线性神经网络模型在新上证综指的预测

利用线性神经网络模型对"新上证综指(000017)"进行拟合预测,选取从"新上证综指"开始发行月份(2006年1月)开始到2011年6月的月度数据,共计66个,用前62个做训练组,最后4个数据做预测组,通过比较不同滞后窗口模型的误差平方和,选择适当的窗口数为最优模型,为了提高模拟的效果,对模型的初始数据进行优化,然后进行预测分析;结果显示,拟合效果很好,除6月份股市波动稍大,其他月份拟合误差不到3%,阐释了股票市场的短期可预测性。

Liner neural network model prediction in the new shanghai Composite Index

By using the model of neural network, this paper selects monthly data of New shanghai Composite Index from beginning of its publication (January 2006) to June 2011,a total of 66. Then it divides data into two groups, previous 62 are training group, the rest 4 are prediction group. By comparing error sum of squares in different models of the time lag window, it selects the appropriate one. In order to improve the simulation, this paper optimizes the initial data before prediction. It shows a good fitting result that the other fitting error less than 3% except the data in June with the reason of larger fluctuation in the stock market that time, and explain the stock market''s predictability in short-term.