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Profit by Concocted Complexities - Catching up with Algorithmic Trading & its light-touch regulation


[All views expressed here are personal and nothing contained herein reflects the views, opinions and beliefs of any organization.]

Scams often hide behind complexities, using legitimate transactions of complex nature, later involving less sophisticated individuals who may not be capable of understanding the nature of the transaction or the necessity of the complexity.[1]

The popular co-location scam, though complex, may be indicative of a deeper issue in the markets. After all, the underlying controversy was primarily a tussle of preference between trading members ("TMs") of the exchange who had paid exorbitant amounts for a rack ("co-location") on the premises of National Stock Exchange of India Limited ("NSE"). This rack afforded them with low latency, i.e., they could place trades quicker. It also provided them with a tick-by-tick ("TBT") data feed. Interestingly, despite severe criticisms and ongoing regulatory actions, NSE’s revenue from co-location charges increased from INR 274.06 Crs. for FY 2020-21 to INR 432.62 Crs. for FY 2021-22. Evidently, the data and low latency are valued like goldmines.


This article, however, does not study the morality of co-location. It attempts to deconstruct algorithmic trading (or "algo trading") from its perceived complexities. It then highlights how it is commonly utilized against market integrity and how the regulatory framework instituted by SEBI fares in protecting the market against the ills of algo trading.


Algo trading presently has 50-55% penetration in the Indian securities market. Under Article 4(1)(39) of the Markets in Financial Instruments Directive II ("European Union"), algo trading is defined as trading where algorithm automatically determines individual parameters of orders such as whether to initiate the order, its timing, price, quantity or how to manage it after its submission, with limited or no human intervention. It excludes systems meant only for routing orders to one or more trading venues or for simply processing orders without determination of any trading parameters.


There is also high-frequency trading ("HFT"), a subset of algo trading that is characterized by high speeds, high turnover rates, and high order-to-trade ratios ("OTR") that leverages high-frequency financial data and technology. Some studies have inferred that HFT facilitates information transfer between investors and increases the price efficiency.[2] While HFT has its own merits and demerits, it is here to stay. It is only prudent to comprehend potential issues that it posits and to study how they can be addressed by suitable regulatory measures. In this regard, evidence suggests that algo trading and HFT can cause market integrity concerns and which if allowed, may nullify the benefits of allowing HFT by decreasing liquidity, increasing costs, increasing short term volatility and large market noises backed by false volume.


Predatory Algo Trading Strategies and Tendencies


As discussed above, HFT is the subset of algo trading that takes advantage at millisecond timelines. It raises critical risk of front-running large or institutional investors as most of these algorithms are capable of detecting a TM's starting to place a big order in smaller tranches. This intel can be utilized to promptly buy those shares and by reselling them to purchasing TM at a profit. This enables HFT firms to gain certain market information and execute trades faster than other participants putting overall fairness and market integrity at risk. Co-located firms stand to benefit by an additional millisecond speed advantage at beating the slower big order.


This raises many concerns including increase in stock price volatility, exposing the lack of level playing field and fairness at large. It also demonstrates how TBT data enables HFT strategies to extract profits from data received at co-location facilities. Such activities also risk increasing execution shortfall for institutional investor's execution, thereby negatively impacting them further.[3] This ought to be concerning considering institutional investors include mutual funds which see humongous retail participation. Lastly, by quick buying and reselling stocks, the HFT firm does not hold an effective position and is not affected by whether the fundamental decision to buy or sell a stock was prudent. However, no such front-running appears to have been detected by SEBI yet.


There is also the risk of HFT firms entering trades merely for rebates as a primary source of profit. In India, these rebates come in the form of liquidity enhancement schemes ("LES") of exchanges, which provide traders with a rebate for providing liquidity to the market when there is a need for it. Mal-elements in rebate trading could look for larger orders, fill a part of that order and offer the shares back to the market by placing a limit order, which makes them eligible to collect the rebate for providing liquidity, irrespective of them making any capital gain.[4]



Some more specifically known ways in which HFT is used to manipulate the market are discussed below.[5]


Statistical arbitrage entails exploiting price discrepancies between different exchanges in India with a view to quickly buy stock on the exchange they are overpriced in.


