Abstract: We consider direct modeling of underlying stock value movement sequences over time in the news-driven stock movement prediction. A recurrent state transition model is constructed, which better captures a gradual process of stock movement continuously by modeling the correlation between past and future price movements. By separating the effects of news and noise, a noisy random factor is also explicitly fitted based on the recurrent states. Results show that the proposed model outperforms strong baselines. Thanks to the use of attention over news events, our model is also more explainable. To our knowledge, we are the first to explicitly model both events and noise over a fundamental stock value state for news-driven stock movement prediction.

Introduction: Stock movement prediction is a central task in computational and quantitative finance. With recent advances in deep learning and natural language processing technology, event-driven stock prediction has received increasing research attention BIBREF0, BIBREF1. The goal is to predict the movement of stock prices according to financial news. Existing work has investigated news representation using bag-of-words BIBREF2, named entities BIBREF3, event structures BIBREF4 or deep learning BIBREF1, BIBREF5. Most previous work focuses on enhancing news representations, while adopting a relatively simple model on the stock movement process, casting it as a simple response to a set of historical news. The prediction model can therefore be viewed as variations of a classifier that takes news as input and yields stock movement predictions. In contrast, work on time-series based stock prediction BIBREF6, BIBREF7, BIBREF5, BIBREF8, aims to capture continuous movements of prices themselves. We aim to introduce underlying price movement trends into news-driven stock movement prediction by casting the underlaying stock value as a recurrent state, integrating the influence of news events and random noise simultaneously into the recurrent state transitions. In particular, we take a LSTM with peephole connections BIBREF9 for modeling a stock value state over time, which can reflect the fundamentals of a stock. The influence of news over a time window is captured in each recurrent state transition by using neural attention to aggregate representations of individual news. In addition, all other factors to the stock price are modeled using a random factor component, so that sentiments, expectations and noise can be dealt with explicitly. Compared with existing work, our method has three salient advantages. First, the process in which the influence of news events are absorbed into stock price changes is explicitly modeled. Though previous work has attempted towards this goal BIBREF1, existing models predict each stock movement independently, only modeling the correlation between news in historical news sequences. As shown in Figure FIGREF1, our method can better capture a continuous process of stock movement by modeling the correlation between past and future stock values directly. In addition, non-linear compositional effects of multiple events in a time window can be captured. Second, to our knowledge, our method allows noise to be explicitly addressed in a model, therefore separating the effects of news and other factors. In contrast, existing work trains a stock prediction model by fitting stock movements to events, and therefore can suffer from overfitting due to external factors and noise. Third, our model is also more explainable thanks to the use of attention over news events, which is similar to the work of BIBREF10 and BIBREF11. Due to the use of recurrent states, we can visualize past events over a large time window. In addition, we propose a novel future event prediction module to factor in likely next events according to natural events consequences. The future event module is trained over gold “future” data over historical events. Therefore, it can also deal with insider trading factors to some extent. Experiments over the benchmark of BIBREF1 show that our method outperforms strong baselines, giving the best reported results in the literature. To our knowledge, we are the first to explicitly model both events and noise over a fundamental stock value state for news-driven stock movement prediction. Note that unlike time-series stock prediction models BIBREF12, BIBREF5, we do not take explicit historical prices as part of model inputs, and therefore our research still focuses on the influence of news information alone, and are directly comparable to existing work on news-driven stock prediction.

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