated crashes in recent years as it has become more involved in the trading process. Consider the Flash Crash of 2010 where the DJIA dropped over six hundred points in only five minutes with the help of high-frequency traders (Lauricella, 2012). Technical glitches have also inhibited trading with the August 2012 “technology breakdown” by market- making company Knight Capital Group causing erratic trading and extremely abnormal volatility in approximately 150 stocks (Farrell, 2012).
Even with all these issues, the stock market has never lost money in any twenty year period and has only lost money in a ten year period once since the Great Depression
(Lichtenfeld, 2012). These consistent returns have led to 54% of the American population owning either individual stock or stock mutual funds (Jacobe, 2011). With this widespread ownership, the equity markets have attracted a significant amount of scholarly research. This chapter reviews the Efficient Market Hypothesis (EMH) and the challenges that followed its discovery (Fama, 1970). The Overreaction Hypothesis (OH), which centers on the idea that investors may overreact to good (bad) news creating opportunity for traders to sell short (buy) stocks that are overvalued (undervalued), is then addressed from both a short-term and long-term time horizon (De Bondt and Thaler,
1985). An overview of stock selection methodology and tendencies in biopharmaceutical
4 (biopharma) stocks follows to set the stage for the research involving abnormal returns available for trading biopharma stocks after major one-day price declines.
The Efficient Market Hypothesis
The Efficient Market Hypothesis was introduced by Eugene Fama’s groundbreaking research forty years ago and “was defined as a market which adjusts rapidly to new information” (Beechey et al., 2000, 5). In a 1991 follow-up study, he defined the term as markets where “security prices fully reflect all available information”
(Fama 1575). The three common forms of the EMH each propose different views of market efficiency. The weak form efficiency suggests that investors cannot earn long-run abnormal returns and that stock prices follow a “random walk” (Fama, 1970). The semi- strong form efficiency further asserts that no excess returns can be gained in the short term as prices adjust to new information instantaneously while the strong form efficiency declares that excess returns are impossible for any market participants, even market makers and company insiders (Hagin, 1979). Investors and scholars have long tried to discover market inefficiencies to counter the EMH. Many suggested market indicators, such as the proposition that there is positive relationship between butter production in
Bangledesh and the Standard & Poor 500 (S&P 500), have been rejected (Investopedia,
2008). Market irregularities that scholars agree are possible ways of achieving outperformance include the overreaction hypothesis (De Bondt and Thaler, 1985; Chopra et al., 1991; Mun et al., 2000; Ma et al., 2005; Avramov et al., 2006), the size effect
(Banz, 1980; Reinganum, 1982; Schwert, 1983), the January effect (Keim, 1982; Haugen and Jorion, 1996; Haug and Hirschey, 2005), the post earnings announcement drift (Ball and Brown, 1968; Bernard and Thomas, 1980; Mendenhall, 2004; Ke and
5 Ramalingegowda, 2005), poor long-run IPO performance (Ritter, 1991; Jain and Kini,
1994; Bossaerts and Hillion, 2001), and strong post-stock-split announcement performance as well as poor post-reverse-stock-split announcement performance (Desai and Jain, 1997; Marchman, 2007; Kim et al., 2008).
Size Effect
Examination of NYSE equity data showed that “the common stock of small firms had, on average, higher risk-adjusted returns than the common stock of large firms”
(Banz, 1980, 3). The results also showed that the size effect could not be represented by a linear or log relationship between performance and market capitalization, but illustrated that the effect was most evident for stocks with very small market values. The size effect was not consistent throughout time as significant variation in the magnitude of the size coefficient was seen. Study of portfolio diversification led to the suggestion that the size effect was caused by investors that avoid investing in sets of securities for which it is impossible to collect the necessary information to determine a possible range of risk and return (Klein and Bawa, 1977). The amount of information available is often related to the size of the corporation, which thus means only a specific group of traders invests in these small firms with very little collectable information available to prospective investors (Banz, 1980). Institutional investors tend to avoid small capitalization stock due to the inability to perform due diligence on them (Del Guercio, 1996; Brandt et al,
2010). Previous study had shown that securities only a subset of traders invest in see higher risk-adjusted returns than securities that all investors consider (Banz, 1978).
6 January Effect
Study of month-by-month equity performance trends determined that mean abnormal return was significantly larger in the month of January than any other month for equity with a small market cap, a calendar trend supporting the theme of small cap outperformance that was termed the January effect (Keim, 1982). Additionally, the negative relationship between the abnormal returns and market value of stocks was more substantial in the month of January than in any of the other months, even in years when large capitalization stocks outperformed their small capitalization counterparts on a risk- adjusted basis. Almost fifty percent of the abnormal returns occurred in the month of
January, with “more than fifty percent of the January premium attributable to large abnormal returns during the first week of trading in the year, particularly on the first trading day” (1982, 13). Later research has shown that the January effect still remains many years after its discovery, which suggests that arbitrageurs are unwilling to take advantage of this profit opportunity, possibly due to the additional risk associated with small caps (Haugen and Jorion, 1996).
Post Earnings Announcement Drift
Major corporate announcements are a possible cause of some market inefficiencies. Initial study into corporate earnings announcements determined that, even after the announcement, stocks that reported “good” news continued to see an upward drift in share price while stocks that reported “bad” news continued to see slow downward movement in stock price (Ball and Brown, 1968). Some research has suggested that errors in the Capital Asset Pricing Model (CAPM) caused by incorrect modification of raw returns for risk could cause inefficiencies like the post earnings
7 announcement drift (PEAD) to appear to exist, but analysis of standardized unexpected earnings and beta shifts showed this to not be the case (Foster 1982; Bernard and
Thomas, 1989). Others have also suggested that a part of the price response to the earnings announcement is delayed (Jones and Litzenberger, 1970; Hong and Stein 1999).
While a significant number of scholars have supported this price delay explanation, there is still significant disagreement over what is causing it. Some have suggested that this delay is caused by transaction or opportunity costs (Jeffrey, 2007; Chung and Hrazdil,
2008). Others argue that “the prices are affected by investors who fail to recognize fully the implications of current earnings for future earnings,” a view they support by noting that a significant portion of the PEAD occurs right before the next quarter’s earning release (Bernard and Thomas, 1989, 2). Active institutional investors take full advantage of this opportunity for abnormal returns. Market data illustrated that investors exploiting this flaw in the EMH “earn a three-month mean abnormal return of 5.1% (or 22% annually) net of transaction costs” (Ke and Ramalingegowda, 2005, 25). This process serves to quicken the length of time it takes stock prices to react to how the current earnings report effects future earnings reports.
Stock Splits and Reverse Splits
Splits and reverse splits are additional examples of corporate announcements that appear to lead to abnormal returns. A sample of approximately 5,600 stock split announcements displayed that the stocks saw an average abnormal return of 7.11% during the announcement month while also showing that these stocks saw a one year abnormal return after the announcement month of 7.05% and a three year abnormal return after the initial month of 11.86% (Desai and Jain, 1997). From a sample of
8 seventy-six reverse splits, results showed that abnormal returns for the announcement month were -4.59%, which was followed by an abnormal performance of -10.76% in the next year and -33.90% in the three year period after the initial month. This study supports the idea that the market underreacts to firm-specific announcements as has been claimed by previous studies (Shiller et al., 1984; De Bondt and Thaler, 1985). A much larger sample of stocks enacting a reverse split totaling over 1,600 firms concurred with previous literature indicating that these stocks exhibit underperformance “beginning in the ex-split month and extending to three years after the split” (Kim et al., 189). The amount of possible profit for investors from this market inefficiency would be capped by the difficulty of short-selling large amounts of lower-priced, illiquid stocks, which is often how stocks that have undergone a reverse split would be categorized.
