Do strategic alliances in a developing country create firm value? Evidence from Korean firms

Journal of Empirical Finance 20 (2013) 30–41

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Journal of Empirical Finance journal homepage: www.elsevier.com/locate/jempfin

Do strategic alliances in a developing country create firm value? Evidence from Korean firms☆ Hyunchul Lee a, Euije Cho b, Chongcheul Cheong c, Jinsu Kim d,⁎ a b c d

School of Business Administration, Kyungpook National University, Republic of Korea Fair Trade Commission, Republic of Korea Department of Business, Economics & Management, Xian Jiaotong-Liverpool, University, China Department of Business Administration, Gyeongsang National University, Republic of Korea

a r t i c l e

i n f o

Article history: Received 26 April 2011 Received in revised form 17 October 2012 Accepted 25 October 2012 Available online 31 October 2012 JEL classification: G10 G14 G30 Keywords: Abnormal returns Cumulative abnormal returns Event study Marketing alliances Technology alliances

a b s t r a c t This paper examines the impact of strategic alliances on the increment of firm value in the case of Korean firms. For this, we apply an event study using OLS and GARCH market models. The results of our study show that, strategic alliances in Korea produce significant positive abnormal returns before and at the announcement date, indicating an increase in firm value. This firm value augmented by alliance announcements does not have any relationship with firms' growth but has an inverse relationship with firms' sizes. Interestingly, non-technological marketing alliances contribute to increasing firm value more than technological alliances do, regardless of partner firms' nationality. This evidence is contrasted to the cases of firms in advanced countries. Particularly, Korean firms' marketing alliances with firms in advanced G7 countries contribute to largely increasing the firm value of the former. © 2012 Elsevier B.V. All rights reserved.

1. Introduction The literature on business strategy suggests that one of the recent changes in the paradigms of managerial activities is a shift of firms' perception about the relationships with competitive firms (see Drucker, 1995). In the past, academics and practitioners in business have mainly focused on improving sustainably competitive advantages against rival firms. In recent years, however, a number of firms have changed their interests into a strategic concept of win–win through effective cooperation with competing firms (see Kang and Sakai, 2000; Porter, 1980 among others). This strategic partnership with competing firms lets individual firms have economies of scale and scope and improve the speed of market entry through close cooperation with rival firms. Since the 1990s, these benefits from the alliance have led many firms to form domestic or international strategic alliances in order to penetrate domestic and overseas markets as well as to effectively obtain technology and resources. There are a variety of strategic alliances, such as technology transfer and improvements, joint research and development, licensing, franchising, marketing agreements, and joint ventures (see Contractor and Lorange, 2002). A number of empirical studies have investigated whether and how these strategic alliances affect an increase in firm value. So far, most of the previous studies pay their attention to the cases of advanced countries (e.g., see Chan et al., 1997; Chiou and White, 2005; Das et al., 1998;

☆ The authors are grateful to anonymous referees and the Editor (T.J. Vermaelen), but are responsible for any remaining errors. ⁎ Corresponding author: Tel.: +82 55 772 1528. E-mail addresses: [email protected] (H. Lee), [email protected] (E. Cho), [email protected] (C. Cheong), [email protected] (J. Kim). 0927-5398/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jempfin.2012.10.003

