Announcement Effects in the Pharmaceutical IndustryAnshu Ankolekar
Submitted in partial fulfillment of the requirements for the degree
MSC. FINANCE
Department of Finance
Tilburg University
i
Abstract
The aim of this study is to investigate the extent to which financial markets incorporate
information about new product innovations in the pharmaceutical industry. This issue is
addressed by means of an event study which analyzes the market reaction of 319 drug
approval announcements. We find that the average abnormal return to a pharmaceutical
company when one of its drugs is approved is 0.77% and that the positive abnormal returns
persist over the following 5 days. Further analysis through OLS regressions reveals that the
market rewards companies that engage in more innovative research, where innovation is
defined in terms of a greater research and development (R&D) intensity and the act of
investing in new molecular compounds as opposed to revising existing formulations of
known drugs.
2. The Pharmaceutical Industry ................................................................................................ 6
2.1 The Structure of the Pharmaceutical Industry ................................................................ 6
2.2 Drug Development Process .............................................................................................. 7
2.3 Issues in Drug Development ............................................................................................ 8
3. Literature Review ................................................................................................................. 10
3.1 Innovation and Market Reaction.....................................................................................10
4. Hypothesis Development ...................................................................................................... 13
4.1 Research Questions ......................................................................................................... 13
4.3 Sources of Abnormal Returns ......................................................................................... 13
5. Data and Methodology .......................................................................................................... 15
5.1 Market Reaction to Drug Approval ................................................................................. 15
5.2 Sources of Abnormal Returns ........................................................................................ 19
6. Findings ................................................................................................................................ 26
6.2 Sources of Abnormal Returns ........................................................................................ 28
7. Conclusions and Recommendations .................................................................................... 34
References ................................................................................................................................ 35
Appendix B: Drug Information ................................................................................................. 41
Appendix C: List of Companies ................................................................................................ 42
iii
Figure 2: Event Study Observation Windows .........................................................................16
Figure 3: Drugs by Chemical Type .......................................................................................... 20
Figure 4: Drugs by Review Classification ................................................................................ 21
Figure 5: Correlation Matrix of Variables ............................................................................... 24
List of Tables
Table 2: Variance Inflation Factors of Independent Variables ............................................... 23
Table 3: FDA Approval and Abnormal Returns ....................................................................... 26
Table 4: FDA Approval and Cumulative Abnormal Returns ................................................... 27
Table 5: OLS Regression Results – Full Sample ........................................................................ 31
Table 6: OLS Regression Results – Positive CARs as Dependent Variable ............................. 32
Table 7: OLS Regression Results – Negative CARs as Dependent Variable ............................ 33
1. Introduction
The process of developing drugs and bringing them to the market is highly complex, risky,
and costly. Total costs to develop a single drug typically reach $1 billion and the entire
process from drug discovery to commercialization may take over 10 years. Furthermore,
before drugs can be made commercially available they must pass through a series of clinical
trials to ensure their safety and effectiveness. The vast majority of drugs fail during these
clinical trials; either they are found to be unsafe or ineffective in treating an illness. For this
reason, pharmaceutical companies invest in a portfolio of drugs in the hope that at least a
few will manage to gain approval from the Food and Drug Administration (FDA). Once
approval has been granted, the company is given a license to sell the drug exclusively for a
pre-determined length of time. In effect this grants the company a virtual monopoly in the
market for that particular drug. After this exclusivity period expires other firms may sell
generic version of the original brand name drug and capture its market share.
In light of this, pharmaceutical firms must continually invest in developing and selling new
drugs to replenish their portfolios as their old patents expire. Producing a continuous
stream of innovative products however requires large expenditures in research and
development (R&D), and in the pharmaceutical industry the risk of failure is considerably
higher than in other technology-intensive industries due to the uncertain nature of drug
compounds, for instance how effective they will be in treating a disease and whether they
are safely absorbed in the human body. Managing the process of developing and testing
them, and then capturing the greatest amount of revenue during the exclusivity period
before rivals come into the picture is a key challenge for major pharmaceutical companies.
We look into how the market values new innovations and what information this may give to
pharmaceutical companies; should they spend more on R&D? Should they focus on more
innovative drugs or does the market reward them for new formulations of existing drugs?).
Indeed it is well-established that innovation is the lifeblood of pharmaceutical companies;
those that spend more on research and development (R&D) experience higher
profitability. Prior research has also found that stock markets react positively to
5
announcements of new product developments and penalize companies who report product
failures. The positive abnormal returns suggest that financial market participants place a
significant value on FDA approval. So far however there is not much information in the
literature about whether the extent of market reaction reflects the specific characteristics of
the drug. Do more innovative drugs experience a higher abnormal return?
The aim of this study is to shed light on this question by investigating the announcement
effect of drugs attaining FDA approval and the relation, if any, between abnormal returns
and certain drug characteristics that represent innovation. The dataset spans the years
1985-2011 and consists of all major drugs approved during this period. In total the sample
contains 319 drug approval events from 37 pharmaceutical companies.
The results corroborate existing findings that FDA approval of a drug is met with a positive
reaction from the stock market. Further analysis in the form of ordinary least squares (OLS)
regressions indicates that this reaction is more pronounced for firms that have a greater
R&D intensity as well as for drugs that are of a new molecular type versus those that are
based on existing molecular compounds.
Although drug development is a risky venture, our results suggest that market participants
recognize and reward companies that engage in more innovative R&D. Taken together, our
findings suggest avenues for pharmaceutical companies to make better strategic allocations
in their R&D budgets.
The remainder of the thesis is as follows. The next chapter summarizes the structure of the
pharmaceutical industry and the issues it faces. Chapter 3 discusses the literature that
relates to the issues identified in the preceding chapter. The hypotheses to be investigated
are developed in Chapter 4. Chapter 5 describes the data and methodologies used. Chapter
6 describes the results and Chapter 7 concludes with a discussion of the main findings and
avenues for future research.
In the pharmaceutical industry, technological innovation and its subsequent
commercialization is a complex process involving multiple parties with complementary
resources such as capital, organizational resources, and marketing and distribution
channels. The industry is dominated by a few large global firms, the result of a wave of
mergers and consolidation that began in the 1980s and continued through the 90s, making
the industry increasingly concentrated. One of the main reasons for this heavy
consolidation was that many large pharmaceutical companies were facing expiration of
their patents. Given that the average drug takes roughly 10 years to progress from a
potential candidate to a fully developed therapeutic treatment, the fastest way to replace
expiring drugs was to acquire other companies and the drugs in their pipeline.
