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Msg  16278 of 16288  at  8/21/2018 4:45:33 PM  by


The following message was updated on 8/22/2018 1:06:51 AM.

R/S Approval Looking Forward Possibility: 2015 32-40 firm study "...biotech firms who conduct a reverse split following a setback experience positive abnormal returns over 1-, 6-, and 12-month periods..liquidity increases after R/S.."...(Repost)



Original Message (# 23945) by moneyonomics on 4/11/2018 3:05:47 PM

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FACT #1 NAVB R/S: 2015 32-40 firm study "...biotech firms who conduct a reverse split following a setback experience positive abnormal returns over 1-, 6-, and 12-month periods..liquidity increases after R/S.."


         Observation: For those who prefer research facts to suppositions and emotions...(Caveat: this is not a guarantee of post reverse split stock price success for NAVB, but facts from detailed 2015 effectuation regression analysis/study of 32-40 biotech firms reverse splits presented below in excepts and study document attached-perform your own due diligence. If one studies the Research they will have a better appreciation of facts along with other prior 4 posts of just facts-2007 1,398 firm study, 2011 and other smaller studies posted)

"...6. Conclusion

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."



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

  • Earlham College

·  ·  Mark Ahn

  • Portland State University

·  ·  Wei Wu

  • · 

Yulianto Suharto

  • 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.

Full-text (PDF)

Available from: Robert Couch, Sep 06, 2015

Download full-text PDF





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


1. Introduction

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

signaling theory.

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

S&P 500

Biotechnology (BTK)



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

the industry.

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

predictive frameworks.


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.

2.2.1. Means

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

later acquisition.

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

exchange trading.

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

affordable losses.

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

abnormal return.

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.

3.2.3. Liquidity

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.

4.1. Methodology

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:


t=0 t



Liquidity= Tr


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. Results

5.1. Analysis of Returns

5.1.1. CARs

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


6. Conclusion


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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