News-based trading strategies

TL;DR

This paper explores how automated trading strategies can leverage news sentiment analysis and machine learning techniques to capitalize on new information entering financial markets, demonstrating the integration of textual data into investment decisions.

Contribution

It introduces novel news-based trading strategies using supervised and reinforcement learning to improve profit generation from market information.

Findings

News sentiment effectively explains stock returns

Automated strategies can profit from news-driven information

Integration of textual data enhances investment decision-making

Abstract

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.