In recent times however these large firms often turn to smaller biotechnology companies
for new drug discoveries. These small biotechnology companies play a key role in
innovation. In turn, they typically lack the resources and infrastructure to commercialize
their discoveries, and so they license their discoveries to large pharmaceutical firms who
have the capabilities to take them further. Typically these large companies are involved in
the development process from start to finish, i.e. the ones who develop drugs also handle
the manufacturing, marketing and distribution.
Indeed the profitability of pharmaceutical companies is wholly dependent on their ability to
develop and distribute innovative drugs. This development phase is lengthy and highly
risky. In the initial phase, a lot of resources are spent on developing chemical compounds
that aim to treat a particular disease. Once a potential molecular compound has been
developed, it must undergo rigorous tests to demonstrate that it is safe and effective before
it can be allowed to be sold. In the United States the Food & Drug Administration (FDA) is a
government body that regulates the pharmaceutical industry. The following section
highlights the procedures it follows before granting approval to a drug.
7
2.2 Drug Development Process
Figure 1 provides an overview of the drug development process. It begins with the
discovery of a molecular or chemical compound (typically in small biotech companies or
universities which then license their discovery to larger pharmaceutical companies for
further development, testing and eventual commercialization). The process of making a
drug begins with finding a specific protein within the body that is involved with the disease
in question. The aim then is to develop a molecular compound that can interact with that
protein in a way that reduces or cures the disease. The next issue is to make the compound
in a way that can be absorbed into the body and bind to the protein (for example by means
of a tablet or injection).
Once a compound is developed the final challenge is to test the drug for safety and efficacy,
which involves rigorous trials on both animal and human subjects. It must be tested to
ensure that it is effective in treating the disease and that it is safe for patients to consume.
In the pre-clinical phase it is tested on animals and once a basic level of safety has been
established further testing on humans takes place in three clinical trial phases described
below. A detailed description of the full process of FDA approval is provided in Appendix A.
Figure 1: The Drug Development Process
.
Clinical Trial: Phase I
The initial tests on humans are conducted on a small group of healthy volunteers as the
purpose is to find out what side effects the drug might have and how it is absorbed in the
8
body. According to the FDA, the typical sample size in a Phase I trial is between 20 to 80
people.
Clinical Trial: Phase II
Once basic safety of the drug has been ascertained it moves into Phase II which is meant to
test efficacy. The drug is administered to a small group of patients who have the disease
and a group of patients who are given an inactive drug (placebo). Phase II tests are
performed on a relatively small scale, with sample sizes usually being less than 300.
Clinical Trial: Phase III
In the final phase, the drug is tested on large samples of people (up to 3000) and in
different types of populations. The purpose of Phase III trials is to find the appropriate dose
level of the drug and to test the how well the human body tolerates the drug and possible
interactions with other drugs. If the dose is not strong enough it will not be effective yet if it
is too strong it could have harmful effects on the patients. If drug is shown to be toxic the
trial may be stopped.
If a drug passes Phase III clinical trials and all other FDA requirements are met (see
Appendix A), the company is given a formal approval and a patent for that drug, giving it
exclusive rights to sell the drug for a limited period of time. In effect, for the duration of the
exclusivity period the company has total monopoly over the revenues from that drug. After
the exclusivity period is over other firms may enter the market for that drug and sell
generic versions of it.
2.3 Issues in Drug Development
While the pharmaceutical industry has enjoyed sustained growth over a several decades, it
is beginning to face a so-called ‘innovation crisis’. While costs of development have been
increasing (DiMasi, 2003), the industry has seen a decline in R&D productivity in recent
years. A report by consultancy Roland Berger estimates that over the last 10 years global
9
R&D costs rose by over 80% while at the same time the number of new drugs launched
declined by 43%. Baines (2010) suggests this is in a large part due to stricter FDA approval
standards resulting in fewer drugs being approved. Simultaneously, many major
pharmaceutical companies are facing what has become known as a ‘patent cliff’ – a situation
in which a number of drugs in a company’s portfolio face expiry at roughly the same time
leading to a sudden gap in the portfolio and consequently its revenue stream.
Another major factor in the decline in profit margins is competition from generic drugs,
which are drugs that are chemically equivalent to brand name drugs but which are sold
after the patent on the original brand name drug has expired. By many accounts, generic
drugs are more profitable than brand name drugs, for several reasons: firstly they are
cheaper and hence they are often more favored by patients. Secondly, the developers of
generics effectively piggy-back on the R&D efforts of the makers of brand name drugs and
so their costs are substantially reduced. Another advantage of generics is that their FDA
approval process is considerably simpler than that of brand name drugs, since they are
based on the same chemical ingredients and so the hard work of testing the compounds has
already been done.
In the face of these developments, the question remains: to what extent does the market
reward innovation? Is it still worthwhile to invest in these expensive NMEs? The following
chapter explores the current state of the research on market returns in innovation and the
drivers of these returns.
It has been well-documented that innovation is associated with increased profitability. For
instance, Roberts (1999) finds that pharmaceutical companies with a high propensity to
innovate enjoyed sustained profitability (measured by their return on assets (ROA)).
However, innovation is generally considered to be an intangible asset and as such does not
appear in a company’s financial statements. The value of its innovations however can be
immense and in the case of the pharmaceutical industry in particular the prime source of
revenues is new innovations. The question arises: to what extent do stock prices reflect the
value of intangible assets like innovation? Chan, Lakonishok and Sougannis (2001) address
this question by constructing portfolios of companies investing heavily in R&D and
comparing the returns of these portfolios to those of companies that are not R&D-intensive.
They find no significant difference between the stock performance of these two groups of
companies; stocks of R&D intensive firms have an average annual return of 19.65% while
the same figure for ‘non-R&D’ firms is around 19.5%. They do however find evidence that
R&D-intensive stocks display greater volatility than their counterparts, and attribute this to
uncertainty arising from the fact that innovation is difficult to quantify.
