Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/162392
Authors: 
Knoll, Julian
Stübinger, Johannes
Grottke, Michael
Year of Publication: 
2017
Series/Report no.: 
FAU Discussion Papers in Economics 13/2017
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 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.
Subjects: 
finance
factorization machine
social media
statistical arbitrage
high-frequency data
Document Type: 
Working Paper

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