Let’s recap: So far we have studied Warren Buffett’s approach to buying a business or selecting stocks (he, of course, considers them the exact same thing). It is an approach built on timeless principles codified into 12 tenets. We have seen how these principles were applied in many Berkshire purchases, including the well-known classic purchase of Coca-Cola and the recent one of IBM. And we have taken the time to understand how the insights from others helped shape his philosophy of investing.
But, as every investor knows, deciding which stocks to buy is only half the story. The other half of the story is managing the portfolio, which, in turn, is a combination of portfolio construction and ongoing management.
When we think about managing our portfolios, we often believe it is a simple process of deciding what to buy, sell, or hold. These decisions are (or should be, if you want to think like Buffett) determined by the margin of safety, which he measures by comparing today’s stock price to its intrinsic value. You buy great businesses when the price is far below that value, hold them when the price is modestly below, and sell them when the price is significantly higher.
However, the margin-of-safety approach, while crucial, is not sufficient in itself. We must also take into account three important portfolio management constructs that Buffett has developed:
Hollywood has given us a visual cliché of what money managers look like: talking into two phones at once, frantically taking notes while trying to keep an eye on a bank of computer screens that blink and blip endlessly, and showing pained expressions whenever one of those computer blinks shows a minuscule drop in a stock price.
Warren Buffett is far from that kind of frenzy. He moves with the calm that comes with great confidence. He has no need to watch a dozen computer screens at once; the minute-by-minute changes in the market are of no interest to him. Warren Buffett does not think in seconds, minutes, days, months, or quarters, but in years. He doesn’t need to keep up with hundreds of companies, because his common stock investments are focused in a select few. He refers to himself as a “focus investor”—“We just focus on a few outstanding companies.”1 This approach, called focus investing, greatly simplifies the task of portfolio management.
Focus investing is a remarkably simple idea, and yet, like most simple ideas, it rests on a complex foundation of interlocking concepts. In this chapter, we look more closely at the effects focus investing produces. The goal here is to give you a new way of thinking about portfolio management. Fair warning: In all likelihood, this new way is the opposite of what you have always been told about investing in the stock market.
The current state of portfolio management appears to be locked into a tug-of-war between two competing strategies: (1) active portfolio management and (2) index investing.
Active portfolio managers are constantly at work buying and selling a great number of common stocks. Their job is to try to keep their clients satisfied, or risk losing clients and ultimately their jobs. To stay on top, active managers try to predict what will happen with stocks in the coming months so at the end of the quarter the portfolio is in good relative shape and the client is happy.
Index investing, in contrast, is a buy-and-hold approach. It involves assembling and then holding a broadly diversified portfolio of common stocks deliberately designed to mimic the behavior of a specific benchmark index, such as the Standard & Poor’s 500.
Active portfolio managers argue that, by virtue of their superior stock-picking skills, they can do better than any index. Index strategists, for their part, have history on their side. Annually, from 1980 through 2011, only 41 percent of large-cap mutual funds beat the S&P 500 index.2
From an investor’s point of view, the underlying attraction of both strategies is the same: to minimize risk through diversification. By holding a large number of stocks representing many industries and many sectors of the market, investors hope to build in a warm blanket of protection against horrific loss that could occur if they had all their money in one arena and that arena suffered some disaster. In a normal period, so the thinking goes, some stocks in a diversified portfolio will go down and others will go up, so let’s keep our fingers crossed that the latter will compensate for the former.
Active managers achieve this protection by loading up their portfolios. In their view, the more stocks the portfolio contains, the better their chances. Ten stocks are better than one, and 100 stocks are better than 10. An index fund, by definition, affords this kind of diversification if the index it mirrors is also diversified. So does the traditional stock mutual fund, which has upwards of 100 stocks.
There’s just one problem: We have all heard this mantra of diversification for so long that we have become intellectually numb to its inevitable consequence: mediocre results. Both active and index funds do offer diversification, but, in general, neither strategy will give you exceptional returns.
What does Buffett say about this ongoing debate? Given these two particular choices, indexing versus an active strategy, he would unhesitatingly pick indexing. So would investors who have a low tolerance for risk, and people who know very little about the economics of a business but still want to participate in the long-term benefits of investing in common stocks. “By periodically investing in an index fund,” Buffett says in his inimitable style, “the know-nothing investors can actually outperform most investment professionals.”3
Buffett, however, would be quick to point out that there is a third alternative, a very different kind of active portfolio strategy that significantly increases the odds of beating the index. That alternative is focus investing. Reduced to its essence, focus investing means this: Choose a few stocks that are likely to produce above-average returns over the long haul, concentrate the bulk of your investments in those stocks, and have the fortitude to hold steady during any short-term market gyrations.
The Warren Buffett tenets, if followed closely, lead inevitably to good companies that make sense for a focus portfolio. The companies chosen will have a long history of superior performance and a stable management, and that stability points toward a high probability that they will perform in the future as they have in the past. That is the heart of focus investing: concentrating your investments in companies with the highest probability of above-average performance. (Probability theory, which comes to us from the field of mathematics, is one of the underlying concepts that make up the rationale for focus investing. We will learn more about probability theory later in this chapter.)
