P R E S E N T S
CFM QUARTERLY IN FINANCE
JULY 2011 EDITION
EDITOR:
DR. PRASANNA CHANDRA
© 2011, Centre for Financial
Management®
|
CONTENTS |
1.
ARTICLES/CASES………………………………………………………………….
Pg 2
·
GLOBAL FINANCIAL CRISIS…………………………………………………..
Pg 2
·
NOISE AND PERFORMANCE IN STOCK MARKET
……………………… Pg 7
·
PROCTOR AND GAMBLE: MASTER IN
INNOVATION…………………… Pg 9
2.
SNIPPETS……………………………………………………………………………..
Pg 10
·
BASICS AND DILUTED EARNINGS PER SHARE…………………………
Pg 10
·
OFFER DOCUMENT……………………………………………………………..
Pg 11
·
MICRO EFFICIENCY AND MACRO
INEFFICIENCY……………………… Pg 12
·
SOME OF THE GREATEST BUSINESS
DECISIONS…………………….. Pg 13
·
DIVIDEND POLICY OF HERO HONDA MOTORS…………………………
Pg 14
3.
WIT
AND WISDOM…………………………………………………………………. Pg 15
·
HUMOUR
·
Wise Saws
·
PERSPECTIVES
CFM Quarterly
in Finance, a publication of the Centre for Financial Management, Bangalore
is primarily a practitioner-oriented journal. It seeks to discuss
contemporary developments, analytical concepts and techniques, research
insights, perspectives, and state-of-the art practices. By and large, the
CFM Quarterly in Finance seeks to convey important developments in the
theory and practice of finance in a rigorous, but relatively non-technical,
manner.
|
SECTION A:
ARTICLES / CASES |
1. GLOBAL FINANCIAL CRISIS
For a quarter of century, beginning in early 1980s, finance enjoyed
its golden age. As an Economist
article put it: “As financial globalisation spread capital more widely, markets
evolved, businesses were able to finance new ventures, and ordinary people had
unprecedented access to borrowing and foreign exchange. Modern finance improved countless lives.”
But more recently something went seriously
wrong and that led to an unprecedented global financial crisis. It surfaced in
the subprime mortgage sector in the U.S. in August 2007 and, following the
collapse of Lehman Brothers in September 2008, snow balled into a global
financial crisis. It led to the bankruptcy or
rescue of the top five investment banks on Wall Street, the biggest
insurance company (AIG), the biggest bank (Citibank), the biggest automobile
company (General Motors), and the
biggest mortgage underwriters (Fannie Mae and Freddie Mae). It is widely regarded
as the greatest crisis in the history of financial capitalism because of the
speed and intensity with which it simultaneously propagated to other countries.
Apart from its huge financial cost, its adverse impact on the real economy has
been severe. According to IMF, in 2009 the world GDP declined by 0.8 and the
world trade volume contracted by 12 percent.
The
crisis has called for re-examining the dominant tenets in macroeconomics. It
has challenged the belief in the
self-correcting nature of financial markets and brought to focus the role of
finance in economic growth.
Contributory Factors
A confluence of factors seems to have caused the global financial
crisis. The major ones are discussed
below:
Macro-economic Imbalances Last decade has witnessed an explosion of
macro-economic imbalances in the world, with a very high savings rate in
countries like China and very low savings rates in countries like the U.S. The high savings rate resulted in a fall in
the real risk-free interest rate to historically low levels. For example, in 1990 the risk-free
index-linked government bonds in U.K. or U.S. provided 3 percent real
rate. In recent years it fell below 2
percent and at times to about 1 percent.
The fall in real interest rates has led to
rapid growth of credit in some developed countries (which fuelled a property
boom) along with a decline in credit standards.
It also drove investors to search for improvement in yield, however
slight that might be. Any product that
appeared to increase yield by 10, 20, or 30 basis points, without adding measurably
to risk, seemed attractive.
Unbridled Financial Innovation The demand for yield enhancement was met by a
wave of financial innovation, focused on securitised credit instruments.
Securitisation involves packaging a designated
pool of assets (mortgage loans, consumer loans, hire purchase receivables, and
so on) and issuing securities which are collateralised by the underlying assets
and their associated cash flow streams.
Securitisation gained in importance from the early 1980s and was
regarded as a major financial innovation that reduced the risk of the banking
system as credit risk was transferred to the end investors.
