NEWS-DRIVEN STOCK PREDICTION USING NOISY EQUITY STATE REPRESENTATION

Abstract

News-driven stock prediction investigates the correlation between news events and stock price movements. Previous work has considered effective ways for representing news events and their sequences, but rarely exploited the representation of underlying equity states. We address this issue by making use of a recurrent neural network to represent an equity state transition sequence, integrating news representation using contextualized embeddings as inputs to the state transition mechanism. Thanks to the separation of news and equity representations, our model can accommodate additional input factors. We design a novel random noise factor for modeling influencing factors beyond news events, and a future event factor to address the delay of news information (e.g., insider trading). Results show that the proposed model outperforms strong baselines in the literature.

1. INTRODUCTION

Stock movement prediction (Ding et al., 2014; 2015) is a central task in computational quantitative finance. With recent advances in deep learning and natural language processing (NLP), event-driven stock prediction has received increasing research attention (Xie et al., 2013; Ding et al., 2015) . The goal is to predict the movement of stock prices according to financial news. Previous work adopts a relatively simple model on the stock movement process, casting price change as a response to a set of news. The prediction model can therefore be viewed as variation of a classifier that takes news as input and yields a movement direction output. Investigations have focused on news representation, where bag-of-words (Kogan et al., 2009) , named entities (Schumaker & Chen, 2009), event structures (Ding et al., 2014) or neural representation features (Ding et al., 2015; Xu & Cohen, 2018) are considered. Intuitively, news events carry information on important changes of company management, market, revenue and other factors, which can affect the fundamental values of equities, and thereby can consequently impact the stock price, as shown in Figure 1 . Properly representing news events is key to modeling such impact on the market. However, the stock market movement can also be influenced by accumulated effects of fundamental changes over time, the overall market sentiment, and other volatile factors, which can be considered as noise to analytical models. These factors have been relatively less modeled by existing work on event-driven stock prediction. For example, although there has been work modeling long-term event impacts by representing event sequences (Ding et al., 2015) , little work has considered representing fundamental values directly. To address these issues, we consider representing the equity state directly using a recurrent neural network over time and propose the stock movement prediction network using Noisy Equity State representation (NES). At each time step, the equity state reflects the current stock price trend, and can be used directly for predicting the next movement. The advantage of separating news representation from equity state representation is that factors beyond news can be modeled as additional input in the recurrent state transition process. Although such factors can be calculated using external tools such as sentiment classification over tweet data, we simply treat them as a random noise factor. The reason is two-fold. First, in practice, noise is inevitable in stock prediction and no single mathematical model can perfectly fit the stock price movement distribution. Second, for fair comparison with existing work on news-driven stock prediction, no additional input should be used on top of standard benchmark input settings.

