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The Super Investors of Graham-and-Doddsville is neat little a 13-page article by Warren Buffet that appeared in 1984 in a Columbia Business School magazine. In it, Buffet talks about the value-investing method of his teachers Graham and Dodd of Columbia University.
Main takeaways from the article:
I’m convinced that there is much inefficiency in the market. These Graham-and-Doddsville investors have successfully exploited gaps between price and value. When the price of a stock can be influenced by a “herd” on Wall Street with prices set at the margin by the most emotional person, or the greediest person, or the most depressed person, it is hard to argue that the market always prices rationally. In fact, market prices are frequently nonsensical.”
An investment operation is one which, upon thorough analysis, promises safety of principal and a satisfactory return. Operations not meeting these requirements are speculative.
… You don’t try and buy businesses worth $83 million for $80 million. You leave yourself an enormous margin. When you build a bridge, you insist it can carry 30,000 pounds, but you only drive 10,000 pound trucks across it. And that same principle works in investing
You have to be like a man standing with a spear next to a stream. Most of the time he’s doing nothing. When a fat juicy salmon swims by, the man spears it. Then he goes back to doing nothing. It may be six month before the next salmon goes by.
I always find it extraordinary that so many studies are made of price and volume behavior, the stuff of chartists. Can you imagine buying an entire business simply because the price of the business had been marked up substantially last week and the week before? Of course, the reason a lot of studies are made of these price and volume variables is that now, in the age of computers, there are almost endless data available about them. It isn’t necessarily because such studies have any utility; it’s simply that the data are there and academicians have worked hard to learn the mathematical skills needed to manipulate them. Once these skills are acquired, it seems sinful not to use them, even if the usage has no utility or negative utility. As a friend said, to a man with a hammer, everything looks like a nail.
Sometimes risk and reward are correlated in a positive fashion. If someone were to say to me, “I have here a six-shooter and I have slipped one cartridge into it. Why don’t you just spin it and pull it once? If you survive, I will give you $1 million.” I would decline — perhaps stating that $1 million is not enough. Then he might offer me $5 million to pull the trigger twice — now that would be a positive correlation between risk and reward! The exact opposite is true with value investing. If you buy a dollar bill for 60 cents, it’s riskier than if you buy a dollar bill for 40 cents, but the expectation of reward is greater in the latter case. The greater the potential for reward in the value portfolio, the less risk there is.
… some of the more commercially minded among you may wonder why I am writing this article. Adding many converts to the value approach will perforce narrow the spreads between price and value. I can only tell you that the secret has been out for 50 years, ever since Ben Graham and Dave Dodd wrote Security Analysis, yet I have seen no trend toward value investing in the 35 years that I’ve practiced it. There seems to be some perverse human characteristic that likes to make easy things difficult. The academic world, if anything, has actually backed away from the teaching of value investing over the last 30 years. It’s likely to continue that way. Ships will sail around the world but the Flat Earth Society will flourish. There will continue to be wide discrepancies between price and value in the marketplace, and those who read their Graham & Dodd will continue to prosper.
Now, all I need to do is figure out how to come up with the true value of a company.
Creativity is just connecting things. When you ask creative people how they did something, they feel a little guilty because they didn’t really do it, they just saw something. It seemed obvious to them after a while. That’s because they were able to connect experiences they’ve had and synthesize new things. And the reason they were able to do that was that they’ve had more experiences or they have thought more about their experiences than other people.
Unfortunately, that’s too rare a commodity. A lot of people in our industry haven’t had very diverse experiences. So they don’t have enough dots to connect, and they end up with very linear solutions without a broad perspective on the problem. The broader one’s understanding of the human experience, the better design we will have.
— Steve Jobs
Buyers and sellers of a good or service set the price by expressing how much they are willing to buy or sell for. When a very large number of buyers and sellers interact in this way, the final price of the good or service settles to one that incorporates all this information. However, in the healthcare market, this mechanism is absent.