But when the crisis broke, it was realised that
most of the holdings of securitised credit instruments were in the books of
highly leveraged banks and financial institutions and not in the books of end
investors. As the Turner Review noted:
“The evolution of the securitised credit model was accompanied by a remarkable
growth in the relative size of the wholesale financial services within the
overall economy, with activities internal to the banking system growing far
more rapidly than end services to the real economy.” For example, in the U.K. the debt of the
financial sector as a proportion of GDP increased from 30 percent in 1987 to
nearly 250 percent in 2007. Naturally,
the growth of the relative size of the financial sector, and in particular the
activities in securitised credit instruments, increased systemic risk,
contributing to the credit boom during the upswing and accentuating the
subsequent downswing.
A worrisome aspect of this growth was the fact
that Collateralised Debt Obligations (CDOs) loomed large in this wave of
financial innovation. A CDO is a product
backed by a diversified pool of debt obligations such as corporate bonds, bank
loans, emerging market bonds, asset-backed securities, mortgages, and other
CDOs. When the underlying pool of debt
obligations represents bond-type instruments, a CDO is called a collateralised
bond obligation (CBO); when the underlying pool of debt obligations represents
bank loans, a CDO is called a collateralised loan obligation (CLO).
The problem with CDOs is that they have a very
high and imperfectly embedded leverage and are very difficult to value. As Emanuel Dreman of Goldman Sachs says:
“With Black-Scholes model you know what you are assuming when you use the
model, and you know exactly what has been swept out of view, and hence you can
think clearly about what you may have overlooked.” With CDOs he says, “you don’t know how to adjust
for its inadequacies.” It appears that
the sophisticated U.S. financial services overwhelmed the relatively
unsophisticated financial services elsewhere.
Misplaced
Reliance on Sophisticated Maths The expansion of financial sector and the complexity of securitised
credit products was accompanied by the development of sophisticated
mathematical models for measuring and managing risks. But these models were based on the assumption
that the distribution of future prices would be similar to their past distribution. This was indeed a fragile assumption that
caused massive damage.
As Warren Buffett notes: “Indeed, the
stupefying losses in mortgage-related securities came in large part because of
flawed, history-based models used by salesmen, rating agencies, and
investors.” He warns “Investors should
be skeptical of history-based models.
Constructed by a nerdy-sounding priesthood using esoteric terms such as
beta, gamma, sigma, and the like, these models tend to look impressive. Too often, though, investors tend to forget
to examine the assumptions behind the symbols.”
In a similar vein, Edmund Phelps, Nobel
Laureate in Economics, says: “Risk assessment and risk-management models were
never well-founded.” He adds: “There was
a mystique to the idea that market participants know the price to put on this
or that risk. But it is impossible to
imagine that such a complex system could be understood in such detail and with
such amazing correctness. The
requirements of information have gone beyond our abilities to gather.”
Flawed VAR Calculations An important abuse of quantitative analysis has been with respect
to value at risk (VAR) calculation. VAR
reflects a limit on the loss of value of a portfolio, on account of normal
market movements, which will be exceeded only with a small pre-specified
probability. Thus if VAR is Rs. 10
million (or whatever) with a confidence level of 95 percent, it means that
there is only a 5 percent probability that the loss in portfolio value will
exceed Rs. 10 million. Quantifying risk
in this fashion requires sophisticated analytical modeling and simulation
analysis. The typical VAR analysis is
based on the assumption that the underlying market movement follows a normal
distribution.
Benoit Mandelbrot, the polymath who invented
fractal theory, calculated the theoretical changes (under normal distribution)
and the actual changes of the Dow Jones Industrial Average (DJIA) over the
period 1916 to 2003, as shown below:
|
Theory |
Reality |
|
·
More than 3.4
percent on 58 days |
·
More than 3.4
percent on 1001 |
|
·
More than 4.5
percent on 6 days |
·
More than 4.5
percent on 366 days |
|
·
More than 7
percent once in 300,000 years |
·
More than 7
percent on 48 days |
Mandelbrot argues that the market movement is
characterised by fat-tail distribution and not normal distribution. The market should have been “mildly stable”
but it was actually “wildly stable.”
This presents a conundrum. As an Economist article put it: “On
the one hand, you cannot observe the tails of the VAR curve by studying extreme
events, because extreme events are rare by definition. On the other hand, you cannot deduce very
much about the frequency of rare extreme events from the shape of the curve in
the middle.” Put differently, while VAR
is good at predicting small losses in the middle of the distribution, it is
unreliable in predicting severe losses that are much rarer, but matter the
most.
