securities appellate tribunal: SEBI must get more empirically invested


The Supreme Court‘s verdict upholding the acquittal of former Gammon Infrastructure Projects Ltd (GIPL) chairman-managing director Abhijit Rajan on insider trading charges is welcome. Sebi had challenged his acquittal by the Securities Appellate Tribunal (SAT) in 2019. The ruling is a pointer to regulatory overreach in the name of clamping down on all shades of insider trading. The court held that the mere possession of unpublished price-sensitive information (UPSI) and trading on its basis are not sufficient to prove the charges. It added that it is equally important to establish that the intent behind the transaction was to gain from the insider information. This sets a precedent for similar cases.

Rajan had sold his shares in GIPL ahead of the disclosure to stock exchanges about the termination of shareholding agreements between GIPL and Simplex Infrastructures. The case began in 2014 with an ex parte order by Sebi based on suspicion. But the regulator remained invested in its original stance taken in the teeth of empirical evidence and against universal first principles. If a person has price-sensitive information that is positive in nature, he would obviously buy the company’s stock before the world gets to know. If such information was adverse, he would sell ahead of its dissemination.

The apex court endorsed SAT’s view that Rajan was in dire need to sell the shares at that time for the purpose of the corporate debt restructuring (CDR) package, and had no motive to make undeserved gains. Given that the closing price of the shares rose after the disclosure of the information, SAT held that any person wanting to indulge in insider trading would have waited till the information went public to sell his holdings. This is a reiteration of the first principles Sebi brushed aside. Sebi must beef up its capacity for investigation and analysis of the huge amounts of data its compliance systems generate using technology such as artificial intelligence (AI) and machine learning (ML) to augment its surveillance capabilities.



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