Firm characteristics, covariances, and cross-sectional expected returns

I investigate the relationship among firm characteristics, conditional covariance structure of stock returns, and cross-sectional expected returns. In the first chapter of my dissertation, I explore the (abnormal) return covariance pattern of S&P 500 stocks. Their covariance pattern, without being associated with any common factors, is explicitly linked to firm characteristics such as size, momentum, and accounting-based fundamentals. I propose a new covariance estimator based on this observation. In comparison to factor-based covariance models, a characteristic-based covariance model brings substantial diversification benefits and utility gains for a risk-averse investor seeking a global minimum variance portfolio and an optimal tangency portfolio, respectively.;In the second chapter of my dissertation, I propose a characteristic-based covariance model that directly links the predetermined firm characteristics to time-varying covariance risk. Using a large cross section of individual stock-level data, I parsimoniously estimate both conditional expected returns and conditional covariances as functions of firm characteristics. I find a strong and positive intertemporal risk-return relation on individual stocks. In addition, the characteristic-based covariance structure of stock returns is systematically related to fundamental risk in the macro economy. In comparison to the Fama-French three-factor model, the characteristic-based covariance model substantially reduces the pricing errors of characteristic-sorted portfolios on size, book-to-market, accruals, asset growth, investment-to-assets, return-on-assets, net stock issues, financial distress, and momentum. Portfolio tests similar to Daniel and Titman (1997) suggest that firm characteristics, mainly through the characteristic-based covariance structure of returns, appears to explain the cross section of average stock returns.