Download Asset Pricing Implication of Innovation (cont`d)

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