In one of the earliest studies on the relationship between FDA decisions and firm value,
Bosch and Lee (1994) conduct an event study spanning the years 1962 to 1989 covering
both approvals as well as negative decisions by the FDA such as disciplinary action or
warnings and recalls on approved products. The main finding was that decisions made by
the FDA have significant impacts on firm valuations. Approvals are associated with an
increase in firm value, while disciplinary action from the FDA leads to negative market
reactions. The fact that there are such large wealth effects even though the approval
process is long and there is some evidence of slight information leakages suggests that the
uncertainty persists until the actual announcement day. This study covers both the
pharmaceutical industry as well as the food industry.
11
In contrast to Bosch and Lee, Lacey and Sharma (2004) focus exclusively on the
pharmaceutical industry and study the market reaction for both FDA approvals and
rejections. They hypothesize that information about new product developments is
incorporated efficiently into the firm’s stock price after announcement, and that there is an
asymmetry in the reactions to positive news and negative news, namely: product
development failures result in greater financial losses than the gains from product
development successes. Using a sample consisting of 344 approvals and 41 rejections they
find that rejections result in negative announcement effects and approvals experience
positive ones in the days following announcement, as one would expect. Furthermore they
find that the decline in stock price after rejections is greater in magnitude than the upswing
following approval. In conjunction with prospect theory the market reacts more extremely
in the case of product failures than successes. Their main conclusion is that market
participants are well informed about the outcomes of specific product development efforts
by pharmaceutical companies. The authors bring to light an interesting implication of this
research for pharmaceutical companies: it might be that only firms willing to take a
significant risk may be willing to engage in innovative drug development. Further questions
remain unanswered: do financial markets discern the differences between products under
development? Do more innovative products gain more positive reaction? If so there may be
greater incentive to innovate because even if the outcome is not favorable the market may
reward the company for pushing boundaries. In practical terms the authors call for
mangers to factor in a substantial risk premium in the form of a higher hurdle rate for new
product developments. They also highlight the need for firms to develop a portfolio of new
products due to the high probability of failure.
Further evidence of positive reactions to approval announcements are reported by Sarkar
and De Jong (2006) and Ahmed (2007). Both are event studies on the announcement effect
of FDA approvals; the former focuses on information disclosures during the FDA approval
process (in particular during Phase I, Phase II and Phase III trials) and conclude that
approval by the FDA results in a positive announcement effect while rejections lead to a
negative announcement effect which is larger in magnitude. Ahmed (2007) studies final
FDA approvals of 11 major pharmaceutical companies between the years 1982 and 2005
12
3.2 Sources of Abnormal Returns
Till date, the research on firm-specific factors and abnormal returns in the pharmaceutical
industry has tended to focus on the sources of gains during mergers and acquisitions. There
has been little attention on whether the market is informed about the specifics of drug
development.
Ciftci and Sougiannis (2011) find that R&D intensity (defined as R&D expense as a fraction
of total sales) and R&D growth (change in annual R&D spending) is indeed associated with
excess returns. This study covers technology-intensive firms in general. It remains to be
seen how this effect manifests in the pharmaceutical industry in particular.
In addition, there has been little discussion in the literature as to whether there may be
drug-specific factors that influence the market reaction. In other words, how well-informed
is the market about the results of clinical trials? Do investors react purely based on the
news of approval or are they also knowledgeable about the details of new innovations in
this field and do they react accordingly to more promising drugs versus those that are more
standard in nature? Sarkar and De Jong (2005) provide some glimpses into this area by
regressing abnormal returns (from announcements during different stages of clinical trials)
on various drug characteristics such as the therapeutic category the drug belongs to (i.e.
what type of diseases it aims to treat – e.g. AIDS, cardiovascular illnesses, cancer) and the
level of priority the FDA gives the drug during its screening (high priority versus standard
priority). They do not find reliable evidence that these factors affect the abnormal returns
in their sample.
This analysis attempts to address some of the gaps identified in the preceding paragraphs.
It investigates the market reaction following approval announcements by the FDA, and to
what extent market participants recognize the difference between new products. While it
has been found that R&D costs are rising while productivity is decline, we study the extent
to which the market rewards companies that have a greater R&D intensity.
13
There are two main research questions addressed in this study:
1. What is the market reaction towards drug approvals?
2. What are the sources of any abnormal returns?
The following subsections present the hypotheses derived from these research questions
that form the basis of this study.
4.2 Market Reaction to Drug Approvals
Prior research has found that markets react to positive as well as negative announcements
in technology-intensive industries. Given that the approval of a drug is a major milestone in
a pharmaceutical company’s operations, the first step is to quantify the relationship
between drug approvals and abnormal returns: does the stock market react positively to
firms that experience approval of their drugs by the FDA?
Hypothesis 1: FDA approval results in positive abnormal returns following announcement
4.3 Sources of Abnormal Returns
If significant abnormal returns are observed in the analysis, the subsequent question is:
what factors influence these abnormal returns? The next issue therefore is to investigate
whether certain firm-specific factors and drug-specific factors influence the size of the
abnormal returns.
Based on the literature surveyed in the previous section, we hypothesize that the following
three factors could play a role in the size of the abnormal returns post-announcement: R&D
intensity of the firm, the drug’s chemical type, and its review status. R&D intensity is
defined as R&D expense as a fraction of total sales and represents the degree to which the
firm engages in innovative activities. A high R&D intensity indicates that a relatively large
14
portion of the company’s profits are reinvested with the purpose of discovering new
molecular compounds that could be developed into potential drugs. It is reasonable to
expect that the R&D efforts of such companies will be rewarded to a greater extent by the
market:
Hypothesis 2: Abnormal returns are higher for firms with greater R&D intensity
The drug’s chemical type is another factor that reflects the extent of the company’s
innovative capabilities; molecular compounds of Chemical Type 1, that is to say those that
are entirely new and have never been used before, represent a significant advancement
over the currently available drugs. To the extent that new molecular entities (NMEs)
represent innovation while reformulations of existing drugs are a sign of imitation, we
expect that if innovation is rewarded then approval announcements of NMEs should be
greater than that of other chemical types:
Hypothesis 3: Abnormal returns are higher for announcements of drugs containing new
chemical compounds (NMEs)
Lastly, certain drugs which provide a major advancement over current treatments or
treatment in an area in which none exists are deemed ‘priority’ drugs by the FDA and are
reviewed on a fast-track basis. This can reduce the length of the clinical trial process by half
a year, which results in a slightly longer exclusivity period. Perez-Rodriguez and Valcarel
(2010) suggest that this extended exclusivity period could represent an opportunity to
capture more sales, in which case we might observe a more positive reaction to drugs that
are branded as ‘priority’ by the FDA. The corresponding hypothesis is:
Hypothesis 4: Abnormal returns are higher for announcements of priority drugs
The first hypothesis will be tested by means of an event study, and regression analysis will
be used to test the remaining three hypotheses.
