Reverse Stock Splits in the Biotechnology Industry: An Effectuation Approach
Article (PDF Available) in Journal of Commercial Biotechnology 21(1):3-18 · January 2015 with 908 Reads
· Robert Couch
· · Mark Ahn
- Portland State University
· · Wei Wu
- Portland State University
Using an effectuation theory lens, we study reverse stock splits in the biotech industry where significant uncertainty makes specific scenarios of success difficult to predict. We conjecture and find that, in contrast to other environments where there is less uncertainty, reverse stock splits in the biotech industry are followed by positive abnormal returns over the subsequent 1- to 12-months. Also consistent with our effectuation-based predictions, we find that these returns are positively related to the reverse split ratio, size, cash holding, and long-term debt, and negatively related to the market-to-book ratio and firm age. We also find that liquidity increases after a reverse stock split. These results suggest that the concept of effectuation theory is better suited to analyzing reverse stock splits in the biotech industry.
Available from: Robert Couch, Sep 06, 2015
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REVERSE STOCK SPLITS IN THE BIOTECH INDUSTRY:
AN EFFECTUATION APPROACH
Using an effectuation theory lens, we study reverse stock splits in the biotech industry where
significant uncertainty makes specific scenarios of success difficult to predict. We conjecture and
find that, in contrast to other environments where there is less uncertainty, reverse stock splits in
the biotech industry are followed by positive abnormal returns over the subsequent 1- to 12-
months. Also consistent with our effectuation-based predictions, we find that these returns are
positively related to the reverse split ratio, size, cash holding, and long-term debt, and negatively
related to the market-to-book ratio and firm age. We also find that liquidity increases after a
reverse stock split. These results suggest that the concept of effectuation theory is better suited to
analyzing reverse stock splits in the biotech industry.
Keywords: biotechnology, effectuation, reverse stock splits, event study, cumulative
abnormal returns (CARs), liquidity
According to standard, predictive signaling theory, reverse stock splits send a negative
signal to the stock market. This prediction arises from the following logic. Managers are
assumed to have superior predictive information about future cash flows. So, when the stock
price is below the optimal range, and there are poor prospects about the arrival of good news
regarding future cash flows, then the decision to undergo a reverse stock split (RSS) reveals the
manager’s negative private beliefs (or non-presence of positive beliefs). Studies show that in
particular, expert managers in highly uncertain business environments do not use a predictive
mental framework; rather, managers think in terms of their ability to effectuate change within
their own firm’s business environment.1. Thus, in business environments with a high degree of
uncertainty, there is reason to question the explanatory relevance of traditional, predictive
Biotech firms operate in a highly uncertain environment. The sequential progression of
products, from pre-clinical and human testing to drug approval requires relatively large sums of
capital and multiple rounds of financing in order to progress through critical phases of
development.2. Obtaining financing at each stage of development is crucial for the survival and
eventual success of these highly volatile biotech firms.3-5. Also, valuation of these firms is very
difficult. Traditional valuation methods, such as discounted cash flow and relative valuation
practices, tend to lead to under-valuation and under-investment in earlier stage drug development
projects.6 Real-option models better capture the stochastic nature of the breakthrough potential
and abandonment options for biotech firms, but these models are still very difficult to implement.
To place the difficulty of valuing this uncertainty in context, the stock market index for the
biotechnology sector (BTK), which has outperformed the overall market, has been 9 times more
volatile than the S&P 500; and 5 times more volatile than the NASDAQ (Figure 1).
Figure 1: Biotechnology Index vs. S&P 500 and
NASDAQ Indices November 1989 – March 2014.
Because of these features of the biotech industry, we hypothesize that the signaling
properties of reverse stock splits for biotech firms will differ from the signaling properties
implied by the traditional, predictive model. Investors’ ability to predict success among biotech
firms does not depend on being able to predict success of specific conceivable scenarios; rather,
success depends more on being able to predict how well, and how likely, firms will be successful
in “taking effectual action and help[ing] stakeholders make effectual commitments” in a
radically uncertain future.7 Also, with effectual processes, the environment is not exogenous to
the firm’s transformative actions and, because of this endogenous relationship between
stakeholder action and the environment, success depends heavily on endogenous factors, like the
ability to obtain stakeholder commitments and the ability to adaptively coordinate and leverage
capabilities both within and without the firm. We analyze these differences between the
traditional, predictive-signaling model and an effectuation-based signaling model and
Index 100=Nov 1989-Mar 2014
hypothesize that, for biotech firms, reverse stock splits should comprise a positive signal about
future prospects of success.
To empirically test our hypothesis, we utilize event study methodology and find that
cumulative abnormal returns (CARs) are positive following a reverse stock split for biotech
firms. We also find that this effect is stronger when the split ratio is higher. These results are
consistent with our hypothesis that, in accordance with effectuation theory, a manager’s
commitment to keeping the firm’s stock price sufficiently high, in order to avoid the risk of
having to delist, is a signal that the manager has positive beliefs about his or her ability to
effectively exercise control over endogenous factors important to the firm’s ability to succeed in
In additional cross-sectional regression analysis, we find that abnormal returns are
associated with firms that are larger, have greater cash holding, are younger, have a positive
amount of long-term debt, and, albeit a less robust finding, have lower market-to-book ratios.
