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One of the great challenges we all face in life is distinguishing between two classes of people: people who know and people who sound like they know. It's called the Batesian Mimicry problem and once you see it, you'll start to notice it everywhere from colleagues and boardrooms, to talking pundits on TV.
From Elon Musk's advice on how to tell if people are lying to how to win an argument, the problem is so pervasive and so fundamental to succeeding in life that I keep a running file whenever people have a clever way to help quickly determine who knows. An unlikely book offered another technique called Racking the Shotgun and it comes from a professional gambler.
80/20 Sales and Marketing by Perry Marshall tells the story of John Paul Mendocha, a friend of Marshall's. At age 17, Mendocha dropped out of high school, hitchhiked to Vegas, and became a professional gambler.
A teenager, however, needs some street smarts so he found himself someone who would take him under his wing for a split of the profits. Mendocha found Rob, a seasoned gambler.
“Son, the first lesson about gambling is, you have to play games you can win. You need to play people who are not as good at poker as you are. Those people are called marks.”
One time, Rob wanted to show John something so they got into the car and headed to the Cabaret. They walked in and sat down amongst the blaring music, dancing women, and copious amounts of alcohol. Rob had a sawed off shotgun in his coat.
He pulled the shotgun out, slipped it under the table. He pressed the lever, popping the chamber open as if to load it. But instead of inserting a shell, he loudly snapped it back shut, with that sharp, signature ratcheting sound shotguns are famous for— what enthusiasts call “racking the shotgun.”
A few heads in the crowd twisted around, trying to see where the racking sound had come from. Everyone else was oblivious, absorbed in their haze of nightclub revelry. Then Rob slipped the gun back into his jacket.
The owner of the club came over to their table and asked if everything was ok.
“Everything’s fine, Bill. Just teaching the lad a lesson,” Rob replied. Then he leaned over and said to John, “John, the people who turned around— those guys are NOT marks. Do not play poker with them. “John, your job is to play cards with everybody else.”
On Monday October 28, 1929, the stock market took one of the worst single-day tumbles anyone alive might have seen, with the Dow Jones averages falling about 13%. The next day, October 29th, the market dropped yet again, a decline of 12%. By the end of the year, the Dow Jones average was down more than 45% from its high of 381. Market historians are familiar with the rest of the story: The sickening slide would not stop at 45%, but continue until 1932 to reach a low of 41 on the Dow, a decline of about 90% from peak to trough. American business was in a major Depression. But at least one businessman would decide that, like General Erwin Rommel would say years later, the path was not out, but through.
International Business Machines, better known as IBM, was created from the ashes of the Computing-Tabulating-Recording Company (C-T-R) in 1917 by Thomas J. Watson, who’d learned his business skills at the National Cash Register Company (NCR). Before Watson’s reorganization of C-T-R, the company was basically in three businesses: computing scales (to weigh and compute the cost of a product being weighed), time clocks (to calculate and record wages), and tabulating machines (which used punch cards to add up figures and sort them). Watson’s first act of genius was to recognize that the future of IBM was not going to be time cards or scales, but in helping businesses do their work more effectively and with a lot less labor. He set out to do just that with his tabulating machines.
The problem was, IBM’s products weren’t yet all that different from its competitors’, and the company was small. IBM’s main tabulating product was the Hollerith machine, created by Herman Hollerith in Washington D.C. in 1890 to improve the Census tabulating process, of all things. (It sounds mundane, but he saved the government $5 million and did the work in about 1/8th of the time.) By the late 1910s, the Hollerith machine had a major competitor in the Powers Accounting Company, which had a similar product that was easier to use and more advanced than the Hollerith.
Watson knew he had to push the research and development of his best product, and he did, hiring bright engineers like Fred Carroll from NCR, who would go on to be known for his Carroll Press, which allowed IBM to mass-produce the punch cards which did the “tabulating” in the pre-electronic days. By the mid 1920s, IBM had the lead. The plan was set in late 1927.
Watson then pointed to where he wanted IBM to go. ”There isn’t any limit for the tabulating business for many years to come,” he said. “We have just scratched the surface in this division. I expect the bulk of increased business to come from the tabulating end, because the potentialities are greater, and we have done so little in the way of developing our machines in this field.”
