Supervising what you cannot inspect

by Maninder Singh Juneja and Renuka Sane.

In traditional financial regulation, supervisors are able to inspect the thing being regulated. For example, a scorecard by a bank was usually a short list of factors that one could comprehend. These factors could be traced to key documents within the bank, banks had a stated rationale for why they were being used, and there was some common sense in making weighted averages. The scorecard itself was static - the same weighted factors were applied over a period of time. Inspecting this process of constructing the scorecard and using it for management decisions was how trust was built. The AI world is different. Models are rented rather than built, change continuously, and behave probabilistically. They cannot be fully inspected even by the institution deploying them let alone by the regulator.

How should we then think of regulation?

One approach is to intensify the traditional approach where regulators demand more explainability, more documentation, and more validation. This will drive up the costs of compliance. But more importantly, this is ill suited to the new world where the technology changes rapidly, where the bank does not control the AI it uses, where there is no clear artefact that the bank can give the supervisor such as a model or a document (Board of Governors et al 2026). When regulators push traditional approaches, banks will respond by choosing AI models which are easy to document rather than the ones best for them, or defer AI deployments altogether. All these are unhappy consequences. What we need are policy makers who understand the live systems of the new world.

In this article, we analyse these emerging problems from first principles. We start from scratch, understand the landscape of market failure in the world of AI in banking, and think about how regulators can grapple with this world.

Our key idea is that AI systems resist replicability. Regulatory strategies that demand replicability will flounder or choke technology deployment. We suggest the regulatory standard applied should be AI deployments that are "supervisable" - the outcomes should be observable, they should be attributable to causes, and reversible by humans.

How AI is actually deployed

Before we get to the puzzles faced by regulators, we need to describe what AI in banking is. This categorisation is not unique to banking. Banking is simply where their consequences are regulated. Five modes span the range.

AI as Tool: AI augments a step, a search, a calculation, a first draft that the human controls. The human is cognitively engaged.

AI as Collaborator: The human and AI co-produce iteratively, and the human participates at every stage.

AI as Recommender: AI generates scores or options, and the human makes the decision. This is the classic human-in-the-loop, but credible only if the human can meaningfully interrogate the recommendation and not degenerate into cognitive surrender.

AI as Preparer: AI does the work and the human signs off. The approval here is closer to a check by a supervisor rather than a re-derivation.

AI as Autonomous executor (agentic AI): AI executes autonomously inside guardrails and the human monitors on exception. Emerging forms of agentic AI for banking run from single-task agents (a payment released, a service query resolved) to multi-step workflows and customer-facing agents that transact.

A central issue here is the true (de facto) role of the human. A reviewer who approves a thousand recommendations a day is not overseeing a model, the model is overseeing her. A feature of any deployment is the measured divergence between the model recommendation (and estimated uncertainty) vs. the human decision. Managers of banks will need to worry about relapses of human behaviour inside the organisation, a bit like how hospital managers worry about bad behaviour by doctors within their organisation.

What AI does to market failure in banking

We now shift gears to look at the standard knowledge on market failure in banking. Regulation may be justified when (and only when) there exist market failures which cannot self-correct fast enough, and there is adequate state capability in banking regulation to be able to correctly identify them and intervene. AI's distinctive feature is that it can cure several classic failures. Better default prediction reduces credit rationing, better fraud detection cuts deadweight loss, richer risk assessment lets banks serve customers they previously could not price, AI advisors help customers avoid some malpractices by the bank. But there are also some new problems that are anticipated.

Information asymmetry: This happens in banking at two levels: borrower-to-lender (adverse selection, hence credit rationing) and firm-to-consumer (product complexity, hence mis-selling). AI narrows the first through alternative data and may widen the second. On one hand, the customer armed with AI can see through many things proposed by the bank which are not in her best interest. But the consumer cannot observe why they were shown a product, offered a price, or steered toward a specific insurance plan. The sales process itself becomes more opaque. Personalised pricing approaches first-degree price discrimination, extracting consumer surplus. A single flawed model can mis-sell to millions simultaneously, converting isolated conduct failures of the pre-AI world into a big correlated event. And redress weakens when a denial comes from a model the firm itself cannot explain.

Systemic externalities from shared infrastructure: Each bank chooses its models, data sources and vendors to optimise its own performance. When multiple entities choose the same ones, the sector's exposures become correlated, which is a cost no individual bank prices in. We list the vulnerabilities below:

Correlated model risk: banks on similar models and the same foundation providers respond identically to an event. The regulator, at the system level, has to manage what happens when institutions move together, because no single entity has experience of such behaviour or of the impact synchronisation adds.

Third party concentration: one vendor's failure propagates everywhere at once. India has already run this experiment, when a ransomware attack on one shared technology provider knocked roughly three hundred cooperative and regional rural banks off the payments network.

Correlated cyber breach: shared stacks mean one exploited vulnerability is every institution's vulnerability. AI lowers the attacker's costs (automated vulnerability discovery, deepfake social engineering) and adds new attack surfaces (data poisoning, model inversion, prompt injection against agents that can move money).

Runs on banks at level 3. The bank runs of old were a queue on the pavement. Then we got to Silicon Valley Bank where over a weekend, customers took away money from the bank. Now we can be at level 3: autonomous agents managing customer cash can turn a shared signal into a self-reinforcing run at machine speed.

The various market failures listed above behave differently across the five modes of AI use. For example, when a human constructs the offer, opaque pricing can get contained. However, when an agent personalises autonomously at scale, this may become severe. Systemic correlation is moderate when AI advises and severe when fleets of similar agents act simultaneously. Any regulation that grades by model type alone, or by use case alone, misses half the object. The next question is the mode of regulation itself.

Regulatory strategy

Regulation can work in two ways. Process-based regulation is ex ante: it prescribes how the firm must operate, defines required controls, mandates oversight, validation standards, limits on autonomy. Outcome-based regulation is ex post: it prescribes ends, fair treatment, solvency, and judges results, leaving the choice of methods to the firm.

Outcome-based regulation is the efficient default. It is technology-neutral, so it does not ossify as methods change; it lets firms find the least cost route to compliance; and it does not require the regulator to understand the firm's production function better than the firm does. But it has important preconditions: the outcome must be observable and measurable; it must be attributable to the firm and, ideally, to the cause; and the harm must be reversible or compensable. Process regulation is the right departure from the default when those preconditions fail and where outcomes are unobservable, harm is catastrophic or irreversible, or damage manifests only systemically or with a lag.

Traditional doctrine treats these preconditions as given: examine the activity, choose the mode. This does not work for AI. Whether an AI deployment's outcomes are observable, attributable, and reversible is an engineering choice, which needs to be settled at design time. We suggest that regulation should mandate observability. This makes it possible to have an "outcome-based supervision" model. There are three ways to ensure observability.

1. Telemetry implies that the institution keeps a track of every decision such that the system records which version of itself it was using, what information it was given, and any time a person stepped in to overrule it. This makes outcomes attributable. That way if a certain group starts getting more (or less) approvals than before, the organisation can evaluate what caused the shift - was it the model, or the group itself. Such a capability is being mandated elsewhere in the world for similar use-cases (European Parliament and Council of the European Union, 2024). The regulator should also consider if it wants to set a minimum common telemetry standard.

2. Boundaries and rollback include putting caps on what the system is allowed to do, rolling out new updates to just a small number of cases first (instead of everyone at once), and having a tested plan for switching back to the older version if something goes wrong. This ensures that if a bad update slips through, it only affects a small slice of decisions.

3. Probes make bias observable. One way is "paired testing": one submits two applications that are exactly the same except for details that hint at things like someone's race or gender, and see if they get treated differently. One can also compare approval rates against the company's own normal levels. Together, these checks can catch an unfair credit model in just a few weeks, instead of waiting years to see who actually pays back their loans. One can also check rejected applicants against credit-bureau data to see which ones got approved by someone else, and how they fared.

These are similar to the idea of decision receipts that record which rules were applied to which facts and in what sequence for every decision made by a government or public system (Srivastava, 2026).

Over time, the supervisor should also build a repertoire of its own test cases drawn from incidents, complaints and examinations across the system and run it against every material AI deployment, much as stress-test scenarios are run against every balance sheet today. This will ensure that what surfaces in one institution becomes a probe for all others. It thus allows the regulator to set its own observability layer.

Process regulation is then reserved for the harms that are systemic, correlated, or irreversible at machine speed. For such events there need to be protections such as circuit breakers that halt things before they cause damage, limits on how much the systems can do on their own, model diversity so they don't all fail the same way, and rehearsed back-up plans.

What follows for the supervisor, the board, and the customer

For the supervisor: The unit of examination shifts from the model to the deployment, and the examiner's question shifts from "show me the validation report" to "show me the behaviour": what boundaries were set, what exceptions were thrown, what overrides were exercised, how far the system drifted from its baseline. Supervisors will also need to find the intellectual clarity to avoid a wide variety of extraneous claims about regulation of AI, e.g. the push for economic nationalism which has nothing to do with market failure.

For the board: A board cannot certify systems it cannot inspect or understand. Its role is to govern the framework which includes the limits on what the system is allowed to do, and making the rules for when a decision must be escalated to a human. The board then needs to continuously check the exceptions and overall performance.

Exception-handling should also be written into policy. If a problem stays unresolved beyond a defined size or time limit, it automatically gets escalated to the board. Internal auditors should double-check that the numbers are real. The board sticks to this audited framework and does not inspect the system directly.

If a board is asked to approve something they can't understand, they will default to saying no. But if you let them govern the limits and the exceptions instead, they can say yes.

The customer: The customer becomes part of the supervisory architecture. The widening firm-to-consumer asymmetry has a structural corrective the pre-AI world lacked: the customer now has AI too. Mainstream assistants abroad have begun connecting directly to users' accounts. If product terms (rates, fees, eligibility criteria) are mandated to be structured and machine-readable, the customer's own AI does the comparing, the explaining, and the policing of mis-selling, continuously and at zero supervisory cost. The redress channel weakened by opacity is restored the same way: an adverse decision should carry its reason to the customer, and what would have had to be different for the answer to change. This restores the ability to contest. The same asymmetry that AI widened, AI-equipped customers can close, but only if regulation hands them the data.

