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Friday, August 23, 2019

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.

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