Problems with evidentiary standards for proving securities fraud in India

by Nidhi Aggarwal and Bhargavi Zaveri.

Introduction

Did O.J. Simpson kill his wife? A criminal jury said no, a civil jury said yes. The standard of proof applied by the two juries made all the difference to the outcome of the case (Vars 2010).

In India, the securities regulator adopts a very low standard of proof for cases involving wrongdoing in the securities market. For many people, standard of proof related questions are procedural and semantic exercises in the dispensation of justice. However, the standard of proof adopted by a judge has direct impact on the outcome of the case and over time, the quality of the investigation conducted by the investigative agency. When the standard of proof is low, there is a high chance that initiating an investigation will induce an adverse order. This creates substantial discretion in the hands of the investigator, to choose the persons against whom State power will be directed. This runs against a basic theme of liberal democracy, of containing executive discretion. The ability of the executive to direct punishment upon chosen ones is inconsistent with the rule of law. It creates policy risk for persons who may consider participating in the Indian financial markets, and creates a bias in favour of participation by politically connected persons.

There are three reasons why the standard of proof in securities fraud cases in India are low. First, the Supreme Court has held that the standard of proof which SEBI must meet to establish securities fraud is the 'preponderance of probability' standard. This is lower than the standard of proof required to establish a crime under criminal law. In civil proceedings, there are usually two versions of the facts. The court, on the basis of the evidence before it, chooses that version which it thinks is 'more probable', that is, it will accept a version which a prudent man will act upon the supposition that it exists. On the other hand, in criminal cases, the prosecutors must satisfy the court that the existence of a fact is not only probable, but that its existence is beyond reasonable doubt. Simply put, the prosecution must satisfy the court that 'a reasonable alternative version is not possible' (185th Report of the Law Commission). Courts have explicitly acknowledged that it is not possible to mathematically define the degree of probability for meeting a certain standard of proof and there is an inherent subjective element within each of these standards. (State of UP v. Krishna Gopal and Anr.)

The other two reasons are inter-connected. SEBI exercises regulation-making, executive and quasi-judicial powers in connection with the securities market. It defines what conduct would constitute fraud for the purpose of exercising its enforcement powers. The concept of fraud under the SEBI (Prohibition of Fraudulent and Unfair Trade Practices) Regulations, 2003 (PFUTP Regulations) - a regulation that defines fraudulent conduct in the securities market - is wider than what is understood as fraud in common law and the codified law applicable to fraud in India. It dispenses with critical elements such as intent, deceit and damages. A wider definition of fraud sets the bar very low for establishing securities fraud before a court or tribunal. SEBI is also responsible for conducting investigations of suspected fraud and makes decisions on whether the conduct investigated meets its definition of fraud. This violates the constitutional scheme of the separation of powers that applies to areas of public administration which are not governed by a technocratic regulatory agency. As an example, the Indian Penal Code defines what constitutes theft, the investigation is conducted by the executive agencies and the decision on whether the investigated conduct amounts to theft (as defined in Parliamentary law) is made by the judiciary.

The concentration of all three powers in a single body creates scope for bias towards a lower standard of proof. Empirical work done on the cases investigated by the Securities Exchange Commission, the securities regulator in the United States, is indicative of such bias. The SEC is empowered to choose whether to pursue a proceeding before one of its own internal administrative law judges or an independent federal court. Reportedly, while the SEC enjoyed a 90% success rate in its own hearings, it had only a 69% success rate against defendants in federal court. (here and a perma link here)

In February 2018, a judgement of the Supreme Court further diluted the standard of proof for securities fraud in India in a case involving synchronised and reverse trades executed on the exchange. The Supreme Court reversed the decision of the Securities Appellate Tribunal which had held that although the trades in question might have been synchronised, they were not manipulative and "market manipulation of whatever kind, must be in evidence before any charge of violating these Regulations could be upheld." The Supreme Court dispensed with the need to show manipulation and relied on the notion of "market integrity" as a standard for adjudging the conduct of market participants. SEBI has extensive powers to sanction wrongdoing in the securities markets, such as the power to bar access to the market, suspend professional licenses and impose hefty monetary penalties. The Supreme Court's ruling has serious implications for the manner in which these powers are exercised as it effectively introduces a new standard of proof of 'market integrity' to be met by persons accused of securities market fraud.

What is market integrity?

The notion of market integrity is both un-defined and hard to measure. World over, there is considerable debate on its meaning in the context of financial regulation (Austin, 2016). Given the subjectivity of the concept, using market integrity as a standard of proof is equivalent to using 'public interest' or 'public good' as a standard for establishing wrongdoing. It leaves tremendous scope for discretion and creates the potential for differential standards of enforcement across a range of practices, depending on the adjudicator's view of whether or not a particular trading practice affects market integrity. This creates uncertainty in the manner in which the law will be applied and enforced and has adverse implications for the rule of law. Ambiguity in the grounds of enforcement and the standard of conduct that could invite legal sanctions, is detrimental to the development of the market as well.

In this article, we advocate the use of empirical approaches for establishing wrongful conduct in the securities markets. We do this by demonstrating the empirical evidence that should have been used to support a claim of fraud in the very same case where the Supreme Court lowered the standard of proof by relying on the vague and problematic ground of market integrity. In a world of electronic trading, empirical evidence of fraud and its impact on the market is not hard to collect and investigators and courts must rely on such evidence instead of holding market participants to a vague and subjective notion of market integrity and unfairness, which the world at large is struggling to define.

Judgement of the Supreme Court in Rakhi Trading

In February 2018, the Supreme Court in the case of Securities and Exchange Board of India v. Rakhi Trading Private Ltd. upheld an order passed by the Securities and Exchange Board of India (SEBI) that levied a penalty on some traders who had synchronised their trades off the exchange before placing them on the exchange. The trades in question were a series of orders placed on the F&O segment of the Nifty index. The modus operandi was to place an order for Nifty options, which matched with a particular party and subsequently reverse the position taken by placing an opposite order, which also matched with the same party. SEBI penalised the party placing such orders on the ground that these "transactions were in the nature of fictitious transactions resulting in creation of misleading appearance of trading in these options." The SEBI order did not elaborate the manner in which the synchronised trades sought to manipulate the price of either the option itself or the underlying securities, which in this case, was the basket of securities included in the Nifty index.

The parties against whom this order was passed appealed against the order before the Securities Appellate Tribunal (SAT). SAT confined its review to whether the synchronised trades in the F&O segment of the Nifty index artificially manipulated the underlying cash segment, which in this case is the Nifty index itself. It observed that:

"To say that some manipulative trades in Nifty options in the F&O segment could influence the Nifty index is too farfetched to be accepted. The only way Nifty index could be influenced is through manipulation of the prices of all or majority of the scrips in the cash segment that constitute Nifty."

This in line with precedent case-law laid down by SAT on the requirement to show manipulative conduct to demonstrate that the synchronised trades constituted fraud under the PFUTP Regulations. On an appeal by SEBI against this order of the SAT, the Supreme Court reversed the order of the SAT without explaining how the synchronised trades in question affected the price discovery system or created a misleading impression of volumes, but emphasised the notion of market integrity as under:

"According to SAT, only if there is market impact on account of sham transactions, could there be violation of the PFUTP regulations. We find it extremely difficult to agree with the proposition...SAT has missed the crucial factors affecting the market integrity, which may be direct or indirect (emphasis supplied) ...By synchronization and rapid reverse trade, as has been carried out by the traders in the instant case, the price discovery system itself is affected."

The problem with synchronised trades

Synchronised trading involves pre-negotiating the trade off the exchange and subsequently placing the order on the exchange such that it matches with the counterparty with whom the trade was pre-negotiated. To synchronise trades on an exchange platform, the buyer and seller of the pre-negotiated trade will enter their respective orders at the same time (with same price and quantity) to maximise the chance of matching their orders against each other. To ensure that the order does not match against another counterparty, the first order may be placed away from the touch, that is, at a price significantly different from the ongoing bid / ask price.

By itself, synchronised trading is not a harmful practice. In fact, block trades for which exchanges have a block trading window are synchronised trades. It is difficult to synchronise trades on liquid securities on the exchange because such orders run the risk of matching against other counterparty(ies). However, even on relatively illiquid securities, synchronised trading is not risk-free. The probability of getting hit by another order (especially a market order) on the opposite side of the book is low, but not zero.

