How Do Firm’s Increases in R&D Affect Long-Run Performance of Intra-Industry Competitors?

1. How Do Firm’s Increases in R&D Affect Long-Run Performance of Intra-Industry Competitors? Weifeng Hung Feng Chia Universty Sheng-Syan Chen National Taiwan University Yanzhi Wang Yuan Ze University

2. R&D investment is a favorable strategy • When a firm increases its R&D outlay, the firm earns positive abnormal return both in the short run (Chan, Martin and Kenisnger, 1990 JFE; Szewczyk, Tsetsekos and Zantout, 1996 FM) and in the long run (Chan, Lakonishok, and Sougiannis, 2001 JF; Eberhart, Maxwell and Siddique, 2004 JF; 2008 JAR).

3. R&D spillover effect • According to Bernstein and Nadiri (1989), • “A feature of R&D investment that distinguishes it from other forms of investment is that firms which do the investing are often not able to exclude others from freely obtaining the benefits from the R&D projects…”.

4. Short-run results are not consistent with the R&D spillover hypothesis • Zantout and Tsetsekos (1994) document that the rivals of firms that make announcements of increases in R&D expenditures suffer a statistically significant negative abnormal return. • Sundaram, John and John (1996) find that the market reaction to competitors varies depending on their competitive strategy measure.

5. Motivations • The long-term impact of R&D increase on intra-industry competitors remains unknown. Particularly, no study explores the long-run market reactions to spillover effect. • Lev and Sougiannis (1996) and Chan, Lakonishok and Sougiannis (2001) suggest that because the R&D valuation is hardly realized and not easily evaluated in a short horizon, long-term study is more adequate to capture the intangible information of the R&D investment. • Managers seldom announce R&D increases formally. There might be a large time elapses between firm’s investment and market’s perception. As a result, the market might take time to fully reflect managers’ investment decision.

6. Motivations • If the market underreacts to the direct future benefits of the R&D increases, it might also underreact to any indirect future benefits, if any, which a firm’s rivals might gain from that firm’s R&D increase. • Fama (1998) argues that the abnormal returns might reflect normal random variations that occur in efficient markets, the long-term results can be viewed as an important challenge to the efficient market hypothesis.

7. The benefits of R&D spillover effect • Bernstein and Nadiri (1988) have indicated that the R&Dinvestment by a firm reduces its own production cost and, as a result of spillovers, costs of other firms are also reduced. • If spillovers do lower rivals’ production cost, then we would expect this effect to show up in the operating performance of rivals. • We use changes in operating performance and analyst forecast revisions to proxy for improvements in profitability.

8. Spillover Hypothesis • Firms undertaking R&D investment are often not able to appropriate the R&D benefits; that is, the benefits from R&D investment may extend to other firms in the same industry and/or economy. • It is possible for rival firms to abstain from R&D investment, and yet to take advantage of the knowledge generated by a firm that does invest in R&D. (Jeffe, 1986; Bernstein, 1989; Bernstein and Nadiri, 1988; Goto and Suzuki, 1989; Nadiri and Kim, 1996; Srinivasan, 1995). • Intra-industry rivals earn positive long-term abnormal return and experience improvements in operating performance and analyst’s forecast revisions

9. Strategic Reaction Hypothesis • A firm that increases its R&D spending might gain unfriendly attention from its rivals. • R&D increasing behaviors might be taken as that firm is moving ahead in the race to be the first to innovate to exploit the future benefits. • Sundaram, John, and John (1996) suggest that firms adopt R&D announcement as for a means of strategic interaction. • Massa, Rehman, and Vermaelen (2007) suggest that a repurchase firm conveys a valuable signal about firm undervaluation, which threatens competitors. To undo this effect, the rival may mimic and repurchase shares. • Massa, Rehman, and Vermaelen (2007) suggest that most strategic reacting behavior occurs in concentrated markets. • Do managers use a non-announcement channel, such as R&D investment racing, to strategically react to their rivals?

10. Sample selection • Source: • The sample includes listed stocks in NYSE/AMEX/NASDAQ during the period 1974 to 2006. Data on stock price and number of shares outstanding to compute market value of equity are obtained from the CRSP database. • Sample selection criterion: • (1) R&D intensity (measured by the ratio of R&D-to-assets (RDA, data46/data6) and R&D-to-sales (RDS, data46/data12) of at least 5%, • (2) increases in dollar R&D by at least 5% (R&D growth rate, or RDI), • (3) increases in ratio of R&D-to-assets (RDAI) by at least 5%.

