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.

Introduction: In a world where traditional financial information is ubiquitous and the financial models are largely homogeneous, finding hidden information that has not been priced in from alternative data is critical. The recent development in Natural Language Processing provides such opportunities to look into text data in addition to numerical data. When the market sets the stock price, it is not uncommon that the expectation of the company growth outweighs the company fundamentals. Twitter, a online news and social network where the users post and interact with messages to express views about certain topics, contains valuable information on the public mood and sentiment. A collection of research BIBREF0 BIBREF1 have shown that there is a positive correlation between the "public mood" and the "market mood". Other research BIBREF2 also shows that significant correlation exists between the Twitter sentiment and the abnormal return during the peaks of the Twitter volume during a major event. Once a signal that has predicting power on the stock market return is constructed, a trading strategy to express the view of the signal is needed. Traditionally, the quantitative finance industry relies on backtest, a process where the trading strategies are tuned during the simulations or optimizations. Reinforcement learning provides a way to find the optimal policy by maximizing the expected future utility. There are recent attempts from the Artificial Intelligence community to apply reinforcement learning to asset allocation BIBREF3 , algorithmic trading BIBREF4 BIBREF5 , and portfolio management BIBREF6 . The contribution of this paper is two-fold: First, the predicting power of Twitter sentiment is evaluated. Our results show sentiment is more suitable to construct alpha signals rather than total return signals and shows predicting power especially when the Twitter volume is high. Second, we proposed a trading strategy based on reinforcement learning (Q-learning) that takes the sentiment features as part of its states. The paper is constructed as follows: In the second section, scraping Tweets from Twitter website and preprocessing the data are described in details. In the third section, assigning sentiment scores to the text data is discussed. In the fourth section, feature engineering and prediction based on the sentiment score is discussed. In the fifth section, how the reinforcement learning is applied to generate the optimal trading strategy is described.

Question: What are the weaknesses of their proposed interpretability quantification method?
Answer: can be biased by dataset used and may generate categories which are suboptimal compared to human designed categories