Stock Volatility Prediction by Hybrid Neural Network

Stock price volatility forecasting is a hot topic in time series prediction research, which plays an important role in reducing investment risk. However, the trend of stock price not only depends on its historical trend, but also on its related social factors. This paper proposes a hybrid time-series predictive neural network (HTPNN) that combines the effection of news. The features of news headlines are expressed as distributed word vectors which are dimensionally reduced to optimize the efficiency of the model by sparse automatic encoders. Then, according to the timeliness of stocks, the daily K-line data is combined with the news. HTPNN captures the potential law of stock price fluctuation by learning the fusion feature of news and time series, which not only retains the effective information of news and stock data, but also eliminates the redundant information of the text. Compared with the state-of-the-art methods, our method combines more abundant stock characteristics and has more advantages in running speed. Besides, the accuracy is averagely improved by nearly 5%.