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

Julian Knoll, Johannes Stübinger and Michael Grottke

Quantitative Finance, 2019, vol. 19, issue 4, 571-585

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.

Date: 2019
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Handle: RePEc:taf:quantf:v:19:y:2019:i:4:p:571-585