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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................................................................................................................
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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
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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
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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.
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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
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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
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(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
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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).
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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
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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
9
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
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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
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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
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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
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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
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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).
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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.
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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
17
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
18
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;
19
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
20
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
21
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
22
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.
23
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
24
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.
25
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
26
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
27
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:
𝐴𝑅!" = 𝑅!" − 𝑅!! (4.1)
where 𝑅! is the return of security x, 𝑅! is the return of the NBI, 𝐴𝑅! 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 𝐴𝑅!:
𝑡 = !"!!!"!
! (4.2)
where
𝐴𝑅! = !!
𝐴𝑅!". (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
28
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:
𝑡! = 𝑛(𝐶 + !!𝛾𝐶! + !
!!𝛾) (4.4)
where
𝐶 = !"!!!"!
and 𝛾 = (!"!"!!"!)!
!(!!"!)! (4.5)
with C 𝑛 being the conventional t-statistic and 𝑦 the skewness estimate (Johnson, 1978;
Lyon et al., 1999). To complete the bootstrapping procedure, one thousand samples of
size 𝑛 /4 will be used to calculate 𝑡! 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:
29
(4.6)
Where
(Yale, 1998). (4.7)
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:
𝑡!"# = +! !
! !
(!− 1) (4.8)
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:
𝑔 = 𝑚!𝑚!!!/! (4.9)
Where
𝑚! is defined as the 𝑟th moment about the pooled mean 𝑥:
𝑚! =!!
(𝑥! − 𝑥)! (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).
( )
+
−=
21
21
11nn
s
xxt
p
pooled
( ) ( )211
21
222
211
−+
−+−=
nnsnsn
s pooled
pooledt 1n 2n
1n 2n 1n 2npooledt
30
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
31
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.
32
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
33
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.
34
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 All Drops Besides Pipeline Results (149)
-0.94 -79.98 -0.80 32.74
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: 𝑥=0, Ha: 𝑥≠0) ** Significant at the 0.10 level ** Significant at the 0.05 level
35
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
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
Outliers n = 7
Sample n = 180
Median AR = -17.01
36
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-
37
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 t-statistic
Bootstrapped 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: 𝑥=0, Ha: 𝑥≠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 𝑡! = 𝑛(𝐶 + !
!𝛾𝐶! + !
!!𝛾) where 𝐶 = !"!
!!"! and
𝛾 = (!"!"!!"!)!
!(!!"!)! was calculated for the long-term return measurement and then bootstrapped with a thousand
repetitions and a sample size of 𝑛/4. The observed coefficient is the mean of the sample means from the repetitions.
38
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: 𝑥! = 𝑥!, Ha: 𝑥! ≠ 𝑥!; 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
39
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.
40
FIGURE 5.2
TWO-DAY ABNORMAL RETURNS
‐5
‐4
‐3
‐2
‐1
0
1
2
3
Abnorm
al Percent Return
Drop Type
Two‐Day Abnormal Returns
Original Sample
After Removal of 8/4‐8/10
Complete Set
Ten to Twenty
Percent Drop
Greater than Twenty
Percent Drop
All Drops Besides Pipeline Results
Earnings Release
Other News
No News Pipeline Results
41
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: 𝑥=0, Ha: 𝑥≠0 * Significant at the 0.05 level
42
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
43
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
‐25
‐20
‐15
‐10
‐5
0
5
10
Abnorm
al Return
Drop Type
Six‐Month Abnormal Return
Original Sample
After Removal of 8/4‐8/10
Complete Set
Ten to Twenty Percent Drop
Greater than Twenty Percent Drop
All Drops Besides Pipeline News
Earnings Release
Other News
No News
Pipeline Results
44
TABLE 5.7
SIX-MONTH ABNORMAL RETURNS WITH REMOVAL OF INDUSTRY CRASH
Data Group Standard t-statistic
Bootstrapped 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: 𝑥=0, Ha: 𝑥≠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: 𝑥! = 𝑥!, Ha: 𝑥! ≠ 𝑥!; 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 𝑡! = 𝑛(𝐶 + !
!𝛾𝐶! + !
!!𝛾) where 𝐶 = !"!
!!"! and
𝛾 = (!"!"!!"!)!
!(!!"!)! was calculated for the long-term return measurement and then bootstrapped with a thousand
repetitions and a sample size of 𝑛/4. The observed coefficient is the mean of the sample means from the repetitions.
45
TABLE 5.9
BOOTSTRAPPED SKEW-ADJUSTED T-TESTS FOR SIX-MONTH
ABNORMAL RETURNS
Data Group Complete Data Set4
After Removal 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: 𝑥=0, Ha: 𝑥≠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 𝑡! = 𝑛(𝐶 + !
!𝛾𝐶! + !
!!𝛾) where 𝐶 = !"!
!!"! and
𝛾 = (!"!"!!"!)!
!(!!"!)! was calculated for the long-term return measurement and then bootstrapped with a thousand
repetitions and a sample size of 𝑛/4. The observed coefficient is the mean of the sample means from the repetitions.
46
valuation when considering that long-term returns for stocks that dropped due to earnings
were significantly negative the rest of the year (-12.74%).
47
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
48
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
49
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.
50
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