Author(s)

Qin Qin, Qing-Guo Wang, Shuzhi Sam Ge, Ganesh Ramakrishnan

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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.

Regression Model, Artificial Neural Network Model, Chinese Stock Market, Technical Indicators, Volatility

Q. 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.