Policy uncertainty in Indian e-commerce

by Megha Patnaik.

Reduced investment in India by private persons is a key part of the present growth challenge. Investment is shaped by macroeconomic uncertainty, sectoral uncertainty, and regulatory risks that firms face. In this article, we think about the risks that an E-commerce firm such as Amazon perceives in India. These include the changing FDI rules, unresolved issues of data localisation and code disclosure, the multiple reports on technology-related activities that various government agencies are releasing, and the problems of rule of law in licensing and investigation.

Economic policy uncertainty

Shrinking investment in the Indian economy is a concern. Investments in new projects fell to a 15 year low in the last quarter according to the Centre for Monitoring the Indian Economy (CMIE) Capex data which tracks large investment projects.

Firms are deterred from investing by policy uncertainty (Bloom, 2009). When firms are unclear about the future economic environment, they hold back on investing till uncertainty declines. This delays the pickup of the investment cycle, where firms generates jobs and business for linked firms, fueling aggregate economic activity. Uncertainty particularly affects long-term investments that are irreversible in nature, and for which horizons for cost recovery run into years. These can be investments in new technologies or market segments, or investments in infrastructure. Such investments are particularly important, as they can benefit other firms in the economy, fueling productivity and long-term growth in addition to their business cycle effects.

Private sector investment is adversely affected by three kinds of policy uncertainty - macroeconomic uncertainty, sectoral policy uncertainty, and regulatory risk. The role of Economic Policy Uncertainty at the macroeconomic level has been measured globally (Baker et al, 2016). In the original measure, an index is created by quantifying newspaper coverage of policy-related economic uncertainty mentions in the national newspapers, through combinations of keywords related to policy and uncertainty. Macroeconomic policy uncertainty has been applied to understand global events. Brexit-driven policy uncertainty in the UK moved closely with the GBP Real Exchange Rate in recent times, and the uncertainty surrounding US trade policy affected importing firms. This measure of macroeconomic policy uncertainty correlates strongly with stock market volatility.

Firms face much more than macroeconomic uncertainty. They also face uncertainty at sectoral, geographical and individual levels. Sectoral-level policy uncertainty can be measured through surveying firms sampled across sectors, asking them about expectations about future growth and costs at various horizons (Altig et al, 2019). For example, firms can report not just their expectations about future profits, but the distribution across the possible profit outcomes that they can expect.

An additional source of uncertainty that firms operating in India face is regulatory risk. Even when regulations are formulated, there is a lack of predictability, and excessive executive discretion, in how a stated regulation will be enforced. For example, the licensing by the RBI of 11 payment banks from 41 applicants who wanted to start payment systems was a non-transparent process inconsistent with the rule of law (Roy and Shah, 2015). Another example is the Copyright Board order of 2010 on statutory licensing fees paid by Radio stations. This order arose out of nine one-on-one disputes between radio stations and music producers, but was applied as an in rem order rather than an in personem order. Thus, music producers who weren't part of the original disputes also became governed by the order, despite the appeals by T-Series and SIMCA against the Copyright Board order applying to them. Aggarwal and Zaveri (2019) show the uncertainty induced for private persons through executive discretion in enforcement at SEBI.

Drivers of uncertainty in the E-commerce sector

In the recent Q2 earnings announcement, the Amazon CFO Brian Olsavsky mentioned uncertainty in India's e-commerce policy. He expressed hope for `stable' and `predictable' policy, for the company to continue with its investments in technology and infrastructure in India. This explicit mention about policy uncertainty in India is a unusual moment, and requires attention by policy thinkers. What is the uncertainty associated with investing in India, as seen by Amazon?

