基于双流LSTM神经网络的股价趋势预测

Stock Price Trend Prediction Based on Dual-Stream LSTM Neural Network

股价波动研究依赖分析金融新闻数据集浅层特征,而忽略了金融新闻句子中单词之间的结构关系,从而导致股价波动预测研究效果不佳。针对该问题,提出了一种基于双流长短时记忆网络(long short term memory network,LSTM)神经网络的股价趋势预测模型(Sent2Vec-DLSTM)。该模型的创新之处在于:提出了基于金融股票新闻数据集和哈佛IV-4情绪词典训练的情感词向量生成模型——Sent2Vec;提出了新型的双流LSTM神经网络(Dual-stream LSTM,DLSTM)。在实验中,首先用标普500指数历史数据以及爬取获得的金融类文章进行标普500指数的趋势预测,然后用VietStock新闻和来自Cophieu68的股票价格数据预测VN指数的变化趋势。结果表明,Sent2Vec-DLSTM相较于现有模型在股价趋势预测中具有更好的效果。

Previous research on stock price volatility prediction relies on analyzing shallow features of financial news datasets and ignores the structural relationship between words in financial news,resulting in poor prediction performance.Aiming at this problem,we propose a stock price trend prediction model(Sent2Vec-DLSTM)based on a dual-stream long short-term memory network(LSTM)neural network.A vector generation model of emotional words called Sent2Vec is first proposed based on financial stock news data set and Harvard IV-4 emotion dictionary training,which is then combined with dual-stream LSTM neural network(DLSTM).In the experiment,the historical data of the S&P 500 index and the financial articles obtained by crawling are used to predict the trend of the S&P 500 index.the VietStock news and stock price data from cophieu68 are then used to predict the trend of the VN index.The results show that Sent2Vec-DLSTM outperforms existing models in stock price trend prediction.