Modern finance perhaps has made the tails
fatter. When all kinds of specific risks
in foreign exchange, interest rates, and stock prices are traded away the
portfolio may appear safer. But in reality every day risk may be swapped for an
exceptional risk like the failure of the insurer, as it happened with AIG.
Explosive Growth in Derivatives Since the early 1970s financial prices –
exchange rates, interest rates, commodity prices, and equity prices – have
become more volatile. To cope with these
risks corporations and banks resorted to the use of derivatives like options,
futures, forwards, and swaps.
Another force that fuelled the explosion in
derivatives was a powerful combination of mathematics and computing. Before the development of Black-Scholes
model, option pricing was more or less educated guesswork. The Black-Scholes model instilled confidence
in buyers and sellers to trade heavily in derivatives. Explains Emanuel Derman of Goldman Sachs: “In
a thirsty world filled with hydrogen and oxygen, someone had finally worked out
how to synthesise H20.”
A significant portion of trading in derivatives
takes place in the OTC (over-the-counter) market. In June 2008, the volume of outstanding OTC
derivatives contracts was of $530 trillion (interest rate derivatives accounted
for $460 trillion, credit default swaps accounted for $60 trillion, and equity
derivatives accounted for $ 10 trillion).
The staggering size and complexity of derivatives market and the fact
that it is mostly an OTC market increases the potential danger of market
disruption.
John Shad, former chairman, Securities Exchange
Commission, expressed concern about this phenomenon. He said: “Futures and options are the tail
wagging the dog. They have escalated the
leverage and volatility of the markets to precipitous, unacceptable levels.”
Warren Buffett echoed a similar warning :“Charlie and I are of one mind in how
we feel about derivatives and the trading activities that go with them: we view
them as time bombs, both for the parties that deal in them and the economic
system.”
Warren
Buffett had expressed his concern in 2003 itself: “Many people argue that
derivatives reduce systemic problems, in that participants who can’t bear
certain risks are able to transfer them to stronger hands. These people believe
that derivatives act to stabilize the economy, facilitate trade, and eliminate
bumps for individual participants. And, on a micro level, what they say is
often true. Indeed, at BH, I sometimes engage in large scale derivatives
transactions in order to facilitate certain investment strategies. Charlie and
I believe, however, that the macro picture is dangerous and getting more so. Large
amounts of risk, particularly credit risk, have become concentrated in the
hands of relatively few derivatives dealers, who in addition trade extensively
with one another. The troubles of one could quickly infect the others.”
Unfortunately, the bulk of the financial
community, enamoured of the derivatives revolution, did not appreciate the
systemic implications of the explosive growth of derivatives.
Regulatory Laxity The general euphoria about the contribution of modern finance to
economic performance seems to have induced complacency in regulators. For example, in 2004, the Securities Exchange
Commission (SEC) exempted the brokerage units of investment banks from a
regulation that limited the amount of debt they could take in return for a
greater oversight of the investment activities of the banks by the SEC. The SEC merely relied on the firms’ own
computer models for determining the riskiness of investments. And it hardly did anything to follow up on
the risky activities uncovered by its examiners. Thanks to the connivance of the regulators,
investment banks could increase their debt equity ratio to such preposterous
levels as 30:1.
A conspicuous example of regulatory laxity was
the introduction of ‘Commodity Futures Modernisation Act’ on the last day of
the last session of a lame duck 106th session of the U.S. Congress
in 2000. This Act removed the various
capital constraints on lending and exempted derivatives and credit default
swaps from legislative purview. This had
a far-reaching impact on the U.S. financial system. As an example, in 2000 when the U.S. Congress
introduced the new legislation the size of the CDS (credit default swaps)
market was $100 billion; in late 2008 the size of the CDS market was $62
trillion. Charlie Munger finds CDS inherently objectionable: “Do you think it
would be desirable if everybody in America could buy life insurance on any
person they wanted to buy life insurance on? He continues “That would be pretty
dangerous for the person who was insured. Some of that danger exists once you
get people who have a vested interest in the destruction of some business.”Even
if CDS are not used to destroy good companies, they induce cynicism and
sloppiness in bank lending. As Cristine Richard says: “In the end, the $62
trillion CDS market allowed Wall Street to lend without having confidence in
the men and women it lent to. Wall Street hedged away the risk of lending and
in the process undermined the entire system.”
