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

Journal article


Publication Details

Author(s): Knoll J, Stübinger J, Grottke M
Journal: Quantitative Finance
Publication year: 2019
Volume: 19
Journal issue: 4
Pages range: 571-585
ISSN: 1469-7688


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.


FAU Authors / FAU Editors

Grottke, Michael Prof. Dr.
Lehrstuhl für Statistik und Ökonometrie
Stübinger, Johannes Dr.
Lehrstuhl für Statistik und Ökonometrie


External institutions with authors

Technische Hochschule Nürnberg "Georg Simon Ohm"


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://dx.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.

BibTeX: 

Last updated on 2019-16-04 at 01:08