JACIII Vol.22 No.6 pp. 831-837
doi: 10.20965/jaciii.2018.p0831


Forecasting Stock Returns Based on a Time-Varying Factor Weighted Density Model

Wentao Gu, Yongwei Yang, and Zhenshan Liu

Department of Statistics, Zhejiang Gongshang University
18 Xuezheng Street, Xiasha Education park, Hangzhou 310018, China

Corresponding author

July 15, 2017
December 25, 2017
October 20, 2018
stock returns, time-varying factor weighted density, forecasting

Stock returns play an important role in the empirical study of asset pricing, and are often applied in portfolio allocation and performance evaluation. The effect of macroeconomic and financial variables on stock returns is a hot topic and many studies have utilized these variables in time series models to improve the forecasts of stock returns. This study imposes macroeconomic and financial variables as weighting factors on kernel density and establishes a new prediction model – the time-varying factor weighted density model. We apply this model to monthly price data of the Chinese stock index and employ the rolling window strategy for out-of-sample forecasting. The result shows that this method improves both statistical and economic measures of out-of-sample forecasting performance.

Cite this article as:
W. Gu, Y. Yang, and Z. Liu, “Forecasting Stock Returns Based on a Time-Varying Factor Weighted Density Model,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.6, pp. 831-837, 2018.
Data files:
  1. [1] E. F. Fama, “Efficient capital markets: A review of theory and empirical work,” The J. of Finance, Vol.25, No.2, pp. 383-417, 1970.
  2. [2] M. C. Jensen, “Some anomalous evidence regarding market efficiency,” J. of Financial Economics, Vol.6, No.2-3, pp. 95-101, 1978.
  3. [3] A. Ang and G. Bekaert, “Stock return predictability: is it there?,” Review of Financial Studies, Vol.20, No.3, pp. 651-707, 2001.
  4. [4] A. Timmermann and C. W. J. Granger, “Efficient market hypothesis and forecasting,” Int. J. of Forecasting, Vol.20, No.1, pp. 15-27, 2004.
  5. [5] J. Y. Campbell and S. B. Thompson, “Predicting the Equity Premium Out of Sample: Can anything beat the Historical Average,” Review of Financial Studies, Vol.21, No.4, pp. 1509-1531, 2008.
  6. [6] X. Chen, K. A. Kim, T. Yao, and T. Yu, “On the predictability of Chinese stock returns,” Pacific-Basin Finance J., Vol.18, No.14, pp. 403-425, 2008.
  7. [7] S. J. Jordan, A. J. Vivian, and M. E. Wohar, “Forecasting returns: new european evidence,” J. of Empirical Finance, Vol.26, No.1, pp. 76-95, 2014.
  8. [8] R. Demirer, C. Pierdzioch, and H. Zhang, “On the short-term predictability of stock returns: A quantile boosting approach,” Finance Research Letters, Vol.22, pp. 35-41, 2017.
  9. [9] D. H. Rapach, M. E. Wohar, and J. Rangvid, “Macro variables and international stock return predictability,” Int. J. of Forecasting, Vol.21, No.1, pp. 137-166, 2005.
  10. [10] T. Cenesizoglu and A. Timmermann, “Do Return Prediction Models Add Economic Value?,” J. of Banking & Finance, Vol.36, No.11, pp. 2974-2987, 2012.
  11. [11] H. Asgharian, A. J. Hou, and F. Javed, “The Importance of the Macroeconomic Variables in Forecasting Stock Return Variance: A GARCH-MIDAS Approach,” J. of Forecasting, Vol.32, No.7, pp. 600-712, 2013.
  12. [12] D. Pettenuzzo, A. Timmermann, and R. Valkanov, “Forecasting stock returns under economic constraints,” J. of Financial Economics, Vol.114, No.3, pp. 517-553, 2014.
  13. [13] J. Li and I. Tsiakas, “Equity premium prediction: The role of economic and statistical constraints,” J. of Financial Markets, Vol.36, pp. 56-75, 2017.
  14. [14] G. Leitch and J. M. Tanner, “Economic forecast evaluation:profits versus the conventional error measures,” American Economic Review, Vol.81, No.3, pp. 580-590, 1991.
  15. [15] J. Chung and Y. M. Hong, “Model-Free Evaluation of Directional Predictability in Foreign Exchange Markets,” J. of Applied Econometrics, Vol.22, No.5, pp. 855-889, 2007.
  16. [16] S. Anatolyev and N. Gospodinov, “Modeling financial return dynamics via decomposition,” J. of Business and Economic Statistics, Vol.28, No.2, pp. 232-245, 2010.
  17. [17] H. Nyberg, “Forecasting the direction of the US stock market with dynamic binary probit models,” Int. J. of Forecasting, Vol.27, No.2, pp. 561-578, 2011.
  18. [18] T. Chevapatrakul, “Return sign forecasts based on conditional risk: Evidence from the UK stock market index,” J. of Banking & Finance, Vol.37, No.7, pp. 2342-2353, 2013.
  19. [19] G. J. Deboeck, “Trading on the edge: neural, genetic, and fuzzy systems for chaotic financial markets,” John Wiley & Sons, Inc, pp. 45-65, 1994.
  20. [20] Y. S. Abu-Mostafa and A. F. Atiya, “Introduction to financial forecasting,” Applied Intelligence, Vol.6, No.3, pp. 205-213, 1996.
  21. [21] J. Zheng, W. Gu, B. Xu, and Z. Cai, “The estimation for Lévy processes in high frequency data,” Econometric Reviews, Vol.37, No.10, pp. 1051-1066, 2018.
  22. [22] T. Liu, N. Qiu, and W. Gu, “A Stock Trading Strategy Based on Time-Varying Quantile Theory,” J. Adv. Comput. Intell. Intell. Inform., Vol.19, No.3, pp. 417-422, 2015.
  23. [23] A. Harvey and V. Oryshchenko, “Kernel density estimation for time series data,” Int. J. of Forecasting, Vol.28, No.1, pp. 3-14, 2012.
  24. [24] A. Goyal and I. Welch, “A comprehensive Look at the Empirical Performance of Equity Premium Prediction,” Review of Financial Studies, Vol.21, No.4, pp. 1455-1508, 2008.
  25. [25] C. W. J. Granger and M. H. Pesaran, “Economic and statistical measures of forecast accuracy,” J of Forecasting, Vol.19, No.7, pp. 537-560, 2000.
  26. [26] M. H. Pesaran and A. Timmermann, “Testing Dependence Among Serially Correlated Multi-category Variables,” J. of the American Statistical Association, Vol.104, No.485, pp. 325-337, 2006.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Nov. 15, 2018