The stock market’s valuation of R&D externalities

Hironobu Miyazaki
School of Economics, North Asia University46-1 Shimokitade-Sakuraaza-Morisawa, Akita-shi, Akita 010-8515 Japan
E-mail: miyazaki@nau.ac.jp

Hiroyuki Aman
School of Economics, Nagasaki University
4-2-1 Katafuchi, Nagasaki-shi, Nagasaki 850-8506 Japan
E-mail: aman@nagasaki-u.ac.jp

Abstract
R&D, particularly basic research, is generally considered as a public good. It provides
positive externalities to other firms. This paper investigates rival firms’ stock-price responses
to an increase in the R&D expenditures of a firm. Examining firms in the pharmaceuticalindustry, we found that the market valuations of some rival firms benefit from R&D
externalities. Moreover, the cross-sectional analysis indicated that R&D-intensive firms
benefit immensely from them. From this result, investors might assess that these firms havethe full potential to absorb new R&D knowledge.

Key words: event study, R&D externalities, rival firms
JEL classification: G14, G30, O32

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1. Introduction

R&D, particularly basic research, is generally considered as a public good. First, it isdifficult to exclude anyone from its benefits. Second, the marginal cost of an additional
individual enjoying a public good is zero. Consumption is considered to be non-rivalrous.
Indeed, sharing the fruits of R&D as soon as they are available can be highly beneficial asother researchers can use this knowledge in their quest for innovation.
Some research has assessed the intra-industry effect in the event of R&D expenditure.
However, this research pertains to the US market. To our knowledge, no research has been
conducted on the Japanese market thus far.
Zantout and Tsetsekos (1994) examined the effects of announcements of plans to increase
R&D expenditures on the stock prices of rival firms. They found that the rival firms sufferedstatistically significant negative abnormal returns at the announcements. This is known as thecompetitive effect. This effect suggests that the announcing firm is moving ahead of its rival
firms with regard to innovation; in other words, the rival firms are lagging behind in the R&D
race.
Sundaram, John, and John (1996) found that competitors’ stock prices are positively
influenced by the interaction between the market’s reaction to the announcing firm and thecompetitive strategy measure (CSM). CSM is the measure that determines whether thecompetition is characterized by strategic substitutes or strategic complements.
Previous studies have reported mainly on the relationship between the announcing firm andrival firms. However, they have not investigated the features of the firms that benefit fromR&D externalities. In contrast, we aim to reveal the rival firms that benefit from R&D
externalities in the Japanese pharmaceutical industry.

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Since the pharmaceutical industry is R&D-intensive and the industry classification is clear,
it is an appropriate industry for our research1.
This paper is organized as follows. Section 2 briefly discusses the theoretical background.
Section 3 presents the data sets, model, and results of the cross-sectional analysis. Section 4
presents the summary and conclusion.

2. Theoretical BackgroundR&D activities promote the technological level of the firm investing in R&D. On the other
hand, they exert both positive and negative effects on rival firms in the corresponding
industry.
The positive effect is the potential for R&D spillovers. Since R&D, particularly basic
research, is generally considered as a public good, rival firms benefit from it. They can enjoy
being free riders by using the intra-industry technology spillovers, since firms that invest in
R&D often cannot prevent others from obtaining the benefits freely.
On the other hand, the negative effect of R&D activities is the competitive threat. The firm
investing in R&D is moving ahead of its rival firms with regard to innovation, thereby
benefiting from first-mover advantages. These advantages arise due to three primary factors:(1) technological leadership, (2) pre-emption of rivals in the market, and (3) switching costs2.
Viewed from a different perspective, these are disadvantages for the rival firms, who lag
behind in the R&D race.
Thus, some rival firms benefit from R&D externalities, while others are under competitive
threat. This paper focuses on the former.
Next, we analyse the factors that significantly influence investors’ assessments of themarket value of R&D externalities. First, according to theoretical studies, there is a positive3

relationship between innovation and firm size. One claim is that among the capital market
imperfections, large firms are favoured in terms of securing finance for risky R&D projects
because firm size is correlated with the availability and stability of internal funds. The otherclaim is that there are economies of scale and scope in R&D activities; the larger the size of afirm, the more its rival firms benefit from R&D externalities. Second, we use R&D intensity
to proxy for technological opportunities. Since firms with high R&D intensity generate more
technological and innovative assets, they have the full potential to absorb new R&D
knowledge. Thus, it can be expected that R&D intensity will be positively correlated withobtaining benefits from R&D externalities.