15
5.1 Market Reaction to Drug Approval
The software Eventus is used to perform the event study to address the first hypothesis
regarding abnormal returns following FDA approval. The function of an event study is to
assess the reaction of the market to announcements made by a company. This is done by
comparing the change in the stock’s return over a certain time window to the expected
return that would have been observed had the event not taken place. The underlying
assumption of the event study is that the market is semi-strong form efficient, that is to say
all publicly available information is incorporated into the firm’s stock price. In order to
conduct an event study using Eventus two types of data are needed: the dates of company
announcements and the stock returns during the chosen estimation and event windows.
In this study the event is the announcement of drug approval by the FDA. Drug data is
compiled from two sources: CenterWatch1 and the Drugs@FDA database from the FDA
website. CenterWatch is a global source of information about clinical trials in the United
States and provides a list of all FDA-approved drugs by company name. These drug names
are then looked up in the Drugs@FDA database which provides full details of all FDA-
approved drugs and their clinical trials. From this database we extract the exact date of final
approval as well as the chemical type and review class of each drug for the regressions in
the later part of the analysis.
We conduct the event study according to the methodology described by De Jong (2007),
which consists of four main steps:
Step 1: Identification of the event
Step 2: Specification of the benchmark model of returns
Step 3: Calculation of abnormal returns around the event date
Step 4: Testing the abnormal returns
1 www.centerwatch.com
Figure 2: Event Study Observation Windows
Step 1: Identification of the event. The event in this study is the date of FDA approval of a
company’s drug as announced by the FDA on their website. The date of approval is taken as
time t = 0. We study abnormal return behavior of the company’s stock around this date, i.e.
during the window [t1, t2] in Figure 3.
Step 2: Specification of the benchmark model of returns. Normal returns are defined as
the returns we would expect to observe during the same time period if no special event had
occurred. Abnormal returns are then calculated as the observed returns over the event
window minus the normal returns which represent the benchmark:
Where:
ARit = abnormal return of firm i at time t
Rit = observed return of firm i at time t
NRit = normal return of firm i at time t
Normal returns are typically estimated as the average returns observed for the firm during
a certain estimation window prior to the event, which is the window [T1, T2] in the graph
above. There are two choices for the benchmark returns: mean returns and market-
adjusted returns. Mean returns are calculated as the average return of the stock over the
estimation window:
∑
In the above calculation, T represents the length of the estimation window in terms of the
number of time periods (days or months). This method however does not account for
market-wide movements in stock prices, which can be remedied by using market-adjusted
returns where the normal returns are calculated as follows:
The market index used in this study to adjust the returns is the CRSP equally-weighted
index. Another choice is the value-weighted index but prior studies find that using the
equally-weighted index versus the value-weighted index does not significantly impact the
results. The length of the estimation window [T1, T2] for calculating the normal returns is
taken as day -310 to day -11.
Step 3: Calculation of abnormal returns around the event date. For the purpose of this
event study the event date is taken to be the date on which approval is made public
knowledge on the FDA website and is designated as time t = 0. The next step is to specify
the event windows [t1, t2] for detecting abnormal returns around the event date. There are
several choices for the width of this event window. We follow the methodology of Lacey and
Sharma (2004) who use an estimation window of 21 days around the event, which they
split into the following event windows:
The primary event window: [t - 5, t +5]
21-Day Window: [t -10, t +10]
11-Day Trailing: [t - 10, t]
11-Day Forward: [t, t + 10]
3-Day Window: [t - 1, t + 1]
2-Day Trailing: [t - 1, t]
2-Day Forward: [t, t + 1]
18
The two trailing windows detect possible information leakages prior to the actual
announcement of the product development outcome, and the forward windows measure
the persistence of post-announcement reactions.
Once the abnormal returns for each event have been calculated they are averaged over the
number of events (represented in the formula below by N):
When this average abnormal return is significantly different from zero it is an indication of
an announcement effect. For the event windows listed above, cumulative abnormal returns
are obtained by summing up the abnormal returns observed over the corresponding
windows:
As before, these cumulative abnormal returns are summed up and averaged across N
observations to get a single figure representing the cumulative average abnormal returns
over all events:
Step 4: Testing the abnormal returns. Statistical significance of the abnormal returns is
checked using the Patell Z test which is a parametric test statistic based on the standardized
abnormal returns obtained. This test is performed within Eventus.
19
5.2 Sources of Abnormal Returns
The second research question concerns the factors that influence the abnormal returns
observed. Are there firm-specific or drug-specific characteristics that influence the market
reaction upon approval? In the previous chapter the possible factors that could be relevant
were identified as the R&D intensity, chemical type of the drug and review class, which led
to the conjecture that abnormal returns are higher for:
Firms with greater R&D intensity
Drugs that are NMEs
Drugs that have priority review status
Regression analysis is used to test these hypotheses. The dependent variable is the
cumulative abnormal return (CAR) for each drug-event obtained during the event study.
Since the 5-day CARs are statistically significant these are selected to be studied. These 5-
day CARs were collected using the cross-sectional analysis tool within Eventus.
The Compustat North America and Compustat Global databases are used to obtain firm-
specific characteristics elaborated upon below. The final sample contains a total of 369 FDA
approvals over the period 1985-2011. Filtering the events for those which have enough
stock return data for the estimation periods reduces the number of events to 319.
The main independent variables are R&D intensity, chemical type and review class. R&D
intensity is widely considered to be an ideal proxy for the level of innovation of a firm
(Chan, Lakonishok and Sougannis, 2001) and is most frequently expressed as a fraction of
R&D expense over total sales. In line with these previous works, we calculate R&D intensity
as annual R&D expense over annual total sales, using the data of the year prior to the one in
which approval was granted. This is based on the assumption that market participants react
on publicly available data about the company and typically consider the most recent
available data. Data for R&D expense and sales was gathered through the Compustat
database using the companies’ gvkey identifiers. The time span of the data is 1985-2011.