These results are consistent with our effectuation-based model in the following ways. With
regard to size, larger firms have greater control over their environment, implying that our
positive abnormal return hypothesis should be greater for larger firms. With regard to cash
holding, firms with more cash have more means to control their environment. With regard to
firm age, firms that are older are “past their prime” in the sense of failing to signal their ability to
be successful even when given a reasonable amount of time to do so. With regard to long-term
debt, the presence of such debt signals an ability to get financial stakeholders to commit to the
future of the firm. Finally, with regard to market-to-book ratios, value firms that have more
depressed market values, relative to their book value, are able to strengthen any positive impact
from the signal to the market and lead to stronger positive abnormal returns. Although not all of
these results are uniquely predicted by our effectuation-based framework, these results
nevertheless make good sense from an effectuation perspective.
We also study liquidity and find that liquidity measures, such as turnover ratio, the
proportion of days with zero returns, and the Amivest liquidity ratio (a measure of the price
impact of a trade), all point to a positive impact on liquidity following a reverse stock split by
biotech firms. This result is consistent with other studies of reverse splits.8-9 In light of the
positive abnormal returns, the improved liquidity implies the positive signal of the RSS has
attracted more participation and trading activity from investors and, consequently, a lower cost
of equity in further rounds of financing.
The remainder of our paper is organized as follows. Section 2 analyzes the biotechnology
industry and effectuation theory. Section 3 discusses forward and reverse splits, and provides
motivation for our empirical hypotheses and predictions. Section 4 describes empirical
methodology, data, and sample summary statistics. Section 5 presents our empirical results from
analyzing stock returns and liquidity. Section 6 concludes and suggests areas for future research.
2. An Effectuation-Based View of the Biotech Industry
The biotech industry is heavily dependent on the research and development of new drugs.
Because of the significant uncertainty and long-term nature of biotech research, which typically
requires multiple rounds of new financing, the biotech industry has many of the characteristics
embedded in effectuation theory. In this section, we first describe the highly uncertain, non-
predictive nature of the biotech industry. Then we describe effectuation theory and argue that it
provides a framework for understanding the biotech industry that is more suitable than standard
2.1. The Non-Predictive Nature of the Biotech Industry
Biotech research is a highly uncertain, long-term affair. Predicting which particular
research efforts will be successful is very difficult. Because of this, successful biotech firms
typically pivot several times, from one area of research to another, before achieving any
significant level of success. Moreover, new lines of promising research frequently appear only
after initial research in some area is already begun.
This underlying uncertainty of the research process is compounded by a fundamental
financial tension that biotech firms face: on the one hand, the vast majority of development-stage
biotech firms have no revenues; on the other hand, these same firms must plan for long product
development cycles (12 years on average from initial research to commercialization). Because of
this tension, financing occurs in successive incremental rounds that provide resources to the next
valuation inflection point (typically 1-3 years). This firm-specific financial risk is compounded
by the volatile nature of market-wide “open windows” for subsequent financings. Thus, a firm
could be progressing on research goals, but end up being unable to raise capital at accretive
terms due either to investor skepticism or a down market. And because biotech firms rely so
heavily on multiple rounds of financing, setbacks in achieving milestones can be devastating to
development-stage biotech firms.10
The following example of Cytokinetics illustrates the compounding effects associated
with the highly uncertain nature of biotech research, long-product cycles, and multiple rounds of
financing. Cytokinetics was founded in 1998 in San Francisco to pursue therapeutics using a
novel technology platform of cytoskeleton and the biology of muscle function to tackle the
pursuit of new treatments for multiple disease areas. The company has completed eight different
financings totaling $308 million since its IPO in 2004. First, the company experienced multiple
setbacks in oncology, notably a Phase 2 trial for SB-715992 (ispinesib) platinum-sensitive and
platinum-refractory non-small cell lung cancer (NSCLC) showed that ispinesib led to a disease
stabilization rate which was insufficient to proceed to the next stage of the development. After
share price decline, Cytokinetics effected a reverse split of 1-for-6, which increased the share
price from $2 to $12 with a corresponding decrease in shares outstanding in June 2013.
Subsequently, the company went on to complete a financing of $40 million in February 2014. In
April 2014, the company announced that tirasemtiv (fast skeletal muscle troponin activator), its
lead unpartnered compound, missed the primary endpoint in the Phase 2b trial in 711 patients to
treat amyotrophic lateral sclerosis (ALS). On the news, its stock price immediately dropped from
$8.40 to $4.59. Despite multiple setbacks, however, Cytokinetics continues to move forward
with large biotech partner Amgen which is evaluating an oral formulation of Cytokinetics'
omecamtiv mecarbil in a Phase 2 trial in patients with chronic heart failure (CHF) and left
ventricular systolic dysfunction.
