This paper sheds new light on the mutual relationship between investor sentiment and excess returns corresponding to the bubble component of stock prices. We propose to use the wavelet concept of the phase angle to determine the lead-lag relation between these variables. The wavelet phase angle...

We perform a large-scale empirical study to compare the forecasting performance of single-regime and Markov-switching GARCH (MSGARCH) models from a risk management perspective. We find that, for daily, weekly, and ten-day equity log-returns, MSGARCH models yield more accurate Value-at-Risk,...

This paper challenges the prevailing view that investor sentiment is a contrarian predictor of market returns at nearly all horizons. As an important piece of "out-of-sample" evidence, we document that investor sentiment in China is a reliable momentum signal at monthly frequency. The strong...

Forecasting stock market returns is one of the most effective tools for risk management and portfolio diversification. There are several forecasting techniques in the literature for obtaining accurate forecasts for investment decision making. Numerous empirical studies have employed such methods...

The empirical literature of stock market predictability mainly suffers from model uncertainty and parameter instability. To meet this challenge, we propose a novel approach that combines the documented merits of diffusion indices, regime-switching models, and forecast combination to predict the...

This paper aims to test whether equity returns are predictable over various horizons. We propose a reliable and powerful nonparametric test to examine the predictability of equity returns, which can be interpreted as a signal-to-noise ratio test. Our comprehensive in-sample and out-of-sample...