Publication: Big data analytics, order imbalance and the predictability of stock returns
Big data analytics, order imbalance and the predictability of stock returns
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Akyildirim, E., Sensoy, A., Gulay, G., Corbet, S., & Salari, H. N. (2021). Big data analytics, order imbalance and the predictability of stock returns. Journal of Multinational Financial Management, 62, 100717. https://doi.org/10.1016/j.mulfin.2021.100717
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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
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- Akyildirim, Erdinc
- Sensoy, Ahmet
- Gulay, Guzhan
- Corbet, Shaen
- Salari, Hajar Novin
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Akyildirim, E., Sensoy, A., Gulay, G., Corbet, S., & Salari, H. N. (2021). Big data analytics, order imbalance and the predictability of stock returns. Journal of Multinational Financial Management, 62, 100717. https://doi.org/10.1016/j.mulfin.2021.100717