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Showing posts with label financial market liquidity. Show all posts
Showing posts with label financial market liquidity. Show all posts

Thursday, June 04, 2026

Chinks in market efficiency: A Melody story

by Ajay Shah and Atibhi Sharma.

The main paradigm in finance is the efficient market hypothesis which suggests that as soon as there is new information, the news is rapidly incorporated in the price. Markets are generally quite efficient, it's hard to find opportunities for supernormal returns.

But there are chinks in the armour. There are clear examples, worldwide, where market prices have been clearly wrong.

In this article, we show a recent Indian story which should be added into the Mistakes of Markets catalog. The share price of Parle Industries soared following the video of Italian prime minister Giorgia Meloni receiving a packet of Parle Products' Melody, gifted by Indian prime minister Narendra Modi. Parle Products, the maker of Melody, is an unlisted private entity. We also show some interesting global examples and offer some thoughts on understanding finance.

The "Signal" Ticker Confusion (2021)

In January 2021, after WhatsApp changed its privacy policy, Elon Musk tweeted 'Use Signal', referring to the free, open-source, and heavily encrypted messaging app. This pushed noise traders to purchase shares of Signal Advance (SIGL). The actual Signal app is managed by the non-profit Signal Foundation and is not publicly traded. Signal Advance, on the other hand, is a tiny medical detection devices manufacturer in Texas.

SIGL was an illiquid penny stock. It surged from \$0.60 to \$70.85 within three days—an 11,700% surge within three days. It took the markets six to nine months to return to pre-tweet levels.

Clubhouse Media Group (2021)

In the same month, on January 31, 2021, Elon Musk tweeted that he would be joining a room on Clubhouse, the then-viral invite-only audio chat application. The next day, shares of Clubhouse Media Group (CMGR), a penny stock entirely unrelated to the privately held Clubhouse app, surged. CMGR had recently rebranded itself as a marketing agency from a healthcare firm. The stock opened at \$10.85 on February 1, 2021, up from \$1.50 - \$2.00 from the previous days. It reached an all-time high of \$28.43 on February 15, 2021 (a near 1,400% increase from its pre-tweet baseline) and it took roughly four months to return to its pre-tweet level.

Zoom Video vs Zoom Technologies (2019-2020)

When Zoom Video Communications (ZM) filed for its IPO in 2019, investors rushed to buy shares, accidentally purchasing the shares of a defunct mobile phone parts manufacturer called Zoom Technologies (Ticker symbol: ZOOM), a penny stock trading at \$0.005. The stock surged 54,000% to around \$5. This confusion was not a one-off incident, as a similar trend was observed during the lockdown in the pandemic of 2020 when ZOOM went up 1,800% to \$20.90 until the US Securities and Exchange Commission physically intervened, suspending trading for Zoom Technologies for 10 business days and forced a ticker symbol change to ZTNO.

Bombay Oxygen Investments (April 2021)

During the second wave of COVID-19, when there was a shortage of medical oxygen in India, noise traders started looking for oxygen in the market. They landed on Bombay Oxygen Investments, a Non-Banking Financial Company that had exited the oxygen manufacturing business in August 2019 and had received RBI's registration certificate on December 31, 2019 for the same. The stock surged 131.3% in under 12 trading sessions, rising from Rs 11,025 on March 31 to Rs 25,500 intraday on April 20 and then, fell by ~ 50% to 12,700 in August of the same year.

L. G. Balakrishnan (2025)

In 2024, shares of LG Balakrishnan & Bros (a manufacturer of automotive chains) surged to a 52-week high because noise traders mistook it for the upcoming IPO of the consumer electronics giant LG Electronics India. The mispricing lasted only a day as investors realised their mistake.

Pan-Homophonic Events

Zhang et al. (2026) documented a new phenomenon of pan-homophonic events, where confusion between linguistics, trending keywords and stock names triggers sudden market volatility, specifically for a Chinese technology firm named Chuan-da-zhi-sheng. Since "Chuan-pu" is a loose and phonetic translation of Trump in Mandarin, noise traders phonetically interpreted the company's name to mean "Trump wins big". This unrelated traffic software stock essentially became a trading proxy for US political events. In 2016, when Donald Trump won the US presidential election, shares of the company surged 7.6% in a single day and then again in 2024, following major turning points in the US election cycle, the same stock repeatedly hit its maximum 10% daily upper circuit limit.

Ticker Confusion and the Limits of Arbitrage

Balashov and Nikiforov (2019) documented the systematic nature of these mix-ups. Investigating 254 pairs of stocks, they found that erroneous trades account for roughly 5% of all trading turnover in the smaller "shadow" companies. A classic example is Ford Motor Company (Ticker: F). Investors systematically assume its ticker is 'FORD' - which is actually the trading symbol for Forward Industries, a micro-cap manufacturer of carrying cases for medical devices. Similarly, a paper by Rashes (2001) studied the mass confusion between MCI Communications and a completely unrelated fund with the ticker MCIC. They found that while the co-movement between the two similarly-named stocks is statistically significant, it is not something that arbitrageurs can easily exploit because shorting an illiquid shadow company is costly.