Latency arbitrage entails strategies from delay in information transfer between exchanges by quick detection of stock price change on one exchange and using that intel to make rapid trades on another exchange before the information is transmitted.


Quote stuffing is a pervasive scheme wherein a HFT places a large number of buy/sell orders and immediately cancels them with a view to influence the movement of the price in the direction of stuffing. The trade prices tend to move in the direction of stuffing, i.e., increasing when it occurs on the ask side or declining when it happens on the bid side. This pattern may last for less than a second.


Layering is used to create a more advantageous execution by manipulating stock price ahead of the manipulator's wish to execute a transaction and involves spoofing. Spoofing involves fooling other traders into thinking that significant buying/selling pressure is mounting by placing large quantities of fake orders in the market with the actual intent of canceling the orders and not executing. This is primarily done with the intent of causing the stock price to rise or fall and to profit from such a rise or fall.


Momentum ignition comprises aggressive trading volumes (without accompanying change in price) followed by a sharp price movement (with larger price volume) and gradual return closer to fairer price levels. This pattern may last for minutes and is used by the manipulator to obtain an advantageous position by instigating other market participants to trade aggressively in response, causing a price move, from which the manipulator benefits.[6]


There are also counter studies on whether HFT truly increases liquidity or whether it is just nonsense noise. Harmful HFT Algorithms can also cause erratic movements and market disruption such as high Intra-Day volatility and high OTR, e.g., flash crashes and technical glitches in the stock exchange.


Thus, there are various concerns arising from HFT, including the cost that HFT poses on other market users, market noise (instead of liquidity), technological arms race in the market, limited opportunities for regulators to intervene during high volatility which may last at most for a few minutes but regardless cause grievous harm to market integrity.


Regulatory Framework


Primarily, SEBI’s first circular on the subject, i.e., Broad Guidelines on Algo Trading (2012) [7] placed the responsibility on stock exchanges to have capacity to achieve consistent response time to all brokers and to continuously study the performance of its systems. It also required the stock exchanges to undertake regular system upgradation to keep pace with the speed of trading and volume of data that may arise from algo trading.


It directed exchanges to enforce economic disincentives with regard to high OTR of algo orders of brokers and to put in place monitoring systems to identify and initiate measures to impede order flooding by algos. This is designed to limit the number of orders that can be placed compared to the number of trades executed. This can be perceived to check spoofing as well as market disruption including slow-down and allowing an entity to establish a dominant presence in a particular security. In order to discourage repetitive instances of high daily OTR, stock exchanges impose an additional penalty in the form of suspension of proprietary trading rights of the broker for a limited duration.


Further, it directed that all algo orders must only be routed through broker servers located in India and that the stock exchange must have appropriate risk control mechanisms such as price check and quality limit check to address the risks emanating from algo orders and trades. This means that the price quoted by orders must not violate the price bands and maximum permissible quantity per order, as set by the exchange for the particular security. While the former can be perceived to protect against mispriced trades and to limit potential for large losses therefrom, the latter may be aimed at preventing algo trading systems from executing trades too large relative to market conditions.


Lastly, it required the stock exchanges to put in place a system to identify dysfunctional algos (those in look or runaway situation) and take suitable measures, including advising the member, to shut down such algos and to remove outstanding orders that may have emanated from such dysfunctional algos. This appears to be the result of the aftermath of a few flash crashes that occurred in the Indian markets in the few years preceding the circular.


Vide another circular in 2013,[8] SEBI mandated brokers/TMs providing algo trading to subject their algo trading system to a system audit and mandated reporting of deficiencies/issues identified to the stock exchange immediately upon completion of the audit by the broker/TM including immediate corrective actions to rectify the same.


Later in 2013, SEBI had also mandated ‘Testing of software used in or related to Trading and Risk Management’ by exchanges before deployment of algos.[9]


SEBI then issued Broad Guidelines on Algorithmic Trading for National Commodity Derivatives Exchanges (2016)[10] with similar capacity building requirements for commodity derivative exchanges. It also directed the exchanges to:

  • disapprove algos not conducive to efficient price discovery or fair play;

  • subject the members’ systems to initial conformance tests to ensure that the checks mentioned therein are in place;

  • put in place economic disincentives for daily algo OTR with a limit of 500 and a limit on the number of orders per second (“OPS”) of 20 OPS;

  • mandate maximum order size check and market price protection in addition to price and quantity check;

  • put in place the system to identify dysfunctional algos;

  • disallow Immediate or Cancel (IOC) orders using algo trading and algos that will take liquidity away.