1. India's draft e-commerce policy rules earlier this year preventing firms from influencing prices or selling products in which they hold stakes disrupted business plans for e-commerce companies. It bring companies back to the drawing board to ensure they can comply with the current regulations while limiting losses that rose from lack of clear direction from the start. The final e-commerce policy has been held back for another year, putting existing investments of firms in this sector at risk during the interim months, and deterring further investments.
2. The uncertainty around data localisation is another deterrent. The recent announcement by a high-level government panel to do away data localisation for non-critical data, and the upcoming announcement of the position of the Prime Minister's Office on data localisation are policy announcements that drive sentiments on this debate, though none are legal instruments. Under data localisation requirements, companies would need to redesign internal algorithms to access data locally, pay up for new servers, and face costs to protect data in less-secure environments. The predictive power of firms' algorithms would weaken with fewer data points to train models on. The due process of discussions with various government bodies and stakeholders on this issue is still in process. The RBI's requirement for financial data localisation despite existing provisions (Bailey and Parsheera, 2018) for access under the Payments and Settlements Act (2007) suggest that any Indian regulator can step in with special requirements at unforeseen times.
3. A related issue is the disclosure requirement of source code under the draft e-commerce policy. E-commerce firms depend on data-driven marketing and use of collaborative filtering for customer recommendations. A code submission requirement is a coercive technique aimed at achieving `the transfer of technology and local needs' described under the proposed e-commerce policy. Technology transfers cannot and should not be coerced: they happen in an organic and legitimate manner through managers and employees developing skills and passing them onward in data communities or by workers moving across companies (Bloom et al, 2019). It is also doubtful how technological transfers can be achieved with segments of code without underlying data. Will code disclosure requirement be combined with data localisation to pass on core business value to competitors? Will companies need to invest in staff and technologies to find workarounds to be able to mask their key assets? Whether such a code disclosure requirement will come into effect remains unresolved. In mid-2020 the final e-commerce policy will describe the stand of the government on this issue, but this is not definitive either.

Multiple guidelines on the same subject can cause delays in the resolution of uncertainty. The RBI Report of the Working Group on FinTech and Digital Banking includes E-aggregators, Robo advisors and Big Data all under Fintech. E-commerce firms, which are data intensive and provide multiple services, will be included under this description. The fintech steering committee report of the Ministry of Finance is still pending. Each of these reports is a statement about how government agencies are likely to move in the future but these are not legal instruments. Government reports can only suggest but not surely state how future laws will change.

Infirmities of the regulatory processes in India also exacerbates uncertainty. As an example, data localisation requirements by RBI for payments firms were translated from an early idea into an enforced law within a matter of days. There was no due process surrounding how officials could change the law.

The last leg of the legal system -- how laws are enforced -- also suffers from concerns about non-equal application of law, as shown in the examples from RBI (Roy and Shah, 2015) and SEBI (Aggarwal and Zaveri, 2019). For a prospective investor, the risk of investing in India lies in how the law might change in the future through an undemocratic process, and in how the law will be applied to her.

Conclusion

For India to have a stable investment environment, we need to provide firms a stable and predictable policy environment. Investments from firms in various sectors will boost the investment cycle for India. Resolving policy uncertainty both at the macroeconomic level as well as in different sectors, and reducing regulatory risk through better rule of law is critical for India in the current investment scenario as well as for long term growth.

References

Aggarwal, Nidhi and Zaveri, Bhargavi. Problems with evidentiary standards for proving securities fraud in India, The Leap Blog, 23 August 2019.

Altig, David, Jose Maria Barrero, Nicholas Bloom, Steven J. Davis, Brent H. Meyer and Nicholas Parker. Surveying Business Uncertainty University of Chicago Working Paper (2009)

Bloom, Nicholas. The impact of uncertainty shocks. Econometrica (2009)

Bloom, Nicholas, Erik Brynjolfsson, Lucia Foster, Ron Jarmin, Megha Patnaik, Itay Saporta-Eksten, and John Van Reenen. What Drives Differences in Management Practices? American Economic Review (2019)

Baker, Scott R., Nicholas Bloom and Steven J. Davis. Measuring Economic Policy Uncertainty. The Quarterly Journal of Economics (2016)

Bailey, Rishab, and Smriti Parsheera. Data localisation in India: Questioning the means and ends, The Leap Blog, 22 February 2018.

Roy, Shubho, and Ajay Shah Payment bank entry process considered inconsistent with the rule of law, The Leap Blog, 1 September 2015.



Megha Patnaik is faculty at the Indian Statistical Institute, Delhi and Fellow at the Esya Centre. The author thanks Radhika Pandey and Ajay Shah for useful inputs.

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.

IBC (Amendment) Bill, 2019: Implications for judicial review of resolution plans

by Pratik Datta and Varun Marwah.

Shroff and Misha (2019) had earlier highlighted that excessive judicial discretion in corporate insolvency resolution is contrary to the express provisions of the Insolvency and Bankruptcy Code, 2016 (“IBC”). Indian policymakers have now recognized this problem. Accordingly, the IBC (Amendment) Bill, 2019 (“2019 Bill”) has been passed by the Parliament.

