# Trading the Twitter Sentiment with Reinforcement Learning

arXiv: https://arxiv.org/abs/1801.02243 · HTML: https://mlsanity.com/paper/1801.02243/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 2018.

## Built on by (most cited first, 3 of 3)

- [A Novel Deep Reinforcement Learning Based Stock Direction Prediction using Knowledge Graph and Community Aware Sentiments](https://mlsanity.com/paper/2107.00931/) (2021)
- [Meta-Learning Reinforcement Learning for Crypto-Return Prediction](https://mlsanity.com/paper/2509.09751/) (2025)
- [When Missing Becomes Structure: Intent-Preserving Policy Completion from Financial KOL Discourse](https://mlsanity.com/paper/2604.14333/) (2026)

## Abstract (arXiv)

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.