An example

Consider a debt-collection example. When borrowers fall behind on payments, the bank has to decide how to chase each overdue account. Contact methods differ in cost. Automated SMS and IVR (the automated phone system - "press 1 to pay") are cheap; having an employee actually phone the customer is expensive. So the bank builds a model that allocates accounts: cheap automated nudges for most, and the costly human call reserved for the accounts where the model predicts that talking to a person will actually "cure" the account (get it back to paying). Human calling works better but it costs more. The model is rationing an expensive resource to where it thinks it'll pay off.

With AI, the collection systems would retrain continuously or run reinforcement-style optimisation against a live reward (cure rate per rupee spent). The model would be moving on its own, faster than the review cycle, toward a target that is a proxy for what the bank may actually want. Under the conventional "inspect-the-artifact" approach, the model is checked before deployment, signed off, and reviewed on a schedule (say quarterly or annually). However, if AI is an active optimiser, a quarterly review discovers deterioration only after it has touched thousands of accounts. It may drift toward a mis-specified target in a way no one can read off the model itself. Further, if the collections model is a shared vendor product, or several banks fine-tune the same foundation model on similar data, they all go bad the same way at the same time when borrower behaviour shifts. In contrast, in a "supervisability-built in by design" approach, a small slice of accounts is deliberately kept on the previous allocation method, running live alongside the new model. This allows comparison between the old and the new in real time.

Every decision should keep a record of a few things: which version of the AI was used, what action it chose, which group of customers it was dealing with, and how things turned out in the end. This is how it helps: Say the AI's overall success rate is going up, but one particular group is quietly getting fewer phone calls from real people and doing way worse because of it. With all that recorded, that gap shows up right as it's happening. You can compare it against what's going well elsewhere, trace it back to a specific version of the AI, and undo it that same day, because the older setup is still up and running as a backup.

The company should never have to dig into the AI's inner "thought process" to realize its behaviour has taken a bad turn. None of this replaces the normal testing that the firm would do anyway. The difference is that here, the ability to observe what's happening, pin down what caused it, and shut it down are all built right into the system from the start, instead of being things you have to go do to the AI afterward.

Conclusion

AI deployments sit uneasily in conventional mores of banking regulation. The intelligence is rented, the behaviour is probabilistic, and harms can move at machine speed. We suggest that the response should not be more inspection of what cannot be inspected, nor blind faith in outcomes that arrive too late. Efficient regulation will emerge from observability, attributability, and reversibility. Regulators should require banks to build these.

References

Board of Governors of the Federal Reserve System, Federal Deposit Insurance Corporation, and Office of the Comptroller of the Currency. "Supervisory Guidance on Model Risk Management." SR Letter 26-2. April 17, 2026. https://www.federalreserve.gov/supervisionreg/srletters/SR2602.pdf.

European Parliament and Council of the European Union. Regulation (EU) 2024/1689 (Artificial Intelligence Act), arts. 12, 26(6), and Annex III(5)(b). https://ai-act-service-desk.ec.europa.eu/en/ai-act/article-12.

Srivastava, Manish. "Digital Governance Needs Decision Receipts." Episode 73 of Big Ideas. XKDR Forum, June 1, 2026. Podcast, video, 13:06. https://youtu.be/WFX4ITb9yok

A cooperative liquidity window for mutual funds: A debate

by Harsh Vardhan vs. Josh Felman and Ajay Shah.

Problem statement

There is a mismatch between the growth of the mutual fund industry versus the maturation of the financial markets (Shah, 2018). This generated trouble after the IL&FS default of August 2018, and will likely make trouble in the future also. Mutual funds are in an awkward place, promising liquidity to their customers but lacking a liquid bond market. Some years ago, the exchanges were getting better, and there was a path to building the Bond-Currency-Derivatives Nexus, so we could hope that progress on both paths would come along and solve the problem of the mutual funds. Now, both elements (exchanges and bond market reform) have a weak outlook. Is there a way out of this conundrum? Can a liquidity window for mutual funds be created, through which the problem of the mutual funds can be solved?

Why we need this and how it can work, by Harsh Vardhan

Indian debt mutual funds have grown rapidly over the past few years. Debt funds got a strong push after demonetisation. Currently the total assets under management (AuM) of debt funds are ~ Rs 15 Trn. There are individual debt fund schemes with AuMs of over Rs 1 Trn.

Debt funds invest their corpus in debt securities. In India there are two main classes of debt securities – those issued by the government including central and state governments and those issued by companies. Both have very poor liquidity. In the case of government bonds, while there is a somewhat liquid interbank market, a large part of the liquidity is in a single ‘benchmark’ paper which is typically a 10 year bond. When a new 10 year bond is issued, the old one ceases to be the benchmark and its liquidity drops sharply. The lack of liquidity is even worse with corporate bonds.

Most debt mutual funds promise high liquidity to their investors. For liquid and short duration funds, redemption proceeds are credited to the investor on T+1 while for most other debt funds it is T+2. MFs suffer the agony of liquid liabilities and illiquid assets. They manage this challenge through two pathways: (a) holding cash (typically less than 5% of AuM) and (b) having credit lines from banks.

There is considerable systemic risk in the Indian financial system, and situations where these two pathways prove to be inadequate. As an example, Franklin Templeton shut down six debt schemes when redemptions were unusually large and the bond market was unusually illiquid. The redemption pressure that they faced had nothing to do with their money management; it was induced by an episode of systemic risk.

In the anatomy of these recurrent debt market crises, one interesting feature is market failure in the form of a negative externality. Purely at random, when large redemptions show up at any one door, the selling that this induces drives down prices (as the overall market is illiquid and impact cost is high), which adversely impacts the NAV of all other funds. For any rational economic agent that sees the first inkling of higher outflows (either by watching flows or by looking at NAV changes), it is rational to yank all debt investments. This creates a channel through which selling by one fund induces redemptions for others.

Another way to locate these problems in the framework of market failure is to see that market liquidity is a public good. As an example, the liquidity of Nifty futures is non-rival (your consumption of liquidity does not adversely impinge on my access to liquidity) and non-excludable (everyone can access the Nifty futures market). When we build liquid markets, we are creating a public good.

All market failure is ultimately a problem of coordination between economic agents. We should look for collective action through which some of the problems of debt mutual funds can be addressed.

There are two solutions going around, for this problem of bond market illiquidity, which just don’t make sense. One strategy is for regulators to demand that mutual funds hold more capital. Mutual funds are not balance-sheet based entities and the journey of trying to amplify their equity capital requirements is conceptually wrong. Another strategy is for the central bank or the government through any other agency, to run a liquidity window for mutual funds. When the full consequences of this play out for mutual funds, it is likely to leave them worse off.

Is there a way out of this jam? I believe we can establish a Cooperative Liquidity Window (CLW), built by mutual funds for mutual funds -- with a small involvement of the state -- which can help solve this problem. For the people who are too used to state leadership in such things, we should point out that the Bank of England played this kind of function -- liquidity support for distressed banks -- for centuries as a purely private organisation; it was only nationalised in 1946. During the great depression in the US in the 1930s, J P Morgan, founder owner of the eponymous bank, orchestrated a bail-out of the American banking system through co-operative efforts of larger, stronger banks. These experiences are food for thought, and the design proposed here draws on this history.

For such an emergency liquidity support mechanism, we should establish five conceptual objectives:

• It should use no public money.
• There should be an extremely low amount of state coercion involved, in getting some MFs to participate in the CLW, and no role for the state in terms of regulation, management, appointments, or rule-making of the CLW.
• The governance of the mechanism should be within the AMCs that participate in it; it should operate as a self regulatory organisation.
• The capital to set up and operate the mechanism should be provided by the participants; it should operate as a mutual co-operative; rules of access to the mechanism should be defined by the participants.
• It should be only an emergency liquidity support system. The criteria for defining an emergency, and the extent of support that can be provided to individual entities, should be defined by members as the by-laws of the mechanism.

How would the proposed CLW work?

1. The participating AMCs would create a vehicle by contributing to the equity of the vehicle. The vehicle could be set up as a trust or any other legal form that minimizes transaction costs.
2. Some members would be coerced by SEBI (the largest firms adding up to perhaps 75% of the category AUM) and others would be voluntary participants (those who would like to benefit from its services even if not forced by SEBI). Apart from this, there would be no role for the state power in the CLW, in any fashion.
3. The equity contribution of each MF should be determined by its debt fund corpus. For example, all MFs with debt fund AuM of over Rs 1 Trn might contribute Rs.5 Billion, those with an AuM of Rs 0.5 Trn to 1 Trn might contribute Rs. 3 Billion, and so on. The CLW governance must write the specific rules of equity contributions.
4. The CLW would leverage up and create a corpus that supports a securities repurchase (repo) operation in the event of stress.
5. When a member AMC faces severe redemption pressure (way beyond what is deemed normal by the members collectively as defined by the governance rule of the CLW) it would pledge its eligible debt securities to raise short term liquidity. This would be akin to a bank accessing the repo window in the event of a run.
6. This window would also accept liquidity from members like a normal repo window.
7. The rules regarding the extent of liquidity support provided, the tenure, the bid-ask spread, acceptable securities as collateral and hair cuts, etc. would all be defined by the members collectively.
8. The CLW would operate as a not for profit entity or provide a modest return on equity to the member shareholders.

Currently there are ~45 AMCs in India. If we assume that 40 of them participate, each contributing an average of Rs 1 billion of equity capital, we would have Rs.40 billion of equity capital in hand. Assuming 4x leverage, the resources of the organisation would be Rs.160 billion. It is easy to go to much higher values.

The CLW should support participating MFs only in dealing with liquidity issues and not credit risk issues. This should be enshrined in the governance and operating rules of the CLW. Considering that the CLW will be managed by the AMCs themselves, who are all deeply informed players, it is reasonable to assume that they will be able to differentiate between liquidity and credit issues, Further, at a security level, the CLW will determine eligibility of securities and haircuts applicable. This will ensure that even in providing liquidity support, credit issues are not ignored. The rules of operation of the CLW should be well known, ex ante, so all the participating MFs face a predictable environment.

Let us simulate how the Franklin Templeton crisis might have played out, if this CLW was in place. The issues faced by Franklin Templeton’s shuttered debt funds schemes were purely liquidity issues: Over the last 18 months or so, they have returned upwards of 90% of the AUM at the time of shutting the schemes. Further, the return on these funds during the time was comparable with other funds in the same asset class. As the Franklin Templeton crisis was a liquidity crisis and not a credit crisis, the CLW would have been in play to support the liquidity crisis at Franklin Templeton. With illustrative assets of Rs.160 billion, it would have had the financial depth to deal with this situation, where all six affected funds put together had a total AUM of about Rs. 250 billion.