How can synchronised orders be used to manipulate the market? Synchronised trades could create misleading, artificial trading interest in a security. High volumes and significant price changes on an otherwise illiquid security may cause participants to believe that there is some news on the security. This may induce them to buy those securities based on unexplained changes in prices and volumes. This is especially possible in an illiquid scrip, which may be perceived to be suddenly liquid if a series of transactions are executed on such a scrip. While the SEBI order levying the penalty does not specify exactly what was manipulated, evidence of manipulation could be demonstrated in either or all of the following ways:

1. Manipulation of Nifty index: One way of determining manipulation in the Rakhi Trading case is by analysing the changes in the value of the underlying security, the Nifty index. The SAT order almost exclusively focused on the possibility of the synchronised trades having manipulated the value of the Nifty index. The order rightly concludes that price manipulation on the index can happen only if an equivalent position is taken on the spot market on all the 50 constituent stocks of the index. Neither the SEBI order nor the Supreme Court order show any evidence of a position by Rakhi Trading on the spot market. This leaves the scope of manipulation on the Nifty index value to the price transmission from derivatives market to the spot market. Empirical evidence on price transmission from derivatives market to spot market suggests that such transmission is subject to the liquidity of the derivatives instrument (see Fleming et al, 1996, Aggarwal and Thomas, 2019). We examine the liquidity of the relevant Nifty options and also examine if the volumes traded by Rakhi Trading in the options segment were significant enough to impact the value of the underlying Nifty index.
2. Volatility in option premium: Manipulation of stock price increases the volatility in returns on such stocks (Aggarwal and Wu, 2006). If the synchronised trades on the Nifty options were manipulative, we would expect that the real price discovery process on option premium would be hampered, resulting in higher volatility in the premium of the Nifty options in question. This manipulation can be established by examining the volatility of the premium on the Nifty options on which the synchronised trades were executed.
3. Jump in traded volumes: Similar to price efficiency, manipulative trades on a security create a misleading impression and induce other market participants to buy the security. This misleading impression will manifest itself through higher traded volumes on that security on the days of such trades. We examine if this was the case by examining the traded volumes of the Nifty options traded by Rakhi Trading around the dates on which such trading occurred.

Size of the market for the relevant Nifty options

Rakhi Trading executed synchronised trades on 13 specific Nifty option contracts (hereafter referred to as "the relevant Nifty options") on four days of the year 2007, namely, on March 21, March 22, March 23 and March 30 (hereafter,"the event days").

We begin by a simple comparison of the liquidity and volatility of the relevant Nifty options on the event days and compare it with other days between March 15, 2007 and March 31, 2007 (hereafter, "non-event days"). Such a comparison should be the beginning of the court's enquiry when dealing with an order punishing a market participant for securities market fraud. Table 1 provides basic summary statistics on the traded volumes (a measure of liquidity) and volatility of the relevant Nifty options.

Table 1: Volumes and volatility of the relevant Nifty options on event days and non event days

	Event days			Non Event days
	Total Volumes	Volatility	Rakhi Trading Volumes	Total Volumes	Volatility
		(%)			(%)
Min	9,550	0.97	2,000	50	0.10
Mean	26,142	6.19	10,015	7,712	2.25
Median	25,750	5.08	10,700	3,925	0.37
Max	38,600	14.85	11,900	50,050	17.62
SD	7,752	3.86	2,477	10,718	4.39

The key observations from Table 1 are as follows:

1. First, the traded volumes of the relevant Nifty options fluctuated significantly on a daily basis on the event and non-event days, ranging from 9,550 to 38,600 on the event days, and 50 to 50,050 on the non-event days.
2. Second, in comparison to the average daily traded volumes on a liquid Nifty option, Table 1 shows that the relevant Nifty options were relatively illiquid. For a frame of reference, the maximum traded volumes on one single Nifty option in March 2007 was 9.2 million.
3. Third, the volatility (measured by the Parkinson's range measure) of the relevant Nifty options was in the range of 1-15% on the event days, while it was in the range of 0-18% on the non-event days.

Did the synchronised trades manipulate the Nifty index?

Table 2 gives a picture of the size of the overall Nifty options market on the event days and compares it for non-event days based on traded volumes. It also shows the traded volumes of the stocks which constitute the Nifty index for these dates. The last column of the table shows the volumes and contracts traded by Rakhi Trading on the event days to provide a perspective on the possible influence of its trades on the overall options and underlying spot market.

Table 2: Size of overall Nifty options market and proportion of synchronised trades

		Non-event days	Event days	Rakhi Trading volumes
All Nifty Options	Traded volumes (in Rs. millions)	49,978	61,041	277
	Contracts (in `000s)	262	316	1.35
Nifty stocks	Traded volumes (in Rs. millions)	36,228	37,932	NA

The key observation from Table 2 is that on the event days, the volumes traded by Rakhi Trading on the Nifty options were less than one percent of the average traded volumes on the stocks that constitute the Nifty index on the spot market. The minuscule proportion of the volumes traded by Rakhi Trading in the Nifty options market relative to the total traded volumes on the stocks constituting the Nifty index on the spot market, re-affirms the finding of the SAT that the synchronised trades executed by Rakhi Trading could not have possibly manipulated the underlying Nifty index.

However, Table 2 also shows that the volumes and number of contracts on the Nifty options segment on the event days were higher than on non-event days. This might or might not have been due to the synchronised trades executed by Rakhi Trading. In the next few paragraphs, we zoom in our analysis on the specific Nifty options which were involved in the synchronised trades executed by Rakhi Trading and examine if those trades did manipulate the individual options traded by Rakhi Trading.

Did the synchronised trades manipulate the option premium?

To test the claim of manipulation of option premium, we examine the volatility of the premium of the relevant Nifty options, and compare it with the volatility of the premium of other Nifty options with similar liquidity. We call the former as the treated set, and latter as the control set. We identify the control set as the options on which the traded volumes were in the same range as that on the ``treated" set, to ensure comparability across the two sets. If there was indeed manipulation on the treated options, we expect the volatility of the treated set to be higher than that of the control set.

We obtain a total of 50 unique options in the control set which we compare with data on the treated set on the event days. Table 3 presents summary statistics on the volatility of the premium for the options in the treated set and control set for our period of analysis.

Table 3: Summary statistics on volatility of options premium on the treated and control sets (in %)

	Treated	Control
Min	0.97	0.25
Mean	6.19	4.78
Median	5.08	2.91
Max	14.85	35.17
SD	3.86	5.09

We observe that the average volatility of the treated set was slightly higher than that of the control set. However, a simple t-test of comparison of means of the treated and control set volatility shows that the difference between the volatility of the two sets is not statistically significant. In a regression analysis (not shown here), we also control for other factors that affect volatility of the option premium of the treated and control sets. We do not find any evidence of significant difference across the two sets even after controlling for other factors such as strike price, days to expiry, value and volatility of the underlying. The analysis finds that the price range in which the option premium varied for the treated set was similar to that of the control set. Thus, we find that the option premium on the Nifty options that were traded by Rakhi Trading was not manipulated.

Did the synchronised trades manipulate the volumes?

We also examine the question whether the synchronised trades in the relevant Nifty options led to higher volumes in these options thereby creating a possibly misleading impression of volumes. For this, we analyse the traded volumes on the relevant Nifty options, after excluding the volumes arising out of synchronised trades themselves. We compare the traded volumes of the relevant Nifty options on the event and non-event days (Table 4). If the synchronised trades did result in higher trading activity from other market participants, we expect to see significant difference in the traded volumes on the event and non-event days.

Table 4: Summary statistics on traded volumes of options traded by Rakhi Trading

	Event days	Non-event days
Min	0	50
Mean	5,342	5,170
Median	4,500	2,450
Max	15,400	21,350
SD	5,736	5,762

Table 4 shows that the traded volumes on the Nifty options involved in the Rakhi Trading case were, on an average and on a median scale, slightly higher on the event days compared to the non-event days. However, a statistical test (t-test) of the comparison of means finds no significant difference across the two sets. A regression analysis on the event and non-event set confirms this finding. This indicates that Rakhi trading trades did not lead to any jump in volumes in the options so traded.

Trading for tax evasion or tax planning

An ancillary concern expressed by the Supreme Court was that Rakhi Trading conducted the trades in question for tax planning or avoidance. While this may or may not be true, the securities markets regulatory framework should not be used for punishing tax evasion. Cases of trades that SEBI has reason to believe were meant for tax avoidance, must be reported to the tax authorities, which is the appropriate forum for addressing questions of tax evasion. The objective of the securities market regulatory regime is not to deal with tax evasion, but to protect investors and develop the securities markets. More importantly, a regulator has scarce resources and dedicating investigative and adjudicatory capacity for dealing with tax evasion cases is not the best use of these resources.