IPOs
The most drastic example of abnormal returns is the poor long run performance of initial public offerings (IPOs). A study of more than 1,500 IPOs determined that in the three years after going public the IPO firms significantly underperformed a set of comparable firms matched by size and industry (Ritter, 1991). The sample of IPOs returned an average of 34.47% during the three-year post-IPO period while the control sample returned an average of 61.86% over the same three-year period. IPOs of younger companies saw even worse performance than the IPO sample average. IPO underperformance after introduction to the market provides evidence to the theory in
Shiller (1990) that the both the market and IPO market are often guilty of throwing significant support behind fads that soon die as shown through price depreciation, such as what occurred during the dotcom bubble (Ritter, 1991). Work on long-run IPO
9 underperformance was expanded to a sample of 4,800 IPOs that studied performance for ten years after introduction to the equity markets finding that IPO underperformance disappears if the first forty-five months of return figures are removed, which supports the abnormal underperformance period suggested by Ritter (Bossaerts and Hillion, 2001).
While it may be possible for proponents of the EMH to dispute the validity of a few proposed flaws, it is very hard to argue that all the proposed market inefficiencies are fabricated. Psychological factors are important to consider when debating the EMH as even early research noted that “the [stock] market is not a weighing machine, on which the value of each issue is recorded by an exact and impersonal mechanism [. . .], rather
[it] is a voting machine, whereon countless individuals register choices which are the product partly of reason and partly of emotion” (Graham and Dodd, 1996, 23). Even
Keynes (1937) asserted that “day-to-day fluctuations in the profits of existing investments [. . .] tend to have an altogether excessive, and even an absurd, influence on the market” (62). These early comments on stock performance support the presence of overreaction in the market, a theory suggesting that over time prices will revert to the appropriate value while investor overreaction may cause short-term improper valuation
(De Bondt, 2000).
Support for the Overreaction Hypothesis
Noting that people tend to overreact to substantial news, researchers have found evidence of this phenomenon in the stock market. Portfolios of stocks that had performed poorly in the past three years outperformed the market by 19.6% on average in the next three years while portfolios of stocks that had seen significant appreciation in the past three years lagged the market by 5.0% over the next three years (De Bondt and
10 Thaler, 1985). The overreaction effect was much more substantial for losers than winners, with significantly greater performance deviation compared to the market for the losers. “In portfolios formed on the basis of prior five-year returns, extreme prior losers outperform extreme prior winners by 5-10% per year during the subsequent five years,” which supports the initial overreaction findings of De Bondt and Thaler (Chopra et al.
1991, 235).
Study of stocks with significant one-day gains or losses highlighted short-term
OH evidence by confirming that the magnitude of the overreaction is exploitable (Howe,
1986). For large one-day drops, most of the rebound occurred within the first week and the above-average returns began to disappear after the fifth week. Further analysis of largest percentage daily winners and losers data illustrated that the best strategy to initiate for losers would be buying the stock at end of the day the drop occurred and selling it two days later, which yielded an average abnormal return of 4.5% (Ma et al., 2005). The best strategy for winners, selling them short at the end of the day the price increased occurred and buying them back two days later, returned 1.76% of abnormal returns on average.
The study also divided the significant price movements into different types of major events (mergers/acquisitions, earnings report, etc.), but it did not come across any statistically significant results on this topic. These results show that the OH is both a short-term and long-term phenomenon.
The OH has been criticized for faulty beta estimates that have significant effects on the parametric techniques used in research (Chan, 1988; Vermaelen and Verstringe,
1986). Estimating the risk coefficients through non-parametric regressions and non- parametrically bootstrapping the results to yield their underlying distributions was able to
11 address this problem (Mun et al., 2000). This method showed that one-year and two-year portfolios of buying previous losers or selling short past winners did yield abnormal returns, but longer-term portfolios (such as three years) did not produce any excess returns. It is also interesting to note that the excess returns for both the winner and the loser portfolios were of the same general magnitude with the one-year portfolios yielding
5.03% and 5.07% of abnormal returns respectively and the two-year portfolios yielding
2.06% and 2.27% of excess returns respectively. While these similar magnitudes do not support previous results suggesting that loser portfolios perform significantly better, the excess returns address the main complaint of critics and still do support the OH.
Research into the possible effects of trading factors on the OH has yielded some results that shed light on some situations where the appearance of the theory will be the greatest. Studying equities on the major indices showed that stocks with high turnover and high illiquidity are more prone to exhibit reversal tendencies as suggested by the OH
(Avramov et al., 2006). The term illiquidity refers to situations where it is difficult for an investor to trade a significant number of shares without having an effect on the market price. This phenomenon may be because “demand for liquidity generates price pressure that is subsequently reversed as liquidity suppliers react to potential profit opportunities that are attributable to price deviations from fundamentals” (2367). These results concurred with previous study illustrating that the OH appears to have a greater effect on loser stocks than winner stocks. Examination of contrarian portfolios determined that high-turnover stocks are much more apt to illustrate the OH phenomenon than stocks with a lower turnover, which supports the results gathered by Avramov et al. (Conrad et al., 1994). Furthermore, stock return examination after a one-day drop of more than ten
12 percent did show a significant reversal, but most of the bounce seen after the initial drop was due to the bid-ask spread (Cox and Peterson, 1994). Nevertheless, there was still the opportunity for profit for the short-term suppliers of liquidity in the plummeting stock, which supports the notion of available profit for those providing liquidity during price drops in illiquid equities (Avramov et al., 2006).
Significant support for the OH exists, which presents a clear challenge to the
EMH and the notion that there are no discernable patterns in the equity market allowing for profit. Even with all the research on the OH as well as all the analysis performed to address issues raised by critics, there are still a number of studies highlighting some possible issues with the OH.
Challenges to the Overreaction Hypothesis
The OH, like all suggested market inefficiencies challenging the EMH, is still being debated as scholars are finding possible mistakes in the studies attempting to document its existence. Many suggesting errors in the research methods in previous works on the OH often have found that even small and simple methodology decisions can have a large effect on the final abnormal return calculations (Kaul and Nimalendran,
1990; Zarowin, 1990; Bremer and Sweeney 1991). Study of possible biases created from methodological decisions showed “that the returns to the typical long-term contrarian strategy implemented in previous studies are upwardly biased because they are calculated by cumulating single-period (monthly) returns over long intervals” (Conrad and Kaul,
1993, 39). Also, measurement errors for the single-period returns appear to be compounded due to the accumulation of the monthly returns over an extended interval.
The groundbreaking work on the OH of DeBondt and Thaler (1985) was specifically
13 singled out as it uses the technique that Conrad and Kaul believe presents inaccurate results. This technique of measuring profitability of the arbitrage portfolio as the average cumulative raw returns difference between the winner and loser securities was compared to a strategy emphasizing a buy and hold mentality that Conrad and Kaul believe has less opportunity for error. The month of January was excluded to avoid confounding the results with the January effect. A 36-month abnormal return of 12.2% was calculated using the average cumulative raw returns method while a return of -1.7% was calculated using the longer holding period method developed by Conrad and Kaul, which suggests that there are actually no long-term profits to be had from a long-term contrarian strategy.
