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Journal of Accounting and Public Policy 27 (2008) 195–216 www.elsevier.com/locate/jaccpubpol

Voluntary disclosure and its impact on share prices: Evidence from the UK biotechnology sector Elisabeth Dedman a, Stephen W.-J Lin b,*, Arun J. Prakash b, Chun-Hao Chang b b

a Manchester Business School, Booth Street West, Manchester, M15 6PB, UK Florida International University, College of Business Administration, Miami, FL 33199, United States

Abstract In the UK, SSAP 13 requires that firms immediately expense most of their R&D expenditures. The reported earnings of high-R&D expenditure firms are therefore likely to convey less value-relevant information to investors than those of less research-intensive firms. Using a sample of firms from the high-R&D UK biotechnology/pharmaceutical sector, we find that earnings announcements have a much lower price impact than drug development announcements. We also find that there are significantly more ‘good news’ voluntary announcements than ‘bad news’ announcements. Furthermore, our findings indicate that these firms are more likely to announce late than early stage developments, and that the pattern of disclosures, and the market’s reaction to them, varies between larger, dominant firms and their smaller counterparts. Ó 2008 Elsevier Inc. All rights reserved. JEL classification: G3; I1; L1 Keywords: Voluntary disclosure; Firm strategy; Market reaction

1. Introduction In this paper we investigate the role of voluntary disclosure and its impact on share prices when the quality of reported earnings is low. To prevent our results from being *

Corresponding author. Tel.: +1 305 348 3253; fax: +1 305 348 2914. E-mail address: lins@fiu.edu (S.W.-J Lin).

0278-4254/$ - see front matter Ó 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.jaccpubpol.2008.02.001

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E. Dedman et al. / Journal of Accounting and Public Policy 27 (2008) 195–216

contaminated by inter-industry factors, we focus our study on only one industry, namely the UK biotechnology/pharmaceutical sector. In this industry the length of time required in developing a marketable product is usually very long, and firms often report negative earnings for a prolonged period of time after being listed due to heavy investment in R&D, most of which must be immediately expensed. Since the UK accounting standard on R&D (SSAP 13) mandates that firms must immediately expense most of their R&D expenditure, reported earnings for biotechs may be less informative with respect to their association with firm value compared to those of less research-intensive sectors. The low probability that any drug in development will ultimately succeed renders the R&D expense figure difficult to interpret, particularly when drugs are in earlier stages of development and when a firm has a smaller portfolio of products in its pipeline. Since financial figures in this industry are less informative compared to other industries, the firm may use more voluntary non-financial disclosures to convey firm prospects to the market. Because the disclosure is discretionary, we conjecture that there may be some manipulation relating to the dissemination of product development information. In this study we examine the regulatory news service (RNS) announcements made and stored in Extel for 22 firms categorized as pharmaceutical by Datastream. For these firms, we identify the exact dates of announcements related to the development of the new drug, rather than information from the firms’ annual reports, to study the instantaneous market reactions, if any, to drug development information. Our sample time period is from 1990 to 1998, prior to the enactment of a code of best practice pertaining to this industry and its disclosure practices.1 We find that average share price reactions to product development announcements are much stronger than responses to any other type of announcements, including earnings announcements. Using earnings response coefficients tests, we find that the impact of reported earnings on share prices is almost negligible in this sector. We describe the drug development process and develop hypotheses about firm disclosure behavior during this process as well as the expected reaction of the market. We find evidence that firms do not disclose as much information in the early stages of drug development as in later stages. When firms do disclose early stage development information, the market does not react as strongly as it does to news of drugs that are closer to attaining market approval. Perhaps one of our most important findings is that firms fail to release negative news to the market, even though low success probabilities suggest that there must be a high incidence of bad news in the drug development process. We also demonstrate that in our sample the disclosure practices of three firms that dominate the pharmaceutical industry in the UK differ from the smaller firms. The rest of the paper is arranged as follows. Section 2 reviews the literature relating to non-financial disclosure; Section 3 describes the current drug development process; Section 4 presents our research questions. The methodology adopted is discussed in Section 5, whilst Section 6 presents our results. Conclusions and suggestions for future research are contained in Section 7.

1

There are practical reasons for ending the sample period at 1998, as this is the year when our announcement data source, Extel, ceased to exist. To the best of our knowledge, Extel has not been replaced by any other as comprehensive and reliable database. Therefore, for consistency, we have opted to study a sample from the same regulatory regime and from the same data source.


