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

Date
2021-09-24
Advisor
Instructor
Source Title
Journal of Multinational Financial Management
Print ISSN
1042-444X
Electronic ISSN
1873-1309
Publisher
Elsevier
Volume
62
Issue
Pages
1 - 19
Language
English
Type
Article
Journal Title
Journal ISSN
Volume Title
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.

Course
Other identifiers
Book Title
Keywords
Fintech, Big data, Data analytics, Order imbalance, Algorithmic trading
Citation
Published Version (Please cite this version)