Using Structured Events to Predict Stock Price Movement: An Empirical Investigation

Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1415–1425, October 25-29, 2014, Doha, Qatar. c⃝2014 Association for Computational Linguistics Using Structured Events to Predict Stock Price Movement: An Empirical Investigation Xiao Ding†∗, Yue Zhang‡, Ting Liu†, Junwen Duan† †Research Center for Social Computing and Information Retrieval Harbin Institute of Technology, China {xding, tliu, jwduan}@ir.hit.edu.cn ‡Singapore University of Technology and Design yue zhang@sutd.edu.sg Abstract It has been shown that news events influ- ence the trends of stock price movements. However, previous work on news-driven stock market prediction rely on shallow features (such as bags-of-words, named entities and noun phrases), which do not capture structured entity-relation informa- tion, and hence cannot represent complete and exact events. Recent advances in Open Information Extraction (Open IE) techniques enable the extraction of struc- tured events from web-scale data. We propose to adapt Open IE technology for event-based stock price movement pre- diction, extracting structured events from large-scale public news without manual efforts. Both linear and nonlinear mod- els are employed to empirically investigate the hidden and complex relationships be- tween events and the stock market. Large- scale experiments show that the accuracy of S&P 500 index prediction is 60%, and that of individual stock prediction can be over 70%. Our event-based system out- performs bags-of-words-based baselines, and previously reported systems trained on S&P 500 stock historical data. 1 Introduction Predicting stock price movements is of clear in- terest to investors, public companies and govern- ments. There has been a debate on whether the market can be predicted. The Random Walk The- ory (Malkiel, 1973) hypothesizes that prices are determined randomly and hence it is impossible to outperform the market. However, with advances of AI, it has been shown empirically that stock ∗This work was done while the first author was visiting Singapore University of Technology and Design Figure 1: Example news for Apple Inc. and Google Inc. price movement is predictable (Bondt and Thaler, 1985; Jegadeesh, 1990; Lo and MacKinlay, 1990; Jegadeesh and Titman, 1993). Recent work (Das and Chen, 2007; Tetlock, 2007; Tetlock et al., 2008; Si et al., 2013; Xie et al., 2013; Wang and Hua, 2014) has applied Natural Language Process- ing (NLP) techniques to help analyze the effect of web texts on stock market prediction, finding that events reported in financial news are important ev- idence to stock price movement prediction. As news events affect human decisions and the volatility of stock prices is influenced by human trading, it is reasonable to say that events can influ- ence the stock market. Figure 1 shows two pieces of financial news about Apple Inc. and Google Inc., respectively. Shares of Apple Inc. fell as trad- ing began in New York on Thursday morning, the day after its former CEO Steve Jobs passed away. Google’s stock fell after grim earnings came out. Accurate extraction of events from financial news may play an important role in stock market pre- diction. However, previous work represents news documents mainly using simple …

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