SEBI Opens Algorithmic Trading to Retail Investors: Opportunities, Risks, and the Future of High-Frequency Trading in India

SEBI has announced plans to soon allow retail investors to engage in algorithmic trading, a privilege extended to institutional investors in 2012. This move led to trades occurring in fractions of a second, with reduced costs and enhanced transparency. A recent SEBI study revealed that 97% of foreign fund profits in FY24 were derived from algorithmic trading.

The new directive aims to create trading parity between institutional and non-institutional investors. However, a level playing field remains unlikely due to the advanced tools, technology, and extensive experience that institutions possess.

The New York Stock Exchange (NYSE) computerized order flow in financial markets in the 1970s, with simple algorithms executing trades at optimal rates. The 2000s witnessed the rise of high-frequency trading (HFT), driven by advancements in AI and machine learning, enabling algorithms to predict market trends. News and social media analysis engines also assessed sentiment. Innovations such as low-latency networks, geolocation data, user-friendly platforms, and cloud computing for scalable resources have further revolutionized trading.

HFT has significantly altered the investment landscape, shifting from long-term, patient investing to rapid arbitrage, generating small profits from price discrepancies across markets. Holding securities for mere milliseconds is now common, with the development and deployment of complex algorithms requiring substantial investments in technology, infrastructure, and skills.

Algorithmic trading can operate at a slower pace, involve fewer trades over longer periods, and employ diverse strategies like trend following, executing trades based on price deviations, and arbitrage.

One notable incident, the "Flash Crash" of May 6, 2010, saw major US equity indices plummet by 9% within minutes before recovering, resulting in $1 trillion in losses due to algorithmic trades triggering a sell-off. Five years later, the US Department of Justice attributed the crash to Navinder Singh Sarao, who placed $200 million in futures orders, repeatedly modifying and canceling them.

Another disaster, not entirely algorithmic, occurred in 2012 when a JPMorgan Chase trader lost $6 billion and the firm incurred $1 billion in fines during the London Whale incident. This underscored the risks of automated systems managing complex derivative strategies without adequate supervision.

In 2012, Knight Capital Group, a major US trading firm, faced a catastrophic failure when a dormant algorithm executed millions of erroneous trades, resulting in $460 million in losses within an hour. The firm was subsequently acquired by a competitor, highlighting how automated trading can turn manageable errors into severe outages with ruinous consequences.

The risk arises from the fact that a malfunctioning algorithm executing HFT can cause massive losses in a short time. Financial markets are interconnected, so shocks can quickly spread, potentially affecting other asset classes like real estate.

HFT is also susceptible to spoofing, where large volumes of fake orders create an illusion of demand before being canceled, as seen in the Sarao case. Computer malfunctions can also cause significant issues, such as the 2014 Intercontinental Exchange Group incident, which required the annulment of 20,000 erroneous trades.

To mitigate risks, measures include volatility filters to adjust trading strategies, monitoring illiquid conditions to prevent losses, diversifying investments across asset classes and markets, and ensuring strict regulatory compliance.

Several major Wall Street players, including Citadel Securities, Tower Research, IMC Financial Markets, and Jump Trading, have registered with SEBI to establish HFT entities, offering proprietary trading and broking services to smaller foreign proprietary traders. India's growing derivatives market is attracting global attention, with firms developing big data-based strategies tailored for India.

SEBI plans to regulate HFT in India through six-monthly audits, strict order-to-trade ratios (OTRs) to control order volumes, timely execution checks, order queue randomization, and disincentives for high daily OTRs.

In 2010, NSE began offering co-location facilities for brokers, allowing them to place servers at NSE's data center for faster market data access. The focus on HFT and the importance of milliseconds led to controversies over preferential access to secondary servers. SEBI's investigations revealed procedural lapses but no collusion.

SEBI's decision to allow non-institutional investors to participate in HFT aims to democratize trading. As the NSE incident demonstrated, there are many known aspects to protect, including volatility, manipulation, and overtrading. However, there are also unknown risks that need to be addressed.