This design is not a substitute for a deep and developed bond market. A liquid market for securities is always the best solution to deal with any liquidity issues. But we face a problem today: We have a situation where the debt mutual funds corpus has grown very significantly and yet the bond market, especially the corporate bond market, remains very illiquid. The CLW is a mechanism where enlightened self interest can create a cooperative which helps the sector deal with a dangerous liquidity challenge.

In my proposal, there is only one use of state power: I feel SEBI should force large debt funds adding up to (say) 75% of the industry AUM to be members of the CLW, and force non-members to communicate this lack of membership in their customer-facing communications. The justification for this use of state power lies in the extent to which this would help reduce systemic risk (innocent bystanders being adversely affected in the next mutual fund crisis). This coercion addresses the free rider problem, where any one MF may derive benefits from the more stable mutual fund / bond market system, but try to be stingy in not paying for this stabilisation. Apart from this, I propose there should be no state involvement / control / regulation of the analysis, design, staffing, rule-making or operation of the CLW.

All members would have the self interest of making the facility work well -- as they are both owners and customers -- and they would thus exert governance. This is a problem where a cooperative solution works well. There is no market failure in the working of the CLW, and thus no role for regulation or any other involvement of the state.

There is one limitation in this design. The CLW will not be adequate if there is a full fledged financial crisis, such as what was experienced in 2008. In that case, the CLW would become one more element of the financial system that would have to be analysed in the crisis management at MOF.

There is no solution which can cover up for the lack of a bond market, by Josh Felman and Ajay Shah

Bond mutual funds are facing a serious dilemma. On the one hand, they promise investors liquidity, the ability to withdraw money at short notice. But on the other hand, they hold assets that are largely illiquid and difficult to sell. As a result, they face a mismatch between what they promise and what they can actually deliver.

Investors typically pay little attention to this mismatch, because most of the time it isn’t apparent. That’s because on most normal days, the investors who want to withdraw their money are more than counterbalanced by the many investors who are putting their money into the funds. It is only when this balance is disrupted, when a large proportion of investors “run” to take their money out, that mutual funds must sell their assets and the liquidity mismatch is revealed (Sane, Shah, Zaveri 2018).

Of course, banks face a similar mismatch problem. They, too, promise that depositors can withdraw funds easily, even as they hold assets (loans) that are even more illiquid than bonds. But in the case of banks there is a firewall against runs, namely the deposit insurance provided by the Deposit Insurance and Credit Guarantee Corporation. With this insurance, depositors know that their deposits are always safe. Accordingly, they have no incentive to rush to banks to withdraw their money, even if they find out that their bank’s loans have turned bad.

Could a Cooperative Liquidity Window (CLW) provide a similar firewall for debt mutual funds? At first blush, it seems like it would. After all, if the problem is that bonds are illiquid, then it seems logical to create a window that would allow funds to exchange bonds for cash. Moreover, the CLW proposal has some particularly attractive features. It would be a private initiative, involving no public money; and it would be employed only in emergencies, reducing the risk that it would distort financial markets. It avoids state failure by having no state involvement, apart from coercing large mutual funds (MFs) to become members.

But we see difficulties in translating this concept into a working liquidity facility. Consider the following problems with the proposal:

• The illustrative corpus – Rs 160 billion – is relatively small, about the size of a single mutual fund group (such as Franklin Templeton). So, if several groups get into trouble at once, there won’t be enough liquidity to go around. In our thinking about the CLW proposal, we should think of something more like Rs.0.5 trillion of dry powder.
• The proposal envisages that lenders will be willing to purchase Rs 120 billion of CLW debt. Would they really be willing to lend so much money to an unknown institution engaged in the risky activity of buying illiquid debt? And even if they did, what interest rate would they charge?
• Assuming that lenders charge a relatively high rate of interest, how will the economics of every day operation of the CLW work out? In most years, its assets will simply be sitting in safe but low-yielding government securities, so it will suffer from a negative cost of carry. That means it will need to make compensating large profits on its occasional liquidity activities, by buying debt at very low prices and selling at high prices.

Let’s assume optimistically that these problems can somehow be overcome. We think the proposal still won’t work, because it has an important flaw: it is based on the premise that mutual funds facing runs are merely suffering from liquidity problems. But things are usually not this simple. Most runs involve credit risk issues, which means that there is a danger of defaults, which could saddle the CLW with large losses. And this makes all the difference. To be concrete: we don’t agree with Harsh’s relatively sanguine assessment of the Franklin Templeton story.

Runs on mutual funds follow a standard sequence. Initially, investors find out that a large bond-issuing firm is in serious trouble. In response, they start examining the portfolios of their mutual funds. And when they find the funds that are heavily exposed to the teetering firm, they run. This is precisely what happened in the case of Franklin Templeton. This firm invested aggressively in risky assets: even its “safe” Ultra Short mutual fund invested more than one quarter of its portfolio in assets rated A or below, rather than the AAA assets that such funds would normally hold. In addition, Templeton invested heavily in zero coupon bonds issued by Yes Bank. So when financial markets turned risk averse and Yes Bank ran into trouble, investors fled the Templeton funds.

In restrospect, it turns out these investors were correct: there was indeed credit risk. It is now almost two years since Templeton shut six of its funds, and the 300,000 investors in these funds still haven’t received all of their money back. Even if investors are reimbursed eventually for their full nominal amounts, they have suffered an opportunity cost. Inflation will have eaten away at the real value of their money, and they will have lost the opportunity to use the funds to meet last year’s expenses (such as Covid hospital bills) or make other investments. In particular, they were unable to place this money in the stock market, which has nearly doubled since withdrawals were frozen in April 2020.

The complexity of correlations and asymmetric information about credit and liquidity risk means that the proposed CLW will run into three problems:

1. It could distort the incentives of mutual funds. Right now, mutual funds face market discipline. They know that if they invest in risky, illiquid bonds, they will get into trouble if investors panic and demand their money back. So most mutual funds – unlike Franklin Templeton – try to confine their purchases to safe, relatively liquid bonds. Precisely for this reason, most funds were able to survive the runs on Templeton largely unscathed.

This discipline could disappear if a liquidity window is established. In this case, mutual funds will feel more free to buy risky, illiquid bonds. In fact, they might try to buy as many such bonds as possible. After all, risky bonds carry higher interest rates, so mutual funds that buy them will be able to advertise higher returns. And if things go wrong, these funds will always be able to pass the problem onto the CLW.

Of course, they will not be able to transfer all their risk, since they have contributed to the equity capital of the CLW. For example, if they own 10 percent of the CLW, they would have to bear 10 percent of any losses faced by the CLW. Still, they might be able to pass on 90 percent of any potential losses. And this is enough to distort incentives.

So, the CLW will try to stop such behavior, by limiting the types of debt they will buy. But this will not be easy.

2. The CLW will find it difficult to use rules or discretion to determine what types of debt are eligible for the facility. If the CLW tries to use rules, that is to define the types of debt that they will buy, firms will employ ‘financial engineering’ to create debt that nominally conforms to the rules but in fact remains highly risky. This was how the US wound up in a financial crisis in the mid-2000s: because firms created synthetic bonds that were rated AAA but were actually highly risky. Closer to home, there are also examples of bonds that were deemed safe – like the AAA-rated bonds issued by ILFS – that nonetheless ended up defaulting.

If the CLW consequently eschews rules and says instead that it will handle episodes using case-by-case discretion, users will fear that they cannot rely on the CLW, since such an approach would mean that other members could veto their attempt to unload their bonds to the facility.

We request the reader to not envision peaceful times, when some trades are taking place and spreads are fine, but instead to think of times when spreads are high, recent trades have stale prices, and a pall of fear hangs over the market. Consider a situation like late 2008, when bond prices were plummeting. At that time, buying bonds was considered a foolhardy act, comparable to ‘catching a falling knife’. Would a consortium of mutual funds really have the courage to intervene in this situation?

It is important to recall that the shareholders of the CLW are, themselves, bond market traders. They are the ones refusing to buy the bonds at any price on their own books – that is why the bonds are illiquid! So why would they allow their agent (the CLW) to do this? Consider the calculation of the other firms. If the CLW purchases the bonds, and the bonds default, the cost will have to be borne by the members of the cooperative. In contrast, if the CLW doesn’t purchase the bonds and the mutual fund is forced to shut down, the other firms might even benefit. Recall how the rest of the financial system `ganged up’ against LTCM in 1998, as they stood to gain from declining prices of LTCM’s positions.

3. Even when the CLW is willing to purchase bonds, it will not be easy to agree on a price. When bonds are illiquid, their price is not known to anyone. The distressed mutual fund will plead for a high price – and it will have a say in the running of the CLW. But other shareholders would object, as they would not want to suffer losses. So the Board of the CLW will work themselves into a tizzy trying to agree on a sale price.

Let’s assume the majority on the Board gets to decide the price.They will face an inherently difficult problem. Because the bonds are illiquid, the Board will need to guess the true value of the bonds on offer. And because the CLW would be running with an elevated leverage ratio, the consequences of guessing too high would be disastrous. At a 3:1 debt-equity ratio, a 30 percent fall in the price of the CLW’s assets would wipe out the entire equity capital. So, the CLW will need to offer a low price.

These three problems would haunt the CLW. It might freeze up with decision-making paralysis precisely at the times when decisive action is most required. Alternatively, it might proceed, but with excessive caution. It might purchase only select assets, meaning that many mutual funds facing runs would find the liquidity window closed. And even where the CLW was willing to purchase their assets, it is likely to offer a low price, which would prove ruinous to already-stressed MFs. These features interfere with the stated function of the CLW.

Many people remember stories from the Panic of 1907, where one person -- J.P. Morgan -- was the buyer of the last resort. This mechanism worked because Morgan was a self-interested profit-maximising individual who made a decision to use dry powder. He drove a hard bargain and purchased assets very cheaply, and turned a tremendous profit. He took enormous risk in the process, for he could have gone bankrupt himself. And, it could easily have been that the demand for liquidity insurance was bigger than his balance sheet, in which case his intervention would have gone badly wrong. For each J.P. Morgan who is celebrated for a 1907 event, there are many others who failed at various moments in history. We dream that a CLW will be able to think and act like J.P. Morgan, but its shareholders + board + management would find it impossible to have the entrepreneurial and risk-taking acumen of an individual. This is perhaps why we don’t see such a co-operative liquidity window in the world today.

