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.

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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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