Prof. Jayanth R. Varma’s Financial Markets Blog

A blog on financial markets and their regulation

Monthly Archives: March 2010

Indian Financial Stability and Development Council

I wrote a column
in the Financial Express today about the proposal to
create a Financial Stability and Development Council in India as a
potential precursor to an apex regulatory body.

The announcement in the Budget speech this year about the setting
up of a Financial Stability and Development Council (FSDC) has revived
the long-standing debate about an apex regulatory body. Much of the
debate on FSDC has focused on the politically important but
economically trivial question of the chairmanship of the council. I
care little about who heads FSDC—I care more about whether it
has a permanent and independent secretariat. And I care far more about
what the FSDC does.

The global financial crisis has highlighted weaknesses in the
regulatory architecture around the world. Neither the unified
regulator of the UK nor the highly fragmented regulators of the US
came out with flying colours in dealing with the crisis. Everywhere,
the crisis has brought to the fore the problems of regulatory overlap
and underlap. In every country, there are areas where multiple
regulators are fighting turf wars over one set of issues, while more
pressing regulatory issues fall outside the mandate of any
regulator. Regulation and supervision of systemically important
financial conglomerates is an area seen as critical in the aftermath
of the crisis. It is an area that has been highly problematic in
India.

The most important failure (and bail-out) of a systemically
important financial institution in India in recent times was the
rescue of UTI, which did not completely fall under any
regulator’s jurisdiction. The most systemically important
financial institution in India today is probably the LIC, whose
primary regulator has struggled to assert full regulatory jurisdiction
over it. Even the remaining three or four systemically critical
financial conglomerates in India are not subject to adequate
consolidated financial supervision. The global crisis has shown that
the concept of a lead regulator as a substitute for effective
consolidated supervision is a cruel joke. The court examiner’s
report in the Lehman bankruptcy released this month describes in
detail how the ‘consolidated supervision’ by the US SEC of
the non-broker-dealer activities of Lehman descended into a
farce. Even before that we knew what happened when a thrift regulator
supervised the derivative activities of AIG.

Consolidated supervision means a lot more than just taking a
cursory look at the consolidated balance sheet of a financial
conglomerate. An important lesson from the global crisis is that we
must abandon the silly idea that effective supervision can be done
without a good understanding of each of the key businesses of the
conglomerate. High-level consolidated supervision of the top five or
top ten financial conglomerates is, I think, the most important
function that the FSDC should perform drawing on the resources of all
the sectoral regulators as well as the staff of its own permanent
secretariat.

Another important function is that of monitoring regulatory gaps
and taking corrective action at an early stage. Unregulated or
inadequately supervised segments of the financial sector are often the
source of major problems. Globally, we have seen the important role
played by under-regulated mortgage brokers in the sub-prime
crisis.

In India, we have seen the same phenomenon in the case of
cooperative banks, plantation companies and accounting/auditing
deficiencies in the corporate sector. Cooperative banks were
historically under-regulated because RBI believed that their primary
regulator was the registrar of cooperative societies. The registrar,
of course, did not bother about prudential regulation. Similarly, in
the mid-1990s, plantation companies and other collective investment
schemes were regulated neither as mutual funds nor as depository
institutions. Only after thousands of investors had been defrauded was
the regulatory jurisdiction clarified.

As far as accounting and auditing review is concerned, the
regulatory vacuum has not been filled even after our experience with
Satyam. Neither Sebi nor the registrar of companies undertakes the
important task of reviewing published accounting statements for
conformity with accounting standards. There is an urgent need for a
body like FSDC that systematically identifies these regulatory gaps
and develops legislative, administrative and technical solutions to
these problems. By contrast, I believe that the role of
‘coordination’ between regulators emphasised in the
current title of the high-level coordination committee is the least
important role of an FSDC. Some degree of competition and even turf
war between two regulators is a healthy regulatory dynamic.

At a crunch, I do not see anything wrong in a dispute between two
regulators (or between one regulator and regulatees of another
regulator) being resolved in the courts. After all, the Indian
constitution gives the judiciary the power to resolve disputes even
between two governments!

