Author:
Xie Haibin,Sun Yuying,Fan Pengying
Abstract
AbstractThis 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.
Funder
National Social Science Fund of China
National Natural Science Foundation of China
Research on Modeling of Return Rate Based on Mixed Distribution and Its Application in Risk Management
Publisher
Springer Science and Business Media LLC
Subject
Management of Technology and Innovation,Finance
Reference24 articles.
1. Anatolyev S, Gospodinov N (2007) Modeling financial return dynamics via decomposition. J Bus Econ Stat 28(2):232–245. https://www.tandfonline.com/doi/abs/10.1198/jbes.2010.07017
2. Breen W, Glosten LR, Jagannathan R (1988) Economic significance of predictable variations in stock index returns. J Finance 44:1177–1189
3. Campbell JY, Thompson SB (2008) Predicting the equity premium out of sample: can anything beat the historical average? Rev Financ Stud 21:1509–1531
4. Christoffersen P, Diebold F (2006) Financial asset returns, direction-of-change forecasting, and volatility dynamics. Manag Sci 52(8):1273–1287
5. Christoffersen P, Diebold F, Mariano R, Tay A, Tse Y (2007) Direction-of-change forecasts based on conditional variance, skewness and kurtosis dynamics: international evidence. J Financ Forecast 1(2):1–22
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献