13 Sep 2021
As part of the ongoing Sasin Research Seminar series, Professor Sirimon Treepongkaruna, Ph.D., gave a fascinating talk looking at whether algorithm traders mitigate insider trading profits. The lecture is based on a published paper, and the data used comes from the Thai stock market (SET). The lecture began by establishing what constitutes legal and illegal insider trading. Legal insider trading is when company insiders buy or sell shares and report all trades to the Securities and Exchanges Commission (SEC). Illegal insider trading is when company insiders benefit from company information and don’t report transactions to the SEC. Often this negatively affects the company and results in an inefficient market. Several well-known cases of illegal insider trading in Thailand were shown and discussed. Next, Professor Sirimon explained Algorithmic Trading (AT). This type of trading uses computers to generate trading signals, send orders, and manage portfolios. AT uses complex mathematical formulas, high-frequency trading, and sophisticated electronic markets and platforms to trade. AT has been receiving academic attention since around 2004, and studies have shown this type of trading can improve efficiency. Professor Sirimon explained the conceptual model of Algorithmic Trading, mentioning previous studies in the literature. These studies examined how AT positively affects market efficiency through aspects such as symmetry information, increased liquidity, and price efficiency. In 2011, Algorithmic Trading accounted for 13% of market share in Thailand, and this figure has continued to rise. As a consequence of this increase, the paper set out to see if AT will improve the efficiency of the Thai stock market. Professor Sirimon then further explained the disciplining role AT plays in restraining insider trading profits and discussed why some types of insider trading are allowed. Arguments include the idea that legal insider trading releases information to the market, allowing the public to make more informed trades. These insights led Professor Sirimon and her co-authors to develop the following hypotheses:
  • H1a: Based on the asymmetric information hypothesis, algorithmic trading reduces insider trading profit.
  • H1b: Based on the attentive insider trading hypothesis, algorithmic trading increases insider trading profit.
  • H2a: Algorithmic Trading reduces non-executive insider trading profit.
  • H2b: Algorithmic Trading increases executive insider trading profit.
Professor Sirimon then explained the data used and the variables involved. These included a dependent variable – cumulative abnormal return after insider trade (CAR); independent variable – AT intensity; and control variables – cumulative abnormal return (pre-event), firm size, the book to market ratio, return on assets, leverage, signed trade size. The final sample included 6,156 stock-month insider transactions for 409 stocks in Thailand from 2010 to 2016. The variables were then examined in more detail as Professor Sirimon explained how they were calculated, including the formulas and methodologies used. The results of the analysis were then presented in a table of summary statistics. The figures were found to be consistent with the findings of previous academic studies. Next, other tables showing AT and insider trade sizes and AT and insider’s management levels were presented, and the figures were discussed. The study’s conclusions found that, to some extent, algorithmic trading can play an important role in increasing the efficiency of the stock market. It does this by processing public information and incorporating it into the price at ultra-fast speeds. Professor Sirimon added that the additional robustness checks based on the instrumental variable approach confirmed their findings. The talk was followed by an engaging Q&A session that discussed topics such as how insider trading was determined, whether algorithmic trading should be encouraged, and other issues relating to the legality of insider trading and the response the paper received for examining the contentious topic.
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