Fifty-three observations are dropped due to missing data for some of the events, resulting
in a complete sample of 266 observations.
20
The next two independent variables, chemical type and review class, involve drug-specific
data obtained manually from the Drugs@FDA database for each drug. The figures below
indicate that most of the drugs in the sample are new molecular entities (NMEs) - evidence
that most companies do innovate to a great extent. Comparatively, new formulations of
existing drugs do not represent a large portion of the types of drugs reviewed. Looking at
the second figure, most drugs are standard review as one would expect. Just over one third
are reviewed on a priority basis by the FDA.
Finally, we also control for firm size in our analysis. Small firms are not as capable due to
lack of resources or experience, yet on the other hand they are thought to be more flexible.
Indeed many innovative breakthroughs occur in smaller biotech companies. Therefore we
expect there to be a difference in the market reaction towards innovations from smaller
firms as compared to larger firms. In the current literature, popular choices of proxies for
size include sales, market capitalization, R&D expense and the number of employees. We
choose not to use market capitalization as it is a reflection of the public opinion of the firm’s
worth. Sales, R&D expense and the number of employees on the other hand are concrete
figures that give a more accurate gauge of the resource capabilities of the firm and for this
reason they are selected as proxies for size.
Figure 3: Drugs by Chemical Type
21
Figure 4: Drugs by Review Classification
The dependent and independent variables and their descriptions are summarized in Table
1 on the following page.
The regression model is as follows:
[ ]
Table 1: List of Regression Variables
The data is checked to ensure that it satisfies OLS regression assumptions. Figure 4 on page
24 displays the correlations between a selection of the variables. While most of the
variables do not exhibit serious correlations there is a high degree of correlation between
the variables SALES and XRD. This is expected since larger firms tend to have more
resources at their disposal and can allocate more of those resources to pursuing R&D
activities. The variable EMP represents the number of employees of the firm during the
relevant years. This data was also collected from the Compustat database however the
quality of the data is suspect and therefore the variable is dropped from the analysis.
Next the data is formally tested for multicollinearity, which refers to the situation in which
two or more independent variables are highly correlated. This is undesirable as it may lead
to unreliable estimations of the regression coefficients. We check for the presence of
multicollinearity by determining the Variance Inflation Factors (VIFs) for each variable. To
Type of Variable Variable Name Symbol Description
Dependent Cumulative
Independent R&D Intensity XRDIt-1 R&D Expense/Sales
Chemical Type 1 CHEMTYPE_1 Dummy variable: 1 = Chemical type 1, else 0
Chemical Type 2 CHEMTYPE_2 Dummy variable: 1 = Chemical type 2, else 0
Chemical Type 3 CHEMTYPE_3 Dummy variable: 1 = Chemical type 3, else 0
Chemical Type 4 CHEMTYPE_4 Dummy variable: 1 = Chemical type 4, else 0
Chemical Type 5 CHEMTYPE_5 Dummy variable: 1 = Chemical type 5, else 0
Chemical Type 6 CHEMTYPE_6 Dummy variable: 1 = Chemical type 6, else 0
Standard Drug REVIEWCLASS_S Dummy variable: 1 = Standard drug, else 0
Orphan Drug REVIEWCLASS_O Dummy variable: 1 = Orphan drug, else 0
Sales logSALESt-1 Logarithm of Sales
R&D Expense logXRDt-1 Logarithm of R&D Expense
23
find the VIFs we regress each variable against the remaining variables and extracting the
R2s from each regression. Using these R2s the VIF is then calculated as:
This method yields one VIF for each variable and is always equal to or greater than 1. The
VIF represents the magnitude of inflation in standard errors due to correlation. For
example a VIF of 1.3 indicates that the variance is inflated 30% more than if there was no
correlation present. It follows from this that it is desirable to have a low VIF, however there
is no specific recommendation in the literature as to what precise values the VIF should
take on. Rules of thumb have been proposed by researchers that a VIF greater than 10
indicates the presence of multicollinearity (Hair, Anderson, Tatham and Black (1995)). By
this criterion the VIFs of the variables in this analysis given below indicate that the different
drug types are highly correlated, as are logXRD and logSALES. The correlation between
sales and R&D expense is expected because larger firms are typically able to allocate more
funds to R&D and so they report higher R&D expenses in their financial statements. Since
the main focus in this research is on R&D intensity which is a fraction of the two variables
the fact that they are highly correlated does not affect our analysis. Indeed XRDI has a fairly
low VIF of 2.64.
Variable: CHEMTYPE_1 CHEMTYPE_2 CHEMTYPE_3 CHEMTYPE_4 CHEMTYPE_5 CHEMTYPE_6
VIF: 68.41 18.75 59.64 16.94 10.72 6.67
Variable: REVIEWCLASS_P REVIEWCLASS_S REVIEWCLASS_O logXRD logSALES XRDI
VIF: 1.22 1.35 1.09 10.30 14.09 2.64
24
Having ensured that the data meets regression requirements, the regressions are run
according to the model described above, first on the full sample and then on two subsets of
the main sample: the first subsample consists of events with positive CARs and the second
subsample contains events with negative CARs. The purpose of this distinction is to
investigate whether the relationships between the variables differ when the markets react
positively versus when the market response is less enthusiastic.
Figure 5: Correlation Matrix of Variables
25
This is prompted by an observation that several studies in the literature find evidence of a
number of negative reactions to FDA approval (Ahmed, 2007, and Bansal, 2010). For
example, Bansal (2010) finds that roughly 38% of the firms in her sample experience
negative abnormal returns on the day following announcement of FDA approval of their
drug. She notes that although the announcement of a drug approval is good news, it may be
outweighed by other (unfavorable) news specific to the company around the event window.
Similarly, Lacey and Sharma (2004) state that negative effects on firm value are observed
when the expectations about a new product are not fulfilled. There could be several reasons
these expectations are not fulfilled by approval, for instance: the drug could be a substitute
for drug that was recalled or had some bad news associated with it. Another explanation is
that in the meantime a rival company developed a comparable drug that is perceived as
more effective, implying that the resources spent on the original drug were wasted. Finally,
Franks, Harris and Titman (1991) point out that post-event estimates of abnormal returns
could be sensitive to the benchmarks used in the analysis and as such could result in
inefficient estimates.
For the purpose of our analysis we assume that negative abnormal returns represent an
underreaction to the news of FDA approval and we focus on how the chosen variables are
.