3. Empirical Analysis

In this section, to examine the effects of R&D externalities, we use investors’ reactions to
R&D expenditure announcements. Although R&D expenditure announcements are
firm-specific, they may contain value-relevant information about other non-announcing firms
in the corresponding industry (rival firms), causing the stock prices of these firms to react tothe same news. This is referred to as the intra-industry transfer of information. Generally,
R&D expenditure announcements result in positive responses to the stock prices of theannouncing firms. However, they are ambiguous in relation to the stock prices of rival firms.
Hence, R&D expenditure announcements can have both positive and negative effects on rival
firms.
First, it is necessary to define the set of rival firms. In this study, rival firms are those firmsthat operate in the same pharmaceutical industry and have the same industry ranking in terms

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of sales as the announcing firms3. Based on the industry ranking criterion, five firms aboveand below the announcing firms are selected. Even if there is only one firm above or below
the announcing firm, that firm is selected.
Next, we use an event study methodology and select 15 announcements from the Nihon
Keizai Shimbun and the Nikkei Business Daily from 2000 to 20044. Sample announcements
satisfy four screening criteria: (1) the announcement is an initial unanticipated one of a future
plan to increase R&D expenditures; (2) the announced plan does not involve funding from
customers or government contracts; (3) the announced plan does not pertain to a joint venture
or corporative agreement with another firm; and (4) the announcing firm and its rivals havesufficient share-price observations on the database of the stock prices CD-ROM (Toyo Keizai).
We choose a two-day event window, which includes the announcement date and thesubsequent day. We use the equally weighted TOPIX index as a proxy for market returns andestimate the parameters of the market model by using data for 250 days around the event
window.
Then, we estimate the following market model for each announcement.

Rit = αˆ + βˆRmt + ε it ,where Rit is the daily return on the stock of firm i at time t , Rmt is the daily return on theequally weighted TOPIX index at time t , and ε it is the zero mean disturbance term.
By using the estimated parameters α̂ i and β̂ i , the abnormal return (AR) on the stock offirm i in period t is obtained by the following:

ARit = Rit − αˆ i − βˆi Rmt .
The cumulative abnormal return (CAR) is calculated by summing up the abnormal returns
over the event window:
t1

CARi (t 0 , t1 ) = ∑ ARit .
t =t0

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We define the event day as t0 , the initial date of the event window as t0 = 0 , and the final
date of the event window as t1 = +1 .
We conclude that if CARi (0,1) > 0, R&D externalities exist.
Next, we estimate the probit equations. The dependent variables in the probit models are
one if CARi (0,1) > 0, and zero otherwise.
We estimate the following regressions:m

Yit = β 0 + β 1 log( ASSETS it −1 ) + β 2 RDit −1 + β 3 PERFORM it −1 + ∑ β l Dummy l + u it , (1)l =4Yit = β 0 + β 1 log( ASSETS it −1 ) + β 2 RDit −1 + β 3 PERFORM it −1m+ β 4 (CFit −1 / SALES it −1 ) + ∑ β l Dummy l +u it ,

( 2)

l =5

where
Yit ：1 or 0
ASSETS it : tangible assets
RDi t : R&D intensity
PERFORM i t : net sales growth or ordinary income divided by sales
CFi t / SALES it : cash flow divided by sales
Dummy l : event dummiesu it : disturbance

We use tangible assets to proxy for firm size. Following Hall (1990) and Blonigen andTaylor (2000), we define R&D intensity as the ratio of a firm’s R&D investment to its
tangible assets. Besides tangible assets and R&D intensity, we include control variables suchas profitability and financial characteristics. The net sales growth and the ordinary income
divided by sales are indicators that a firm is well managed and is performing satisfactorily. In
6addition, we include a liquidity measure for a firm’s cash flow. This suggests that a firm witha higher free cash flow is performing well. Cash flow is defined as retained earnings plus
depreciation. We also include event dummies to remove event-specific characteristics. Each
event Dummy l takes one when firm i belongs to event l , and zero otherwise. Finally, toreduce the simultaneity bias problem, the explanatory variables—excluding the dummy
variables—are lagged.
Table 1 shows the descriptive statistics for the main variables in this analysis. Tables 2 and3 summarize the coefficient estimates for equations (1) and (2) for the probit models. The
coefficient estimates of tangible assets are not significant in all the models. We found that
there is no positive relationship between R&D externalities and firm size. On the other hand,the coefficient estimates of R&D intensity are significant in all the models. Based on theseresults, as theory indicates, firms with high R&D intensity benefit from R&D externalities.
That is to say, investors might perceive that firms with high R&D intensity have the fullpotential to absorb new R&D knowledge. The net sales growth and the ordinary income
divided by sales are not significant in all the models. Finally, even the coefficient estimates ofcash flow divided by sales are not significant in all the models.

4. Concluding Remarks

Using an event study methodology, this paper investigated how the stock prices of rival
firms respond to the announcement of an increase in the R&D expenditures of a firm.
Examining firms in the pharmaceutical industry, we discovered that some firms benefit fromR&D externalities. Moreover, the cross-sectional analysis indicated that R&D-intensive firms
benefit immensely from them. From this result, investors might assess that these firms havethe full potential to absorb new R&D knowledge.
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Acknowledgement
We are grateful for the financial support received from the Murata Science Foundation.

