JILSA  Vol.3 No.4 , November 2011
Chinese Stock Price and Volatility Predictions with Multiple Technical Indicators
ABSTRACT
While a large number of studies have been reported in the literature with reference to the use of Regression model and Artificial Neural Network (ANN) models in predicting stock prices in western countries, the Chinese stock market is much less studied. Note that the latter is growing rapidly, will overtake USA one in 20 - 30 years time and thus be-comes a very important place for investors worldwide. In this paper, an attempt is made at predicting the Shanghai Composite Index returns and price volatility, on a daily and weekly basis. In the paper, two different types of prediction models, namely the Regression and Neural Network models are used for the prediction task and multiple technical indicators are included in the models as inputs. The performances of the two models are compared and evaluated in terms of di- rectional accuracy. Their performances are also rigorously compared in terms of economic criteria like annualized return rate (ARR) from simulated trading. In this paper, both trading with and without short selling has been consid- ered, and the results show in most cases, trading with short selling leads to higher profits. Also, both the cases with and without commission costs are discussed to show the effects of commission costs when the trading systems are in actual use.

Cite this paper
nullQ. Qin, Q. Wang, S. Ge and G. Ramakrishnan, "Chinese Stock Price and Volatility Predictions with Multiple Technical Indicators," Journal of Intelligent Learning Systems and Applications, Vol. 3 No. 4, 2011, pp. 209-219. doi: 10.4236/jilsa.2011.34024.
References
[1]   B. G. Malkiel, “A Random Walk Down Wall Street,” W. W. Norton & Company, New York and London, 1999.

[2]   M. H. Pesaran and A. Timmermann, “Forecasting Stock Returns: An Examination of Stock Market Trading in the Presence of Transaction Costs,” Journal of Forecasting, Vol. 13, No. 4, 1994, pp. 335-367. doi:10.1002/for.3980130402

[3]   M. T. Mitchell, “Machine Learning,” The McGraw-Hill Companies, New York, 1997.

[4]   M. H. Eng and Q.-G. Wang, “Modeling of Stock Markets with Mean Reversion,” The 6th IEEE International Conference on Control and Automation (IEEE ICCA 2007), Guangzhou, 30 May-1 June 2007, pp. 2615-2618.

[5]   J.-H. Wang and J.-Y. Leu, “Stock Market Trend Prediction Using ARIMA-Based Neural Networks,” The 1996 IEEE International Conference on Neural Networks, Washington DC, 3-6 June 1996, pp. 2160-2165.

[6]   W. Wang, D. Okunbor and F. C. Lin, “Future Trend of the Shanghai Stock Market,” ICONIP '02: Proceedings of the 9th International Conference on Neural Information Processing, Singapore, 18-22 November, pp. 2320-2324.

[7]   L.-X. Liu and J.-H Ma, “Multivariate Nonlinear Prediction of Shenzhen Stock Price,” The 3rd International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM 2007), Shanghai, 21-23 September 2007, pp. 4120-4123.

[8]   S.-H. Chen, C.-Q Tao and W. He, “A New Algorithm of Neural Network and Prediction in China Stock Market,” Pacific-Asia Conference on Circuits, Communications and Systems, PACCS 2009, Chengdu, 16-17 May 2009, pp. 686-689.

[9]   X. Xiong, X,-T, Zhang, W, Zhang and C,-Y, Li, “Wavelet-Based Beta Estimation of China Stock Market,” Proceedings of 2005 International Conference on Machine Learning and Cybernetics, Guangzhou, 18-21 August 2005, pp. 3501-3505.

[10]   W.-R. Pan, “Empirical Analysis of Stock Returns Volatility in China Market Based on Shanghai and Shenzhen 300 Index,” 2010 International Conference on Financial Theory and Engineering (ICFTE), Dubai, 18-20 June 2010, pp. 17-21.

[11]   X.-M. Song and H.-X. Pan, “Analysis of China Stock Market: Volatility and Influencing Factors,” 2010 International Conference on Management and Service Science (MASS), Wuhan, 24-26 August 2010, pp. 1-5. doi:10.1109/ICMSS.2010.5578224

[12]   S. Haykin, “Neural Networks: A Comprehensive Foundation”, Prentice-Hall, Saddle River, 1999.

[13]   Http://www.stockstar.com/

 
 
Top