Forecasting high‐frequency excess stock returns via data analytics and machine learning

Date
2023-01
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Source Title
European Financial Management
Print ISSN
1354-7798
Electronic ISSN
1468-036X
Publisher
John Wiley and Sons Inc.
Volume
29
Issue
1
Pages
22 - 75
Language
en
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Abstract

Borsa Istanbul introduced data analytics to present additional information about its market conditions. We examine whether this product can be utilized via var ious 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.

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Published Version (Please cite this version)