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1 Long Run Stock Returns Following R&D Increases - A Test of R&D Spillover Effect Yanzhi Wang Yuan Ze University Taiwan By Sheng-Syan Chen, Wei-Ju Huang.

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Presentation on theme: "1 Long Run Stock Returns Following R&D Increases - A Test of R&D Spillover Effect Yanzhi Wang Yuan Ze University Taiwan By Sheng-Syan Chen, Wei-Ju Huang."— Presentation transcript:

1 1 Long Run Stock Returns Following R&D Increases - A Test of R&D Spillover Effect Yanzhi Wang Yuan Ze University Taiwan By Sheng-Syan Chen, Wei-Ju Huang and Yanzhi Wang

2 2/21 R&D and Firm Valuation What is research and development (R&D)? –The investments on innovations and patents. –In accounting treatment, R&D is expensed. –Related to intangible assets –R&D has the externality (spillover effect) ASKEY ( 亞旭 ) obtained the technologies via patents from ATEONIX NETWORKS in 2004. These patents are about –Method and system for providing remote storage for an internet appliance –Method and system for providing a modulized server on board ASUS ( 華碩 ) obtained the technologies via patents from ASKEy via acquisition in 2008. Then, ASUS sued IBM.

3 3/21 R&D and Stock Return R&D level is positively related to future stock return (Lev and Sougiannis, 1996; Chan, Lakonishok, and Sougiannis, 2001). –Given that R&D generates intangible assets which is difficult to be valued, the R&D is normally underreacted. R&D level affects future long term stock return. R&D increase is also positively related to future stock return (Chan, Martin and Kensinger, 1990; Eberhart, Maxwell and Siddique; 2004) –R&D increase indicates the improvement on profitability that investors slowly react to. –R&D outlay reduces the current earnings while R&D is beneficial to future profitability. Investors extrapolate R&D increase firm’s future earnings too low, and this biased estimation causes positive return.

4 4/21 Ebarhart, Maxwell and Siddique (2004) The firms with R&D intensity over 5% and R&D increase over 5% are investigated. The R&D information is obtained from the annual report. The five-year long run abnormal return is about 0.74% and 0.53% per month based on equal- and value-weighted Carhart (1997) four-factor model, respectively. The operating performance improves in consequent of the R&D increase.

5 5/21 Some Comparisons EventArticleMethodsAbnormal Return per Month IPORitter and Welch (2002) Three-factor: EW-0.21% (t = -1.23) SEOLoughran and Ritter (2000) Three-factor: EW Three-factor: VW -0.47% (t = -5.42) -0.32% (t = -3.00) RepurchaseChan, Ikenberry and Lee (2007) Four-factor: EW Four-factor: VW 0.28% (t = 4.24) 0.25% (t = 3.35) M&AMitchell and Stafford (2000) Three-factor: EW Three-factor: VW -0.20% (t = -3.70) -0.03% (t = -0.48) R&D increase Eberhart, Maxwell and Siddique (2004) Four-factor: EW Four-factor: VW 0.74% (P-value=0.001) 0.53% (P-value=0.001)

6 6/21 The Issue Why the R&D increase that is a non-timing event experiences abnormal return so high? There could be other factor affecting the long run abnormal return of the R&D increase. Economics literature has widely discussed the spillover effect of the R&D for past decades (Arrow, 1962; Griliches, 1979; Bernstein and Nadiri, 1988; Hanel and St-Pierre, 2002; Agarwal, Echambadi, Franco and Sarkar, 2004; Hunt, 2006), but few papers mention this in finance literature. This could be the factor.

7 7/21 R&D Spillover Effect R&D spillover describes the fact that privately owned firm does not (or cannot) appropriate the outcome of its R&D investment. Due to the industrial competition, the rival firms may follow to increase the R&D after the EMS sample firm increases R&D.

8 8/21 Hypothesis Eberhart, Maxwell and Siddique (2004) find significantly high abnormal return for R&D increase firms. We hypothesize that this result is related to the R&D spillover effect. For the Eberhart, Maxwell and Siddique (2004) sample firm with more R&D followers, then the abnormal return of the sample firm should be higher.

9 9/21 Sample Collection Our sample is collected from U.S companies listed on the NYSE/Amex/Nasdaq during January 1974 to December 2006. We start our R&D sample collection from 1974 because the requirement of reporting R&D became effective from 1974. We mainly follow EMS and set up five criterions for our sample that includes firm-year observations with significant increases in R&D: (i) the ratio of R&D expenditures divided by sales over 5%, (ii) the ratio of R&D expenditures divided by average total assets over 5%, (iii) the change of the ratio of R&D expenditures divided by sales over 5%, (iv) the ratio of change of R&D expenditures divided by average total assets over 5%, and (v) the ratio of change of R&D expenditure and than divided by R&D expenditures over 5%. As a result, the sample with significant increase in R&D expenditures includes 10,280 U.S firm-year observations.