Remember Buffett’s advice to a “know-nothing” investor, to stay with index funds? What is more interesting is what he said next: “If you are a know-something investor, able to understand business economics, and can locate five to ten sensibly priced companies that possess important long-term competitive advantages, conventional diversification makes no sense for you.”4
What’s wrong with conventional diversification? For one thing, it greatly increases the chances that you will buy something you don’t know enough about. “Know-something” investors, applying the Buffett tenets, would do better to focus their attention on just a few companies—five to 10, Buffett suggests. For the average investor, a legitimate case can be made for investing in 10 to 20 companies.
As we know from Chapter 2, Buffett’s thinking was greatly influenced by Philip Fisher, and we clearly see Fisher’s hand in this area—portfolio management. Fisher was known for his focus portfolios; he always said he preferred owning a small number of outstanding companies that he understood well, rather than a large number of average companies, many of which he understood poorly. As we have seen, Fisher generally limited his portfolios to fewer than 10 companies, of which three or four represented 75 percent of the total investment.
Fisher’s influence on Buffett can also be seen in his belief that the only reasonable course, when you encounter a strong opportunity, is to make a large investment. Today, Buffett echoes that thinking. “With each investment you make, you should have the courage and conviction to place at least ten percent of your net worth in that stock.”5
You can see why Buffett says the ideal portfolio should contain no more than 10 stocks, if each is to receive a 10 percent weighting. Yet focus investing is not a simple matter of finding 10 good stocks and dividing an investment pool equally among them. Even though all the stocks in a focus portfolio are high-probability bets, some will inevitably be higher-probability than others, and they should be allowed a greater proportion of the investment.
Blackjack players understand this strategy intuitively; when the odds are strongly in their favor, they put down a big bet. Investors and gamblers draw from the same science: mathematics. Along with probability theory, mathematics provides another piece of the focus investing rationale: the Kelly optimization model, which is described in detail later in the chapter. The Kelly formula uses probability to calculate optimization—in this case, the optimal size bet one should make in the portfolio.
Focus investing is the antithesis of a broadly diversified high-turnover approach. Although focus investing stands the best chance, among all active strategies, of outperforming an index return over time, it does require investors to patiently hold their portfolios when it appears that other strategies are marching ahead. In shorter periods, we realize that changes in interest rates, inflation, or near-term expectations for a company’s earnings can affect share prices. But as the time horizon lengthens, the trendline economics of the underlying business will increasingly dominate its share price.
How long is that ideal time? There is no hard-and-fast rule, although Buffett would probably say five years since this is the time period he is focusing on for Berkshire Hathaway’s results. The goal is not a zero turnover ratio; that’s foolish in the opposite direction, for it would prevent you from taking advantage of something better when it comes along. I suggest that, as a general rule of thumb, we should be thinking of a turnover rate between 10 and 20 percent. A 10 percent turnover rate suggests that an investor holds the stock for 10 years; a 20 percent rate implies a five-year period.
Focus investing pursues above-average results, and, as we will see, there is strong evidence, both academic research and actual case histories, that the approach is successful when thoughtfully applied. There can be no doubt, however, that the ride is bumpy, for an increased level of price volatility is a necessary by-product of the focus approach. Focus investors tolerate the bumpiness because they know that, in the long run, the underlying economics of the companies will more than compensate for any short-term price fluctuations.
Buffett is the master bump ignorer. So is his partner Charlie Munger, who came to the fundamental concept of focus investing by a slightly different route. “Back in the 1960s,” he explained, “I actually took a compound interest rate table, and I made various assumptions about what kind of edge I might have in reference to the behavior of common stocks generally.”6 Charlie worked through several scenarios to determine the number of stocks he would need in the portfolio of his investment partnership and what kind of volatility he could expect.
“I knew from being a poker player that you have to bet heavily when you’ve got huge odds in your favor,” Charlie said. He concluded that as long as he could handle the price volatility, owning as few as three stocks would be plenty. “I knew I could handle the bumps psychologically, because I was raised by people who believed in handling bumps.”7
Maybe you also come from a long line of people who can handle bumps. But even if you were not born so lucky, you can acquire some of their traits. You need to consciously decide to change how you think and behave. Acquiring new habits and thought patterns does not happen overnight, but gradually teaching yourself not to panic or act rashly in response to the vagaries of the market is certainly doable.
It is a vast oversimplification, but not an overstatement, to say that the stock market is a giant warehouse full of countless probabilities. In this warehouse are thousands of single forces that combine to set prices, all of which are in constant motion, any one of which can have a dramatic impact, and none of which is predictable to an absolute certainty. The task for investors, then, is to narrow the field by identifying and removing that which is most unknown, and then focusing on the least unknown. That task is an exercise in probability.
When we are unsure about the situation but still want to express our opinion, we often preface our remarks by saying “the chances are,” or “probably,” or “it’s not very likely but. . . .” When we go one step further and attempt to quantify those general expressions, we then are dealing with probabilities. Probabilities are the mathematical language of risk.