Underneath that statement lay a number of reasons—other than the thrill of new technology—why Watson zeroed in on the punch card business. When seen together, the reasons clicked like a formula for total domination. IBM would never be able to make sure it was the world leader in scales or time clocks, but it could be certain that it was the absolute lord of data processing.
Watson had no epiphanies. No voice spoke to him about the future of data processing. He didn’t have a grand vision for turning IBM into a punch card company. He got there little by little, one observation after another, over a period of 10 to 12 years.
(Source: The Maverick and his Machine)
Watson's logical, one-foot-at-a-time approach was reminiscent of Sir William Osler's dictum: Our main business is not to see what lies dimly at a distance, but to do what lies clearly at hand. And with a strategy of patenting its proprietary punch-cards, making them exclusively usable with IBM tabulators and sorters, IBM was one of the market darlings in the lead-up to 1929. Between 1927 and 1929 alone, IBM rose about four-fold on the back of 20-30% annual growth in its profits.
But it was still a small company with a lot of competition, and the punch card system was notoriously unreliable at times. He had a great system to hook in his customers, but the data processing market was still young — many businesses wouldn't adopt it. And then came the fall.
As the stock market dropped by the day and the Depression got on, the economy itself began to shrink in 1930. GDP went down 8% that year, and then another 7% the following year. Thousands of banks failed and unemployment would eventually test 30%, a figure that itself was misleading; the modern concept of “underemployment” hadn’t been codified, but if it had, it probably would have dwarfed 30%. An architect working as a lowly draftsman had a job, but he’d still fallen on hard times. Everyone had.
Tom Watson’s people wondered what was to become of IBM. If businesses didn’t have money, how could they purchase tabulators and punch cards? Even if it would save them money in the long run, too many businesses had cut their capital spending to the bone. The market for office spending was down 50% in 1930.
Watson's response was to push. Hard. So hard that he’d take IBM right up to the brink.
IBM could beat the Depression, Watson believed. He reasoned that only 5 percent of business accounting functions were mechanized, leaving a huge market untapped. Surely there was room to keep selling machines, even in difficult times. Watson also reasoned that the need for IBM machines was so great, if businesses put off buying them now, certainly they’d buy them later, when the economy picked up. His logic told him that the pent-up demand would explode when companies decided to buy again. He wanted IBM to be ready to take advantage of that demand.
He’d keep the factories building machines and parts, stockpiling the products in warehouses. In fact, between 1929 and 1932, he increased IBM’s production capacity by one-third.
Watson’s greatest risk was running out of time. If IBM’s revenue dropped off or flattened because of the Depression, the company would still have enough money to keep operating for two years, maybe three. If IBM’s revenue continued to falter past 1933, the burden of running the factories and inventory would threaten IBM’s financial stability.
Watson’s logic led him to make what looked to outsiders like another insane wager. On January 12, 1932, Watson announced that IBM would spend $1 million—nearly 6 percent of its total annual revenue— to build one of the first corporate research labs. The colonial-style brick structure in Endicott would house all of IBM’s inventors and engineers. Watson played up the symbolism for all it was worth. He would create instead of destroy, despite the economic plague.
(Source: The Maverick and his Machine)
Most companies pulled back, and for good reason. Demand was rapidly shrinking, and IBM's decision to spend money expanding productive capacity, research, and employment would be suicide if demand didn’t return soon. All of that unused capacity was costly and would go to waste. Watson took an enormous risk, but he also had faith that the American economy would recover its dynamism. If it did, IBM would come out on the other side untouchable.
Somehow, Watson had to stimulate demand. He had to come up with products that companies couldn’t resist, whatever the economic conditions. Again, thanks to Charles Kettering’s influence, Watson believed that R&D would drive sales. (ed: Kettering was chief engineer at General Motors.) So Watson decided to build a lab, pull engineers together, and get them charged up to push the technology forward.
Throughout the 1930s, IBM cranked out new products and innovation, finally getting its technology ahead of Remington Rand or any other potential competitors.
