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.................................................................................................

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4.1 Data Selection……………………................................................................ 25 4.1 Method……………………........................................................................... 27 5 EMPIRICAL RESULTS........................................................................................

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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

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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

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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

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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

Sources Consulted

Avik, R. (2012). How the FDA stifles new cures, part I: The rising cost of clinical trials.

Retrieved from http://www.forbes.com/sites/aroy/2012/04/24/how-the-fda-stifles-

new-cures-part-i-the-rising-cost-of-clinical-trials/

Avramov, D., Chordia, T., & Goyal, A. (2006). Liquidity and autocorrelations in

individual stock returns. The Journal of Finance, 61(5), 2365-2394. Retrieved from

http://www.jstor.org/stable/10.2307/3874713

Balkin, S. D., & Mallows, C. L. (2001). An adjusted, asymmetric two-sample t test. The

American Statistician, 55(3), 203-206. Retrieved from

http://amstat.tandfonline.com/doi/abs/10.1198/000313001317098176

Ball, R., & Brown, P. (1968). An empirical evaluation of accounting income numbers.

Journal of Accounting Research, 159-178. Retrieved from

http://www.jstor.org/stable/10.2307/2490232

Bansal, A. (2010). As you sow, so shall you reap: Evidence of innovation and drug

portfolio diversification from the stock market. Retrieved from

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1605127

51

Banz, R. W. (1981). The relationship between return and market value of common

stocks. Journal of Financial Economics, 9(1), 3-18. Retrieved from

http://www.sciencedirect.com/science/article/pii/0304405X81900180

Banz, R. W. (1978). Limited diversification and market equilibrium: An empirical

analysis. Unpublished Manuscript, University of Chicago, Chicago, IL. Retrieved

fromshttp://books.google.com/books/about/Limited_Diversification_and_Market_Eq

uil.html?id=Bf0fNgAACAAJ

Barber, B. M., & Lyon, D. (1997). Detecting long-run abnormal stock returns: The

empirical power and specification of test statistics. Journal of Financial Economics,

43(341), 372. Retrieved from

http://www.sciencedirect.com/science/article/pii/S0304405X96008902

Barber, B. M., & Odean, T. (2008). All that glitters: The effect of attention and news on

the buying behavior of individual and institutional investors. Review of Financial

Studies, 21(2), 785-818. Retrieved from

http://rfs.oxfordjournals.org/content/21/2/785.short

Beechey, M., Gruen, D., Vickery, J., & Reserve Bank of Australia. (2000). The efficient

market hypothesis: A survey. Reserve Bank of Australia, Economic Research

Department. Retrieved from

http://www-ho.rba.gov.au/publications/rdp/2000/pdf/rdp2000-01.pdf

Bernard, V. L., & Thomas, J. K. (1989). Post-earnings-announcement drift: Delayed

price response or risk premium? Journal of Accounting Research, 27, 1-36.

Retrieved from http://www.jstor.org/stable/10.2307/2491062

52

Bosch, J. C., & Lee, I. (1994). Wealth effects of Food and Drug Administration (FDA)

decisions. Managerial and Decision Economics, 15(6), 589-599. Retrieved from

http://onlinelibrary.wiley.com/doi/10.1002/mde.4090150606/abstract

Bossaerts, P., & Hillion, P. (2001). IPO post-issue markets: Questionable predilections

but diligent learners? Review of Economics and Statistics, 83(2), 333-347. Retrieved

from http://www.mitpressjournals.org/doi/abs/10.1162/00346530151143860

Brandt, M. W., Brav, A., Graham, J. R., & Kumar, A. (2010). The idiosyncratic volatility

puzzle: Time trend or speculative episodes? Review of Financial Studies, 23(2), 863-

899. Retrieved from http://rfs.oxfordjournals.org/content/23/2/863.short

Bratic, W., Tilton, P., & Balakrishnan, M. (2000). Navigating through a biotech

valuation. Journal of Biotechnology in Healthcare Research and Regulation, 4, 1-6.

Retrieved from

http://www.canbiotech.com/UserResourcesCB/BusinessDev/NavBiotech.pdf

Bremer, M., & Sweeney, R. J. (1991). The reversal of large stock-price decreases.