Note: IOC Orders require immediate execution of the entire order at best available price or cancellation of any unfulfilled portion of the order, they can result in trades being executed at prices that deviate significantly from the fair value of the underlying commodity.


Further, it cautioned exchanges to be wary in permitting algo trading in mini and micro contracts (targeted towards small participants) only after taking into account liquidity in the contract and ascertaining small participants in disadvantage.

Lastly, the 2016 Guidelines noted that “Co-Hosting or any other facility or arrangement which puts some members in a disadvantageous position vis-a-vis other members shall not be allowed. Algorithmic trading shall not be permitted from exchange hosted CTCL terminals”

Thereafter, in 2018, SEBI relaxed the OTS limit from 20 to 100 and also relaxed the requirement to have empanelled system auditors for algo trading in commodity derivatives.[11] By another circular in 2018 it directed stock exchanges to provide TBT feeds to all TMs, free of cost, to create a more level playing field.[12] Notably, it brought orders placed under LES under OTR framework, [13] implying LES orders which may have been placed merely for rebates were not mandated to OTR limit until 2018. In 2020, OTR limit was raised to 2000[14] and in 2022, OPS limit was relaxed to 120.[15]


Concluding Remarks

While SEBI's circulars have laid a foundation to tackle operational risk and concerns raised by algo trading, there is a need for a deeper study to assess its impacts on market integrity. There is also a need to design qualitative regulatory measures including considering modification of SEBI's FUTP Regulations to account for devices and schemes in algo trading that may affect market integrity in order to see more regulatory action on this front. Additionally, in light of exchange's shift in their role as co-location service providers (co-location being NSE's highest revenue source), and consequently arisen conflict of interest, SEBI must reconsider its delegation-based regulatory model to analyze whether it needs to play a more active role in regulating on this front.


Sources:

[1] https://uncitral.un.org/sites/uncitral.un.org/files/media-documents/uncitral/en/recognizing-and-preventing-commercial-fraud-e.pdf

[2] Austin Gerig, U.S. Securities and Exchange Commission “High-Frequency Trading Synchronizes Prices in Financial Markets”; European Central Bank Working Paper “High Frequency Trading and Price Discovery”

[3] Professor Lin Tong “A blessing or a curse? The impact of High Frequency Trading on Institutional Investors” (2015)

[4] Incentives may include discount in fees, adjustment in fees in other segments, cash payment or issue of shares, including options and warrants. SEBI's Revised guidelines for Liquidity Enhancement Scheme in the Equity Cash and Equity Derivatives Segments (April 23, 2014).

[5] DEA NIFM Report on Algo Trading

[6] Paper-High Frequency Trading – Measurement, Detection and Response by Tse , Lin and Vincent(2012); An Empirical detection of High Frequency Trading strategies by Dimitar Bogoev1 and Arz´e Karam of Durham University.

[7] SEBI Circular No. CIR/MRD/DP/ 09 /2012 dated March 30, 2012.

[8] SEBI Circular No. CIR/MRD/DP/ 16 /2013 dated May 21, 2013.

[9] SEBI Circular No. Circular no. CIR/MRD/DP/24/2013 dated August 19, 2013

[10] SEBI Circular No. SEBI/HO/CDMRD/DMP/CIR/P/2016/97 dated September 27, 2016

[11] SEBI Circular No. SEBI/HO/CDMRD/DRMP/CIR/P/2018/60 dated April 03, 2018.

[12] SEBI Circular No. SEBI/HO/MRD/DP/CIR/P/2018/62 dated April 09, 2018.

[13] Ibid.

[14] SEBI Circular No. SEBI/HO/MRD1/DSAP/CIR/P/2020/107 dated June 24, 2020.

[15] SEBI Circular No. SEBI/HO/CDMRD/CDMRD_DRM/P/CIR/2022/30 dated March 17, 2022.


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