Conclusion

Our empirical analysis finds that the synchronised trades did not manipulate the underlying Nifty index, the premium on the relevant Nifty options or the traded volumes of the relevant Nifty options. However, we recognise that our analysis is limited to daily data. An analysis of this kind must be underpinned by examining the intra-day data around the time of synchronised trades. By using such data, the regulator can further make a case for whether the trades in question were indeed manipulative.

The objective of this article is not to establish the guilt or innocence of any specific market participant. By using publicly available inter-day trading data on the security involved in the Rakhi Trading case, we make a case for using empirical strategies to establish fraudulent conduct under the Indian securities regulatory regime. As demonstrated above, the advanced nature of the securities market infrastructure in India and the availability of data ensures that this is not difficult. The use of empirical evidence to substantiate charges of fraudulent conduct will ensure that enforcement orders pass the muster of the appellate forums without having to compromise on evidentiary standards for establishing guilty conduct. More importantly, backing enforcement with robust underlying evidence will help the regulator build the trust of the regulated and testify to the high standards of proof that our society should place for the deprivation of liberty.

References:

Stock market manipulations by Aggarwal R and Wu G, The Journal of Business, 2006.

When stock futures dominate price discovery by Aggarwal N and Thomas S, Journal of Futures Markets, 2019.

What exactly is market integrity? An Analysis of One of the Core Objectives of Securities Regulation by Austin J, William and Mary Business Law Review, Vol.8(2), 2017.

Trading costs and the relative rates of price discovery in stock, futures, and option markets by Fleming J, Ostdiek B and Whaley R, Journal of Futures Markets, 1996.

State Of U.P vs Krishna Gopal & Anr 1988 AIR 2154.

185th Report of the Law Commission of India on a Review of the Indian Evidence Act, 1872.

Toward a General Theory of Standards of Proof, Frederick E. Vars, Catholic University Law Review, Vol. 60(1) (Fall 2010).

 

Nidhi is faculty at IIM-Udaipur and Bhargavi is a researcher at IGIDR.

Are fleeting orders by high frequency traders a source of market abuse?

by Nidhi Aggarwal and Chirag Anand.

SEBI recently released its discussion paper on algorithmic trading. The paper proposes several measures to address various concerns that have been expressed about the rise of new technology in the field of financial markets. One of the candidate interventions that SEBI has proposed is the imposition of 'minimum resting time for orders'. SEBI proposes imposing a resting time of 500 milliseconds (ms) during which an order will not be allowed to be amended or cancelled. In this article, we bring evidence to bear on this one candidate intervention.

The rationale for the proposed measure is to curb 'fleeting orders' or orders that appear and disappear within a very short period of time. SEBI's proposed regulatory intervention, that there should be a minimum resting time of 500 ms, may suggest that orders modified/cancelled in less than 500 ms are considered by SEBI to be fleeting orders, though the discussion paper does not say this explicitly.

A central objective of the regulation of financial markets is to block market abuse. How can fleeting orders be connected with market abuse? Orders without a clear intent to trade may falsify perceived liquidity and price in the marketplace. Through this, placing fleeting orders could be a tool for misleading other traders. In the market abuse literature, there is a concept known as "order spoofing". This involves placing a visible order in the opposite direction of the trade that is genuinely desired. For example, a seller might post a small buy order priced above the current bid, in the hope of convincing other buyers to match or outbid this. If that occurs, the trader can then sell into this (higher) price.

Fleeting orders can also contribute to "quote stuffing", which can affect the ability of other traders to send their orders to the exchange by essentially flooding the systems. This is tantamount to the strategem in the field of computer security that is called `denial of service attacks'.

There are, however,  legitimate and  important reasons for rational persons to place fleeting orders. A trader may cancel and resubmit a limit order when the market moves away from the original limit price. This will especially occur during volatile times when information arrival in the market is high. Many trading strategies look at the touch -- at the bid and the ask price -- and not just at the last traded price.

A perfectly legitimate trading strategy runs as follows:

Watch the bid and the ask price, continuously compute (bid+ask)/2 which is the reference price, always have a limit order to sell at 0.1% above this reference price. 

A person may use such an algorithm to sell a large block of shares while hoping to get a good execution price. This algorithm would dance continuously, refreshing the limit order every time (bid+ask)/2 changes, which is much more often than a change in the last traded price. That is, this trading strategy would undertake more revisions per unit time when compared with the number of trades per unit time.

Every now and then, a trader might just switch a limit order to a market order to get immediate execution (Hasbrouck and Saar, 2009). This would look like a fleeting order as the trader changed his mind and scrapped a limit order after a very short time.

Before designing an intervention, SEBI needs to examine the data to look at the fraction of and nature of fleeting orders in the market. The discussion paper has no evidence about the existence of fleeting orders, or evidence that there are problems in what is going on at Indian exchanges. Without a clear demonstration that the issue exists, the coercive power of the State should not be used. If we go down the path of using State coercion without the foundations of hard evidence, then there is a high chance that State power will merely reflect competing political pressures where various factions try to use State power as a tool for furthering their business objectives.

In this article, we analyse the questions surrounding fleeting orders using data for orders and trades from the National Stock Exchange of India.

Data description

The database and computational challenges of such work are immense. Hence, we use two months of data for the analysis.

Ideally, this work should have been done using data for June and July 2016, but our computational infrastructure broke down in January 2014, and we were forced to make do with the most recent available complete months, which were November and December 2013. The intensity of algorithmic trading in November and December 2013 is the same as that which has prevailed in the following months. Hence, we are on sound grounds when we analyse that data.

There were 6.5 billion records of data in these two months, which were studied for the purpose of this article.

Order cancellations

A large fraction of orders on NSE are cancelled. In an analysis that we did in July 2015, where we studied the same months of November and December 2013, we found that 56.97% of new orders that entered the spot market, 94.11% of the orders on the single stock futures (SSF) market, 88.55% of orders on the  single stock options (SSO) market, 82.58% of the orders on the Nifty futures market, and 87.51% of the orders on the Nifty Options market were cancelled.

Order cancellation is clearly a valuable tool for most traders on electronic markets. This is seen internationally also. For example, Hasbrouck and Saar (2002) find that 93% of limit orders are cancelled on INET. This is true for other exchanges including NYSE, the Australian Securities Exchange and so on.

In a deep sense, algorithmic trading is merely trading by other means.  Using data from NASDAQ, Subrahmanyam and Zheng (2015) document that cancellation ratios of high frequency traders are similar to that of the non-high frequency traders.

A large percentage of cancellations does not imply the existence of fleeting orders. A fleeting order involves placing a limit order inside the touch (i.e. between the bid and the ask) and then quickly cancelling it (Fong and Liu, 2010). There are three steps in identifying fleeting orders: We must count (a) Cancelled orders, (b) Which were cancelled quickly and (c) Which were near the touch. We will now do these calculations for the NSE spot and SSF markets.

Duration of cancelled orders

We analyse the securities which were traded on the derivatives market in 2013. These were the top 150 firms. We group these securities by market capitalisation. The securities with the highest market capitalisation are in Q1, and the securities with the lowest market capitalisation are in Q4. For each quartile, we measure the fraction of orders which were cancelled. We go on to measure the fraction of these order cancellations which took place in under 1 second. This is a conservative value when compared with SEBI's proposal of 0.5 seconds. If SEBI's proposed threshold of 0.5s were used, the fraction of orders seen would be lower.

Table 1: Order cancellations on Spot and SSF market in 2013

All values as % of total unique orders entered
Panel A	Orders cancelled
Market Cap Quartiles	Spot	SSF
Q1 (Highest)	67.23	94.06
Q2	58.83	91.15
Q3	51.58	90.62
Q4 (Lowest)	41.19	85.12
Panel B	Orders cancelled in less than 1 second
Q1 (Highest)	36.84	70.06
Q2	28.11	61.05
Q3	22.23	58.06
Q4 (Lowest)	12.60	45.23

Panel A of Table 1 shows the share of cancelled orders in all unique orders, while Panel B shows the share of orders cancelled within one second of arrival in all unique orders. We see that in comparison to the SSF market, the spot market experiences a lower percentage of orders cancellations within one second of arrival. In addition, we see that the percentage of order cancellations within one second is higher for large market capitalisation stocks. This is consistent with the fact that the biggest firms are the subject of the most intensive scrutiny by the financial markets.