Others have tried to determine if there may be another explanation behind the long-run reversal pattern. George and Hwang (2007) proposed “long-term reversals in
U.S. stock returns are better explained as the rational reactions of investors to locked-in capital gains than an irrational overreaction to news” (2865). This tax explanation suggests that investors delay selling stocks with capital gains since taxes are not paid until the gains are realized, which may cause these stocks to have higher prices and thus lower projected price appreciations due to many investors delaying sales of the stocks.
The fact that overreaction is not present on the Hong Kong market where investment gains are not taxed is provided as evidence supporting this theory. Some claim that extensive data mining by econometricians will lead to identifying possible market inefficiencies, but that it is difficult for a significant number of investors to create an implementable strategy to harvest the possible abnormal profits (Lewellan and Shanken,
2002; Chen and Kuo, 2001).
14 While there are certainly compelling evidence against the OH, the overwhelming support for it by the academic community suggests it is a viable theory. Many past critics claimed that any market inefficiencies from OH are almost impossible to profit from due to trading fees, but this has now become an ineffective argument as fees have plummeted in the past couple decades with the help of technology. Liquidity, another issue highlighted by critics, has also improved significantly over the past few decades thanks to high-frequency traders and rule changes by the NYSE. Research on the OH has suggested that traders can garner abnormal profits from both short-term and long-term reversals. The existence of the OH and the possibility for consistent abnormal returns directly challenges the idea of the EMH. Bossaerts and Hillion (2001) developed the term efficient learning market (ELM) to describe the hypothesis that prior beliefs about an equity may be biased. The ELM suggests that investors gather newly released information that helps alter their previously held biased beliefs and allows the stock of the corporation to approach the value suggested by the EMH. This explanation of market performance allows for small inefficiencies to exist that arbitrageurs quickly exploit to garner profits. With a high likelihood that additional market inefficiencies allowing consistent abnormal profits have yet to be discovered, investors and academics alike continue to pour money and resources into research with the hope of discovering new market flaws. Thus, it comes as no surprise that market participants have created numerous trading techniques and quantitative analysis procedures that offer the best opportunity for minimized risked and maximized profit.
15 Stock Selection
Investing is characterized by a variety of methods to generate profits. The most common investment technique involves buying and holding stock in stable companies with predictable revenue streams to harvest consistent returns (fundamental investing).
Others believe that trading around volume spikes and stock price fluctuations offers better opportunities to make money (technical trading). Many investors even champion the idea of buying low-cost funds that follow broad market indexes, justifying this strategy by noting that about eighty percent of actively managed mutual funds underperform the market in a given year (Farzad, 2012). This section covers the strategies used by practitioners of both technical and fundamental analysis to make investment decisions.
Technical Analysis
Academia’s disapproval of technical analysis is represented by Malkiel (2003), which compared predicting future equity performance from past returns to astrology.
Nevertheless, both institutional and individual investors have embraced this form of quantitative analysis as a key indicator of future price movements, especially in short- term time horizons (Sullivan et al., 2002; Repkine, 2008). The main tool of those trading based on technical indicators is the stock chart, which technicians believe allows them to predict future stock action with some certainty. Resistance levels (areas where a stock sees significant selling pressure) and support levels (areas where a stock sees a considerable number of buyers) show traders where a position should be initiated or closed (Edwards et al., 2007). Many technicians also put significant study into moving averages (especially the 50- and 200-day moving averages), which are the average price
16 of a stock over a number of time units (Scottrade, 2012). While the chart is very important for technicians, a number of statistics are also used to make decisions. The relative strength index (RSI), a momentum oscillator with range 0-100 comparing average price change in declining periods to average price change in advancing periods, is used to determine if a stock is overbought or oversold (Scottrade, 2012). Traders typically consider stocks with an RSI under thirty to be oversold while stocks with an
RSI over seventy are considered overbought, but developing a strategy to profit from RSI is not that simple as study has shown simply buying when a stock is “oversold” and selling when a stock is “overbought” yields no abnormal returns (Schwager, 1996; Wong et al., 2002). Many technicians also consider what percent of the company’s share float is sold short, as many believe that stock of a company issuing good news that also has a high percentage of its shares sold short will see a skyrocketing price with both investors buying outright and the short-sellers buying back their shorted shares (Kusick, 2007).
While there are traders that make decisions by just religiously studying charts and technical indicators, many investors look at some technical information along with a basket of fundamental statistics.
Fundamental Analysis
The goal of fundamental analysis is to find company stocks trading at discounts to intrinsic value. Data gathered from the balance sheet, income statement, and statement of cash flows allows investors to generate statistics to investigate five main characteristics: profitability, asset turnover, liquidity, leverage, and current market valuation (Kieso,
2010). Figures including current ratio, return on assets, debt ratio, total asset turnover, and price-to-earnings ratio will help investors screen for undervalued stocks to buy and
17 overvalued stocks to short-sell (Manley, 1999). Skeptics of stock picking based on market discounts to company valuation do exist with a fundamental analyst being compared to the “erudite major general in ‘The Pirate of Penzance,’ with his many cheerful facts about the square of the hypotenuse” (Graham, 1959, 130).
Even practitioners of fundamental analysis concede that there are types of corporations not appropriate for this valuation technique, especially those with little revenue and large research and development investments (Chan et al., 2001; Lev at al.,
2008). High-technology companies like early-stage drug developers are especially reliant on research and development to discover new compounds that will be drivers of future revenues. Biopharma reliance on drugs advancing through the FDA process to create new revenue streams or partnership opportunities makes it unique compared to most sectors that are valued by typical financial statistics, which triggers the question of what variables effect their stock volatility and valuation (McClure, 2011).
Investing in biopharmaceutical equities
Biopharma companies are “involved in the research, development, manufacturing and/or marketing of biotechnology-based pharmaceutical products or surrogates, including gene and protein sequences” (Rader 42). With the typical drug taking 10-15 years to develop at an average cost of roughly $1.3 billion (as of 2004), developing a biopharma pipeline is time consuming and expensive (Bansal 3). Nevertheless, significant life sciences funding from government agencies like the National Institute of
Health ($30 billion a year) as well as the ever-growing portion of biopharma revenues in the $325 billion of annual U.S pharmaceutical sales ensures that there is significant financial incentive for industry firms (International Network of M&A Firms, 2011;
18 Heath, 2012). The many Food and Drug Administration (FDA) hurdles on the way to drug approval, along with the huge financial stakes, make the almost 500 biopharma stocks trading on United States exchanges extremely volatile (Huggett et al., 2011).
Biopharma’s uniqueness in stock performance being driven by drug progression in the
FDA process as opposed to the typical financial measures renders great opportunities for research into equity returns in this industry.
The main drivers of the immense volatility in the equities of biopharma companies are the major events in the production of new drugs, with the results of FDA- required clinical studies causing significant price gyrations (Houston, 2010). An executive of a major pharmaceutical company described Wall Street’s betting on clinical trial results through positions in drug companies as a “sporting event” (Bosch and Lee
590). Phase I, which is the first FDA-required clinical study and is geared at drug safety, has just 20 to 100 participants and takes only a month (University of Pittsburgh, 2002).