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2. Literature survey This is a study of the impact of voluntary product development disclosures in an industry where accounting numbers are relatively uninformative due to high levels of R&D expenditure. It has been argued that ‘‘The theory of voluntary disclosure is a special case of game theory with the following central premise: any entity contemplating making a disclosure will disclose information that is favorable to the entity, and will not disclose information unfavorable to the entity.” (Dye, 2001) Of course, accounting and stock exchange regulations ensure some minimum level of disclosure; but some firms may have incentives to make additional, voluntary, disclosures if they will benefit the firm. There is ample empirical evidence supporting this point of view. For example, Botosan (1997) and Botosan and Plumlee (2001) predict and observe a negative association between disclosure levels and the cost of equity capital. Greater disclosure has also been associated with lower cost of debt (Sengupta, 1998). Welker (1995) argues that lower bid-ask spreads for firms with more disclosure indicate that information asymmetry has been reduced by disclosure. Further support for this is provided by Healy et al. (1999) in their investigation into changes in bid-ask spreads following increased disclosure by firms. Gelb and Zarowin (2002) provide evidence on the informativeness of extra disclosure with regard to predicted future earnings. They demonstrate that firms whose disclosure rating is in the top quartile for their industry have significantly higher future earnings response coefficients than firms ranked in the bottom quartile of their industry. Given the benefits of voluntary disclosures to firms, it may be argued that firms whose mandated accounting disclosures are relatively uninformative, have stronger incentives to publish information outside of that required in the accounting statements. Empirical studies support this contention. Using analysts’ ratings of firms’ disclosure practices from AIMR reports on the level of information provided by a company in (1) its annual reports, (2) quarterly reports and other voluntary publications and (3) investor relations programs, Gelb (2002) finds that companies obtaining significantly higher analysts’ ratings for (2) and (3) in relation to (1) tend to have higher levels of R&D and advertising expenditures. Further evidence that firms with less informative financial statements are more likely to make voluntary disclosures is presented by Tasker (1998) who finds that firms with higher market-to-book ratios are more likely to host conference calls. Amir and Lev (1996) investigate the value relevance of financial information in independent US cellular firms. They find that earnings and changes in earnings are not significantly related to stock returns or share prices in this industry and argue that GAAP procedures are inappropriate for high technology firms. However, they find that nonfinancial information such as the population size of areas in which service licenses are held, are value-relevant. Amir and Lev then suggest that the reported earnings of firms in the biotechnology sector are likely to suffer the same inadequacies as those of cellular companies. Testing a sample of US biotech firms they find that book values are positively related, while earnings are negatively related to share prices. They argue that, ‘. . .similarly to the cellulars’ case, the earnings of biotechnology companies are of dubious relevance for securities pricing’. (p. 17)

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Ely et al. (2003) examine the relationship between the market value of equity and, inter alia, product development information in the US biotechnology sector over a sample period (1988–1998) close to ours (1990–1998). This sector is much more mature in the US than the UK, as Ely et al. (2003) only examine US biotechnology firms whose initial public offering occurred after 1978. In contrast, the first UK ‘pure play’ biotechnology firm did not attain listing status until 1992. Furthermore, the US biotechnology sector is larger than that of UK, having four times as many firms in 2001, as per a report prepared for the UK department of trade and industry (DTI) in 2004. Comparing European biotechnology, which is dominated by UK firms, to US biotechnology, the report states, ‘‘The European group taken as a whole tends to be smaller, slower, less intensive and generally more prone to failure (or, at least, less prone to success) than their US counterparts. They attract less money, spend less, and generate less income.” The report provides evidence that there is more funding available to US biotechnology companies. For example, US human healthcare companies raised an average of €6.85 m in 2002, compared with €1.44 m for their European counterparts. This extra investment allows US firms to progress to profitability more rapidly. The differences in sector maturity and funding availabilities suggest that we cannot simply assume that results of US studies in this industry apply to UK and that comparative studies are necessary. Ely et al. (2003) find that aggregate earnings are not significantly related to firm value in the US biotech sector, but that non-financial information such as the number of drugs-in-progress is value-relevant, as is the net book value of assets. They test a conjecture that investors will not capitalize biotech R&D until some final success probability threshold is crossed. In support of this they find that R&D expenditure is positively related to firm value for firms which are above the median by their PORTWEIGHT measure of portfolio potential.2 Conducting event studies around drug development announcements released to PR Newswire reveals that the market responds significantly positively to news of the initiation of Phase I and Phase II trials (and also to update announcements in these categories) but has no significant reaction to announcements relating to later stages in the development process. The literature reviewed above suggests that firms with low accounting quality, including biotechs, are more likely to make non-financial disclosures to the market, and that these disclosures are likely to be more informative to investors than mandated accounting information. There is evidence in support of this from studies of the US biotech industry, but since there are important differences in the UK and US environments, we cannot assume these findings apply to the UK setting. We therefore examine the informativeness of financial as well as non-financial disclosures during the drug development phases in the UK biotech sector.

2

PORTWEIGHT is the sum of drugs in development weighted by their probability of eventual success. Probabilities are taken from Standard & Poor’s 1997 Biotechnology Industry Analysis.