Journal of Finance, 747-754. Retrieved from

http://onlinelibrary.wiley.com/doi/10.1111/j.1540-6261.1991.tb02684.x/abstract

California Biomedical Research Assocation. (2012). Fact sheet--new drug development

process. Retrieved from http://ca-biomed.org/pdf/media-kit/fact-

sheets/cbradrugdevelop.pdf

Chan, K. (1988). On the contrarian investment strategy. Journal of Business, 147-163.

Retrieved from http://www.jstor.org/stable/10.2307/2352897

53

Chan, L. K. C., Lakonishok, J., & Sougiannis, T. (2001). The stock market valuation of

research and development expenditures. Journal of Finance, 2431-2456. Retrieved

from http://www.nber.org/papers/w7223

Chen, S. H., & Kuo, T. W. (2001). Trading strategies on trial: A comprehensive review

of 21 practical trading strategies over 56 listed stocks. Proceedings of the Fourth

International Conference on Computational Intelligence and Multimedia

Applications (ICCIMA 2001), 66-71. Retrieved from

http://www.m-finance.net/hfe/Trading%20Strategies%20on%20Trial.pdf

Chopra, N., Lakonishok, J., & Ritter, J. R. (1992). Measuring abnormal performance: Do

stocks overreact? Journal of Financial Economics, 31(2), 235-268. Retrieved from

http://www.sciencedirect.com/science/article/pii/0304405X9290005I

Chung, D., & Hrazdil, K. (2008). Market efficiency and the post-earnings announcement

drift. Contemporary Accounting Research, 28(3), 926-956. Retrieved from

http://onlinelibrary.wiley.com/doi/10.1111/j.1911-3846.2011.01078.x/full

Conrad, J., Hameed, A., & Niden, C. (1994). Volume and autocovariances in short-

horizon individual security returns. Journal of Finance, 1305-1329. Retrieved from

http://onlinelibrary.wiley.com/doi/10.1111/j.1540-6261.1994.tb02455.x/abstract

Conrad, J., & Kaul, G. (1993). Long-term market overreaction or biases in computed

returns? Journal of Finance, 39-63. Retrieved from

http://onlinelibrary.wiley.com/doi/10.1111/j.1540-6261.1993.tb04701.x/abstract

54

Cox, D. R., & Peterson, D. R. (1994). Stock returns following large one-day declines:

Evidence on short-term reversals and longer-term performance. Journal of Finance,

255-267. Retrieved from http://onlinelibrary.wiley.com/doi/10.1111/j.1540-

6261.1994.tb04428.x/abstract

De Bondt, W. F. M. (2000). The psychology of underreaction and overreaction in world

equity markets. Publications of the Newton Institute, 9, 65-81. Retrieved from

http://fac.comtech.depaul.edu/wdebondt/Publications/PsychologyUnderOver.pdf

De Bondt, W. F. M., & Thaler, R. (1985). Does the stock market overreact? Journal of

Finance, 793-805. Retrieved from

http://onlinelibrary.wiley.com/doi/10.1111/j.1540-6261.1985.tb05004.x/abstract

Dedman, E., Lin, S. W. J., Prakash, A. J., & Chang, C. H. (2008). Voluntary disclosure

and its impact on share prices: Evidence from the UK biotechnology sector. Journal

of Accounting and Public Policy, 27(3), 195-216. Retrieved from

http://www.sciencedirect.com/science/article/pii/S0278425408000215

Del Guercio, D. (1996). The distorting effect of the prudent-man laws on institutional

equity investments. Journal of Financial Economics, 40(1), 31-62. Retrieved from

http://www.sciencedirect.com/science/article/pii/0304405X95008412

Desai, H., & Jain, P. C. (1997). Long-run common stock returns following stock splits

and reverse splits. The Journal of Business, 70(3), 409-433. Retrieved from

http://www.jstor.org/stable/10.1086/209724

55

DiMasi, J. A., Hansen, R. W., & Grabowski, H. G. (2003). The price of innovation: New

estimates of drug development costs. Journal of Health Economics, 22(2), 151-186.

Retrieved from

http://www.manhattan-institute.org/projectfda/health_econ_price_of_innovation.pdf

Edwards, R. D., Magee, J., & Bassetti, W. (2007). Technical analysis of stock trends.

New York: CRC Press, Inc.

Fama, E. F. (1991). Efficient capital markets: II. Journal of Finance, 1575-1617.

Retrieved from

http://onlinelibrary.wiley.com/doi/10.1111/j.1540-6261.1991.tb04636.x/abstract

Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work.