A final point. We have stayed within the construct of no state intervention other than forcing MFs adding up to 75 percent of category AUM to become members. We fear, however, that when faced with the difficulties described above, the Indian state will not hold back even though there is no market failure. Once this happens, the familiar litany of state failure would commence.

We see this debate as a special case of a general principle. An economic policy strategy that addresses the surface symptoms is unlikely to work; the scope for financial engineering in public policy is very small. For a policy to succeed, it needs to engage in a root cause analysis, to address the underlying economic problem. If the problem is that investors are running from bond funds because they are inherently illiquid, then the only way to solve this problem is by reducing the mismatch between what these funds promise and what they can actually deliver. And that requires some fundamental financial reforms.

Hence, we would argue that the future of Indian finance remains along the strategy of the Financial Sector Legislative Reforms Commission (FSLRC). Once this is done, the need for a liquidity window will gradually fade away.

Bibliography

Mutual funds with feet of clay. Ajay Shah, Business Standard, 22 January 2018.

Runs on mutual funds. Renuka Sane, Ajay Shah, Bhargavi Zaveri. The Leap Blog, 12 October 2018.

RBI vs. Covid-19: Understanding the announcements of March 27

by Rajeswari Sengupta and Josh Felman.

When the first cases of Covid-19 started getting reported in India, the economy was already in a precarious situation and the space for a macroeconomic policy response was limited. Even so, the Reserve Bank of India has come up with a number of initiatives to combat the crisis. In this article, we consider the broad principles that should guide the macro policy response, summarise the RBI announcements of March 27, and assess the announcements against the principles.

Background

The "corona crisis" consists of three interlinked problems: a health shock, an economic shock following from the lockdown, and a global economic downturn. Each one of these shocks on its own is significant. Put together, they have created considerable pressure upon policy makers to act quickly and decisively.

Coming up with an effective policy response is not an easy task. For one thing, the corona crisis poses some exceptional difficulties. It is clear that the human and economic toll will be serious, but it is unclear how long the crisis will last or how deep the damage will be. And without a clear understanding of the size and duration of the problem, it is difficult to know how to calibrate the policy response. For example, monetary easing could take a year to have a significant effect. By then the problem might be over, and inflation might have re-emerged, at which point painful measures would be required to bring it down. This is not just a theoretical possibility, it is precisely what happened in the aftermath of the Global Financial Crisis in 2009-13.

Principles of policy response

Policy making is difficult in the best of times. It is harder in exceptional times, when there is pressure for quick actions, grounded in reduced analysis. It is in exceptional times that the toolkit of good governance becomes even more important:

• The lowest cost actions are those which are grounded in root cause analysis.
  
• Each action needs to be carefully weighed in terms of the costs and benefits imposed upon society.
  
• As much as possible, policy responses should be fitted into existing rules and frameworks.
  
• All state actions should be preceded by public debate and consultation.
  
  

This toolkit is a valuable discipline, an institutionalised application of mind. Why is root cause analysis important? Consider the problem of weak banks lending to firms in recent years. From 2018 onwards, RBI has been trying to address this problem by injecting more and more liquidity into the banking system, in the hope that banks would deploy these resources and lend more (link, link, link). But liquidity issues were not at the root of the problem, the twin balance sheet (TBS) stresses at firms and banks were the real issue. Bank lending has also been discouraged by the government’s measures to investigate and prosecute bank officials for their lending decisions. As a result of these factors, banks have remained reluctant to lend to the private corporate sector, curtailing credit to industry to a year-on-year growth rate of just 0.67 percent in February 2020.

As an example of poor cost-benefit analysis, consider the regulatory decisions after the Global Financial Crisis. At the time, it was felt that exceptional times called for exceptional deviation from prudent financial regulation. A series of restructuring schemes followed, allowing banks to postpone NPA recognition and hide bad news. With the benefit of hindsight, we know that this restructuring worked poorly, and helped prepare the ground for the twin balance sheet crisis of 2011-2020.

As for respecting frameworks, there is a temptation during crises to abandon rules and resort to discretion. But recent experience warns us that "temporary measures" are often difficult to reverse (consider the 2010 fiscal stimulus), while inadvertent consequences (such as NPAs) are difficult to resolve. More fundamentally, temporary measures disrupt the stable configuration of expectations of economic agents, which hamper the recovery. It takes many decades of consistent behaviour in a rules-based framework to shape the rhythm of the working of state institutions, to build up policy credibility. This credibility can be rapidly dissipated.

Hence, policy makers need to proceed cautiously.

The March 27 announcements

It is in this context that we need to examine the March 27 announcements. Four bold actions were taken, following an "out of cycle" i.e., unscheduled Monetary Policy Committee (MPC) meeting:

• The repo/reverse repo rates were cut by sizeable amounts, to 4.40/4.00 percent from 5.15/4.90 percent. The 91-day treasury bill rate, which measures the de facto stance of monetary policy, dropped to 4.31 percent from 5.09 percent on 26 March.
  
  
• Ordinarily, banks can borrow on a short-term basis from the RBI using the repo window. To supplement this facility, a new `targeted long-term repo operations' (T-LTRO) mechanism, with a limit of Rs.1 trillion, was announced. Banks may find this attractive because they do not have to mark to market the investments made with these borrowed funds for the next three years. However, there is a condition: the money that is borrowed here must be deployed in investment-grade corporate bonds, commercial paper, and non-convertible debentures, over and above the outstanding level of their investments in these bonds as on March 27, 2020.
  
  
• The cash reserve ratio (CRR) was reduced by 1 percentage point, bringing it down to 3% of deposits ("net demand and time liabilities"). This is the first time the CRR has been changed in the last 8 years. RBI's initiatives appear to be motivated by the desire to increase liquidity, as their statement highlights that these measures will free up Rs 3.74 trillion in banks' funds.
  
  
• Banking regulation requires banks to recognise and provide for a loan when there is a delay in payment. According to the Prudential Framework for Resolution of Stressed Assets, banks are required to classify loan accounts in special mention categories in the event of a default. The account is to be classified as SMA-0, SMA-1 and SMA-2, depending on whether the payment is overdue for 1-30 days, 31-60 days or 61-90 days, respectively. RBI has now modified this regulation, so that banks can offer a moratorium of 90days for term loans and working capital facilities for payments falling due between March 1, 2020 and May 31, 2020. However interest on the term loans will continue to accrue during this period. If a firm applies for and receives a moratorium, the loan account in consideration will continue to be recognised as a standard asset and the SMA classifications will no longer apply. Interest on term loans will continue to accrue during this period. 

Analysing the monetary policy announcements

Monetary policy is most effective when economic agents understand and can anticipate the behaviour of the MPC. This process of learning and understanding is still underway, given that India is in the early years of building up the credibility of the inflation targeting framework and the MPC process. So, one would have expected that the MPC statement would go into great details and spell out its macroeconomic forecast, explaining why it believed the 75 basis points rate cut was consistent with its commitment to the 4 percent inflation target.

However, tt did not explain the rate decision in the context of a revised inflation forecast, or any other element of a macroeconomic forecast. It did not offer a justification for the magnitude of rate cut chosen.

Since the rate cut announcement was not couched in the standard IT framework, the public does not have the assurance that the rate cuts will be reversed when inflation begins to rise again. To remedy this problem, monetary policy actions could henceforth be couched in terms of this framework, as a way of assuring the public that the RBI is keeping its eye on this critical objective, and that the mistakes of the past will not be repeated.

Analysing the banking regulation announcements

We know that the corona crisis is a temporary shock. Standard economic theory tells us that the optimal response to a temporary shock is for (viable) firms and households to obtain financing, so that they can tide over the difficult period. Over the next few months, three categories of firms will emerge: a) firms that are able to pay their dues throughout the crisis period, b) firms that are fundamentally viable and can survive provided they are given adequate credit support, and c) firms whose business is faulty and who should become bankrupt as a result of this shock.

It will be important for the banks to distinguish among these firms. Banks should ideally do nothing with firms in category (a), extend credit support to firms in category (b), and take the firms in category (c) to the insolvency and bankruptcy courts as and when that process resumes.

Under the 27 March package, the RBI has given regulatory approval to banks and other lending institutions to decide which of their customers needs a 90-day deferral. This decision, to allow banks but not require them, to grant moratoria is a good one, as it allows banks to distinguish among the three types of firms.

However, the plan is not without drawbacks.

• No mechanism has been created to classify the loans that will be rescheduled, so transparency has been lost. Investors – already nervous because of accounting surprises at Yes Bank and other financial institutions – will consequently provide capital only at a cost marked up to reflect this information risk premium. And this increase in banks’ costs will be passed on to the borrowing corporate sector.
  
• Moratoria will create problems for pass-through certificates, i.e. loans that have been bundled as bonds and sold to mutual funds, because there are no provisions in these certificates for loan rescheduling.
  
• Finally, and most importantly, there is no clarity on what happens once the moratorium period is over. How will banks clean up the mess that will be created later, as many of the firms which benefited from the moratorium end up defaulting? There will be a new wave of NPAs, which we know from experience will be difficult to resolve.
  

There is also a risk: now that a "temporary" moratorium has been introduced, there will be pressure for it to be extended again and again. If the RBI is unable to resist, we will quickly find ourselves back in the 'extend and pretend' era of post-2008. Banks, investors, the RBI, will all be navigating in a fog, since no one will know – and hence, be able to deal with -- the true size of the bad loan problem.

In other words, under the current design, there are risks that the costs of the moratoria could end up exceeding the benefits. Is there an alternative? In fact, two supplementary actions could reduce potential costs, while preserving the benefits.

First, RBI could announce that firms seeking a moratorium would be marked in a separate category. This would give transparency regarding the true financial situation of the banks. There will also have been a bit of a stigma for borrowers, helping to preserve debtor discipline. If a firm has no choice, it will still postpone repayment. But if a firm can afford to pay, it will do so, in order to escape the stigma.

