Deep Attentive Learning for Stock Movement Prediction From Social Media Text and Company Correlations

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, pages 8415–8426, November 16–20, 2020. c⃝2020 Association for Computational Linguistics 8415 Deep Attentive Learning for Stock Movement Prediction From Social Media Text and Company Correlations Ramit Sawhney* Netaji Subhas Institute of Technology ramits.co@nsit.net.in Shivam Agarwal* Manipal Institute of Technology shivamag99@gmail.com Arnav Wadhwa MIDAS, IIIT Delhi arnavw96@gmail.com Rajiv Ratn Shah IIIT Delhi rajivratn@iiitd.ac.in Abstract In the financial domain, risk modeling and profit generation heavily rely on the sophisti- cated and intricate stock movement prediction task. Stock forecasting is complex, given the stochastic dynamics and non-stationary behav- ior of the market. Stock movements are in- fluenced by varied factors beyond the conven- tionally studied historical prices, such as social media and correlations among stocks. The ris- ing ubiquity of online content and knowledge mandates an exploration of models that factor in such multimodal signals for accurate stock forecasting. We introduce an architecture that achieves a potent blend of chaotic temporal signals from financial data, social media, and inter-stock relationships via a graph neural network in a hierarchical temporal fashion. Through experiments on real-world S&P 500 index data and English tweets, we show the practical applicability of our model as a tool for investment decision making and trading. 1 Introduction Stock prices have an intrinsically volatile and non-stationary nature, making their rise and fall hard to forecast (Adam et al., 2016). Investment in stock markets involves a high risk regarding profit-making. Prices are driven by diverse fac- tors that include but are not limited to company performance (Anthony and Ramesh, 1992), histori- cal trends (Kohara et al., 1997), investor sentiment (Neal and Wheatley, 1998). Uninformed trading decisions can leave traders and investors prone to financial risk and experience monetary losses. On the contrary, careful investment choices can maxi- mize profits (de Souza et al., 2018). Conventional research focused on time series and technical anal- ysis of a stock, i.e., using patterns from historical price signals to forecast stock movements (B et al., * Equal contribution. 2013). However, price signals alone fail to capture market surprises and impacts of sudden unexpected events. Social media texts like tweets can have huge impacts on the stock market. For instance, US President Donald Trump shared tweets express- ing negative sentiments against Lockheed Martin, which led to a loss of around $5.8 Billion to the company’s market capitalization.1 The Efficient Market Hypothesis (EMH) (Malkiel, 1989) states that financial markets are informationally efficient, such that stock prices re- flect all known information. Existing works (Sec. 2) mainly focus on subsets of stock relevant data. Although useful, they do not jointly optimize learn- ing over modalities like social media text and inter stock relations limiting their potential to capture a broader scope of stock movement affecting data, as we show in Sec. 6. Multimodal stock predic- tion involves multiple challenges (Hu et al., 2018). Both price signals and tweets exhibit sequential context dependencies, where singular samples may not be informative enough but can be considered a sequence for …

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