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The following section reviews the history of medicine control and describes the current drug development process. 3. Stages in the drug development process and their economic significance The control of medicines by the state was originated in ancient Greece and Egypt around 3000 years ago. In the UK such controls date back to the reign of Henry VIII (1491–1547), when the Royal College of Physicians of London had the power to inspect the products of local apothecaries and destroy any stock of inferior quality. The legislation in the UK regarding licensing for medicinal products was first introduced in 1925 with the Therapeutic Substances Act. After the creation of the National Health Service in 1948, the Cohen Committee considered the issue of limiting or prohibiting the prescribing of certain medicines. However, it was not until after the Thalidomide tragedy of the early 1960s that the committee on safety of drugs (CSD) was formed. The pharmaceutical industry agreed to submit data on their products to the CSD and abide by their decisions. This process has now been developed in both the US and UK along similar lines, being regulated by the medicines control agency (MCA) in the UK and the food and drug administration (FDA) in the US. Following the discovery of a new chemical entity (NCE), there is a highly regulated development process before effective medicines reach the patient. The probability of a NCE reaching the market is very low. Only five in 5000 NCEs survive pre-clinical testing on animals to proceed to human clinical testing; of these 5 only one is likely to be granted approval. At this early stage, an R&D group conducts laboratory and animal studies to show that the agent elicits the desired biological activity against the targeted disease. The new compound must also demonstrate that it has a high safety profile in animals. Pre-clinical testing takes an average of 3.5 years. Some companies file for patent protection during this stage. However, with patents generally lasting only 17 years, and the lengthy drug development process, firms are likely to be conscious of ‘the clock ticking’. Given the extremely low probability (1/5000) of final success at this early stage of drug development, one would expect to observe little market reaction to announcements of the discovery of a NCE. Indeed, if these events are not price-sensitive, and may reveal information to competitors, very few announcements of this type are to be expected. 3.1. Investigational new drug application After completing pre-clinical testing, the developing company files an application with the appropriate authority to request permission to begin to test the drug in human subjects. The application shows results of previous experiments: how, where and by whom the new studies will be conducted; the chemical structure of the compound; how it is thought to work in the body; any toxic effects found in the animal studies; and how the compound is manufactured. All clinical trials must be reviewed and approved by the review body where the human clinical trials will be conducted. Progress reports on clinical trials must be submitted at least annually to the review body. There are then 3 key clinical test phases drugs must pass through before applying for approval from the relevant state agencies.

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3.2. Phase I These tests are to ascertain the safety of the drug on humans. They usually involve 20– 100 normal, healthy volunteers. Data are collected relating to how a drug is absorbed, distributed, metabolised, and excreted. This process takes about one year. A rule of thumb reportedly used by analysts3 is that during these trials, drugs have a 10–20% chance of ever reaching the market. 3.3. Phase II This stage examines the new drug’s safety and effectiveness at various doses and involves approximately 100–500 volunteer patients (people with the disease). It takes around two years to complete this phase. Successful completion of this phase is believed to increase the chances of reaching the market to more than 50%. 3.4. Phase III In phase III, clinic and hospital physicians administer the drug to large number of patients (1000–5000) who meet selection criteria. Thus the drug is tested for effectiveness and safety in a larger sample. These patients are closely monitored to confirm efficacy and safety and to identify any adverse events. Phase III trials take an average of three years. Success at this stage reportedly lifts the odds of reaching the market to more than 75% in the UK.4 Following successful Phase III trials, firms submit a licence application to the regulatory authority. As compiling the application is costly, firms are unlikely to proceed unless they are confident of success. It may therefore be predicted that the announcement of successful Phase III trials will substantially increase the market’s estimation of the drug being approved and will result in a significantly positive share price reaction. Conversely, failure at this stage means that the substantial costs of getting this far will not be recouped; the probability of the product reaching the market will fall and the market value of the firm will decrease accordingly, as soon as the market receives this information. 3.5. License application For such applications, firms analyze and compile their data on the scientific development, efficacy and safety of the drug, often resulting in a document of over 100,000 pages. The regulatory body then assesses the data and information in the application to ensure the agent has successfully demonstrated patient safety as well as effectiveness. Finally the

3

Durman (1996). Studies related to the US indicate that the probabilities may differ across the Atlantic, with commencement of Phase II trials being associated with a final success probability of almost 30%; success of Phase II trials increasing final success probability to about 60%; and success at Phase III (leading to an NDA) increasing the odds to over 70%. 4


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ultimate governmental regulatory agencies such as FDA in the US and MCA in the UK take approximately 2.5 years to review a new drug’s license application.5 This brings the average total time of 12 years from discovery to market of a new drug. 3.6. Approval After gaining approval, the new drug may be prescribed by physicians. However, the firm is obliged to continue to submit periodic reports to the regulator, including details of quality controls and any cases of adverse reactions. 3.7. Post-marketing approval clinical trials (Phase IV) In some cases, the regulatory body requires additional clinical trials (post market surveillance) to evaluate the longer-term safety and efficacy of new drugs. Due to the change in success probabilities over the life of the drug development process, we expect to see more announcements relating to positive later stage developments than to earlier stage developments. This is because such announcements are likely to have a greater impact on firm value than earlier ones, and possibly because competitors are less likely to enter any race at the later stage.

4. Hypotheses Based on the discussion above we formulate several testable hypotheses. These are described and expressed in alternative form below. Since, in the early stages of development, drugs have very low chance of final success, we expect to see higher numbers of later stage drug development announcements than early stage announcements. H1: Firms will make more later than early stage drug development announcements Following Amir and Lev (1996) we expect that non-financial information, in the form of product development announcements, will be more value-relevant in the biotech/pharmaceutical industry than any financial information such as earnings announcements. Therefore, we hypothesize, H2: On average, drug development announcements will have a greater impact on the valuation of shares than earnings announcements. Ely et al. (2003) argue that drugs may need to reach a certain point in the development process before investors are willing to capitalize their costs. Therefore, we hypothesize,

5 By law, FDA (or MCA) is allowed six months to review an NDA. In almost all cases, the period between the first submission of an NDA and final FDA approval exceeds that limit; the average NDA review time for new molecular entities approved in 1992 was 29.9 months.

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Table 1 Sample details

a

The three firms in shaded boxes will comprise our ‘dominant’ firm subset in later tests.

