Fuwei Jiang

Forecasting Government Bond Risk Premia Using Technical Indicators

While economic variables have been used extensively to forecast bond risk premia, little attention has been paid to technical indicators which are widely used by practitioners. In this paper, we study the predictive ability of a variety of technical indicators vis-a-vis the economic variables. We find that technical indicators have significant in both in- and out-of-sample forecasting power. Moreover, we find that using information from both technical indicators and economic variables increases the forecasting performance substantially. We also find that the economic value of bond risk premia forecasts from our methodology is comparable to that of equity risk premium forecasts.

Forecasting Chinese Stock Market Volatility With Economic Variables

ABSTRACT This article investigates the forecasting power of economic variables for the Chinese stock market volatility. We find that several economic variables strongly forecast the future monthly volatilities for the aggregate Chinese stock market and a number of industry portfolios. The forecasting power of economic variables remains strong in out-of-sample setting. The predictability of Chinese stock market volatility can be further improved when combining information in all economic variables together.

Manager Sentiment and Stock Returns

This paper constructs a manager sentiment index based on the aggregated textual tone of corporate financial disclosures. We find that manager sentiment is a strong negative predictor of future aggregate stock market returns, with monthly in-sample and out-of-sample R2s of 9.75% and 8.38%, respectively, which is far greater than the predictive power of other previously studied macroeconomic variables. Its predictive power is economically comparable and is informationally complementary to existing measures of investor sentiment. Higher manager sentiment precedes lower aggregate earnings surprises and greater aggregate investment growth. Moreover, manager sentiment negatively predicts cross-sectional stock returns, particularly for firms that are difficult to value and costly to arbitrage.

Forecasting Stock Returns in Good and Bad Times: The Role of Market States

This paper proposes a state-dependent predictive regression model and finds that a market momentum predictor predicts the excess market return negatively in good times and positively in bad times. The out-of-sample R-square is 2.06% in NBER expansions and 1.84% in NBER recessions. There are similar predictability patterns in the cross-section of U.S. stocks and in the international markets. Our study shows the importance of market states in predicting stock returns, and finds that the concentration of return predictability in bad times, a well documented fact in the literature, is largely due to the assumption of a one-state predictive regression model.

Asset Allocation in the Chinese Stock Market: The Role of Return Predictability

In this article the authors investigate asset allocation in the Chinese stock market from the perspective of incorporating return predictability. Based on a host of return predictors, they find significant out-of-sample return predictability in the Chinese stock market. They then examine the performance of active portfolio strategies—such as aggregate market timing as well as industry, size, and value-rotation strategies—designed to profitably exploit return predictability. Strong evidence is found by the authors that these portfolio strategies incorporating return predictability can deliver superior performance—up to 600 basis points per annum and almost double the Sharpe ratios—compared with the passive buy-and-hold benchmarks that ignore return predictability.

The Chinese Bond Market: Risk, Return, and Opportunities

After reviewing the unique characteristics of the Chinese bond market, the authors investigate the factors that determine the returns and risks of Chinese bonds. Bond returns are found to be highly predictable and the predictability is largely driven by various institutional characteristics and China’s unique monetary policy: the Chinese Central Bank controls the whole term structure of deposit rates and leading rates. The authors also show that a U.S. investor can gain potentially sizable economic benefits by investing in the Chinese bond market for diversification, given the market’s high returns and low correlation with the U.S. bond market.

Cost Behavior and Stock Returns

This paper shows that investors fail to fully incorporate cost behavior information into valuation. Firms with higher growth in operating costs generate substantially lower future stock returns. A long-short spread portfolio earns an average return of about 12% per year after controlling for extant risk factors and firm characteristics. Mean-variance spanning tests show that an investor can benefit from investing in this spread portfolio in addition to well-known factors. Firms with high cost growth also suffer from deterioration in future operating performance. The negative cost growth-return relation is much stronger around earnings announcement days, among firms with lower investor attention, higher idiosyncratic volatility, and higher transaction costs, suggesting that investor underreaction and limits to arbitrage mainly drive the effect.