Parle Industries

The Parle Industries episode is the latest entry in this ledger. Parle Industries is not a confectionary giant; it is a micro-cap company involved in infrastructure development, real estate, and paper waste recycling. Prior to the viral Modi-Meloni video, its market capitalization hovered around Rs.360 million, the price of a few apartments in Bombay. It was a penny stock with a share price of about Rs.5 and an average trading volume of 20,000 to 60,000 shares daily in the 6 months prior window.

  • 9:30 AM (IST) - The Baseline: Market opened in India. Parle Industries opened at INR 4.95.
  • 10:00 AM (IST) - The Event: Italian Prime Minister Giorgia Meloni uploaded a video to her official Instagram account and then her X account with the caption, "Thank you for the gift."
  • 10:00 AM to 3:35 PM (IST) - Amplification: The video went viral. On X, the hashtag #Melodi trended, and the reel became the most viewed reel on PM Meloni's Instagram account. Simultaneously, Google Trends intraday data showed a spike: searches for "Parle share" and "Melody".
  • 3:30 PM (IST) - Noise trading: By the market close, Parle Industries was locked into a 5% upper circuit closing at INR 5.25. It closed at a volume of 857,248 shares.

The stock hit the upper circuit for five consecutive trading sessions. BSE historical data shows that from May 21 through the end of the month, the stock's delivery percentage reached exactly 100%. The noise traders were taking delivery, believing it was worth holding this for multi day horizons.

Market Efficiency and the Role of Liquidity

These are examples of how prices can go wrong, exposing failures in market efficiency.

A better interpretation of market efficiency comes from focus on how clever people could exploit the mistakes of the noise traders. Here, we see the problems of financial market completeness (can you take an opposing trade?) and financial market liquidity (is the size of your winning trade big enough to matter?). A market inefficiency that is not exploitable will not be readily solved by the market. With small cap penny stocks there are no single stock derivatives that rational traders can short. In India, stock lending does not work so it is not possible to short sell and profit from the mistakes in the price. To the extent that better financial economic policy increases liquidity, it will, in turn, increase access to the correct tools for trading (single stock derivatives and stock lending). As a result, these problems will be diminished.

These problems are a reminder of the difficulties of small capitalisation stocks. Financial market trading works extremely well for large firms. We may perhaps apply a thumb rule in India of a minimum point of a market capitalisation of Rs.10 billion. But we do wrong to assume it is equally useful and equally effective for small firms. There is a certain social justice instinct in India, where we like to bring the glory of stock market listing to small firms, thinking that we are giving a helping hand to a weak firm. We need to be more cautious in the usefulness of this approach.

Financial markets are a remarkable information processing system. It is easy to disrespect the drama that is ceaselessly afoot. What is going on is that millions of clever people have been harnessed to constantly look at the world and make prices. These prices are the commanding heights of the economy and shape the resource allocation. Markets are not perfect, they are the best aggregation of what humans can figure out based on their self-interest.

Bibliography

Parle Industries' upper circuit to Signal's 5,100% surge: 5 mistaken stocks that triggered market frenzy, Surabhi Pandey, Moneycontrol, 20 May 2026.

#Melodi trends on X as PM Modi gifts Melody toffees to Italian PM Giorgia Meloni, DH Online, Deccan Herald, 20 May 2026.

190 Million And Counting: Meloni's Melody Moment With PM Modi Is Mega Viral, Abhinav Singh, NDTV, 21 May 2026.

Publicly Listed Zoom Video Communications: Traders Buying Zoom Technologies, Jonathan Garber, Markets Insider, 18 April 2019.

Want to Invest in the Zoom IPO? Make Sure You Buy ZM, Not ZOOM, Minda Zetlin, Inc.com, 18 April 2019.

Traders mistakenly invest in Clubhouse Media Group after Elon Musk tweets about a separate, private app with the same name, Natasha Dailey, Business Insider, 2 February 2021.

COVID-19: Bombay Oxygen shares up 256%; it doesn't even make oxygen, Business Today, 20 April 2021.

Massively Confused Investors Making Conspicuously Ignorant Choices (MCI-MCIC), Michael S. Rashes, The Journal of Finance, Vol. 56, No. 5, 2001.

How much do investors trade because of name/ticker confusion? Vadim S. Balashov and Andrei Nikiforov, Journal of Financial Markets, Vol. 46, 2019.

Quantifying the Linguistic Complexity of Pan-Homophonic Events in Stock Market Volatility Dynamics, Yunfan Zhang, Jingqian Tian, Yutong Zou, Xu Zhang, and Xiao Cai, Entropy vol. 28, no. 1, 12 January 2026.

Mistaken Identity: LG Balakrishnan Shares Surge as Investors Confuse It for LG Electronics India, Nishanth Vasudevan, Economic Times, 15 October 2025.

The authors are researchers at XKDR Forum. The authors would like to thank Susan Thomas, Amrita Agarwal, Aditi Mascarenhas and Jay Kulkarni for their valuable feedback and discussions on this piece.

Monday, January 10, 2022

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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

Monday, August 09, 2021

Sudden Rise of the Floaters

by Rajeswari Sengupta and Harsh Vardhan.