H3: The abnormal share price reaction to positive drug development announcements will be greater for later stage announcements than early stage announcements. Our investigation over the structure of the UK biotech/pharmaceutical industry during the test period shows that the entire industry was dominated, in terms of sales and market


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capitalization, by three pharmaceutical firms. They are Glaxo Wellcome, Smithkline Beecham and Zeneca Group and have much larger product portfolios than their counterparts (for details refer to Section 5.1 and Table 2). We argue that, ceteris paribus, drug development announcements will be more value-relevant for firms with fewer or no approved products. This is because these firms generally have smaller product portfolios and are smaller in size. More importantly, they are not followed by analysts and the media to the same extent as large firms.6 There is therefore greater information asymmetry in these firms. We hypothesize that H4: Firms with fewer products will experience larger abnormal share price returns around drug development announcements than firms with more products. Our fifth hypothesis focuses on how disclosure behavior is likely to vary between dominant and large pharmaceutical firms and pure play biotechnology firms. In the UK, compounds discovered by smaller biotech firms are often taken through the early stages of development by the small firms alone. The small firm then enters a collaborative agreement with a larger firm to take the product through the later and more costly stages. We would therefore expect to see smaller firms making more early stage announcements, with the later stage announcements dominated by the larger firms. This hypothesis of interest is expressed as H5: Early stage announcements are more likely to come from smaller firms, whilst later stage announcements are more likely to come from dominant and larger firms. In the next section we describe the sample and methodology employed in the study. 5. Methodology 5.1. Sample selection The original sample comprises 35 active and inactive UK listed pharmaceutical firms in Datastream. Twelve out of 35 firms make no announcements regarding drug development and we are unable to find share returns data from Datastream for one other firm. The final sample is therefore composed of 22 firms as listed in Table 1. These firms released 239 drug trial announcements during the period 1990–1998, five of which are excluded due to missing share returns in Datastream, leaving a total of 234 announcements. After deleting firms with other information released around drug trial announcements (i.e. day 1, day 0, and day +1), there are 165 non-contaminated drug trial announcements. Due to the small number of negative announcements made by these firms, we concentrate on the market response to positive announcements. Furthermore, due to the very small number of very early stage announcements, we concentrate on announcements of events beyond pre-clinical testing and early patent applications. Thus, we are left with a sample of 151 positive-toned, non-contaminated announcements.

6

Indeed we suffered significant sample attrition in the later tests which required analyst forecast data because many firms are not closely followed by analysts.

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Table 2 Levels of market concentration and impact of dominant firms Year

Panel 90 91 92 93 94 95 96 97 98 Year

Total sales (A)

Sales by three dominant firms (B)

A: Market concentration 3,010,827 2,854,000b 3,602,493 3,397,000b 4,395,455 4,096,000b 15,951,786 15,534,000c 17,098,662 16,628,000 19,332,610 18,902,263 22,170,595 21,629,000 21,198,015 20,969,000 21,881,776 21,575,000 Total Market Capitalization (A)

Sales by other firms (A–B)

Percentage of total sales from dominant firms (B/A) (%)

Herfindahl– Hirschman indexa

Normalised Herfindahl index

156,827 205,493 299,455 417,786 470,662 430,347 541,595 229,015 306,776

94.79 94.30 93.19 97.38 97.25 97.77 97.56 98.92 98.60

9000.41 8908.60 8706.21 3225.81 3225.13 3267.52 3280.68 3369.85 3329.46

0.87 0.86 0.84 0.27 0.28 0.29 0.30 0.31 0.31

Market value of three dominant firms (B)

Panel B: Market capitalization 90 17,092,234 16,461,276d 91 25,334,420 24,302,669d 92 28,153,532 27,187,016d 93 31,985,168 30,539,405e 94 33,012,490 31,146,580 95 57,089,451 53,758,944 96 77,741,373 71,388,167 97 111,604,065 106,150,800 98 150,548,210 146,627,904

Market value of other firms (A–B)

Percentage of total market cap in 3 dominant firms (B/A) (%)

630,958 1,031,751 966,516 1,445,763 1,865,910 3,330,507 6,353,206 5,453,265 3,920,306

96.31 95.93 96.57 95.48 94.35 94.17 91.83 95.11 97.40

Sample is 22 UK pharmaceutical firms from 1990 to 1998. Firms classified as dominant are Glaxo Wellcome, Smithkline Beecham and Zeneca Group. There is no sales data for Smithkline Beecham Plc until 93. Consistent with Panel A, the above three companies have the highest market capitalization during our test period. P a 2 Herfindahl index is derived from H ¼ i¼n i¼1 S i , where Si is the market share of firm i (measured by the sales of firm i dividend by the total sales of the industry in which firm i is operating). H great than 0.18 indicates high market concentration; n indicates number of firms in the same industry. Normalised Herfindahl index is derived H 1 from H ¼ 11n. H* greater than 1800 indicates high market concentration. n b Sales from Glaxo Wellcome Plc. c Zeneca Plc joined in 93. d Market value from both Glaxo Wellcome Plc and Smithkline Beecham Plc. e Zeneca Plc joined in 93.

