Nishad Kapadia

 

Description: Description: Description: Description: X:\Public\www\Photo.jpg    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

Published and accepted papers

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.

Death and Jackpot: Why Do Individual Investors Hold Overpriced Stocks? (with Jennifer Conrad and Yuhang Xing) 2012, Accepted, Journal of Financial Economics. A potential for ‘jackpots’ (a small probability of really high  returns) explains the low average returns of stocks with high default risk shown by Campbell, Hilscher, and Szilagyi (2008).

Working papers

Estimating the cost of equity: Why do simple benchmarks outperform factor models  (with Bradley Paye), July 2013: (new version) Compares the performance of ‘naïve’ estimators of cost of equity such as the historical market mean with plug-in estimators from factor models. One key insight of the paper in a figure: even under extremely favorable conditions for the CAPM (most notably, the CAPM is true), the historical market mean is more accurate than the plug-in CAPM estimator for a little over 50% of the cross-section of stocks! We also develop a Bayesian framework that takes into account estimation error in factor loadings and factor premia, and also potential mispricing, and delivers more accurate estimates of cost of equity than standard approaches in simulations and in the data. Internet Appendix

Davids, Goliaths and Business Cycles (with Jefferson Duarte) 2013:  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 the Menzly, Santos, and Veronesi (2004) model)

Slopes as Factors: Characteristic Pure Plays (with Kerry Back and Barbara Ostdiek), July 2013:  The four factor model does not explain returns of size, value, and momentum strategies. We build pure play portfolios for size, value, and momentum using Fama-Macbeth regressions and show that these portfolios earn four factor alphas. The alphas for value and momentum remain statistically and economically significant even after excluding microcaps and revising factors monthly. A five factor model with the pure-plays and a size-value interaction portfolio makes alphas of eight (out of thirteen we consider) anomalies insignificant. These include several prominent anomalies, such as Gross Profitability, Betting Against Beta, Idiosyncratic Volatility, and Firm failure probability.

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

Click here for my resume

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