10 10/21 Methodology - Return From July 1976 to December 2006, each R&D increase portfolio p is formed by including sample firms which were increase firm within past 60 months. For example, the R&D increase calendar-time portfolio is composed of firms classified as the R&D increase firm in any of the past 5 years. As a result, the R&D increase portfolio monthly returns are regressed on Carhart (1997) four-factors as follows: Coefficient tests are adjusted with Newey-West autocorrelation- heteroskedasicity estimation. Year t-1/DecYear t /DecYear t+1 /July Compute ΔR&D ΔR&D is publicly available Holding period Year t+6/June end

11 11/21 Methodology - Operating Performance Using EBITDA/Assets as ROA and as well as the measure of operating performance (Barber, Lyon, 1996) We look at the median of changes of ROAs. We compute abnormal operating performance by matching firm approach. 1.Minimize |OP t -OP t | around 80%~120% of OP t with the same 2-digit SIC code. 2.Minimize |OP t -OP t | around 80%~120% of OP t with the same 1-digit SIC code if it’s not found at step 1. 3.Minimize |OP t -OP t | around 80%~120% of OP t without industry requirement if it’s still not found at step 2. 4.Minimize |OP t -OP t | without industry and filter requirement for all remaining sample.

12 12/21 Summary Statistics R&D increase firms are small growth firms R&D increase firms are high R&D firms About 30% of R&D increasing firms are followed by their industry peers. R&D increasing firms cluster in two industries: manufacturing and pharmaceutical. Table 1

13 13/21 Abnormal Return of R&D Increase RD followed ratio is the percentage of rival firms that follow the EMS sample firms to increase R&D over at least 1% High RD followed ratio implies higher R&D spillover effects. Get a closed look at resultGet a closed look at result

14 14/21 Some Robust Checks Fama and French (1993) three-factor model Control the time-varying risk betas in factor model Control the delisting return in factor model Remove the repeating R&D increase events Change the definitions of the R&D increase follower All these approaches appear consistent result.

15 15/21 Operating Performance We use Fama and French (2000) earnings regression β 1 and β 2 are coefficients for RD followed rank and RD followed ratio, respectively Abnormal ROA t+5 - Abnormal ROA t =β 0 +β 1 RD followed rank t +β 2 RD followed ratio t +(γ 1 +γ 2 NDFED t +γ 3 NDFED t ×DFE t +γ 4 PDFED t ×DFE t )×DFE t +(λ 1 +λ 2 NCED t +λ 3 NCED t ×CE t +λ 4 PCED t ×CE t )×CE t +ε t

16 16/21 R&D Mimicking Rival firms engage in R&D mimicking to undo the negative effect of the sample firm’s R&D investment. In a concentrated industry, the strategic reactions are more active, thus the R&D increasing benefit could be offset by rivals’ following R&D increases. The R&D increasing firms earn lower return in a more concentrated industry.

17 17/21 Stock Return and Industry Concentration Firms in high- concentration industry earn lower return This confirms the mimicking hypothesis.

18 18/21 Fama and MacBeth Regression For monthly stock returns during July at year t+1 to June year t+2, we include the monthly stocks with significant R&D increases in any of past five years (t to t–4) in the regression model. We regress the monthly returns on independent variables including the RD-followed ratio. The Fama-MacBeth estimates are obtained by the time-series average and tested by time-series volatility.

19 19/21 Fama-Macbeth (1973) Regression

20 20/21 Industry R&D Growth as Spillover Proxy The R&D spillover describes the impact of rivals’ R&D inputs on sample firms’ outputs. So the aggregative industrial R&D inputs (excluding sample firm) could be an alternative. We use the industry-wide R&D growth. Link to Table 9Table 9

21 21/21 Conclusion The long-term positive abnormal stock return following a firm’s R&D increase is argued by Eberhart, Maxwell and Siddique (2004). In this paper, we turn to propose an economical hypothesis, the R&D spillover effect, to account for the long-term stock return post to the R&D increase. As a firm increases the R&D investment, its industry peers may follow and increase their R&D investments under a competitive industry. Given that R&D investment has spillover effect, the follower’s R&D investment is beneficial to the firm that has significantly invested in R&D projects. Hence we argue and find that the firm with significant R&D increases and with sufficient R&D investment followers tends to outperform that with few R&D followers. This economic explanation also helps to answer the puzzle why the R&D increase, which is a non-timing event, is followed by significant abnormal stock return.

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