基于双流LSTM神经网络的股价趋势预测

doi:10.3969/j.issn.0255-8297.2023.02.013

Abstract

Abstract: 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.

Key words: financial news, dual-stream long short-term memory (LSTM) network, emotional word embedding, stock price trend prediction, sentiment analysis

CLC Number:

TP301

WU Feng, XIE Cong, JI Shaopei. Stock Price Trend Prediction Based on Dual-Stream LSTM Neural Network[J]. Journal of Applied Sciences, 2023, 41(2): 344-358.

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

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