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Contribution Details
| Type | Journal Article |
| Scope | Discipline-based scholarship |
| Title | Forecasting high‐frequency excess stock returns via data analytics and machine learning |
| Organization Unit | |
| Authors |
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| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Journal Title | European financial management |
| Publisher | Wiley-Blackwell Publishing, Inc. |
| Geographical Reach | international |
| ISSN | 1354-7798 |
| Volume | 29 |
| Number | 1 |
| Page Range | 22 - 75 |
| Date | 2023 |
| Abstract Text | 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. |
| Free access at | Official URL |
| Official URL | https://onlinelibrary.wiley.com/doi/10.1111/eufm.12345 |
| Digital Object Identifier | 10.1111/eufm.12345 |
| Other Identification Number | merlin-id:21948 |
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