Evaluation of news-based trading strategies

Abstract

The marvel of markets lies in the fact that dispersed information is instantaneously processed by adjusting 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 attempt to design trading strategies that are built on textual news in order to obtain higher profits than benchmark strategies achieve. Essentially, we succeed by showing evidence that a news-based trading strategy indeed outperforms our benchmarks by a 9.06-fold performance.