INTRODUCTION
The Indian securities market has undergone remarkable transformation in recent years, driven by the rapid adoption of cutting-edge technologies and the ever-increasing integration of Artificial Intelligence (AI) systems. A well-regulated and transparent securities market is crucial for sustainable economic growth. The secondary market, where securities are traded by investors on an everyday basis reflects the health of the economy. While the Indian securities market has witnessed the initial applications of AI, its full potential remains untapped. Among the most disruptive and controversial developments in this domain is the rise of ‘Algorithmic trading’, a trading strategy that leverages advanced algorithms and high-speed execution capabilities to capitalize on fleeting market opportunities. Within Algorithmic trading falls High-frequency Trading (HFT), which has become a defining feature of modern capital markets, characterized by its lightning-fast execution speeds. While HFT has undoubtedly enhanced market liquidity and efficiency, its unbridled growth has also raised concerns regarding potential market manipulation and unfair advantages for certain market participants. The regulatory framework established by the Securities and Exchange Board of India (SEBI) remains unclear. This article delves into the functioning of Algorithmic trading/HFT, strategies employed by High-frequency traders, regulatory challenges, and opportunities surrounding Algorithmic trading/HFT in India.
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DECODING HIGH-FREQUENCY TRADING
HFT relies on computer algorithms to execute trades rapidly, based on menial price movements. These traders employ high-speed computer algorithms, low-latency networks, and massive data centers to identify and execute trades at a faster rate than human traders. HFT strategies typically involve executing a large number of trades within a short period of time, using predictive models to identify patterns in the market and profit from these patterns.
One of the essentials for successful HFT is ‘speed’. The algorithms employed by HFT firms are designed to exploit even the most minuscule inefficiencies in the market by executing trades at blistering speeds, measured in fractions of a second. This blazing-fast execution capability gives HFT algorithms an almost unfair advantage over human traders, who are simply unable to react and make decisions at such superhuman speeds. By the time a human trader has processed market data and decided to act, HFT algorithms have already identified the opportunity, executed the trade, and moved on to the next opening – leaving traditional investors in their dust. However, speed alone is not enough – these algorithms also require a constant stream of data to fuel their trading decisions. HFT firms invest heavily in obtaining access to a vast array of data sources, which is further ingested and analyzed by the algorithms in real-time, allowing them to potentially detect and predict market movements before they even materialize on traditional traders’ screens.
Another essential apart from speed, and availability of data is, ‘colocation’ (physical location) and ‘low latency networks’. Colocation is crucial in HFT because it minimizes latency, or the delay between sending and receiving data, thereby allowing firms to execute trades faster. By colocating their servers with exchange servers, algorithmic traders can reduce the physical distance that data needs to travel, resulting in quicker access to market information and faster execution of trades. Additionally, low latency is critical as even milliseconds of delay can make a huge difference in getting orders executed at desired prices in fast-moving markets. Latency is introduced by the physical distance the data has to travel, as well as by the number of network components (routers, switches etc.) it has to pass through. To reduce latency, algorithmic trading firms invest heavily in setting up networks with the most direct fiber optic cable routes between trading centers, datacentres, and exchanges.
STRATEGIES EMPLOYED UNDER HIGH-FREQUENCY TRADING
The realm of HFT is dominated by a diverse array of intricate strategies, each designed to exploit specific market dynamics or inefficiencies at speeds far surpassing human capabilities. One prominent approach is ‘statistical arbitrage’, which involves exploiting temporary pricing inefficiencies between related securities across different markets. By leveraging powerful algorithms and ultra-low latency data feeds, HFT firms can identify and act upon these fleeting arbitrage opportunities before they disappear. For instance, if the price of a stock deviates slightly from the underlying stock’s price due to temporary imbalances in supply and demand, an HFT algorithm can instantly execute trades to profit from this discrepancy, buying the underpriced asset and selling the overpriced one simultaneously.