During our sample period, three of these firms, Glaxo Wellcome, Smithkline Beecham and Zeneca Group, dominated the UK pharmaceutical market, both by sales and market capitalization. In Panel A of Table 2 we report that the industry was highly dominated by these three giants during the period under study, with the Herfindahl Index measuring over 3000 each year.7 These concentration levels are due to the sales of our dominant firms, whose market share totals between 93% and 99% in the test period. Panel B shows that the combined market capitalization of these three dominant firms represent over 90% of the total market value of all listed firms in this industry throughout our sample period. Table 1 also shows that 54% (127/234) of the drug trial-related announcements were released by these three dominant firms during the test period. 7

A Herfindahl Index measure exceeding 1800 indicates a highly concentrated market.

Table 3 Description and classification of drug trial-related announcements E. Dedman et al. / Journal of Accounting and Public Policy 27 (2008) 195–216

Shaded areas show where several similar types of announcement have been put in the same category. 205

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5.2. Drug trial announcements The announcements are classified to represent different stages of the drug development process. There are originally 27 different categories assigned to the announcements. However, several of these relate to similar stages in the process and are therefore grouped together. The classification process is depicted in Table 3. The shaded areas indicate where we have grouped several classes of announcements together. Simple analysis of the entire sample of 234 announcements reveals support for our earlier conjecture regarding the release of good versus bad news. Only 12 of the announcements are classified as bad news. These are announcements of types D, F, J, L, O, P and R in Table 3. Binomial tests confirm that firms are more likely to release good news than bad news with respect to drug development. This supports the US evidence of Ely et al. (2003) who also found very low levels of negative-toned announcements. We then partition the 234 announcements into two categories. They are early stage announcements – those announced up to the end of Phase II, and later stage announcements – those made after the completion of Phase II. We classify 70 announcements as early stage (types B–J) and 164 announcements as later stage (types K–R). Using the binomial test again, we find that firms are more likely to announce later stage than early stage developments, supporting H1. According to Table 3, firms released 113 announcements in relation to final approval (category Q), comprising 48% of the 234 announcements examined. These observations are consistent with Dye’s (2001) ‘theory of voluntary disclosure’, with firms more likely to announce good than bad news. Firms in this sample also announce later stage announcements which are likely to be more price-sensitive, as the final success probabilities of the drug are starting to exceed 0.5. Table 4 divides the 165 non-contaminated announcements by category of announcement and whether or not the firm is ‘dominant’. Our ‘dominant’ firm category comprises the three major players in this industry. Overall these dominant firms, namely, Glaxo Wellcome, Smithkline Beecham and Zeneca (see Table 2) make more announcements per firm than their smaller counterparts. The pattern, as well as the number, of announcements also varies across groups. Consistent with the fact that the smaller firm group contains many biotech firms with no approved products, most of the early stage announcements come from this group. Similar evidence is also found in the US, with Ely et al. (2003) reporting that biotech firms make more than twice as many Phase I and Phase II announcements compared to the later stage announcements. The larger, dominant firms may well have no incentive to make such announcements, as they may Table 4 Comparison of announcement patterns between dominant and other firms Groups

Event

Dominant firms

Other firms

Total

Binomial t test

EGHI KMN Q

All positive announcements Phases I and II Phase III Final success (drug makes it to market)

81 5 9 67

70 42 18 10

151 47 27 77

0.90 5.40a 1.73b 6.50a

Dominant firms in this study are Glaxo Wellcome, Smithkline Beecham and Zeneca Group (see Table 2). The groups and events are as categorized in Table 3. Sample is 151 positive, non-contaminated announcements. a Significant at 1% level. b Significant at 10% level.


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not be as price-sensitive as the smaller firms, due to their larger product portfolios and market capitalizations. Large firms do, however, make announcements regarding final drug approval, with nearly seven times the number of such announcements coming from the dominant firm group in the UK sample compared to the smaller firms. The information contained in Table 4 is therefore consistent with the description of the UK pharmaceutical industry presented earlier (development of H5), where smaller firms develop products through early, less costly stages of development, then either sell their technology to, or develop collaborative venture with, larger firms who fund the drugs’ development through the later, more costly stages. 5.3. Event study methodology In order to examine abnormal share price returns around drug development announcements this paper uses standard event study research methodology, with a short test window (i.e. including day 1, day 0, and day +1) and a 150-day estimation period (i.e. a period between day 160 and day 11). The market model is used to measure abnormal returns. Both Student’s t and Patel’s (1976) standardized residual tests are used to examine whether the abnormal returns for the test period are statistically different from zero. 5.3.1. Share returns Daily logarithmic price relatives are calculated as follows: Rit ¼ log½ðP it þ Dit Þ=P it1

ð1Þ

where for the firm i, R, P, Pit1 and Dit are, respectively the logarithm of price relatives on day t, the share price on day t, the share price on day t 1, and the cash dividend paid on ex-dividend date t. All the above variables are based on per share basis and are also adjusted for capitalization. 5.3.2. Market returns Daily logarithmic market price relatives are calculated as follows: Rmt ¼ log½FTAIt =FTAIt1

ð2Þ

where FTAIt is the financial times all-share index on day t.8 5.3.3. Abnormal returns For the market model Rit ¼ ai þ bi Rmt þ eit

ð3Þ

î . We first obtain the estimates of the regression parameters and denote these by aî and b The abnormal returns for the estimation period (between day 160 and day 11) are computed as: î Rmt Þ ARit ¼ Rit ð^ ai þ b 8

ð4Þ

Following Fama et al. (1969) we use the logarithmic rather than the standard form of the market model to account for any skewness in the rates or return probability distribution.

