Forecasting high‐frequency excess stock returns via data analytics and machine learning
buir.contributor.author | Şensoy, Ahmet | |
buir.contributor.orcid | Şensoy, Ahmet|0000-0001-7967-5171 | |
dc.citation.epage | 75 | en_US |
dc.citation.issueNumber | 1 | |
dc.citation.spage | 22 | |
dc.citation.volumeNumber | 29 | |
dc.contributor.author | Akyildirim, E. | |
dc.contributor.author | Nguyen, D.K. | |
dc.contributor.author | Şensoy, Ahmet | |
dc.contributor.author | Šikić, M. | |
dc.date.accessioned | 2024-03-19T06:31:01Z | |
dc.date.available | 2024-03-19T06:31:01Z | |
dc.date.issued | 2023-01 | |
dc.department | Department of Management | |
dc.description.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. | |
dc.description.provenance | Made available in DSpace on 2024-03-19T06:31:01Z (GMT). No. of bitstreams: 1 Forecasting_high_frequency_excess_stock_returns_via_data_analytics_and_machine_learning.pdf: 3363608 bytes, checksum: 75b8d22b27d3dc38637374e374d4ffc3 (MD5) Previous issue date: 2023-01-01 | en |
dc.identifier.doi | 10.1111/eufm.12345 | |
dc.identifier.eissn | 1468-036X | |
dc.identifier.issn | 1354-7798 | |
dc.identifier.uri | https://hdl.handle.net/11693/114926 | |
dc.language.iso | en | |
dc.publisher | John Wiley and Sons Inc. | |
dc.relation.isversionof | https://dx.doi.org/10.1111/eufm.12345 | |
dc.rights | CC BY-NC-ND (Attribution-NonCommercial-NoDerivs 4.0 International) | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.source.title | European Financial Management | |
dc.subject | Big data | |
dc.subject | Data analytics | |
dc.subject | Efficient market hypothesis | |
dc.subject | Forecasting | |
dc.subject | Machine learning | |
dc.title | Forecasting high‐frequency excess stock returns via data analytics and machine learning | |
dc.type | Article |
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