Flaw in the Business Model of Investment Banks Investment banks originally started off as
brokerage firms and then diversified into underwriting of securities and
advisory services. None of these
businesses requires huge amounts of capital.
When commissions on their traditional
businesses declined, investment banks further diversified into proprietary
trading and then to private equity, businesses which require large amounts of
capital to be committed to risky and illiquid assets. To finance these risky businesses they
recklessly levered themselves. In August
2008, even after additional equity infusions, Lehman Brothers had a debt-equity
ratio of 20:1. With such vulnerability,
the acquisition of a property investment company at the height of the property
bubble was sufficient to kill Lehman Brothers.
There were serious flaws in the model followed
by investment banks. First, their assets
were financed in the wholesale markets.
If there is uncertainty about the value of the assets, access to funds
is cut off, triggering a collapse.
Second, high leverage incentivises managers to take huge risks. If the bets succeed, managers get outsized
rewards; if the bets fail, shareholders get screwed up.
One can argue that the irresponsible behaviour
of financial institutions is a manifestation of moral hazard to a certain extent. The involvement of the Federal Reserve Bank
of New York in rescuing Long Term Capital Management perhaps prodded large
financial institutions to assume more risk.
Excessive Leverage in European Banks While Europeans criticised the U.S. investment
banks for their casino capitalism, their own banks such as UBS, Credit Suisse,
ING, Dexia, ad B N P Paribas had debt-equity ratios nearing 50:1. Using the Basel norms European banks
justified their high leverage by arguing that their assets (including much sovereign
debt) were of high quality.
Yet the crisis of late 2008 taught some
sobering lessons. First, even the
highest rated assets can get tainted in a crisis thereby inflicting huge losses
on highly leveraged banks. Second, in a
panic, even the biggest financial institutions are vulnerable to a run on
deposits or panic sales of securities.
Third, practices like capital adequacy norms and mark-to-market are
pro-cyclical, not anti-cyclical.
Reverse Natural Selection in Finance In financial services, there is always a
temptation to play. This tendency has
been heightened with the evolution of financial services from a guild of small
partnerships to a joist of gigantic multinational corporations and clashing
egos. As Chuck Prince, CEO of Citigroup
in 2007, said: “As long as the music is playing you have got to get up and
dance.” A bank of Citi’s size cannot sit
on the sidelines without inviting criticism from investors and commentators.
The perturbing message in Prince’s words is
that bit by bit boom induces excessive risk taking, thereby causing reverse
natural selection. As an Economist
article says: “The end of partnerships turned private rivalries into a public
tournament. The senior managers’ wealth,
careers and status were completely wrapped up in their firm’s
pre-eminence. League tables, quarterly
results, daily share-price movements, total shareholder returns, all are ways
of keeping score.” It adds: “If you did
not compete you were a dullard. If you
pulled back your career may be cut short.”
To paraphrase Keynes, the market can stay
irrational longer than you can stay in your job. So in the last 35 years it appeared that
everyone in finance tried to be someone else.
As an Economist article put it: “Hedge funds and private equity
wanted to be as cool as a dotcom.
Goldman Sachs wanted to be as smart as a hedge fund. The other investment banks wanted to be as
profitable as Goldman Sachs. America’s
retail banks wanted to be as cutting edge as investment banks. And European banks wanted to be as aggressive
as American banks. They all ended up
wishing they could be back precisely where they started.”
2. NOISE AND PERFORMANCE IN STOCK
MARKET
In general, if returns are independent over time (which means that
they behave like a random walk), the standard deviation of the average return
over n years is s / n,
where s is the standard deviation of one-year return and n is
the length of investment horizon. This means that as the investment
horizon elongates the standard deviation of average return decreases and as the
investment horizon contracts the standard deviation of average return
increases. For example, if equities earn an average annual return of
15 percent with a standard deviation of 10 percent, the standard deviation of
average return will be as follows for different investment horizons:
Investment Horizon Standard
Deviation of Average Return
2
years 10 / 2 = 7.07 percent
1
year
10 /
1 = 10.00
percent
3
months 10 /
0.25 = 20.00
percent
Note that while the average annual return
remains the same, viz., 15 percent, the standard deviation of average return
varies inversely with the investment horizon. This means that as the
observation period shortens, noise (volatility) dominates performance (average
return) and vice versa. To appreciate the significance of this let us
assume that equities provide an average annual return of 15 percent with a
standard deviation of 10 percent and answer two questions:
·
What is the probability of
success (defined as a positive return) for different observation periods?
