基于多类别特征体系的股票短期趋势预测

Short-term Trend Forecasting of Stocks Based on Multi-category Feature System

随着经济和科技的快速发展,股市已成为当前金融市场的重要组成部分。传统机器学习方法在处理非线性、高噪声、波动性强的股票时序预测问题时存在局限性,而近年来深度神经网络的兴起,给股票趋势预测问题提供了新的解决方案。采用长短期记忆网络(Long Short-Term Memory,LSTM)来处理长距离的股票时序问题,构建了一个多类别特征体系作为长短期记忆网络的输入进行训练,包括常用技术指标、多种关键转折点特征和个股真实事件信息等。同时,通过实验全面分析了各类特征对股票趋势预测的有效程度,对比结果表明了多类别特征体系在预测中的良好表现,其能够达到68.77%的短期涨跌预测准确率。另外还将LSTM与CNN,RNN和MLP等模型进行了比较,实验结果表明LSTM在解决该时序预测问题上优于其他模型。

With the rapid development of economy and technology,the stock market has become an important part of the current financial market.Traditional machine learning methods have limitations in processing stock prediction problems with nonlinearization,high-noise or strong volatility.In recent years,the rise of deep neural networks has provided new solutions to stock trend forecasting problems.In this paper,long short-term memory network(LSTM)is used to deal with long-distance stock temporal problems,and a multi-category feature system is constructed as the input for long-term and short-term memory networks for training,including common technical indicators,multiple key features,and real event information for individual stocks.Meanwhile,the experimental part comprehensively analyzes the effectiveness of various characteristics for stock trend prediction,and the comparison results show that the multi-category characteristic system performs well in the prediction,and can reach a short-term forecast accuracy of 68.77%.In addition,LSTM is compared with other models such as convolutional neural network(CNN),recurrent neural network(RNN)and multilayer perceptron(MLP).Experimental results show that LSTM is superior to other models in solving this problem.