The biggest value in Panel B is 70.06%: A full 70.06% of the SSF orders for top quartile stocks are cancelled within 1s. The smallest value is 12.60%: Just 12.60% of the spot market orders for bottom quartile stocks are cancelled within 1s. We should not that this is the bottom quartile within the top 150 stocks on NSE, i.e. it is the stocks from rank 113 to 150.

We now turn to measuring the extent to which these fast cancelled orders could be termed fleeting orders. We only focus on the orders cancelled within one second of their arrival.

Position of 'fast' cancelled orders before exit

The table below shows the position, in the limit order book, of fast cancelled orders before they were cancelled. 'At best' indicates that the order was at the best prices in the book, (1,3] indicates that the order was placed at depth two or three in the order book, (3,5] indicates that the order was placed at depth four or five in the order book, and (>5) indicates that the order was placed beyond the top five prices in the order book.

Table 2: Position of fast cancelled orders in the order book in 2013

All values as % of total unique orders entered
	'Fast' cancelled orders: Orders cancelled in less than 1 second
Market Cap Quartiles	At best	(1, 3]	(3, 5]	(>5]	Sum
Panel A: Spot
Q1 (Highest)	2.47	5.46	5.59	23.31	36.83
Q2	5.22	7.25	5.09	10.55	28.11
Q3	7.12	6.54	3.07	5.50	22.23
Q4 (Lowest)	5.14	3.82	1.56	2.08	12.60
Panel B: SSF
Q1 (Highest)	3.66	8.19	9.83	48.37	70.05
Q2	6.18	11.18	10.89	32.82	61.07
Q3	4.96	10.75	12.09	30.27	58.07
Q4 (Lowest)	5.70	13.04	11.30	15.19	45.23

The value 2.47 in the first row of the table indicates that for the stocks with highest market capitalisation, 2.47% of orders were at the best prices, i.e. at the touch, and were rapidly cancelled. Similarly, the value 5.59 in the first row shows that for the highest market capitalisation stocks, 5.59% of the orders were at the best fourth or the fifth price level in the order book, and were rapidly cancelled. The last column adds up all the previous columns, and matches up with the share of orders cancelled within one second, which is Panel B of Table 1.

This table offers fascinating evidence about high frequency trading in India:

1. The incidence of fleeting orders is very small: The biggest value seen is for Q3 stocks on the spot market, where 7.12% of orders were at the best prices and were cancelled within one second of their arrival.
2. An overwhelming majority of fast order cancellations occur away from the best prices. As an example, in the 1st row, fast order cancellations were 36.83% of orders, of which 2.47 percentage points were at the touch.
3. Stocks with the highest market capitalisation, where algorithmic trading is the most intense, experience a low incidence of fleeting orders as a share in total orders.

We cannot examine the intent behind these cancellations since it requires the knowledge of trader-identities for further analysis, which we do not have in our data.

Implications

Good regulation making requires data analysis and scientific evidence. The legislative function of regulators (i.e. the drafting of regulations) is primarily a research function: it requires deeply understanding the world, identifying market failures, and identifying parsimonious instruments of intervention that go to the root cause of the market failure. Globally, regulators such as the SEC and ASIC have deployed empirical research to determine the need for an intervention. The FSLRC handbook requires that regulators must do cost-benefit analysis before issuing any new regulation, where such research would be an early first stage of the regulation-making work. These capabilities are required in regulators in India if we are to build high performance organisations.

Our analysis above has many important implications for the policy analysis of the proposed minimum resting time of 500 ms:

1. We used a more conservative measure -- order cancellation within 1 s. We find little evidence of fleeting orders in India.
2. We have undertaken the first stage of the research -- counting fleeting orders. If the coercive power of the State were to be used in proscribing fleeting orders, SEBI needs to show evidence that this small proportion of fleeting orders is adversely affecting market quality.
3. A regulation that interferes with all orders in order to influence the tiny proportion of fleeting orders is placing a burden upon society at large because it wishes to block a rare event. We should think more about the tradeoffs between prevention and enforcement. Perhaps it would be better for SEBI to build knowledge about how to enforce against market abuse in the HF environment, instead of imposing the costs of prevention upon society. SEBI's proposal raises concerns about the possibility of faulty tradeoffs in security. There is a public choice theory problem here: It is a stroke of the pen for SEBI to impose restrictions upon citizens, while it is hard work for SEBI to build State capacity in enforcement.

SEBI's discussion paper proposes seven interventions:

1. Minimum resting time for orders
2. Frequent batch auctions
3. Random speed bumps of delays in order processing/matching
4. Randomisation of orders received during a period (say 1-2 seconds)
5. Maximum order message-to-trade ratio requirement
6. Separate queues for colo orders and non-colo orders (2 queues) 
7. Restrict access to tick-by-tick data feed.

This article deals with the first: minimum resting time. As emphasised above, our work is limited: We have only counted fleeting orders, we have not gone into the question of demonstrating that fleeting orders have an adverse impact upon market quality. This kind of research is required on all the other six proposed interventions before policy decisions can be taken. This suggests the scale of research capabilities which are required before wielding the coercive power of the State in the legislative wing of a financial regulator.

References

The causal impact of algorithmic trading on market quality by Aggarwal N, Thomas S, 2014, IGIDR Working Paper.

The changing landscape of equity markets by Aggarwal N, Anand C, 10 July 2015, Ajay Shah's blog.

Limit Orders and Volatility in a Hybrid Market: The Island ECN by Hasbrouck J and Saar G, 2002, Working Paper, New York University.

Technology and liquidity provision: The blurring of traditional definitions by Hasbrouck J, Saar G, 2009. Journal of Financial Markets, Volume 12, Issue 2, May 2009, p. 143-172.

Limit order revisions by Fong K and Liu W, 2010, Journal of Banking and Finance, Volume 34, Issue 8, August 2010, p. 1873-1885.

Limit Order Placement by High-Frequency Traders by Subrahmanyam A and Zheng H, Working Paper, 2016.

SEBI's new "trade annulment" policy

by Nidhi Aggarwal, Chirag Anand.

One of the main functions of organised financial markets is efficient price discovery. Under certain circumstances, prices fail to reflect the correct information. These include erroneous trades or market manipulation. Trades at non informative prices impose significant costs on other market participants and the public at large. There can be a market failure in the form of a negative externality imposed on other market participants by way of distorted prices.

As has been found around the world, incidents of erroneous trades and market manipulation have been experienced in India, which have adversely affected the functioning of markets. Erroneous trades can occur either due to the so-called fat-finger trades, trades caused by bad algorithms, or buggy software. With the growth of algorithmic trading and an overall increase in the number and pace of orders entering the exchanges, the probability of such occurrences may increase.

In an attempt to address these concerns, in a circular dated July 16, 2015, the Securities and Exchange Board of India issued guidelines for annulment of trades by stock exchanges. These new provisions are required to be implemented by the exchanges within one month of the issue of the circular. The policy comes after a discussion paper released by SEBI in October 2013 on the same topic. The paper had sought public comments on a policy that proposed trade annulment on occurrence of erroneous trades under "exceptional circumstances". The Finance Research Group at IGIDR had responded to the discussion paper with an analysis of the proposed intervention by SEBI. We argued that the costs of cancelling trades under any circumstances are much higher than the benefits envisioned. The published regulation suffers from the same flaws that were present in the discussion paper.

The new framework

Traditionally, stock exchanges have been empowered to annul trades either suo moto, or on receipt of requests from stock brokers. Under the new regulation, the regulator has directed the stock exchanges to consider a trade annulment request if it is sent within 30 minutes of execution of the trade(s). This time limit can be extended to 60 minutes in case of "exceptional circumstances". The stock exchanges are required to communicate the receipt of such requests in a time bound manner to all market participants and convey a decision on the request not later than the start of next trading day. With respect to examining such requests, the circular mentions:

"2.5. .. While examining such requests, stock exchanges shall consider the potential effect of such annulment on trades of other stock brokers/investors across all segments, including trades that resulted as an outcome of trade(s) under consideration."

"2.7. Stock exchanges shall undertake annulment or price reset only in exceptional cases, after recording reasons in writing, in the interest of the investors, market integrity, and maintaining sanctity of price discovery mechanism."

