Analysis of financial market trend based on autoregressive conditional heteroscedastic model and BP neural network prediction
Analysis of financial market trend based on autoregressive conditional heteroscedastic model and BP neural network prediction
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DOI：
10.3233/jifs-189060
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
Xin Zhang
中科院分区：
文献类型：
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作者：
Xin Zhang
将股票价格等高频数据汇总为低频月度数据进行建模。但求和法只适用于流形式的高频数据，且求和法减少了样本量。基于此，本文采用混合模型构建财务状况指数，可以对不同频率的数据进行建模，在一定程度上弥补了同频率数据建模的缺陷。并且，基于主成分分析和文本挖掘技术，构建了两类情绪指数，研究了两类情绪指数对股市收盘价的影响和预测。另外，在实证分析中，本文建立了GARCH模型和BP神经网络预测模型，并对收盘价进行了预测。最后，本文比较了预测模型和情绪指数的优缺点。研究表明，以Web文本情绪指数的滞后变量作为输入层变量建立的BP神经网络模型更加可靠，可以广泛应用于股票市场。
High-frequency data such as stock prices are aggregated into low-frequency monthly data for modeling. However, the summation method only applies to high frequency data in the form of flow, and the summation method reduces the sample size. Based on this, this paper uses the mixing model to construct the financial status index, which can model the data of different frequencies and compensate for the defects of the same frequency data modeling to some extent. Moreover, based on principal component analysis and text mining technology, this paper constructs two kinds of sentiment indexes, and studies the influence and prediction of two sentiment indexes on the closing price of stock market. In addition, in the empirical analysis, this paper establishes the GARCH model and BP neural network prediction model and predicts the closing price. Finally, this paper compares the pros and cons of predictive models and sentiment indices. The research shows that the BP neural network model established by using the lag variable of the Web text sentiment index as the input layer variable is more reliable and can be widely used in the stock market.