Another lucrative strategy is ‘market making’, where HFT firms essentially act as automated market makers, providing continuous buy and sell quotes for various securities. By rapidly updating their quotes and adjusting their positions, they can capture the bid-ask spread – the difference between the prices at which they are willing to buy and sell. While this spread may seem negligible for individual trades, the sheer volume of transactions executed by HFT firms can translate into substantial profits. Some HFT market makers even engage in rebate trading, capitalizing on the rebates or fee discounts offered by exchanges to provide liquidity by rapidly posting and canceling orders.
‘Order anticipation’ is another controversial HFT strategy that involves detecting and front-running large institutional orders. When a major institutional investor, such as a mutual fund or hedge fund, places a large buy or sell order, it can temporarily impact market prices. HFT algorithms can identify these imminent large orders and execute trades ahead of them, effectively front-running the institutional order and profiting from the temporary price impact.
Despite the sophistication and speed of HFT algorithms, their success ultimately hinges on identifying and exploiting even the most transient market inefficiencies or informational advantages. By leveraging cutting-edge technology, vast data resources, and complex mathematical models, HFT firms aim to stay one step ahead of traditional traders and capitalize on opportunities that would be virtually imperceptible to human eyes and minds.
POTENTIAL THREATS OF HIGH-FREQUENCY TRADING
While HFT has undoubtedly brought about significant benefits in terms of increased liquidity and tighter bid-ask spreads, its rapid growth and the accompanying strategies employed by some market participants have raised concerns about potential market manipulation and unfair advantages. It becomes crucial to understand that not all HFT strategies are legal or ethical. Practices like ‘layering’ and ‘spoofing’, which involve creating a false impression of supply and demand by rapidly placing and canceling orders, are considered forms of market manipulation and are illegal. Similarly, ‘quote stuffing’, which is flooding exchanges with an excessive number of orders and cancellations to overload their systems and gain an advantage, is also an unlawful tactic.
HFT traders have access to vast amounts of data and advanced analytical tools, which can give them an unfair advantage over other market participants. This informational asymmetry can lead to a distorted understanding of market prices and a subsequent inability to accurately price risky assets. Another major concerns surrounding HFT is its potential to amplify market volatility and contribute to extreme price movements, also known as ‘flash crashes’. HFT algorithms are designed to execute trades at lightning-fast speeds based on predefined rules and market signals. However, in periods of market stress or unexpected events, these algorithms can react in ways that exacerbate price swings. If multiple HFT firms’ algorithms start to buy or sell aggressively in response to the same market conditions, it can create a feedback loop of cascading buy or sell orders, leading to sudden and severe price movements. The infamous “Flash Crash” of May 6, 2010, in the United States serves as a stark reminder of this risk, when the Dow Jones Industrial Average plunged nearly 1,000 points in a matter of minutes before recovering, largely attributed to the effects of HFT activity.[1]
SEBI REGULATORY MEASURES STRENGTHENING ALGORITHMIC TRADING IN INDIA
Recognizing the potential risks posed by HFT, the SEBI has taken steps to regulate this trading strategy in the Indian securities market. The regulatory framework remains a work in progress, and there is an ongoing debate about striking the right balance between fostering innovation and ensuring market integrity. Nonetheless, SEBI has time again laid down guidelines regulating Algorithmic trading and HFT.
In its circular on ‘Broad guidelines on Algorithmic trading’, SEBI mandated that Stockbrokers or trading members offering Algorithmic trading must conduct a system audit every six months to verify compliance with SEBI and stock exchange regulations pertaining to algorithmic trading. Moreover, if serious deficiencies or issues arise, or if a stock broker or trading member fails to address them adequately, the stock exchange will suspend their use of trading software until the issues are resolved and a satisfactory system audit report is submitted.