11. Sample selection criterion: • Further, we exclude the non-common stock ADRs, SBIs, unit trusts, closed-end funds, REITs, and financial firms, as the work done by Fama and French (1992, 1993). • Sample stocks are also excluded if they have the following conditions: • (1) non-positive book equity, (2) without sales, operating income before depreciation (data13), earnings before interest and taxes (data178), total assets, or market value (3) without industry concentration measures (4) a firm have not appeared in COMPUSTAT for more than two years (Banz and Breen, 1986). • The final sample consists of 10,452 firm-year observations, in which the sample includes 3,646 R&D increasing firms.

12. Definition of industry rivals • Throughout the paper, we use CRSP four-digit SIC classification to define industry membership. • We measure the industry concentration using Herfindahl-Hirschman Index (HHI). • HHI is a commonly accepted as the measure of the product market concentration. HHI is the sum of squared market share of each firm in the industry. • Each year, we classy three groups based on HHI, where low concentration portfolio corresponds to the 30% of industries with the lowest concentration, while high concentration portfolio corresponds to the 30% of industries with the highest concentration.

13. Definition of industry rivals cont. • For each sample firm, we construct its corresponding industry portfolio as all stocks, except the sample firm itself, in the same four-digit SIC industry as the sample stock. • The returns on industry portfolio are equally and value weighted. • That is, if we have 10,452 firm-year observations, then 10,452 industry portfolios will be obtained.

14. Methodologies • Calendar time abnormal returns • For each calendar month t in our sample period, we form a portfolio of all sample firms that have significantly increased their R&D investment in the previous five years (60 months). • We then run the Fama and French three-factor model and Carhart four-factor model for long-term abnormal stock returns shown in the following equation: • Both equally- and value-weighted portfolio returns are calculated.

15. Rolling-over method: • A firm’s risk may change in response to its R&D change (Berk, Green, and Naik, 1999; Chan, Lakonishok, and Sougiannis, 2001) • We use the first 60 monthly returns (e.g., from April 1975 to March 1980) of the portfolio to estimate its factor loadings, and calculate the expected portfolio return in month 61 (e.g., April 1980) based on these factor loadings estimated over the previous 60 months multiplied by their corresponding factor returns in month 61. • The abnormal return in month 61 is the difference between the actual portfolio return and expected portfolio return.

16. Delisted-adjusted returns: • To mitigate survival-ship bias in returns for firms delisted from CRSP for performance reasons, we follow the procedure of Shumway (1997) and Shumway and Warther (1999). • Specifically, for firms delisted for performance reasons, we substitute -30% as the last return for NYSE and Amex stocks and -55% for Nsadaq firms. • Cumulative benchmark adjusted returns: • Our procedure for calculating benchmark-adjusted returns follows the methodology outlined in the Daniel, Grinblatt, Titman, and Wermers (1997, JF) study that developed benchmarks to evaluate mutual fund performance. • Specifically, we form 25 benchmark portfolios that capture three stock characteristics namely book-to-market equity and size which are significantly related to the cross-sectional variation in returns.

17. Each stock, in each year, is assigned to a benchmark portfolio according to its rank based on SZ and BM. Excess monthly returns of a particular stock are then calculated by subtracting the stock’s corresponding benchmark portfolio’s returns from the stock’s returns. Specifically, the characteristics-adjusted return is defined as: • where and are the return on security i and the return on a SZ-BM-matched portfolio in month t, respectively. • Each month, we use characteristics-adjusted return to calculate portfolio’s abnormal returns, then the abnormal monthly returns after formation period are cumulated as cumulative abnormal returns.

18. RATS approach : • Sock excess returns are regressed on the Carhart (1997) four-factor for each month in event time, and the estimated intercept represents the monthly average abnormal return for each event month. • The long-run abnormal returns between 1 month and 60 months (j) after a large increase in R&D at a sample firm are adopted. • The following regression is run each event month j: • ri,t are the equally- and value-weighted portfolio returns on industry portfolios in calendar month t that corresponds to the event month j, with j = 0 being the month of the beginning of the fourth month following fiscal year-end in which there is a large increase in R&D at a sample firm.