Second, forward planning could help deal with the consequences of the inevitable surge in defaults. Even before the corona crisis, bankruptcy cases were taking far longer than what the law stipulates. Large cases were taking several years to resolve. If this situation is not addressed, there is a risk that large sections of the economy will be tied up in bankruptcy courts, making it impossible for the economy to return to normal, even after the virus abates. To make sure this does not happen, the Insolvency and Bankruptcy Code (IBC) needs to be reformed urgently in order to ensure faster and effective resolution. Such reforms would also have an immediate benefit: banks would be more confident in lending now if they knew the IBC would not be overwhelmed by cases after the crisis is over.

Reviving credit growth

The need of the hour is to revive credit to the private corporate sector. But the marginal benefit of the RBI adding more liquidity to a system that is already in a surplus mode is not clear. This strategy has already been tried, without success. It is unclear why it would work now, especially now that uncertainty about firms' prospects has only increased.

For a proper root cause analysis, let’s go back to economic fundamentals. Consider a loan decision. When a bank decides to approve a loan, it is performing two functions simultaneously: it is assuming risk, and it is allocating capital. In the current circumstances, it is still possible for banks to allocate capital. They can assess which firms are more likely to be hit badly by the crisis and which firms are going to be less affected. That is, banks can figure out the relative risk. The problem for the banks is that right now they cannot assess the absolute level of risk, because they do not have any idea about how long the crisis is going to last, or how deep the crisis is going to be. And this shock has come at a time when banks have already become risk-averse given the last few years of balance sheet problems. Hence, it is difficult for them to lend, especially to new customers.

In these circumstances, giving them liquidity, exhorting them, coming up with any number of subsidy schemes, will not work. But there is a possible solution. The government-- not the RBI -- could relieve the banks of the burden that they cannot manage: the burden of risk.

This can be done through a mechanism as follows. The government can capitalise a fund which will then give loan guarantees. The scheme would have some selection criteria, say MSMEs that have been current on their bank loans. It would also specify the maximum rupee amounts per firm, pegged say to the annual revenues of the company. Once the eligibility criteria are specified by the government, the actual selection of the firms would be done by the banks. They would identify the best firms, originate the loans, and then apply to the fund for guarantee coverage. The banks should be charged a fee for this, to discourage them from using the fund unnecessarily.

In this way, we could use the law of comparative advantage to obtain better economic outcomes: the government would do what it does best in crises, namely bearing risk, while the banks would continue to do what they do best, namely allocating capital.

Conclusion

The RBI’s March 27 announcements were bold and decisive. In particular, the reduction in the repo rate by 75 basis points will provide significant debt service relief to firms and households. This is a welcome measure, at a time when their cash flows are going to be seriously strained. The announcement that banks will be allowed to grant temporary debt moratoria to firms and households could also prove a major help, for exactly the same reasons.

That said, the announcements could have been better grounded in basic principles. The root causes of the banks’ reluctance to lend have not been addressed. At the same time, the way the policy actions were designed and announced run the risks of damaging confidence in the existing frameworks. The public may not be so sure that the authorities remain committed to preserving low inflation or financial stability. Nor is it clear that there is an "exit strategy", to ensure that the defaults will be resolved expeditiously, allowing the economy to return quickly to normal, once the health crisis is over.

There is still time to clear up these ambiguities, and remove any doubts. Initial actions can be followed by supplementary steps, and initial problems can always be remedied. This will take careful root cause analysis, cost-benefit calculations, and a determination to reinforce existing policy frameworks.



Josh Felman is a researcher specialising on India. Rajeswari Sengupta is a researcher at IGIDR.

Credit stress in large Indian firms

by Ajay Shah and Pramod Sinha.

We in India are used to thinking about banks and NPAs. We infer the state of difficulty of the banks, and indirectly of their borrowers, by using data from banks. There are many advantages in looking directly at the state of credit stress in the large non-financial firms, and identifying the firms where there is high credit stress:

1. Do not rely on bank information. This evidence is not filtered through the difficulties of banking regulation. Whether a bank classified Kingfisher Airlines as an NPA or not, we can see credit stress in the financial statements of Kingfisher Airlines.
2. Look beyond banks. Banks are not the only financial lenders to the non-financial firms. As an example, bank-centric thinking is not useful in understanding runs on mutual funds. When there is stress in a borrower, this impacts not just on banks but on all lenders. Pulling together information about stressed borrowers helps us see the difficulties of lenders, on a financial system scale, and not just in banks.
3. Micro-prudential considerations. The stressed firms face likely defaults. The debt of such firms is likely to be worth less than book value. Under sound micro-prudential regulation, banks and other lenders should mark down these assets, even if no default has taken place. The extent of stress, as seen here, gives us insights into the fragility of banks and other lenders.
4. The bankruptcy process, the distressed debt industry. There is a new world opening up in India, of distressed firm transactions and the bankruptcy process. We will see the empirical contours of this industry, and the bankruptcy process, by examining the state of credit stress in the non-financial firms.
5. A drag on growth. A firm that is in a state of credit stress is likely to face difficulties meeting payments to creditors. It might often be liquidity constrained, and may struggle to obtain cash to pay its suppliers. The mind space of the leadership of such a firm is likely to be absorbed in the struggle for survival. Such firms are unlikely to fare well on growth through increasing the resources utilised or through increased productivity. To understand what is coming in Indian macroeconomics, we should look at the non-financial firms and their balance sheet difficulties.

Identifying stressed firms

The interest cover ratio is defined as PBIT/interest. If a firm has to make interest payments of 100, and if its profit before interest and taxes is 150, then its interest cover ratio ("ICR") is 1.5. Such a firm has the 100 required to pay interest in the year, but there may be a task in terms of juggling the dates on which interest has to be paid versus the dates on which the business produces cash. And, such a firm is left with just 50 after paying interest, which can be used for debt repayment and the regular capital expenditures required for the upkeep of the business.

A good thumb rule which identifies a firm in a state of stress at time $t$ is: The firm has ICR$ < 1.5$ in year $t$ and in year $t-1$. This avoids the false positive of a firm which only hits ICR$<1.5$ for one year.

A `stressed firm', by this definition, is not necessarily one that has defaulted (and is thus eligible for the bankruptcy code), and it is not necessarily one that is classified as a non-performing asset by RBI's rules of recognition. We would, however, suggest that a firm with two consecutive years at an ICR of below 1.5 is under stress, has an enlarged risk of default, and has a management team that is absorbed in dealing with this stress.

Methodology

We study all the non-financial firms in the CMIE database. At each year, we isolate the firms which are observed for two consecutive years. Some additional sanity checks are applied. Through this, we are able to construct two sets at each point in time: The set of all firms observed and the subset of this, which is the stressed firms.

Here are some counts of the firms in the two sets.

Year	Total	Stressed
2014-15	9,289	3,674
2015-16	9,208	3,702
2016-17	6,687	2,573

In the table above, the total number of firms (9,289) for 2014-15 is the number
of non-financial firms that are observed, and pass some sanity checks, in both 2013-14 and 2014-15. Of these, 3,674 were stressed. The last year that we utilise here -- 2016-17 -- has fewer firms when compared with the years prior to
it, where information for a larger number of firms has trickled into the CMIE database. In this last year, we see 2,573 non-financial firms in the database, where the ICR was worse than 1.5 in both 2015-16 and 2016-17.

Conditions in 2016-17

Parameter	Value (Rs. Tln)
Balance sheet size
Stressed firms	29.79
All firms	77.24
Bank borrowing
Stressed firms	8.87
All firms	14.94
Total borrowing
Stressed firms	15.58
All firms	27.59

This shows that the sum of the balance sheet size for all the 6,687 firms for 2016-17 was Rs.77.24 trillion. Of this, Rs.29.79 trillion was in the 2,573 stressed firms.

Totally, borrowing of Rs.27.59 trillion was visible. This is small when compared with the total assets of these firms of Rs.77.24 trillion. Of this borrowing, Rs.15.58 trillion was in the stressed firms.

Finally, we are able to see Rs.14.94 trillion of borrowing from banks, in this sample of 6,687 firms, in 2016-17. Of this, Rs.8.87 trillion was in the stressed firms.

How has credit stress evolved over time?

We are able to do these calculations for all years from 1998-99 onwards. We will express the time-series evidence using confusingly similar graphs, all of which produce important stylised facts for our understanding of the economy.

The share of bank debt to stressed firms, in the total bank debt seen in the sample firms

The health of banks is related to the health of their borrowers. Hence, in the graph above, we compare the sum of bank credit to stressed firms (in our sample) against the sum of bank credit to all firms (in our sample).

The business cycle is clearly visible here. In the last tough downturn, 2000-2003, this ratio was at about 50%. That is, about half of the bank borrowing seen in the CMIE database was in stressed firms.

This ratio dropped all the way to about 15% in 2007. It climbed steadily thereafter and is now at about 60%. There is a tiny gain in 2016-17 when compared with the previous year.

In the last recession, this measure improved through the recovery of the economy. Firm exit took place through the sluggish traditional ways. When the bankruptcy reform resolves or liquidates a large volume of stressed firms, this will deliver improvements in this measure. To the extent that the bankruptcy reform works, we may expect the next recovery to proceed faster than the last one, where it took five years of a powerful expansion to get from about 50% to about 15%.

We apply this same thinking to total borrowing -- instead of just bank borrowing:

The share of total borrowing by stressed firms, in the sum of borrowing seen by all sample firms

The last business cycle downturn got to values of above 50%, there was a great decline to about 20%, and then it has risen to about 55%, with a slight improvement in 2016-17.

Implications

There is considerable balance sheet stress. In the latest year, the aggregate balance sheet size of the stressed firms -- observed in the CMIE database -- was Rs.30 trillion. The stressed firms had Rs.15.6 trillion in borrowings of which Rs.9 trillion was from banks. This has implications in other parts of finance, beyond banking.

Bank debt in stressed firms is about 60% of total bank debt seen in the sample. Similarly, the borrowing by stressed firms is about 55% of all borrowing in the sample. Under sound micro-prudential regulation, asset-based lenders would mark down these assets based on the price at which the loan/bond could be sold on the market.

In the conventional wisdom, there is about Rs.10 trillion of bad debt on the balance sheet of banks. Our analysis shows that in the 6,687 large non-financial firms, where Rs.15 trillion of bank debt is located, we see 2,573 stressed firms with Rs.9 trillion of bank debt. The 2,573 stressed firms that we see in this sample, alone, account for 11.4% of the overall bank debt ("non-food credit") in the economy.

The stressed firms are about 40% of the overall corporate balance sheet. These firms are likely to fare poorly in investing or in productivity growth, and are thus a drag upon overall economic growth.

