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| Type | Journal Article |
| Scope | Discipline-based scholarship |
| Title | Big data analytics, order imbalance and the predictability of stock returns |
| 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 | Journal of Multinational Financial Management |
| Publisher | Elsevier |
| Geographical Reach | international |
| ISSN | 1042-444X |
| Volume | 62 |
| Page Range | 100717 |
| Date | 2021 |
| Abstract Text | Financial institutions have adopted big data to a considerable extent to provide better investment decisions. Consequently, high-frequency algorithmic traders use a vast amount of historical data with various statistical models to maximize their trading profits. Until recently, high-frequency algorithmic trading was the domain of institutional traders with access to supercomputers. Nowadays, any investor can potentially make high-frequency trades because of easy access to big data and software to analyze and execute trades. With that in mind, Borsa Istanbul introduced real time big data analytics as a product to its customers. These analytics are derived in real time from order book and trade data and aim to level the playing field between investment firms and retail traders. Using classical benchmark models in the literature, we show that Borsa Istanbul’s order imbalance-based data analytics are useful in predicting both time-series and cross-sectional intraday excess future returns, proving that this product is extremely beneficial to market participants, particularly day traders. |
| Official URL | https://doi.org/10.1016/j.mulfin.2021.100717 |
| Digital Object Identifier | 10.1016/j.mulfin.2021.100717 |
| Other Identification Number | merlin-id:21946 |
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