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Table 5 Comparison of announcement types and share price return effects

A summary of total standardized absolute abnormal share returns for individual news categories for biotech firms provided by the EXTEL news cards. Abnormal share returns are derived from the market model. Standardized absolute abnormal share returns are derived from absolute abnormal share returns divided by standard deviations. The rankings are based on the mean standardized absolute abnormal returns. a

Including commercial operations, trading statements, and activities.


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To control for non-synchronous trading, this paper uses Scholes and Williams’s (1977) unbiased parameters.9 5.3.4. Statistical tests To achieve our objective, we utilize three standard statistical tests in our study. They are namely, Student’s t, Patel’s Z and Corrado’s rank tests. The details of these tests are provided in Appendix 1. 6. Empirical results Table 5 summarizes total standardized absolute values of abnormal share returns for individual news categories for biotech firms provided by the EXTEL news cards. Abnormal share returns are derived from the market model. Standardized abnormal share returns are derived from the absolute values of the abnormal share returns divided by their standard deviations.10 The rankings are based on the mean standardized absolute abnormal returns. The rankings change slightly while using the median standardized absolute abnormal returns, with listing announcements inducing the highest median return. Drug development announcements, however, dominate earnings announcements irrespective of any average we employ. In Table 5 we present a comparative evaluation of the average share price response to our 165 non-contaminated drug trial announcements with other types of announcements made by firms in our sample. This table contains 1367 non-contaminated announcements released by the 35 bio/pharma firms that are available in the Datastream. Classifying them in 47 news categories as per the EXTEL news cards, we find that around 40% (547) of total non-contaminated announcements are related to shareholdings in the company, with 12.07% of ‘clean’ announcements pertaining to drug development. Furthermore, only 2.93% (40) of our non-contaminated announcements are earnings-related.11 It is clear from the results presented in Table 5 that, in terms of average share price changes, drug trial-related announcements induce much stronger average market reactions than earnings announcements. The reaction to non-financial disclosures is about twice the size of the reaction to earnings announcements when using mean (median) standardized absolute values of abnormal share returns. The results presented in Table 5 therefore support the view that voluntary, non-financial disclosures are important determinants of the market valuation of high R&D firms. Amir and Lev (1996) use earnings response coefficients to demonstrate that earnings and changes in earnings are not significantly related to share prices in the US Cellular industry. Following them, we use the models described below to test the earnings response coefficients: 6.1. Model 1: Price and earnings P it ¼ a1 þ b1 EPSit þ eit ^sw ¼ bî þbî þbî , where b ^t is the slope coefficients derived from regressing the SW unbiased beta estimator: b i 1þ2^ qm ^m . The first-order serial share returns of firm i at time t against the market returns at time t, t 1 and t + 1; q correlation coefficient of the market returns at time t. 9

1

o

þ1

10 Absolute values are used here as almost all drug announcements are good news, whereas earnings announcements could be either good news or bad news. 11 This category consists of a combination of quarterly, interim, and final earnings announcements.

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6.2. Model 2: Price, earnings and book value of equity ^1 EPSit þ b ^2 BVit þ êit P it ¼ ^ a1 þ b where Pit, EPSit and BVit are, respectively share price, earnings and book value of equity ^1 indicate earnings response coefper share of firm i in period t. The parameters b1 and b ficients before and after controlling for book value of the equity. Book value is controlled in the second model as it is expected to be the dominant valuation variable when earnings are not informative (Ohlson, 1995). 6.3. Model 3: Annual abnormal return, earnings and change in earnings CARit ¼ a2 þ b1

EPSit DEPSit þ b2 þ eit P it1 P it1

where CARit is the cumulative abnormal share return for firm i for period t (starting from three months after prior year end and ending three months after current year end), derived using the market adjusted model, EPSit is the level of earnings per share for firm i for period t, DEPSit is the change in earnings per share for firm i for period t and Pit1 is the share price at the beginning of the above return period for firm i. 6.4. Model 4: Abnormal return around earnings announcements and unexpected earnings b it ¼ a3 þ b3 CA R

UEPSit þ eit P t1

b it is the cumulative abnormal share return for firm i for period t (i.e. three days where CA R centred on earnings announcement day). The cumulative abnormal share return is derived from the market adjusted model. UEPSit is the unexpected earnings for firm i for period t, proxied by the difference between actual reported earnings and the latest analysts’ forecasts (mean forecasted earnings obtained from I/B/E/S) before earnings announcements. Pit1 is the share price at the beginning of the above return period for firm i. As presented in Table 6, the coefficient on the level of earnings is small and not significant in either Model 1 or 2, a result consistent with the findings of Amir and Lev (1996).12 Further, the insignificant results using Model 3 imply that both earnings per share and changes in earnings per share are not value-relevant in this industry. Model 4 tests the announcement effect of earnings surprises by regressing the 3-day CAR (centred on earnings announcement day) on unexpected earnings, measured as the difference between reported earnings and the latest analyst forecast of earnings before earnings announcements. As some firms have no analyst following, we lose some observations here. The coefficient on our unexpected earnings variable is statistically insignificant and the model as a whole is not significant. These test results provide partial support for our H2 that earnings are not value-relevant and earnings announcements do not contain any unexpected information in this industry. Similar to our findings, Ely et al. (2003) report book value to be significantly positively related to firm value while earnings having no significant association with the market value of equity in US biotechnology firms.