It is likely that many of these stressed firms will be sold, or go into the bankruptcy process. There is a substantial task ahead, in terms of resolving these firms and paying for the losses experienced. These 2,573 firms are the happy hunting ground for this new industry. This process of resolution is central to India's economic recovery.

There is much value in understanding the balance sheet stress in the economy using such methods. We obtain insights into difficulties of the financial system going beyond a bank-centric view, we get a view of the new distressed debt industry, and we get insights into the drag on GDP growth that the stressed firms represent.



The authors are researchers at NIPFP.

There be dragons: Off-balance-sheet liabilities of the Indian State

by Ila Patnaik and Ajay Shah.

Conventional fiscal stability analysis looks at the stock of debt and wonders whether a country can pay off this debt, under reasonable scenarios for future interest rates and fiscal surpluses. In many countries, though, the fiscal sustainability story has turned on promises made by a government which were not explicitly counted in the debt. There are obvious liabilities that are kept off the books - such as debt in public sector companies or state electricity boards. In this article we look deeper, at less obvious ways in which off-balance-sheet liabilities have arisen, and the checks and balances that can contain them.

Off balance sheet liabilities of the government

Off balance sheet items come in two kinds.

1. A promise that looks like the cashflows on a bond. Example: A pension promise to a person is no different from a series of coupons that are paid out every year. Promising a pension is exactly like issuing that comparable bond.
2. A promise that looks like an option payoff. Example: If a government is in hock to pay the lenders of a firm when it goes bankrupt, it is much like being the seller of an option. When governments write guarantees, this changes the risk profile of the exchequer and generates possibilities of large payouts when those options mature in the money.
   It should be noted that organisations backed by statute are not automatically backed by a government guarantee. As an example, in the UTI crisis of 2001, the government had no legal obligation to make good the losses of investors, but a political decision was made to use fiscal resources to pay half the loss. There is a mixture of financial risk ("Will X get into trouble?") and political risk ("Will the government backstop X?").

A correct reckoning of the liabilities of a government should add in these off-balance-sheet liabilities of both kinds. The FRBM Act brought control on one kind of off-balance-sheet liability of the Indian State: explicit guarantees given by the government. But there is more to the problem of off-balance-sheet liabilities than explicit guarantees.

Differences in cost versus differences in transparency

In the field of pensions, an interesting distinction is made between an unfunded defined benefit program vs. a funded defined benefit program that has assets invested in government bonds. In the conventional wisdom, a funded DB program is always superior to a pay-as-you-go unfunded program.

However, these two approaches are exactly the same in terms of the cashflows: both involve a highly predictable set of claims on the exchequer at future dates. To promise a pension is to implicitly issue a bond. This equivalence, between the cashflows of a bond and the cashflows of a pension, has an interesting implication. Consider a funded DB public pension program that invests in government bonds. The two streams of cashflows cancel out.

This approach to funding (holding government bonds) does not make things cheaper: it is only superior in that it is transparent and connects into the fiscal planning process. Cost savings only come about when a funded DB program invests in higher return assets, such as equities, through which the claims upon the exchequer at future dates are reduced on expectation.

What are the important off-balance-sheet liabilities of the Indian State?

Some important components of the off-balance-sheet liabilities are:

• Promises made for defined benefit pensions of civil servants, in particular the new `one rank one pension' (i.e. wage indexed) pensions for uniformed folk, and the underfunded `Employee Pension Scheme' (EPS) that is run by the EPFO. For the civil servants recruited after 1/1/2004, there is no such problem, as these new recruits are in the New Pension System.
• Promises made in a variety of health-related entitlement programs (Patnaik et. al., 2018).
• The temptation to make good the promises made by public sector financial firms, that experience distress in the future, even when there is no explicit guarantee. Of these, LIC has a balance sheet of Rs.28 trillion.
• The temptation to make good the promises made by private financial firms that experience distress in the future, even when there is no explicit guarantee. As an example, will the failure of IL&FS -- a private financial firm -- induce a direct or indirect fiscal impact upon the exchequer? So far, the government has not put money on the table, but could this change?
• The use of fiscal resources in responding to a full blown financial crisis, that may occur at a future date.
• The Parliament has enacted many laws, which could potentially evolve into large inflexible expenditures. These include `Right to education', `Right to food' and NREGS. On a similar note, the promises which are being made under `minimum support price' (MSP) could turn into large expenditures if the future brings together a certain combination of political pressures, jurisprudence and development of State capacity. Until repeal, these laws are a genotype that could, under the right combination of events at future dates, get expressed in a way that involves major fiscal risk.

These liabilities add up to large sums of money, of the same order of magnitude as the overt stock of public debt. Hence, off-balance-sheet liabilities should become more prominent in the Indian fiscal discourse.

How do the incentives of politicians and officials change?

At present, there is no check-and-balance influencing these opaque promises and risks. Each party in power looks to enter into greater off-balance-sheet obligations so as to get re-elected. How might this change?

The key thing that shapes these incentives is financial repression. At present, government debt is mostly sent into involuntary lenders. When the fiscal system graduates from financial repression to voluntary lenders, off-balance sheet liabilities would matter. There are numerous gains from removing financial repression: voluntary borrowing is more efficient than forced borrowing, the magnitude of resources available in a crisis would become greater, etc. But this requires a government that faces a skeptical bond buyer who demands a risk premium based on the extent to which the Indian State may engineer inflation or default.

In India today, there are many loose ends, which periodically induce fiscal surprises. This creates an adverse risk profile of Indian government bonds, and would drive up the required interest rate for borrowing when faced with voluntary buyers of bonds. In such a world of market discipline, when a government dips into LIC's resources, this would induce a higher cost of borrowing.

In India today, most of the attention in fiscal reforms lies upon tax policy reforms, such as the GST and the Direct Tax Code, and there is some interest in FRBM. There is much more to a mature fiscal system, including the issues of tax administration, debt management, the bond-currency-derivatives nexus, off-balance-sheet liabilities, accrual-based accounting, and the budget process. We need to broaden our research and policy work to address this full range of problems.

Tracking and understanding off-balance-sheet liabilities, communicating them to lenders, and communicating these concerns back into the budget process, is part of the work program of the future Public Debt Management Agency (PDMA) (Pandey and Patnaik, 2017). A Fiscal Council will help. Accrual based accounting will help.

Once we start paying attention to off-balance-sheet obligations, this creates fresh impetus for economic reform in many areas. As an example, if a monsoon failure induces a farm loan waiver paid for by the government, this is like a monsoon derivative that has (maybe) been written by the government. When reforms of personal insolvency and reforms of agriculture remove this possibility, the risk profile of the Indian exchequer will improve, and the cost of borrowing will go down.

Off-balance-sheet liabilities and financial reform

There is a close connection between public finance and finance, centering around the government bond market and the PDMA. For public finance, PDMA and the government bond market are the source of debt. For finance, the PDMA is the biggest investment banker of the country and the government bond market is the tool for low risk transfers of resources across time. What is less widely noticed is the intimate connection, between public finance and finance, through the question of off-balance-sheet liabilities.

How will off-balance-sheet liabilities change when micro-prudential regulation improves and the resolution corporation is setup? Financial firms will face distress less often, we will discern that distress early, and we will have an institutional mechanism to put the distressed firm down. Conversely, under present conditions, we get surprised by the difficulties in an IL&FS or in a UTI. These crises lead to a political question being thrust upon the leadership: Will you make liability-holders happy by using taxpayer money? We should, of course, have a mature political system which is able to turn down such requests most of the time, but we should have a mature financial regulatory system so that these situations do not arise in the first place.

Governments worldwide have faced claims on fiscal resources when dealing with full blown financial crises. The probability of occurrence of such crises, and the severity of such crises, is shaped by the institutional capacity in systemic risk regulation. The FSLRC apparatus for systemic risk regulation -- the Financial Stability and Development Council (FSDC) and its information system, the Financial Data Management Centre (FDMC) -- will reduce fiscal risk and thus the cost of government borrowing. As an example of the work program which should take place through FSDC/FDMC: At present, we have the possibility of runs on mutual funds (Sane et. al., 2018), which can lead to a full blown financial crisis, which requires policy thinking and reforms on a financial system scale.

Our objective in financial economic policy should be: to be as sparing as possible in ever asking for resources from public finance policy. For a sound fiscal system, we require financial sector reform. This will have a beneficial impact upon contingent off-balance-sheet liabilities and thus the cost of borrowing.

The need for a research community and a research literature

A remarkable feature of the existing Indian policy process is that no fiscal estimation was done in the policy process that led up to the announcements  about one rank one pension, or the various health insurance programs.

Even if policy makers had tried to reach into the research community to obtain such estimates, the state of data and knowledge is weak, and it is difficult for policy makers to obtain policy support from researchers. Some early work on the civil servant's defined benefit pension (Bhardwaj and Dave, 2005), one rank one pension (Sane and Shah, 2015) and banking (Shah and Thomas, 2000) is available. Much more needs to be done in this important field.

In an ideal world, record level data would be available from the government which would permit estimation of the value of the implicit debt or the implicit derivatives that the government has issued. The state of information systems and transparency of government is often a bottleneck, and creative research strategies have to be employed. As an example, Bhardwaj and Dave, 2005, utilise data from a national scale household survey to identify present and future beneficiaries of the traditional DB civil servants pension, and extrapolate the sample estimates to an estimate of the implicit pension debt associated with the traditional civil servant's DB pension. Similarly, Shah and Thomas, 2000, exploit information in stock prices to estimate the equity capital gap in banks, which helps overcome the opacity of banks and banking regulation.

A research community is required, which will build a research literature in estimating these expenditures based on exploiting diverse datasets. There will, of course, be multiple different estimates, as different researchers search for useful approximations through different assumptions and modelling strategies. A coherent picture will emerge from these debates. The PDMA, and buyers of government bonds, will be important users of this research community.

Off balance sheet liabilities and GDP growth volatility: A conjecture

There is a big gap between short spurts of GDP growth and sustained GDP growth. A mature market economy is a turtle, it plods along for a century, obtaining a low rate of growth on average, and harnessing the power of compounding. Poor countries fail to get sustained growth. The striking fact in cross-country comparisons is how volatile the GDP growth of poor countries is.

