Exploiting social media with higher-order Factorization Machines: Statistical arbitrage on high-frequency data of the S&P 500

Arbeitspapier

Over the past 15 years,there have been a number of studies using text mining for predicting stock market data. Two recent publications employed support vector machines and second-order Factorization Machines, respectively, to this end. However, these approaches either completely neglect interactions between the features extracted from the text, or they only account for second-order interactions. In thispaper, weapply higher-order Factorization Machines, for which efficient training algorithms have only been available since 2016. As Factorization Machines require hyperparameters to be specified, we also introduce the novel adaptive-order algorithm for automatically determining them. Our studyis the first one tomake use of social media data for predicting high-frequency stock returns, namely the ones of the S&P 500 stock constituents. We show that, unlike a trading strategy employing support vector machines, Factorization-Machine-based strategies attain positive returns after transactions costs for the years 2014 and 2015. Especially the approach applying thea daptive-order algorithm outperforms classical approaches with respect to a multitude of criteria, and it features very favorable characteristics.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: FAU Discussion Papers in Economics ; No. 13/2017

Classification and Topics

• Classification: Wirtschaft
• Subject: finance ; factorization machine ; social media ; statistical arbitrage ; high-frequency data

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Knoll, Julian ; Stübinger, Johannes ; Grottke, Michael
• Event: Veröffentlichung
• (who): Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute for Economics
• (where): Nürnberg
• (when): 2017

Further information

• Handle: http://hdl.handle.net/10419/162392
• Last update: 10.03.2025, 11:42 AM CET