How SEBI can use advanced analytics to limit insider trading | Ankura

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There have been several reports of recent crackdowns by India’s capital markets regulator, Securities and Exchange Board of India (SEBI) on companies and individuals found to be involved in insider trading, front-end trading and trading anomalies such as the big finger error. This shows how seriously the regulator takes trade compliance as a serious area of ​​concern. It is unclear whether these cases were the result of a whistleblower complaint or were uncovered as part of a proactive review.

Incidents of insider trading in the Indian market have had their fair share of media attention, mostly for all the wrong reasons. However, as is often the case when something like this happens, it also brought to light some very real issues that need to be addressed. The regulator has implemented several new insider trading rules. Simply put, insider trading occurs when someone in a privileged position in a company (usually executives or board members) buys or sells stock in that company based on inside information. which can only have been obtained from another person having access to this information. The problem is not limited to insiders who sell their shares before bad news becomes public, but also extends to friends or family of people in leadership positions who may be aware of confidential information and in gain an advantage.

Ambiguous definition of “insiders”

Persons considered insiders of a company are generally the officers or members of the company’s board of directors. However, there are also times when other people may have access to confidential information and may be considered insiders. For example, lawyers or accountants who work with a company may have access to confidential information and could potentially trade in it. In India, SEBI has taken a very broad view of what constitutes insider trading. In fact, the current definition of an insider under the SEBI (Insider Trading) Regulations, 1992 is so broad that it includes anyone connected with the company or believed to have access to unpublished price sensitive information relating to securities.

This definition raises some important questions. First of all, what do we mean by link with the company? Second, what kind of information is price sensitive? And finally, how does SEBI determine if a person has access to price-sensitive unpublished information?

SEBI has not provided any specific guidance on what is meant by connection to the company. However, it is safe to assume that this would include people who are employed by the company, people who have been associated with the company in the past, and people who are believed to have access to price-sensitive unpublished information.

Price sensitive information is defined as any information which is not generally available and which, if made available, could affect the price of securities. This would include things like financial results, new products or services, mergers and acquisitions, and changes in corporate strategy.

SEBI said it will presume a person has access to unpublished price-sensitive information if that person is in a position to influence the company’s decision-making regarding the disclosure of that information. This would include people such as senior management, board members, key employees and external service providers.

Evolution of SEBI Using Analytics

SEBI has several rules and regulations in place to prevent insider trading. These include the Prohibition of Insider Trading (SEBI) Regulations 2015, which establish specific prohibitions on insider trading. The regulations also require companies to put in place a code of conduct to prevent insider trading. In addition, SEBI has also set up a dedicated department to investigate insider trading cases.

SEBI has been very proactive in recent years in trying to curb insider trading. However, there are still several challenges facing the regulator in this area. First, it can be very difficult to prove that someone traded inside information because there is often no direct evidence to support it. Second, even if SEBI can prove that someone exchanged inside information, it can be very difficult to prosecute them because the burden of proof is very high.

One of the ways SEBI can try to overcome these challenges is through the use of advanced analytics. Advanced analytics is a type of data analysis that uses sophisticated techniques, such as machine learning, to extract insights from data.

SEBI may use advanced analytics to detect patterns in trading activity that may be indicative of insider trading. SEBI may consider conducting periodic look-back activity in conjunction with other agencies such as stock exchanges and other private and public entities to detect instances of insider trading. One of the biggest flaws in detecting insider trading is identifying the individuals who may be potential offenders and the trading accounts under which these illegal transactions took place. Most knowledgeable offenders will never use their own trading accounts for insider trading. Many also do not take delivery of stocks to keep their Demat accounts clean, but would take positions using Futures & Options.

The regulator should create a big data framework that connects individuals within the firm, its external service providers who have access to price-sensitive information, and connects everyone involved to family members’ trading accounts, extended family members and friends based on advanced information. link analysis. This link analysis will identify a network of trading accounts associated with a person who can manage these accounts directly or indirectly. Link analysis will also employ pattern matching techniques using data points such as mobile phone numbers, landline numbers, addresses, emails, IP addresses, bank details, social media, geolocation and advanced techniques such as artificial neural networks.

The framework will also need to take into account that some people may use multiple email accounts, mobile numbers, and bank accounts to avoid detection. The framework should be able to identify these people using sophisticated techniques such as clustering and outlier detection.

SEBI can also use advanced analytics to predict which stocks are likely to experience unusual trading activity. This can be done by reviewing historical data to identify stocks that have shown signs of insider trading in the past. SEBI can then use this information to flag stocks that may be subject to insider trading in the future.

Using advanced analytics is just one way SEBI can try to curb insider trading. However, it is important to note that this is not a silver bullet and that SEBI will need to continue to use other means, such as monitoring and investigation, to detect and prosecute insider trading. .

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