‘Self-trade’ manipulation issue back on SEBI radar

PALAK SHAH | | Updated on: Dec 06, 2021

Files an affidavit with SAT which considers ‘self-trades’ as illegal

After announcing that it will penalise nearly 15,000 entities for non-genuine derivative trading, SEBI has decided to turn the heat on thousands of cases of ‘self-trade’. Orders that are matched on the exchange platform without any change in ownership pattern of shares are termed as ‘self-trades,’ which SEBI considers as bad market practice. But brokers say these are inadvertent and incidental.

In 2017, SEBI had decided that cases of ‘self-trades’ may not be punished unless an ‘intention’ to indulge in such trades was established on the broker’s part. Based on such thinking, which was also made part of SEBI’s enforcement action policy, the regulator exonerated some of those who were show-caused in the matter. Now, the regulator has filed an affidavit with the Securities Appellate Tribunal (SAT), which considers mere occurrence of ‘self-trades’ as illegal.

Algo matching

“The self-trade matter could run in several thousands or even lakh of cases,” said a regulatory source following the matter. “Rough estimates suggest self trades could be a huge chunk of daily trading volumes. If SEBI is out to penalise all, the matter may go to the court.”

Reportedly, when SEBI’s secondary market advisory committee discussed the self-trades problem in 2014, it acknowledged that such trades can occur when multiple algo orders of the same broker get matched or different traders in the same brokerage house place buy-sell orders. It recommended that SEBI consider imposing a penalty only for manipulative self-trades.

A letter by the Association of NSE Members of India has requested SEBI to put on hold its action on self-trades as the regulator’s board in August 2017 had decided to review all cases of self-trades. But the regulator has put the matter back on its radar now.

Published on April 12, 2018
This article is closed for comments.
Please Email the Editor

You May Also Like

Recommended for you