Phase II, which is focused on drug effectiveness, has more participants (100 to 300) and lasts a few months (Avik, 2012; California Biomedical Research Association, 2012).
Phase III, the largest (1,000 to 3,000 participants) and final clinical study in the FDA approval process that determines whether the drug can be both safe and effective for humans, lasts several years and dwarfs these previous studies in importance (California
Biomedical Research Association, 2012). Size of the potential market for the drug, probable market share the product will gain, and chance of approval are the three main factors used to value a pharmaceutical, yet there has been no widely-accepted valuation method developed (Pietersz, 2012). Valuing projects through a real-options technique has demonstrated the ability to value a company at 15-20% off of actual market
19 valuations by assessing its entire pipeline, but many estimates for projected revenue and probability of success have to be used (Kellogg et all., 2000). The lack of importance of typical financial metrics in the biopharma industry is demonstrated by the stock of
Human Genome Sciences Inc. (HGSI), which rose from $0.45 to $32.07 between March
2009 and January 2010 even though its profit margin went from significantly positive in the first quarter of 2009 to negative in the fourth quarter of 2010 (Fan, 2010). This huge rise in Human Genome was caused by positive late-stage results of a drug to treat Lupus, which emphasizes how it is necessary to pay significant attention to the company’s pipeline (Human Genome Sciences Inc., 2009).
Literature on the relationship between clinical studies and stock response has concentrated on late-stage trials. Stock reactions from over 100 failed phase III trials showed that negative results led to an average market value decline of $405 million
(Girotra et al., 2006). Failure in Phase III has more of a negative effect on a company’s market valuation than failure in an earlier clinical trial, as companies are required to invest more money in a drug as it successfully proceeds through the necessary FDA hurdles. This study also found support for the hypothesis that the negative effect on market value was mitigated if the company had a pipeline with an additional drug in
Phase III or a large number in Phase II. The New Drug Application (NDA), a submission to the FDA occurring after Phase III trial success highlighting the results of all the clinical trials, is the final step before a drug is approved (California Biomedical Research
Association). While the FDA typically approves most drugs at the NDA stage (80% of drugs that reach this stage are approved), failure at this stage is especially costly, as all drugs at this stage have required enormous time and resources from the sponsoring
20 company (FDA, 2008; Dimasi et al., 2003). Research of NDA approvals demonstrated that there was an abnormal return of 1.08% at the time of a positive NDA announcement, but additional study of over 300 NDA applications showed that losses in market valuation due to NDA failure were much larger than gains in company equity value from positive NDA results, illustrating the disastrous market valuation effect of a late-stage failure (Himmelmann and Schiereck, 2010; Sharma and Lacey, 2004).
Major events for biopharmaceutical stocks cause enormous increases in liquidity and volume as well as the drastic price oscillations. This liquidity is partially due to the fact that individual investors are net-buyers of both stocks that are in the news and stocks seeing extreme one-day price changes, phenomena that are caused by the difficulty investors have sorting through the thousands of possible stocks that they could buy
(Barber and Odean 785). Institutional investors on the other hand do not seem to be affected by this attraction to attention-grabbing stocks. Financial analysts of pharmaceutical stocks can also cause increased individual investor interest in a stock through issuing a rating and a price target on the stock, which was demonstrated in a study showing that these type of new events caused increased liquidity and price volatility as the market found a new appropriate price for the stock (Gonzalez and
Gimeno, 2008).
Summary
Early research on biopharma equities has illustrated that, due to the long time frames necessary for drug discovery and eventual FDA approval coupled with the complicated nature of biotechnology, nonfinancial information is often not automatically completely factored in to the stock price (York et al., 2011; Dedman et al., 2008; Guo et
21 al., 2004; Liu, 2006). Even considering this unique aspect of biopharma equities, there has been little research looking for possible market inefficiencies associated with the significant price movements from these nonfinancial events. While research on the EMH and the OH can be helpful for understanding some of the factors at play when biopharma stocks see significant price movements, looking at industry-specific news may help highlight market inefficiencies allowing abnormal profits for arbitrageurs. The following chapters hope to shed light on this and determine if there are any inefficiencies present in the market response to major negative biopharma events.
22
CHAPTER III
THEORY
The OH has shown that investors tend to overreact to major news (especially of the negative variety) making large price drops in the securities of the volatile early-stage biopharma industry an interesting avenue to investigate the OH as well as factors that may affect the variation of returns in post-event stock performance.
Hypothesis 1: Overreaction as defined by the OH will be present after significant
price drops in small cap biopharma stocks.
The OH states that investors will often overreact to major negative news, causing the stock of the company issuing the negative news to become oversold and allowing an opportunity for positive abnormal returns for arbitrageurs. Previous study has shown that overreaction is more evident for major negative events than major positive events. Small cap biopharma provides a great opportunity for research of overreaction to negative news as significant events are common and cause enormous price gyrations.
Hypothesis 2: Small cap biopharma company stock returns after a large price
decline will vary according to the type of news that caused the drop and the size
of the drop.
Large one-day stock depreciations in small cap biopharma stocks can be triggered by a number of factors. Market participants may react differently to financing news than
23 negative study results, as financing events will have positive long-term effects on company success as they provide funding for pipeline development. Financing news is especially common in this industry as thirty-three percent of companies only have enough cash for one year and fifty percent have enough funds for just two years (Bratic et al.,
200). Negative pipeline results may see a different reaction from investors as they can cause prior spending on research and development to become obsolete and lead to large decreases in projected future earnings. The size of the price decline could also shed some light on future returns, as investors may be more apt to initially misprice stocks when the news that has a very large effect on future company prospects, which should coincide with larger price drops. Looking at post-event price performance through the lens of the news causing the drop and the magnitude of the initial one-day drop will highlight the best opportunities for abnormal profits in trading biopharmaceutical equities after significant one-day declines.
24
CHAPTER 4
METHODOLOGY
This study analyzes the OH through the unique lens of small-cap biopharmaceutical stocks. Investigation concentrates on significant one-day declines as previous results have shown that the OH is more evident in losers than winners (De
Bondt and Thaler, 1985, Ma et al., 2005). Two-day abnormal returns are tested against a mean hypothesis of zero with a standard t-statistic while six-month abnormal returns are tested against the same hypothesis with both the standard t-statistic and a bootstrapped skew-adjusted t-statistic. Means and test statistics are also calculated for different groups based on the size of the initial drop and the type of news that caused the drop.
Data Selection
Small cap was defined as companies with market capitalization under $2.5 billion, as designated by small cap expert Royce Funds (Forbes, 2012). Stock returns from the close of the day the event occurred to the market close two days after the event were calculated to determine if there is evidence to support short-term existence of the OH (Ma et al., 2005). While exploration looking for long-term evidence of the OH often looks at periods a few years in length, this was not be very feasible for small-cap biopharma as it would have made it very hard to collect an unbiased data set due to the difficulty of collecting price data for companies bought out or delisted. To ensure an unbiased
25 sample, examination of possible long-term OH was through six-month post-event stock performance.
