中国科学院大学学报2025，Vol.42Issue(3)：371-381,11.DOI:10.7523/j.ucas.2023.069
基于DTW-SACP-LSTM模型的个股新闻信息挖掘及价格预测
News information mining and price prediction of individual stock based on DTW-SACP-LSTM model
摘要
Abstract
Aiming at the rapid changes and complex relations in the stock market,this paper proposes a stock price prediction method based on individual stock news.First,through dynamic time warping algorithm,the benchmark sequence with the highest similarity to the target individual stock sequence is found,and then we can extract the length and time of news impact through smooth-and-abrupt change point model,which is converted into time series data.We introduce the relationship between stocks into time series forecasting through statistical models,examine the relationship between news influence and historical stock price data,and combine news influence with individual stock data for price forecasting by using long-and-short-term memory network.The results show that the stock sector's influence of news in the technology sector is the most obvious.Compared to existing stock prediction methods,the prediction performance of the fusion model has been improved,and the prediction accuracy has decreased slightly over time.The fusion model can more accurately describe the changes in stock prices,achieving an average return of 14.50％under the conditions of simulating investment strategies.关键词
金融新闻/股票预测/动态时间规整(DTW)/平滑突变点(SACP)/长短期记忆网络(LSTM)Key words
financial news/stock forecast/dynamic time warping(DTW)/smooth-and-abrupt change point(SACP)/long-and-short-term memory network(LSTM)分类
经济学引用本文复制引用
王子平,金百锁..基于DTW-SACP-LSTM模型的个股新闻信息挖掘及价格预测[J].中国科学院大学学报,2025,42(3):371-381,11.基金项目
国家自然科学基金(7201101228)资助 （7201101228）