In addition, stock exchanges are required to define "suitable" criteria so as to discourage frivolous trade annulment requests from stock brokers. The regulator has asked exchanges to charge stock brokers an annulment application fee which could range between Rs 1 lakh to Rs 10 lakh. The circular also says that the exchange shall penalise brokers who put in erroneous orders.

Evaluating the new framework

A clear policy on erroneous trades is a welcome step from the regulator, since a) it addresses the market failure induced by hurting the normal course of price discovery process, and b) it removes the uncertainty for other market participants on how such trades will be handled in the event of their occurrence. However, as has been argued on this blog before (see here, here and here), trade annulment is a bad solution for numerous reasons:

1. Moral hazard: Bailing out trading firms by cancelling trades introduces moral hazard. These firms are supposed to have adequate risk control systems. With such an option in place, trading firms will be less careful in building high quality algorithms or trading systems.
2. Deters market stabilising trading strategies: In the case of extreme events, two types of trading strategies help markets to recover: First, strategies that place orders far away from the touch, and second, the presence of active traders who come into the market to take opposite position. These strategies are often high risk strategies. Trade annulment will deter these traders to enter the market during stress events. This will reduce market resilience. The idea should instead be to make markets more resilient to such shocks.
3. Hurts other market participants: Several market participants (especially the liquidity providers) generally take positions across several asset classes (example: equity spot and futures). Cancellation of trades on one asset class leaves them exposed to risk on the other leg.
4. Leaves space for regulatory capture: The current policy essentially leaves the decision for trade annulment to the subjective satisfaction of the exchanges. Such vague powers give the exchanges undue power and leaves space for regulatory capture -- a dominant group of traders or large firms with large share in trading will stand to benefit under such a system, while day traders and other small traders will lose out. A previous instance of trade annulment by BSE after a trading error shows how giving powers to the exchanges to decide on trade annulment can result in undesired outcomes.
5. Moral hazard in trading strategies: It is difficult to ascertain whether trade cancellation requests are made in good faith. Once the law allows that fat finger trades can be annulled, rogue traders can take advantage of that rule to enter into trades and get them cancelled subsequently. As an example, imagine the following steps: Long nifty vol followed by a big fat finger trade on Nifty spot followed by closeout of the options position.
6. Ambiguity in language: Section 2.7 of the regulation uses the phrase "in the interest of the investors, market integrity". These are very broad terms, and, as discussed above, open to subjective interpretation by the exchanges. Such words should not be used when drafting law.

The issue of erroneous trades is analogous to the issues of industrial safety, where a failure occurs when a firm fails to deploy adequate safety measures to prevent catastrophic events. The Bhopal gas tragedy in 1984 is an example of such a failure. The gas leak accident at Union Carbide India Ltd. caused several deaths and affected many thousands of people. The firm failed to deploy enough resources to have developed a safety mechanism to avoid such a catastrophe.

By this reasoning, trade annulment should be prescribed only if the exchange's order matching software fouls up, or if there was a systematic breakdown of connectivity to the exchange. In all other circumstances, trade annulment is a poor strategy. It messes up the sanctity of order matching processes and questions the finality of trades. It provides wrong incentives to the doers of such acts and sets all the wrong precedents. A trading firm should be mandated to place proper checks and balances in their operations, and in the event of such a failure, should be penalised to compensate for the damage caused to other participants in the system.

Taking the example of other jurisdictions, internationally, such trades are cancelled only if the price movements are beyond certain thresholds. Even though such a practice is not recommended and often debated, there are clearly laid rules on when a trade will be termed as clearly erroneous. This leaves no ambiguity with respect to an exchange's decision on when a trade will be annulled versus not. The question of such thresholds does not arise in India since Indian exchanges already have hygiene checks in the form of margin money, price bands, circuit filters, pre-trade order limits, that should not allow large price movements beyond thresholds. Exchanges are responsible for maintaining and running perfectly functional systems with all these hygiene checks well in place. These systems should guarantee functionality and is a service provided by the exchange to the investors. Any failure in a service should be considered a breach of contract between the exchange and the investor, and the exchange should be penalised subject to the terms of the contract.

Good governance practices

The Handbook on adoption of governance enhancing and non-legislative elements of the draft Indian Financial Code issued by the Ministry of Finance in December 2013 states the good governance practices for issuing regulations. Section 4.4 'Comments on draft Regulations' of the handbook says:

"The regulator has to publish all representations received, and at least a general account of the response to the representations while publishing the final regulations."

SEBI has failed to publish an account of the representations made and explain why the clear criticisms were rejected.

Conclusion

SEBI's new policy on trade annulment does not address the issue of erroneous trades appropriately. If anything, it only leaves a great deal of ambiguity. Public policy thinking and regulation-making should be done in a more rational way.

Self trading is not synonymous with market abuse

by Nidhi Aggarwal, Chirag Anand, Shefali Malhotra, and Bhargavi Zaveri.

1   Introduction

Orders that match with each other with no resultant change in the ownership are termed as self-trades. Lately, there have been increased concerns regarding self-trades in equity markets in India. With no genuine trading intent, these trades are seen as manipulative in nature, aimed at artificially pumping up the turnover to portray a false picture of liquidity. Self-trades are prohibited under the present law, and SEBI has punished several firms on this score.

In this article, we argue that there are some kinds of self-trades which do not constitute market abuse. With no manipulative or fraudulent intent, a trading firm can hit its own bid or offer. Penalising firms in such situations is wrong, and can act as a deterrent to trading in capital markets. Internationally, regulators have realised such possibilities, and taken necessary steps to ensure that legitimate cases of self-trades do not get punished. The Indian regulator needs to undertake similar steps.

2   Legitimate self-trades

Self-trades are generally considered to be non bona fide transactions. However, there can be instances where genuine trading intentions within the same firm result in self-trades. Such trades can occur in the course of normal trading when i) orders from two independent trading strategies coincidentally interact with each other, or orders originated from the same trading desk match with each other due to technical and operational limits of the existing infrastructure (such as matching engine technology). The following text illustrates such situations in detail.

2.1   Manual trading

Proprietary trading firms typically have several dealers operating in multiple securities. These dealers, independently, deploy trading strategies to make profit and to manage their own risk. Orders from these independent dealer desks originate from accounts with common ownership. Such orders, though initiated with legitimate purposes, can result in self-trades.

As an example, suppose a firm has two independent dealers. Both these dealers could be separated from each other by information barriers. Suppose they pursue following strategies:

• Dealer 1: Arbitrage between BSE-NSE stock prices
Under this strategy, arbitrage opportunities arise when the price of a security trading on both BSE as well as NSE diverges significantly. By selling the security on the exchange with higher price, and buying on the one with lower price, a trader can make arbitrage gains. 

• Dealer 2: Bullish strategy
In this strategy, the trader has a view about the direction of a security's price based on his analysis. If he anticipates that the price of the security is likely to go up in the future, he will buy that security. The trader makes a profit if the price actually moves upward at a later time-period.

Suppose that at a certain point of time, Dealer 1 sees a significant divergence between NSE and BSE prices of a security, with higher price on NSE and lower price on BSE. He, thus, sends a buy order on BSE, and a sell order on NSE to pursue his arbitrage strategy. Dealer 2, at the same time, places a buy order on NSE in pursuit of his bullish strategy on the same security.

Though completely legitimate, and without an intent to manipulate, the two buy-sell orders on NSE from two independent dealers can end up matching with each other. This trade, while being unintentional and completely co-incidental, when tracked at the legal entity level of the parent proprietary firm, will be characterised as a self-trade.

2.2   Algorithmic trading

The incidence of self-trades increases much more in the case of automated trading due to higher speed and the use of algorithms for making trade decisions. Similar to manual trading, two different algorithms within the same firm could be trading completely unrelated strategies. However, orders originating from these algorithms can also interact with each other without any malicious intention.

2.3   Latency issues

Another source of legitimate self-trades could be technological limitations. Exchanges and trader terminals are situated at different physical locations which affect order placement and trade confirmation timings. Orders sent to two different exchanges could reach with a delay because of difference in the speed of computer network lines. This time delay is known as "latency".

Algorithmic trading strategies doing arbitrage across two exchanges continuously send buy and sell orders. Due to latency differences across the two exchanges, traders may get "trade confirmations" exchanges at different time-points. A possible scenario where a self-trade can happen due to such technological issues and with absolutely no malicious intent is described below:

• The arbitrage algorithm keeps sending buy orders to BSE and sell orders to NSE based on a price difference.
• For a particular set of orders, trade confirmation on one leg of the order is received from one exchange, but not from the second exchange.
• Meanwhile, the trader's algorithm sends a second pair of a buy and sell order to BSE and NSE respectively.
• Later, it is realised that the second leg of the first order on BSE did not get execution. The trader will, thus, have to reverse the executed position on NSE for the first order.
• To reverse the position, the trader sends a buy order to NSE.
• This buy order on NSE ends up interacting with the sell order sent in Step 3 resulting in a self-trade.