12

Amir and Lev (1996) test the ERC of US biotech firms and find an insignificant coefficient of 0.148.


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Table 6 Earnings response coefficients

Observations Intercept Earnings (EPS)

Model 1

Model 2

Model 3

Model 4

228 2.96 (11.16)a 1.99 (1.05)

228 1.07 (3.67)a 1.13 (0.79)

193 0.12 (2.65)a 0.01 (0.03) 0.26 (1.05)

78 0.003 (0.47)

Change in earnings (DEPS) Unexpected earnings (UEPS)

0.00 (0.18)

Book value (BV) F-statistic R-squared

14.41a 5.58%

Model 1: Earnings and price Pit = a1 + b1EPSit + eit Model 2: Earnings, book value of equity, and rice ^1 EPSit þ b ^2 BVit þ êit P it ¼ ^ a1 þ b

Model 3: Annual abnormal return, earnings and change in earnings EPSit DEPSit CARit ¼ a2 þ b1 þ b2 þ eit P it1 P it1

Model 4: Abnormal return around earnings announcements and unexpected earnings b it ¼ a3 þ b3 UEPSit þ eit CA R P it1

3.36 (5.79)a 75.10a 39.50%

0.96 0.05%

0.03 1.27%

Pit indicates the share price for firm i at the end of year t EPSit indicates earnings per share for firm i for year t Pit indicates the share price for firm i at the end of year t EPSit indicates earnings per share for firm i for year t BVit indicates the book value of equity for firm i for year t ^1 indicate the earnings response coefficients before b1 and b and after controlling for book value of equity, respectively. Book value is controlled for in the second model as book value is expected to be the dominant valuation variable when earnings are not informative (Ohlson, 1995). CARij is the cumulative abnormal share return for firm i for period t (starting from three months after prior year end and end three months after current year end); cumulative abnormal share return is derived from the market adjusted model. EPSit is the level of earnings per share for firm i for period t. DEPSij is the change in earnings per share for firm i for period t. Pit1 indicates the share price at the beginning of the above return period for firm i. b ij is the cumulative abnormal share return for firm i for CA R period t (e.g. earnings announcement day and three days centred at earnings announcement day); cumulative abnormal share return is derived from the market adjusted model. UEPSij is the unexpected earnings per share for firm i for period t, proxied by the difference between actual reported earnings and the latest analysts’ forecasts (mean and median forecasts obtained from I/B/E/S) before the earnings announcement. Pt1 indicates the share price at the beginning of the return period for firm i.

Note: All the t statistics use White’s unbiased standard deviation if there is evidence of heteroskedasticity. a Significant at the 1% level.

In Table 7 we provide some preliminary results of our event study. Using both traditional Student’s t and Patel’s standardized Z statistics for our 165 non-contaminated announcements (151 positive-toned announcements plus 14 non-positive tone announcements) we find that, on average, the market reacts positively and strongly to all non-contaminated announcements. This is in line with our previous observation (Table 3) that these firms announce more good than bad news. Consistent with H3, reactions to good

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Table 7 Market reactions to drug trial-related announcements


213

The shaded boxes represent pre-clinical, negative, or patent, announcements, which we exclude from later tests, along with patent and pre-clinical announcements because of the small number of such observations. This leads to a drop in sample size from 165 to 151 observations.

news appear to get stronger later in the development process. There are very few negativetoned announcements, which make it difficult to draw any concrete conclusions about the apparently mixed reactions to such announcements. However, the results do seem to present indirect evidence of negative news being ‘leaked’ to, or anticipated by, the market prior to being formally announced, as positive reactions to what is obviously bad news (e.g. Failure at Phase I) indicate the market had even worse expectations about such events. From Table 7 it is evident that some groups of announcements have a very small number of observations, which may lead to unreliable results. To focus our subsequent tests on whether the market reacts differently to early versus late stage drug development announcements, we rearrange the group announcements into three categories, each of which contains only positive tone announcements. They are: Phase I and Phase II; Phase III; and marketing announcements. Phase I and Phase II announcements include the drug reaching Phase I (E), positive interim results at Phase I (G), drug reaches Phase II (H), and positive news during Phase II (I). Phase III announcements include drug reaches phase III (K), positive interim results at Phase III (M), and drug makes it to NDA stage (N). Marketing announcements include final approval announcements (Q). This sample rearrangement results in losing 14 observations, leaving us with 151 observations grouped into three stages of the development process, which are investigated in Table 8. Panel A of Table 8 shows that the mean (median) Day 0 abnormal return for positive tone announcements is 0.92 (0.57), which is significant at the 1% level. Of 151 announcements, 105 are associated with a positive share price response, 46 with a negative response. Binomial tests confirm that there are significantly more positive than negative responses. Disaggregating the full set of announcements into broad development stages reveals that later stage announcements are associated with stronger price reactions, as hypothesized in H3. Phase III and final success announcements generate more significant reactions than earlier stage announcements. All subsets of announcements in Panel A have significantly more positive than negative share price responses. We also examine whether there are different reactions to these announcements between large firms with well-developed portfolios of products (dominant firms from Table 2), and smaller firms. The results are shown in Panels B and C of Table 8, which report details of

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Table 8 Market reaction to Phases I, II, III, and final success (drug makes it to market) announcements Groups

