Big data analytics, order imbalance and the predictability of stock returns

Erdinc Akyildirim, Ahmet Sensoy, Guzhan Gulay, Shaen Corbet and Hajar Novin Salari

Journal of Multinational Financial Management, 2021, vol. 62, issue C

Abstract: 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.

Keywords: Fintech; Big data; Data analytics; Order imbalance; Algorithmic trading (search for similar items in EconPapers)
JEL-codes: C21 C22 G11 G14 G17 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:mulfin:v:62:y:2021:i:c:s1042444x21000402

DOI: 10.1016/j.mulfin.2021.100717

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