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.

Key words: financial news, stock forecast, dynamic time warping (DTW), smooth-and-abrupt change point (SACP), long-and-short-term memory network (LSTM)