·
How much of noise and how
much of performance do we see over different observation periods?
Probability of Success
What would be the probability of success (defined as a positive
return) in any given year? Since the standard deviation of average
return over a one-year period is 10 percent and the average return is 15 percent,
0 percent (which separates success from failure) is 1.5 s to the left
of the mean (15 percent). So, the probability of success is equal to
the shaded area in the following distribution:
Consulting the table of standard normal distribution, we find that
the probability of the shaded area is 0.93 or 93 percent.
What would be the probability of success over a
quarter (0.25 years)? The standard deviation of return when the
period is 0.25 is: 10% / 0.25 = 20 percent. Now,
given a mean return of 15 percent and a standard deviation of 20 percent, 0
percent is 0.75 s to the left of the mean. So, the
probability of success is equal to the shaded area in the following
distribution.
From the standard normal distribution table, we find that the probability
of the shaded area is 0.77 or 77 percent.
Thus we find that when the time scale is 1 year
the probability of success is 93 percent and when the time scale is a quarter,
the probability of success is 77 percent. As the time scale reduces
the probability of success falls as shown below:
Scale Probability
of Success
1
year 93%
1
quarter 77%
1
month
67%
1
day 54%
1
hour 51.3%
1
minute
50.17%
1
second 50.02%
From the above it is clear that as the
observation period shortens noise dominates performance.
Proportions of
Performance and Noise over Different Observation Periods
Average return represents performance and
standard deviation (volatility) represents noise. If equities earn
an average annual return of 15 percent with a standard deviation of 10 percent,
then the performance and noise for various observation periods are:
Observation Period Performance
(Average Return) Noise
(Standard Deviation)
2
years
15
percent 7.07 percent
1
year 15
percent 10.00 percent
3
months 15 percent 20.00
percent
1
month 15
percent 34.64 percent
1
week
15
percent 72.11 percent
1
day 15
percent 191.10
percent
Thus we find that over 2 years, we observe 0.47 parts noise for one
part performance; over 1 year we observe 0.67 parts noise for one part
performance; over one-quarter we observe 1.33 parts noise for one part performance;
over one month we observe 2.31 parts noise for one part performance; over one
week we observe 4.81 parts noise for one part performance; and over 1 day we
observe 12.74 parts noise for one part performance. As the
observation period contracts, noise dominates performance.
3. Proctor and Gamble: Master in Innovation
Proctor and Gamble (P&G) has excelled in innovation, which is
at the heart of their business model, for decades. P&G has innovated
consistently, reliably and successfully. Its long list of innovation firsts
includes Tide (the first heavy-duty laundry detergent), Crest (the first
fluoride toothpaste clinically proven to prevent tooth decay), Head and
Shoulders (the first pleasant-to-use shampoo effective against dandruff),
Pampers (the first affordable, mass-marketed disposable diaper), Bounty (the
first three-dimensional paper towel), Always (the first feminine protection pad
with an innovative, dry-weave topsheet), Febreze (the first fabric and air care
product that actually removes odors from fabrics and the air), and Crest White
Strips (the first patented in-home teeth whitening technology).
P&G has created a
unique design for innovation. P&G defines innovation broadly, invests in
innovation at industry-leading levels, manages innovation with discipline,
delivers innovation that builds customer trust and loyalty over time, and leads
innovation with global brands and an outstanding team of innovation leaders.
P&G’s integrated, end-to-end approach,
to innovation, complemented by its global scale and scope, enables it to win
customers and generate sustainable long-term growth and shareholder value.
P&G invests more
than $2 billion a year a R&D, nearly twice the level of its closest
competitor, Unilever and roughly equal to the combined total of its other major competitors – Avon,
Clorox, Colgate, Energizer, Henkel, Kimberly Clark, L’Oreal, and Reckitt
Bencksier. It also maintains a high
level of marketing investment in its brands. Its advertising budget has
averaged 10% of sales over the past 15 years.
P&G takes a very
comprehensive approach to productivity improvement leading to systematic growth
in productivity. Sales per employee have grown more than three-fold and net
earnings per employee have grown eight-fold since 1980. A good example of
P&G’s productivity improvement is its Global Business Services – P&G’s
shared services business model.