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Gulati et al., 2009; Ho et al., 2010; Neill et al., 2001; Ozcan and Overby, 2008; Swaminathan and Moorman, 2009). Unfortunately, there is no study that investigates the firms in developing countries, possibly due to unavailability of sufficient data. Grant (1995) argues that, since firms in developing countries are in an embryonic or a growth stage, a quick market penetration of new products into existing markets is very crucial for their survival and growth rather than technological competence of products. Thus, unlike firms in advanced countries, which mainly engage in exploring the alliances of technological cooperation for new business opportunities, firms in developing countries generally form marketing alliances as one of the strategies in increasing the sales of their products. Given that, in terms of marketing and technology levels, managerial capability, and resource accessibility, firms in developing countries are less competitive than those in developed countries, and strategic alliances would be an important strategic instrument for the growth of firms in developing countries. In this context, it is interesting to investigate the impact of strategic alliances on the increment of firm value in these countries and to explore which type of strategic alliance, technological alliances and marketing alliances, is more advantageous in increasing firm value. To bridge the lacuna in the existing literature, this paper aims to investigate whether the announcement of strategic alliances affects the increase of firm value in Korea, which is one of the leading developing countries, and which type of alliances is important in augmenting the firm value. The country has lots of firms which form a variety of strategic alliances with other domestic or overseas firms, in order to overcome marketing and technological difficulties. Our study for this economy is interesting in that the Korean evidence may shed light on the importance of strategic alliances to increase firm value in other developing countries and would provide their usefulness in designing some policies for investors, firm managers, and policymakers in these countries. Following Chan et al. (1997) and Das et al. (1998) who investigate US firms, we only concentrate on the announcement effect of non-equity strategic alliances on an increase in firm value. 1 We first employ an event study, using the OLS market model commonly used in the literature of finance (see Brown and Warner, 1985). For robust results, we additionally apply the GARCH market model used by Booth et al. (1996), Coakley et al. (2008), and Corhay and Tourani (1996), in order to capture the existence of possible heteroskedasticity in stock returns. Furthermore, to identify the major factors that affect the increase of firm value, we do a cross-sectional analysis. The major findings of our study can be summarized as follows. Strategic alliances yield significantly positive abnormal returns at the announcement date, reflecting an increase in firm value. But, there is evidence of some information leakage effects on the date before the announcement date. Furthermore, our cross-sectional analysis indicates that the firm value increased by the announcement of strategic alliances has an inverse relationship with firms' size, regardless of partner firms' nationality, that marketing alliances contribute to increasing firm value more than technology alliances do, and that marketing alliances with the firms in overseas countries-particularly in G7 countries — increase firm value more than with domestic firms. These results may reflect that, due to the export-oriented structure of the Korean economy in the growing stage of the industry life cycle, Korean firms prefer strategic alliances related to an easy accessibility of marketing resources already established in advanced countries (e.g., partners' distribution channel or brand reputation, etc.). This evidence is different from that of the previous studies, which show the importance of technology alliances rather than marketing alliances in increasing firm value in the case of USA firms (see Chan et al., 1997; Das et al., 1998 among others). The paper is organized as follows. Section 2 reviews theoretical backgrounds and empirical studies about the announcement effect of strategic alliances on the change of firm value. Section 3 discusses methodology and data used in the study. Section 4 provides empirical results. Finally, conclusions are summarized in Section 5. 2. Literature reviews 2.1. Theoretical backgrounds Theoretical arguments about the relationship between strategic alliances and firm value are very broad. In general, the literature can be categorized into the intent of strategic alliances, resource alliances, technology alliances, and marketing (non-technology) alliances. In relation with the purpose of strategic alliances, Koza and Lewin (1998) propose a co-evolutionary process of strategic alliances. According to this process, strategic alliances are an outcome of adaptation choice of firms. Specifically, they divide the purpose of alliances into two strands: exploitation and exploration. The former is based on a desire to exploit the core marketing capability of licensing and franchising to obtain excess revenues by forming a network alliance in stable markets. This intent is generally more essential for the alliances of firms in developing countries under an embryonic or growth stage in the evolutionary progress of industrial structure and competition. On the other hand, the latter is due to a desire for a new knowledge and technology to explore new opportunities for firms in advanced countries under a mature stage in the evolutionary progress. For instance, a learning alliance is a prototypical type of exploration alliances (Grant, 1995). In this vein, Levinthal and March (1993) argue that the benefit of exploitation alliances is in general short but certain, whereas the benefit from exploration alliances is long but highly variant for new opportunities. A resource-based theory proposed by Pfeffer and Salancik (1978) suggests that firms, which are insufficient in essential resources for their growth, would depend on other firms' resources. This theory involves not only tangible resources but also intangible resources such as brand names, in-house knowledge of technology, and employment of skilled personnel. Using these resources, firms can obtain managerial resources that rival firms cannot duplicate and catch up (Wernerfelt, 1984). In addition, 1 Non-equity alliances do not allow partner firms to share equity control as in a minority equity investment and to create a new organizational entity as in a joint venture, but simply agree to pool resources.

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firms' resources are important components for having a sustainably competitive advantage which current or latent rivals cannot implement or duplicate simultaneously (Barney, 1991). From this point of view, firms can modify their organizational power with other organizations to procure some resources from the latter. Based on the theory associated with strategic alliances, Das and Teng (2000) suggest four critical components for resources: rationale, formation, structural preferences, and performance. They argue that strategic alliances can increase firm value represented by rationale, be used to hold a competitive position, and realign partner firms' resources to contribute to firms' performance. These actions allow firms to exploit their competitive advantages. A knowledge based theory proposed by Grant and Baden-Fuller (2004) supports the view that strategic alliances contribute to increasing the efficiency of knowledge application. Building upon a distinction between knowledge generation and application, they contend that alliances raise the efficiency of knowledge application by improving the efficiency with which knowledge is integrated into the production of complex goods and services and by increasing the efficiency with which knowledge is utilized. They argue that technology resources are more critical than the marketing ones due to the lack of alternative sources in supply. Jensen and Meckling (1991) suggest that strategic alliances are useful because they are more cost effective than integrated corporations. Due to efficiency and flexibility, managers may access new combinations of knowledge to sell their new products to markets. 2.2. Empirical studies There are not many studies that investigate the effect of non-equity strategic alliances on firm value. A substantial body of empirical studies focuses on investigating the announcement impact of joint ventures on the increase of firm value mainly for the case of USA firms. For example, McConnell and Nantell (1985) find evidence that the alliance announcements of USA firms yield positive abnormal returns at the announcement date. By extending this study, Koh and Venkatraman (1991) also examine the impact of joint ventures on the market value of parent firms in the U.S. information technology sector. The study reports that the announcement of a joint venture formation contributes to a positive growth of firm value. Interestingly, they further provide evidence that horizontal joint ventures with the partners in the same industry increase more firm value than those with the partners in the different industry. On the contrary, Mohanram and Nanda (1998) provide the opposite result that the announcement of joint ventures with the firms in the same industry negatively affects firm value. On the other hand, by analyzing different types of strategic decisions in investment (e.g., joint ventures of R&D projects and major capital expenditures), Woolridge and Snow (1990) find that the US stock market generally reacts in a positive way to the announcement of individual firms' strategic plans. Contrastingly, Chung et al. (1993) and Lee and Wyatt (1990) provide evidence that the announcement of joint ventures reduces the overall value of US firms. In relation with the announcement effect of non-equity strategic alliances on firm value, Chan et al. (1997) and Das et al. (1998) suggest that, as in the cases of joint ventures, non-equity strategic alliances of US firms yield significant positive abnormal returns at the announcement date. They further show that horizontal alliances with firms in the same industry increase firm value more than non-horizontal alliances with firms in the different industry. Horizontal technology alliances produce a strong, positive reaction of stock prices. More specifically, Chan et al. (1997) show that horizontal alliances are associated with a large increase in firm value either by pooling complementary skills and technical linkages or by enhancing firm's market power in its product markets. In a similar vein, Das et al. (1998) show that, in the same industry, technology resources are more critical than marketing ones, due to a lack of alternative sources in supply. They suggest that relatively small firms enjoy the largest abnormal returns from the announcements of technology alliances. In recent years, Neill et al. (2001), who analyze the announcement effect of 89 non-equity alliances in the US information and technology sector, report that the announcement yields a positive abnormal return at the event date. They also find an information leakage effect of positive abnormal returns at the dates before the announcement date. Very recently, Ho et al. (2010) and Swaminathan and Moorman (2009), who only focus on the marketing alliances of US firms, provide evidence that marketing alliances of US firms significantly contribute to an increase in firm value. Using the data of Japanese financial institutions, Chiou and White (2005) find that strategic alliance announcements contribute to a significant increase in the Japanese financial institutions' firm value. However, they show no difference between domestic–domestic alliances and domestic–foreign alliances on their impacts to the change of the institutions' firm value. 3. Data and methods 3.1. Data Our study used the data related to non-equity strategic alliances announced by Korean listed firms over the sample period from 2001 to 2007. We excluded the data after the early 2008, because the Korean stock prices have extremely fluctuated due to the severe global financial crisis. All the firms involved in alliance announcements were obtained from Korea Investor's Network for Disclosure (KIND) and Korea Integrated News Database System (KINDS). 2 In these database systems, we combined key words 2 KIND operated by the Korean Stock Market Division provides investors with all disclosure information of Korean listed firms (http://engkind.krx.co.kr). The database source provides investors with nearly 190 different types of disclosure documents (e.g., IPO, SEO, M&A, Alliances, etc.) of Korean listed firms. KINDS operated by the Korea Press Foundation also provides investors with the news associated with firm disclosure of the Korean listed firms, which can make a crucial effect on firm value. By simply inputting keywords and clicking, users can obtain verified, high-quality disclosure information about the Korean firms (http:// www.kinds.or.kr).