The first two months of 2021-22 have witnessed a remarkable new trend in the corporate bond market—a sudden rise in the issuance of floating rate bonds or “floaters” and the use of the 91-day treasury bill yield as the reference rate in these bonds, instead of the yields on dated government securities (G-Secs).

We conjecture that one possible reason behind this new development could be an increase in the perception of interest risk on the part of the bond market participants. This in turn may have been a result of the active yield curve management undertaken by the Reserve Bank of India (RBI). If indeed dated government bonds such as the 10-year G-Secs have lost relevance as benchmark securities then this can lead to serious mispricing of risk in the economy, an unintended consequence of the RBI’s bond market intervention.

An interesting development in the bond market

Over the three-month period from April to June 2021, about 7 percent of the total corporate bond issuance of Rs 1.02 trillion consisted of floating rate bonds. While this percentage looks small, it is important to keep in mind that for the previous ten years or more, the share of floating rate bonds in the total issuance of corporate bonds has been less than 1 percent.

It is also important to note that the firms issuing these bonds and the investors investing in them are not a new class of issuers and investors. They are the same issuers and investors who were issuing and buying fixed-rate bonds until recently. In particular, 100 percent of the floating rate bond issuers now are non-banking finance companies (NBFCs) who were earlier issuing fixed rate bonds, and the investors are the same mutual funds and banks who were investing in fixed rate bonds earlier. This could imply that their behaviour has now changed due to external developments. It is as if the bond issuers and investors have suddenly developed a taste for floaters.

Corporate bonds are typically issued with a maturity of more than one year, along with a coupon, which is the rate of interest to be paid on the bond. Most bonds have a ‘fixed’ coupon—the rate of interest on the bond is decided at the time of issuance of the bond and remains fixed over the life of the bond.

This rate is a function of two factors – (i) the prevailing risk-free interest rate for the maturity matching that of the bond, and (ii) the credit risk spread that is added to compensate the investors for the default risk associated with the issuer.

The risk-free reference rate is ideally the interest rate on the government security of similar maturity. The credit spread is the function of the credit rating of the issuer. For example, if a AAA-rated issuer wants to issue a 5-year maturity corporate bond, then the risk-free reference rate will be the rate for a 5-year government security (let’s say 5.7 percent). If the credit spread of the AAA-rated issuer is an additional 100 basis points (1 percent), then the bond will be issued with a fixed coupon of roughly 6.7 percent. Note that this rate will apply to all the future interest payments by the issuer until the bond matures even if the underlying risk-free rate changes. This means that the investor in this bond is taking the interest rate risk. The secondary market price of these bonds reacts to changes in the underlying interest rates – the bond prices fall if the risk-free interest rate increases and bond prices go up if the risk-free rate decreases.

In the case of a floating rate bond, the main components of determining the coupon remain the same—a reference rate and a credit risk premium. The crucial difference is that the reference rate is no longer fixed but changes over time. Hence, these bonds are referred to as ‘floating’. The coupon on these bonds clearly specifies the reference-floating rate.

If the bond in the example cited above were a floating rate bond, then the coupon on it will not be a fixed rate of 6.7 percent. Instead, it will be the rate on 5-year government security at the time of interest payment plus 1 percent. In other words, for a floating bond, the applicable interest is computed at the time of payment of interest. If the 5-year government security rate moves up by 0.5 percent in a year then the interest rate payable will become 7.2 percent. The investor in such a bond is more protected from interest rate risk and the prices of these bonds in the secondary market fluctuate much less with movements in interest rates.

In the last two months, floating rate bonds worth Rs 70 billion have been issued in the corporate bond market, almost entirely by private companies. Overall, bonds worth Rs 793 billion have been issued by the private sector including NBFCs. The floating rate bond issues in these two months thus represent around 10 percent of private sector bond issuance.

An interesting feature of these floaters issued in the last two months is that all of them have used the yield on 91-day treasury bills (T Bills) as the reference rate. Notwithstanding the fact that these corporate bonds have maturities ranging from 2 to 4 years, yields on dated government securities (i.e., G-Secs with maturity of more than 1 year) have not been used as a reference.

What might explain this sudden preference on the part of the issuers and investors for these floating bonds?

What might be going on?

One possibility could be a heightened perception of interest rate risk. Bond investors might be harbouring the belief that the interest rates on dated G-Secs are unlikely to remain at their current levels. As discussed earlier, issuing floating rate bonds is one way to mitigate interest rate risk. This raises the next question – why would the perception of interest rate risk suddenly go up now?

We conjecture that this could be a result of the manner in which the RBI has been managing interest rates in the government bond market. The Covid-19 pandemic presented the Indian economy with an unprecedented challenge. A combination of falling tax revenues and rising expenditure on account of fiscal stimulus resulted in a massive increase in the fiscal deficit of the government, and a corresponding rise in government borrowing from the bond market. In 2020-21 the consolidated government borrowing was a whopping Rs 21.5 trillion and the planned borrowing for 2021-22 is roughly Rs 19.6 trillion. The overall government debt to GDP ratio is roughly 90 percent, the highest ever.