When you are sick and you visit a doctor, you do not get to ask up front what you will be charged for seeing the doctor! Instead, you present your health insurance card to the receptionist, thereby disclosing information about your ability to pay, and creating information assymetry. The other party (i.e. the healthcare provider) can now select a price , taking into account the information you have just disclosed, and that too, after you have made use of their services, rather than before. This is akin to getting to know the price of a haircut in a barber shop, after you have had the haircut. There is no price discovery mechanism in the healthcare industry in US. I recommend:
Ostensibly, providers check a patient’s insurance card in order to make sure that the patient can pay for the services she is about to purchase. Instead, this function should be done by a ‘lookup’ over the internet on a server run by a government agency: Input = ‘name of the medical procedure’, Output = ‘Covered or not’. This will prevent the provider from learning about the extent of coverages available to the patient. Not learning about this will force providers to more honestly assess fees, and compete with each other more realistically, bringing down costs borne by the patients. I think this will also prevent providers from passing on large unnecessary fees to insurance companies.
A formal mechanism design formulation of the problem would be interesting to tackle. 4 players: patients, providers, insurance companies, government.
The percentage of a country’s population that feels religion plays an important role in their lives (x-axis) seems negatively correlated with the GDP per capita (Y-axis, in log-scale):
One cannot help but be reminded of the famous verse in the bible that alludes to camels and needles. But, of course correlation does not imply causation!
Here are the heatmaps, first for religion:
and per capita GDP (PPP):
From the heat maps above, several countries in Africa and Asia stand out as having low per capita GDP and high importance of religion, while all of Scandinavia and Australia stand out as places with high per capita GDP and low importance of religion. The US is somewhat of an outlier with with highest per capita GDP in the world of ~$35k and about 65% of the population reporting that religion plays an important role in their lives.
For completeness sake, here is the heat map for life satisfaction, or long-term happiness:
Combining data on religion and GDP with data with data on long term happiness, we get roughly the following picture:
My guess is that the causal structure between the 3 variables: Religion (R), Happiness (H) and per capita GDP is the following:
When per capita GDP is high, the basic needs of life are met, people are relatively comfortable, and therefore long term happiness is high, but for the same reason, very few people feel compelled to ask fundamental questions of the type that people turn to religion for. This has the effect of creating a negative correlation between Religion and Happiness. In the causal structure above, religion and happiness are related, but given per capita GDP, they are independent.
Happiness is as hard to define as it is to achieve. Everybody wants to be happy. Even masochists. I think it is best to use a non-constructive definition:
Happiness is the goal that drives all human actions and desires.
If long term happiness if everybody’s ultimate goal, then it is worth learning how to achieve long term happiness. In fact, if being happy is the ultimate goal (as opposed to say, being wealthy), then our education system should also be teaching us how to be happy over a life time, rather than purely technical or vocational skills. Simple GDP growth does not imply an increase in the happiness of a society — as indicated by data from the last ~40 years in the US, comparing per capita GDP and happiness levels:
While per capita GDP has risen more or less steadily, happiness levels have remained more or less stagnant in the last ~40 years.
Should countries develop public policy with the goal of making a society happier, rather than with the goal of increasing GDP? I think it is an idea worth exploring (Scandinavian countries seem to rank highest in in the world in happiness scores, despite high taxes). The government of Bhutan came up with the Gross National Happiness index, which measures the average life satisfaction of the citizens in a country.
This correlates well with health, access to education, and wealth (GDP per capita). At any given time, the relationship between average happiness of a country and per capita GDP seems to log-linear, meaning that happiness is roughly linear in the log of the per capita GDP.
This is because in order to increase the happiness level of a society by 1 unit, the increase in wealth required is proportional to the current welath. For e.g., if the required amount of increase in personal wealth for a group with per capita income of $1000 is $x, then it is $10x for a group with per capita income of $10,000.
Near the end of this talk, Daniel Kahneman says that in a study done with the Gallup organization, he found that:
Below an income of … $60,000 a year, people are unhappy, and they get progressively unhappier the poorer they get. Above that, we get an absolutely flat line. … Money does not buy you experiential happiness, but lack of money certainly buys you misery.
Kahneman distinguishes between two types of happiness: that of the experiencing self and that of the reflecting self. It is possible to be happy in the experiencing self but have a poor happiness score when reflecting on a long time frame in the past, and vice-versa. For the type of happiness that measure life satisfaction in retrospect, there is no flat time — i.e. it continues to increase with increasing wealth. I don’t find this too surprising. It is the difference between short term and long term happiness. It is easy to be happy in the short term at the expense of the long term. On the other hand, tolerating displeasure during hard work in the present can have a huge payoff in long term happiness in the future.
