Do algorithm traders mitigate insider trading profits?: Evidence from the Thai stock market

Area of Science:

• Financial Economics
• Market Microstructure
• Algorithmic Trading

Background:

• Insider trading poses a significant threat to stock market integrity.
• Algorithmic trading (AT) has become a dominant force in modern financial markets.
• The impact of AT on mitigating insider trading profitability remains an active area of research.

Purpose of the Study:

• To investigate whether algorithm traders (AT) mitigate insider trading profits in the Thai stock market.
• To analyze the effectiveness of AT across different trade types (buy-side, big trades, executive trades).
• To assess the role of AT in enhancing stock market efficiency.

Main Methods:

• Empirical analysis of Thai stock market data from 2010-2016.
• Examination of insider trading profitability in the presence of algorithmic trading.
• Instrumental variable approach for robustness checks.

Main Results:

• Algorithmic trading generally mitigates insider trading profits.
• This mitigation effect is not observed for buy-side, large, or executive trades.
• Findings suggest AT contribute to market efficiency through rapid information incorporation.

Conclusions:

• Algorithm traders play a role in reducing insider trading profitability in the Thai market.
• Market efficiency is enhanced by AT's speed in processing public information.
• Further research may explore AT's nuanced impact across diverse trading scenarios.