P&G systems,
infrastructure, and services are focused on improved service levels and greater
value creation. Its Global Business Services (GBS)is recognised as the best
shared-services organisation in the world. It has brought about substantial
savings in cost. The GBS, in collaboration with R&D and Engineering
functions, is making P&G a more productive and effective innovator.
P&G exercises tight
control over overhead spending. For businesses projected to grow significantly
faster than the balance of the portfolio, the overhead target growth is set
equal to or less than half their projected sales growth, for slower-growing
businesses and all corporate functions, no overhead growth is allowed; for
businesses growing below company goals and / or having significant cost
structure issues, overhead spending must reduce each year.
P&G’s Corporate
Innovation Fund is in essence an in-house venture capital fund that does
initial concept, design, engineering, and qualification work. It hands over
successful ideas to the appropriate business units.
|
SECTION B:
SNIPPETS |
Basic and Diluted
Earnings per Share
As per Accounting Standard 20 all listed companies should present
basic and diluted earnings per share for each class of equity share.
To calculate the basic earnings per share, the
net profit or loss for the period attributable to equity shareholders is
divided by the weighted average number of equity shares during the period.
To calculate the diluted earnings per share,
the net profit or loss for the period attributable to equity shareholders and
the weighted average number of shares outstanding during the period should be
adjusted for the potential dilution arising from conversion of debt into
equity, exercise of warrants and stock options, and so on. The
nature of adjustment is illustrated below:
Convertible Debentures To illustrate the diluted EPS is calculated,
when a company has outstanding convertible debentures let us consider an
example. Magnum Company has 10 million equity shares of Rs. 10 each
and 200,000 convertible debentures of Rs. 100 each carrying a coupon rate of 8
percent. Each convertible debenture is convertible into 4 equity
shares. Magnum’s profit after tax for the year ended March 31, 20X5,
was Rs. 25 million and its tax rate is 30 percent.
The basic earnings per share is:
|
Basic earnings per share |
= |
Rs. 25,000,000 |
= |
Rs. 2.50 |
|
Rs. 10,000,000 |
The diluted earnings per share is calculated as follows:
|
Number of existing equity shares |
10,000,000 |
|
Equivalent number of equity shares
corresponding to convertible debentures |
800,000 |
|
Number of equity shares for
calculating the diluted earnings per share |
10,800,000 |
|
Profit after tax |
Rs. 25,000,000 |
|
Add: After-tax debenture
interest 200,000
x 100 x .08 x 0.70 |
1,120,000 |
|
Adjusted profit after tax |
26,120,000 |
|
Diluted earnings per share 26,120,000 / 10,800,000 |
Rs. 2.42 |
Stock Options To illustrate how the diluted earnings per
share is calculated when a company has issued stock options, assume that the
Magnum Company does not have convertible debentures but has issued stock
options for 1 million shares which are exercisable at a price of Rs.
24. The fair value of an equity share is Rs. 30.
The excess of fair value (Rs. 30) over the
exercise price (Rs. 24) is translated into an equivalent number of equity
shares for calculating the diluted earnings per share. The
calculation of the diluted earnings per share is shown below:
|
Number of existing equity shares |
10,000,000 |
|
|
Number of equity shares |
|
10,000,000 |
|
Number of equity shares under stock
option |
1,000,000 |
|
|
Number of equity shares that would
have been issued at fair value: 1,000,000 x 24/30 |
800,000 |
|
|
Dilution impact in terms of
equivalent number of shares |
|
200,000 |
|
Number of equity shares for
calculating the diluted earnings per share |
|
10,200,000 |
|
Diluted earnings per share : Rs.
25,000,000 / 10,200,000 |
|
Rs. 2.45 |
Offer Document
‘Offer document’ is a document used for inviting subscription to
the issue being made by the issuer. It contains information about
the company, promoters, projects, financial details, objects for raising money,
terms of the issue, and so on.
Depending on the stage or type of the issue,
the terms used for the offer document are as follows:
·
Draft
Offer Document This is the
offer document filed with SEBI for specifying changes, if any, before it is
filed with the Registrar of Companies (ROCs). Through SEBI website,
the draft offer document is made available in public domain to enable public to
give comments.
·
Red
Herring Prospectus This is the
offer document used in case of a book built public issue. It
contains all the relevant details excepting the price and the number of shares
being offered. It is filed with the ROC before the issue opens.