The RBI on its part has taken multiple steps to ensure that interest rates are kept low in the bond market so that the government’s cost of borrowing remains under control. It has allowed several primary auctions of G-Secs to devolve on primary dealers and has even canceled auctions when it did not receive bids at rates that were low enough. In addition to its standard open market operations (OMOs), it initiated the Operation Twist program whose objective was to bring down interest rates at the long end of the yield curve and push up rates at the short end. This meant that the RBI was buying long-dated G-Secs and selling shorter maturity bonds.

In March 2021 the RBI launched a program called the G-SAP wherein for the first time it pre-committed to buying a specific amount of G-Secs. These bond market interventions are mostly aimed at capping the interest rate on the benchmark 10-year G-Sec at 6 percent. As a consequence of these actions, the RBI has ended up owning a substantial amount of the 10 year benchmark government bonds (link).

It is possible that bond investors believe that the RBI will not be able to suppress the interest rates for too long, and the rates will rise sharply and suddenly. This could be either because of the large volume of G-Secs the government needs to issue to finance its deficit or because of growing inflationary concerns in the Indian economy (CPI inflation has exceeded the upper limit of 6 percent of the RBI’s targeted inflation band in both May and June 2021), or because of external factors such as rising inflation in the US.

This is akin to a spring that has been forcefully compressed but can bounce back anytime. If the rates suddenly go up, holding fixed coupon bonds will lead to losses, as explained earlier. This increased risk perception might be one possible explanation as to why the investors now prefer floating rate bonds.

Arguably, another unintended consequence of the steps taken by the RBI to lower the long-term G-Sec yields and suppress the organic evolution of the yield curve in response to market forces may have been that the bond market participants have lost confidence in the yield curve.

In the past whenever inflation went up, 10-year G-Sec yields would also go up, implying a positive correlation between the two variables. The underlying idea is that rising inflation is usually followed by a tightening of the monetary policy stance which in turn leads to higher long term bond yields.

For instance, figure 1 below plots the 10-year G-Sec yield alongside CPI (consumer price index) inflation from 2004-05 to 2013-14. This was a period of high and rising inflation. CPI inflation went up from 3.8 percent in 2004-05 to more than 10 percent in 2012-13. Concomitantly, the 10- year rate went up from 6.6 percent in 2004-05 to more than 8 percent by 2012-13.

Figure 1: CPI Inflation and 10year G-Sec yield, 2004-05 to 2013-14

But recently this correlation seems to have broken down. We can see this clearly in figure 2, which plots the two series using monthly data, focusing on the period from March 2020 to June 2021. CPI inflation began rising from May 2020 onward. It consistently breached the 6 percent upper limit of the RBI’s targeted inflation band during the period April-October 2020, increasing from 5.8 percent in March to 7.6 percent in October. More recently it went up from 4.2 percent in April 2021 to 6.3 percent in June 2021.

Figure 2: CPI Inflation and 10year G-Sec yield, March 2020 to June 2021

However, this time around, rather than increasing, the 10-year G-Sec yield actually fell from 7.5 percent in April 2020 to 5.8 percent in May, since then holding more or less steady around 6 percent. These developments suggest that G-Sec rate might be distorted by the RBI’s interventions, which in turn might explain why some investors are turning to the T Bill rate as a preferred reference rate.

Other explanations are, of course, possible. The rise of floaters could also be a result of companies expecting interest rates to come down, in which case they would not want to issue long-term debt at higher rates. This however seems unlikely. Given that inflation continues to be a concern, interest rates are more likely to go up rather than down, and sooner or later RBI would need to start normalising the surplus liquidity situation that the financial system is currently in.

Alternatively, floaters could be issued if the private sector is tapping a new class of investors, who are interested in buying bonds but do not want to run any interest rate risk. But the issuers of and the investors in the floaters are exactly the same entities that were participating in fixed-rate bond transactions earlier.

Finally, it is also possible that the funding requirements of the NBFCs (the sole issuers of floating rate bonds right now) have undergone some changes which might have increased their preference for these bonds.

Conclusion

We are observing an interesting new development in the corporate bond market. The rise of floating rate bond issuances by private NBFCs, and the use of the 91day T Bill rate as the reference rate seem to indicate a change in the preferences on the part of both issuers and investors.

We conjecture that one reason that might explain this development is the intervention in the bond market by the RBI to control G-Sec yields. Specifically, it is possible that the RBI’s persistent interventions have caused some market participants to lose trust in the yield curve. This possibility needs to be explored further in the future.

If there has indeed been an erosion of credibility in the yield curve, then this would be a serious problem. The yield curve is a fundamental construct in a market economy, as it defines the interest rate structure that is used to price debt. As a result, if the yield curve is distorted, then interest rate risk is being mispriced. The associated misallocation of resources could prove to be costly, damaging the economy just as it struggles to recover from the Covid crisis.


Harsh Vardhan is Executive in Residence at the Center for Financial Studies (CFS) at the SP Jain Institute of Management and Research. Rajeswari Sengupta is an Assistant Professor of Economics at the Indira Gandhi Institute of Development Research (IGIDR). The authors thank Josh Felman and an anonymous referee for their useful suggestions.