Within a few years, Watson’s gamble of manufacturing looked disastrous. As IBM pumped increasing amounts of money into operations and growth, revenue from 1929 to 1934 stalled, wavering between $17 million and $19 million a year. IBM edged toward insolvency. In 1932, IBM’s stock price fell to 1921 levels and stayed there—11 years of gains wiped out.
(Source: The Maverick and his Machine)
By 1935, IBM was still stagnating. Watson made the smart move to get out of the money-losing scale business and use the money to keep the remaining businesses afloat, but he was drowning in excess capacity, inventions be damned.
Then IBM got a stroke of luck that it would ride for almost 50 years.
After all of his pushing and all of his investment, after the impossible decision to push IBM to the brink, Tom Watson was rewarded with The Social Security Act of 1935, part of FDR’s New Deal. It was perfect.
No single flourish of a pen had ever created such a gigantic information processing problem. The act established Social Security in America—a national insurance system that required workers to pay into a fund while employed so they could draw payments out of it once they retired, or if a wage-earning spouse died. To make the system work, every business had to track every employee’s hours, wages, and the amount that must be paid to Social Security. The business then had to put those figures in a form that could be reported to the federal government. Then the government had to process all those millions of reports, track the money, and send checks to those who should get them.
Overnight, demand for accounting machines soared. Every business that had them needed them more. An officer for the store chain Woolworth told IBM that keeping records for Social Security was going to cost the company $250,000 a year. Businesses that didn’t have the machines wanted them. The government needed them by the boatload.
Only one company could meet the demand: IBM. It had warehouses full of machines and parts and accessories, and it could immediately make more because its factories were running, finely tuned, and fully staffed. Moreover, IBM had been funding research and introducing new products, so it had better, faster, more reliable machines than Remington Rand or any other company. IBM won the contract to do all of the New Deal’s accounting—the biggest project to date to automate the government…
This period of time became IBM’s slingshot. Revenue jumped from $19 million in 1934 to $21 million in 1935. From there it kept going up: $25 million in 1936, $31 million in 1937. It would climb unabated for the next 45 years. From that moment until the 1980s, IBM would utterly dominate the data processing industry—a record of leadership that was unmatched by any industrial company in history.
(Source: The Maverick and his Machine)
By combining aggressive opportunism and a great deal of luck, IBM was forged in the depths of the Great Depression. Like John D. Rockefeller before him, who bought up refineries during periods of depression in the oil industry, and Warren Buffett after him, who scooped up loads of cheap stocks when the stock market was crumbling in the 1970s, Watson decided that pushing ahead was the only way out.
History certainly didn’t have to go his way — FDR might not have been elected or might not have been able to enact Social Security. Even if he’d done it two years later, IBM still might never have made it.
But Watson’s courage and leadership did open the possibly of serendipitous fortune for IBM if the world didn’t end. Like oxygen combining with fuel to create internal combustion, those elements forged a monstrous competitive advantage when the match was finally lit.
Still Interested? Check out the excellent The Maverick and his Machine by Kevin Maney, where the excerpts above come from.
“Experience is what you got when you didn’t get what you wanted.”
— Howard Marks
Successful decision making requires thoughtful attention to many separate aspects.
Decision making is as much art as science. The goal, if we have one, is not to make perfect decisions but rather to make better decisions than average. To do this we require either good luck or better insight. And since luck isn’t really much of a plan, we should probably focus on better insight.
In most of life you can get a step ahead of others by going to the gym or the library, or even a better school. In thinking, however, a lot of what you'd think gets you ahead is only window dressing.
Would be thinkers and deciders can attend the best schools, take the best courses and, if they are lucky, attach themselves to the best mentors. Yet only a few of them will achieve the skills and superior insight necessary to be an above average thinker. And we live in a world that, if it rewards anything, rewards better decisions. The rest is increasingly automated.
But how do we get there in a world where everyone else is also smart and well-informed? How do we get there in a world that is increasingly becoming computerized? You must find an edge. You must think differently.
In his exceptional book, The Most Important Thing, Howard Marks hits on the concept of second-level thinking.
