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.

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 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.