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‘strategic’ and ‘alliance’ with different types of cooperative agreements (e.g., licensing, marketing, distribution, supply, production, research and development, and technology) to obtain information related to strategic alliances. Initially, we obtained a total sample of 545 associated with strategic alliances. Then, we filtered the initial sample by excluding some firms that are not directly related to our interests on the basis of the following criteria: – firms not listed on the Korea Stock Exchange (KSE) in the sample period, – firms which are related to equity strategic alliances, such as joint venture and consortium, because they are not non-equity strategic alliances (see Das et al., 1998), – firms not matched with the daily price index of the Korea Composite Stock Price Index (KOSPI) from DataStream International, – firms with missing accounting data during the sample period, – firms in the financial industry, because the firms' financial statements in this industry are substantially different from those in the manufacturing industry. With this filtering, the total samples related to non-equity strategic alliances in this study are 297 over the full sample period. Daily stock returns were calculated as the difference in natural logarithm of daily closing stock prices for two consecutive trading days,
Rit ¼ ln

pt pt−1

  100

where Ri,t is a daily actual return of stock i at time t and p is a daily price of individual stocks. Table 1 describes the total samples of strategic alliances from Korean listed firms. Panel A classifies them into years. The announcements in Table 1 were evenly spread across the full sample period. There are, on average, 42 strategic alliance announcements every year with a maximum of 57 in 2003 and a minimum of 32 in 2004. Classifications by alliances and industry types are presented in panel B. For the former, our study followed the classification used by Chan et al. (1997) (i.e., I. Licensing, II. Distribution, III. Development or Research, IV. Technology transfer or systems integration). 3 For the latter, we classified the whole sample by two-digit Korean Standard Industrial Classification (KSIC) codes. Panel B shows that, industrial groups in KSIC-20, 21, 26, 30, and 71 relatively take a large portion of the total samples, while industrial groups in the remaining KSICs take a small portion. Panel C presents the geographical distribution of partner firms involved in strategic alliances with Korean firms. Of the total firms, domestic (Korean) partner firms (91) take the largest portion (30%). Among partner firms in overseas countries, the portion (73) of the USA firms amounts to about 24%. The alliances involved with Japanese, European, and Asian firms comprise 18% (53), 14% (44), and 11% (34) of the total firms, respectively. Strategic alliances involved with Canadian firms compose the remaining 0.67% (2). Overall, the geographic distribution of partner firms is spread across various regions. Since the nationality of partner firms in overseas countries spans 26 countries in various regions, our study may provide robust and comprehensive evidence on the relationship between firm value and strategic alliances in Korea. 3.2. Method 3.2.1. Event study We conducted an event study, using the OLS-market model applied by Brown and Warner (1985). It is assumed that the security market is informationally efficient so that an increase of firm value to any information disclosure can be assessed by observing a change in abnormal stock returns around the release of the information. The abnormal returns (ARi,t) of individual stocks were obtained by applying the conventional market model as follows: First, a regression between an individual daily stock return (Ri,t) and the market portfolio return (Rm,t) was estimated to obtain the coefficients of αi and βi in Eq. (1) over the estimation period of − 220 to − 21. εit is an error term. The expected return [E(Rit)] of an individual stock i was obtained by ^ into Eq. (2). For the event period of − 20–+20, the daily abnormal return (ARit) of an individual ^ i and β plugging the estimated α i stock was then obtained by calculating the difference between the actual daily return and the return predicted by the market model in Eq. (3), Rit ¼ α i þ βi Rmt þ εit

ð1Þ

^R ^i þ β EðRit Þ ¼ α i mt

ð2Þ

ARit ¼ Rit −EðRit Þ:

ð3Þ

3 In order to focus on the announcement effect of the strategic alliances involved with pure technology and marketing activities, we excluded the combination of the four agreement types (I–IV) used by Chan et al. (1997). However, the results from the sample of such combination are not so much different from our main results. Upon request, the specific results can be provided.