All of the above are cases of self-trades that can occur within the same firm, but from separate or distinct underlying strategies with genuine trading interest. These examples show that it is wrong to think that all self-trading is market abuse.

3   Regulatory mechanism worldwide

Self-trading in securities is a concern for regulators worldwide. It has, however, been recognised that such trades can also happen with legitimate purposes. As a result, regulators globally have made various changes to accommodate for such transactions.

3.1   The US securities law

In an amendment to the securities law, the US SEC approved a rule change proposed by the Financial Industry Regulatory Authority, Inc. (FINRA) relating to self-trades in 2014. In its description of the proposed rule change, FINRA noted:

• Transactions resulting from orders that originate from unrelated algorithms or from separate and distinct trading strategies within the same firm would generally be considered bona fide self-trades.
  
  Thus, the proposed rule allowed for legitimate cases of self-trades arising from unrelated trading strategies. Caution is however taken in allowing this form of activity by the use of the word "generally". In its response to a comment, FINRA noted:
  

  "although self-trades between unrelated trading desks or algorithms are generally bona fide, frequent self-trades may raise concerns that they are intentional or undertaken with manipulative or fraudulent intent".

• FINRA issued guidelines for members to have policies and procedures in place that are reasonably designed to review their trading activity for, and prevent, a pattern or practice of self-trades resulting from orders originating from a single algorithm or trading desk, or from related algorithms or trading desks.
  
  FINRA noted that even if not purposeful, a material percentage or regularity of such transactions from related desks, may give a misimpression of active trading in the security. This can adversely affect the price discovery process. It is therefore recommended that members must put in place effective systems to prevent such trades. But it also stated:
  

  "the rule will not apply to isolated self-trades resulting from orders originating from a single algorithm or trading desk, or from related algorithms or trading desks, provided the firm's policies and procedures were reasonably designed".

  In defining "related", FINRA stated its understanding that discrete units within a firm's system of internal controls typically do not coordinate their trading strategies or objectives with other discrete units of internal controls, but that multiple algorithms or trading desks within a discrete unit are permitted to communicate or are under the supervision of the same personnel and thus, are presumed to be related. It also stated that the proposed rule permits firms to rebut this presumption, suggesting that a firm could demonstrate that "related" algorithms or trading desks are in fact independent or are subject to supervision or management by separate personnel.

Subsequently, after receiving comments from market participants on the proposed rule change, and minor amendments to the proposed law, the SEC approved the proposed rule change in May 2014.

The current rule reads as follows: Under the FINRA/SEC rule 5210(.02):

"Transactions in a security resulting from the unintentional interaction of orders originating from the same firm that involve no change in the beneficial ownership of the security, ("self-trades") generally are bona fide transactions for purposes of Rule 5210; however, members must have policies and procedures in place that are reasonably designed to review their trading activity for, and prevent, a pattern or practice of self-trades resulting from orders originating from a single algorithm or trading desk, or related algorithms or trading desks. Transactions resulting from orders that originate from unrelated algorithms or separate and distinct trading strategies within the same firm would generally be considered bona fide self-trades. Algorithms or trading strategies within the most discrete unit of an effective system of internal controls at a member firm are presumed to be related."

3.2   The UK securities law

The Financial Conduct Authority's (FCA) guidance also allows self-trades for legitimate cases. As per FCA MAR.1.6.2(2):

"Wash trades: that is, a sale or purchase of a qualifying investment where there is no change in beneficial interest or market risk, or where the transfer of beneficial interest or market risk is only between parties acting in concert or collusion, other than for legitimate reasons".

3.3   Self-trade prevention mechanisms by exchanges

With no information barriers and no technological limitations, it will be optimal that trading firms implement mechanisms to prevent self-trades at their own end. However, such an ideal world does not exist. In such a scenario, could we demand that trading firms establish systems to ensure that self-trading does not happen? Since matching occurs at the exchange's order matching platform, and hence, some degree of self-trades could be difficult to detect at the trading firm's level.

For example, when two separate dealers within the same firm send their orders to the exchange at different time points, those orders may still end up matching with each other if there are no other orders in the book. This can happen if one dealer sends a `buy' limit order at some point, while the other sends a `sell' market order at some later point of time. Similarly, if there is a large `aggressive' buy order sent by one dealer, and a normal sell limit order by another dealer which sits in the book, the `aggressive' buy order will first interact with the higher priority sell orders. If still some balance of this buy order is left, it may then end up matching with the second dealer of the same firm. In yet another case, it can also happen that one dealer sends a limit `buy' order at a point of time, but due to limitations in the exchange's order matching technology, it stands in the queue. After a point, another dealer from the same firm sends a 'sell' limit order and that order stands behind the first dealer's order. These two opposite orders can also ultimately end up matching with each other.

With no malicious intent, in all the above cases, these self-trades are inadvertent and difficult to identify at a trading firm's level. Several exchanges including the NYSE, CME, Euronext, Canadian Securities Exchange, ICE, NASDAQ have implemented self-trade prevention (STP) mechanisms that alert the traders to the occurrence of a self-trade from the same member, and let them make a choice to either, cancel the resting order, or the aggressive order. Some of the exchanges including the ICE and NASDAQ give the a choice to opt for the use for this service at either the company, group, or trader level.

In India, BSE introduced a similar system in January 2015 on its equity derivatives and currency derivatives segment. It extended the facility to the equity segment in March 2015. NSE will be introducing self-trade prevention mechanism in the currency derivatives segment starting August 3, 2015. The systems, on both the exchanges, however, only cancel the incoming (active) order of the client.

4   The current regulatory framework in India

Under the current regulatory framework, Regulation 4(2) of the Securities and Exchange Board (Prevention of Fraudulent and Unfair Trade Practices of Securities) Regulations, 2003 (SEBI (FUTP) Regulations) prohibits a person from indulging in a fraudulent or unfair trade practice.

The operative part of the regulation 4(2) reads as under:

"Dealing in securities shall be deemed to be a fraudulent or an unfair trade practice if it involves fraud and may include all or any of the following, namely:-

(a) ...
(b) dealing in a security not intended to effect transfer of beneficial ownership but intended to operate only as a device to inflate, depress or cause fluctuations in the price of such security for wrongful gain or avoidance of loss; ...
(g) entering into a transaction ...without intention of change of ownership of such security;..."

Since the buyer and seller in a self-trade are the same entity, there is no change in ownership of the shares. Clause (b) of Regulation 4(2) prohibits self-trades originated with manipulative intentions.

5   Issues with past SEBI orders on self-trades

We outline a case below and highlight how SEBI has failed to provide sufficient evidence of market manipulation, and refused to recognise co-incidental and unintentional self-trading activity which occurs (a) within a firm or (b) as a result of algorithmic trading.

In the case of Crosseas Capital Services Pvt. Ltd:

(a) The Adjudicating Officer (AO) said:

"It may be noted that these different CTCL ids belong to the same Stock Broker / legal entity i.e., noticee, therefore, matching of trades amongst them will have to be considered as a 'self-trade'."
...
"Further, the argument of the noticee that final trader id may be identified by CTCL id is not the right interpretation and the self-trades at the member level has to be considered because the UCC for each client is different in case of trading for clients whereas in the case of proprietary trading the trades are executed in member's 'PRO' code irrespective of number of dealers / traders employed to execute the proprietary trading."

The AO, here, considered a proprietary firm's trading activities solely from the viewpoint of the legal entity, and not at the trader id or dealer level.

As described above as legitimate cases, self-trades occurring from unrelated trading desks, and functioning independently may not be manipulative, and need to be considered separately. Exchanges themselves, register the user id and terminal ids for each dealer. It is therefore, inappropriate to not consider trades at the level of traders or terminals.

(b) The AO said:

"... the total self-traded volume is 78,927 shares at BSE and 38,229 shares at NSE which is very high."
...
"The number of instances of self-trades executed by the Noticee is extremely high i.e. 6,051 trades at BSE and 2,985 trades at NSE which is not miniscule by any stretch of imagination as contended by noticee."