References

Blonigen, B. A. and Taylor, C. T. (2000) R&D intensity and acquisitions in high-technology
industries: Evidence from the US electronic and electrical equipment industries, Journalof Industrial Economics, 48, 47–70.
Eckbo, B. E. (1985) Mergers and the market concentration doctrine: Evidence from thecapital market, Journal of Business, 58 (3), 325–349.
Hall, B. H. (1990) The impact of corporate restructuring on industrial research anddevelopment, Brooking Papers of Economic Activity: Microeconomics, 85–124.
Lieberman, M. B. and Montgomery, D. B. (1988) First-mover advantage, Strategic
Management Journal, 9, 41–58.
MacKinlay, A. C. (1997) Event studies in economics and finance, Journal of Economic
Literature, 35, 13–39.
Porter, M. (1979) The structure within industries and companies’ performance, Review of
Economics and Statistics, 61, 214–227.
Sundaram, A. K., John, T. A., and John, K. (1996) An empirical analysis of strategiccompetition and firm values: The case of R&D competition, Journal of Financial
Economics, 40, 459–486.
Zantout, Z. Z. and Tsetsekos, G. P. (1994) The wealth effects of announcements of R&D
expenditure increases, Journal of Financial Research, 17 (2), 205–216.

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1 As many firms in the other industries are diversified, it is difficult to define the industry market. This refers to

the problem of market definition.
2 For example, see Lieberman and Montgomery (1988).
3

First, in defining rival firms, we attempted to follow Eckbo’s (1985) selection procedure. However, many firms

are so diversified that it is difficult to classify them into their corresponding industries. Thus, we follow Zantout
and Tsetsekos (1994) and Porter (1979).
4 The standard event study methodology has been described by MacKinlay (1997).

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Table 1. Descriptive statistics of the variables

Variablelog(ASSETS)RDPERFORM1PERFORM2
CF/SALES

Description
tangible assets
R&D intensity
net sales growthordinary income divided by sales
cash flow divided by sales

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Mean
18.160
0.330
0.039
0.155
0.173

Median Maximum Minimum Std. Dev.
18.29819.88815.675
0.765
0.333
0.611
0.061
0.135
0.032
0.262
-0.321
0.084
0.143
0.464
0.037
0.093
0.150
0.434
0.002
0.087

Table 2. The results of equation (1)Variable
Coefficient Asymptotic t-value
Constant-6.548-1.192log(ASSETS)
0.279
0.935
RD
2.993
2.677 ***PERFORM1
0.670
0.383
PERFORM2
Dummy1-0.520-0.701
Dummy20.746
1.217
Dummy3-0.226-0.339Dummy40.474
0.773
Dummy59.049
17.245 ***Dummy61.030
1.531
Dummy7-7.667-10.106 ***Dummy8-0.896-1.185
Dummy90.804
1.128
Dummy10-8.443-15.905 ***Dummy11-0.338-0.526
Dummy120.823
1.249
Dummy130.518
0.689
Dummy14-0.067-0.098Sample size
117
Log-likelihood-56.763

Coefficient Asymptotic t-value-4.887-0.862
0.180
0.583
2.522
2.135 **
2.525
-0.6790.645-0.248
0.336
9.028
0.919
-7.741-0.9070.790-8.254-0.4250.772
0.464-0.138117-55.7371.404-0.9501.037-0.3670.532
17.609 ***1.353-10.219 ***-1.192
1.110-16.845 ***-0.6861.141
0.604-0.207

Notes :*** significant at the 1% level, ** significant at the 5% level, * significant at the 10% level
PERFORM1 is the net sales growth
PERFORM2 is the ordinary income divided by sales

11Table 3. The results of equation (2)Variable
Coefficient Asymptotic t-value
Constant-3.071-0.522log(ASSETS)
0.078
0.243
RD
2.365
1.861 **PERFORM1
0.977
0.534
PERFORM2
CF/SALES
2.686
1.417
Dummy1-0.751-1.089Dummy20.706
1.096
Dummy3-0.282-0.420
Dummy40.372
0.577
Dummy58.920
17.036 ***Dummy60.628
0.951
Dummy7-7.387-11.975 ***Dummy8-0.842-1.092
Dummy90.804
1.132
Dummy10-8.218-14.808 ***Dummy11-0.416-0.655
Dummy120.796
1.167
Dummy130.487
0.648
Dummy140.138
0.193
Sample size
107
Log-likelihood-53.758

Coefficient Asymptotic t-value-3.237-0.549
0.088
0.273
2.375
1.729 *
1.438
1.463
-0.7070.715-0.233
0.367
9.043
0.663
-7.515-0.8530.823-8.219-0.3910.803
0.459-0.005107-53.839

0.320
0.312
-0.9911.119-0.3450.572
17.537 ***0.983-11.987 ***-1.121
1.163-16.565 ***-0.6241.174
0.558-0.008Notes :*** significant at the 1% level, ** significant at the 5% level, * significant at the 10% level
PERFORM1 is the net sales growth
PERFORM2 is the ordinary income divided by sales

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