With a six-month time frame being used to study possible long-term OH, equity data from 2011-2012 was collected to create the sample. The NASDAQ Biotechnology
Index (NBI) was selected for the comparison index to determine if overreaction allowed opportunity for abnormal return. Though a few large companies including Amgen and
Gilead Sciences are in the NBI, this index provides an effective representation of small- cap biopharma performance as over eighty percent of the companies had market caps under $2.5 billion during at least part of 2011. Price data for both the comparison index and the sample biopharma companies was gathered from the historical price section of
Yahoo! Finance. Creation of the biopharma company sample began with the 116 companies of the NBI as of the beginning of 2012. The current NBI was not used for sample selection because companies are removed from the index in May and November of every year if they do not meet certain criteria such as a market cap of at least $200 million and daily trading volume of at least 100,000 shares (NASDAQ, 2012). Using the
NBI from the beginning of 2012 removes the upward rebalancing bias that would be present from the current NBI (Barber and Lyon, 1997). Of the 116 companies selected, seven companies that have been bought out since the beginning of 2012 were removed since price data is no longer available on Yahoo! Finance. Two companies were removed because they were in the medical technology business and do not rely on pipeline development. Fifteen additional companies were removed because they had a market cap of more than $2.5 billion during all of 2012, leaving a sample of ninety-two companies remaining for examination. The bias caused because IPOs are often excluded
26 from abnormal return studies causing positively biased test statistics is avoided as five of the sample companies joined the market though 2010 or 2011 IPOs (Lyon et al., 1999).
Method
As has been the practice in recent OH investigation, price data from the companies in the sample was used to identify all instances of one-day price decline of at least ten percent, which provided 180 data points (Cox and Peterson, 1994; Larson and
Madura, 2003). For companies that had market caps both below and above $2.5 billion at some point during 2012, only data points where the pre-drop company market valuation was below $2.5 billion were included. Each data point consisted of the two-day post- event performance reading (short-term OH) as well as the six-month post-event performance figure (long-term OH). Abnormal returns for each data point during the short-term and long-term period were calculated as the difference from the index return:
��!" = �!" − �!! (4.1) where �! is the return of security x, �! is the return of the NBI, ��! is the abnormal for security x, and T is the time period. To identify the significance of the OH in the two-day time frame, the following two-tailed t-statistic was calculated for ��!:
!"! � = ! (4.2) !"! ! where
�� = ! �� . (4.3) ! ! !"
While a standard two-tailed t-statistic was calculated for the six-month time frame, long- run stock returns are thought to be positively skewed so a Shapiro-Wilk Test was run on the six-month relative return data. These tests confirmed that this data was not normal, thus questioning the accuracy of the standard t-test. The positively skewed distribution is
27 logical as long-term biotech returns in excess of one hundred percent are not uncommon while long-term index returns of this size are highly unusual. Also, there is no limit to stock returns on the upside while stock returns on the downside are maxed out at 100%.
To compensate for the skew, we used a bootstrapped skewness-adjusted t-statistic, which prevents positively skewed returns and negatively biased t-statistics that occur with the use of a conventional t-statistic when testing the significance of long-term abnormal returns (Barber and Lyon, 1997; Lyon et al., 1999). The skew-adjusted t-statistic is defined:
� = �(� + ! ��! + ! �) (4.4) ! ! !! where
! � = !"! and � = (!"!"!!"!) (4.5) ! !(! )! !"! !"! with C � being the conventional t-statistic and � the skewness estimate (Johnson, 1978;
Lyon et al., 1999). To complete the bootstrapping procedure, one thousand samples of size � /4 will be used to calculate �! to determine if the hypothesis that the mean abnormal return is equal to zero can be rejected.
Returns for stocks seeing drops between ten and twenty percent one-day price decline (approximately eighty percent of the data set) were compared to stock returns for companies seeing a share price drop greater than twenty percent for both the two-day and six-month post-drop time period to test for significance in the return variation. A pooled two-sample t-test of the following form was performed for both time frames to determine whether there were statistically significant return differences between the two groups:
28 (x1 − x2 ) t pooled = 1 1 s + p n n 1 2 (4.6)
Where
2 2 (n1 −1)s1 + (n2 −1)s2 s = (Yale, 1998). (4.7) pooled n n 2 1 + 2 −
Since long-term stock return distributions are often positively skewed, adjusted asymmetric two-sample t tests in the following form were also run on the six-month comparison returns:
n1 ! n2 ! ! t pooled �!"# = + (t pooled − 1) (4.8)
! n1 n2 n1 ! n2 where g is the third standardized moment (Balkin and Mallows, 2001). Considering that the third standardized moment is the skewness of a random variable, g was defined as:
!!/! � = �!�! (4.9)
Where
�! is defined as the �th moment about the pooled mean �:
� = ! (� − �)! (StataCorp, 2012). (4.10) ! ! !
Each return value was also tested for significance against the hypothesis that the mean return equaled zero though a standard t-test. Skew-adjusted bootstrapped t-statistics were calculated for the six-month returns, but it is important to also consider the standard t-test for the six-month return as bootstrapping has been shown to be somewhat ineffective for small sample sizes (Goetzmann and Jorion, 1993).
29 To test whether there were post-event return differences based on the type of news that occurred, drops were split up into four major news categories:
1. Pipeline Results
2. Earnings Release
3. Other News Unrelated to the Pipeline
4. No News
News causing the drop was gathered from investor relations’ websites as well as articles on Google Finance. Pipeline results include drops caused by news directly affecting the future success of a drug. Applicable categories include:
1. FDA Panel Recommendations
2. Drug Put on Hold by FDA
3. Clinical Trial Results
4. FDA Response Letter
5. Drug Partnership Termination
6. Patent‐related Court Ruling
7. Revenue‐sharing Court Ruling
8. FDA Response to Competitor Drug
9. Commercialization Update
Earnings releases are corporate updates where a company provides updated financials as well as the outlook for future quarters. Other news includes a variety of categories that often cause market devaluation in all industries:
1. Executive Change
2. Analyst Downgrade and/or Lowered Price Target
30 3. Secondary Offering
4. Listing Deficiency
5. Insider Sale
6. Merger or Acquisition
7. Federal Contract Update
No news is an important category because biopharmas have a tendency to see large price changes for no clear reasons due to institutional investors or insiders unloading large positions or just speculation in relationship to a near-term catalyst. Two-day and six- month return figures were calculated for each of the four news types and tested against the hypothesis of a mean return of zero. Both the two-tailed t-test (two-day and six- month returns) and the bootstrapped skewness-adjusted t-statistic (six-month returns) were used to test return figures against the hypothesis that the abnormal performance equaled zero. Stock declines caused by pipeline results were also directly compared to all the other drops through a pooled two-sample t-test to determine if returns were different for negative pipeline results compared to the other types of news. It would be expected for poor pipeline results to have a different effect on the company than other forms of news as they often highlight big problems with a product expected to provide the company with a significant revenue stream in the future.
31
CHAPTER 5
EMPIRICAL RESULTS
To determine whether overreaction is present in equities within the volatile small- capitalization biopharmaceutical industry, a sample of 180 large one-day declines was developed from all stocks in the NBI with a market cap of under 2.5 billion that dropped at least 10% in a sing day during 2011. Each data point was associated with the following: (1) the two-day relative performance compared to the NBI; (2) the six-month relative performance compared to the NBI; (3) the size of the initial one-day drop; (4) and the type of news that caused the drop.