19. Summary statistics • The statistics reported in Table 1 are very similar to the those reported in EMS. • The average (median) HHI is 0.245 (0.176), suggesting that the most of sample firms are within less concentrated industries. • The average (median) number of rival firms in each industry portfolio is around 91 (58).

20. Table 1 Summary statistics

21. Spillover effect • Consistent with EMS, Panel A of Table 2 shows that both equally and value-weighted long-run abnormal returns on sample stocks are significantly positive. The abnormal returns are 0.86% and 0.34% for equally- and value-weighted method. The results are quantitatively similar to EMS. • There are significantly positive abnormal returns for the rival portfolio.

22. Table 2 Long-Term Abnormal Return for Large R&D-Increase Firms and Rival Portfolios

23. The influence of strategic reaction • Table 3 shows that the coefficient of the Concentration x R&D increase wave term is 0.261 (Model 3), which is significant at 1% confident level. • This indicates that the higher the concentration of the industry and the higher total number of firms that largely increase R&D expense over past five years in the industry, the more likely that the firms located in that industry will increase their R&D expense.

24. Table 3. Probit Regression of Indicator for Large Increases in R&D

25. The influence of strategic reaction • Table 4 shows significant positive abnormal returns for the rival portfolio in less concentrated industries. • Instead, in the concentrated industries, the abnormal returns for the rival portfolios are not significant, and some rival portfolios even earn negative abnormal returns.

26. Table 4 Long-Term Abnormal Return for Rival Portfolio Sorted by Industry Concentrations

27. The influence of strategic reaction • Fig. 1 shows that the long-term return of the rival portfolio in low concentration industry experiences high return. In particular, the rival portfolio in high concentration industry earns negative BHARs. • Table 5 demonstrates that over 12 (24, 36, 48, 60) months, for the full sample, the cumulative equally-weighted average abnormal returns of 10.05% (22.28%, 34.93%, 46.54%, 58.50%), are all significant at the 1% level. The results of the subsample indicate that for the low industry concentration group, the CARs are all significant at the 1% level. • Therefore, these results further provide supports for the strategic reaction hypothesis

28. Figure 1 Cumulative Abnormal Return for Rival Portfolios

29. Table 5. Long-Term Cumulative Abnormal for Rival Portfolios

30. Cross-sectional regression analysis • The dependent variable is 60-month buy-and-hold abnormal returns (BHAR) of each industry portfolio, in which the buy-and-hold abnormal return is controlled for the size, B/M matching portfolio return.

31. Further spillover evidence • In Model 1 and 2, the results show that the BHAR of sample firm term is positive and highly significantly across all the models indicating that the higher the buy-and-hold abnormal returns to sample firms occur following the R&D increases, the greater the buy-and-hold abnormal returns to rival portfolios will earn. • The long-run abnormal returns of industry portfolio are also positively associated with the level of R&D increases by largely R&D-increase firm. • This clearly suggests that the R&D increases has spillover effect on rival firms, and is consistent with the spillover hypothesis.

32. Further strategic reaction evidence • The coefficient estimate of Concentration is significantly negative. Thus, the higher the concentration of the industry, the lower the long-run abnormal returns to industry portfolios will be. • The coefficients of the interacting terms are negative.

33. Table 6 Cross-Sectional Analysis of Long-Run Abnormal Returns to Rival Portfolios

34. Changes in operating performance • First, the operating performance of the rival portfolios deteriorates prior to the event year and increased subsequent to the event year. • Second, the improvements in post-event operating performance are the higher for rivals with low concentration and lower for rivals with high concentration.

35. Figure 2 Changes in Return on Assets (ROA) of Rival Portfolios

36. Figure 3 Changes in Profit Margins (PM) of Rival Portfolios

37. Cross-sectional regression analysis • The dependent variable is five-year average post-event changes in operating performance (ROA and PM) of each industry portfolio. • First, for all models, the intercept indicates that industry portfolio experiences positive changes in ROA (PM) post to the R&D increasing year. • The long-run post-event changes in ROA of industry portfolio are positively associated with the level of R&D increases by sample firm. • Second, the coefficient estimate of Concentration is significantly negative.

