Intelligent Trading Systems: A Sentiment-Aware Reinforcement Learning Approach

Author(s)

Lima Paiva, Francisco Caio ; Felizardo, Leonardo Kanashiro ; da Costa Bianchi, Reinaldo Augusto ; Reali Costa, Anna Helena ; ACM

• Document type: Journal article
• Source: ICAIF 2021: THE SECOND ACM INTERNATIONAL CONFERENCE ON AI IN FINANCE; v. N/A, p. 9-pg., 2021-01-01.

Abstract

The feasibility of making profitable trades on a single asset on stock exchanges based on patterns identification has long attracted researchers. Reinforcement Learning (RL) and Natural Language Processing have gained notoriety in these single-asset trading tasks, but only a few works have explored their combination. Moreover, some issues are still not addressed, such as extracting market sentiment momentum through the explicit capture of sentiment features that reflect the market condition over time and assessing the consistency and stability of RL results in different situations. Filling this gap, we propose the Sentiment-Aware RL (SentARL) intelligent trading system that improves profit stability by leveraging market mood through an adaptive amount of past sentiment features drawn from textual news. We evaluated SentARL across twenty assets, two transaction costs, and five different periods and initializations to show its consistent effectiveness against baselines. Subsequently, this thorough assessment allowed us to identify the boundary between news coverage and market sentiment regarding the correlation of price-time series above which SentARL's effectiveness is outstanding. (AU)

• FAPESP's process: 19/07665-4 - Center for Artificial Intelligence
• Grantee: Fabio Gagliardi Cozman
• Support Opportunities: Research Grants - Research Program in eScience and Data Science - Research Centers in Engineering Program