The immediate trigger for this reform was the National Company Law Appellate Tribunal (“NCLAT”) judgment dated July 4, 2019, in the resolution of Essar Steel Ltd. (“Essar judgment”). In this case, the NCLAT found ArcelorMittal’s resolution plan to be discriminatory. Accordingly, it went on to modify the plan such that the financial and operational creditors enjoy the same recovery rate. In the process, it obliterated the distinction between secured and unsecured creditors. Moreover, the NCLAT also held that the statutory waterfall under section 53 of IBC does not apply to distribution under a resolution plan. By disrupting the basic fundamentals of banking, this decision caused much concern in the Indian financial sector. Among others, the 2019 Bill seeks to address these concerns as well.

In this backdrop, we contextualize the amendments proposed by the 2019 Bill within a hypothetical theoretical framework to analyse their implications on the scope of judicial review of resolution plans under IBC.

Theoretical framework

An insolvency law should have two broad objectives. First, it must achieve the most efficient economic outcome for the insolvent company. This outcome could be going concern sale, restructuring, liquidation or a combination of these outcomes. Second, the value (cash/non-cash) received from the outcome must be distributed among the claimants of the company according to the waterfall provided in the insolvency law.

In a going concern sale, the business of the insolvent company is marketed for price discovery, that is, auctioned. Potential buyers submit their price bids in their respective resolution plans. Once the best price is discovered, the price signifies the precise value of the business. The successful buyer deposits this value in cash or its equivalent in an escrow account and takes over the business, immediately putting the assets to their best use. The resolution plan need not deal with how the deposited value would be distributed among the insolvent company's claimants.

The value deposited in the escrow could be separately distributed by the resolution professional among the claimants of the insolvent company as per the statutory waterfall. This scheme of distribution cannot be altered unless a claimant(s) affected by such alteration specifically consent to it. However, the resolution plan need not deal with any of these distribution issues. Therefore, the resolution plan cannot in any way unfairly discriminate against any claimant. Consequently, there is no need for judicial review of resolution plans to ensure fair and equitable distribution in a going concern sale for cash or its equivalent. The same logic applies to distribution in liquidation involving sale of assets for cash or its equivalent.

However, going concern sales are not always possible or desirable. For instance, during a recession, there could be no buyers in the market. Or even if there are buyers, there could be an oversupply of similar assets in the market due to industry wide factors, pushing down the price for such assets. In such circumstances, instead of a going concern sale to a new buyer, the claimants of the insolvent business may be better off by “selling” the business to some or all of the existing claimants themselves. Such “hypothetical sale” is commonly referred to as restructuring.

In such a restructuring, there is no auctioning of the insolvent business to potential outside buyers. As a result there is no price discovery and the precise value of the insolvent business in cash or its equivalent is not evident. Therefore, the notional distribution of rights (that is, securities in the restructured company) among the claimants is conceptually very different from the distribution of value in cash or its equivalent in a going concern sale.

Unlike a going concern sale, the notional distribution of rights in a restructuring has to be determined by the resolution plan. This creates two unique problems. First, given the uncertainty regarding the value of the restructured business itself, there could be ambiguity about how much value should each claimant receive under the waterfall. Second, even if the value payable to each claimant is agreed upon, there could be ambiguity regarding the value of the rights (that is, securities of the restructured company) allocated to each claimant by the resolution plan. These unique problems create a peculiar risk in restructuring - the rights distributed by the resolution plan may give one or more classes of claimants a value lesser than what they are entitled to under the statutory waterfall. This is often referred to as “unfair discrimination”. To prevent such unfair discrimination, judicial review of resolution plans in non-cash restructuring transactions is necessary.

This conceptual distinction between sale (cash) and restructuring (non-cash) transactions is critical. Minority creditor protection mechanisms including judicial review is necessary primarily for restructuring (non-cash) transactions; not so much for sale (cash) transactions. For example, Chapter 11 of the US Bankruptcy Code deals with restructuring. Section 1129 in this chapter requires a restructuring plan to provide every creditor at least the liquidation value. This safeguard is not applicable to sale transactions (for cash) under Section 363 of Chapter 3 of the US Bankruptcy Code. Evidently, the US law clearly recognises the difference between sale (cash) and restructuring (non-cash) transactions.