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Table 1 The total samples of strategic alliance announcements from Korean listed firms. Panel A. Announcements of strategic alliances by year Year of announcements

Number of announcements

Percent of total

2001 2002 2003 2004 2005 2006 2007 Total

33 46 57 32 46 42 41 297

11.11% 15.49 19.19 10.77 15.49 14.14 13.81 100.00

Panel B. Announcements by industry composition and alliance types Two-digit KSIC code

Industry groups

Type of alliances I

10 12 13 17 19 20 21 22 25 26

Food products Tobacco products Textiles, except apparel Pulp, paper and paper products Coke, hard-coal and lignite fuel briquettes and refined petroleum products Chemicals and chemical products except pharmaceuticals, medicinal chemicals Pharmaceuticals, medicinal chemicals and botanical products Rubber and plastic products Metal products, except machinery and furniture Electronic components, computer, radio, television and communication equipment and apparatuses Medical, precision and optical instruments, watches and clocks Electrical equipment Other machinery and equipment Motor vehicles, trailers and semi-trailers Furniture Electricity, gas, steam and air conditioning supply Wholesale trade and commission trade, except of motor vehicles and motorcycles Retail trade, except motor vehicles and motorcycles Transport Motion picture, video and television program production Telecommunications Information service activities Professional services Total

27 28 29 30 32 35 46 47 50 59 61 63 71

II

III

5 1

1

1 14

1 6 3

10

2

19

2 12

4 1 27

total

7 1 9 1 3 22 25 4 3 14

12 1 11 1 4 29 52 4 5 47

1

1 3 13 33 2 3 18 1 6 1 14 7 29 297

1 2 3

2

2 9 1 5

1 3

3

4 3 7 72

7

1 1 3

3 45

18 153

5

1 3

IV

11 25 1

Panel C. Geographic distribution of partner firms involved with strategic alliances Location

Number of announcements

Percent of total

Home Asia Japan Europe USA Canada Total

91 34 53 44 73 2 297

30.64% 11.45 17.85 14.81 24.57 1.35 100

Notes: Asian countries excluding Japan and Korea consist of 9 countries: China, Hong Kong, India, Iran, Malaysia, Taiwan, Thailand, Uzbekistan, and Vietnam. European countries include 13 countries: Austria, Belgium, Republic of Czech, Denmark, France, Germany, Italy, the Netherlands, Portugal, Spain, Sweden, Swiss, and the UK.

The average abnormal return (AARt) for the sample period is the arithmetic mean of summed abnormal returns (ARit) of all individual stocks, AARt ¼

N 1X ARit : N i¼1

ð4Þ

The t-statistic of the AARt is obtained by AARt : t AARt ¼  S AARt−200 −t −21

ð5Þ

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To overcome a potential cross-sectional dependence across average abnormal returns on the dates of each event, we used the standard deviation [S(AARt)] of average abnormal returns over the estimation period from − 220 to − 21 days. 4   Cumulative average abnormal returns CAARt 1− t n were obtained by the sum of averaged abnormal returns as follows, CAARt 1 −t n ¼

tn X

AARt :

ð6Þ

t1

Then, the t-statistics of CAARt 1 −t n was estimated by t CAARt

1 −t n

CAARðt 1 −t n Þ ; ¼  S CAARt −220 p ffiffiffi −t−21

ð7Þ

N

  where S CAARt 1 −t n is a standard deviation of CAARs over the estimation period from − 220 to − 21 days. N is the number of partitioned event dates. See also Appendix A in detail for the GARCH market model for robustness. In order to account for the major determinants of the abnormal returns, we did a cross-sectional analysis. 4. Empirical results 4.1. Stock price reaction to strategic alliance announcements Using both OLS and GARCH market models, we conducted an event study to measure the reaction of stock prices to the strategic alliances announced by Korean listed firms. Before a GARCH market model was employed, both GARCH(1,1) − t and GARCH(1,1) − N models were fitted for 122 (41%) out of the total samples of 297. Although the two GARCH models seemed to fit well the residual terms, we focused on the GARCH(1,1) − t model employed by Booth et al. (1996), Coakley et al. (2008), and Corhay and Tourani (1996). 5 Based on the result of the GARCH(1,1) − t model, nearly half (41%) of our sample follows conditional t-distribution error processes which depart from the normal-distribution assumption of the standard OLS specification. This justifies our use of a GARCH(1,1) − t market model for a robust study on this topic. Table 2 presents the specific results of average abnormal returns (AARs) and cumulative average abnormal returns (CAARs) estimated by OLS and GARCH(1,1) − t market models. Both market models indicate significantly positive AARs (1.264% and 1.001%) at the 1% level, respectively, at the announcement date. This suggests that, in the case of the Korean stock market, strategic alliance announcements contribute to increasing firm value (i.e., shareholder wealth) on the event date. This finding is in line with the existing literature (e.g., Chan et al., 1997; Chiou and White, 2005; Das et al., 1998; Ho et al., 2010; Neill et al., 2001; Swaminathan and Moorman, 2009), which shows that non-equity strategic alliance announcements in US and Japanese firms produce significant positive AARs at the announced date. Interestingly, the two market models in Table 2 yield significant positive AARs (0.416% and 0.314%), respectively, at the date just before the announcement date as well. This implies an information leakage effect by traders with inside information. This result is not in line with that of Chan et al.'s (1997), which shows that, in the case of US firms, there is no pre-announcement information leakage, as a full market reaction to the alliance announcements immediately occurs at the announcement date. However, our finding coincided with that of Neill et al.'s (2001) that provide evidence of positive AARs at the earlier date than the announcement date. It would be worthwhile to note that the AARs of the GARCH market model are smaller than those of the OLS market model although the AARs of the two market models show similar patterns. 6 In Fig. 1, the AARs of OLS and GARCH market models move a similar way over the event period from − 20 to +20. The AARs of both market models rise sharply one day before the event date. Fig. 2, which plots the CAARs obtained from the OLS and the GARCH market models, presents that the difference between the two CAARs gradually increases after the announcement date. Interestingly, Fig. 2 shows a smoothing effect of the GARCH market model on the CAARs estimated by the OLS market model. Table 3 reports the CAARs estimated by the two market models for various event intervals around the announcement date. Both of the market models show statistically significant and positive CAARs at the 1% significance levels for all of the selected intervals. In particular, the two market models yield the highest CAAR (1.680% and 1.289%) at the short interval of [− 1–0], respectively. The result of the positive stock reactions to the Korean firms' alliance decisions is different from those of Chan et al.'s (1997) and Das et al.'s (1998) that provide evidence of no significant CAARs for most intervals. Note that the CAARs of the GARCH market model are smaller than the ones of the OLS market model at all the partitioned intervals. This may reflect that the bigger 4