What might be going on? An analogy from a different field is useful. A well known problem in financial portfolio management is the returns that can be obtained, in the short term, by selling out-of-the-money options. For some time, the option seller seems to make a lot of money. But in time, some of those options get exercised and the portfolio gets into a lot of trouble. In similar fashion, for some time, a government that takes on option-like off-balance-sheet liabilities can gain votes and possibly accelerate economic activity, at the cost of sustainability.

Perhaps one element of the high GDP growth volatility of poor countries runs as follows. Mature fiscal systems create checks-and-balances which reduce the extent to which debt or off-balance-sheet liabilities can surge. Perhaps less developed countries have weak institutions, and then the political leadership sees a different optimisation. Short bursts of GDP growth can then be achieved in many bad ways, such as a surge in debt, piling up off-balance-sheet liabilities, etc. But this is not sustained growth: We get a spurt of high growth, and then things go wrong. This yields one more element of the translation of bad institutions into high GDP growth volatility.

References

Bhardwaj, Gautam and Surendra A. Dave, 2005. Towards estimating India's implicit pension debt, Working paper.

Pandey, Radhika and Ila Patnaik, 2017. Legislative strategy for setting up an independent debt management agency. NUJS Law Review, 10(3).

Patnaik, Ila, Shubho Roy and Ajay Shah, 2018. The rise of government-funded health insurance in India. NIPFP Working paper.

Sane, Renuka and Ajay Shah, 2015. What is the cost of one-rank-one-pension? The Leap Blog.

Sane, Renuka, Ajay Shah, Bhargavi Zaveri, 2018. Runs on mutual funds, The Leap Blog.

Shah, Ajay and Susan Thomas, 2000. Systemic fragility in Indian banking: Harnessing information from the equity market. IGIDR Working Paper.



The authors are researchers at the NIPFP in New Delhi. We are grateful to Shubho Roy, M. Govinda Rao and Arbind Modi for useful discussions.

Runs on mutual funds

by Renuka Sane, Ajay Shah, Bhargavi Zaveri.

Runs on banks

Runs on banks are to finance what supernovae are to astronomy. R. K. Narayan's book The financial expert vividly tells the tale of the chaos and misery of a bank run.

Bank runs are not random events. What makes a bank run happen is the fact that each depositor has the incentive to run when faced with a slight chance of a run developing. Robert K. Merton used runs as a motivating example of his introduction of the concept of 'self-fulfilling prophecies'. In order to understand runs, we must understand the incentives of each customer.

Suppose a bank is not protected by the government or by the central bank. Suppose you see many depositors running to take their money out of the bank. Now there are two possibilities.

If you believe the bank is unsound, then it is efficient for you to stand in the queue and try to take your money out. If others do this before you, then you may be left with nothing.

Even if you believe the bank is sound, you know that a bank with illiquid assets and liquid liabilities will default when faced with a run. While you will get your money back (as the bank is sound), you will suffer the loss of time value of money and you will suffer the administrative costs of it all working out. Hence, it's efficient for you to stand in the queue and try to take your money out.

Runs on mutual funds

We know a lot about runs on banks. What about runs on mutual funds? At first blush, the simple technology of a mutual fund -- NAV based valuation, full mark-to-market, liquid assets -- seems run-proof. But in September 2018, Rs.2.35 trillion exited Indian mutual funds. Why did such an outsized exit take place? It cannot be just coincidence that many people felt like leaving at the same time.

Large exits have taken place with mutual funds elsewhere in the world also. During the 2008 crisis, the Reserve Primary Fund (with exposure to Lehman Brothers' commercial paper) witnessed a similar run-like situation. The US District Court's order directing the liquidation of the fund records how redemption requests aggregating to two-thirds of the value of the assets of the fund were received in a span of less than 48 hours immediately following Lehman Brothers having declared bankruptcy:

Date	Time	Value of redemption requests received (in USD bn)
15th Sep	08:40 am	5
15th Sep	10:30 am	10
15th Sep	01:00 pm	16.5
16th Sep	3:45 pm	40

What are the incentives that shape the behaviour of an investor in a mutual fund? How can a mutual fund industry be susceptible to runs?

Channel 1: Over-valuation can lead to runs

Suppose a mutual fund has 100 bonds that are liquid, where the true price (market price) is Rs.100. In addition, it has 100 bonds that are illiquid. The market does not give a reference price for the illiquid bond. If we tried to find out a prospective sale price, it would be Rs.50. Suppose the mutual fund claims that this bond is worth Rs.75. The true portfolio value is 10000+5000 = 15000, but the mutual fund claims the value is Rs. 17500. Suppose there are 100 units. In this case, the NAV should be Rs.150, but it is shown as Rs.175.

Over-valuation destabilises rational investors. The rational investor knows that each unit is truly worth Rs.150, but if she redeems right away, before the mistake in the NAV calculation is corrected, she will get Rs.175.

When one unit runs at Rs.175, where does the excessive payment of Rs.25 come from? It comes from the investors who did not run. This is unfair, and it creates strong pressure to run.

Channel 2: False promises can lead to large redemptions

Suppose a mutual fund has been sold to investors under the false promise of it being a safe product. In this scenario, investors do not expect fluctuations in the NAV, and believe that their investments will be shielded from turmoil in the markets. If, for any reason, this expectation is belied, then investors may get spooked by sharp falls in the NAV. This may induce large redemptions.

In the US, there was a claim that the NAV of money market mutual funds would not drop below USD1. This was termed `breaking the buck'. In 2008, when the NAV did drop below USD1, this caused panic and the flight of investors who had been told all along that the scheme would not break the buck.

Channel 3: Runs in an illiquid market

The Indian bond market is extremely illiquid, but even within this landscape, there is heterogeneity in the extent of illiquidity. Fund managers will be sensitive to the transactions costs faced when trading in alternative instruments, and choose the most liquid ones first.

Suppose a mutual fund has some cash in a liquidity buffer, and has 100 bonds that are more liquid, where the true price (market price) is Rs.100. In addition, it has 100 bonds that are illiquid. Suppose fair value accounting is indeed done, and we correctly value the illiquid bonds at Rs.100. The trouble is, the illiquid bonds incur large transactions costs when selling in large quantities. While there is a (bid+offer)/2 of Rs.100, in truth, when a large quantity is sold, the price realised will be Rs.90. This is an `impact cost' of 10%.

Therefore, when the first redemptions come in, the mutual fund will adjust by using cash and then it will adjust by selling the liquid bond. At first, things seem fine. But in time, the mutual fund will have to rebuild its cash buffer. It will have to get back to a more diversified and more liquid portfolio. For this, it is going to have to sell the illiquid bond, and at that time the NAV will go down.

After large redemptions, there is an overhang of selling of illiquid bonds that is coming in the future.  In this situation, investors are better off leaving early as they get the clean exits associated with the early use of cash and the early sale of liquid bonds.

Channel 4: Systemic spillovers in an illiquid market

When large redemptions take place in even one or two schemes, at first they will use cash buffers and sell liquid instruments. But when they start selling illiquid instruments, this changes the price of those illiquid instruments. Now declines in prices hit the NAVs of all schemes that hold those instruments. Through this, large redemptions on a few schemes propagate into reduced NAVs (at future dates) across the entire mutual fund industry. Prediction: In periods of large inflows/redemptions, we will get a pattern of autoregression in the mutual fund NAVs across days, across the multiple funds that hold a pool of illiquid instruments.

Rational investors anticipate this phenomenon, and have an incentive to run when they see large redemptions in even a few mutual fund schemes (and vice versa).

Runs on mutual funds are a complex phenomenon

We have shown four distinct channels through which large redemptions on mutual funds can develop:

1. Overstatement of NAV; it is efficient to leave at a higher price.
   
2. Consumers who thought it was very safe, get spooked, and leave.
   
3. Sales of illiquid securities that are pent up; it is efficient to get out before those transactions hit the NAV.
   
4. Market impact by a few schemes under stress will ricochet into NAVs of other schemes and the problem will worsen; it is efficient to get out early.

In India, a large proportion of the customers of fixed income funds are institutional (e.g. page 4 of this AMFI document). These customers are likely to be pretty rational in understanding problems 1, 3 and 4. Households are likely to be more vulnerable on account of problem 2.

It is interesting to see the `curse of liquidity'. When redemptions come in, mutual funds will sell their most liquid bonds first. Through this, innocent bystanders -- the issuers of liquid securities -- will suffer from price impact and a higher interest rate.

Thinking about runs on mutual funds thus requires a full view of the problems of consumer protection (if all consumers accurately understood the risks that they were taking, they would be less spooked when events unfold), financial market development (the lack of a liquid bond market) and systemic risk (channels of contagion through which disruption of some parts of finance induces disruption of other parts of finance).

Interesting recent experiences in India

While India has not seen a full blown run on mutual funds as was seen in the US in 2008, a few instances of defaults on bond repayments followed by falls in NAVs and rise in redemption requests offer useful insights.

Amtek Auto (2015): In September 2015, Amtek Auto defaulted on a bond redemption of Rs.800 crore. In the Indian corporate bond market, once a default takes place, the bond tends to become highly illiquid. The Amtek Auto bonds were held by two debt schemes of J. P. Morgan Mutual Fund.

J. P. Morgan did an unusual thing: they put a cap on redemptions. It subsequently used something analogous to a good-bank-bad-bank structure, where the scheme was split into two, and the second part held the Amtek Auto bonds, and could not be redeemed.

This did not go down well with SEBI. SEBI sought to penalise J.P. Morgan for, among other things, not following "principles of fair valuation under mutual fund norms" and for changing fundamental attributes of the scheme without giving an exit option to the investors. Nearly three years after the incident, J.P.Morgan settled the matter by paying a settlement fee of about Rs. 8.07 crore under the provisions of the SEBI Act, 1992 providing for settlement of civil and administrative proceedings.

Ballarpur Industries (2017): In February 2017, Ballarpur Industries defaulted. At the time, Taurus Mutual Fund held their commercial paper. Unlike J P Morgan's response, Taurus Mutual Fund reportedly marked down the value of the paper to zero. This is a sound and conservative strategy as it gives a bad deal to the persons who run.

Other mutual funds reportedly sold such paper to group companies or took it on their own balance sheet to shield the investor from the NAV hit.

ILFS (2018): More recently, ILFS group firms have defaulted on bonds issued by them. These bonds are present in certain mutual fund schemes. Some mutual funds have portrayed this event as a loss of 100%, while others have portrayed a 25% loss. Credit rating agencies were very late in understanding the problems of ILFS, and to the extent that credit ratings are used in computing the NAV of a mutual fund scheme, these NAVs would have been overstated.

