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Long Run Stock Returns Following R&D IncreasesLong Run Stock Returns Following R&D Increases- A Test of R&D Spillover Effect - A Test of R&D Spillover Effect

Yanzhi WangYanzhi Wang

Yuan Ze UniversityYuan Ze University

TaiwanTaiwan

By Sheng-Syan Chen, Wei-Ju Huang and Yanzhi WangBy Sheng-Syan Chen, Wei-Ju Huang and Yanzhi Wang

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R&D and Firm ValuationR&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 appli

ance – 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.

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R&D and Stock ReturnR&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 val

ued, 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 fu

ture profitability. Investors extrapolate R&D increase firm’s future earnings too low, and this biased estimation causes positive return.

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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.

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Some Comparisons

Event Article Methods Abnormal Return per Month

IPO Ritter and Welch (2002)

Three-factor: EW -0.21% (t = -1.23)

SEO Loughran and Ritter (2000)

Three-factor: EW

Three-factor: VW

-0.47% (t = -5.42)

-0.32% (t = -3.00)

Repurchase Chan, Ikenberry and Lee (2007)

Four-factor: EW

Four-factor: VW

0.28% (t = 4.24)

0.25% (t = 3.35)

M&A Mitchell 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)

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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.

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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.Sample firms’ long run return R&D increases

w/ R&D followers impact of R&D spillover on return R&D increases w/o R&D followers

May April time Year t+1 Year t+6

Sample firms Some R&D followers Some other followers incease R&D increase R&D increase R&D Events

over 5% over 1% over 1%

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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.

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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.

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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/Dec Year t /Dec Year t+1 /July

Compute ΔR&D ΔR&D is publicly availableHolding period

Year t+6/June end

tttttftmtftp emMOMhHMLsSMBRRRR )( ,,,,

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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 fir

m approach.1. Minimize |OPt-OPt| around 80%~120% of OPt with the same

2-digit SIC code.2. Minimize |OPt-OPt| around 80%~120% of OPt with the same

1-digit SIC code if it’s not found at step 1.3. Minimize |OPt-OPt| around 80%~120% of OPt without industr

y requirement if it’s still not found at step 2.4. Minimize |OPt-OPt| without industry and filter requirement for

all remaining sample.

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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

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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 result

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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.

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Operating Performance

• We use Fama and French (2000) earnings regressionβ1 and β2 are coefficients for RD followed rank and RD followed ratio, respectivelyAbnormal ROAt+5 － Abnormal ROAt=β0+β1RD followed rankt+β2RD followed ratiot

+(γ1+γ2NDFEDt+γ3NDFEDt×DFEt+γ4PDFEDt×DFEt)×DFEt +(λ1+λ2NCEDt+λ3NCEDt×CEt+λ4PCEDt×CEt)×CEt+εt

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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.

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Stock Return and Industry Concentration

• Firms in high-concentration industry earn lower return

• This confirms the mimicking hypothesis.

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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.

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Fama-Macbeth (1973) Regression

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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 9

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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.