Brown and Warner (1985) suggest using time-series standard deviations, which can be estimated from mean excess returns over the estimation period. Many studies in the literature of econometrics suggest that the GARCH(1,1) − t model parsimoniously better performs in capturing a conditional heteroskedasticity effect of financial return data (Baillie and De Gennaro, 1990; Bollerslev, 1986; Hsieh, 1989 among others). In particular, to capture the high level of kurtosis in the distribution of the observed return, a leptokurtic GARCH(1,1) − t model also provides a better fit to the residual terms than other conditional leptokurtic distributions (Baillie and De Gennaro, 1990). 6 We also estimated the AARs and the CAARs by applying a GARCH(1,1) − t market model. The estimated AARs and the CAARs based on this model are almost identical to those of the OLS-market model. The detailed results can be provided upon request. 5

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Table 2 Average abnormal returns (AARs) for different event windows. Windows

−20 −10 −5 −4 −3 −2 −1 0 1 2 3 4 5 10 20

GARCH(1,1) − t market model (n = 297)

OLS market model (n = 297)

AAR Difference

AAR (%)

t-Value

CAAR (%)

AAR (%)

t-Value

CAAR (%)

OLS − GARCH

0.081 −0.236 0.000 0.073 −0.138 0.090 0.416 1.264 −0.036 −0.254 0.110 −0.178 −0.283 0.042 −0.124

0.442 −1.282 −0.001 0.400 −0.748 0.492 2..262 ⁎⁎ 6.876⁎⁎⁎ −0.196 −1.379 0.597 −0.969 −1.537 0.226 −0.676

0.081 −0.073 0.306 0.380 0.242 0.332 0.748 2.013 1.977 1.723 1.833 1.654 1.372 1.386 1.169

0.068 −0.079 0.093 0.027 −0.101 0.137 0.314 1.001 −0.026 −0.143 0.052 −0.149 −0.294 0.045 −0.085

0.371 −0.431 0.506 0.148 −0.548 0.745 1.710⁎ 5.445⁎⁎

0.068 0.057 0.346 0.373 0.273 0.410 0.724 1.725 1.699 1.555 1.607 1.458 1.165 1.058 0.694

0.013 −0.156 −0.093 0.046 −0.037 −0.046 0.102 0.263 −0.010 −0.110 0.058 −0.029 0.011 −0.003 −0.039

−0.144 −0.779 0.281 −0.810 −1.596 0.243 −0.463

Average abnormal returns (AARs) and cumulative average abnormal returns (CAARs) were estimated by OLS and GARCH(1,1) − t market models. ⁎ Indicate significance at 10% level. ⁎⁎ Indicate significance at 5% level. ⁎⁎⁎ Indicate significance at 1% level.

CAARs derived from the OLS market model are somewhat overestimated due to the ignorance of a heteroskedastic effect in the stock return series over the estimation period. 4.2. Cross-sectional analysis This section examines which factors mainly explain the increment of firm value, due to strategic alliance announcements. For this purpose, we run linear OLS regressions for both ARs and G-ARs at the announcement date. For reference, Chan et al. (1997) and Chiou and White (2005) use ARs at the announcement date or the date after the announcement date as the dependent variable of their cross-sectional regressions, while Das et al. (1998) use CARs for selected event windows. The cross-sectional regressions in our study were modeled as follows: ARi;0 ¼ a þ b1 Sizei;t−1 þ b2 SGi;t−1 þ b3 Dummy MA þ b4 Dummy HA þb5 Dummy Overseas þ b6 Dummy G7 þ ν i

ð8aÞ

G−ARi;0 ¼ c þ d1 Sizei;t−1 þ d2 SGi;t−1 þ d3 Dummy MA þ d4 Dummy HA þd5 Dummy Overseas þ d6 Dummy G7 þ τi

ð8bÞ

where ARi,0 and G­ARi,0 are the dependent variables of OLS and GARCH abnormal returns for stock i at the announcement date (t0), respectively. Independent variables consist of two variables related to firms' characteristics and four dummy variables

Fig. 1. AARs for alliance announcements over the full event window (−20–+20).

