Title: Trading the Twitter Sentiment with Reinforcement Learning,
Abstract: This paper is to explore the possibility to use alternative data and
artificial intelligence techniques to trade stocks. The efficacy of the daily
Twitter sentiment on predicting the stock return is examined using machine
learning methods. Reinforcement learning(Q-learning) is applied to generate the
optimal trading policy based on the sentiment signal. The predicting power of
the sentiment signal is more significant if the stock price is driven by the
expectation of the company growth and when the company has a major event that
draws the public attention. The optimal trading strategy based on reinforcement
learning outperforms the trading strategy based on the machine learning
prediction.