Sebi embraces new-age tools for information to prevent insider trading

The regulator will use data analytics to scrutinise what it finds in its 'data lakes' and monitor information on social media

Heard of Black Edge? In her famous book on the hedge fund industry which has the same title, Sheelah Kolhatkar describes Black Edge as the “most valuable information of all” in that it is proprietary, non-public, and certain to move markets.

No doubt, securities regulators around the world are putting in huge efforts to prevent the flow of such information, as well as to punish those making illicit gains using these insights. But it is not as easy as it may sound in an era when WhatsApp, Instagram, LinkedIn, Facebook and Telegram have almost become primary mediums of communication.

Given this, Indian market regulator Sebi (the Securities and Exchange Board of India) is going all out to embrace new age tools and technologies to analyse large-scale data to prevent market manipulation such as insider trading.

Sebi has drawn up a four-year road-map for beefing up its technological prowess with a Rs 500 crore budget. It is looking at building a “data lake”, a vast repository of both structured and unstructured data, and creating data modelling and analytical capabilities on top of it through the use of AI and Machine Learning. A tender to this effect was launched last November. 

Presently, several industries including e-commerce, telecom, banking, and financial services are using data modelling by leveraging new age tools and technologies to gain business insights and make faster and smarter decisions.

 

Several global regulators across the banking and securities markets have also started using data analytics extensively to stay ahead of the curve when it comes to unscrupulous activities.

“By creating a data lake architecture, Sebi can use analytics to identify a pattern to detect instances of market manipulation. Using a combination of these can make the analysis sharper and bring actionable insights,” said Kunal Pande, partner, KPMG India.

Getting access to the data and acquiring the ability to harness it, he added, will boost Sebi’s confidence.

At present, Sebi’s surveillance architecture is designed to act on what is called “structured data”, that is, the data obtained from market intermediaries such as stock exchanges, brokers, depositories and mutual funds.

It also has access to ‘semi-structured data’ in the form of bank statements and income tax filings which are also relatively easy to process. But where Sebi lags is in the handling of “unstructured data” which could be blogs, videos, and even random chatter posted online.

“Structured data analysis is not helping much and manipulators are using all kinds of techniques to evade them,” said Sebi chairman Ajay Tyagi. “The analysis of unstructured data and language processing is a must in addition to analysing changes in prices and volumes. We intend to acquire new technology to do this.”

Gaining access to information posted on social media is also a key part of this strategy. There have been several orders issued by Sebi which have established links through matrimonial apps and Facebook or through using in-house technology. However, industry players say that in the absence of a data modelling platform or analytics tools, Sebi’s capabilities could be limited.

At present, a huge amount of stock market-related information is shared and distributed by individuals as well as companies on social media and discussion forums. Monitoring the flow of this information is critical to prevent insider trading and ensure transparency.

The implementation of data lake capabilities will arm Sebi to scrutinise such data. This ability, combined with Sebi’s traditional surveillance tools, can act as potent tools to catch violators.

For example, scores of alerts on stocks that see unusual volumes or price movements are generated by stock exchanges daily. While these alerts draw Sebi’s attention, it has to establish if any participant made unlawful gains.

By leveraging the “data lake”, Sebi will be able to comb through social media, news websites, discussion forums, videos and podcasts, to find any potential pattern. If, for instance, the results show that a company insider passed key information illegally, Sebi can hold the company accountable.

Also, listed companies are supposed to disseminate sensitive and credible information on the stock exchange platform in order to ensure all investors get uniform access to it. However, some companies tend to give out information on Twitter or television news channels which could be prohibited under the law.

A famous example was Telsa boss Elon Musk’s tweet in August 2018 that the company had secured funding to go private. The US market regulator, the Securities and Exchange Commission, later pulled up Musk for giving information without authorisation. The case was settled last year after Musk agreed to follow Twitter usage guidelines in future.

Industry players say such instances are possible in India as the use of Twitter is on the rise. Regulators will need to deploy technology to ensure that information that is passed on is not in violation of disclosure norms.