Event

Panel A: All firms All positive announcements EGHI Phases I and II

Number of observations

151

Day 0 mean (median) abnormal returns%

T-stats mean (median) Day 0 abnormal return

0.92 5.51 (0.57) (3.41) 47 0.77 1.83 (0.58) (1.37) KMN Phase III 27 1.56 4.45 (0.71) (2.02) Q Final success 77 0.79 4.88 (0.43) (2.65) Panel B: Dominant firms (Glaxo Wellcome, Smithkline Beecham, Zeneca) All positive 81 0.58 4.26 announcements (0.38) (2.79) EGHI Phases I and II 5 1.15 1.93 (1.22) (2.03) KMN Phase III 9 0.03 0.11 (0.01) (0.04) Q Final success 67 1.04 4.07 (0.48) (2.49) Panel C: Other firms All positive 70 1.89 4.05 announcements (1.19) (2.80) EGHI Phases I and II 42 0.73 1.56 (0.58) (1.24) KMN Phase III 18 2.35 4.70 (1.50) (2.99) Q Final success 10 1.87 2.70 (1.08) (1.54)

Patel (Corrado) statistics

Observations with positive/negative abnormal return

8.52 (3.82) 3.91 (2.77) 6.45 (2.52) 5.06 (3.27)

105/46 (4.80)*** 32/15 (2.48)*** 18/9 (1.73)* 55/22 (3.76)***

4.28 (3.46) 1.74 (1.54) 0.01 (0.25) 4.24 (3.07)

55/26 (3.22)*** 4/1 (1.34) 4/5 (0.33) 47/20 (3.30)***

7.91 (5.76) 3.54 (2.42) 7.91 (3.10) 3.07 (1.95)

50/20 (3.59)*** 28/14 (2.16)** 14/4 (2.36)** 8/2 (1.90)*

Sample comprises 151 ‘good news’, non-patent, drug development announcements. Groups correspond to categorizations in Table 3. Notes: ***, ** and * denote the binomial test is significant at the 1%, 5%, and 10% level, respectively, indicating there are more observations with positive than negative share returns.

Day 0 abnormal returns divided into three broad event categories and partitioned by whether the firm is dominant or not. In Panel B, which contains results for our dominant firms, we provide the evidence that it is the ultimate stage announcements which are most important to these firms. Their drug development announcements are concentrated in the final success category and are associated with significant positive average abnormal returns. In Panel C, we report the results for the other 19 smaller firms. It is clear that, whilst the pattern of announcements for these firms tends towards earlier stage announcements, all types of announcement are associated with significant share price movements for these smaller firms which have less well-developed portfolios of products. In contrast to the findings of Ely et al. (2003) regarding US firms, our results suggest that it is the later stage announcements (Phase III and final success) which are most value-relevant in the UK. Ely et al. (2003) find it is the earlier stage announcements (Phase I and II) which have a more significant association with US firm value. They argue that the market capitalizes R&D expenses at early


215

stages of drug development in the US but, in case of UK firms, this capitalization appears to occur at a later stage. 7. Summary and conclusions This is a study of the market impact of voluntary disclosure in an industry where the quality of reported earnings is low. In order to control for inter-industry effects we concentrate on one industry, namely, biotechnology and pharmaceuticals in the UK. We find that drug development announcements dominate earnings-related announcements in terms of stock market impact. Consistent with this finding, we also show that earnings are not informative for share valuation. We provide evidence that managers release more good news than bad news. In fact, there is so little bad news released to the market that we are unable to study negatively toned disclosures, even though low success probabilities would suggest that there may be preponderance of bad news in this industry. All of these findings are in line with results from a US study over a similar period of time. Our results differ from those in the US study, however, in that investors in UK firms appear to demand further progress through the drug development process before they capitalize R&D expenditure. Acknowledgement We gratefully acknowledge the comments from the anonymous referee and from participants of research seminars at Manchester Business School and Florida International University. Appendix 1. Statistical tests The statistical tests employed in this paper are detailed below. (1) Students t test The t test statistic is computed as follows (MacKinlay, 1997): ARt tt ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi N ð0; 1Þ varðARt Þ

ðA:1Þ

where ARt is the mean abnormal returns on day t; varðARÞ is the variance of mean abnormal returns during the estimation period. (2) The standardized residual statistic A standardized residual statistic is computed as follows (Patel, 1976): Pi¼N SARit Z t ¼ h i¼1 N ð0; 1Þ ðA:2Þ Pi¼N T i 2i1=2 i¼1 T i 4

SARit ¼

ARit ^ ei r

ðA:3Þ

where ARit is the abnormal return of firm i on day t; ^dei is the standard deviation of abnormal share returns of firm i during the estimation period. Ti is the number of observations of firm i in the estimation period; N is the number of companies.

216


(3) Corrado’s rank test (Corrado, 1989) This test is based on the distribution of the differences between the actual and expected ranks of abnormal returns during the test period. Corrado’s test statistics is computed as follows: h i Þ N K it ðLþ1 2 1 X N ð0; 9Þ ðA:4Þ h¼ N i¼1 sðkÞ ffi vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u T " #2 N 2 u1 X X 1 ðL þ 1Þ sðkÞ ¼ t K it ðA:5Þ L t¼T 1 N i¼1 2 where L is the number of days in the test period from T1 (day 10) to T2 (day +10); Kit is the actual rank of the abnormal return of company i on day t; N is the number of companies.

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