Nishad Kapadia

    Assistant Professor of Finance,

    Jones Graduate School of Business,

    Rice University,

    Houston, TX 77030

    Tel: (1) 713-348-5392

    Email: nishadk at rice dot edu

Research

Publications

Firm-specific risk and equity market development (with Gregory W. Brown), 2007, Journal of Financial Economics 84, 358-388. The changing composition of publicly listed firms explains the increase in idiosyncratic volatility.

Tracking down distress risk, 2011, Journal of Financial Economics 102, 167-182. Exposure to aggregate distress risk explains the size and value premiums. HML and SMB hedge increases in aggregate defaults. A single factor chosen to optimally predict aggregate defaults works as well as SMB and HML in pricing size and b/m sorted portfolios. The key innovation in this paper is to measure distress risk using covariance with an aggregate factor rather than the firm-specific measure of default probability.

Working papers

Death or glory: What explains the distress risk puzzle? (with Jennifer Conrad and Yuhang Xing) 2012 : A potential for ‘glory’ (a small probability of really high returns) explains the low average returns of stocks with high distress risk shown by Campbell, Hilscher, and Szilagyi (2008). Conditionally accepted, JFE

Estimating the cost of equity: Does theory help or hurt? (with Bradley Paye) 2012: Internet Appendix. Compares the performance of ‘naïve’ estimators of cost of equity such as the historical market mean with plug-in estimators from factor models. Even if the CAPM is true, the historical market mean does better than the plug-in CAPM estimator for a little over 50% of stocks! The paper presents analytical MSEs for different estimators with and without mispricing, calibrates them to the cross-section of stocks and industries, and does an empirical evaluation.

Davids, Goliaths and Business Cycles (with Jefferson Duarte) 2012: A cool new predictor of market excess returns, bond excess returns, GDP growth, investment growth, SMB, and, HML that is intuitive, grounded in theory and works out-of-sample. Old version (focuses on predicting market returns and includes a calibration of MSV model)

The next Microsoft? Skewness, idiosyncratic volatility, and expected returns

Click here for my resume