Forecasting high‐frequency excess stock returns via data analytics and machine learning. (7th December 2021)

• Record Type: Journal Article
• Authors: Akyildirim, Erdinc; Nguyen, Duc Khuong; Sensoy, Ahmet; Šikić, Mario
• Is Part Of: European financial management. Volume 29:Number 1(2023)
• Page Start: 22
• Page End: 75
• Publication Date: 2021-12-07
• Subjects: big data -- data analytics -- efficient market hypothesis -- forecasting -- machine learning; Finance -- European Union countries -- Periodicals; International finance -- Periodicals; Corporations -- European Union countries -- Finance -- Periodicals; 332.094
• DOI: 10.1111/eufm.12345
• Languages: English
• ISSNs: 1354-7798

Abstract

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.