1. Empirical Research in Innovation
Dongmei Li
University of South Carolina

2. Roadmap Literature review Data on innovation Empirical methods
Asset pricing implication of innovation
Corporate finance
Data on innovation
Input: R&D
Output: Patents
Empirical methods
Portfolio sorts/factor regressions
Fama-MacBeth regressions

3. 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

4. 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

5. 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)

6. Asset Pricing Implication of Innovation (cont’d)
Innovative originality
Hirshleifer, Hsu, and Li (2014)

7. 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)

8. 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

9. Asset Pricing Implication of Innovation (cont’d)
R&D growth
Eberhart, Maxwell, and Siddique (2004)
R&D spillover

10. What drives innovation?
Financial constraints and innovative efficiency
Heitor, Hsu, and Li (2014)

11. 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

12. Data on Innovation (cont’d)
Output — patents, citations, company/security identifiers (gvkey, permno)
NBER

13. 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)

14. 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)

15. 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