We investigate the implications of time-varying expected return and volatility on asset allocation in a high dimensional setting. We propose a dynamic factor multivariate stochastic volatility (DFMSV) model that allows the first two moments of returns to vary over time for a large number of assets. We then evaluate the economic significance of the DFMSV model by examining the performance of various dynamic portfolio strategies chosen by mean-variance investors in a universe of 36 stocks. We find that the DFMSV dynamic strategies significantly outperform various bench-mark strategies out of sample. This outperformance is robust to different performance measures, investor's objective functions, time periods, and assets.

[We model a microstructure effect on daily security returns, embodied by zero returns and the bid-ask spread, and derive a closed-form solution for the resulting bias in the estimated idiosyncratic volatility. Our empirical tests show that controlling for the bias eliminates the ability of idiosyncratic volatility estimates to predict future returns. We also find a significant reduction in the pricing ability of idiosyncratic volatility after exogenous shocks to liquidity evidenced in the 1997 reduction in the quotes to sixteenths and the 2001 decimalization. Finally, minimizing liquidity's influence on the estimated idiosyncratic volatility, by orthogonalizing the percentage of zero-return and spread effects on the estimated idiosyncratic volatility, demonstrates that the resulting idiosyncratic volatility estimate has little pricing ability.]

This paper shows that trends typically used for monetary policy guidance are also effective in predicting market excess returns. Using a linear combination method across 14 economic and financial predictor variables, we find that moving-average trends outperform the variables’ current values in forecasting market returns. Incorporating neural networks further improves these predictions. Our findings underscore the importance of trends, supporting the Federal Reserve’s emphasis on integrating trends with lagged variables. When accounting for nonlinearity, we find that market return predictability is significantly greater than commonly believed. Our results are robust across both U.S. and global equity markets. JEL C52, C53, C55, C58, G17

We investigate the implications of time-varying expected return and volatility on asset allocation in a high dimensional setting. We propose a dynamic factor multivariate stochastic volatility (DFMSV) model that allows the first two moments of returns to vary over time for a large number of assets. We then evaluate the economic significance of the DFMSV model by examining the performance of various dynamic portfolio strategies chosen by mean-variance investors in a universe of 36 stocks. We find that the DFMSV dynamic strategies significantly outperform various benchmark strategies out of sample. This outperformance is robust to different performance measures, investor’s objective functions, time periods, and assets.

We propose a novel Bayesian framework to incorporate uncertainty about the state of the market. Among others, one advantage of the framework is the ability to model a large collection of time-varying parameters simultaneously. When we apply the framework to estimate the cost of equity we find economically significant effects of state uncertainty. A state-independent pricing model overestimates the cost of equity by about 4% per annum for a utility firm and by as much as 3% for industries. We also observe that the expected return, volatility, risk loading, and pricing error all display state-dependent dynamics that coincide with the business cycle. More interestingly, the forecasted market and Fama–French factor risk premiums can predict the future real GDP growth rate even though the model does not use any macroeconomic variables, which suggests that the proposed Bayesian framework captures the state-dependent dynamics well.

We model a microstructure effect on daily security returns, embodied by zero returns and the bid-ask spread, and derive a closed-form solution for the resulting bias in the estimated idiosyncratic volatility. Our empirical tests show that controlling for the bias eliminates the ability of idiosyncratic volatility estimates to predict future returns. We also find a significant reduction in the pricing ability of idiosyncratic volatility after exogenous shocks to liquidity evidenced in the 1997 reduction in the quotes to sixteenths and the 2001 decimalization. Finally, minimizing liquidity's influence on the estimated idiosyncratic volatility, by orthogonalizing the percentage of zero-return and spread effects on the estimated idiosyncratic volatility, demonstrates that the resulting idiosyncratic volatility estimate has little pricing ability.

In this paper, we provide a trend factor that captures simultaneously all three stock price trends: the short-, intermediate-, and long-term, by exploiting information in moving average prices of various time lengths whose predictive power is justified by a proposed general equilibrium model. It outperforms substantially the well-known short-term reversal, momentum, and long-term reversal factors, which are based on the three price trends separately, by more than doubling their Sharpe ratios. During the recent financial crisis, the trend factor earns 0.75% per month, while the market loses −2.03% per month, the short-term reversal factor loses −0.82%, the momentum factor loses −3.88%, and the long-term reversal factor barely gains 0.03%. The performance of the trend factor is robust to alternative formations and to a variety of control variables. From an asset pricing perspective, it also performs well in explaining cross-section stock returns.

We model and test the relations between the team management of mutual funds, managers’ ability, performance, and holdings. Our model predicts that team-managed funds perform better and behave more conservatively than single-manager funds. However, the effect of team management is masked in equilibrium because high-ability managers rationally self-select into single-manager funds. Consistent with the model’s prediction, we find that team-managed funds perform better and deviate less from their benchmark allocations than single-manager funds with the same characteristics. These differences are marked after we control for the endogenous self-selection of managers.

This paper examines data from 45 world markets and shows that the previously documented relation between mean returns and idiosyncratic volatility arises because of biases in volatility estimates that we can attribute to the bid–ask bounce in trade prices. We show that no significant relation exists between mean returns and idiosyncratic volatility estimated from quote-midpoint returns. Further, there is no significant relation between mean returns and the portion of transaction-price-based idiosyncratic volatility that is orthogonal to bid–ask spreads. The pricing of idiosyncratic volatility is due to the negative pricing of the bid–ask spread.

This study investigates the performance of trend factors across different markets around the world and demonstrates that the trend factors perform well across most of developed markets and many emerging markets, outperforming the market portfolio, short-term reversal, momentum, and long-term reversal. We further examine how cultural and legal differences influence the performance of the trend factor trading strategy and find it is more profitable in countries where the individualism is higher and securities laws are better enforced. Finally, the global trend factor aggregating individual market trend factors performs well and explains various global portfolios’ returns. The findings suggest that the trend factors present a challenge to traditional risk-based asset pricing theories, and trend factor trading strategies may deserve more attention in international portfolio management.