Empirical asset pricing models seek to capture characteristic-based patterns in average stock returns in a parsimonious fashion. I propose a new approach for constructing these models, and investigate its performance with respect to estimating the cost of equity capital. Using a model that accounts for the cross-sectional relation between five firm-level characteristics and average stock returns, I obtain cost-of-equity estimates that outperform the estimates produced by the recently-developed Fama and French (2014) five-factor model. Because my approach to model construction builds directly on standard cross-sectional regression techniques, it provides complete flexibility in choosing the firm characteristics used to formulate the cost-of-equity estimates. This stands in sharp contrast to competing methods that utilize a small number of prescribed factors.
Researchers typically employ cross-sectional regression methods to identify firm-level characteristics that help to explain the cross-section of average stock returns. I develop a straightforward approach for testing whether the coefficient estimates produced by these methods satisfy the pricing restrictions imposed by a given stochastic discount factor. The empirical analysis reveals that the evidence from cross-sectional regression studies poses a substantial challenge to existing asset pricing models. The tests produce emphatic rejections for several candidate SDF specifications that perform well in prior research. It appears that the rejections are driven in part by the presence of nonlinearities in the data.
We propose a comprehensive empirical strategy for optimizing the out-of-sample performance of sample mean-variance efficient portfolios. After constructing a sample objective function that accounts for the impact of estimation risk, specification errors, and transaction costs on portfolio performance, we maximize the function with respect to a set of tuning parameters to obtain plug-in estimates of the optimal portfolio weights. The methodology offers considerable flexibility in specifying objectives, constraints, and modeling techniques. Moreover, the resulting portfolios have well-behaved weights, reasonable turnover, and substantially higher Sharpe ratios and certainty-equivalent returns than benchmarks such as the 1/N portfolio and S&P 500 index
Characteristic-based sorting rules and fund-of-fund optimization strategies, with E. Chiang and B. Ostdiek
Income Shifting as an Aspect of Tax Avoidance: Evidence from U.S. Multinational Corporations, with A. Cordis, forthcoming in Review of Pacific Basin Financial Markets and Policies
We use jurisdiction-specific effective tax rates (ETRs) to investigate income shifting as an aspect of tax avoidance by U.S. firms. Our central prediction is that tax-based incentives for shifting income, as measured by the spread between domestic and foreign ETRs, should be reflected in the share of pre-tax income earned by U.S. firms in foreign jurisdictions. The data lend substantial support to this prediction. We find robust evidence of a positive correlation between the foreign share of pre-tax income and the ETR spread that is consistent with firms shifting income both into and out of the United States. The evidence also indicates that firms respond asymmetrically to positive and negative ETR spreads. Specifically, the response to a negative spread is stronger than to a positive spread of the same magnitude.
Capital Expenditures and Firm Performance: Evidence from a Cross-Sectional Analysis of Stock Returns, with A. Cordis, forthcoming in Accounting and Finance
It is well established that firms that undertake high levels of capital investment relative to their scale of operations, as measured by total assets, sales, or similar criteria, tend to have lower subsequent stock returns than firms with the opposite characteristic. Intuitively, this finding is consistent with the hypothesis that firms evaluate investment projects using hurdle rates that reflect expected stock returns, thereby inducing a negative cross-sectional correlation between realized stock returns and observed investment levels. We use a simple two-period model of firm investment to formalize this intuition, and show that the model predicts that the function that relates stock returns to investment is nonlinear, i.e., its slope varies with the level of investment. This prediction finds substantial support in the data. The evidence indicates that the slope of the investment function is negative at low investment levels, close to zero at intermediate investment levels, and negative at high investment levels. Our results, which are robust to the use of narrowly- and broadly-defined measures of capital investment, pose a challenge to the hypothesis that the negative correlation between investment and stock returns is attributable to some sort of overinvestment phenomenon.
Discrete stochastic autoregressive volatility, with A. Cordis, Journal of Banking and Finance 43, June 2014
Component-driven regime-switching volatility, with J. Fleming, Journal of Financial Econometrics 11, Spring 2013
It’s all in the timing: Simple active portfolio strategies that outperform naive diversification, with B. Ostdiek, Journal of Financial and Quantitative Analysis 47, April 2012
Regime-switching factor models in which the number of factors defines the regime, with A. Cordis, Economics Letters 112, August 2011
Long memory in volatility and trading volume, with J. Fleming, Journal of Banking and Finance 35, July 2011
Most Frequently Cited Articles
The economic value of volatility timing using ‘realized’ volatility, with J. Fleming and B. Ostdiek, Journal of Financial Economics 67, March 2003
The economic value of volatility timing, with J. Fleming and B. Ostdiek, Journal of Finance 56, February 2001
Information and volatility linkages in the stock, bond, and money markets, with J. Fleming and B. Ostdiek, Journal of Financial Economics 49, July 1998