For the two-day period,1 the abnormal return of -1.04% suggests that there is short-term underreaction after a significant drop, but the result was not significant (p =
0.18). This negative yet not significant result provides inconclusive evidence concerning post-drop stock performance and provides no evidence to support the overreaction hypothesis. Breaking down short-term returns by size illustrated stocks seeing initial drops between ten and twenty percent had an average abnormal decline of -0.57% during the two-day post-drop period (p = 0.51) while stocks seeing an initial drop of greater than twenty percent saw an abnormal decline in the subsequent two days averaging -3.58%, which was significant at the α = 0.05 level. This significant negative result suggests that
1 Measured from the close on the event day to the close two trading days immediately following the event
32 investors initially underreact to news causing a one-day decline greater than twenty percent leading to further price decline in the following two days. As far as short-term return based on the news causing the drop, stocks that dropped due to an earnings release saw continued downward momentum in the following two days with abnormal loss of -
2.26%, which was significant (α = 0.10). The two-day post-event returns for stocks dropping from no news (x̅ = -0.88) or other general corporate news such as executive changes or insider sales (x̅ = 1.38) were each not found to not be significant. Different results were expected for drops caused by pipeline results compared to other news types because poor drug news often coincides with failure of large amounts of R&D investment as well as significant reduction in projected future revenues so post-event return data was gathered for an all drops besides pipeline results (a combination of earnings releases, other news, and no news) as well as the comparison pipeline results category. No significant difference was found in short-term returns as stocks that initially dropped from the pipeline results saw a -1.52% two-day post-event abnormal return (p = 0.51) and the combination group of all other drops saw an abnormal return of -0.94% (p = 0.25), neither of which were determined to be significant (Table 5.1 and Table 5.2). A pooled two-sample t-test confirmed that there was no significant difference in the means of these two groups (p = 0.98). There was no evidence of short-term overreaction in either any of the data subsets or the sample overall, but there was support for the belief that investors underreact to significant news in the short term.
33 TABLE 5.1
SUMMARY STATISTICS
Data Group (Observations) Mean Min Median Max
Two-Day Abnormal Returns Complete Set (180) -1.04 -79.98 -0.77 50.29 Ten to Twenty Percent Drop (152) -0.57 -79.98 -0.31 50.29 Greater Than Twenty Percent Drop (28) -3.58 -27.09 -3.41 16.09 -0.94 -79.98 -0.80 32.74 All Drops Besides Pipeline Results (149) Earnings Release (37) -2.36 -21.71 -1.59 16.09 Other News (20) 1.38 -7.92 0.00 32.74 No News (92) -0.88 -79.98 -0.07 19.34 Pipeline Results (31) -1.52 -27.09 -0.74 50.29
Six-Month Abnormal Returns Complete Set (180) -9.50 -108.11 -17.01 303.28 Ten to Twenty Percent Drop (152) -7.68 -108.11 -15.36 303.28 Greater Than Twenty Percent Drop (28) -19.33 -83.13 -29.28 80.63
All Drops Besides Pipeline Results (149) -13.11 -108.11 -19.79 303.28 Earnings Release (37) -4.02 -108.11 -6.02 303.28 Other News (20) -23.42 -74.22 -28.73 84.99 No News (92) -14.52 -106.51 -20.55 281.11 Pipeline Results (31) 7.88 -67.96 5.09 133.11
TABLE 5.2
TWO-DAY ABNORMAL RETURNS: TESTING FOR SIGNIFICANCE
Data Group Mean t-statistic (p-value)
Complete Set -1.04 -1.34 (0.18) Ten to Twenty Percent Drop -0.57 -0.67 (0.51) Greater Than Twenty Percent Drop -3.58 -2.05** (0.05)
All Drops Besides Pipeline Results -0.94 -1.16 (0.25) Earnings Release -2.36 -1.99* (0.05) Other News 1.38 0.73 (0.47) No News -0.88 -0.76 (0.45) Pipeline Results -1.52 -0.67 (0.51)
(Ho: �=0, Ha: �≠0) ** Significant at the 0.10 level ** Significant at the 0.05 level
34 Previous exploration has shown that long-term stock returns are positively skewed, which can create negatively biased t-statistics. A Shapiro-Wilk Test for normality on the six-month abnormal returns yielded a Z-score of 6.352 and p = 0 suggesting that the distribution is indeed positively skewed, which was confirmed through a box plot representation of the distribution (Figure 5.1).
FIGURE 5.1
DISTRIBUTION OF SIX-MONTH ABNORMAL RETURNS
Median AR = -17.01
Outliers n = 7
Sample n = 180
Bootstrapped skew-adjusted t-tests were used to test the significance of the data to compensate for the positive skew, but standard t-tests were still calculated as previous tests have shown that the bootstrapping method is ineffective for smaller sample sizes.
For the six-month post-event period, stocks saw an abnormal return of -9.50%, which was significant (α = 0.10) with the bootstrapping method (p = 0.094) suggesting the
35 presence of long-term underreaction. While stocks that initially saw drops between ten and twenty percent yielded abnormal returns of -7.68% that were not significant under either test, declines of greater than twenty percent saw a six-month post-event return of
-19.33%, which was significant for the standard t-test (p = 0.011). The bootstrapping method produced a p-value of 0.281 for the ten to twenty percent drop group, but this is likely high due to the small sample size (28 observations). A p-value of 0.32 produced through a pooled two-sample t-test for mean comparisons for the two different drop size subsets suggests that there is no statistically significant difference between their abnormal return averages, which may again be affected by the small sample size. For long run performance in relationship to news, stocks dropping due to earnings saw a six-month abnormal return of -4.02%, which was not found to be significant. The bootstrapped technique p-value of 0.106 for the no news category abnormal return of -14.52% was just outside the 0.10 significance level. The -23.42% abnormal return for the other news category was found to be significant (α = 0.01) with the standard t-test while the bootstrapping method provided a p-value of 0.837, which again is likely high due to the small sample size (20 observations; Table 5.1 and 5.3). The all other drops category saw an abnormal six-month return of -13.11%, which was found significant at a p-value of
0.10 by the bootstrapped skew-adjusted test (p = 0.076), yet the abnormal returns for the pipeline results group provided the only positive six-month abnormal return for any group (7.88%). While the pipeline result figure was not found to be significant through either test (only 31 observations), the pooled two-sample t-test showed the difference in means between drops caused by pipeline results and all other drops was significant at
0.10 (p = 0.06), which was confirmed through an adjusted asymmetric pooled two-
36 sample t-test (p = 0.08) that corrects for skew (Table 5.4). This suggests that investors actually overestimate the effect negative drug results will have on long run company performance, which is in stark contrast to the hypothesis that this news category would see the worst performance in the long term. There was no support of long-term overreaction in the sample overall, but this positive abnormal return suggests that investors do overreact to negative pipeline results. There also was substantial evidence for the belief that investors underreact to significant news in the long term through other data subsets and the sample as a whole.