·
Prospectus This is the offer document in case
of a public issue that contains all the relevant details, including the price
and the number of shares being offered. The prospectus has to be
filed with ROC before the issue opens in case of a fixed price issue and
after the issue closes in case of a book built issue.
·
Letter
of Offer This is the offer document
in case of a rights issue. It is filed with the stock exchanges
before the issue opens.
·
Abridged
Prospectus This
is an abridged version of the offer document for a public issue, which has to
be issued along with the application form of a public issue.
·
Abridged
Letter of Offer This
is an abridged version of the letter of offer, which is sent to all
shareholders along with the application form.
·
Shelf
Prospectus This
is a prospectus that enables an issuer to make a series of issues within a
period of 1 year without the need to file a fresh prospectus every
time. This facility is available to public sector banks / public financial
institutions.
·
Placement
Document This is an offer document
used for the purpose of Qualified Institutional Placement.
Micro
Efficiency and Macro Inefficiency
Paul Samuelson has argued that modern markets show considerable
micro efficiency because the minority that spots deviations from micro
efficiency can make money by exploiting those deviations and in doing so they
eliminate persisting inefficiencies. In contrast, Paul Samuelson hypothesized
that markets display considerable macro inefficiency in the sense that
aggregate indexes of security prices remain below or above various definitions
of fundamental values for long periods of time. There seems to be substantial
evidence in support of Samuelson’s dictum where inefficiency is defined as predictability
of future (excess) returns.
Samuelson’s dictum is
plausible because much more information is available about future changes in
fundamentals of individual firms than about future changes in the fundamentals
of the aggregate stock market. Activities and prospects of individual firms are
highly diverse. Some firms may be poised to grow rapidly in profitable segments
because of major technological breakthroughs or favourable market developments;
other firms may be experiencing declining fortunes.
The wide variations
in the prospects of individual firms overwhelm the effect on price of time as
speculative booms and busts. Hence the efficient markets model works fairly
well for individual firms.
In contrast, the
market has lesser clarity about changes in aggregate dividend or earnings
flows. It is harder for investing public to understand the changes in aggregate
dividends and earnings as they are influenced by factors like overall economic
growth, fiscal and monetary policies, profitability margins, and the like.
Given this difficulty in predicting aggregate dividends, we might expect that
factors like market psychology would dominate the effect of information about
aggregate future dividends in determining prices. Hence the efficient markets
model may be a bad approximation for the aggregate stock market.
(This
note draws on “Samuelson’s Dictum and
the Stock Market,” by Jeman Jung and Robert J. Shiller (Cowles Foundation Paper
No. 1183))
Risk
Management at L&T
L&T has to manage risks across currencies and commodities. To
execute billion dollar projects that range from power to hydrocarbons, L&T
has to deal in at least a dozen commodities (which serve as inputs) and a
number of currencies (in which it borrows or is paid for turnkey projects).
L&T’s greatest challenge is to budget for a project in terms of costs and
expected revenues 3-5 years after the bidding is done. As Y.M. Deosthalee,
L&T’s CFO, says: “The projects business has its own peculiarities. It is
lumpy which means that orders are spread unevenly and cash flows are not
uniform. It is also risky; all the risks – be it in people, foreign currency,
commodity price – are on you.” He further adds “What complicates matters
further is that in the projects business you cannot pad up for contingencies.
Your margins are already very low – about 11 to 12 percent – so if you provide
for adversity, you’ll never bag a project.”
Given the nature of
risks faced by L&T, its treasury team is involved right from bidding to
completion, to ensure that risks arising from fluctuations in exchange rates
and commodity prices are minimised. The treasury team makes explicit
assumptions about these rates and reviews these assumptions periodically. This
has helped L&T in maintaining its operating margins, despite large
fluctuations in exchange rates and commodity prices.
L&T has a treasury
team of about 50 persons to manage these risks. Its track record in managing
these risks is one of the best in corporate India. Here are some of its notable
successes.
·
Like many domestic and
global companies, L&T had raised huge yen borrowings (equivalent to $1
billion) because of rock-bottom interest rates in Japan which were unhedged. In
July 2007, the treasury people noticed an erratic pattern of yen’s appreciation
against the US dollar, in contrast to the earlier trend of depreciating yen.
L&T quickly shifted from an open position to a completely hedged position
by August 2007, between 117 and 122 yen per dollar.
·
L&T had forex loans of
$1 billion which it hedged in April 2008 at Rs. 40 and Rs. 40.50 per dollar.