In this TED talk, Dan Gilbert showcases his research that shows that happiness can be synthesized by individuals. So happiness is not some finite resource that needs to be distributed among people, instead one can simply choose to be happy, despite seemingly adverse conditions. This is fascinating, because it provides experimental evidence that happiness has to do not just with our external circumstances (such as GDP per capita), but also with how we process information in our minds. Several religions have the concept of expressing gratitude. The act of being grateful basically synthesizes happiness out of thin air.
Intelligence is a slippery thing to define. The following definition recently struck me:
That which allows one to maximize happiness over long term.
I like this definition because it is short (c.f. MDL, Occam’s Razor), it makes logical sense, and it carries a lot of meaning without going into details of how to be intelligent. It is logical to me because of the following argument: Suppose a person is allowed to life two versions of his life starting from some fixed point in his life. All events and circumstances in the two versions are the same except for actions taken by the person. Then he can be said to be more intelligent in that version of his life in which he achieves greater happiness over the rest of his life.
Intelligence is needed in order to understand what actions will make us happy, for how long, and whether there will be any effects of those actions on our future happiness. Making decisions to maximize cumulative happiness is certainly a non-trivial task. Sometimes one must put oneself through short-term adversity (e.g. graduate school at little on no stipend, or an athlete undergoing gruelling training for a race) to be able to do well later. Sometimes, one decides to undertake an action that provides short term happiness, but at the cost of long term happiness. It takes intelligence to learn to avoid such behaviour n the future.
Modern definitions of intelligence from the scientific and psychology community are incredibly long-winded [Wikipedia]
A very general mental capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience. It is not merely book learning, a narrow academic skill, or test-taking smarts. Rather, it reflects a broader and deeper capability for comprehending our surroundings—”catching on,” “making sense” of things, or “figuring out” what to do.
Individuals differ from one another in their ability to understand complex ideas, to adapt effectively to the environment, to learn from experience, to engage in various forms of reasoning, to overcome obstacles by taking thought. Although these individual differences can be substantial, they are never entirely consistent: a given person’s intellectual performance will vary on different occasions, in different domains, as judged by different criteria. Concepts of “intelligence” are attempts to clarify and organize this complex set of phenomena. Although considerable clarity has been achieved in some areas, no such conceptualization has yet answered all the important questions, and none commands universal assent. Indeed, when two dozen prominent theorists were recently asked to define intelligence, they gave two dozen, somewhat different, definitions.
The same Wikipedia page also lists various different definitions given by researchers. The long-windedness of these definitions is somewhat excusable as an attempt to be all-inclusive and general. But in the end, the notion of intelligence is a man-made model, invented to try and explain phenomena. I think a focus on happiness as the central phenomenon to be explained goes a long way in simplifying our understanding of intelligence.
The average age of first time mothers in the developed countries of the world has been rising for the last ~40 years.
Here is another plot that shows the rate of occurrence of Down Syndrome, a chromosomal defect, as a function of the age of the mother at the time of child birth.
The curve really starts to shoot up at 30. In the UK, the average age of a first time mother is 30 years. It is well known that the fertility rate in women decreases after the age of 30 and drops rapidly after 35. Older mothers are likely to find it harder to have a baby and if they do, then they run a higher risk of chromosomal defects. Given the possibilities of all these negative consequences, the increase in the average age is a bit disturbing. It seems like there is a hidden cost to more women working and for longer.
Why is it that women are waiting longer before having their first born despite the risks? Most of my hypotheses (of which more than one, or none, may be true) have to do with women working:
One source of further information is the following map, showing the absolute increase, in years, in the age of a first time mother, over the last 40 years, state by state in the US:
This number is highest in the North Eastern states of NY, NJ, MA, CT, etc. The intensity of the colors in the map above correlates well with population density, and with economic activity in general (meaning more working women). Here are two more plots I came across in a US based study done by P&G, that suggest that at least in the US, employer policies may be responsible.
What would a mother value most from their employers?:
How much guilt to mothers feel about work-life balance?:
Also, less surprisingly, GDP density, and the night lights as seen by a low Earth orbit NASA satellite are very well correlated: