Timing the market: the economic value of price extremes

By decomposing asset returns into potential maximum gain(PMG)and potential maximum loss(PML)with price extremes,this study empirically investigated the relationships between PMG and PML.We found significant asymmetry between PMG and PML.PML significantly contributed to forecasting PMG but not vice versa.We further explored the power of this asymmetry for predicting asset returns and found it could significantly improve asset return predictability in both in-sample and out-of-sample forecasting.Investors who incorporate this asymmetry into their investment decisions can get substantial utility gains.This asymmetry remains significant even when controlling for macroeconomic variables,technical indicators,market sentiment,and skewness.Moreover,this asymmetry was found to be quite general across different countries.

Return direction forecasting:a conditional autoregressive shape model with beta density

This paper derives a new decomposition of stock returns using price extremes and proposes a conditional autoregressive shape(CARS)model with beta density to predict the direction of stock returns.The CARS model is continuously valued,which makes it different from binary classification models.An empirical study is performed on the US stock market,and the results show that the predicting power of the CARS model is not only statistically significant but also economically valuable.We also compare the CARS model with the probit model,and the results demonstrate that the proposed CARS model outperforms the probit model for return direction forecasting.The CARS model provides a new framework for return direction forecasting.