·
In February 2009, the
price of copper, a commodity that L&T uses heavily across a range of
projects, was at a year’s low of $3400 per tonne. At this price, L&T locked
into long-term contracts quantities totaling 6,600 tonnes. (In early 2010, the
price of copper rose to $8000 a tonne).
·
In 2008-2009, L&T
generated an average return of almost 14.5 percent on its investment portfolio.
Some
of the Greatest Business Decisions
1.
Walt Disney decided to
call his cartoon mouse Mickey rather than Mortimer, on the advice of his wife
Lillian. Entertainment has never been the same after the debut of Mickey and
Minnie in Steamboat Willie.
2.
Richard Sears decided to
put all his products together in a catalogue and laid the basis for the huge
success of Sears Roebuck.
3.
Coca Cola decided to hold
a competition for the design of its new bottle. One of the best icons of the 20th
century was created without any charge, gathering unusual publicity along the
way.
4.
Pier Du Pont of General
Motors decided to adopt Alfred P. Sloan’s reorganisation plan for General
Motors.
5.
In 1981, Bill Gates
decided to license MS-DOS to IBM, while retaining the control of the license
for all non-IBM personal computers. This laid the foundation for Microsoft’s
stellar success and IBM’s fall from grace.
6.
Akito Morito decided to
develop the Walkman. Sony has pioneered many product innovations and Akita
Morito subscribed to the view: “The public does not what is possible, we do.”
7.
Michael Dell decided to
sell personal computers directly to consumers and build it to order.
Dividend Policy of Hero Honda Motors
Hero Honda Motors is a BSE Sensex and NSE Nifty company that has
steadily paid the highest dividends to shareholders over the same period. For
example, for the financial year 2009-2010, Hero Honda Motors paid a dividend of
5,500 percent on each share (inclusive of a special 4,000 percent “Silver
Jubilee” payout). On a share that had a face value of Rs. 2, the company paid a
dividend of Rs. 110 when the earnings per share was Rs. 111.77.
Several inter-related
factors explain the dividend policy of Hero Honda Motors:
1.
The company has been a
debt-free company for more than a decade.
2.
The company operates in a
sector which is not capital-intensive.
3.
The company is highly
profitable and generates a lot of cash.
4.
To maintain the ROCE at a
reasonable level, the company has to pay high dividends. As Ravi Sud, Senior VP
& CFO, Hero Honda Motors says: “The best returns I can generate on that
cash, therefore, are 9 or 10 percent. So why not give the money back to the
people it belongs to – shareholders.”
|
SECTION C:
WIT AND WISDOM |
Humour
·
Here is an old Wall Street
Joke
Customer : “Thanks for putting me in stock A at 10.
I find that it has climbed to 30.”
Broker
: “Yes, and that’s just the
beginning. As a matter of fact, the company’s prospects have improved so much that it is
an even better buy at 30 than it was when you made the purchase.”
Customer : “Damn, I knew I should have waited.”
·
After their death, a
priest and a stock broker qualified for heaven. Since the heaven was
overcrowded, the angel at the gate of heaven admitted the stock broker but
asked the priest to wait for six months. The priest protested and said that he
should be given priority because all his life he preached and propagated the
message of God. The angel clarified: “When you were preaching, the audience was
sleeping, but when the stock broker was preaching, the audience was praying.”
Wise Saws
·
Ronald E. Osborn “Unless
you try to do something beyond what you have already mastered, you will never
grow.”
·
A compromise is the art of
dividing a cake in such a way that everyone believes he has the biggest piece.
·
The best job goes to the
one who can get it done without passing the buck or coming back with alibis.
Perspectives
·
A balanced perspective
cannot be acquired by studying disciplines in pieces but through pursuit of the
consilience among them. Such unification will come hard. But I think it is inevitable.
Intellectually it rings true, and it gratifies impulses that rise from the
admirable side of human nature. To the extent that the gaps between the great
branches of learning can be narrowed, diversity and depth of knowledge will
increase.
Edward O. Wilson, Consilience
·
Individual decisions can
be badly thought through, and yet be successful, or exceedingly well thought
through, but be unsuccessful, because the recognized possibility of failure in
fact occurs. But over time, more thoughtful decision-making will lead to better
overall results, and more thoughtful decision-making can be encouraged by
evaluating decisions on how well they were made rather than on outcome.
Robert Rubbin, Harvard Commencement Address, 2001