My favourite example from the US is the court battle between the
SEC and the derivative exchanges (supported by their regulator, the
CFTC) that led to the introduction of index futures in that country. A
truly independent regulator should be able and willing to go to court
against another arm of the government in order to perform its
mission.

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Lehman and its computer systems

Perhaps, I have a perverse interest in the computer systems of
failed financial firms – I blogged
about Madoff and his AS400 last year. Even while struggling to cope
with the fantastic 2,200 page report of the court
examiner on Lehman, I homed in on the discussion about Lehman’s
computer systems:

At the time of its bankruptcy filing, Lehman maintained a patchwork
of over 2,600 software systems and applications. … Many of Lehman’s
systems were arcane, outdated or non-standard. Becoming proficient
enough to use the systems required training in some cases, study in
others, and trial and error experimentation in others.
… Lehman’s systems were highly interdependent, but their
relationships were difficult to decipher and not well documented. It
took extraordinary effort to untangle these systems to obtain the
necessary information.

My limited experience suggests that outdated and unusable software
is a problem in most large organizations. I do hope that the ongoing
consumerization
of information technology will help reduce these problems by putting
intense pressure on corporate IT to reform their ways. Perhaps,
organizations should consider releasing the source code of most of
their proprietary software on their own intranet to help manage the
complexity and user unfriendliness of their systems. Consumerization
plus crowd sourcing might just be able to tame the beast.

Law, Madoff, fairness and interest rates

I would grant that there is probably no fair way for the courts to
deal with the mess created by the Madoff fraud. But I am intrigued by
the discussions about fairness in the ruling
of the US Bankruptcy Court about the rights of the Madoff victims.

I have nothing to say about the part of the judgement which
interprets the law, and will confine myself to the fairness example
that the judge discusses (page 32):

Investor 1 invested $10 million many years ago, withdrew $15
million in the final year of the collapse of Madoff’s Ponzi
scheme, and his fictitious last account statement reflects a balance
of $20 million. Investor 2 invested $15 million in the final year of
the collapse of Madoff’s Ponzi scheme, in essence funding
Investor 1’s withdrawal, and his fictitious last account
statement reflects a $15 million deposit. Consider that the Trustee
is able to recover $10 million in customer funds and that the Madoff
scheme drew in 50 investors, whose fictitious last account statements
reflected “balances” totaling $100 million but whose net
investments totaled only $50 million.

The judge believes that Investor 1 has no net investment
“because he already withdrew more than he deposited” while
Investor 2 has a $15 million net investment. Since the recovery of
$10 million is 20% of the $50 million net investment of all investors
put together, Investor 2 is entitled to $3 million and Investor 1 is
entitled to nothing.

The court states that Madoff apparently started his Ponzi scheme
(“investment advisory services”) in the 1960s. Since the
fraud was exposed at the end of 2008, the Ponzi scheme went on for
maybe 40 years. Let us therefore take “many years ago” in
the judge’s example to mean 20 years ago.

Between 1988 and 2008, the 3 month US Treasury Bill yield averaged
a little over 4% so that the present value of Investor 1’s $10
million at the risk free interest rate would be about $22
million. After the withdrawal of $15 million, there would still be $7
million left – a little less than half of Investor 2’s $15
million. If you believe in the time value of money, Investor 1 should
get a little less than half of Investor 2. The judge thinks Investor 1
should get nothing.

Alternatively, if you believe that the purchasing power of money is
important, then the US consumer price inflation during those 20 years
averaged about 3%. The $10 million that Investor 1 put in two decades
ago would be worth $18 million in 2008 dollars and Investor 1 would
still have $3 million of net investment left after the withdrawal of
$15 million. Yet the judge thinks he should get nothing.

