Covariance Risk, Mispricing, and the Cross Section of Security Returns

This paper offers a multisecurity model in which prices reflect both covariance risk and misperceptions of firms' prospects, and in which arbitrageurs trade to profit from mispricing. We derive a pricing relationship in which expected returns are linearly related to both risk and mispricing variables. The model thereby implies a multivariate relation between expected return, beta, and variables that proxy for mispricing of idiosyncratic components of value tends to be arbitraged away but systematic mispricing is not. The theory is consistent with several empirical findings regarding the cross-section of equity returns, including: the observed ability of fundamental/price ratios to forecast aggregate and cross-sectional returns, and of market value but not non-market size measures to forecast returns cross-sectionally; and the ability in some studies of fundamental/price ratios and market value to dominate traditional measures of security risk. The model also offers several untested empirical implications for the cross-section of expected returns and for the relation of volume to subsequent volatility.

A Protocol for Factor Identification

We propose a protocol for identifying genuine risk factors. The underlying premise is that a risk factor must be related to the covariance matrix of returns, must be priced in the cross-section of returns, and should yield a reward-to-risk ratio that is reasonable enough to be consistent with risk pricing. A market factor, a profitability factor, and traded versions of macroeconomic factors pass our protocol, but many characteristic-based factors do not. Several of the underlying characteristics, however, do command premiums in the cross-section

Common determinants of bond and stock market liquidity: The impact of financial crises, monetary policy, and mutual fund flows

We study common determinants of daily bid-ask spreads and trading volume for the bond and stock markets over the 1991-98 period. We find that spread changes in one market are affected by lagged spread and volume changes in both markets. Further, spread and volume changes are predictable to a considerable degree using lagged market returns, lagged interest rates, lagged spreads, and lagged volume. During periods of financial crisis, stock and bond spreads and volume are more volatile and become more highly correlated; moreover, at these times, money supply positively affects financial market liquidity, albeit with a lag of two weeks. During normal times, increases in mutual fund flows enhance stock market liquidity and trading volume, but during financial crises, U.S. government bond funds see higher inflows, resulting in increased bond market liquidity. Overall, this study deepens our understanding of the dynamics of liquidity in financial markets and suggests how asset allocation strategies might be designed to reduce trading costs

Common Liquidity Shocks and Market Collapse: Lessons From the Market for Perps

We show how a high degree of commonality in investor liquidity shocks can diminish incentives for intermediaries to keep markets open and lead to market collapse, even without information asymmetry or news affecting fundamentals. We motivate our model using the perpetual floating-rate note market where two years of explosive growth – in which issues by high quality borrowers were placed with institutional investors and traded in a liquid secondary market – were followed by a precipitous collapse when market intermediaries withdrew due to large order imbalances. We shed new light on the trade-off between ownership concentration and market liquidity

A Protocol for Factor Identification

We propose a protocol for identifying genuine risk factors. The underlying premise is that a risk factor must be related to the covariance matrix of returns, must be priced in the cross-section of returns, and should yield a reward-to-risk ratio that is reasonable enough to be consistent with risk pricing. A market factor, a profitability factor, and traded versions of macroeconomic factors pass our protocol, but many characteristic-based factors do not. Several of the underlying characteristics, however, do command premiums in the cross-section