A standard discounted-cash-flow framework for analyzing biotech firms, like
Cytokinetics, has significant weaknesses which lead to under-valuation and under-investment.
Although real option techniques can be used to improve valuation accuracy, these models
quickly become very complex as the number of development pathways increases. On the
contrary, effectuation theory provides an alternative and more suitable way to value and
understand the biotech industry.
2.2. Effectuation Theory
Effectuation theory refers to “a set of means as given and focus on selecting between
possible effects that can be created with that set of means,” while predictive models rely on
predictable processes that “take a particular effect as given and focus on selecting between
means to create that effect”.11 The original effectuation model consists of four dimensions:
means, affordable loss, partnership, and expecting the unexpected.12 In the remainder of this
section, we describe how these four dimensions of effectuation theory fit the biotech industry.
In previous studies, effectuation has been widely explored in entrepreneurship, 13 but it
has also been considered in the context of corporate R&D,14 management,15-16 economics,17
finance1 and marketing.12 However, to our knowledge, our research is the first to explore its use
specifically within the context of the biopharma industry and to apply it to analyzing reverse
stock splits for development stage companies. Also, although the concepts of effectuation theory
have been empirically tested at both the individual and the firm level, surveys have been the
predominant method of data collection. Dew, Sarasvathy11 study individual decision-making in
exploring new venture success with data collected from surveys of expert entrepreneurs and
compared to MBA student responses. Wiltbank, Read1 surveyed angel investors and analyze how
effectuation framing relates to success. Our research differs from these studies in that we look
only at existing firm level variables, a precedent suggested and supported by 14 who collected
their data using surveys of European technology firms rather than adopting archival financial
data as proxies. As such, their survey-based results are based on management perceptions.
The “means” construct is a three dimensional variable: “what I know,” “who I am,” and
“who I know.” “What I know” tends to be defined as domain specific expertise as well as more
general variables such as personality, gender, and management experience. In the biotech
industry, this dimension is largely comprised of knowledge about the R&D process. “Who I am”
is operationalized at both the individual level of analysis (such as propensity for risk and self-
efficacy) and the firm level (such as patents, capital, and internal R&D). “Who I know” includes
family and friends who are resourceful or well connected, including entrepreneurs, university
personnel, scientists, or others experts in the innovation process.17
Because pharmaceutical firms enjoy high profit margins, most multinational
biopharmaceutical companies have significant financial means or resources to deploy, including
large cash balances, borrowing capacity and stock market values. These means allow them to
invest heavily in R&D, among other things. However, their decisions on how much to invest
and on what segments can differ significantly depending on their degree of diversification and
priorities. For example, a diversified biopharma firm like Johnson and Johnson (J&J) gains about
37% of sales from its biopharma segment, but a more focused biopharma firm such as Biogen
gains 100% of revenues from drug sales. While both earn about the same profit margins on their
biopharma sales (24.4% for J&J and 23.5% for Biogen), in absolute terms, the internally
generated cash available to a corporate giant like J&J ($15 billion total, $6.1 billion from
biopharma) dwarfs the internally generated cash available to a stand-alone biotech firm like
Biogen ($1.2 billion). However, this advantage in financial means is a disadvantage when it
comes to managing affordable losses, as discussed below.
The relatively diminished means for development-stage biotech companies can be
crippling. For example, development stage Aveo Oncology completed a Phase 3 trial for
ASP4130 (tivozanib) in advanced renal cell carcinoma (RCC), and found a co-promotion partner
in Japan-based multinational pharmaceutical Astellas Pharma. However, a FDA advisory
committee known as Oncologic Drugs Advisory Committee (ODAC) voted 13-1 to recommend
the agency reject tivozanib for RCC in June 2013. The FDA subsequently rejected the
company’s application which it faulted as uninterpretable and inconclusive, and requested a new
trial be conducted in December 2013. Aveo restructured with the layoff of 140 staffers—62% of
its workforce—following the advisory committee rejection. Its share price reduced from $7 to
$2. Three weeks later, Astellas Pharma informed Aveo it would not pursue European approval
for the drug candidate, and would stop funding RCC trials under their collaboration, which ended
the company’s programs.
2.2.2. Affordable Loss
Rather than using expected return as a criterion for investment, “each effectual
stakeholder strives to invest only what he or she can afford to lose.”7 Although large firms have
more financial resources than development-stage firms, implementing an affordable-loss
approach is easier in smaller biotech firms. This is because multiple rounds of financing are
frequently needed to keep biotech firms afloat, a mechanism that naturally limits losses. The pros
and cons of the different ways that large versus small biotech firms manage investment decisions
can be illustrated in the following examples.