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37

Fig. 2. CAARs for alliance announcements over the full event window (−20–+20).

related to the characteristics of strategic alliances. Size, which is one of the control variables in Eq. (8a), is the first lag of the book value of individual firms' total assets in logarithm at the fiscal year end, immediately proceeding to the date of alliance announcements. It captures the effect of the individual firms' size on the increased firm value, due to non-equity strategic alliance announcements (Adams et al., 2009; Ammann et al., 2011 among others). The variable of SG is the first lag of the 3 year averages in the annual growth of individual firms' sales at the fiscal year end, immediately proceeding to the announcement date. It is added to capture the impact of firms' future growth on the increment of firm value (Ammann et al., 2011). 7 The data associated with the above two control variables are obtained from the KIS-value database provided by the Korean Information Service. The variable, Dummy _ MA, which takes 1 for marketing alliances with partner firms and 0 otherwise, is added to examine the effect of non-technology strategic alliances on the change of firm value. Dummy _ HA takes 1 for horizontal alliances with partner firms in the same industry and 0 otherwise (non-horizontal ones) on the basis of the International Standard Industrial Classification (ISIC) code. Following Chan et al. (1997), this dummy is included to capture whether strategic alliances with firms in the same industry contribute to increasing firm value. Dummy _ Overseas takes 1 for alliances with partner firms in overseas countries and 0 otherwise (i.e., ones with domestic firms). This is included to capture the national impact of alliances on the change of firm value. Finally, the variable, Dummy _ G7, which takes 1 for alliances with firms in G7 countries and 0 otherwise, is included to examine whether strategic alliances with firms in G7 countries affect the increment of firm value. Table 4 provides a correlation matrix across the whole variables used in our regression. Most pairs of the variables show low correlations, except for the pair of overseas and G7 dummies which has a relatively high value of 0.676. Table 5 reports the results of the cross-sectional OLS regressions on the ARs of the announcement date from the two market models. Panel A shows results for the ARs of the OLS market model. Regression 1 indicates that the size variable has a significant negative coefficient (− 0.355) at the 1% level, which suggests that firm size is inversely related with positive abnormal returns at the announcement date. This result still remains valid for regression 2, which excludes the overseas dummy to avoid a possible multicolinearity across the two kinds of country dummies. This finding is in line with the existing literature of Chan et al. (1997), Chiou and White (2005), and Das et al. (1998), which suggests a small effect of strategic alliance announcements on the change of firm value in advanced countries (e.g., Japan and the USA). No regression in panels A and B of Table 4 indicates a significant coefficient for the variable of sales growth (SG). This suggests that any opportunities for firms' future growth have no significant relationship with the change in firm value. This finding is not consistent with that of Das et al.'s (1998) which shows that the growth opportunities are negatively associated with firm value for strategic alliance announcements in the case of the US firms. The Dummy _ MA, a marketing alliance dummy, in panel A has highly significant and positive coefficients (1.153 and 1.140). This suggests that marketing alliance (non-technology alliance) announcements contribute to increase firm value more than that of technology alliances. Unlike our finding, Chan et al. (1997) provide evidence that, in the case of US firms, the growth of firm value is mainly affected at the announcement date by the latter rather than the former. In addition, our study shows quite a different result from that of Das et al.'s (1998) that find no effect of marketing alliance announcements on US firms' value. From the perspective of co-evolutionary progress for firm (or industry) structure and competition, firms in developing countries are generally under an embryonic or growth stage, of which a quick market penetration of new products into stable markets is very crucial for their survival and growth (Grant, 1995). In this vein, our finding provides evidence that, in Korea, marketing alliances are more vital than technology ones for firms' survival and growth and for investors' wealth. Importantly, this reflects that, unlike US firms which mainly engage in exploring technology alliances for new business opportunities in competitive markets, Korean firms are rather engaged in exploiting marketing alliances for excess revenues through a network alliance in stable markets. By contrast, Grant and Baden-Fuller (2004), who propose a knowledge-based theory of firm's strategic

7 Alternatively, Das et al. (1998) use the data at the fiscal year end, immediately proceeding to the alliance announcement date in order to find the determinants of US firm value created by strategic alliance announcements.

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H. Lee et al. / Journal of Empirical Finance 20 (2013) 30–41

Table 3 CAAR for partitioned intervals. Intervals

OLS market model

GARCH(1,1) − t market model

OLS–GARCH (difference)

[−3–+3]

1.453⁎⁎⁎ (3.066) 1.481⁎⁎⁎

1.234⁎⁎⁎ (2.604) 1.283⁎⁎⁎

0.219

(3.698) 1.644⁎⁎⁎ (5.300) 1.680⁎⁎⁎

(3.204) 1.289⁎⁎⁎ (4.155) 1.316⁎⁎⁎

(6.633)

(5.196)

[−2–+2] [−1–+1] [−1–0]

0.198 0.355 0.364

This table reports the CAARs obtained from the two market models for partitioned event intervals around the announcement date. ⁎⁎⁎ Denotes statistical significance at the 1% level.

alliances, maintain that, due to a lack of alternative sources of supply, technology alliances are more critical than the marketing ones in forming firms' strategic alliances. In the case of the horizontal alliance dummy, Regressions 1 and 2 indicate insignificant coefficients for the variable, suggesting that horizontal alliances seem to have no effect on the increment of firm value. Given the characteristic of the export-oriented Korean economy, it is worthwhile to investigate the effect of partner firms' nationality on firm value. Regression 1 in panel A indicates an insignificant coefficient for the overseas country-dummy used to capture the nationality impact of partner firms on the Korean firms' value. However, Regression 2 indicates a marginally significant coefficient (0.772) for the G7-dummy, which is another overseas-country dummy. This would be interpretable that strategic alliances with firms in G7 countries are associated with a larger positive effect on the growth of firm value than the ones with domestic firms. This result is not consistent with that of Chiou and White's (2005) that find no difference between domestic–domestic alliances and domestic–foreign alliances. The results of G-ARs in panel B are similar with the ones of the ARs in panel A. Exceptionally, the G7-dummy in the G-ARs regression shows an insignificant coefficient (0.515) which is slightly different from the significant one (0.772) for the variable in the case of ARs regression.