So far we have only dealt with neat categories of going concern sale and restructuring. However, in reality, resolution plans could propose transactions that may not squarely fall within these simple categories. For instance, some new investors may pay up cash to buy some equity or take on some debt of the restructured company, while existing lenders may swap some of their debt into equity of the restructured company. Such transactions would then be a hybrid
exhibiting features of both a going concern sale for cash as well as that of restructuring for non-cash considerations. It is important to recognise that such transactions effectively help preserve the going concern value of the insolvent business. Therefore, in such transactions, each claimant of the insolvent company must get at least the amount of value (cash, non-cash or both) that it would have received had the company been sold off as a going concern for cash or its equivalent. In other words, even in such transactions, the distribution of value must follow the statutory waterfall. And this distribution function has to be performed by the resolution plan itself. Consequently, the risk of unfair discrimination may arise even in such transactions, rendering judicial review of resolution plans necessary for minority creditor protection.

Shortcomings in IBC and the proposed solutions

The IBC, as initially envisaged, differed from this ideal theoretical framework on two counts.

First, the statutory waterfall under section 53 of the IBC was initially envisaged only for distribution in liquidation. The Essar judgment held that section 53 cannot apply to distribution proposed by a Resolution Applicant. Consequently, in sale (cash) or restructuring (non-cash) transactions, a resolution plan would have to follow a scheme of distribution different from the statutory waterfall, opening up flood-gates of unfair discrimination allegations.

The 2019 Bill attempts to address this problem. Section 30(2)(b) is proposed to be amended to make the statutory waterfall under section 53 applicable to distribution in resolution as well. This clarity should reduce allegations of unfair discrimination in resolutions and help cut down on unnecessary judicial review in going concern sales.

Second, the IBC does not clearly recognise the conceptual difference between cash and non-cash transactions. Unlike US Bankruptcy Code, section 30(2) of IBC applies minority creditor protection mechanisms, meant primarily for restructuring (non-cash) transactions, to even sale (cash or its equivalent) transactions. This provision has been liberally interpreted by the NCLAT in Binani Industries to hold that every resolution plan must be fair and equitable – a vague judicially determined standard. Consequently, every resolution plan is now open to judicial review without any clear purpose. This has unnecessarily delayed many corporate insolvency resolutions. For example, the Essar resolution has been ongoing for almost 2 years, even after approval of a resolution plan by the CoC.

The 2019 Bill attempts to address this problem too. The proposed Explanation 1 to section 30(2)(b) explicitly clarifies that if a resolution plan follows the order of priority under section 53, it will satisfy the ‘fair and equitable’ standard. This explanation should narrow down the focus of judicial review to ensure distribution as per the statutory waterfall.

Conclusion

Judicial review under IBC should be focused to ensure that distribution of value is as per the statutory waterfall under section 53. In going concern sales for cash or its equivalent, distribution of value among claimants based on the waterfall would be relatively unambiguous and should not require judicial review of resolution plans. In restructuring or other hybrid transactions involving non-cash consideration, notional distribution of non-cash value in form of rights to the restructured business (that is, its securities) is likely to be contentious, given that distribution will be provided by the resolution plan and/or issues relating to price discovery and valuation. This could create opportunities for value extraction from minority to majority claimants through resolution plans in such restructuring or hybrid transactions. Judicial review of resolution plans may be necessary only to prevent such unfair value extractions in violation of the statutory waterfall. But once distribution among claimants as per the statutory waterfall is achieved, there is nothing further to be gained by subjecting each and every resolution plan to judicial review. The 2019 Bill has now modified the law to achieve this optimal outcome. Whether this outcome is achieved in practice will depend largely on how the judiciary interprets and implements these new provisions.

References

Shroff, S. and Misha, A question of balance, via the code or otherwise, Asia Business Law Journal, May 13, 2019.

Datta, P, Value Destruction and Wealth Transfer under the Insolvency and Bankruptcy Code, 2016, NIPFP Working Paper No. 247, Dec. 2018.

 

Pratik Datta is a Senior Research Fellow, and Varun Marwah is a Research Fellow at Shardul Amarchand Mangaldas & Co. We thank Mr. Shardul S. Shroff and four anonymous referees for useful discussions and comments.