38. Table 7 Cross-Sectional Analysis of Changes in Long-run Operating Performance of Rivals

39. Analysts forecast revisions • The dependent variable is the post-event 60-month average of abnormal analysts’ EPS forecast revisions of industry portfolios. • The evidence indicates that the long-run averages of abnormal analysts’ forecast revisions of industry portfolio are positively associated with the level of R&D increases by largely R&D-increase firm. • On the other hand, the coefficient estimate of Concentration is significantly negative.

40. Table 8 Cross-Sectional Analysis of Changes in Analysts’ EPS Forecast Revisions of Rivals

41. Institutional Trading Surrounding Share Repurchase Announcements (SRA) Weifeng Hung (洪偉峰) Department of Finance, Feng Chia University, Taiwan

42. Agenda • Motivations • Contributions • Data and methodologies • Empirical Results • Conclusions

43. Motivation: SRA attracts institutions? • Allen, Bernardo, and Welch (2000) argue that undervalued firms who want to signal their worth would like to attract institutions because institutions are better at assessing the firm’s true worth. • Several studies indicate that SRA attracts institutions (Grinstein and Michaely, 2005; Shleifer and Vishny, 1986; Allen, Bernardo, and Welch, 2000). • On the other hand, unlike individual investors, institutions are expected to be less prone to attention-driven trading behavior (Barber and Odean, 2008).

44. Motivation: Institutional response • Institutional investors are expected to have ability to move prices directly through their own trading, as well as indirectly, by influencing the trading decisions of other market participants who may follow their actions. • Institutional trading affects stock returns(Bannet et al., 2003; Gompers and Metrick, 2001). • Understanding of whether SRA attracts institutions is of great importance for firms announcing share repurchases.

45. Motivation: Superior information? • Institutions would be expected as sophisticated investors in processing information to arbitrage the repurchases anomaly to earn superior returns. • Prior studies indicate that institutional investors are able to correctly identify corporate events, such as IPO and SEO. • Why SRA? • The buyback anomaly has persisted for 25 years in U.S. stock market (Peyer and Vermalen, 2008).

46. Motivation: Superior information cont. • SRA in Taiwan: • On average, firms announcing share buyback earn significantly positive abnormal returns. • However, about 45% of events in Taiwan experience negative cumulative abnormal returns in the 30 days following SRA. • If institutional investors do have informational advantages in processing corporate activities, it is intuitively credible that individual investors can profit from the buy-sell information by imitating institutional trades surrounding the corporate announcements.

47. Motivation: Unique datasets • Since daily institutional trading data is not easily assessed, most empirical studies of institutional trading have focused on quarterly or annual data, such as 13(F) database. • Few studies have explored the relationship between institutional daily trading behavior and SRA. • Puckett and Yan (2010) suggest that trading performance shown by prior studies using quarterly data are biased downwards because of inability of publicly accessing interim trades.

48. Motivation: Unique datasets, cont. • We argue that the quarterly holdings data cannot capture the intra-quarter institutional trading, such as the exact timing of institutional trading surrounding the share repurchases announcements. • Particularly, we show that institutional trading occurs very near to the SRA date, about 10 days before SRA and a month after. • Daily institutional trading data in Taiwan allows us, for the first time, to contribute to the literature by examining the daily institutional trading behavior in response to SRA.

49. Contributions • 1. SRA significantly attracts institutions, switching their trading behavior from net selling to net buying. This finding is consistent with the argument that SRA attracts institutions (Grinstein and Michaely, 2005; Shleifer and Vishny, 1986; Allen, Bernardo, and Welch, 2000). • 2. There is an institutional price impact before and after SRA. • 3. Institutional trading seems to have predictive ability for the post-SRA stock performance. • 4. However, this trading skill disappears after controlling for their post-SRA price impact.

50. Data • We obtain daily data from Taiwan Economic Journal (TEJ), including stock repurchases announcement events (for the interests of shareholders), market index returns (including dividends), and institutional trading volumes. • Annual accounting data, such as book equity, are also retrieved from TEJ. This paper includes 610 repurchasing samples from October 13, 2000 through December 31, 2006. • We exclude events without institutional trading, stock returns, market value, and accounting variables at announcement date. The stocks with less than 130 trading days prior to the share repurchase announcement are also dropped.