Quantitative Day Trading from Natural Language using Reinforcement Learning

2021-06-01 · NAACL 2021 4 · Ramit Sawhney, Arnav Wadhwa, Shivam Agarwal, Rajiv Ratn Shah

It is challenging to design profitable and practical trading strategies, as stock price movements are highly stochastic, and the market is heavily influenced by chaotic data across sources like news and social media. Existing NLP approaches largely treat stock prediction as a classification or regression problem and are not optimized to make profitable investment decisions. Further, they do not model the temporal dynamics of large volumes of diversely influential text to which the market responds quickly. Building on these shortcomings, we propose a deep reinforcement learning approach that makes time-aware decisions to trade stocks while optimizing profit using textual data. Our method outperforms state-of-the-art in terms of risk-adjusted returns in trading simulations on two benchmarks: Tweets (English) and financial news (Chinese) pertaining to two major indexes and four global stock markets. Through extensive experiments and studies, we build the case for our method as a tool for quantitative trading.

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Deep Reinforcement Learningreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)Stock Prediction

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