# News-based trading strategies

arXiv: https://arxiv.org/abs/1807.06824 · HTML: https://mlsanity.com/paper/1807.06824/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2018.

## Built on by (most cited first, 5 of 5)

- [The Evolution of Reinforcement Learning in Quantitative Finance: A Survey](https://mlsanity.com/paper/2408.10932/) (2024)
- [Intelligent Trading Systems: A Sentiment-Aware Reinforcement Learning Approach](https://mlsanity.com/paper/2112.02095/) (2021)
- [Reinforcement Learning Applied to Trading Systems: A Survey](https://mlsanity.com/paper/2212.06064/) (2022)
- [Predictive AI for SME and Large Enterprise Financial Performance Management](https://mlsanity.com/paper/2311.05840/) (2023)
- [Towards Explainable and Reliable AI in Finance](https://mlsanity.com/paper/2510.26353/) (2025)

## Abstract (arXiv)

The marvel of markets lies in the fact that dispersed information is instantaneously processed and used to adjust the price of goods, services and assets. Financial markets are particularly efficient when it comes to processing information; such information is typically embedded in textual news that is then interpreted by investors. Quite recently, researchers have started to automatically determine news sentiment in order to explain stock price movements. Interestingly, this so-called news sentiment works fairly well in explaining stock returns. In this paper, we design trading strategies that utilize textual news in order to obtain profits on the basis of novel information entering the market. We thus propose approaches for automated decision-making based on supervised and reinforcement learning. Altogether, we demonstrate how news-based data can be incorporated into an investment system.