Friday, August 07, 2020

The Indian corporate bond market: From the IL&FS default to the pandemic

by Rajeswari Sengupta and Harsh Vardhan.

The banking sector is the most important financial intermediary in India's debt market. Over the last few years the bond market has emerged as an alternative to the banking sector especially for the top rated firms. This trend has been pronounced ever since the banking sector started reporting high levels of non performing assets. Figure 1 below shows the flow of commercial credit in India from various sources and highlights the growing relative importance of bond issuance especially from 2015 onwards.

The bond market has faced two big shocks in recent years: (i) the default by IL&FS (Infrastructure Leasing and Financial Services Limited) in September 2018, followed by other relatively low-impact shocks due to problems in companies such as DHFL (Dewan Housing and Finance Limited) and IndiaBulls Housing Finance as well as Yes Bank, and (ii) the outbreak of the Covid-19 pandemic in India since March 2020. As a result of these shocks the risk perceptions in the bond market have gone up. In this article, we take a look at changes in the risk perceptions in the corporate bond market especially in the ongoing context of the pandemic and ensuing economic slowdown. We also highlight the asymmetry in the risk perceptions of the markets towards private sector corporate bonds vis-a-vis public sector unit (PSU) bonds and discuss the likely implications of changes in the risk perceptions, for the future funding model of non-banking finance companies (NBFCs).

Figure 1: Flow of Commercial Credit in India (Source: RBI)

Measuring risk perception

The most important metric for assessing risk perception in the bond market is the credit spread which is the difference between the yield of a corporate bond and of a government security of comparable maturity. Highly rated bonds (with ratings of AAA and AA) are traded relatively actively and their yields reflect changing perceptions of investors regarding the riskiness of these bonds. Movement over time of credit spreads on corporate bonds is therefore a good indicator of the bond market's perception of risk.

We look at the credit spreads of AAA rated bonds of 3 years and 5 years maturity from April 2018 to June 2020. The data is sourced from Bloomberg. The bonds in our data are separated into 3 categories - NBFCs (non-banking finance companies) and HFCs (housing finance companies), private corporations and public sector undertakings (PSUs), which may include public sector NBFCs such as Power Finance Corporation (PFC) and Rural Electrification Corporation (REC). The figures 2 and 3 below show the evolution of credit spreads for these three categories of bonds for the two specific maturities.

The IL&FS default

Figure 2: Credit Spreads on 5 Year AAA Paper (Source: Bloomberg)

As we see from figure 2 above, prior to September 2018, the credit spreads on the NBFC, private corporate and PSU bonds were fairly stable, between 50 and 100 basis points for the 3 year paper and between 40 and 60 basis points for the 5 year paper. In the rest of our discussion we focus on the credit spreads on the 5 year paper. The pattern is more or less the same for the 3 year paper, only the absolute levels of credit spreads are different.

Figure 2 shows that credit spreads on NBFC AAA paper of 5 year maturity nearly doubled between September 2018 and November 2018 and reached 160 basis points by February 2019. This shows that the IL&FS episode that unfolded in the 3rd week of September significantly enhanced the risk perception of the bond market regarding all top rated NBFCs.

After a small dip, the spreads went back to around 140-150 basis points by July 2019 and stayed at this high level, with some fluctuations, till November 2019. During this period, crisis in other NBFCs (such as the Dewan Housing and Finance Limited (DHFL)) as well as in Yes bank, added to the overall risk perception of the bond market. This is reflected in the credit spreads remaining high one year after the IL&FS default.

Private corporate and PSU bonds' credit spreads also widened in the aftermath of the IL&FS default, but not by the same magnitude as the NBFCs. The IL&FS default triggered a liquidity crunch primarily for the NBFC sector. The corporate sector experienced spill over effects owing to a rise in risk aversion in the bond market.

While in the pre IL&FS default period the spreads of all three categories of bonds were closely bunched together, the difference between them began increasing from October 2018 onwards. The difference was particularly acute between the NBFC and private corporate bond spreads on one hand and the PSU bond spreads on the other hand especially in the second half of 2019. This is despite the fact that these bonds were all rated AAA. This reflects the implicit government guarantee enjoyed by the PSU bonds.

The government and the RBI took several actions to deal with the ensuing crisis in the NBFC sector. Government appointed a new Board for IL&FS. RBI took several steps including open market operations to inject liquidity into the system, reducing the risk weights on bank lending to NBFCs, instructing banks to disburse sanctioned but undisbursed credit to NBFCs etc.

These eventually resulted in enhanced credit flow to the NBFCs which reduced the credit spreads in the later part of 2019. For both NBFCs and private corporate sector, the spreads declined by about 50 basis points to settle at about 100 and 50 basis points respectively. These spreads, especially for the NBFCs, were still higher than pre-IL&FS episode but much lower than their peak. We see a similar dynamic with the 3 year maturity bonds as well as shown in figure 3 below, except the absolute levels of the spreads were different.