6.1 Market Reaction to Drug Approvals
The results of the event study are presented in the following tables. They report the
(cumulative) abnormal returns over all windows specified in the previous chapter and the
corresponding test statistics.
Table 3: FDA Approval and Abnormal Returns
Overall the results show that that the stock market rewards companies when their drugs
gain approval from the FDA. The abnormal return the day after announcement is 0.77%
under the market model and this figure is statistically significant at the 1% level (test
statistic: 7.04). In addition, cumulative abnormal returns are positive and detectable in the
days following approval announcement. The CAR in the window [-1, 1] and [0, 5] are 0.95%
Day AR (%) t-statistic
t + 1 0.77% 7.036***
t + 2 -0.05% -0.396
t + 3 -0.10% -1.313*
t + 4 -0.18% -1.246
t + 5 0.19% 0.394
Number of events 319
* significant at 10% level
** significant at 5% level
*** significant at 1% level
Abnormal Returns
27
and 0.77% and both are significant at the 5% level. Positive abnormal returns are also
observed over the 10-day window however these returns are not statistically significant.
Table 4: FDA Approval and Cumulative Abnormal Returns
These results are comparable to those found in the literature. Lacey and Sharma (2004)
find an abnormal return of 0.88% on the day following the announcement of successful
product outcomes. Bansal (2010) finds an abnormal return of 0.64% on the day following
FDA approval, at a 5% level of significance. In line with the results of Bosch and Lee (1994),
Lacey and Sharma (2004) and Bansal (2010) we also note some signs of information
leakage prior to announcement.
The fact that FDA approval is greeted enthusiastically by the market highlights the
importance of FDA approval as a milestone for pharmaceutical companies. The cumulative
abnormal returns persist over the 5 days following approval. This could signal that the
market has an optimistic outlook for the future of the company when it gains regulatory
approval.
Overall we conclude that there is ample support for Hypothesis 1. Pharmaceutical
companies experience significant positive abnormal returns after earning approval and
Window CAR (%) t-statistic
28
these abnormal returns persist in the days following announcement. Having established the
existence of post-approval abnormal returns, the next section investigates the sources of
these abnormal returns.
6.2 Sources of Abnormal Returns
The first set of regressions is performed on the full sample of events, with the 5-day CAR as
the independent variable. The results are presented in Table 5 below.
Model 1 includes all variables, while Models 2-6 include a subset of the dependent
variables. In these initial regressions R&D intensity (XRDI) has a positive coefficient and is
statistically significant. As R&D intensity increases the cumulative abnormal returns go up
as well. In other words, a firm that innovates more is rewarded more by the market. This
conforms to our expectations and to prior research described in previous chapters. The
variables representing chemical type, review class and size are not statistically significant.
The next set of regressions is performed on the events that experienced positive CARs.
Table 6 presents the results of the regressions performed on the subsample with positive
CARs as the dependent variable. R&D intensity remains positive and statistically significant
in all the models.
We see that not only is R&D intensity significant as it was in the main sample but so is
CHEMTYPE_1 which represents new molecular entities – the most innovative type of drug.
The positive coefficient for NMEs is evidence that markets reward companies for drugs
which are innovative.
The highly correlated variables logSALES and logXRD are regressed separately and are
observed to have negative coefficients that are significant at the 5% and 1% levels.
Approval is still rewarded but the magnitude of the reaction is smaller if the firm is larger
and spends more on R&D. This could be because larger firms are perceived to be less risky
or better equipped to handle risks, therefore an additional approval, while good news, does
not significantly affect the market's perception. It is also likely that larger and more
29
established firms have a bigger portfolio of drugs so the marginal effect of one additional
approval is not as pronounced.
Moving on to drug types, the variable CHEMTYPE_1 which represents new molecular drug
types (NMEs) shows a positive coefficient that is statistically significant at the 10% level.
When a firm develops a very new and innovative drug and this drug successful passes the
rigorous clinical trials to obtain approval the stock of such a firm experiences a positive
reaction.
As in the case of the aggregate sample, the review class of the drug does not appear to have
any relation to how the news of approval is greeted by market participants.
For the subsample with negative CARs as the dependent variable (Table 7) we observe
some striking results. Increasing R&D intensity is generally associated with a negative effect
on abnormal returns. Also, when the market response is unenthusiastic, the effect is more
pronounced for drugs that are of the ‘least innovative’ type (CHEMTYPE_6). This is as
expected; the market considers that the drug is simply a variant of an existing drug and as
such it could represent the perception that the R&D resources were not well spent. In such
cases, high R&D intensity is perceived in a negative light; as XRDI increases the magnitude
of abnormal returns decreases. This could represent the view that the R&D budget and
efforts were wasted.
In terms of firm size we observe the opposite effect as that in the positive CAR regressions.
Here, the abnormal return is larger if the firm is larger. This may be an indication that even
if the market reacts negatively to an old drug the news is not perceived as negatively for a
large company compared to a smaller one, as a large company typically has a larger
portfolio of drugs as well as the resources to develop more drugs. Having more resources
and experience means they are in a better position to withstand unfavorable events in their
product development.
In all three cases, the review class is not a significant factor in abnormal returns. This is
surprising as priority review extends the exclusivity period of the drug and as such should
represent a larger time window to obtain revenue.
30
On the whole we find support for Hypotheses 1, 2 and 3. Firms experience significant
positive abnormal returns the day after their product is granted approval.
The size of the cumulative abnormal return is related to both the innovative efforts of the
company as well as some specific drug characteristics. Greater R&D intensity is associated
with higher CARs if the news is positive but becomes a liability if the news is not received
favorably by the market. Drug type is also a significant factor in abnormal returns following
drug approval. The market rewards firms for developing innovative new drugs, while
punishing those that are less innovative, i.e. those that bring out variants of existing drugs.
We do not find evidence to support Hypothesis 4; the abnormal returns are not affected by
whether the drug is of priority class. A possible explanation of this could be that when a
drug is granted priority review the news is made public earlier in the approval process
(albeit not in a major press release). If indeed investors respond to these smaller
announcements earlier in the process it is likely that the information is incorporated into
the stock price at that time itself rather than when the final approval is made public news.
