A former managing director of India’s National Stock Exchange (NSE), who sought a “Himalayan yogi’s” advice in official decision-making, has been arrested for allegedly leaking crucial information.
Chitra Ramkrishna was arrested yesterday (March 6) by the Central Bureau of Investigation (CBI), NDTV reported.
The development comes nearly a month after it was reported that she had shared confidential information about the NSE’s business plan, financial results, and performance appraisals of employees, with the ascetic. In a bizarre twist, the mysterious yogi reportedly turned out to be NSE’s group operating officer Anand Subramanian himself.
Subramanian was the prime beneficiary of Ramkrishna’s abuse of powers and he advised her through e-mail during her tenure at NSE from April 2013 to November 2016. He was arrested on Feb. 24.
The specific case for Ramkrishna’s arrest pertains to allegations of providing a select group of nine brokers preferential access to the stock exchange’s algorithmic trading platform.
What is the NSE’s algorithm trading scam?
In May 2018, an FIR was filed against Delhi-based brokerage OPG Securities and unnamed NSE and Securities and Exchange Board of India (Sebi) officials in the co-location or algorithm trading scam following a whistleblower’s complaint in 2015.
Co-location facilities are dedicated spaces in NSE’s Bandra-Kurla Complex building in Mumbai, right next to its servers, where high-frequency and algorithm traders can place their systems or programmes for a fee. The scam involved nine brokers getting an unfair advantage over others while accessing NSE’s algorithm-trading platform, and making undue gains or avoiding losses.
OPG Securities and its directors earned unfair profits of 15.7 crore rupees with an interest of 12% from April 2014, a Sebi order in 2019 said.
The FIR registered by CBI in 2018 had not named Ramkrishna in the matter and she was allowed to resign in November 2016 without any action. All this while, the NSE board was aware that Ramkrishna was passing on confidential information to an unknown third party.