Figure 3: Credit Spreads on 3 Year AAA Paper (Source: Bloomberg)

The Covid-19 outbreak

Just as the bond market was recovering from the shock of IL&FS default followed by crises in DHFL and Yes bank, the Indian economy got hit by another massive shock in the form of the ongoing Covid-19 pandemic. Credit spreads in the bond market began rising sharply from the middle of March once again reflecting growing risk perceptions. Figure 2 shows the increase in the spreads around the time when the nationwide lockdown was announced on 24 March.

For both NBFC and corporate bonds, the spreads rose by about 30-40 basis points between February 2020 and April 2020. For both categories of bonds the credit spreads reached their peak in the first half of May, close to 180 basis points for NBFCs and 170 basis points for the corporate bonds. The peak of the credit spreads during the pandemic has so far been higher than the peak reached in the aftermath of the IL&FS default episode.

Spreads on PSU paper also went up, but by a smaller amount. The average spread on these bonds in March and April was only 30-35 basis points. The difference between the credit spreads on NBFC and corporate bonds on one hand and PSU bonds on the other widened significantly to about 100 basis points. The large gap in spreads for bonds of the same ratings is worth noting. Similar to the post-IL&FS period, this too is a reflection of the market's perception of implicit government guarantee to the public sector units.

The impact of policy actions on credit spreads

The sharp rise in credit spreads of NBFC and corporate bonds in April 2020 could be attributed to the announcement by the RBI to grant moratorium on loan repayments for all borrowers in order to alleviate the financial stress triggered by the pandemic and the lockdown. Following this announcement, NBFCs had to offer moratorium to their borrowers but at the time it was not clear whether they themselves would also receive a moratorium from banks on their repayment obligations.

In the second half of May, the government announced a package to boost the economy. This included Rs 20 lakh crore of 'benefits' and effectively entailed an outlay of around Rs 3 lakh crore for 2020-21. RBI also adopted several policy initiatives such as cutting the policy interest rates aggressively and establishing new long term targeted repo operations (T-LTRO) that would provide 3 year funding to banks under a repo arrangement. RBI made the repo arrangement `targeted' so as to ensure that the funds raised by the banks were made available to the NBFCs.

These policy actions increased the credit supply to all issuers. Consequently, by the 3rd week of June, the credit spreads on both NBFC and corporate bonds came down from their respective peak levels of mid May by about 50 basis points.

However, the RBI and government actions notwithstanding, the credit spreads for NBFCs and private corporate sector continue to be substantially high. In fact the spreads in June 2020 were similar to the spreads in December 2018 in the aftermath of the IL&FS default. For PSUs the spreads have come down to around the same levels that prevailed before the IL&FS crisis.

This shows that the bond market remains concerned about the riskiness of the corporate sector and the NBFCs. PSUs on the other hand, benefit from implicit government guarantee. The significantly lower credit spreads they are experiencing in the time of the pandemic reflect a `flight to safety' by the bond investors.

Credit spreads and funding costs

As we interpret the bond market data, it is important to understand the difference between credit spreads and funding costs. Credit spreads going up does not necessarily mean that the cost of funding for the issuer is going up. Cost of funding for a company that raises capital in the debt market depends on the market determined yield on the security it issues This yield on debt consists of two components: risk free rate and credit spreads. RBI's monetary policy impacts the risk free rate but not the credit spreads. Credit spreads reflect the premium that the investor charges over and above the risk free rate, taking into account the inherent riskiness of the underlying bond.

Since the IL&FS episode, the risk free rate has been coming down steadily due to the actions by the RBI such as reduction in the policy interest rates (repo and reverse repo rate) and large scale open market operations to inject liquidity in the financial system. Figure 4 below depicts the yield on 5 year and 3 year government securities from the April 2018 to June 2020 period.

Figure 4: Government Securities Yield

The 5 year risk free interest rate has come down from about 8.4% in September 2018 (before the IL&FS episode) to about 5.5% in June 2020 indicating a decline of 300 basis points. The 3 year risk free interest rate has declined even more to about 4.5% over this period, a decline of nearly 350 basis points.

Since RBI's monetary policy does not affect the credit spreads, the impact of policy action on the actual cost of funding will not be the same as the reduction in the risk free rate. If risk aversion in the market goes up, then investors will demand higher price for the credit risk which will result in rising credit spreads. Thus, the net cost of funding for an issuer may decline to a lower extent compared to the reduction in the policy rates.

This is what has been happening since the IL&FS episode. Risk free rate has been declining but owing to high risk aversion, credit spreads have remained elevated. As a result, funding costs of companies have not come down by as much as the risk free rate. This implies that in an environment of high and rising risk perception such as the ongoing Covid-19 period, the effectiveness of policy rate cuts will be constrained.

The widening gap between the credit spreads on PSU debt versus private sector points to lower risk perception for PSU entities which are perceived to have implicit sovereign guarantees. The combined effects of rising risk perception, widening gap between credit spreads of identically rated issuances and reduction in the policy interest rates would mean that the debt market will skew towards government owned issuers who might experience the greatest reduction in funding cost.

Conclusion

Bond market credit spreads provide important information about the risk perception of an important class of investors. Sustained high credit spreads (compared to long term average levels) suggest elevated risk perception and imply heightened risk aversion. Specifically, it also points to the role that individual episodes of corporate defaults and the associated policy responses (or lack thereof) play in shaping risk perceptions.