Journal of Finance, 25(2), 383-417. Retrieved from

http://onlinelibrary.wiley.com/doi/10.1111/j.1540-6261.1970.tb00518.x/abstract

Fan, P. (2010). The impacts of non-corporate events on biotech firms. Unpublished

Manuscript, Ghent University, Ghent, Belguim. Retrieved from

http://lib.ugent.be/fulltxt/RUG01/001/459/856/RUG01-

001459856_2011_0001_AC.pdf

Farrell, M. (2012). Knight's bizarre trades rattle markets. Retrieved from

http://buzz.money.cnn.com/2012/08/01/trading-glitch/

Farzad, R. (2012). Mutual funds confront not-quite-as-dumb money. Retrieved from

http://www.businessweek.com/articles/2012-05-03/mutual-funds-confront-not-quite-

as-dumb-money

56

FDA. (2008). Trends in NDA and BLA submissions and approval times. Retrieved from

http://www.fda.gov/AboutFDA/ReportsManualsForms/Reports/UserFeeReports/Perf

ormanceReports/PDUFA/ucm209349.htm

Forbes, S. (2012). Small-cap master royce: Many myths about small stocks--and even

more opportunities! Retrieved from

http://www.forbes.com/sites/steveforbes/2012/07/03/small-cap-master-royce-many-

myths-about-small-stocks-and-even-more-opportunities/

Foster, G. (1982). Capital market efficiency: Definitions, testing issues and anomalies.

Unpublished Manuscript, Stanford University, Stanford, California. Retrieved from

https://gsbapps.stanford.edu/researchpapers/library/RP643.pdf

George, T. J., & Hwang, C. Y. (2007). Long‐Term return reversals: Overreaction or

taxes? The Journal of Finance, 62(6), 2865-2896. Retrieved from

http://onlinelibrary.wiley.com/doi/10.1111/j.1540-6261.2007.01295.x/full

Girotra, K., Terwiesch, C., & Ulrich, K. T. (2007). Valuing R&D projects in a portfolio:

Evidence from the pharmaceutical industry. Management Science, 53(9), 1452-1466.

Retrieved from http://mansci.journal.informs.org/content/53/9/1452.short

Goetzmann, W. N., & Jorion, P. (2012). Testing the predictive power of dividend yields.

The Journal of Finance, 48(2), 663-679. Retrieved from

http://onlinelibrary.wiley.com/doi/10.1111/j.1540-6261.1993.tb04732.x/abstract

57

Gonzalez, C., & Gimeno, R. (2008). Financial analysts impact on stock volatility: A

study on the pharmaceutical sector. FEDEA Working Paper no. 2008-19. Retrieved

from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1132938

Google Finance. (2012). Dow Jones Industrial Average. Retrieved from

http://www.google.com/finance?q=.dji&ei=93xPULiiJsiOrgHsNg

Graham, B., & Dodd, D. L. F. (1996). Security analysis. New York: McGraw-Hill.

Graham, B. (1959). The intelligent investor: A book of practical counsel (2nd ed.). New

York, Harper & Brothers.

Guo, Re-Jin., Lev, B., & Zhou, N. (2004). Competitive costs of disclosure by biotech

IPOs. Journal of Accounting Research, 42(2), 319-355. Retrieved from

http://onlinelibrary.wiley.com/doi/10.1111/j.1475-679X.2004.00140.x/abstract

Hagin, R. (1979). The Dow Jones-Irwin guide to modern portfolio theory. New York:

McGraw-Hill/Irwin.

Haug, M., & Hirschey, M. (2006). The January effect. Financial Analysts Journal, 78-88.

Retrieved from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=831985

Haugen, R. A., & Jorion, P. (1996). The January effect: still there after all these years.

Financial Analysts Journal, 27-31. Retrieved from

http://www.jstor.org/stable/10.2307/4479893

Heath, E. (2012). Overview of the FY 2013 NIH budget. Retrieved from

www.aaas.org/spp/rd/rdreport2013/13pch07.pdf

58

Himmelmann, A., & Schiereck, D. (2010). The effects of innovation on stock liquidity

and systematic risk: Evidence from the pharmaceutical industry. Unpublished

Manuscript, Tech University Darmstadt, Darmstadt, Germany. Retrieved from

http://www.campus-for-finance.com/filebrowser/file/Papers/49a40609594_name.pdf

Hong, H., & Stein, J. C. (1999). A unified theory of underreaction, momentum trading,

and overreaction in asset markets. The Journal of Finance, 54(6), 2143-2184.

