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.
Highlights
From the beginning of time it has been man’s common goal to make his life easier
We do the prediction of Shanghai Composite Index return and the prediction of Shanghai Composite Index volatility based on regression model and neural networks (NNs) model using the daily and weekly data of Shanghai Composite Index
For the prediction of Shanghai Composite Index return, both trading with and without short selling has been considered, and the results show in most cases, trading with short selling leads to higher profits
Summary
From the beginning of time it has been man’s common goal to make his life easier. The prevailing notion in society is that wealth brings comfort and luxury, so it is not surprising that there has been so much work done on ways to predict the markets. In Technical analysis, it is believed that market timing is keypoint It involves the study of historical data of the stock market to predict trends in price and volume. Fundamental analysis, on the other hand involves making estimates on the intrinsic value of a stock This technique uses information such as earnings, ratios, and management effectiveness to predict future outcomes. Tools that used modeling techniques to discover patterns within the historical data of the stock market were put to test, with an attempt to predict and bene-. One such example is the Linear Time Series Models, where univariate and multivariate regression models [2] were used to identify patterns in the historical data of the stock market.
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