The AO states that there was a very high scale of self-traded volume. The said volumes are respectively 0.53% and 0.10% of the total quantity traded that day on the security on BSE and NSE. SEBI failed to establish materiality by comparing these numbers to an appropriate benchmark.

6   Judicial treatment of unintentional self-trades

The Securities Appellate Tribunal (SAT) has, previously, refused to acknowledge unintentional self-trades that emanate from independent terminals and traders, and has obligated firms to prevent self-trading by all means.

• In Systematix Shares & Stocks (India) Limited vs SEBI, SAT held:
  

  "..Trades, where beneficial ownership is not transferred, are admittedly manipulative in nature".

• In Anita Dalal vs. SEBI, SAT held:
  

  "Self-trades admittedly are illegal. This Tribunal has held in several cases that self-trades call for punitive action since they are illegal in nature."

• In Triumph International Finance Ltd. vs. SEBI, the Tribunal held:
  

  "The buyer and the seller were also the same. It is obvious that these trades were fictitious to which the appellant was a party. They were fictitious because the buyer and the seller were the same."

 

7   Solution

In India, FUTP regulations do not deal with legit self-trading activity which may happen within a firm without intent of manipulation. Since there is no clear law which deals with self-trades specifically, even genuine self-trades activity often falls under the "unfair trade practice" category.
In light of these issues, the following changes are proposed as a solution to deal with self-trading activity.

7.1   Legislative actions

The current SEBI FUTP regulation 4(2), 2003 should be amended as:

1. Clause (g) of 4(2) treats all self-trades as manipulative and should be removed.
2. The following clauses should be included in this section:
• Transactions in a security resulting from the unintentional interaction of orders originating from the same firm that involve no change in the beneficial ownership of the security, generally are bona fide transactions. Transactions resulting from orders that originate from unrelated algorithms or separate and distinct trading strategies within the same firm would generally be considered bona fide self-trades.
• Algorithms or trading strategies within the most discrete unit of an effective system of internal controls at a member firm are presumed to be related.
• Members must have policies and procedures in place that are reasonably designed to review their trading activity for, and prevent, a pattern or practice of self-trades resulting from orders originating from a single algorithm or trading desk, or related algorithms or trading desks.

7.2   Improvements in SEBI processes on the executive functions

The following measures should be adopted by the regulator to deal with, and investigate self-trading activity:

1. Before starting the investigation, the number of shares traded via self-trades should be significant i.e. above an appropriate benchmark, in terms of volume and value of transactions.
2. The regulator should be able to reasonably demonstrate the impact of self-trades on the price.
3. Patterns and practice of self-trades should be looked at before considering them as manipulative.
4. Exchanges should implement Self-Trade Prevention systems and offer these services to its members on a voluntary basis.
5. The regulator should issue guidelines regarding self-trading in line with the proposed changes to the law.

These rules need to be woven into the internal process manuals at SEBI on enforcement against market abuse.

8   Conclusions

At present, subordinate legislation by SEBI, enforcement actions by SEBI and rulings at SAT are unanimous in viewing all self-trading as being synonymous with market abuse. In this article, we have demonstrated that this presumption is incorrect. Some but not all self-trading is market abuse. Financial regulators elsewhere in the world have obtained greater precision in enforcing against market abuse while not punishing legitimate actions. We have shown actions that need to be undertaken at SEBI on the legislative and the executive side in order to address this problem.

The discussion above has been couched in the language of the equity market, which is the most sophisticated component of the Indian financial system. It is, however, completely general and pertains to all organised financial trading. As an example, if SEBI implements the above improvements, all this progress will immediately accrue to commodity futures as SEBI is now the regulator for commodity futures trading also. In the future, when the Bond-Currency-Derivatives Nexus moves from RBI to SEBI, similar gains will accrue there also.

Acknowledgements

We thank Pratik Datta, Shubho Roy, Anjali Sharma, and Susan Thomas for their valuable comments.

Author: Nidhi Aggarwal

• Problems with evidentiary standards for proving securities fraud in India, 23 August 2019. 
• SEBI's new "trade annulment" policy, 7 August 2015.
  
• Self trading is not synonymous with market abuse, 29 July 2015.
• The changing landscape of equity markets, 10 July 2015.
  

• What does algorithmic trading do to market quality?, 1 September 2014.
  
• What role is played by commodity futures in India?, 10 September 2014.
  
• Concerns about individual investors on the Indian equity derivatives market, 29 July 2014.
  

The changing landscape of equity markets

by Nidhi Aggarwal and Chirag Anand.

The arrest of a London based algorithmic trader, Navinder Singh Sarao, on charges of triggering the US flash crash of 2010 has once again brought regulatory concerns on high frequency trading (HFT) to the forefront. With the underlying fear that the use of high speed complex algorithms can pose systemic risk, regulators worldwide are considering actions to tighten their grip on HFT. The Indian securities markets have not remained immune to such concerns, and the securities market regulator, SEBI, has indicated that steps will be taken to keep the level of algorithmic trading (AT) in check. Very recently, even RBI in its annual Financial Stability Report expressed its concerns regarding high levels of algorithmic orders in the Indian securities market.

Despite all the fears and the measures that are being taken to curb HFT, one needs to note that the evidence regarding how HFT (or AT) hurts the market is yet to be established. Concerns such as higher percentage of algorithmic orders creates higher level of systemic risk in the financial system are not backed by strong empirical evidence. Studies examining AT/HFT trading only find evidence contrary to this popular notion (Brogaard et al., 2015; Thomas and Aggarwal, 2014). Other studies (Biais and Faoucault, 2014) examining the overall effect of AT/HFT on market quality find that higher levels of AT/HFT improves market quality by increasing liquidity and price efficiency. In spite of this overwhelming evidence on the effect of AT, regulatory fears on how increased market complexity can disrupt the financial markets remain.

An analysis at the Finance Research Group, IGIDR aims to provide a few insights on the proliferation of HFT (or AT) in the Indian markets. Using a unique tick by tick orders and trades dataset from one of the most liquid stock exchanges in the country, the National Stock Exchange (NSE), we examine how AT/HFT has changed the equity market structure in India. In addition to the usual details of price and volume, the data contain details of whether an order was sent by an AT or a non AT, and whether the order was a new order, or an old order that was modified or cancelled. A clear demarcation of orders sent by AT versus non AT, enables us to examine the characteristics of how AT's trade in the markets vis-a-vis non AT.

We analyse two periods: a low AT period (November-December 2009) and a high AT period (November-December 2013). Few points emerge:

• Between the two periods, percentage of orders entered by algorithmic traders increased from 11.36% to 62.76% on equity spot, from 38.93% to 93.72% on single stock futures (SSF), and from 21.29% to 86.79% on single stock options (SSO).
• On the most liquid segment of NSE, that is the Nifty options, the percentage of new orders entered by AT increased from 19.60% to 93.56%.
• On Nifty futures contract, it increased from 21.57% to 91.23%.

The values indicate that a large proportion of the orders that are entered on NSE today are by algorithmic traders (AT). A majority of these orders are limit orders, indicating that instead of going for the special orders that the exchange offers, AT prefer the traditional limit orders which offer them greater flexibility to manage their orders.

Do AT supply liquidity or demand liquidity?

The increase in percentage of AT orders in the market raises the concern on whether that increase corresponds to a similar increase in liquidity supply, or, whether they consume liquidity from non algorithmic traders. For each segment on NSE, we analyse the percentage of trades in which AT supplied liquidity versus the trades where they demanded liquidity. When an order that comes to the market trades against an existing order in the book, the new order is said to have taken (demanded) liquidity, while the existing order is said to have provided (supplied) liquidity.

The graph above indicates the share of AT orders in total liquidity demanded increased across all the segments between the two periods. However, this matches with their share of orders in total liquidity supplied to the market in all except the Nifty options market. We further break this analysis into who supplies liquidity to whom. This is depicted in the following graph.

In the above graph, the top-left panel indicates the percentage of trades in which AT demanded liquidity from another AT. On the spot market, for example, AT took liquidity from other AT in 6.34% of trades in 2013. The top-right panel indicates the percentage of trades in which non AT demanded liquidity from AT. The bottom left panel indicates the percentage of trades in which AT demanded liquidity from non AT. Finally, the bottom right panel indicates the percentage of trades in which non AT demanded liquidity from non AT.

A difference in the values in the bottom right panel from 100 indicates the AT-intensity, that is the percentage of trades that occurred on NSE in which AT was either on one or both sides of the trade. For example, on the spot market, in 2013, the percentage of trades in which AT were present atleast on one side of the trade was (100 - 44.3)% = 55.7%.