Additionally, to enhance surveillance of Algorithmic trading and prevent market manipulation, stock exchanges are instructed to enhance monitoring and surveillance of orders and trades generated by trading algorithms. Stock exchanges must regularly assess their surveillance methods to improve the detection and investigation of market manipulation and disruptions. SEBI has directed the exchanges to monitor, in real-time, the positions, margins, and leverage build-up by Algorithmic traders. Any abnormal spikes in trading volumes or order-to-trade ratios must trigger alerts for detailed investigation. SEBI has mandated rigorous testing and certification of all Algorithmic trading systems used by brokers and traders. Once tested, the systems must be certified by registered exchanges or authorized agencies.
Subsequently, SEBI proposed measures strengthening Algorithmic trading and Co-location, penalising Order to Trade Ratio (OTR), and tagging Algorithms to provide a playing field between both Algorithmic and traditional trading. Managed Co-location Services facilitate the participation of small and medium-sized trading members who face challenges like high costs and lack of expertise. Under this, exchanges will allot space/racks to eligible vendors, who will provide the necessary technical support, hardware, software, etc. as services to their trading member clients. Exchanges must allow multiple vendors to ensure fair competition while remaining responsible for data integrity, security, and privacy. To enhance transparency around latency, exchanges must now publish additional metrics like minimum, maximum, and mean latencies, as well as latencies at the 50th and 99th percentile. They must also publish a reference latency between a designated co-location rack and the exchange’s core router.
SEBI aims to put in place effective economic disincentives for high daily OTR of algorithmic orders placed by trading members to protect investors’ interests and promote the development of the securities market. Stock exchanges can introduce additional slabs up to OTR of 2000; where OTR is more than 2000, deterrent incremental penalties decided jointly by exchanges may be imposed. On the third instance of OTR being 2000 or more in the last 30 days, the concerned trading member shall not be permitted to place orders for the first 15 minutes on the next trading day as a cooling-off action.
Other than this, SEBI modified the existing order-to-trade ratio (OTR) penalty framework. Only orders within ±0.75% of the last traded price will be exempted from OTR penalties instead of the current ±1% threshold. Additionally, cash segment orders and those under liquidity enhancement schemes will now be included in the OTR framework. Further, for enhanced surveillance, exchanges must allocate a unique identifier to each approved trading algorithm. All algorithmic orders must be tagged with this unique ID, which should be part of the data shared with SEBI for surveillance purposes. Finally, exchanges may provide a simulated market environment specifically for testing software and algorithms, in addition to the existing mock trading sessions.
Recently, SEBI issued another circular aiming to address the issue of unregulated platforms offering Algorithmic trading services/strategies to investors with claims of high returns on investment and assigning ratings to the strategies, which could lead to mis-selling of such services. SEBI observed that some stock brokers provide algorithmic trading facilities to investors through these unregulated platforms. To prevent mis-selling and to protect investors’ interests, the circular directs that stock brokers providing Algorithmic trading services shall not directly or indirectly make any reference to the past or expected future return/performance of the algorithm or associate with any platform providing such references. Stock brokers currently doing so must remove/disassociate themselves within seven days. Stock exchanges are instructed to take necessary steps, amend relevant bylaws/rules, disseminate this circular, monitor compliance by brokers, and submit a compliance report to SEBI within 60 days. The provisions are applicable with immediate effect, and SEBI may take appropriate action under securities laws for non-compliance.
CONCLUSION
While algorithmic trading offers the potential for faster, more efficient, and cost-effective transactions, it necessitates robust monitoring mechanisms to prevent market abuse and safeguard investor interests. To balance the benefits and risks, comprehensive regulatory oversight is crucial. Frameworks should promote transparency, fair competition, and robust risk management systems to prevent and mitigate market disruptions. Through proactive and adaptive regulations, SEBI has endeavored to fortify the integrity and stability of Indian markets amidst technological disruptions. However, regulators must continuously refine their approach in tandem with financial innovations to fully harness the benefits of algorithmic finance while upholding principles of market fairness and systemic stability. By staying agile and responsive to evolving market dynamics, SEBI can facilitate the responsible adoption of cutting-edge trading technologies while ensuring a level playing field and mitigating potential risks to market integrity.
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