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.

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.

We extend the Fama–MacBeth regression framework for cross-sectional return prediction to incorporate big data and machine learning. Our extension involves a three-step procedure for generating return forecasts based on Fama–MacBeth regressions with regularization and predictor selection as well as forecast combination and encompassing. As a by-product, it provides estimates of characteristic payoffs. We also develop three performance measures for assessing cross-sectional return forecasts, including a generalization of the popular time-series out-of-sample R2 statistic to the cross section. Applying our extension to over 200 firm characteristics, our cross-sectional return forecasts significantly improve out-of-sample predictive accuracy and provide substantial economic value to investors. Overall, our results suggest that a relatively large number of characteristics matter for determining cross-sectional expected returns. Our new method is straightforward to implement and interpret, and it performs well in our application.

In this paper, we document that an application of a moving average timing strategy of technical analysis to portfolios sorted by volatility generates investment timing portfolios that substantially outperform the buy-and-hold strategy. For high-volatility portfolios, the abnormal returns, relative to the capital asset pricing model (CAPM) and the Fama-French 3-factor models, are of great economic significance, and are greater than those from the well-known momentum strategy. Moreover, they cannot be explained by market timing ability, investor sentiment, default, and liquidity risks. Similar results also hold if the portfolios are sorted based on other proxies of information uncertainty.

We provide evidence that a simple moving average timing strategy, when applied to portfolios of commodity futures, can generate superior performance to the buy-and-hold strategy. The outperformance is very robust. It can survive the transaction costs in the futures markets, it is not concentrated in a particular subperiod, and is robust to short-sale constraints, alternative specifications of the moving average lag length, alternative construction of the continuous time-series of futures prices, and impact from data mining. The outperformance of the timing strategy is not driven by the backwardation and contango. It is stronger during recession and can not be explained by macroeconomic variables. Finally, we confirm that the outperformance of the moving average timing strategy in the commodity futures comes from the successful market timing.