Investigating the Performance of an Order Imbalance based Trading Strategy in a High-Frequency Trading

Abstract

One of the most interesting and challenging issues in the field of finance at the moment is high-frequency trading (HFT). This is because HFT is a relatively new issue for most of the financial markets and its activity is becoming more common all over the world. So, the study aim is to investigate the performance of an order imbalance based trading strategy in high-frequency trading. Besides, we used Multivariate GARCH models such as BEKK and DCC GARCH to estimate volatility, return and order imbalance relations. Our dataset includes stocks traded on the Tehran Stock Exchange from April 1, 2014, until March 30, 2016 (1095 trading days). Our dataset includes stocks traded on the Tehran Stock Exchange from April 1, 2014, until March 30, 2016 (1095 trading days). Also, the results of lagged return-order imbalance relations show that the percentage of positively significant lagged order imbalances is 1.00% and the percentage of negatively significant coefficients of lagged order imbalance is only 8.21% at confidence level 95% in 2016. Finally, it concluded that order imbalance is a proper measure for predicting future returns. Indeed, order imbalance could be proper measures for predicting returns in HFT.