Exploiting social media with higher-order Factorization Machines: statistical arbitrage on high-frequency data of the S&P 500
Knoll J, Stübinger J, Grottke M (2019)
Publication Type: Journal article
Publication year: 2019
Journal
Book Volume: 19
Pages Range: 571-585
Journal Issue: 4
DOI: 10.1080/14697688.2018.1521002
Abstract
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 this paper, we apply 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 a novel adaptive-order algorithm for automatically determining them. Our study is the first one to make use of social media data for predicting minute-by-minute 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 the adaptive-order algorithm outperforms classical approaches with respect to a multitude of criteria, and it features very favorable characteristics.
Authors with CRIS profile
Johannes Stübinger
Lehrstuhl für Statistik und Ökonometrie
Michael Grottke
Lehrstuhl für Statistik und Ökonometrie
Related research project(s)
Involved external institutions
Germany (DE)How to cite
APA:
Knoll, J., Stübinger, J., & Grottke, M. (2019). Exploiting social media with higher-order Factorization Machines: statistical arbitrage on high-frequency data of the S&P 500. Quantitative Finance, 19(4), 571-585. https://doi.org/10.1080/14697688.2018.1521002
MLA:
Knoll, Julian, Johannes Stübinger, and Michael Grottke. "Exploiting social media with higher-order Factorization Machines: statistical arbitrage on high-frequency data of the S&P 500." Quantitative Finance 19.4 (2019): 571-585.
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