Business Commons^™

Forecasting Stock Returns In Good And Bad Times: The Role Of Market States, Dashan Huang, Fuwei Jiang, Jun Tu, Guofu Zhou

Research Collection Lee Kong Chian School Of Business

This paper proposes a two-state predictive regression model and shows that stock market 12-month return (TMR), the time-series momentum predictor of Moskowitz, Ooi, and Pedersen (2012), forecasts the aggregate stock market negatively in good times and positively in bad times. The out-of-sample R-squares are 0.96% and 1.72% in good and bad times, or 1.28% and 1.41% in NBER economic expansions and recessions, respectively. The TMR predictability pattern holds in the cross-section of U.S. stocks and the international markets. Our study shows that the absence of return predictability in good times, an important finding of recent studies, is largely driven by …

Essays On Investor Sentiment In Asset Pricing, Liya Chu

Dissertations and Theses Collection

The dissertation addresses three topics on investor sentiment in asset pricing.

The first essay investigates the impact of market sentiment on the recent debate on equity premium forecasting. Particularly, market sentiment may break the link between fundamental economic predictors and equity premium. We find that economic predictors tend to lose their power and various remedies proposed in recent studies, such as non-negativity constraints, no longer work during high sentiment periods. In contrast, economic predictors actually do have strong performances even without using any such remedies, as long as the sentiment stays low enough so as not to distort the link. …

Upper Bounds On Return Predictability, Dashan Huang, Guofu Zhou

Research Collection Lee Kong Chian School Of Business

Can the degree of predictability found in data be explained by existing asset pricing models? We provide two theoretical upper bounds on the R² of predictive regressions. Using data on the market portfolio and component portfolios, we find that the empirical R² are significantly greater than the theoretical upper bounds. Our results suggest that the most promising direction for future research should aim to identify new state variables that are highly correlated with stock returns instead of seeking more elaborate stochastic discount factors.

International Volatility Risk And Chinese Stock Return Predictability, Jian Chen, Fuwei Jiang, Yangshu Liu, Jun Tu

Research Collection Lee Kong Chian School Of Business

This paper investigates the predictive ability of international volatility risks for the daily Chinese stock market returns. We employ the innovations in implied volatility indexes of seven major international markets as our international volatility risk proxies. We find that international volatility risks are negatively associated with contemporaneous Chinese daily overnight stock returns, while positively forecast next-day Chinese daytime stock returns. The US volatility risk (ΔVIX) is particularly powerful in forecasting Chinese stock returns, and plays a dominant role relative to the other six international volatility measures. ΔVIX's forecasting power remains strong after controlling for Chinese domestic volatility and is robust …