I was playing around with the historical data of Sensex and its valuations (available from BSE website).I took the monthly data from Jan 1991 to September 2012 (261 data points). A basic analysis of the data points:
PE Multiple percentile range (For e.g. In 70% of the instances, the PE ratio was above 16.4):
|PE Multiple Percentile Range|
PB Multiple percentile range (For e.g. In 80% of the instances, the PB ratio was above 2.6):
|PB Multiple Percentile Range|
At present (23/11/2012) the Sensex PE multiple is 16.84 and PB Ratio is 2.86 – The PE valuation has been greater than 16.84 in 65% of the instances (171/261 months) and PB ratio has been greater than 2.86 in 71% of the instances (185/261). The odds are slightly in our favour and the market appears to be slightly undervalued compared to historical valuations.
Another point to note is that usually only PE ratio is focused upon in the general media and PB ratio is ignored. But the PB ratio is an important indicator too. The ROE for the Sensex can be computed by (PB Ratio/PE Ratio) for a given month. There have been occasions when the PE multiple is depressed and even the ROE is low (which can be the case when the interest rates are high since larger portion of the earnings is shared with debt holders as interest). When the tide turns, the ROE also improves and the PE multiple also expands. The impact of both these can be seen in the PB ratio. I would hazard a guess that since the ROE at present is lower than historical average, valuation based on PB ratio shows that we are further down in the cheaper territory compared to the valuation based on PE ratio.
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A good piece which highlights how stupid can Media sometimes be!
As an aside, I do not have an intelligent opinion on whether Apple must be paying a dividend or not or what is the rational level of cash it needs to have on its books. But a lot of comments which focus on “Apple must be taking debt since it has such high FCF’s” reeks of academic financial theory. Apple is in a business in which winners and losers can change in a very short span of time. Its FCF can drop to zero in a jiffy if it does not continue innovating. If scenarios do change, having debt on its balance sheet will not give it time to think. Its like being on a ventilator.
The logic of using debt to prep equity returns can be applied to firms like P&G or Coke, defensive businesses which need not bother whether something is going to ruin their revenue stream in next 3 months. But not for Apple. Not too long ago, Nokia was considered as “the” firm in consumer understanding. Blackberry/RIM was considered to have “the moat” amongst business users. No longer. Technology changes are ruthless.
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You come across many rich people (Really rich!) who in a way apply value investing precepts to their businesses. Usually in private transactions it is difficult to find inefficiencies caused by an irrational seller since one would be buying from the owner himself and usually owners would have a very good understanding of their business to sell it cheap. They would also not be treating their shares as just pieces of paper. Inefficiencies in private transactions can probably arise either when buying or selling:
– When the owner is a forced seller because the owner desperately needs cash
– When the owner is a forced seller and desperately wants to exit the asset for whatever reasons (lawsuit, potential liabilities, unable to run it, labour problems etc.)
– A boom is going on in the specific sector that someone pays crazy valuations to your business (E.g. Telecom deals by Telenor, Etilsalat etc, Ranbaxy Sale, Piramal sale of Abbott)
In the first category, returns are made while buying while in the second category, returns are made while selling. Structurally it is better to develop capabilities on the former and make use of situations which throw up to enable the latter.
Some of the people who come to my mind who have made money on the former from the industry (i.e. they imbibe value investing principles while operating their business) are L.N. Mittal, Vedanta’s Anil Agarwal, Piramal’s Ajay Piramal (Can readers think of few more examples?).
Li Ka-Shing falls under that category. A wonderful read about him.
With profits from plastics, Li began buying up apartment buildings and factories throughout the city during the 1960s, a period of intense social unrest marked with Maoist-tinged riots and bombings, and reaped huge returns when the market recovered. In 1979 he became the first Chinese to buy a controlling stake in one of the old British trading houses, then struggling Hutchison Whampoa. In 1987, the year he appeared on our first-ever global billionaires ranking, Li and affiliates paid $500 million for about half of Canada’s moneylosing Husky Oil; it has been through restructurings and mergers, but he still personally has a stake worth over $8 billion.
He bought when, as Sir Templeton would say, “there was blood was on the streets”
I liked these:
*I do not get overly optimistic when the market is good, nor overly pessimistic when the market is down.
*A good reputation for yourself and your company is an invaluable asset not reflected in the balance sheets.
*Though a universal formula for success is difficult to come by, caution signs for failure are posted everywhere. Establishing a structure that serves to minimize failure will prove to be a shortcut to success.
It is very similar to some of Warren Buffet’s principles.
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This is a wonderful read for multiple reasons. Some of them:
– I supposedly read a lot of Crisis related material but did not come across David Li. Either I did not “get it” or his formula was not covered in depth. We have seen a lot of Black Scholes bashing, but not enough of David Li bashing.
– Amazing quotes which explain in simple words how some of these need to be thought through
“Investors like risk, as long as they can price it. What they hate is uncertainty—not knowing how big the risk is. As a result, bond investors and mortgage lenders desperately want to be able to measure, model, and price correlation. Before quantitative models came along, the only time investors were comfortable putting their money in mortgage pools was when there was no risk whatsoever—in other words, when the bonds were guaranteed implicitly by the federal government through Fannie Mae or Freddie Mac.”
