EMD-BiLSTM Stock Price Trend Forecasting Model based on Investor Sentiment

Tianyu Xu, Xiaoling He
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Abstract

The movement of stock prices is the focus of investors' attention in the stock market, so stock price trend prediction has always been a hot topic in quantitative investment research. Traditional machine learning forecasting models are difficult to process nonlinear, high-frequency, high-noise stock price time series, which makes the prediction accuracy of stock price trends low. In order to improve the prediction accuracy, according to the temporal characteristics of stock price data, it is proposed to use a combination of empirical mode decomposition (EMD), investor sentiment and two-way long short-term memory neural network to predict the rise and fall of stock prices. Firstly, the empirical mode decomposition algorithm is used to extract the characteristics of the stock price time series on different time scales, and the investor sentiment indicators of the text from the close of the previous stock trading day to the opening of the next trading day are extracted by constructing a financial sentiment dictionary, and finally the EMD-BiLSTM model is used to predict the rise and fall of the next index trading day. Experiments on the dataset of stock price series show that the optimized BiLSTM model has strong predictive ability for the trend of consumer sector indexes.
基于投资者情绪的EMD-BiLSTM股价趋势预测模型
股票价格的走势是股票市场中投资者关注的焦点,因此股票价格走势预测一直是定量投资研究中的热点问题。传统的机器学习预测模型难以处理非线性、高频、高噪声的股价时间序列,这使得对股价趋势的预测精度较低。为了提高预测精度,根据股票价格数据的时间特征,提出了将经验模式分解(EMD)、投资者情绪和双向长短期记忆神经网络相结合的方法来预测股价涨跌。首先,利用经验模态分解算法提取不同时间尺度上的股价时间序列特征,并通过构建金融情绪词典提取上一个股票交易日收盘至下一个交易日开盘文本的投资者情绪指标,最后利用EMD-BiLSTM模型预测下一个指数交易日的涨跌。在股票价格序列数据集上的实验表明,优化后的BiLSTM模型对消费板块指数的走势具有较强的预测能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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