First-level thinking is simplistic and superficial, and just about everyone can do it (a bad sign for anything involving an attempt at superiority). All the first-level thinker needs is an opinion about the future, as in “The outlook for the company is favorable, meaning the stock will go up.” Second-level thinking is deep, complex and convoluted.
Second-level thinkers take into account a lot of what we put into our decision journals. Things like, What is the range of possible outcomes? What’s the probability I’m right? What’s the follow-on? How could I be wrong?
The real difference for me is that first-level thinkers are the people that look for things that are simple, easy, and defendable. Second-level thinkers push harder and don't accept the first conclusion.
“It’s not supposed to be easy. Anyone who finds it easy is stupid.”
— Charlie Munger
First-level thinkers think the same way other first-level thinkers do about the same things, and they generally reach the same conclusions. By definition, this can’t be the route to superior results.
This is where things get interesting. Extraordinary performance comes from being different. It must be that way. Of course, below average performance comes from being different too — on the downside.
So it's not enough to be different — you also need to be correct. “The problem is that extraordinary performance comes only from correct nonconsensual forecasts, but nonconsensual forecasts are hard to make, hard to make correctly and hard to act on,” writes Marks. The goal is not blind divergence but rather rather a way of thinking that sets you apart from others.
In short, you can’t do the same things that other people are doing and expect to outperform.
We can look at this as a simple two-by-two matrix (via The Most Important Thing).
I’m generalizing a bit here, but if your thoughts and behavior are conventional, you’re likely to get conventional results. Steve Jobs was right.
This is where loss aversion comes in. Most people are simply unwilling to be wrong because that means they might look like a fool. Yet this is a grave mistake. The ability to risk looking like an idiot is necessary for being different. You never look like a fool if you look like everyone else. (Bringing to mind Keynes' dictum: Worldly wisdom teaches that it is better for reputation to fail conventionally than to succeed unconventionally.) Only by doing — or, in our case, thinking — something different do you put yourself at risk.
Conventional thinking and behavior is safe. But it guarantees mediocrity. You need to know when your performance is likely to be improved by being unconventional.
Here’s a pro tip. If you want to have fun at work this week, do one of two things. First, start digging below the surface of people’s opinions. Ask them why they think what they think. Second, ask them to take the other side of the argument.
Investing money can seem a little rudderless at times.
One day you hear that stocks are risky and the next that they’re indispensable. Some days it seems like stocks only go up and sometimes that they only go down. Real estate used to seem like an automatic path to wealth, and then the housing crisis hit. For the uninitiated, it sometimes seems there are no central truths. And unlike certain fields, we all have to deal with money. We can’t “opt out” from financial concerns unless we plan to live in a monastery, and it is useful for all of us to understand the basic ideas.
Investing is not a science but a craft, and a craftsman needs tools. In the case of investing, the tools are mostly mental. If we accumulate a few simple mental tools, we can start evaluating the claims of experts, salesmen, or simply well-intentioned friends.
(1) The value of an asset depends entirely on the net cash it will
generate from now to the hereafter.
This goes for a stock, an apartment house, a convenience store, a bakery, or an iPhone app startup. An asset only has value (in a financial sense) if it can generate net cash flow to its owner. The amount and timing of that cash determines the value of the business. The more cash to be expected and the sooner it’s expected to come, the more valuable the asset is today. This is the fundamental truth about investing. Nothing escapes the orbit of future cash flows.
The other determinant of value is interest rates: The average future interest that could have earned if you bought a “risk-free” asset is the opportunity cost of the asset you’re considering purchasing today. That risk-free interest rate determines the value of an asset’s future cash flows to you today. (Although we don’t recommend trying to compute the figure out to three decimal places.) If average risk-free interest rates are 6% over time and I offer you a chance to buy an apartment house that pays you 4% on cost, is that a good buy? You better feel confident that the 4% will grow over time, right? This basic form of reasoning can be applied to all types of cash-producing assets.
