Nishad Kapadia

        Nishad Kapadia

        Assistant Professor of Finance,
        Freeman School of Business,
        7 McAlister Drive,
        Tulane University,
        New Orleans, LA 70118.

        Email: firstname.lastname@tulane.edu
        Office: (504) 314-7454

        Click here for a recent C.V.
    
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 economy-wide defaults 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) 2014, Journal of Financial Economics 113, 455–475. 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).

Davids, Goliaths and business cycles (with Jefferson Duarte), 2013:  Forthcoming, Journal of Financial and Quantitative Analysis. 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)

Safe Minus Risky: Do Investors Pay a Premium for Stocks that Hedge Stock Market Downturns? (with Barbara Ostdiek, James Weston, and Morad Zekhnini), 2015. Accepted, Journal of Financial and Quantitative Analysis. Internet Appendix.   Stocks that hedge against sustained stock market downturns -- peaks to troughs in S&P 500 index levels at the business cycle frequency -- should earn low returns, but they do not. A tradeable Safe Minus Risky portfolio that delivers returns of 4.6% per month during stock market downturns, also earns large unconditional mean returns and alpha. There appear to be no costs to hedging 'bad times', only benefits. Sentiment predicts returns to SMR.

Working papers

Estimating the cost of equity: Why do simple benchmarks outperform factor models? 2015:  Compares the performance of ‘naive’ estimators of cost of equity such as the historical market mean with plug-in estimators from factor models. One key insight of the paper: even under extremely favorable conditions for the CAPM (most notably, the CAPM is true), the historical market mean is more accurate than the standard plug-in CAPM estimator for a little over 50% of the cross-section of stocks! We also develop a Bayesian framework that delivers more accurate estimates of cost of equity than standard approaches in simulations and in the data by taking into account estimation error in factor loadings and factor premia, as well as potential mispricing. Internet Appendix

Do Idiosyncratic Jumps Matter? (with Morad Zekhnini), 2016  (Revision requested by Journal of Financial Economics). The entire annual return of a typical stock accrues on the four days (on average) on which its stock price experiences jumps, or large idiosyncratic movements relative to its volatility, Stock prices drift down by about 2% before jumps. These patterns are likely due to a premium for idiosyncratic jump risk. A trading strategy that buys stocks with high ex-ante jump probability earns high average returns and alphas. Returns for the strategy are higher when/where costs of arbitrage are high.

Testing Factor Models on Characteristic and Covariance Pure Plays (with Kerry Back and Barbara Ostdiek), 2015: We find that returns are associated with characteristics rather than covariances with risk factors for the new Fama-French and Hou-Xue-Zhang factor models. We generate test assets from Fama-MacBeth regressions that are bets on a particular characteristic or covariance and are neutral to all others. Our methods rely on the portfolio interpretation of Fama-MacBeth regression coefficients to construct tests that are unbiased in Monte-Carlo simulations despite errors-in-variables. 

The next Microsoft? Skewness, idiosyncratic volatility, and expected returns, 2007





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