Report on Rating Agency Regulation in India

Last week, the Reserve Bank of India published the Report
of the Committee on Comprehensive Regulation of Credit Rating
Agencies
appointed by the Government of India (more precisely, the
High Level Coordination Committee on Financial Markets). This was also
accompanied by a study by the National Institute for Securities
Markets entitled An
assessment of the long term performance of the Credit Rating Agencies
in India

The report provides a comprehensive analysis of the issues
mentioned in the terms of reference for the committee. Unfortunately,
those terms of reference did not include what I believe are the only
two questions worth looking at about credit rating in the aftermath of
the global financial crisis:

  • How should India eliminate or at least reduce the use of credit
    ratings in financial sector regulations?
  • How should India try to introduce greater competition in credit
    rating?

Rating agencies are fond of saying that “AAA” is just
the shortest editorial in the world. Regulators should take the rating
agencies at their word and act accordingly. They should give as little
regulatory sanction for these ratings as they do to the editorial in a
newspaper. Also, regulators should make it as easy to start a rating
agency as it is to start a newspaper. These are the two issues that I
think need urgent consideration.

As I pointed out in this blog
post
last year, the US is an outlier in terms of the use of credit ratings
in its regulations, and since India has largely adopted US style
regulations, it too is an outlier. By unilateral action, India can
eliminate all use of credit ratings except what is required by
Basel-II. Even Basel-II is not something for which Indian regulators
can disown responsibility – India is now a member of the Basel
Committee. Indian regulators should be providing thought leadership on
eliminating credit rating from Basel-III or Basel-IV.

I am disappointed that India’s apex regulatory forum (High
Level Coordination Committee on Financial Markets) having recognized
the important role of credit rating agencies in the global crisis, did
not bother to ask the truly important questions. All the more so,
because the report did a good job of addressing the questions that
were referred to it in the terms of reference. If only the same bunch
of competent people had been asked the right questions!

Bayesians in finance redux

In November last year, I wrote a brief post
about Bayesians in finance. The post was brief because I thought that
what I was saying was obvious. A long and inconclusive exchange with
Naveen in the comments section of another
post
has convinced me that a much longer post is called for. The
Bayesian approach is perhaps not as obvious as I assumed.

When finance professors walk into a classroom, they want to build
on what the statistics professors have covered in their courses. When
I am teaching portfolio theory, I do not want to spend half an hour
explaining the meaning of covariance; I would like to assume that the
statistics professor has already done that. That is how division of labour is supposed to work in a
pin factory or in a university.

Unfortunately, there is a problem with this division of labour
– most statistics professors teach classical statistics. That is
true even of those statisticians who prefer Bayesian techniques in
their research work! The result is that many finance students wrongly
think that when the finance professors talk of expected returns,
variances and betas, they are referring to the classical concepts
grounded in relative frequencies. Worse still, some students think
that the means and covariances used in finance are sample means and
sample covariances and not the population means and covariances.

In business schools like mine where the case method dominates the
pedagogy, these errors are probably less (or at least do less damage)
because in the case context, the need for judgemental estimates for
almost everything of interest becomes painfully obvious to the
students. The certainties of classical statistics dissolve into utter
confusion when confronted with messy “case facts”, and
this is entirely a good thing.

But if cases are not used or used sparingly, and the statistics
courses are predominantly classical, there is a very serious danger
that finance students end up thinking of the probability concepts in
finance in classical relative frequency terms.

Nothing could be farther from the truth. To see how differently
finance theory looks at these things, it is instructive to go back to
some of the key papers that established and developed modern portfolio
theory over the years.

Here is how Markowitz begins his Nobel prize winning paper
(“Portfolio Selection”, Journal of Finance, 1952) more
than half a century ago:

The process of selecting a portfolio may be divided into two stages.
The first stage starts with observation and experience and ends with
beliefs about the future performances of available securities. The
second stage starts with the relevant beliefs about future performances
and ends with the choice of portfolio.

Many finance students would probably be astonished to read words
like observation, experience, and beliefs instead of terms like
historical data and maximum likelihood estimates. This was the paper
that gave birth to modern portfolio theory and there is no doubt in
Markowitz’ mind that the probability distributions (and the
means, variances and covariances) are subjective beliefs and not
classical relative frequencies.