TABLE 5.3
SIX-MONTH ABNORMAL RETURNS: TESTING FOR SIGNIFICANCE
Data Group Standard Bootstrapped t-statistic Skew-Adjusted t- statistic2
Mean Observed Coefficient Complete Set -9.49** (0.03) -1.99* (0.09) Ten to Twenty Percent Drop -7.68 (0.11) -0.59 (0.61) Greater Than Twenty Percent Drop -19.33** (0.01) -2.32 (0.28)
All Drops Besides Pipeline Results -13.11*** (0.01) -2.32* (0.08) Earnings Release -4.02 (0.73) -0.27 (0.82) Other News -23.42*** (0.01) -1.96 (0.84) No News -14.52** (0.01) -2.10 (0.11) Pipeline Results 7.88 (0.41) 0.89 (0.47)
Ho: �=0, Ha: �≠0; p-value in parenthesis *** Significant at the 0.10 level *** Significant at the 0.05 level *** Significant at the 0.01 level
! ! !" 2 Following Lyon, Barber and Tsai (1999), skew adjusted t-statistic � = �(� + ��! + �) where � = ! and ! ! !! ! !"! (!" !!" )! � = !" ! was calculated for the long-term return measurement and then bootstrapped with a thousand !(! )! !"! repetitions and a sample size of �/4. The observed coefficient is the mean of the sample means from the repetitions.
37
TABLE 5.4
TESTING FOR DIFFERENCE IN MEANS: NEWS FORM AND DROP SIZE
Data Group Two-Day AR Six-Month AR
Pooled Two-Sample t-test Greater than 20% Drop Versus 10 to 20% Drop -1.35 (0.18) -1.00 (0.32) All Other News Versus Pipeline News -0.02 (0.98) -1.87* (0.06)
Adjusted Asymmetric Pooled Two-Sample t-test Greater than 20% Drop Versus 10 to 20% Drop -1.00 (0.32) All Other News Versus Pipeline News -1.78* (0.08)
Ho: �! = �!, Ha: �! ≠ �!; p-value in parenthesis * Significant at the 0.10 level
Means and significance tests were recalculated without data from 4-10 August
2011 (henceforth Industry Crash), a period when the NBI dropped -13.8%. The data was rerun without numbers from this time period because it is likely that the significant decline of the NBI caused stocks to drop 10% or more in one day when they wouldn’t have under normal market conditions, allowing the thirty-five percent of the data set coming from this short time frame to easily confound test results (Figure 5.2). Ninety- seven percent of the data points from the Industry Crash saw initial drops between ten to twenty percent compared to only seventy-seven percent for the rest of the sample, which suggests that a large portion of the declines from this period were triggered by the poor market performance. Furthermore, sixty-six percent of the Industry Crash drops were not caused by any major company news, which was only forty-four percent for the rest of the sample and again shows the effect of poor market conditions on this portion of the sample. Two-day abnormal returns for this sample were 0.15% suggesting there are no
38 abnormal returns after a large one-day decline. The 1.33% two-day abnormal return for stocks that saw an initial drop of ten to twenty percent was not found to be significant (p
= 0.30), but the -4.16% abnormal return during the two-day period for stocks seeing a greater than twenty percent initial drop was found to be significant at the 0.05 level (p =
0.03), which supports the significant finding for the complete data set. A pooled two- sample t-test found the difference in means for these two groups to be significant at the
0.05 level, which had not been found significant in the complete sample (p = 0.04). None of the short-term returns for the news categories were found to be significant, with the p- value for each mean being at least 0.55. Stocks that dropped due to earnings saw 0.20% of abnormal returns in the following two days, which was not found significant (p = 0.88) and contradicts the -2.36% abnormal return that was found be to significant at the 0.05 level in the complete data set (Table 5.5 and 5.6). Many biopharma companies in the sample reported earnings during the Industry Crash, and many likely dropped more on the earnings results than they would have on a normal day due to adverse market conditions, which may have led to an initial false positive for the earnings release subset.
39 FIGURE 5.2
TWO-DAY ABNORMAL RETURNS
3 Two‐Day Abnormal Returns
Other News 2 Ten to All Drops Twenty Besides Percent Drop Pipeline Results 1 Complete Greater than Earnings No News Pipeline Set Twenty Release Results Percent Drop
0 Drop Type
‐1 Abnormal Percent Return ‐2
‐3 Original Sample
After Removal of 8/4‐8/10 ‐4
‐5
40 TABLE 5.5
SUMMARY STATISTICS WITH REMOVAL OF INDUSTRY CRASH
Data Group (Observations) Mean Median Min Max
Two-Day Abnormal Returns Complete Set (116) 0.15 0.77 -79.98 50.29 Ten to Twenty Percent Drop (91) 1.33 1.30 -79.98 50.29 Greater Than Twenty Percent Drop (25) -4.16 -3.35 -27.09 8.46
All Drops Besides Pipeline Results (87) 0.69 0.99 -79.98 32.74 Earnings Release (20) 0.20 1.07 -11.61 14.73 Other News (17) 1.91 0.08 -7.92 32.74 No News (50) 0.47 1.43 -79.98 17.11 Pipeline Results (29) -1.48 -0.74 -27.09 50.29
Six-Month Abnormal Returns Complete Set (116) -11.31 -15.90 -108.11 133.11 Ten to Twenty Percent Drop (91) -10.09 -12.97 -108.11 133.11 Greater Than Twenty Percent Drop (25) -15.73 -28.52 -67.96 80.63
All Drops Besides Pipeline Results (87) -17.68 -20.04 -108.11 84.99 Earnings Release (20) -12.73 -4.53 -108.11 46.52 Other News (17) -19.67 -24.87 -74.22 84.99 No News (50) -18.98 -20.55 -106.51 76.88 Pipeline Results (29) 7.81 2.36 -67.96 133.11
TABLE 5.6
TWO-DAY ABNORMAL RETURNS WITH REMOVAL OF INDUSTRY CRASH
Data Group Mean t-statistic (p-value)
Complete Set 0.15 0.14 (0.89) Ten to Twenty Percent Drop 1.33 1.05 (0.30) Greater Than Twenty Percent Drop -4.16 -2.35* (0.03)
All Drops Besides Pipeline Results 0.69 0.58 (0.57) Earnings Release 0.20 0.16 (0.88) Other News 1.91 0.88 (0.40) No News 0.47 0.25 (0.81) Pipeline Results -1.48 -0.61 (0.55)
Ho: �=0, Ha: �≠0 * Significant at the 0.05 level
41 T The significant six-month abnormal return of -9.50% suggesting underreaction during the initial drop was supported by the -11.31% figure for the sample excluding the
Industry Crash, which was significant at the 0.05 level with the bootstrapped skew- adjusted t-test (p = 0.026; Figure 5.3). Both the -10.06% abnormal six-month return for the ten to twenty percent drop group and -15.73% figure of the greater than twenty percent drop group were found to be significant at the 0.05 level with the standard t-test
(p = 0.043 and p = 0.042 respectively), but only the ten to twenty percent drop group was found significant with the bootstrapped skew-adjusted statistic at the 0.10 level (p =
0.091). The bootstrapping procedure provided a p-value of 0.667 for the greater than twenty percent drop group, which may have been caused by the small sample size (25 observations). The -18.98% abnormal return of the no news group was significant at the
0.05 level with the bootstrapping method, contradicting the not significant finding for the complete sample. Both the -12.73% six-month abnormal return of the earnings release group and -19.67% return relative to the NBI for the other news group were found not significant with the bootstrapping method (possibly due to sample size). Regardless, the
-17.68% return for all drops besides pipeline results group was found significant at a lower level (α = 0.01) than the group had been found significant at with the complete data set (α = 0.10). Once again, the pipeline results group was the only one to show positive long-run abnormal returns (7.81%). Every other group in the sample without 4-10
August had negative double-digit abnormal return. While the positive relative return for the pipeline group was not found to be significant, a pooled two-sample t-test showed there was a significant difference in means between the drops triggered by pipeline results and all other drops (p = 0.01; Table 8). This positive return for the group that saw
42 a drop triggered by poor pipeline results directly contradicts the general trend of negative abnormal returns and suggests that investors initially overreact to bad pipeline news.
