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Empirical Research in Innovation Dongmei Li University of South Carolina Roadmap • Literature review – Asset pricing implication of innovation – Corporate finance • Data on innovation – Input: R&D – Output: Patents • Empirical methods – Portfolio sorts/factor regressions – Fama-MacBeth regressions Asset Pricing Implication of Innovation • R&D intensity – Chan, Lakonishok, and Sougiannis (2001): • R&D/sales does not predict returns • R&D/market equity predicts significantly higher returns • Explanations: over-extrapolation Asset Pricing Implication of Innovation (cont’d) • Li (2011): interaction between R&D and financial constraints – Motivation: two asset-pricing puzzles • Financial constraints and stock returns • R&D and stock returns – Findings (theoretical and empirical): • Among constrained firms, risk increases with R&D-intensity • Among R&D-intensive firms, risk increases with financial constraints Asset Pricing Implication of Innovation (cont’d) • R&D efficiency (Hirshleifer, Hsu, and Li 2012) – Motivation: limited attention – Measurement: patents or citations generated per dollar of R&D – Results: • R&D skills (Cohen, Diether, and Malloy 2012) Asset Pricing Implication of Innovation (cont’d) • Innovative originality – Hirshleifer, Hsu, and Li (2014) Asset Pricing Implication of Innovation (cont’d) • Capital investment in innovative capacity (IC) – Kumar and Li (2015) • Motivation: Investment anomalies – Behavioral explanations: underreaction to empire building (Titman, Wei, and Xie 2004); overreaction to investment (Cooper, Gulen, and Schill 2008) – Real options (Carlson, Fisher, Giammarino 2004, 2006) – Q-theory (Li and Zhang 2010) Asset Pricing Implication of Innovation (cont’d) • Examples of IC investment: building research infrastructure, purchasing R&D equipment, buying patents • Uniqueness of IC investment: helps firms generate options with uncertainty • We study dynamic implications of IC investment on – Expected returns – Future investment – Profitability Asset Pricing Implication of Innovation (cont’d) • R&D growth – Eberhart, Maxwell, and Siddique (2004) • R&D spillover What drives innovation? • Financial constraints and innovative efficiency – Heitor, Hsu, and Li (2014) Data on Innovation • Input — R&D expenditure • Compustat • Data quality is higher after 1975 • FASB No. 2: R&D reporting rules are standardized • Missing R&D is generally equivalent to zero or very small amount of R&D Data on Innovation (cont’d) • Output — patents, citations, company/security identifiers (gvkey, permno) – NBER – https://sites.google.com/site/patentdataproject/Home – http://eml.berkeley.edu/~bhhall/patents.html – https://iu.app.box.com/patents Empirical Methods — Portfolio Sorts • References: Fama and French (1992, 1993) • Timing: • Portfolio formation: end of June of year t (based on characteristics in year t-1) • Holding period: July of year t to June of year t+1 • Purposes: • Avoid look-ahead bias: make sure sorting variables are observable to investors at formation (financial reporting lag) • Rebalance once a year (reduce transaction cost) Measure of Abnormal Returns • Time-series regressions of portfolio excess returns on factors returns • Intercepts: alphas (returns that cannot be explained by existing factor models) • Slopes: factors loadings (quantity of risk) Empirical Methods — Fama-MacBeth Regression • References: Fama and MacBeth (1973) • Conduct monthly cross-sectional regressions of individual firms’ returns (July of year t to June of year t+1) on lagged variables • Compute average and t-statistics of the time-series slopes from regressions above • Benefits: • Control more variables • Address cross-sectional correlation • Allow “more” samples