本文通过采用投资者情绪指数公式,获取了相关的投资者情绪数据,并结合时间序列分析方法,进一步深入探讨了投资者情绪对股市的影响。首先对所获得的情绪数据与股价数据进行了ADF检验,以确保数据的平稳性,为后续的分析提供可靠的基础。在此基础上,构建了向量自回归模型,以探索投资者情绪与中证500指数收盘价之间的相互关系。为了进一步探讨两者之间的因果关系,使用了Granger因果检验。通过该检验判断投资者情绪是否在统计意义上能够预测股市的走势。最后还采用了脉冲响应分析和方差分解分析,通过这两种方法可以追踪和量化投资者情绪对中证500指数收盘价的冲击效应及其在短期和长期内的逐步变化。In this paper, the relevant investor sentiment data are obtained by using the investor sentiment index formula, and the influence of investor sentiment on the stock market is further discussed by combining the time series analysis method. Firstly, ADF test is carried out on the obtained emotional data and stock price data to ensure the stability of the data and provide a reliable basis for subsequent analysis. On this basis, a vector autoregressive model is constructed to explore the relationship between investor sentiment and the closing price of the CSI 500 index. In order to further explore the causal relationship between the two, the Granger causality test is used. Through this test, it is judged whether investor sentiment can predict the trend of the stock market in a statistical sense. Finally, impulse response analysis and variance decomposition analysis are used to track and quantify the impact of investor sentiment on the closing price of the CSI 500 Index and its gradual changes in the short and long term.
股票市场快速发展,股票价格波动性研究备受关注,准确预测股价走势对投资者决策和市场稳定意义重大。鉴于股票价格波动的不确定性与非线性特征,单一模型预测效果欠佳。为此,本文提出将GARCH与BP神经网络相结合的组合预测方法,以中国农业银行股票日收盘价数据为例,基于误差修正思想构建组合模型,运用BP神经网络对GARCH模型的残差数据进行预测校正。研究结果表明组合模型预测效果优于单一模型,验证了该组合模型在提高股票价格预测准确度方面的有效性。With the rapid development of the stock market, the study of stock price volatility has attracted much attention, and accurate prediction of stock price movements is of great significance to investors’ decision-making and market stability. In view of the uncertainty and nonlinear characteristics of stock price volatility, the prediction effect of a single model is not good. For this reason, this paper proposes a combined prediction method combining GARCH and BP neural network, taking the daily closing price data of Agricultural Bank of China as an example, constructing a combined model based on the idea of error correction, and utilizing BP neural network to correct the residual data of the GARCH model for prediction. The results show that the combination model predicts better than a single model, which verifies the effectiveness of the combination model in improving the accuracy of stock price prediction.