Retrieved from http://onlinelibrary.wiley.com/doi/10.1111/0022-

1082.00184/abstract

Houston, C. A. (2010). Biotechnology Valuation: An Examination of the Drug

Development Pipeline and Board of Director Composition. Unpublished Manuscript,

University of North Carolina Wilmington, Wilmington, NC. Retrieved from

http://dl.uncw.edu/etd/2009-3/r1/houstonc/chadhouston.pdf

Howe, J. S. (1986). Evidence on stock market overreaction. Financial Analysts Journal,

42(4), 74-77. Retrieved from http://www.jstor.org/stable/10.2307/4478954

Huggett, B., Hodgson, J., & Lähteenmäki, R. (2011). Public biotech 2010—The numbers.

Nature Biotechnology, 29(7), 585-591. Retrieved from

http://www.ncbi.nlm.nih.gov/pubmed/21747381

Human Genome Sciences Inc. (2009). Human genome sciences and glaxosmithkline

announce positive results in second of two phase 3 trials of benlysta™ in systemic

lupus erythematosus. Retrieved from http://www.hgsi.com/latest/human-genome-

sciences-and-glaxosmithkline-announce-positive-results-in-second-of-two-phase-3-

trials-of-benlysta-in-systemic-lupus-erythema-2.html

59

International Network of M&A Partners. (2011). Pharmaceuticals & biotech industry

global report. Retrieved from

http://imap_pharmareport_8_272B8752E0FB3-2.pdf

Investopedia. (2008). World's wackiest stock indicators. Retrieved from

http://www.investopedia.com/articles/stocks/08/stock-market-indicators.asp

Jacobe, D. (2012). In U.S., 54% have stock market investments, lowest since 1999.

Retrieved from http://www.gallup.com/poll/147206/stock-market-investments-

lowest-1999.aspx

Jain, B. A., & Kini, O. (1994). The post-issue operating performance of IPO firms.

Journal of Finance, 1699-1726. Retrieved from

http://onlinelibrary.wiley.com/doi/10.1111/j.1540-6261.1994.tb04778.x/abstract

Jeffrey, N. G. (2007). Implications of transaction costs for the post-earnings-

announcement. Unpublished Manuscript, Singapore Management University,

Singapore. Retrieved from

http://www2.accountancy.smu.edu.sg/research/seminar/pdf/JeffreyNG_paper.pdf

Johnson, N. J. (1978). Modified t tests and confidence intervals for asymmetrical

populations. Journal of the American Statistical Association, 73(363), 536-544.

Retrieved from

http://www.tandfonline.com/doi/abs/10.1080/01621459.1978.10480051

Jones, C. P., & Litzenberger, R. H. (1970). Quarterly earnings reports and intermediate

stock price trends. The Journal of Finance, 25(1), 143-148. Retrieved from

http://onlinelibrary.wiley.com/doi/10.1111/j.1540-6261.1970.tb00420.x/abstract

60

Kaul, G., & Nimalendran, M. (1990). Price reversals: Bid-ask errors or market

overreaction? Journal of Financial Economics, 28(1), 67-93. Retrieved from

http://www.sciencedirect.com/science/article/pii/0304405X90900485

Ke, B., & Ramalingegowda, S. (2005). Do institutional investors exploit the post-

earnings announcement drift? Journal of Accounting and Economics, 39(1), 25-53.

Retrieved from

http://www.sciencedirect.com/science/article/pii/S0165410104000552

Keim, D. B. (1983). Size-related anomalies and stock return seasonality: Further

empirical evidence. Journal of Financial Economics, 12(1), 13-32. Retrieved from

http://www.sciencedirect.com/science/article/pii/0304405X83900259

Kellogg, D., & Charnes, J. M. (2000). Real-options valuation for a biotechnology

company. Financial Analysts Journal, 76-84. Retrieved from

http://www.jstor.org/stable/10.2307/4480249

Keynes, J. M. (1937). The general theory of employment. The Quarterly Journal of

Economics, 51(2), 209-223. Retrieved from

http://qje.oxfordjournals.org/content/51/2/209.short

Kieso, D. E., Weygandt, J. J., & Warfield, T. D. (2010). Intermediate accounting: IFRS

edition. Hoboken, New Jersey: Wiley & Sons, Inc.

Kim, S., Klein, A., & Rosenfeld, J. (2008). Return performance surrounding reverse

stock splits: Can investors profit? Financial Management, 37(2), 173-192.

