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Forecasting high-frequency excess stock returns via data analytics and machine learning
Authors:Erdinc Akyildirim  Duc Khuong Nguyen  Ahmet Sensoy  Mario Šikić
Institution:1. Department of Banking and Finance, Mehmet Akif Ersoy University, Burdur, Turkey

Department of Banking and Finance, University of Zurich, Zurich, Switzerland;2. IPAG Lab, IPAG Business School, Paris, France;3. Faculty of Business Administration, Bilkent University, Ankara, Turkey;4. Department of Banking and Finance, University of Zurich, Zurich, Switzerland

Abstract:Borsa Istanbul introduced data analytics to present additional information about its market conditions. We examine whether this product can be utilized via various machine learning methods to predict intraday excess returns. Accordingly, these analytics provide significant prediction ratios above 50% with ideal profit ratios that can reach up to 33%. Among all the methods considered, XGBoost (logistic regression) performs better in predicting excess returns in the long-term analysis (short-term analysis). Results provide evidence for the benefits of both the analytics and the machine learning methods and raise further discussion on the semistrong market efficiency.
Keywords:big data  data analytics  efficient market hypothesis  forecasting  machine learning
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