Wide spreads between bonds of the same ratings issued by private companies and those owned by the government clearly indicates a strong perception of the implicit government guarantee enjoyed by public sector companies. This raises important questions as to whether the debt of government owned companies should be treated as a part of government's debt.

Finally, economic recovery in India in the post Covid-19 period will depend crucially on the flow of credit in the economy. The economic package recently announced by the government depends largely on the financial sector. Nearly 70% of the 'benefits' of Rs 20 lakh crore in the package are expected to be routed through the financial sector. In a recent article we discussed the rise in risk aversion in the banking sector. With both the banks and the bonds markets showing high levels of risk aversion, growth of credit may be less than envisaged in the package. This may dilute the overall effectiveness of government's monetary and fiscal policy actions.


Harsh Vardhan is an Executive-in-Residence at the Center for Financial Studies and an Adjunct Faculty at the SP Jain Institute of Management and Research, Mumbai. Rajeswari Sengupta is an Assistant Professor of Economics at IGIDR, Mumbai.

Wednesday, November 09, 2016

Watching markets work: The `surgical strikes' of 29 September 2016

by Susan Thomas.


At 12:15 in the afternoon on 29 September, the Indian government said that `surgical strikes' had been conducted in Pakistan-occupied-Kashmir in the early morning of 29 September. The graph above spans across the previous and the next trading days also, i.e. it runs from the start of trading on 28th to the close of trading on 30th.

There was a close-to-open positive jump from 28th close to 29th open. There is no hint that news had leaked, ahead of time. Nifty and the near month futures fell sharply the moment this news came out. It looks like by late 30th the market had digested the information and was more calm.


The best measure of overall equity market liquidity in India is the `impact cost' faced when doing a portfolio trade on Nifty. We use the transaction size of Rs.5 million. In peacetime, the impact cost is very small, of around half a basis point. This tripled after the news was revealed at 12:15. By late 30th, we were back to the very low impact cost values of early 29th.

What about traded volume?


The blue dots are turnover on the Nifty futures and the gray dots are turnover on the ATM Nifty options. Turnover for the options jumped on 29th, once the news came out, but this was not the case with the futures. Futures turnover along with options turnover was enhanced on 30th. We don't know why the futures turnover slumbered on 29th but not on the 30th.

Turnover was also very large on the morning of 30th. People seem to have slept over the news of 29th and come back with views on the 30th. This may partly reflect the slow decision processes of institutional investors, particularly foreign institutional investors.

Here, even by late 30th, the market had not found its pre-announcement levels.

We now turn to measures of deviation from no-arbitrage on the Nifty futures market. When very large turnover takes place, this can stress the limited capital of rational arbitrageurs.


It's interesting to focus inside the 29th. There is a small violation of no-arbitrage and the basis was fluctuating to a modest extent. When the news broke, the violations got bigger and basis variability went up. This suggests there are `limits of arbitrage' : there is not enough capital and not enough algorithmic trading to hold the futures price at the rational value in the aftermath of a news shock like the surgical strike.

Similar issues are visible in the deviation from put-call parity on the Nifty options market.


Put call parity holds quite well before 12:15. Once the news breaks, the volatility of the pricing error goes up, and large errors are visible in absolute terms. It's interesting to see that with both the Nifty futures and the Nifty options, the largest pricing errors are found at the end of 30th.

The picture seems to be one where there was a huge surge in futures and options turnover, and during this process of price discovery, the garbage collectors of the market (the arbitrageurs) were a bit overwhelmed.

What about the USD/INR exchange rate?


The spot market for USD/INR trades 24 hours a day. That's the blue line above. The gray line is the USD/INR futures contract at NSE, which only trades for limited hours of the day. We see that there was a bit of a depreciation in this market even before 12:15 and it went further on 29th and also on the 30th. The futures in particular moved more.

What happened to the turnover?


We see an enormous surge in turnover on the ATM options after 12:15. The extent of this surge is much bigger than that seen with Nifty. With Nifty, turnover went up by a factor of 5x. Here, options turnover seems to have gone up by 10x. It's striking how almost nothing happened with the futures. The market seems to be using ATM options as a way to express views on USD/INR and not USD/INR futures.

With the Nifty futures and options, 30th was a very active day. With USD/INR, the market slumbered on 30th.

Finally, we look at violations of no-arbitrage on the USD/INR futures.


As with the Nifty futures, basis vol goes up after 12:15, the violations are larger in magnitude, and the ill effects are visible all the way to the end of 30th.



The author is at the Finance Research Group in the Indira Gandhi Institute for Development Research.

Tuesday, December 22, 2015

Looking beyond the label `algorithmic trading'

by Ajay Shah.

At the EMF 2015 conference, I attended a talk by Pradeep Yadav, where he presented a paper: Raman, Robe, Yadav, 2015. This paper analyses algorithmic traders ("AT") and manual traders ("MT") on one of the world's largest electronic limit order book exchanges, the National Stock Exchange ("NSE").