61

Retrieved from http://onlinelibrary.wiley.com/doi/10.1111/j.1755-

053X.2008.00009.x/full

Klein, R. W., & Bawa, V. S. (1977). The effect of limited information and estimation risk

on optimal portfolio diversification. Journal of Financial Economics, 5(1), 89-111.

Retrieved from

http://www.sciencedirect.com/science/article/pii/0304405X77900319

Kothari, S., & Warner, J. Econometrics of event studies, E. Eckbo, editor. Handbook of

Empirical Corporate Finance, 1, 4-32. Retrieved from

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=608601

Kusick, L. (2007). How do I Find short interest for a stock? Retrieved from

http://www.thestreet.com/story/10345483/1/how-do-i-find-short-interest-for-a-

stock.html

Larson, S. J., & Madura, J. (2003). What drives stock price behavior following extreme

one-day returns. The Journal of Financial Research, 26(1), 113. Retrieved from

http://onlinelibrary.wiley.com/doi/10.1111/1475-6803.00048/full

Lauricella, T. (2010). Market plunge baffles Wall Street. Retrieved from

http://online.wsj.com/article/SB10001424052748704370704575228664083620340.h

tml

Lev, B., Radhakrishnan, S., & Ciftci, M. (2008). The stock market valuation of R&D

leaders. Unpublished Manuscript, New York University, New York, New York.

Retrieved from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1280696

62

Lewellen, J., & Shanken, J. (2002). Learning, asset‐pricing tests, and market efficiency.

The Journal of Finance, 57(3), 1113-1145. Retrieved from

http://onlinelibrary.wiley.com/doi/10.1111/1540-6261.00456/abstract

Lichtenfeld, M. (2012). Why investors shouldn't worry about the next 10 years. Retrieved

from http://money.usnews.com/money/blogs/the-smarter-mutual-fund-

investor/2012/08/22/why-investors-shouldnt-worry-about-the-next-ten-years

Liu, Q. (2006). How good is good news? Technology depth, book-to-market ratio, and

innovative events. Journal of Accounting, Auditing, and Finance, 21(3), 293.

Retrieved from http://jaf.sagepub.com/content/21/3/293.short

Lyon, J. D., Barber, B. M., & Tsai, C. L. (1999). Improved methods for tests of long-run

abnormal stock returns. Journal of Finance, 165-201. Retrieved from

http://onlinelibrary.wiley.com/doi/10.1111/0022-1082.00101/abstract

Ma, Y., Tang, A. P., & Hasan, T. (2005). The stock price overreaction effect: Evidence

on NASDAQ stocks. Quarterly Journal of Business and Economics, 113-127.

Retrieved from http://www.jstor.org/stable/10.2307/40473407

Malkiel, B. G. (2003). A random walk down Wall Street. New York: WW Norton &

Company.

Manley, N. (1999). Financial ratios. Retrieved from

http://www.stfrancis.edu/content/ba/ghkickul/stuwebs/btopics/works/ratios.htm

63

Marchman, B. (2007). Reverse stock splits: Motivations, effectiveness, and stock price

reactions. Unpublished Manuscript, Florida State University, Tallahassee, FL.

Retrieved from http://diginole.lib.fsu.edu/etd/2744/

McClure, B. (2011). How to do qualitative analysis on biotech companies. Retrieved

from http://www.investopedia.com/articles/stocks/11/qualitative-analysis-biotech-

companies.asp

Mendenhall, R. R. (2004). Arbitrage risk and Post‐Earnings‐Announcement drift. The

Journal of Business, 77(4), 875-894. Retrieved from

http://www.jstor.org/stable/10.1086/422627

Mun, J. C., Vasconcellos, G. M., & Kish, R. (2001). The Contrarian/Overreaction

hypothesis: An analysis of the US and Canadian stock markets. Global Finance

Journal, 11(1), 53-72. Retrieved from

http://www.sciencedirect.com/science/article/pii/S1044028300000119

NASDAQ. (2012). NASDAQ biotechnology index. Retrieved from

http://www.nasdaq.com/dynamic/nasdaqbiotech_activity.stm

NYSE Euronext. (2012). About us--history--timeline. Retrieved from

http://www.nyse.com/about/history/timeline_trading.html

Pietersz, G. (2012). Drug pipeline. Retrieved from

http://moneyterms.co.uk/drug_pipeline/

Rader, R. A. (2005). What is a biopharmaceutical? BioExecutive International. May

2005, 60-65. Retrieved from http://www.biopharma.com/bioexec_pt2.pdf

64

Reinganum, M. R. (1982). A direct test of roll's conjecture on the firm size effect.

