/pdf/value-and-momentum-everywhere-value-and-momentum-everywhere-4c8fblmeeh.pdf

Value and Momentum Everywhere: Value and Momentum Everywhere

Investor Psychology and Security Market Under- and Overreactions

Motivated from investment-based asset pricing, we propose a new factor model that consists of the market factor, a size factor, an investment factor, and a return-on-equity factor The new model [i] outperforms the Carhart (1997) four-factor model in pricing portfolios formed on earnings surprise, idiosyncratic volatility, financial distress, equity issues, as well as on investment and return-on-equity; [ii] performs similarly as the Carhart model in pricing portfolios on momentum as well as on size and book-to-market; but [iii] underperforms in pricing the total accrual deciles Our model's performance, combined with its clear economic intuition, suggests that it can serve as a new workhorse model for academic research and investment management practice

Digesting Anomalies: An Investment Approach

An empirical q-factor model consisting of the market factor, a size factor, an investment factor, and a profitability factor largely summarizes the cross section of average stock returns. A comprehensive examination of nearly 80 anomalies reveals that about one-half of the anomalies are insignificant in the broad cross section. More importantly, with a few exceptions, the q-factor model's performance is at least comparable to, and in many cases better than that of the Fama-French (1993) 3-factor model and the Carhart (1997) 4-factor model in capturing the remaining significant anomalies.

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We find that innovative efficiency (IE), patents or citations scaled by R&D, is a strong positive predictor of future returns after controlling for firm characteristics and risk. The IE-return relation is associated with the loading on a mispricing factor, and the high Sharpe ratio of the Efficient Minus Inefficient (EMI) portfolio suggests that mispricing plays an important role. Further tests based upon attention and uncertainty proxies suggest that limited attention contributes to the effect. The high weight of the EMI portfolio return in the tangency portfolio suggests that IE captures incremental pricing effects relative to well-known factors.

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Innovative Efficiency and Stock Returns

http://rady.ucsd.edu/docs/faculty/Innovative-Efficiency-and-Stock-Returns.pdf

Innovative efficiency and stock returns

This paper uses research and development (R&D) data to examine the link between asset prices and financing constraints. Through a continuous-time real options model I show that there is a strong positive relation between financing constraints and stock returns, but only for high-R&D firms. Conversely, the model also predicts a strong positive relation between R&D and returns for highly constrained firms. Empirical results confirm these predictions. These findings not only explain the puzzling flat relation between financial constraints and stock returns documented in the literature, but also shed light on the economic source of the predictability of R&D investment on stock returns.

/pdf/financial-constraints-r-d-investment-and-stock-returns-3e92tx5cdx.pdf

Financial Constraints, R&D Investment, and Stock Returns: Theory and Evidence

Through the interaction between financial constraints and R&D, I study two asset-pricing puzzles: mixed evidence on the financial constraints--return relation and the positive R&D-return relation. Unlike capital investment, R&D is more inflexible. A financially constrained R&D-intensive firm is more likely to suspend/discontinue R&D projects. Therefore, R&D-intensive firms' risk increases with their financial constraints. Conversely, constrained firms' risk increases with their R&D intensity. I find a robust empirical relation between financial constraints and stock returns, primarily among R&D-intensive firms. Moreover, R&D predicts returns only among financially constrained firms. This evidence suggests that financial constraints potentially drive the positive R&D-return relation. The Author 2011. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com., Oxford University Press.

https://rady.ucsd.edu/faculty/directory/li/pub/docs/financial-constraints.pdf

Financial Constraints, R&D Investment, and Stock Returns

Q-theory predicts that investment frictions steepen the relation between expected returns and firm investment. Using financing constraints to proxy for investment frictions, we document only weak evidence that the investment-to-assets and asset growth effects in the cross-section of returns are stronger in financially more constrained firms than in financially less constrained firms. There is no evidence that q-theory with investment frictions explains the investment growth, net stock issues, abnormal corporate investment, or net operating assets anomalies. Limits-to-arbitrage proxies dominate q-theory with investment frictions in explaining the magnitude of the investment-to-assets and asset growth anomalies in direct comparisons.

Dongmei Li

Papers

Innovative Efficiency and Stock Returns

Innovative efficiency and stock returns

Financial Constraints, R&D Investment, and Stock Returns: Theory and Evidence

Financial Constraints, R&D Investment, and Stock Returns

Does Q-Theory with Investment Frictions Explain Anomalies in the Cross-Section of Returns?