Markowitz is also crystal clear that what matters is not the
historical data but beliefs about the future – historical data
is of interest only in so far as it helps form those beliefs about
the future. He also seems to take it for granted that different people
will have different beliefs. He is helping each individual solve his
or her portfolio problem and is not bothered about how these choices
affect the equilibrium prices in the market.

When William Sharpe developed the Capital Asset Pricing Model that
won him the Nobel prize, he was trying to determine the market equilibrium
and he had to assume that all investors have the same beliefs but did
so with great reluctance:

… we assume homogeneity of investor expectations: investors are
assumed to agree on the prospects of various investments – the
expected values, standard deviations and correlation coefficients
described in Part II. Needless to say, these are highly restrictive
and undoubtedly unrealistic assumptions. However, … it is far from
clear that this formulation should be rejected – especially in
view of the dearth of alternative models

But finance theory quickly went back to the idea that investors had
different beliefs. Treynor and Black (“How to use security
analysis to improve portfolio selection,” Journal of
Business
, 1973) interpreted the CAPM as saying
that:

…in the absence of insight generating expectations different from
the market consensus, the investor should hold a replica of the market
portfolio.

Treynor and Black devised an elegant model of portfolio choice
when investors had out of consensus beliefs.

The viewpoint in this paper is that of an individual investor who
is attempting to trade profitably on the diiference between his
expectations and those of a monolithic market so large in relation to
his own trading that market prices are unaffected by it.

Similar ideas can be seen in the popular Black Litterman model
(“Global Portfolio Optimization,” Financial Analysts
Journal,
September-October 1992). Black and Litterman started
with the following postulates:

  1. We believe there are two distinct sources of information about
    future excess returns – investor views and market equilibrium.
  2. We assume that both sources of information are uncertain and are
    best expressed as probability distributions.
  3. We choose expected excess returns that are as consistent as
    possible with both sources of information.

Even if we stick to the market consensus, the CAPM beta itself has
to be interpreted with care. The derivation of the CAPM makes it clear
that the beta is actually the ratio of a covariance to a variance and
both of these are parameters of the subjective probability
distribution that defines the market consensus. Statisticians
instantly recognize that the ratio of a covariance to a variance is
identical to the formula for a regression coefficient and are tempted
to reinterpret the beta as such.

This may be formally correct, but it is misleading because it
suggests that the beta is defined in terms of a regression on past
data. That is not the conceptual meaning of beta at all. Rosenberg and
Guy explained the true meaning of beta very elegantly in their paper
(“Prediction of beta from investment fundamentals”,
Financial Analysts Journal, 1976) introducing what are
now called fundamental betas:

It is instructive to reach a judgement about beta by carrying out an
imaginary experiment as follows. One can imagine all the various
events in the economy that may occur, and attempt to answer in each
case the two questions: (l) What would be the security return as a
result of that event? and (2) What would be the market return as a
result of that event?

This approach is conceptually revealing but is not always practical
(though if you are willing to spend enough money, you can access the
fundamental betas computed by firms like Barra which Barr Rosenberg
founded and later left). In practice, our subjective belief about the
true beta of a company involves at least the following inputs:

  • The beta is equal to unity unless there is enough reason to
    believe otherwise. The value of unity (the beta of an average stock)
    provides an important anchor which must be taken into account even
    when there is other evidence. It is not uncommon to find that simply
    equating beta to unity outperforms the beta estimated by naive
    regression.
  • What this means is that betas obtained by other means must be
    shrunk towards unity. An estimated beta exceeding one must be reduced
    and an estimated beta below one must be increased. One can do this
    through a formal Bayesian process (for example, by using a Bayes-Stein
    shrinkage estimator), or one can do it purely subjectively based on
    the confidence that one has in the original estimate.
  • The beta depends on the industry to which the firm belongs. Since
    portfolio betas can be estimated more accurately than individual
    betas, this is often the most important input into arriving at a
    judgement about the true beta of a company.
  • The beta depends on the leverage of the company and if the
    leverage of the company is significantly different from that of the
    rest of the industry, this needs to be taken into account by
    unlevering and relevering the beta.
  • The beta estimated by regressing the returns of the stock on the
    market over different time periods provides useful information about
    the beta provided the business mix and the leverage have not changed
    too much over the sample period. Since this assumption usually precludes very
    long sample periods, the beta estimated through this route typically
    has a large confidence band and becomes meaningful only when combined
    with the other inputs.
  • Subjective beliefs about possible future changes in the beta
    because of changing business strategy or financial strategy must also
    be taken into account.