Journal of Finance, 27-35. Retrieved from

http://onlinelibrary.wiley.com/doi/10.1111/j.1540-6261.1982.tb01093.x/abstract

Repkine, A. (2008). Charting technical trading rules and the lottery of technical

analysis: Empirical evidence from foreign exchange market. Unpublished

Manuscript, Konkuk University, Seoul, South Korea. Retrieved from

http://mpra.ub.uni-muenchen.de/7849/

Ritter, J. R. (1991). The long-run performance of initial public offerings. Journal of

Finance, 3-27. Retrieved from http://onlinelibrary.wiley.com/doi/10.1111/j.1540-

6261.1991.tb03743.x/abstract

Schwager, J. D. (1996). Technical analysis. Hoboken, New Jersey: John Wiley & Sons.

Schwert, G. W. (1983). Size and stock returns, and other empirical regularities. Journal

of Financial Economics, 12(1), 3-12. Retrieved from

http://schwert.ssb.rochester.edu/size.pdf

Scottrade. (2012). Knowledge center: The indicators. Retrieved 9/16, 2012, from

http://research.scottrade.com/public/knowledgecenter/education/article.asp?docId=e

c9ae5f8d0de4737bf9575832f6b74be

Sharma, A., & Lacey, N. (2004). Linking product development outcomes to market

valuation of the firm: The case of the US pharmaceutical industry. Journal of

Product Innovation Management, 21(5), 297-308. Retrieved from

http://onlinelibrary.wiley.com/doi/10.1111/j.0737-6782.2004.00084.x/full

65

Shiller, R. J. (1990). Speculative prices and popular models. The Journal of Economic

Perspectives, 4(2), 55-65. Retrieved from

http://www.jstor.org/stable/10.2307/1942890

Shiller, R. J., Fischer, S., & Friedman, B. M. (1984). Stock prices and social dynamics.

Brookings Papers on Economic Activity, 1984(2), 457-510. Retrieved from

http://www.jstor.org/stable/10.2307/2534436

StataCorp. (2009). Stata: Reference Q-Z. R, 1431-2106.

Sullivan, R., Timmermann, A., & White, H. (2002). Data‐snooping, technical trading rule

performance, and the bootstrap. The Journal of Finance, 54(5), 1647-1691.

Retrieved from http://onlinelibrary.wiley.com/doi/10.1111/0022-

1082.00163/abstract

Terrell, E. (2010). History of the New York stock exchange. Retrieved from

http://www.loc.gov/rr/business/hottopic/stock_market.html

University of Pittsburgh. (2002). Comparison of phases of clinical trials. Retrieved from

www.clinicalresearch.pitt.edu/docs/comparison_of_clinical_trial_phases.pdf

Vacca, M. (2006). Philadelphia Stock Exchange. The Historical Society of Pennsylvania.

Retrieved from

http://hsp.org/sites/default/files/legacy_files/.../findingaid3070phlx.pdf

Vermaelen, T., & Verstringe, M. (1986). Do Belgians overreact? Unpublished

Manuscript, Université Catholique De Louvain, Louvain-La-Neuve, Belguim.

66

Retrieved from

http://books.google.com/books/Do_Belgians_Overreact.html?id=f6RAuAAACAAJ

Wong, W. K., Manzur, M., & Chew, B. K. (2003). How rewarding is technical analysis?

Evidence from Singapore stock market. Applied Financial Economics, 13(7), 543-

551. Retrieved from

http://www.tandfonline.com/doi/abs/10.1080/0960310022000020906

Yahoo! Finance. (2012). NASDAQ biotechnology (^NBI). Retrieved from

http://finance.yahoo.com/q/cp?s=%5eNBI

York, A. S., Dunham, L. M., & Ahn, M. (2011). Vertical versus horizontal integration in

the biopharma industry: The link between acquisition announcements and stock

market performance. Advances in Mergers and Acquisitions, 10, 121-143. Retrieved

from http://www.emeraldinsight.com/books.htm?chapterid=17006854

Zarowin, P. (1990). Size, seasonality, and stock market overreaction. Journal of

Financial and Quantitative Analysis, 25(1), 113-125. Retrieved from

http://www.jstor.org/stable/10.2307/2330891