Its now not surprising to understand why everyone loves “beta”. It is a single number which measures the risk of a stock. In finance circles, if you understand “beta” and master how it is computed (and the various nuances involved in computing it), you are considered to be “real good in finance”. The interviews with most I-banks (have either sat through or have heard first hand interview experience) revolve around various questions around computing beta (Simple ones: E.g. How long should the price data be taken to compute beta effectively, how do we know the fight frequency – daily or weekly prices, In what kind of instances would the time period taken not be correct etc.)
“To understand the mathematics of correlation better, consider something simple, like a kid in an elementary school: Let’s call her Alice. The probability that her parents will get divorced this year is about 5 percent, the risk of her getting head lice is about 5 percent, the chance of her seeing a teacher slip on a banana peel is about 5 percent, and the likelihood of her winning the class spelling bee is about 5 percent. If investors were trading securities based on the chances of those things happening only to Alice, they would all trade at more or less the same price.
But something important happens when we start looking at two kids rather than one—not just Alice but also the girl she sits next to, Britney. If Britney’s parents get divorced, what are the chances that Alice’s parents will get divorced, too? Still about 5 percent: The correlation there is close to zero. But if Britney gets head lice, the chance that Alice will get head lice is much higher, about 50 percent—which means the correlation is probably up in the 0.5 range. If Britney sees a teacher slip on a banana peel, what is the chance that Alice will see it, too? Very high indeed, since they sit next to each other: It could be as much as 95 percent, which means the correlation is close to 1. And if Britney wins the class spelling bee, the chance of Alice winning it is zero, which means the correlation is negative: -1.
If investors were trading securities based on the chances of these things happening to both Alice and Britney, the prices would be all over the place, because the correlations vary so much.
But it’s a very inexact science. Just measuring those initial 5 percent probabilities involves collecting lots of disparate data points and subjecting them to all manner of statistical and error analysis. Trying to assess the conditional probabilities—the chance that Alice will get head lice if Britney gets head lice—is an order of magnitude harder, since those data points are much rarer. As a result of the scarcity of historical data, the errors there are likely to be much greater.
In the world of mortgages, it’s harder still. What is the chance that any given home will decline in value? You can look at the past history of housing prices to give you an idea, but surely the nation’s macroeconomic situation also plays an important role. And what is the chance that if a home in one state falls in value, a similar home in another state will fall in value as well?”
– And this:
“Li wrote a model that used price rather than real-world default data as a shortcut (making an implicit assumption that financial markets in general, and CDS markets in particular, can price default risk correctly).
It was a brilliant simplification of an intractable problem. And Li didn’t just radically dumb down the difficulty of working out correlations; he decided not to even bother trying to map and calculate all the nearly infinite relationships between the various loans that made up a pool. What happens when the number of pool members increases or when you mix negative correlations with positive ones? Never mind all that, he said. The only thing that matters is the final correlation number—one clean, simple, all-sufficient figure that sums up everything.”
– The key:
“”The relationship between two assets can never be captured by a single scalar quantity,” Wilmott says. For instance, consider the share prices of two sneaker manufacturers: When the market for sneakers is growing, both companies do well and the correlation between them is high. But when one company gets a lot of celebrity endorsements and starts stealing market share from the other, the stock prices diverge and the correlation between them turns negative. And when the nation morphs into a land of flip-flop-wearing couch potatoes, both companies decline and the correlation becomes positive again. It’s impossible to sum up such a history in one correlation number, but CDOs were invariably sold on the premise that correlation was more of a constant than a variable.”
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I really have come to believe teaching MBAs that one of the most important things you learn as an MBA is how to pretend you know the answer to any question even though you have absolutely no idea what you’re talking about. And I’ve found it’s really one of the most destructive factors in business — is that everyone masquerades like they know the answer and no one will ever admit they don’t know the answer, and it makes it almost impossible to learn.
Though the author specifically relates this to MBAs, it is applicable to all and sundry. I think the higher you are in status and social standing, the more difficult it is to say I Don’t Know. Especially if highly paid!
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I pretty much got convinced when I read this that Facebook is in no way having a better business proposition than google when it comes to ads.
These made perfect sense:
Search is taking over the world. Click for more >The reason the search business has swallowed such a huge percentage of global ad spending in only a decade is that it is advertising space that can capture the consumer’s attention at the exact moment that the consumer is looking for something to buy.
When you search for a product, you are telling the world you are interested in that product. And there is no better and more efficient time for those who make and sell that product to try to get your attention than at the moment you announce that you are interested in it.
That is why search spending has gone through the roof.
And what about social networking?
In contrast to search, social networking advertising is like hanging signs on the walls of a house during a party and sending sales reps to mingle with the crowd.
Yes, you can target which parties you pay to hang your signs on the walls of.
Yes, you can make those signs appealing to those at the party.
But the fact remains that the people at the party, who are sharing stories and photos and news and gossip, are not at the party because they want to buy something.
They’re at the party because they want to socialize.
And any time you do more than passively hang in the background at the party, they will likely be annoyed by your intrusion. And, annoyed or not, when they do notice your ads, their reaction will most likely be, “Cool–if I ever decide to buy a car/boat/stereo/meal/flowers/bull-whip, maybe I’ll look at that kind.” Then they’ll go right back to their party.
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