This is the thing you should be thinking about when you’re instead thinking about what the Fed will be doing or what Jim Cramer said on TV or what the hot industry is or what the CEO of some company had for breakfast. Do you have any idea what the business or property will earn over the next five, ten, or twenty years in relation to what it earns now? This Grand Unifying Theory of investing gets discussed surprisingly little.
It also generates some sub-conclusions that aren’t always recognized by lay investors and are frequently forgotten by professionals.
(A) What an asset has earned in the past does not determine its value. In stocks, looking at the last ten years of earnings is a useful exercise in trying to understand what type of business you’re dealing with, but while it’s a good guide, past earnings do not generate value. Future earnings do, and that goes for all types of assets. This means you must develop a view about the future, which we’ll address again in point (2) below.
(B) An asset that never earns any net profit after all expenses has no financial value. Please let that sink in. It is common for businesses to obscure this basic fact, and promote all sorts of alternative methodologies with which you’re supposed to see value. Book value, EBITDA, number of page views, number of users, brand recognition, and years of managerial experience do not, in and of themselves, tell you about the value of a business or an asset. The bare fact is that an asset must eventually generate net cash flows to its owner which are commensurate with the price paid in order for the investment to be worthwhile. Investing on another basis is, by definition, speculation.
(C) If you have no idea what an asset will earn in the future (at least in a general sense), then you have no idea what it’s worth. And if you do not know what the asset is worth, then you have no idea whether you are over-paying or under-paying for it, and as an intellectually honest person, you should consider both possibilities at least equally likely.
(D) Any future cash flows out of an asset must also be subtracted in determining today’s value. If a business is going to lose money for 10 years and only then start making it, it’s worth a hell of a lot less than one which will make the same amount of money starting this year. This is another idea which gets surprisingly little play in relation to its obvious importance.
(2) Buying a share of stock is a buying share of the underlying business.
Although we’re discussing general investment concepts, the stock market needs a bit more attention because of its seemingly abstract nature. No one gets confused as to what they’re buying when they invest in a local dry-cleaner. Most fixed income instruments are priced in a fairly straightforward manner. But when it comes to businesses traded on the public exchanges, which we call stocks, all sorts of weird theories abound.
Stocks, for all of their labels and all of the strange fears and speculations around them, are no more than a piece of an underlying business pie. When you buy stock in a business, you are buying the right to the net cash flows that its assets produce. Stocks do not escape the orbit of financial gravity no matter what the Fed is doing or what CNBC is saying. And buying into an index fund that owns many stocks means that you’re now part-owner in all of the underlying businesses; your return comes from the success of their business operations. If American business as a whole keeps on trucking, the index fund will reflect their success, assuming you paid a rational price. That’s why averaging into the indexes is such a common recommendation for non-professional investors. Buying individual businesses in the form of stocks carries a heavier burden of proof and much more specialized work.
One corollary to this idea is that stock prices tend to move around much more than intrinsic business values. If you were to take the ten year business record of any of a number of very stable corporations and then guess their high and low stock prices in the same period, you would almost certainly be surprised at the degree of variation. The reason for this can be found in point (3).
(3) Investing in any asset with uncertain cash flows requires an
element of speculation about the future.
Discerning an asset’s cash flows requires that we make intelligent guesses about a cloudy future. This idea has some deep corollaries:
(A) The more speculation needed to determine the value of the asset, the riskier it is all else equal, due to the higher probability of getting our estimates wrong. In the case of a 1-Year U.S. Treasury bill, we don’t have to offer any speculation beyond assuming that the U.S. government will be solvent and paying 12 months from now and that the U.S. dollar will continue to be accepted as legal tender. (If this ceases to be true, we’re all in big trouble.)
In the case of a biotechnology startup, on the other hand, our entire valuation is going to be based on speculation. In essence, the whole exercise of valuing such a start-up would be making difficult guesses about the future. The probability that we get all of our guesses correct approaches zero, although if we’re correct enough about one or two important factors we might still make money. But we’ll need great luck in doing so, whereas with the Treasury bill, we hardly need any luck at all.