31
Table 5: OLS Regression Results – Full Sample
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Intercept -0.026 -0.029 -0.024 0.037 0.016 0.000
(-0.325) (-0.564) (-0.500) (0.946) (0.359) (-0.023)
XRDIt-1 0.008 0.007 0.006 0.006
(1.786)* (1.968)* (2.100)*** (2.177)***
logXRDt-1 -0.004 0.001 -0.001
Adj. R-Squared 0.013 0.010 0.007 0.001 -0.003 0.014
T-statistics are reported in parentheses
* significant at 10% level; ** significant at 5% level; *** significant at 1% level
32
Table 6: OLS Regression Results – Positive CARs as Dependent Variable
Model 7 Model 8 Model 9 Model 10 Model 11 Model 12
Intercept 0.1478 0.149 0.130 0.211 0.186 0.0383
(3.586)*** (3.865)*** (3.489)*** (7.245)*** (5.359)*** (13.359)***
XRDIt-1 0.005 0.006 0.008 0.010
(1.657)* (2.588)** (4.209)*** (5.887)***
logXRDt-1 0.002 -0.005 -0.009
Adj. R-Squared 0.240 0.218 0.206 0.189 0.104 0.193
* significant at 10% level; ** significant at 5% level; *** significant at 1% level
T-statistics are reported in parentheses
33
Table 7: OLS Regression Results – Negative CARs as Dependent Variable
Model 13 Model 14 Model 15 Model 16 Model 17 Model 18
Intercept -0.108 0.151 -0.146 -0.204 -0.192 -0.039
(-1.527) (-2.839)*** (-3.069)*** (-5.004)*** (-4.276)*** (-10.964)***
XRDIt-1 -0.012 -0.010 0.013 -0.016
(-1.852)* (-2.069)** (-3.200)*** (-4.104)***
logXRDt-1 0.003 0.006 0.009
Adj. R-Squared 0.139 0.125 0.129 0.105 0.074 0.113
* significant at 10% level; ** significant at 5% level; *** significant at 1% level
T-statistics are reported in parentheses
34
7. Conclusions and Recommendations
In this thesis we performed an event study to determine the nature of the market reaction
on a pharmaceutical company’s stock after news of its drug approval is made public. We
found positive abnormal returns, highlighting the fact that regulatory approval is a major
milestone that marks the beginning of the revenue stream related to that particular drug.
By running OLS regressions on the cumulative abnormal returns with various explanatory
variables we found that the abnormal returns were higher for companies that have a higher
R&D intensity (defined as R&D expenditure as a fraction of sales). These findings support
previous work and add another dimension to the existing literature, namely that the
financial market rewards companies that engage in more innovative drug development.
One of the limitations of this study is that it suffers from sampling bias because only major
drugs (as listed in the Centerwatch database) are included. Missing data is also a cause of a
significant number of observations being dropped from the analysis. In this sense the
sample of drugs studied may not be truly representative of drug approvals in general.
Also, there are many dimensions of technological innovation in the pharmaceutical industry
of which we consider just two: R&D intensity and chemical type. Future research might
explore other dimensions, such as portfolio diversity. For instance, it is possible that
companies with larger and/or more diverse portfolios experience a more positive reaction
from the market. At the drug level, it may be interesting to see whether the market
response to a drug approval or failure is affected by the number and/or type of drugs the
company has in its portfolio at that time. Perhaps the negative response to a failure may be
dampened if the company has a large and diversified portfolio so that the marginal effect of
failure of one drug out of many is fairly low.
35
References
Ahmed, I., (2007). The Market Return to Pharmaceutical Product Approval. VDM Publishing
House. Retrieved on 6th April, 2012 from:
https://dspace.uta.edu/bitstream/handle/10106/380/umi-uta-1730.pdf?sequence=1
Baines, D., (2010). Problems Facing the Pharmaceutical Industry and Approaches to Ensure
Long Term Viability. Master Dissertation. University of Pennsylvania.
Bansal, A., (2010). As You Sow, so Shall You Reap: Evidence of Innovation and Drug
Portfolio Diversification from the Stock Market. Towers Watson Report. Available at SSRN:
http://ssrn.com/abstract=1605127
Blau, G., Pekny, J., Varma, V., Bunch, P., (2004). Managing a Portfolio of Interdependent New
Product Candidates in the Pharmaceutical Industry. The Journal of Product Innovation
Management, 21: 227-245
Bosch, J., Lee, I., (1994). Wealth Effects of Food and Drug (FDA) Decisions. Managerial and
Decision Economics 15 (6): 589-599
Chan, Lakonishok and Sougannis (2001). The Stock Market Valuation of Research and
Development Expenditures. The Journal of Finance, Vol. LVI, No. 6
De Jong, F., (2007) Event Studies Methodology, Tilburg University
DiMasi, J., Hansen, R., Grabowski, H., (2003). The Price of Innovation: New Estimates of Drug
Development Costs. Journal of Health Economics 22, 151-185
Franks, R., Harris, R., Titman, S., (1991) The Post-Merger Share Price Performance of
Acquiring Firms. The Journal of Financial Economics 29, 81-96
36
Girotra, K., Terwiesch, C., Ulrich, K., (2007). Valuing R&D Projects in a Portfolio: Evidence
from the Pharmaceutical Industry. Management Science Vol. 53, No. 9, pp. 1452–1466
Grewal, Chakravarty, Ding and Liechty (2008). Counting Chickens Before the Eggs Hatch:
Associating New Product Development Portfolios with Shareholder Expectations in the
Pharmaceutical Sector. Intern. Journal of Research in Marketing 25, 261-272
Hara, T. (2003) Innovation in the Pharmaceutical Industry: The Process of Drug Discovery
and Development, Edward Elgar, Cheltenham, UK.
Hair, F., Anderson, R., Tatham, R., Black, W., (1995) Multivariate Data Analysis with
Readings, 5th ed., Eaglewood Cliffs, NJ: Prentice-Hall
Lacey, N., Sharma, A., (2004) Linking Product Development Outcomes to Market Valuation
of the Firm: The Case of the US Pharmaceutical Industry. Journal of Product Innovation
Management 21(5): 297-308
Myers, S., Shyam-Sunder, L., (1991) Cost of Capital Estimates for Investment in
Pharmaceutical Research and Development. Contract report prepared for the Office of
Technology Assessment, US Congress, Washington, DC.