NSE is an ideal laboratory for studying these questions, as it is a simple plain limit order book market, without the confusion caused by market makers. In addition, the overall equity market structure is simple, with two exchanges (NSE and BSE) where NSE has dominant market share. The complexities of the fragmented order flow and multiple trading venues, of the US, is not present. At NSE, both spot and futures trade in the same time zone, with orders emanating from the same co-location facility, which also facilitates research. NSE data has thus shaped up to be a very nice foundation for understanding markets, in recent years, with some of the cleanest microstructure work getting done here.

The puzzle


Pradeep Yadav and his coauthors find that under conditions of market stress, ATs withdraw from the market. This immediately lends itself to a pejorative interpretation: "ATs are good for liquidity under good times, but are quick to withdraw when the going gets difficult, and this is creating a new set of problems".

I wondered how this squares with a powerful result from Aggarwal & Thomas, 2014. This is a modern causal econometrics paper, and in this they find that the incidence of mini flash crashes goes down when there is more AT. They look for mini flash crashes defined as 2%, 5% and 10% declines of the price within a five minute window (see Table 6, page 28). All three coefficients are negative; more AT gives fewer flash crashes. For the 2% and 10% case, the differences are not statistically significant, but for the case of a 5% drop of prices in 5 minutes, bigger AT gives a statistically and economically significant decline in the incidence of flash crashes.

Both papers seem to have persuasive empirical strategies. How do we square the results? How is it that ATs are more likely to step away in difficult times (Raman, Robe, Yadav) but at the same time how it is that when there is more AT, mini flash crashes are less frequent (Aggarwal, Thomas)?

A better classification system


In order to figure out what's going on, I think we should break with the classification AT vs. MT. Instead, it's better to think in terms of simple, mechanistic trading strategies vs. complex strategies that involve human judgment. For the purpose of argument, let's call these "SI" for simple vs. "CO" for complex strategies.

Let's start with the old world, before algorithmic trading. In that world, we very much had many humans running SI strategies and many humans running CO strategies. `Technical analysis', and other trend following mechanistic strategies, were around well before algorithmic trading came along!

As Friedman, 1953, reminded us, there is a Darwinian process at work where speculators who lose money tend to exit the market. Because markets are competitive, the dumb adherence to a SI strategy would induce losses, and the people who did this would exit the market. Hence, the only sensible approach for a trader who uses a SI strategy is to either stop some times (i.e. have a "kill switch") or switch to a CO strategy at certain times.

In the good old days, SI speculators had "kill switches". When the market got weird, they would just stop trading. Nobody expected a simple trend following speculator to behave unchanged when volatility changed or when big news broke. We had trading floors where a boss would shut down some strategies from time to time. This was akin to a "kill switch" applied to a large number of SI strategies.

All that has happened with algorithmic trading is that we now have powerful clerks, i.e. computers, who are the foot soldiers implementing SI strategies. Nothing else has changed. Humans are still in charge!

Some human traders keep a swarm of SI strategies under leash, and when market conditions get difficult, they hit the kill switch. Some of them switch to CO strategies when the going gets difficult, and because CO strategies are much harder to program, they may well do this trading by hand. Some bosses of trading floors yank hundreds of SI strategies when the going gets weird.

Resolving the puzzle


There were always SI strategies and CO strategies. These have been around ever since organised financial trading began. In the older data, we are not able to disentangle the two.

In recent years, the SI strategies have gotten automated. We have reduced the use of humans in mechanistic tasks, and got computers to do this clerical work. For the first time in human history, we are now seeing a flag on orders where orders from SI strategies are now called "AT" orders. The CO strategies continue to be mostly done by hand, as it's quite hard doing this programming.

There is nothing wrong or unusual in SI strategies backing out of the market when conditions become confusing. SI strategies can only work in peaceful times. A trader who ran SI strategies all the time would exit the market as his wealth would run out (Friedman, 1953).

SI strategies done through AT give us more eyeballs looking at the millions of traded products in the modern exchange environment. When there's a dislocation in a market (e.g. a crash in the futures price), immediately, hundreds of traders come through with a mechanistic response (reverse cash and carry arbitrage), which stabilises the price. In contrast, in the manual world, the field of view of each human was limited, and when a little crash got started, there were fewer people available to interfere with it. This gave more mini flash crashes in the pre-AT world. This is how both statements are correct:

  1. In times of market stress, the AT orders shy away (Raman, Robe, Yadav, 2015)
  2. Greater AT intensity reduces the incidence of mini flash crashes (Aggarwal & Thomas, 2014).

References


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

Milton Friedman. The case for flexible exchange rates. In Essays in Positive Economics, The University of Chicago Press, 1953.

Vikas Raman, Michel A. Robe, Pradeep K. Yadav. Man vs. Machine: Liquidity Provision and Market Fragility, Working Paper, 2015.

Friday, July 10, 2015

The changing landscape of equity markets

by Nidhi Aggarwal and Chirag Anand.

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

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

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

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

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

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

Do AT supply liquidity or demand liquidity?


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


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


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

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

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

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

Changing market structure due to high speed access


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

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

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

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

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

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

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

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


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

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

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

And its relevance in informing the policy debate:

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


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

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

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


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

Summary


In a nutshell, the findings can be summarised as:

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

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

References:

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

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

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

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


 

Footnotes

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