EconPapers    
Economics at your fingertips  
 

News-based trading strategies

Stefan Feuerriegel and Helmut Prendinger

Papers from arXiv.org

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.

New Economics Papers: this item is included in nep-mst
Date: 2018-07
References: View references in EconPapers View complete reference list from CitEc
Citations Track citations by RSS feed

Published in Feuerriegel, Stefan, and Helmut Prendinger. "News-based trading strategies." Decision Support Systems 90 (2016): 65-74

Downloads: (external link)
http://arxiv.org/pdf/1807.06824 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1807.06824

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().

 
Page updated 2018-09-15
Handle: RePEc:arx:papers:1807.06824