(B) Investing in uncertain assets, including any kind of business-based investment like a farm or a technology stock, involves some difficult speculation, so it’s easy to predict that at times, investors will get caught up in their enthusiasms and mis-price assets. Charlie Munger has commented that stocks are valued partly like Treasury bonds, with obvious cash flows estimated and discounted at rational rates, and partly like art or collectibles, with speculation that the price will go higher or lower because of popularity, trend, or hope. The riskiest assets are the ones valued primarily on speculation because of our lack of ability to see into their economic future. That’s why a corporate bond tends to be less risky than a stock — you only need to establish that the corporation will be solvent for the bond to be a good investment, whereas with the stock, you must make much more complicated estimates.
(C) We know from watching horse-race betting, casino gambling, and lottery participation that people are frequently willing to speculate on odds-against bets that can only hurt them financially in the long-run. We observe the same behavior in the stock market and in other markets as well. (There was, for example, a speculative farming boom in the 1980s.) Financial markets cannot be perfectly efficient because of the speculative element. As with (B) above, more uncertain assets tend to have a greater speculative element attached.
(D) Assets with predictable cash flows tend to be inherently lower-risk than ones without predictable cash flows. Let’s use two different types of businesses to understand this point.
We can be essentially certain that over the next year, Visa and MasterCard will make a tremendous amount of money which is closely related to the amount they made last year — their cash flows are based on the number of transactions made on their cards and the amount of money they collect per transaction. Both elements tend to be extremely stable on a day to day and year to year basis, with a tendency towards growth as new cards make it into circulation. Unless hundreds of millions of people stop using their credit and debit cards or million of merchants find a way to pay a lot less to these intermediaries (who collect very little to start), the businesses will maintain a useful degree of economic predictability. The number of transactions you made on your card last month and the month before pretty closely predicts how much you’ll use it this month and the following one.
As we move out further to year 2, we can still be pretty sure that these characteristics will continue to hold, and thus we can predict with useful accuracy the kind of money Visa/Mastercard will make. The same goes for year 3. However, the longer we continue this exercise, the more our accuracy declines. Although things look pretty good this year, next year, and the year after, what about 30 years from now? By then, one can speculate on the possibility of certain changes to the financial system which might affect the economics of Visa/Mastercard. Our earnings estimate 30 years out is certain to be inaccurate even for a predictable business.
The opposite case is Twitter. Twitter has never shown a net profit to its shareholders and has not established a consistent business model which would allow it to do so. Thus, it would be very difficult to say what Twitter’s earnings might be in the next year, let alone 30 years from now. On this basis, investing in the common stock of Twitter contains a much larger speculative element than Visa/Mastercard. An investment in Visa/Mastercard at a fair price in relation to future earnings can be said to have far less risk than an investment in Twitter. Notice that we don’t come to this conclusion by saying that Visa/Mastercard are riskless, that we can predict their earnings forever, or that Twitter will never be a profitable investment. We are simply ranking potential investments on a sliding scale based on the predictability of their future cash flows. Any estimates will be necessarily imprecise, but they still have great value to us as investors.
(4) The price you pay determines the return you get.
Lower prices = higher returns.
You’ll often hear fables about how now is the time to invest because “The market has done really well over the past few years” or because “My friend has made a lot of money on this stock, I think it’s a good investment,” or some similar statement about other kinds of financial assets; real estate properties, oil royalties, McDonald’s franchises, etc.
Conversely, one frequently hears things like “That stock’s gone way down recently, it seems pretty risky” or “My friend bought a bunch of real estate that went way down, I think real estate is risky” and other notions to that effect.
These thoughts are 180 degrees wrong because they fail to understand the point that low prices create high future returns and that high prices create low future returns. (“High” and “low” being in relation to underlying value.) If a stock trades at 50% of its recent high price, then you are buying the same future cash flows for half the price. If a stock trades at 200% of a recent low price, then the opposite is true; you’re getting exactly half the value you would have before.
You should seek to buy assets with future cash earnings you can (roughly) estimate at prices that offer a fair return. The rest is almost always noise.
(5) Everyone has a unique circle of competence which allows them to
understand certain things best and other things not at all.
