News-based trading strategies
This work addresses the challenge of leveraging news data for investment systems, but it appears incremental as it builds on existing sentiment analysis methods.
The paper tackles the problem of designing profitable trading strategies by using textual news sentiment to explain stock returns, proposing automated decision-making approaches based on supervised and reinforcement learning.
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.