As an example of a large multinational biopharma leveraging its resources to shift from a
traditional internal R&D model to biopharmaceutical alliances to further its product pipeline,
consider Bristol-Myers Squibb Company (BMY). BMY has been strategically aligning with
small and mid-sized drug developers and biotech companies by targeting companies whose
products and technologies address unmet medical needs and build on BMY’s R&D strengths
and/or create new areas of expertise.18 The String of Pearls strategy, formalized in 2007, threads
together a library of compounds and portfolio of technologies for the purpose of accelerating the
discovery, clinical development and commercialization of new therapies across a broad range of
therapeutic areas. However, BMY’s acquisition of Inhibitex in Phase 3 clinical development for
HCV (hepatitis C virus) for $2.5 billion or 167% premium resulted in a total failure. After only
eight months, the lead drug trial was discontinued when a patient death resulted in a $1.8 billion
While BMY withstood the Inhibitex setback, consider the smaller, development-stage
Ziopharm Oncology clinical study for ZIO-201 (palifosfamide) in metastatic soft tissue sarcoma
in March 2013. The DNA alkylating agent did not meet its primary endpoint of progression-free
survival (PFS) in a Phase 3 trial, designed to assess the drug as a first-line treatment for
metastatic soft tissue sarcoma. The setback resulted in the elimination of the company’s entire
oncology portfolio, the elimination of 65 positions (leaving approximately 30 employees), and
the complete change in strategic focus on its synthetic biology programs being developed with
Intrexon. Ziopharm Oncology survived, but the failure resulted in more drastic changes
compared to post-setback changes implemented in larger firms like BMY.
2.2.3. Stakeholder Commitments
Because of the greater reliance on multiple rounds of financing, smaller firms depend
more than larger firms on commitments from external stakeholders. Effectuation theory frames
partnerships as collaborations with stakeholders and organizations willing to make a significant
commitment to product and market development. Read, Dew12 distinguish the means-based
“who I know” dimension from the stakeholder-commitment-based “partnerships” dimension by
determining whether success depends on the firm itself (“means) or the other party (“who I
know”—typically as a result of money, equity or a product changing hands).
In the biotech industry, the vast majority of the over 600 public and 8,000 private
companies worldwide have no revenues or earnings, which means that their investment is funded
through grants, public or private equity, and/or through partnerships with larger, better
capitalized publicly traded firms. The small percentage of these firms that are successful in
moving into later stages of clinical trials or actually receiving FDA approval to market a drug are
often acquired by larger biopharma firms in these later stages.19 Thus, for large, well-established
firms, partnering with and/or later acquiring smaller biotech companies provides a viable option
to committing a firm’s R&D investment capital to internal development programs.20 These
partnerships, collaborative agreements, and joint ventures create powerful innovation network
effects, 21 as well as allow both firms to learn to work together, providing an option for possible
Despite the greater information asymmetry associated with early stage novel technologies
(e.g., stem cells, checkpoint inhibitors, gene therapy, cancer vaccines, RNAi), signaling
mechanisms can help investors discriminate among firms’ pipelines. The relevant data in this
process includes clinical data (such as announcement of clinical results at medical conferences),
publicly announced partnership deals (such as licensing, co-development, co-promotion), and
institutional investment by specialist mutual and hedge funds.22,23 For example, Agios
Pharmaceuticals, an early stage drug development company which focused on cancer
metabolism with a marquee research partnership with large biopharma Celgene, successfully
completed an IPO at $18 which overshot the range of $14-16, raised an additional $106 million,
and soared 60% on its first day of trading—sending the market capitalization to over $800
External stakeholder commitments can also be critical in allowing a development stage
company to survive a major setback. For example, development stage Rigel partnered R788
(fostamatinib) with multinational pharmaceutical company AstraZeneca (AZ). In June 2013,
Rigel announced that R788 (oral spleen tyrosine kinase inhibitor) with methotrexate (MTX) did
not show statistically significant improvement compared to placebo in the Phase 3 OSKIRA-3
clinical study. Of note, AZ was solely responsible for all costs and expenses, and subsequently
recorded a $136 million pre-tax impairment charge to R&D expense. AZ announced that it
would not proceed with regulatory filings, and returned its rights to the compound to Rigel which
has since turned its primary focus to other programs.
2.2.4. Expecting the Unexpected
The “expect the unexpected” effectuation principle encourages companies to embrace
surprises that arise from uncertain situations, remaining flexible rather than tethered to existing
goals 12. Wiltbank, Dew7 refer to this effectuation dimension as “leveraging contingencies”
defined as a willingness to dramatically change goals, products, or strategies.
While all large biopharmaceutical companies have a pressing and ongoing need for new
products, they have approached pipeline and product investment and development differentially,
in the sense that some rely on internal development and research partnerships, while others rely
on purchasing external R&D and/or smaller firms typically in later stages of FDA approval
through mergers and acquisitions). Illustrating these different approaches, Pfizer has heavily
relied on multibillion dollar acquisitions such as Warner-Lambert in 2000, Pharmacia in 2002,
Wyeth in 2009, King Pharmaceuticals in 2010; and Roche has relied on internal development
and partnerships (e.g., Genentech partnership to grow a pipeline of blockbuster oncology
products such as Herceptin® (trastuzumab), Rituxan® (rituximab), and Avastin® (bezicuzimab)
each with greater than $5 billion in 2012 annual revenues. There is a tradeoff between the
perceived risk of overpaying for late-stage products, often obtained through mergers and
acquisitions, and the uncertainty of valuing internally developed earlier stage products.24
An example of an “unexpected” event is the emergence of an unanticipated safety signal,
even after extensive clinical studies. For example, Biogen-Idec and Elan’s Tysabri (natalizumab)
was originally approved for all relapsing forms of multiple sclerosis (relapse-remitting,
secondary-progressive, and progressive-relapsing) in 2004. However, four months after its
approval in February 2005, the manufacturer withdrew natalizumab voluntarily after two fatal
cases of progressive multifocal leukoencephalopathy, and the stock price fell from $66 to $38.