FIGURE 5.3
SIX-MONTH ABNORMAL RETURNS
10 Pipeline Results Six‐Month Abnormal Return 5 Complete Ten to Greater All Drops Earnings Other No News Set Twenty than Twenty Besides Release News Percent Percent Pipeline Drop Drop News 0
Drop Type ‐5 Abnormal Return
‐10
‐15
‐20 Original Sample
After Removal of 8/4‐8/10 ‐25
43
TABLE 5.7
SIX-MONTH ABNORMAL RETURNS WITH REMOVAL OF INDUSTRY CRASH
Data Group Standard Bootstrapped t-statistic Skew-Adjusted t- statistic3
Mean Observed Coefficient Complete Set -11.31*** (0.01) -2.58** (0.03) Ten to Twenty Percent Drop -10.09** (0.04) -1.97* (0.09) Greater Than Twenty Percent Drop -15.73** (0.04) -1.83 (0.67)
All Drops Besides Pipeline Results -17.68*** (0.00) -4.15*** (0.00) Earnings Release -12.73 (0.16) -1.65 (0.21) Other News -19.67** (0.04) -1.63 (0.94) No News -18.98*** (0.00) -3.19** (0.04) Pipeline Results 7.81 (0.45) 0.82 (0.47)
Ho: �=0, Ha: �≠0; p-value in parenthesis *** Significant at the 0.10 level *** Significant at the 0.05 level *** Significant at the 0.01 level
TABLE 5.8
TESTING FOR DIFFERENCE IN MEANS WITH INDUSTRY CRASH REMOVED
Data Group Two-Day AR Six-Month AR
Pooled Two-Sample t-test Greater than 20% Drop Versus 10 to 20% Drop -2.10** (0.04) -0.55 (0.58) All Other News Versus Pipeline News -0.02 (0.98) -2.70*** (0.01)
Adjusted Asymmetric Pooled Two-Sample t-test Greater than 20% Drop Versus 10 to 20% Drop -0.56 (0.58) All Other News Versus Pipeline News -2.64*** (0.01)
Ho: �! = �!, Ha: �! ≠ �!; p-value in parenthesis *** Significant at the 0.05 level *** Significant at the 0.01 level
! ! !" 3 Following Lyon, Barber and Tsai (1999), skew adjusted t-statistic � = �(� + ��! + �) where � = ! and ! ! !! ! !"! (!" !!" )! � = !" ! was calculated for the long-term return measurement and then bootstrapped with a thousand !(! )! !"! repetitions and a sample size of �/4. The observed coefficient is the mean of the sample means from the repetitions.
44
TABLE 5.9
BOOTSTRAPPED SKEW-ADJUSTED T-TESTS FOR SIX-MONTH
ABNORMAL RETURNS
Data Group Complete After Removal Data Set4 of Industry Crash
Observed Coefficient Observed Coefficient Complete Set -1.99* -2.58** Ten to Twenty Percent Drop -0.59 -1.97* Greater Than Twenty Percent Drop -2.32 -1.83
All Drops Besides Pipeline Results -2.32* -4.15*** Earnings Release -0.27 -1.65 Other News -1.96 -1.63 No News -2.10 -3.19** Pipeline Results 0.89 0.82
Ho: �=0, Ha: �≠0 *** Significant at the 0.10 level *** Significant at the 0.05 level *** Significant at the 0.01 level
Analysis of the data subset from just the Industry Crash shows that stocks seeing a one- day drop of at least ten percent continued to decline at an average of -3.23% in the following two days, which was significant (p = 0.001). The -6.07% abnormal return for the six-month period for this subset was greater than the abnormal return for the complete data set (-9.50%) and not significant (p = 0.51). The long-term abnormal return for stocks that dropped in conjunction with an earnings release was 6.24%, which suggests that investors overreact to biopharma earnings during a dramatic decline in industry
! ! !" 4 Following Lyon, Barber and Tsai (1999), skew adjusted t-statistic � = �(� + ��! + �) where � = ! and ! ! !! ! !"! (!" !!" )! � = !" ! was calculated for the long-term return measurement and then bootstrapped with a thousand !(! )! !"! repetitions and a sample size of �/4. The observed coefficient is the mean of the sample means from the repetitions.
45 valuation when considering that long-term returns for stocks that dropped due to earnings were significantly negative the rest of the year (-12.74%).
46
CHAPTER 6
CONCLUSION
Small-cap biopharmaceutical stock performance was investigated through the lens of the overreaction hypothesis. While there was no evidence supporting the OH, performance figures provided substantial evidence of underreaction after large one-day stock price declines suggesting that a short sale after the initial one-day drop will lead to abnormal profit in a longer-term time horizon. Return data illustrated that stocks with a greater than twenty percent initial drop continued to see abnormal decline in the short term with returns that were substantially more negative than what occurred with stocks that had a smaller initial drop. Furthermore, every news group saw the significant negative abnormal returns in the long term with the exception of the pipeline group, which actually saw positive returns compared to the comparison index.
While results for the two-day post-drop period initially appeared to suggest underreaction, reevaluation of the sample without the industry crash period illustrated that the short-term performance data was inclusive. There was substantial support for short-term underreaction for stocks that saw initial drops greater than twenty percent, suggesting abnormal returns for a short-term short sale of an equity after negative news that sees an initial one-day drop greater than twenty percent. The long-run performance results provided conclusive evidence for underreaction even after rerunning return data
47 without the industry crash, suggesting that a six-month short position for a stock seeing a large one-day drop will yield consistent profit. Significant underreaction evidence was present for most news categories for the six-month period, but stocks initially dropping from pipeline results saw positive returns in the next six months, which suggests overreaction in this subsample. While negative pipeline news will have the most significant effect on a company’s valuation as it will significantly reduce future revenue projections, the positive long-run abnormal return suggests that traders actually initially overestimate how much the negative results will effect the company’s future.
While results were illuminating, this investigation merely serves to open the door to an area of study and industry that has been largely ignored by the academic community, yet has provided significant opportunities for profit to traders. Data investigation was performed only through a snapshot in time, meaning that confirmation of results through exploration of a different time period would be very constructive.
Expansion of the data set to companies outside of the NBI would also be useful as it would provide more drop points and a wider variety of biopharma companies. A larger sample size would especially make the performance results for news category subsets more conclusive and would allow each news category to be split up by drop size in order to calculate abnormal returns associated with different news and drop size combinations.
It would also be interesting to determine whether technical indicators like relative strength index or moving averages are correlated with performance.
The most important area to address in the study of abnormal stock performance in this industry is development of a near term catalyst variable. Biopharma stock trading is often dictated by stocks substantially appreciating in anticipation of the release of clinical
48 data or an FDA decision. Coming up with an accurate near-term catalyst valuation methodology based on projected event date, projected revenue stream of product, and chance of a positive result would provide a huge advantage to a biopharma equity trader.
The development of this variable would highlight large one-day drops where traders overreacted and did not consider the upside of a future catalyst. While this study does not provide significant evidence for the OH as was the initial goal, performance results have opened the door to an area of study likely to discover opportunities for significant abnormal profit.
49
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