Much of the above discussion is valid for estimating Fama-French
betas and other multi-factor betas, for estimating the volatility
(used for valuing options and for computing convexity effects), for
estimating default correlations in credit risk models and many other
contexts.

Good classical statisticians are quite smart and in a practical
context would do many of the things discussed above when they have to
actually estimate a financial parameter. In my experience, they
usually agree that (a) there is a lot of randomness in historical
returns; (b) the data generating process does not remain unchanged for
too long; (c) therefore in practice there is not enough data to avoid
sampling error; and (d) hence it is desirable to use a method in which
sampling error is curtailed by fundamental judgement.

On the other side, Bayesians shamelessly use classical tools
because Bayes theorem is an omnivore that can digest any piece of
information whatever its source and put it to use to revise the prior
probabilities. In practical terms, Bayesians and classical
statisticians may end up doing very similar stuff.

The advantage of shifting to Bayesian statistics and subjective
probabilities is primarily conceptual and theoretical. It would
eliminate confusion in the minds of students on the ontological status
of the fundamental constructs of finance theory.

I am now therefore debating in my own mind whether finance
professors must spend some time in the class room discussing
subjective probabilities.

How would it be like to begin the first course in finance with a
case study of subjective probabilities – something like the
delightful paper by Karl Borch (“The monster in Loch
Ness”, Journal of Risk and Insurance, 1976)? Borch
analyzes the probability that the Loch Ness
monster
exists (and would be captured within a one year period)
given that a large company had to pay a rather high 0.25% premium to
obtain a million pound insurance cover from Lloyd’s of London
against that risk? This is obviously a question which a finance
student cannot refuse to answer; yet there is no obvious way to
interpret this probability in relative frequency terms.

Greek bond issue

That Greece could borrow money at all (even if it is at 3% above
the risk free rate) seems to have calmed the markets a great deal. I
am reminded of this piece of Rothschild wisdom:

You are certainly right that there is much to be earned from
a government which has no money. But you have to take risks.

That is James Rothschild writing to Nathan Rothschild nearly two
centuries ago as quoted by Niall Ferguson, The House of
Rothschild: Money’s Prophets 1798-1848
, Chapter 4.

Regulation by placebo

This is a very nice phrase that I picked up from SEC Commissioner
Kathleen Casey’s speech
dissenting from the short selling rules that the SEC introduced
recently:

But this is regulation by placebo; we are hopeful that the pill
we’ve just had the patient take, although lacking potency, will
convince him that everything is all right.

Casey’s speech itself was a bit of political grandstanding
and was in the context of an SEC vote that went on predictable party
lines. I am not therefore inclined to take the speech too
seriously. But the phrase “regulation by placebo” very
elegantly captures a phenomenon that is all too common in financial
sector regulation all over the world.

Securities regulators, banking regulators and other financial
regulators have this great urge to be seen to be doing something
regardless of whether that something is the right thing or not. The
result is often a half hearted measure that does not stop the wrong
doing but convinces the public that the evil doers have been kept at
bay.

Regular readers of my blog know that I am against short sale
restrictions in general. At the very least, I would like short sale
restrictions to be accompanied by corresponding and equally severe
restrictions on leveraged longs. If you are not allowed to short a
stock when it has dropped 10%, then you should not be allowed to buy a
stock (with borrowed money) when the stock has risen 10%. Market
manipulation is done far more often by longs than by shorts!