Stock Market Prediction with Deep Learning: A Character-based Neural Language Model for Event-based Trading

Stock Market Prediction with Deep Learning: A Character-based Neural Language Model for Event-based Trading Leonardo dos Santos Pinheiro Macquarie University Capital Markets CRC lpinheiro@cmcrc.com Mark Dras Macquarie University mark.dras@mq.edu.au Abstract In the last few years, machine learning has become a very popular tool for an- alyzing financial text data, with many promising results in stock price fore- casting from financial news, a devel- opment with implications for the Effi- cient Markets Hypothesis (EMH) that underpins much economic theory. In this work, we explore recurrent neu- ral networks with character-level lan- guage model pre-training for both in- traday and interday stock market fore- casting. In terms of predicting di- rectional changes in the Standard & Poor’s 500 index, both for individual companies and the overall index, we show that this technique is competitive with other state-of-the-art approaches. 1 Introduction Predicting stock market behavior is an area of strong appeal for both academic researchers and industry practitioners alike, as it is both a challenging task and could lead to increased profits. Predicting stock market behavior from the arrival of new information is an even more interesting area, as economists frequently test it to challenge the Efficient Market Hypothe- sis (EMH) (Malkiel, 2003): a strict form of the EMH holds that any news is incorporated into prices without delay, while other interpreta- tions hold that incorporation takes place over time. In practice, the analysis of text data such as news announcements and commentary on events is one major source of market infor- mation and is widely used and analyzed by investors (Oberlechner and Hocking, 2004). Financial news conveys novel information to broad market participants and a fast reaction to the release of new information is an impor- tant component of trading strategies (Leinwe- ber and Sisk, 2011). But despite the great interest, attempts to forecast stock prices from unstructured text data have had limited success and there seems to be much room for improvement. This can be in great part attributed to the difficulty in- volved in extracting the relevant information from the text. So far most approaches to ana- lyzing financial text data are based on bag-of- words, noun phrase and/or named entity fea- ture extraction combined with manual feature selection, but the capacity of these methods to extract meaningful information from the data is limited as much information about the structure of text is lost in the process. In recent years, the trend for extracting fea- tures from text data has shifted away from manual feature engineering and there has been a resurgence of interest in neural networks due to their power for learning useful representa- tions directly from data (Bengio et al., 2013). Even though deep learning has had great suc- cess in learning representations from text data (e.g. Mikolov et al. (2013a), Mikolov et al. (2013b) and Kiros et al. (2015)), successful ap- plications of deep learning in textual analysis of financial news have been few, even though it has been demonstrated that its application to event-driven stock prediction …

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