Investigating the Effect of Imbalance in Algorithmic Trading Orders on Abnormal Stock Return Rates in Turbulent Markets

Document Type : Original Article

Authors
1 Ph.D. Student of Accounting Department, Bandar Abbas Branch, Islamic Azad University, Bandar Abbas, Iran
2 Assistant Professor of Accounting and Financial Management Department, Bandar Abbas Branch, Islamic Azad University, Bandar Abbas, Iran
3 Associate Professor of Accounting and Financial Management Department, Bandar Abbas Branch, Islamic Azad University, Bandar Abbas, Iran
4 Assistant Professor of Accounting Department, Bandar Abbas Branch, Islamic Azad University, Bandar Abbas, Iran
10.30495/jik.2025.23593
Abstract
Technological developments in the last decade have led to a significant increase in the use of computer algorithms by investors. In the light of extreme market events such as the "sudden collapse", researchers, market makers and financial professionals are interested in understanding the role of algorithmic trading in volatile markets; Therefore, the main purpose of this study is to investigate the effect of imbalance in algorithmic trading orders on abnormal stock return rates in turbulent markets. Turbulent market days are defined in this study when the absolute values ​​of market returns are greater than 2%. For this purpose, a sample of 276 companies listed on the Tehran Stock Exchange during 1398 has been studied using panel regression and logistics. The results showed that in the bullish days of the market, stocks that demand more algorithmic trading have lower abnormal stock returns and have lower price fluctuations. The results for the downtrends showed that the stocks that are traded mostly by algorithmic trading show more downtrends during the downtrends. The results also showed that the net imbalance in supply and demand liquidity orders of algorithmic trades has lower price pressure compared to the net imbalance of non-algorithmic trading orders. These findings suggest that the effect of algorithmic trading on stock returns is probably due to lower price pressure exerted by algorithmic trading. In addition, the results showed that the algorithmic trades follow the weighted average volume weight and counter-trend price strategies.
Keywords

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