4.3. Cross-effects between types of alliances and partner firms' nationality In association with the firm value created by strategic alliance announcements of Korean listed firms, we found a significant difference between technology and marketing (non-technology) alliance announcement effects in the above section. For a further understanding of the difference, we additionally investigate the cross-effects of the types of alliance and partner firms' nationality on average abnormal returns at the announcement date. For this purpose, we partitioned the total samples into six subgroups based on technological alliances and partners' nationality. Then we conducted the t-test of mean difference across the AARs of the subgroups partitioned by the alliance types and partners' nationality. Out of the total of 297 sample alliances, 198 were classified into technology alliances and the remaining 99 were classified into non-technological marketing ones. Table 6 presents the AARs of the subgroups partitioned in the alliance types and partners' nationality. First, panel A provides the AARs obtained from the OLS market model and their t-statistics. The estimated coefficient (− 1.037) of the mean difference between technology and marketing alliance groups in the total (column 1) is significant at the 5% level. Regarding the subsamples, except for the case of domestic alliances, technology and marketing alliances in both overseas and G7 samples show significant t-statistics (− 1.038, − 1.618) at 5% and 1% levels, respectively. Overall, the results show a statistically significant difference between marketing and technology alliances for overseas and G7 country samples. Besides, it is noticeable that the AAR of marketing alliances of Korean firms with firms in G7 countries has the highest significant positive value (2.530%) at the announcement date. This result suggests that Korean firms' marketing alliances with

Table 4 The correlation matrix across the whole variables.

ARs G­ARs Size SG Dummy Dummy Dummy Dummy

_ MA _ HA _ Overseas _ G7

ARs

G­ARs

Size

SG

Dummy _ MA

Dummy _ HA

Dummy _ Overseas

Dummy _ G7

1.000 0.908 −0.201 −0.064 0.141 −0.049 −0.007 0.037

1.00 −0.173 −0.028 0.100 −0.046 −0.000 0.030

1.000 0.093 0.061 −0.232 0.084 0.176

1.000 0.066 −0.006 0.096 0.092

1.000 −0.234 −0.227 −0.119

1.000 0.286 0.089

1.000 0.676

1.000

H. Lee et al. / Journal of Empirical Finance 20 (2013) 30–41

39

Table 5 Results of the cross-sectional OLS regressions. Variables

Constant Size SG Dummy _ MA Dummy _ HA Dummy _ Overseas

Panel A. Regression on ARs of OLS market model Regression 1

Regression 2

Regression 1

Regression 2

10.539⁎⁎⁎ (2.480) −0.355⁎⁎⁎ (0.089) −1.163 (1.089) 1.153⁎⁎

10.949⁎⁎⁎ (2.486) −0.371⁎⁎⁎ (0.090) −1.191 (1.083) 1.140⁎⁎⁎

7.748⁎⁎⁎ (2.074) −0.257⁎⁎⁎ (0.075) −0.370 (0.911) 0.666⁎

8.014⁎⁎⁎ (2.083) −0.267⁎⁎⁎ (0.075) −0.382 (0.907) 0.652⁎

(0.435) −0.638 (0.440) 0.596 (0.458)

(0.429) −0.568 (0.424)

(0.364) −0.516 (0.368) 0.427 (0.383)

(0.359) −0.461 (0.355)

0.772⁎ (0.403) 0.082 5.250⁎⁎⁎ 297

Dummy _ G7 Adjusted R2 F-value Observations

Panel B. Regression on G-ARs of GARCH market model

0.076 4.830⁎⁎⁎ 297

0.515 (0.338) 0.038 3.357⁎⁎⁎ 297

0.051 3.130⁎⁎⁎ 297

This table reports the results of the cross-sectional OLS regressions on the ARs and G-ARs (on the announcement date) from the two types of market model, respectively. Figures in parenthesis are standard errors. ⁎ Indicate significance at 10% level. ⁎⁎ Indicate significance at 5% level. ⁎⁎⁎ Indicate significance at 1% level.

firms in G7 countries positively contribute to increasing their firm value, whether it is for importing overseas products toward domestic markets or for exporting domestic products toward advanced G7 countries. Panel B reports the AARs obtained from the GARCH market model and their corresponding t-statistics. The results provide significantly positive estimates, suggesting a difference across the partitioned subgroups. These are essentially similar to those in panel A based on the OLS market model. Overall, panels A and B show evidence that both technology and marketing alliances involved with firms in G7 countries contribute to increasing firm value more than those involved with domestic firms at the announcement date. Taking into account the insufficient resources of the Korean economy, it is reasonable for Korean firms to prefer alliances with foreign firms in overseas, particularly in G7 countries, to the ones with domestic firms, in order to access more resources.

Table 6 The average abnormal returns (AARs) by type of alliance and nationality of the partner firm. Total Panel A. The AARs calculated from the OLS market model and their significance Technology alliance AAR 0.897%⁎⁎⁎ t-Value 4.398 Obs. 198 Marketing alliance AAR 1.934%⁎⁎⁎ t-Value 6.190 Obs. 99 t-Statistic Mean difference −1.037⁎⁎ (t-value) (−2.441) Panel B. The AARs calculated from the GARCH market model and their significance Technology alliance G-AAR 0.769%⁎⁎⁎ t-Value 3.768 Obs. 198 Marketing alliance G-AAR 1.402%⁎⁎⁎ t-Value 4.486 Obs. 99 t-Statistic Mean difference −0.633⁎ (t-value) (−1.797)

Domestic

Overseas

G7

0.728%⁎ 1.641 46 1.851%⁎⁎⁎ 3.416 45 −1.123 (−1.411)

0.948% ⁎⁎⁎ 4.187 152 1.986%⁎⁎⁎ 4.918 54 −1.038⁎⁎

0.912%⁎⁎⁎ 3.403 109 2.530%⁎⁎⁎ 5.219 42 −1.618⁎⁎⁎

(−1.984)

(−2.475)

0.714%⁎ 1.609 46 1.243%⁎⁎

0.785%⁎⁎⁎ 3.467 152 1.508%⁎⁎⁎

0.778%⁎⁎⁎ 2.904 109 1.804%⁎⁎⁎

2.294 45 −0.529 (−0.865)

3.734 54 −0.723⁎ (−1.601)

3.720 42 −1.026⁎ (−1.790)

This table reports the t-statistic of mean difference across the announcement date-AARs of the subgroups partitioned by technology and nationality. ⁎ Indicates significance at the 10% level. ⁎⁎ Indicates significance at the 5% level. ⁎⁎⁎ Indicates significance at the 1% level.