We discussed above in point (3) that certain investment situations are inherently speculative, as with the case of Twitter common stock. But even within the realm of knowable investment choices, each investor has his or her own unique circle of competence which they bring to the analysis. In the circle are the things that, through life experience and/or accumulated study, one can fairly evaluate and expect to end up in the right ballpark. Outside of the circle are things we don't have the experience to understand.
Although this point seems simple to the point of banality, it is constantly violated even by smart and financially-savvy people. Many an expert in construction businesses or plumbing businesses or restaurant businesses have tried their hand at buying apartment houses or energy stocks only to find out that their expertise did not carry over. And it is thus for all of us: We are prisoners to our talents, and we’re wise to think long and hard about what we really know and don’t know.
For example, if you do not have the ability to read financial statements, understand microeconomics, and assess the future underlying cash flows of an individual business, are stocks truly in your circle of competence? If you don’t know cap rates from Captain America, is it wise for you to try to get rich buying real estate properties? Unless and until we learn to be honest with ourselves, we will make mistakes that we don’t need to make.
Investing does not have to be rocket science. Once you understand the central concepts and begin learning to apply them, all it takes is discipline and intellectual honesty. Anyone can be a successful investor, broadly defined, by sticking to their circle of competence and not straying outside of it, by not speculating when they think they’re investing, and by always looking to pay a fair price in relation to what they’re buying. These three central tenets, closely followed, can allow any intelligent person to operate safely in the financial world.
Still Interested? Read more about Warren Buffett and Charlie Munger, whose ideas on investing have influenced generations of wise and successful investors. The best books we know of on the topic are The Intelligent Investor, Poor Charlie's Almanack, Berkshire Hathaway's Letters to Shareholders, and John Bogle's book The Little Book of Common Sense Investing to learn about indexing.
If you're a knowledge worker you make decisions everyday. In fact, whether you realize it or not, decisions are your job.
Decisions are how you make a living. Of course not every decision is easy. Decisions tend to fall into different categories. The way we approach the actual decision should vary based on category.
Here are a few basic categories that decisions fall into.
There are decisions where:
We often think we're making decisions in #2 but we're really operating in #3. The difference may seem trivial but it makes a world of difference.
Ignorance is a state of the world where some possible outcomes are unknown: when we've moved from #2 to #3.
One way to realize how ignorant we are is to look back, read some old newspapers, and see how often the world did something that wasn't even imagined.
Some examples include the Arab Spring, the collapse of the Soviet Union, the financial meltdown.
We're prepared for a world much like #2 — the world of risk, with known outcomes and probability that can be estimated, yet we live in a world with a closer resemblance to #3.
Read part two of this series: Two types of ignorance.
References: Ignorance: Lessons from the Laboratory of Literature (Joy and Zeckhauser).
In Mobs, Messiahs, and Markets: Surviving the Public Spectacle in Finance and Politics, Will Bonner writes:
…you don't win by predicting the future; you win by getting the odds right. You can be right about the future and still not make any money. At the racetrack, for example, the favorite horse may be the one most likely to win, but since everyone wants to bet on the favorite, how likely is it that betting on the favorite will make you money? The horse to bet on is the one more likely to win than most people expect. That's the one that gives you the best odds. That's the bet that pays off over time.
And here is Charlie Munger speaking about the same topic:
The model I like to sort of simplify the notion of what goes on in a market for common stocks is the pari-mutuel system at the racetrack. If you stop to think about it, a pari-mutuel system is a market. Everybody goes there and bets and the odds change based on what's bet. That's what happens in the stock market.
Any damn fool can see that a horse carrying a light weight with a wonderful win rate and a good post position etc., etc. is way more likely to win than a horse with a terrible record and extra weight and so on and so on. But if you look at the odds, the bad horse pays 100 to 1, whereas the good horse pays 3 to 2. Then it's not clear which is statistically the best bet using the mathematics of Fermat and Pascal. The prices have changed in such a way that it's very hard to beat the system.
And then the track is taking 17% off the top. So not only do you have to outwit all the other betters, but you've got to outwit them by such a big margin that on average, you can afford to take 17% of your gross bets off the top and give it to the house before the rest of your money can be put to work.