The drug was eventually re-approved in June 2006 after an extensive safety review and heavy
lobbying by patients, and Tysabri reached $5.5 billion in 2012 revenues.
3. Reverse Stock Split and Empirical Hypotheses
In this section, we first provide a review of the existing literature on forward and
reverse stock splits, paying particular attention to standard predictions based on predictive
signaling theory. Then we motivate our empirical hypotheses and predictions by analyzing
reverse stock splits from the perspective of effectuation theory.
3.1. Forward and Reverse Stock Splits
There are two types of stock splits, forward splits and reverse splits. A forward split is
when one share becomes multiple shares, resulting in more shares but a lower price per
share. Between 1933 and 2007 the average share price of major U.S. stocks remained
remarkably constant, rarely straying far from $25 to $35. The average forward split was $50
pre-split. Anytime a stock went much higher, the company reduced it back down with a stock
split. Conversely, a reverse split occurs when multiple shares are combined into one share,
resulting in less shares but a higher price per share. The average reverse split is $1.21 pre-
Typically, reverse splits are done from a position of weakness such as a setback of
some kind (e.g., unanticipated loss of intellectual property protection, loss of market share,
natural disaster, adverse regulatory action, significant market correction) which significantly
reduces the share price and threatens the company’s viability as an exchange traded stock.28-
31 Further, companies must maintain minimum standards to ensure continued compliance and
For example, to maintain a listing on the NASDAQ stock exchange, corporations are
required to meet minimum standards for their share price, market value and corporate
governance. Generally, stocks must have a share price of at least $1 and a minimum market
value of $1 million. In addition, companies listed on the NASDAQ must adhere to federal
disclosure requirements for publicly traded securities and pay annual listing fees. The
exchange issues a deficiency notice to any company in violation of any of the minimum
standards for 30 consecutive days—after which the company has 90 days to regain
compliance. For example, if the price were under $1 a company could choose to effect a
reverse split to increase its share price. Companies which are delisted from the NASDAQ can
continue to trade on the over-the-counter markets and the Pink Sheets, and some can reapply
to NASDAQ and regain their listing. Regardless, delisting is often hard on a company,
because it can impair its access to capital (e.g., Blue Sky laws which limit retail brokerages
to sell stock with a price under $5 per share can reduce the depth and breadth of investors),
the ability to borrow and exemptions from various state laws.32
There are three main stock split theories: (1) The optimal price/tick theory posits that
splits return the stock price and relative tick size to their optimal range in their industry and
market; (2) signaling theory posits that splits reveal information about future performance;
and (3) the procedure/structure theory explains how a particular feature/structure/rule can
cause a certain phenomenon in relation to splits.27 According to the traditional signaling
model, managers have better predictive information about outcome scenarios and so, when a
firm is near its delisting boundary, a reverse stock split (RSS) signals negative information
about the probability distribution of specific future scenarios. As Rhee and Wu explain:
A broadly accepted explanation . . . is that RSS signals to the market that management
has either lost confidence in future price increases or exhausted all other means of
maintaining the listing. RSS is the last straw before a stock is delisted to less liquid and
less transparent markets, which becomes especially apparent after the NASDAQ
introduced the one-dollar rule.33
3.2. Empirical Predictions Based on Effectuation Theory
3.2.1. Main Prediction for Stock Returns
Relative to the traditional prediction-based signaling framework, signaling works
differently in a non-predictive, “effectual” environment. When there is a large amount of
uncertainty and firms have a significant amount of control over their future outcomes, then an
RSS signals that the firm’s manager is bullish about its own means, its stakeholders’ willingness
to commit to the future of the company, and that the firm will be able to successfully adapt to
unexpected outcomes. Moreover, the RSS is a means by which the firm can, ipso facto, increase
stakeholder commitments. However, because multiple rounds of financing are to be expected,
this increase in commitment is done in a way that is consistent with the effectual logic of
Thus, in contrast to the predictive framework of traditional signaling theory where the
manager and the stakeholders of the firm have relatively little control over outcomes, in an
effectual environment this relationship is reversed: the firm operates in a non-predictive
environment and the manager and stakeholders of firm have a relatively significant amount of
control over the firm’s outcomes. Because of this, an RSS strengthens commitments to the firm’s
future and signals the manger’s confidence that the firm will be able to continue its operations in
a propitious way. And because it is not possible in an effectual environment to exhaustively
specify these possible scenarios, the signaling effect about the firm’s confidence in its ability to
control its own fate has a greater effect than any effect based on predictions about any specific
future scenarios. Based on this logic and our previous argument that biotech firms are, in fact, in
an effectual environment, we hypothesize the following:
H1: Biotechnology firms who conduct a reverse stock split will experience a positive
3.2.2. Explanatory Variables for Stock Returns
Our key hypothesis, that RSS-firms will have positive ex post abnormal stock returns, is
rooted in effectuation theory. Effectuation theory can also be used to predict the sign of the
coefficient for various explanatory variables. These predictions cannot be cleanly contrasted with
predictions obtained using a predictive signaling model. Nevertheless, to better understand the
empirical implications of effectuation theory in the context of biotech RSSs, below we discuss
the predicted sign on the coefficient for various variables in a regression where abnormal stock
returns are the dependent variable.
If a firm chooses a larger split ratio, then this comprises a stronger signal and effects a
greater commitment. Thus, the effect underlying H1 will be more significant and we predict that
the estimated coefficient for the split ratio will be positive. Because of economies of scale, larger
firms tend to have greater control of their own destiny. This is because larger firms have more
means and resources to survive and adapt when setbacks occur. All else equal, size is also an
indicator of commitment by internal and external stakeholders. Thus, in an effectual
environment, measures of size should have a positive coefficient. Similarly, if a firm has a great
deal of cash, the cash can be used as a means of increasing the firm’s ability to prolong projects
and successfully navigate or adapt in the face of setbacks. Cash thus comprises an alternate form
of control and implies a positive predicted coefficient.
In a slightly different vein, research and development (R&D) spending represents an
alternative indicator of means, commitment, and adaptability. This is because firms with larger
R&D spending will, all else equal, have a greater resources to spur innovation, a larger network
of synergistic partners and potential partners, and a larger number of options to adapt in the face
of setbacks. Thus, the estimated coefficient for R&D should have a positive sign. In a similar
vein, but with a stronger emphasis on commitments, long-term debt signals that a firm has
committed financial partners (debt holders). This comprises a positive signal with respect to the
firm’s commitments from existing financial investors and prospects for successfully navigating
future rounds of financing. Additionally, the structure of debt more strongly parallels the logic of
affordable losses than the structure of equity. Thus, the estimated coefficient for an indicator of
long-term debt should be positive.
If the market is bearish about a firm’s future prospects, this will lead to a lower market-
to-book ratio, all else equal. If a reverse stock split sends a positive signal to investors, the
reversal in investors’ expectations is apt to be greater for these firms with low market-to-book
ratios. This implies that the market-to-book ratio should have a negative coefficient estimate.
Finally, with regard to firm age, older biotech firms can be understood as being less likely to face
setbacks, since they should have more controls and means compared to younger firms, all else
equal. So, when an older firm does experience a setback, as indicated by the need to undergo an
RSS, then this is likely to comprise a negative signal about the firm’s ability to successfully
control its environment. Thus, we predict that the coefficient for firm age will be negative.
With regard to stock liquidity, reverse stock splits are known to improve liquidity due to
reduced effective (percentage) bid-ask spread that captures round-trip trade execution costs.8,9 In
an effectual environment, an RSS draws attention from investors. This, in turn, leads to a
stronger signaling effect. Also, because the signaling effect is positive, as we have argued above,
the greater attention also leads to improved commitments from investors and other stakeholders.
Also, inasmuch as an RSS increases the firm’s stock price, this leads to a positive feedback effect
in the form of a lower cost of capital, thus improving the firm’s ability to adapt to unexpected
setbacks. We thus hypothesize the following:
H2: Biotechnology firms who conduct a reverse split following a setback will experience an
improvement in liquidity.
4. Methodology and Data
In this section, we first explain our empirical methodology. We then discuss our
sample, and provide basic summary statistics.
We utilize the methodology of event study to test our hypotheses. An event study
attempts to measure the valuation effects of a corporate event, such as a reverse stock split
announcement, by looking at the response of the stock price around the announcement of the
event and determine whether there is an abnormal return or not. One underlying assumption
is that the market processes information about the event in an efficient and unbiased manner.
To alleviate this assumption, we consider a number of lengths of event windows from one
day to one year.
To estimate the normal return of a stock, we first use market model with CRSP value-
weighted index as the proxy for the market return. We then use a four-factor model with
Fama-French three factors (market, size, and book-to-market) and momentum factor34,35.
The four-factor model has the advantage to control for risk premiums associated with size,
growth, and market momentum. It is important to control for size and growth when
estimating the normal return as our sample firms are relatively small and still in their early
growth stage. We then calculate the abnormal return and the cumulative abnormal return
(CAR) based on the estimated normal return and test the average of CAR using the
methodology in Brown and Warner.36,37
In addition to the CAR approach, we follow Barber and Lyon38 and analyze buy-
and-hold returns using matched control firms. Barber and Lyon point out a potential bias
induced by cumulating short-term abnormal returns, such as CARs, over long periods (see
also Conrad and Kaul39 and conclude that the matched control firm approach leads to
unbiased test statistics. Because of this potential bias, we measure stock performance by
computing holding-period returns (HPRs) for each adopting firm and its matched control
firm over one-month, six-month, and one-year periods following RSs. The holding periods
start on the RS announcement day.
Following Spiess and Affleck-Graves40, we first choose our control firms on the
basis of industry, size (market capitalization), and book-to-market ratio. We avoid look-
ahead bias by using only the information available at the time of RS announcement. For each
RS sample firm, we identify all public firms in CRSP that have not undergone a RS in the
previous three years and belong to the biotech industry as defined by their 2-digit NAICS.
We select the first matched firm from the set of potential matches such that the sum of
absolute percentage difference between the size and book-to-market ratio of the sample firm
and the control firm is minimized. If the first-best matched firm is delisted, we substitute
returns from the second-best matched firm, starting at the close of trading on the date of the
delisting payment and including the delisting return. If the first-best matched firm
subsequently undergoes a RS, we substitute the second-best matched firm on the next trading
In our liquidity analysis, we use four measures of Chordia, Huh 41. First, we
construct a share turnover ratio, Turnover, by dividing the total number of shares traded by
the number of shares outstanding for a trading day and then average the daily ratios over a
sample period to have the mean share turnover ratio:
1 number of shares traded on day t
Turnover= T number of shares outstanding on day t
Lesmond, Ogden 42 consider the proportion of days with zero returns as a proxy for
liquidity. There are two key arguments that support this measure. First, stocks with lower
liquidity are more likely to have days with little to no trading activity, and thus zero volume
and zero return on these days. Second, stocks with higher transaction costs have less private
information acquisition because of the higher transaction costs which gives traders a low
incentive to obtain private information. Thus, even on positive volume days, these illiquid
stocks can experience no-information-revelation and therefore zero return on these days.
number of days with zero returns
Zeros= total number of days in the subsample
The Amivest liquidity ratio is a measure of price impact which can be interpreted as
the dollar volume of trading associated with a 1-percent change in the price of a security:
where volumet is the dollar volume on day t and
is the return on day t. The average is
calculated over all non-zero-return days since the ratio is undefined for zero-return days. A
larger value of Liquidity implies a lower price impact. This measure has been used by
Amihud, Mendelson 43, Berkman and Eleswarapu 44, and others.
Finally, we define two volatility variables: Volatilityd as the standard deviation of
daily returns, annualized by multiplying by the square root of 252; Volatilitym as the standard
deviation of monthly returns, annualized by multiplying by the square root of 12. A reverse
split reduces the relative bid-ask spread due to an increase in share price. This change in
market microstructure alone may cause volatility to decrease.26 As monthly returns are less
impacted by bid-ask bounce, Volatilitym can reflect the level of volatility due to trading
activities, which we intend to measure.
4.2. Sample and summary statistics
We use the Biocentury database to identify 40 biotech firms with RSS and collect
split-related information. All 40 biotech firms were listed on NASDAQ and announced their
reverse stock split during the 2011-2013 period. Table 1 summarizes the 40 reverse stock
splits by split ratio and by their announcement year. Company financial data and stock return
data are collected from COMPUSTAT (active and research) and CRSP tapes, respectively.
The COMPUSTAT data includes “research” firms that have failed or been acquired
eventually and CRSP stock returns include delisting returns if a firm’s stock is delisted.
Imposing that firms need to have data in all three sources leaves us with a total of 35 RSS
firms. The choice of the sample period is governed by the availability of data.
We show summary statistics for our sample in Table 2. As shown in Panel A, the average
(median) split ratio is 14.38 (7) with a range from 2 to 125 and an interquartile range from 6 to
15. The average (median) 30-day closing price for RSS firms prior to their reverse split event is
0.62 (0.52), with a range from 0.16 to 1.96 and interquartile range from 0.37 to 0.66. Thus, the
majority of our biotech RSS firms have a prior price below $1. Average (median) market
capitalization three days prior to the RSS event is 40.06 (28.58) million dollars.
Panel B of Table 2 shows summary statistics of our key variables. Panel C shows
correlations between the explanatory variables that we intend to use in subsequent analysis. The
variables with absolute correlation greater than 0.40 are as follows: LogEmp, LogSales, and
LogTA are all highly correlated, with correlations ranging from 0.45 to 0.77. All three variables
are proxies of size. As half of our sample firms do not have any sales, we use LogTA to measure
size in our regression analysis. Also, there is a high degree of negative correlation between
LogSales and LogSplitRatio, LogSales and Cash/TA, and LogEmp and Cash/TA, ranging
from -0.42 to -0.54.
5.1. Analysis of Returns
Table 3 shows cumulative abnormal returns (CARs) for RSS biotech firms over the
following six time windows, relative to each firm’s RSS event: (1) 30 days before to 1 day
before [-30, -1]; (2) the day of [0, 0]; (3) the day after [+1, +1]; (4) two days after to one month
after [+2, +30]; (5) 1 month after to 6 months after [+31, +180]; (6) the day after to one year
after [+1, +365]. Panel A shows CAR results using the market model with CRSP value-
weighted index as the proxy for the market return whereas Panel B shows CAR results using a 4-
factor model with Fama-French 3 factors (market, size, and B/M) and the momentum factor.
The results in Table 33 tell a fairly clear empirical story: biotech RSS firms experience
positive abnormal returns prior to the RSS event, negative returns on the day of and the day after,
and positive returns in 1-, 6-, and 12-month periods following the RSS event. These results are
generally statistically significant, although if the Z-statistic is adjusted for both time-series and
cross-sectional dependence, following Mikkelson and Partch37, then the day-of and month-after
results are not significant. The economic significance of these results is, on average, quite
large: -16% for the one-month prior; about -2.5% on the event day; -6% for the day-after; 33%
for the month after; an additional 61% for the next 5 months; an additional 59% for the next 6
months, or 120% for the 1-year post-RSS window. The stock market initially reacted negatively
to the announcement as shown by the negative CARs on the event day and the day after (albeit a
less robust finding), and quickly reversed to strong positive returns in longer event windows.
5.1.2. Cross-sectional CAR Regressions
Table 4 shows the results of cross-sectional regressions with CARs for various event
windows as the dependent variable. In accordance with our effectuation-based prediction, we
find that the coefficient on LogSplitRatio is positive and significant for each event window, and
that the magnitude of the effect is larger for longer horizons.
LogTA, our size measure, has a positive and significant coefficient for the 6-month and
1-year post-RSS returns. This result is consistent with our effectuation-based prediction. For M-
B, the coefficient is negative, in line with our prediction, but it is only significant for the one-
month prior, event-day, and day-after returns. With regard to cash holding, we find that the
coefficient on Cash/TA is significant only for the 1-year-after event window. The coefficient on
Cash/TA is positive, in accordance with our prediction.
The coefficient on R&D/TA is positive and significant, as predicted, for the 1-month
prior, 6-month after, and 1-year-after event windows. The coefficient on Age is, as predicted,
negative and significant for the 1-month prior, day-after, 6-month-after, and 1-year-after event
windows. The coefficient on IndLTDebt is positive and significant, as predicted, for the 6-
month-after and 1-year-after event windows.
5.1.3. Matched Returns
Table 5 shows holding-period returns (HPRs) for RSS biotech firms compared to a
matched sample of non-RSS firms on industry, size, and book-to-market. The returns for our
biotech RSS firms are significantly higher than our control sample. This difference is 7.5% at 1
month, 20.7% at 6 months, and 27.0% at 1 year. These results corroborate our CAR findings
reported in Table 3 and strengthen our H1 hypothesis.
5.2. Analysis of Liquidity
Table 6 reports mean and median values for each liquidity measure and the
corresponding difference of the measure of the same firm in the windows of 180 days before and
after the announcement day. We conduct the paired sample t-tests and Wilcoxon signed-rank
tests of differences in means and medians respectively, and report corresponding p-values. The
number of firms in this table is 34.
In accordance with H2, we find that the share turnover ratio and the Amivest liquidity
ratio are higher and Zeros is lower after the RSS by the mean and median. Both volatility
measures indicate a slight increase in return volatility after the RSS despite insignificant p-
values. When combining with the positive abnormal returns, these data support the idea that the
reverse split draws positive attention and trading activities from investors. The enhanced
liquidity can enhance the ability of biotech firms to raise capital in subsequent rounds of
The highly volatile nature of the biotechnology industry possesses several features that
make it an ideal fit to evaluate effectuation theory. In particular, there is significant uncertainty
in developing specific product development scenarios which makes it confounding to predict
results, as firm success depends on their internal means and ability to procure stakeholder
commitments, limit losses, and being prepared to adapt to unexpected results (i.e., expecting the
unexpected). Because this environment differs substantially from the presumed predictable
environment of traditional stakeholder theory, the usual negative-signal predictions regarding
reverse stock splits are not appropriate. We conjecture, instead, that reverse stock splits
following a setback comprise a positive signal for biotech firms regarding their own
competencies and commitments pertaining to operations and future rounds of financing.
In our empirical analysis, we find that biotech firms who conduct a reverse split
following a setback experience positive abnormal returns over 1-, 6-, and 12-month periods. We
also find, in accordance with the effectuation-theory perspective, that the abnormal returns are
positively related to the reverse split ratio, size, cash holding and long-term debt, and negatively
related to the market-to-book ratio and firm age. Moreover, we find that liquidity increases after
reverse stock splits.
In sum, we believe this study contributes to the research literature by expanding and
extending the use of effectuation theory as an integrative and highly relevant framework for
assessing biotech firms, especially with regard to financial decisions. More specifically, our
analysis suggests that the concept of effectuation theory is better suited to analyzing reverse
stock splits in the biotech industry. To the best of our knowledge, this is the first effectuation
research to use archival financial data as opposed to relying on surveys of management
perceptions, as in prior research. Further, our integration of effectuation and stock split theories
provides a lens from which to explore emerging approaches for breakthrough innovation and
technology development. Future research, particularly in the biotech industry, should therefore,
pay more careful attention to the distinct aspects of effectuation theory.
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