On October 29, Tata Money Market Fund and Tata Short Term Mutual Fund saw a 5.94 per cent and 3.2 per cent dip in their NAVs respectively after the funds wrote-off their balance investment in the commercial paper of IL&FS. This suggests that they took a long time to actually write-off their investment in IL&FS. This shows that the people who redeemed from them when the ILFS news came out, fared better than those that did not.

Why might NAV be overstated?

The IFRS notion of fair market value came about first in finance, on a global scale, and much later got enshrined into IFRS. For example, in the US, under the Investment Company Act of 1940, the definition of `value' for mutual fund securities holdings is construed in one of two ways. Securities for which `readily available' market quotations exist must be valued at market levels. All other securities must be priced at `fair value' as determined in good faith according to processes approved by the fund's board of directors. Marking a particular security at a fair value requires a determination of what an arm's-length buyer, under the circumstances, would currently pay for that security.

The US SEC's framework recognizes that no single standard exists for determining fair value. By the SEC's interpretation, a board acts in good faith when its fair value determination is the result of a sincere and honest assessment of the amount that the fund might reasonably expect to receive for a security on its current sale. Fund directors must "satisfy themselves that all appropriate factors relevant to the value of securities for which market quotations are not readily available have been considered" and "determine the method of arriving at the fair value of each such security."

Supervisory strategies can be developed, to identify if the management is overstating prices of illiquid securities. As an example, imagine that there are three mutual funds who hold an illiquid bond and have claimed a certain fair value of the bond. Now imagine that one of those three sells the bond on date T. The market price obtained on date T by this fund should not be too far from the internal notion of fair value that was used by the other two funds.

It is ironic that in India, IFRS concepts of valuation have come to the non-financial sector first and the financial sector last. RBI and IRDA have explicitly resisted the adoption of IFRS. The lack of fair value accounting lies at the core of the Indian banking crisis and the concerns about the soundness of LIC.

Currently, the valuation norms prescribed by SEBI ask AMCs to value non-traded and/or thinly traded securities "in good faith" based on detailed criteria. This appears similar to the IFRS principles of fair value accounting. As an example, bonds issued by ILFS should be marked down to the prospective resale value, even if the event of a default has not yet taken place on a particular security.

There are two problems with SEBI's norms on fair value. First, it is not clear that SEBI has the commensurate supervisory strategy, to verify that mutual funds are indeed marking down securities to prospective market values. SEBI does not show supervisory manuals on its website through which we can assess its processes in this regard. When some mutual funds have marked down ILFS bonds by 100%, while others have marked down by 25%, this raises concern about the drafting of the regulation and/or the supervisory process.

Second, the SEBI-prescribed valuation norms entrench the use of credit-rating agencies for valuation by mutual funds. As rating agencies emphasise, their opinion on a security is just an opinion: ratings should not be used in the drafting of regulations. We should be skeptical about using rating agencies to override IFRS principles of valuation. For questions of valuation, the only question should be: What is the prospective price that would be obtained if this security is sold? There should be no role for the opinions of credit ratings.

We recognise that when an active market is lacking, it is very hard for anyone to figure out a notion of fair price. This is a problem ever-present in fair value accounting. E.g. when a non-financial firm has a piece of land, the market value of that land is not clearly visible. What we need to fight is not individual instances of estimation error but estimation bias. By default, the fund managers and the shareholders of the fund are likely to suffer from a bias in favour of over-optimistic portrayal of NAV. It is the job of regulators to fight the bias, not at the level of individual decisions about a benchmark price, but at the level of the expected value of the estimation error.

How can we improve truth in advertising?

When debt mutual fund investors lose money, there is a tendency to force the AMC to make good the loss. This may be driven by regulatory populism, or the temptation to improve the sales of other schemes. This is a dangerous and unviable course.

Getting the micro-prudential framework correct. Let's think about the micro-prudential regulation of a mutual fund. The fund manager is merely an intermediary who pools funds and invests them in a basket of assets, and issues "units" that represent an undivided share in such a basket. The risks in holding these assets are borne by consumers.

The balance sheet of the asset management company is nowhere in the picture, when it comes to the customer. Micro-prudential regulation in a mutual fund, therefore, is restricted to procedures for ensuring that the NAV of the fund is calculated correctly, and does not concern itself with issues of solvency.

SEBI's regulatory framework governing mutual funds, however, entrenches the notion of `safe' funds. This is inconsistent with the concept of agency fund management. For example, the valuation norms for mutual funds specified by SEBI require mutual funds to provision (that is, set aside capital) in respect of defaulted assets as under:

• Where a debt security in the mutual fund's portfolio has defaulted on an interest payment, the mutual fund must classify it as a NPA at the end of a quarter after the due date of payment. For example, if the due date for interest is 30th June, 2000, it will be classified as NPA from 1st October, 2000.
• The mutual fund must provision for the principal plus interest accrued upto the date on which the asset is classified as a NPA. SEBI prescribes a schedule for provisioning that mandates the mutual fund to provision upto 100% of the book value of the asset.

This is conceptually flawed. It is perhaps inspired by notions from banking. But mutual funds are not banks. A regulatory framework that mandates such provisioning is inconsistent with the idea that a mutual fund is merely a manager of funds, and entrenches the idea of a promised return in a debt mutual fund scheme.

If we start thinking that the AMC must pay debt mutual fund schemes for losses, then a wholly different problem in micro-prudential regulation will arise. Large AMCs today manage assets worth Rs.1 trillion on a balance sheet of Rs.0.001 trillion. The risk absorption capacity of such a balance sheet is negligible when compared with the magnitude of assets. The entire concept of a mutual fund as an agency mechanism for fund management breaks down, if investors are to have recourse to the balance sheet of the fund manager.

If we go down the route of asking mutual funds to have equity capital on their balance sheets, then this changes the very nature of the fund management business. This sets the stage for confused thinking such as increasing the minimum capital requirements from firms. In 2014, SEBI increased the minimum networth requirements from Rs.10 crore to Rs.50 crore, which has been seen as anti-competitive.

Fix the mismatch of expectations among consumers One more way in which truth in advertising is contaminated is the behaviour of mutual funds themselves.

Suppose some mutual funds dip into their own pockets when faced with a small default like Ballarpur Industries. What kinds of expectations does this setup in the minds of consumers? Do consumers then invest in mutual funds expecting that they will be protected from credit defaults? Such an expectation will inevitably be violated, when a large default such as ILFS comes along. For an analogy, if the central bank smooths the fluctuations of the exchange rate, this contaminates the expectations of the economy about the ex-ante risk embedded in exchange rate exposure, and actually causes greater harm when large exchange rate changes inevitably come along.

The only sound foundation for the mutual industry is one in which customers bear all losses. It is incorrect for AMCs to absorb the loss for small defaults, build an expectation that customers are shielded from such defaults, and not make good the promise when defaults are large. This risk needs to be communicated to the mutual fund investor at the time of investing, and through actions that are "true to label".

One final mechanism through which truth in advertising can be improved is though enhanced disclosures about liquidity. Customers need to know more about the ex-post transactions costs experienced by the fund on various instruments.

How can we reduce systemic risk spillovers?

The root cause of these problems lies in India's failure to build a bond market. We have a large debt mutual fund industry backed by a poor foundation of bond market liquidity. Even the most liquid bonds are fairly illiquid. Hence, when such selling pressure comes about, these bonds will suffer from price impact. Their prices will go down, their yields will go up. When redemptions take place, for whatever reason, yields of the most liquid bonds will shoot up. If the selling pressure is large enough, these markets will stop working.

Critical policy work on building the bond market was begun in 2015, but was rolled back. We need to get back to this important reform.

If the underlying corporate bond market is not adequately liquid, debt schemes should not promise liquidity. This promise is a recipe for trouble.

In the limit, regulators could restrict open-end schemes to very liquid instruments. The right institutional mechanisms to hold illiquid assets are closed-end funds or private equity funds, where the promise of liquidity is not made.

If open-end schemes must be offered to customers, and if they hold illiquid securities, there must be limitations on liquidity. SEBI has allowed restrictions on redemption in "circumstances leading to a systemic crisis". Specifically, it allows a mutual fund to restrict redemptions when the "market at large becomes illiquid affecting almost all securities rather than any issuer of (sic) specific security". Further, the circular provides that a "restriction on redemption due to illiquidity of a specific security in the portfolio of a scheme due to a poor investment decision, shall not be allowed". This creates considerable confusion on the situations in which mutual funds may restrict redemptions. For instance, in the current situation, it is unclear whether a mutual fund having exposure to the defaulted paper of ILFS would be allowed as it has the potential of systemic risk spillovers or whether such a restriction would not be allowed due to the poor investment decision of the mutual fund scheme.

Mutual funds should be allowed to ring fence losses to ensure that 'all investors are treated fairly', that is, when there is a run on the fund, those who choose or are unable to redeem their units do not suffer at the expense of those who do redeem. SEBI was reported to have rejected a proposal from AMFI that specifically allowed mutual funds to adopt such ring-fencing approaches.

Market liquidity is the commons

These episodes are a reminder of the importance of market liquidity. The ultimate foundation of the financial system is liquid asset markets. When asset markets are liquid, marking to market is sound, financial intermediaries work well, firms can raise resources through primary market issuance, etc. All this rests on the edifice of exchanges, instruments, derivatives, arbitrage, algorithmic trading, etc.

Liquid asset markets have the nature of a public good. Once they exist, they are non-rival (your consumption of liquidity or price information does not reduce my access to the same) and non-excludable (it is not possible to exclude a new-born child from living under their benign influence).

The very public goods character of liquid markets implies that nobody will expend effort on building a liquid market. In the political economy of finance, there are always narrow agendas which want to harm liquid markets. A steady stream of regulatory and other actions comes along, seeking to harm liquid markets. There is a tragedy of the commons, when each regulatory action pollutes market liquidity. Private persons will not mobilise to solve the financial economic policy problems that harm market liquidity. This is the role of the leadership in economic policy.



Renuka Sane and Ajay Shah are researchers at NIPFP. Bhargavi Zaveri is a researcher at the Finance Research Group, IGIDR. We thank Harsh Vardhan, Josh Felman, Kayezad Adajania and Susan Thomas for useful discussions.