Office of Technology Assessment, 1993. Pharmaceutical R&D: Costs, Risks and Rewards,
OTA-H-522
Perez-Rodriguez, J., Valcarcel, B., (2010). Do Product Innovation and News about the
R&D Process Produce Large Price Changes and Overreaction? The Case of Pharmaceutical
Stock Prices. Applied Economics. DOI : 10.1080/00036846.2011.562172
Roberts, P., (1999) Product Innovation, Product-Market Competition and Persistent
Profitability in the US Pharmaceutical Industry. Strategic Management Journal, 20, 655-670
37
Roland Berger. (2013). Global Pharmaceutical Industry is in a Strategic Crisis – Business
Models Must be Adjusted. Press release. Retrieved from:
http://www.rolandberger.com/media/press_releases/Pharmaceutical_industry_in_a_strate
gic_crisis.html
Sarkar, S., De Jong, P., (2005). Market Response to FDA Announcements. The Quarterly
Review of Economics and Finance. 46 (2006) 586-597
38
Stage 1: Tests on laboratory animals
When a potential drug has been developed it must be tested for effectiveness, and the first
test is on laboratory animals. These tests are carried out by pharmaceutical companies
themselves and the results must be presented to the FDA along with a plan on how the
drug will be tested on humans.
Stage 2: IND Application
If the drug shows some promise in preclinical trials the company files an Investigational
New Drug (IND) application. This is a document stating the results of the preclinical trials
as well as a detailed overview of how the company intends to test the drug on humans.
Decisions such as the testing methods, number of patients, dosage levels etc. The IND is
reviewed both by the FDA as well as an independent panel of scientists and specialists from
hospitals and research groups. If the FDA considers the preliminary results safe it allows
the company to initiate testing on humans.
Stage 3: Phase I Testing
The initial tests on humans are conducted on a small group of healthy volunteers as the
purpose is to find out what side effects the drug might have and how it is absorbed in the
body. According to the FDA, the typical sample size in a Phase I trial is between 20 to 80
people.
Stage 4: Phase II Testing
Once basic safety of the drug has been ascertained it moves into Phase II which is meant to
test efficacy. The drug is administered to a small group of patients who have the disease
and a group of patients who are given an inactive drug (placebo). Phase II tests are
performed on a relatively small scale, with sample sizes usually being less than 300.
Stage 5: Phase III Testing
39
In the final phase, the drug is tested on large samples of people (up to 3000) and in
different types of populations. The purpose of Phase III trials is to find the appropriate dose
level of the drug and to test the how well the human body tolerates the drug and possible
interactions with other drugs. If the dose is not strong enough it will not be effective yet if it
is too strong it could have harmful effects on the patients. If drug is shown to be toxic the
trial may be stopped.
Stage 6: Review Meeting
The FDA holds a meeting with the pharmaceutical company to exchange additional
information about the drug and the trial results.
Stage 7: New Drug Application
The pharmaceutical company submits a New Drug Application (NDA) to the FDA which
includes all details about the drug and how it is produced, results of the animal and human
trials, and all observed effects the drug has on the body.
Stage 8-9: Application Review
After the NDA has been submitted the FDA has a 60 day period to decide whether to review
the application. Common reasons for not reviewing the application are that it is incomplete
or there is missing information. If the application is accepted a special team reviews the
research conducted on the safety and efficacy of the drug.
Stage 10: Drug Labeling
The FDA reviews the drug's labels to ensure that information and instructions for usage are
communicated properly to the end users (medical practitioners and patients).
Stage 11: Facility Inspection
The FDA conducts a review of the facilities where the drug will be manufactured to ensure
that it meets safety and quality standards.
Stage 12: Approval
40
In this final stage of the process the FDA announces whether the drug is approved. If the
drug does not pass the final decision the FDA publishes a response letter which in its words
is "a neutral mechanism to convey that our initial review of an application is complete and we
cannot approve the application in its present form.”
41
Chemical Types
The drug’s chemical type is a number indicating the ‘newness’ of the drug. For example, the
number 1 is assigned to drugs containing an active ingredient that is completely new and
has never been used in drug formulations previously. The figure below lists all chemical
types and their meanings.
New indication
New combination
Letter
P
S
O
Meaning
Priority review drug: A drug that appears to represent an advance
over available therapy
Standard review drug: A drug that appears to have therapeutic
qualities similar to those of an already marketed drug
Orphan drug - a product that treats a rare disease affecting fewer than
200,000 Americans
3 Allergan Inc. 015708 01849010 75646
4 Alliance Pharmaceutical 011793 01877330 60090
5 Amgen 001602 03116210 14008
6 AstraZeneca 028272 04635310 79363
7 Bausch & Lomb Inc 002085 07170710 26518
8 Bayer 100080 07273030 17209
9 Biogen Idec Inc 024468 09062X10 76841
10 Bristol-Myers-Squibb 002403 11012210 19393
11 Celgene Corp 013599 15102010 11552
12 Cephalon Inc 023945 15670810 76625
13 Eli Lilly 006730 53245710 50876
14 Endo Pharmaceuticals Holdings Inc 063645 29264F20 88436
15 Forest Laboratories 004843 34583810 45241
16 Genentech Inc 005020 36871040 87031
17 Genzyme Corp 012233 37291710 10324
18 Gilead Sciences 024856 37555810 77274
19 GSK 005180 37733W10 75064
20 Johnson & Johnson 006266 4786010 22111
21 King Pharmaceuticals Inc. 112033 49558210 86176
22 Merck & Co Inc. 007257 58933Y10 22752
23 Millennium Pharmaceuticals Inc. 062784 59990210 83531
24 Novartis 101310 66987V10 88233
25 Novo Nordisk 008020 67010020 63263
26 Pfizer 008530 71708110 21936
27 Regeneron Pharmaceuticals Inc 023812 75886F10 76614
28 Salix Pharmaceuticals Ltd 062977 79543510 88811
29 Sanofi-Aventis 101204 80105N10 89475
30 Savient Pharmaceuticals Inc 002222 80517Q10 18033
31 Schering-Plough 009459 80660510 25013
32 Sepracor Inc. 024473 81731510 76845
33 Shire 212340 82481R10 85888
34 Teva Pharmaceutical 014538 88162420 75652
35 Vertex Pharmaceuticals Inc 024344 92532F10 76744
36 Watson Pharmaceuticals Inc. 027845 94268310 78916
37 Wyeth 001478 98302410 15667