Of particular interest are the top-right and bottom-left graphs. These two graphs indicate non AT demand for liquidity from AT, and AT demand for liquidity from non AT, respectively. The values in the graph reinforce the observation that AT demand as much liquidity from non AT as they supply to them for all except the Nifty options market.¹ This suggests that the concern that AT consume liquidity from non AT does not hold.

We now proceed on to examining how the order placement strategies of AT have changed the market structure on NSE.

Changing market structure due to high speed access

Q:1 How have order placement strategies changed after faster market access? With a majority of the orders coming from AT, we first examine if there has been a change in the order placement strategies by market participants. Specifically, we examine if the increase in the number of orders has translated into a larger number of trades, or are most of the orders that are entered are eventually cancelled?

The table below indicates the percentage of orders that get traded and cancelled by AT and non AT.

All values as % of total orders entered
	Spot		SSF		SSO		Nifty futures		Nifty options
	2009	2013	2009	2013	2009	2013	2009	2013	2009	2013
AT	12.42	62.19	39.18	93.30	20.56	84.89	11.11	87.84	21.71	93.38
Traded	3.91	12.37	1.59	2.20	0.74	2.61	3.02	7.73	1.49	7.47
Cancelled	8.31	49.73	37.52	90.91	19.65	82.03	7.99	79.88	20.15	85.88
Non AT	87.58	37.81	60.82	7.70	79.44	15.11	88.89	12.16	78.29	6.62
Traded	56.11	25.69	14.17	3.00	24.95	6.03	45.37	8.18	32.76	4.43
Cancelled	21.75	7.24	44.88	3.20	44.05	6.52	39.67	2.70	43.22	1.63

The first row in the table indicates the percentage of orders entered by AT. As discussed eariler, the share of AT in the total number of orders sent to the NSE has risen significantly. The second row in the table indicates the percentage of AT orders that got traded.

The table shows that the increase in percentage of new orders entered by AT is not matched with a higher percentage of orders that got traded. Instead, we see a decline in the percentage of traded orders across all the five segments (spot, SSF, SSO, Nifty futures, Nifty options). For example, on the spot market, the percentage of orders that got traded declined from 60.02% in 2009 to 38.06% in 2013. We also find a significant increase in the percentage of orders that got cancelled in the high AT period (2013). Of the total unique orders that came to NSE, the percentage of orders that got cancelled increased from 30.06% in 2009 to 56.97% in 2013 on the spot segment, from 82.40% to 94.11% on the SSF and from 63.70% to 88.55% on the SSO. On Nifty futures, this percentage increased from 47.66% to 81.58% and on Nifty options from 63.37% to 87.51%.

While there could be legitimate reasons for such cancellations (Hasbrouk and Saar, 2009), the increase in the percentage of cancelled orders raises concerns about phantom liquidity (also known as spoofing, flickering quotes, or fleeting liquidity), that is, the fear that high speed access allows the trader to post an order for everyone to see, but withdraws it before anyone can act on it. We examine this concern in the next question.

Q:2 Do order cancellations occur at very short intervals? Higher percentage of order cancellations, by itself is not a matter of concern. The concern instead is that these orders might be getting cancelled in such short a time that other traders, who do not have the advantage of fast market access, are unable to execute their orders against such orders. Or, these orders could be sending signals of false liquidity. In order to pin down these concerns, the evidence of cancellations needs to be combined with evidence of speed of cancellations - or the lifespan of the orders. If a majority of the orders are cancelled in very short time intervals, then it could be suggestive of phantom liquidity in the markets.

Cancelled orders as a percentage of total orders entered on Nifty options

Cancelled orders as a percentage of total orders entered on SSF

The graphs above indicate the percentage of orders that got cancelled in less than a second on the two most liquid NSE segments: Nifty options and single stock futures (SSF). The graphs suggests that in the high AT period (2013), more than 70% of the orders entered on the SSF and about 54% of the orders entered on the Nifty options market got cancelled within a second.² These values are substantially higher than the values in the low AT period of 2009, during which 7.83% and 14.96% of orders got cancelled within one second on SSF and Nifty options.

A useful question to ask is how these numbers compare with the global markets. A similar analysis for the US equity markets by SEC indicates that 45.9% of the orders were cancelled within a second during Q2 2013.³
 
Q:3 Is fast too fast? The analysis above indicates that high speed access has made cancellations too fast. The next question that becomes important to ask is, ``Is this too fast''? To characterise the intensity of what is fast, we use the SEC's approach. In a speech in April 2014, by the then Associate Director of SEC, Gregg Berman, noted:

``If the speed of cancellation is much quicker than the speed at which those quotes can be accessed, then I would say quote cancellations are not only fast, but perhaps they are too fast. However, if market participants can lift quotes just as quickly as others can cancel them, I would say that the cancellations might be fast, but not necessarily too fast."

And its relevance in informing the policy debate:

``If quote cancellations are indeed too fast for the rest of the market to keep up, it might make sense to slow down this particular aspect of the markets, perhaps with some sort of minimum quote-life requirement. But it the data shows that at least some market participants can access quotes just as quickly as they can be canceled, this suggest that both sides of the market are very fast and if you want to slow down the market -- in a way that does not bias one side, you would need to not only address the speed of quote cancellations, but also the speed at which liquidity is taken."

We examine this by comparing the lifespan of cancelled orders with that of the traded orders. We once again restrict our discussion to the two most liquid segments on NSE: Nifty options and SSF.

The graph above shows the results for Nifty options for cancelled (top-panel) and traded (bottom-panel) orders. A shift from the red to yellow region indicates increase in the speed of order cancellations or execution. In 2009, while about 30% of all cancelled orders remained in the book for less than a second, about 55% of all traded orders were the result of some trader hitting limit orders within that same time period. These numbers rose to 65% and 80% respectively in 2013. Also noticeable is that the number of modifications on these cancelled orders is in the range of 0-5. This suggests two features of trading activity on the Nifty options market:

1. A majority of the orders that get cancelled do not undergo large number of modifications.
2. Access to speed has indeed increased the speed of order cancellations, but this speed is lower than the speed of execution.

The Nifty options inferences do not however hold for the SSF. In 2009, the percentage of cancelled orders within a lifespan of less than a second on SSF was almost negligible, while the percentage of traded orders within the same lifespan was less than 40%. The numbers changed dramatically in 2013. The graph shows a shift from red to yellow region for cancelled orders, but only a shift from red to orange region for traded orders. The percentage of cancelled orders with a lifespan of less than a second was about 75%, while the percentage of traded orders within the same lifespan was about 45%. This indicates that the speed of order cancellations surpassed the speed of trade executions in 2013.

Summary

In a nutshell, the findings can be summarised as:

1. The share of algorithmic orders in total orders that come to the market has risen significantly.
2. Except for the Nifty options market, the share of algorithmic traders in liquidity demand matches with their share in liquidity supply.
3. A large majority of the orders on NSE are cancelled, with most of them occurring within a second of order entry.
4. The speed of order execution is higher than the speed of order cancellations on Nifty options. This is however not true of the SSF segment of the NSE.

The above analysis does imply that the order placement activities have changed significantly with a lot of cancellations occurring within short time-frames. However, to analyse whether this degree of cancellations could be hurting the other market participants, it is critical to examine the where these quote cancellations occur? If most of these cancellations are occurring around the best bid and ask prices (or even the upto level 5 depth of the market), such cancellations could be a cause of concern. Further analysis aims to capture this aspect.

References:

 
High-frequency trading and extreme price movements by Brogaard J, Carrion A, Moyaert T, Riordan R, Shkiklo A and Sokolov K, 2015, Working Paper

The causal impact of algorithmic trading on market quality by Susan Thomas and Nidhi Aggarwal, 2014. IGIDR Working Paper.

HFT and market quality by Biais B and Foucault T, 128, 2014 in Bankers, Markets and Investors, p. 5-19.

Technology and liquidity provision: The blurring of traditional definitions by Hasbrouck J, Saar G, 2009. Journal of Financial Markets, Volume 12, Issue 2, May 2009, p. 143-172.

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Footnotes

1. The reason for the difference in the nature of AT liquidity demand and supply on the options market needs further investigation.
2. We record similar values for the rest of the market.
3. The findings are also comparable to studies investigating fleeting orders. For example, Hasbrouck and Saar (2009) find that 36.69% of the limit orders get cancelled in less than two seconds on INET.