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H. Lee et al. / Journal of Empirical Finance 20 (2013) 30–41

5. Conclusions Using Korean data series, this paper has shed light on the relationship between an increase in firm value and strategic alliance announcements. For this purpose, we used an event study by applying OLS and GARCH market models. Overall, both models provide evidence that, in the case of Korea, strategic alliances contribute to increasing firm value at the announcement date but there are some information leakages before the announcement date. Using a cross-sectional analysis, we further examined which are the major factors explaining the increment of firm value. The specific results can be summarized as follows: first, an increase in firm value has an inverse relationship with firm size, but has no relationship with the growth opportunity of individual firms; second, unlike the case of strategic alliances in advanced countries' firms, non-technology (marketing) alliances with partners holding a good marketing capability lead the value of Korean firms to rise more than that of technology alliances; strategic alliances with partner firms in foreign countries, particularly firms in G7 countries, contribute to a larger increase in firm value; and finally, non-technology (marketing) alliances, particularly with the firms in G7 countries, increase more firm value, irrespective of partner firms' nationality. An implication of our study for Korean firms is that firm managers need to actively seek for various strategic alliances with firms in overseas countries, particularly in G7 countries, to increase firm value (i.e., shareholders' wealth) and to effectively compete with other firms in rapidly changing global business environments. In this direction, marketing alliances are recommended rather than technology alliances. Some limitations of our study should be mentioned. First, we provide that Korean firms' strategic alliances with firms in G7 countries positively affect the increment of firm value. This evidence can be interpreted as a result of Korean firms' marketing alliances with the latter for the purpose of either importing foreign products toward domestic markets or exporting domestic products to advanced overseas markets. However, the actual motivation of marketing alliance often cannot be identified due to limited information. Second, in the study we mainly focused on the role of non-equity strategic alliances related to the aggregated levels of marketing and technology resources for the increment of firm value. In the case of the technology, the announcements of strategic alliances related to disaggregated non-equity, contractual forms of R&D partnerships, such as joint R&D pacts and joint development agreements, can have the effect of different levels on firm value. In a similar vein, strategic technology alliances between different levels of firms (high level technology and low level technology) and between complementarily related firms in technology can also have the impacts of different levels on the change of firm value. We leave these two issues for future research. Appendix A. GARCH market model The classical OLS assumption of homoskedastic residuals may not hold for an event study. Previous studies on event studies argue that OLS market model may mislead inferences and the test statistics may be biased if the variances of ARs in the estimation period are not constant for the event period (see Giaccoto and Ali, 1982 and Morgan and Morgan, 1987 among others). Empirically, Booth et al. (1996), Coakley et al. (2008), and Corhay and Tourani (1996) combine the standard OLS market model with a GARCH method to account for a heteroskedasticity effect in stock return series. Inspired by these studies, we apply the GARCH market model which allows us to consider some possible heteroskedasticity effects. GARCH market models assume that residuals are conditionally heteroskedastic. The market model with the GARCH effect can be represented as, εit jΩit−1 eDð0; hit ; dÞ εit ¼ Rit −α i −βi Rmt hit ¼ γ þ κ i hi;t−1 þ θi ε2i;t−1

ð9Þ

where Ωit is an information set through time t on stock i, hit is the conditional variance of a stock i, D is a student-t distribution with zero mean and time dependent variance hit, and d is a degree of freedom. A GARCH model with conditionally normal distribution allows its unconditional error distribution to be leptokurtic. However, the GARCH model might not fully explain the high level of kurtosis often observed in the distributions of return series. Various leptokurtic conditional distributions have been applied in the literature (see Baillie and Bollerslev, 1989; Hsieh, 1989 among others). It is generally accepted that the t-distribution performs better. Thus, our study focuses on a GARCH(1,1) − t process, since in fitting stock returns it is better than a GARCH(p,q) − t model with p + q ≥ 3 (Corhay and Tourani, 1996). The maximization for the GARCH(1,1) model with t-distributed conditional errors is based on the log-likelihood function. In the model, the sum of the estimated parameters, (α + β), which measures persistence of the volatility of stock i, should be less than the unity for a stability condition of the variance process. If this sum equals one, the process becomes an integrated GARCH (IGARCH) process (Engle and Bollerslev, 1986). An IGARCH process implies not only the persistence of a forecast of the conditional variance over all future horizons but also an infinite variance of the unconditional distribution of εt. After estimating the parameters of the GARCH(1,1) − t for each stock i, we compute the predicted errors of abnormal returns over the test period from − 20 to +20 by iteratively solving the GARCH market model using εi,− 20 and hi,− 20 as a starting point. This can be represented in the following Eqs. (10)–(12),   ^R ^i þ β ε^ it ¼ Rit − α i mt

ð10Þ

H. Lee et al. / Journal of Empirical Finance 20 (2013) 30–41

41

^ i þ κ^ i h^ i;t−1 þ θ^i ε2i;t−1 h^ it ¼ γ

ð11Þ

−0:5 G−ARit ¼ ε^it h^ it

ð12Þ

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