What is the probability of a cat giving birth to a bird? Zero. What is the probability the sun will rise tomorrow? That event, which is considered certain, is given a probability of 1. All events that are neither completely certain nor completely impossible have a probability somewhere between 0 and 1, expressed as a fraction. Determining the fraction is what probability theory is all about.
In 1654, Blaise Pascal and Pierre de Fermat exchanged a series of letters that formed the basis of probability theory. Pascal, a child prodigy gifted in both mathematics and philosophy, had been challenged by the Chevalier de Méré, a philosopher and gambler, to solve the riddle that had stumped many mathematicians. How should two card players decide the stakes of the game if they had to leave before the game was completed? Pascal approached Fermat, a mathematical genius in his own right, about Méré’s challenge.
“The 1654 correspondence between Pascal and Fermat on this subject,” said Peter Bernstein in Against the Gods, his wonderfully written treatise on risk, “signaled an epochal event in the history of mathematics and the theory of probability.”8 Although they attacked the problem differently (Fermat used algebra whereas Pascal turned to geometry), they were able to construct a system to determine the probability of several possible outcomes. Indeed, Pascal’s triangle of numbers solves many problems, including the probability that your favorite baseball team will win the World Series after losing the first game.
The work by Pascal and Fermat marks the beginning of the theory of decision making. Decision theory is the process of deciding what to do when you are uncertain what will happen. “Making that decision,” wrote Bernstein, “is the essential first step in any effort to manage risk.”9
• • •
Although Pascal and Fermat were both credited with developing probability theory, another mathematician, Thomas Bayes, wrote the piece that laid the groundwork for putting probability theory to practical action.
Born in England in 1701, exactly 100 years after Fermat and 78 years after Pascal, Bayes lived an unremarkable life. He was a member of the Royal Society, but published nothing in mathematics during his lifetime. After his death, his paper titled “Essays Towards Solving a Problem in the Doctrine of Chances” appeared. At the time, no one thought much of it. However, according to Bernstein, the essay was a “strikingly original piece of work that immortalized Bayes among statisticians, economists, and other social scientists.”10 It provides a way for investors to make use of the mathematical theory of probability.
Bayesian analysis gives us a logical way to consider a set of outcomes of which all are possible but only one will actually occur. It is conceptually a simple procedure. We begin by assigning a probability to each of the outcomes, on the basis of whatever evidence is then available. If additional evidence becomes available, the initial probability is revised to reflect the new information. Bayes’s theorem gives a mathematical procedure for updating our original beliefs, thus changing our relevant odds.
How does this work? Let’s imagine that you and a friend have spent the afternoon playing your favorite board game, and now, at the end of the game, you are chatting about this or that. Something your friend says leads you to make a friendly wager: that with one roll of a die from the game, you will get a 6. Straight odds are one in six, a 16 percent probability. But then suppose your friend rolls the die, quickly covers it with his hand, and takes a peek. “I can tell you this much,” he says; “it’s an even number.” Now you have new information and your odds change dramatically to one in three, a 33 percent probability. While you are considering whether to change your bet, your friend teasingly adds: “And it is not a 4.” With this additional bit of information, your odds have changed again, to one in two, a 50 percent probability.
With this very simple example, you have performed a Bayesian analysis. Each new piece of information affected the original probability, and that is a Bayesian inference.
Bayesian analysis is an attempt to incorporate all available information into a process for making inferences, or decisions, about the underlying state of nature. Colleges and universities use Bayes’s theorem to help their students study decision making. In the classroom, the Bayesian approach is more popularly called the decision tree theory; each branch of the tree represents new information that, in turn, changes the odds in making decisions. “At Harvard Business School,” explains Charlie Munger, “the great quantitative thing that bonds the first-year class together is what they call decision tree theory. All they do is take high school algebra and apply it to real life problems. The students love it. They’re amazed to find that high school algebra works in life.”11
As Charlie points out, basic algebra is extremely useful in calculating probabilities. But to put probability theory to practical use in investing, we need to look a bit deeper at how the numbers are calculated. In particular, we need to pay attention to the notion of frequency.
What does it mean to say that the probability of guessing heads on a single coin toss is one in two? Or that the probability that an odd number will appear on a single throw of the dice is one in two? If a box is filled with 70 red marbles and 30 blue marbles, what does it mean that the probability is three in 10 that a blue marble will be picked? In all these examples, the probability of the event is what is referred to as a frequency interpretation, and it is based on the law of averages.
If an uncertain event is repeated countless times, the frequency of the event’s occurrence is reflected in its probability. For example, if we toss a coin 100,000 times, the number of times that are expected to be heads is 50,000. Note I did not say it would be equal to 50,000. The law of large numbers says the relative frequency and the probability need be equal only for an infinite number of repetitions. Theoretically, we know that, in a fair coin toss, the chance of getting heads is one in two, but we will never be able to say the chance is equal until an infinite number of tosses have passed.
In any problem that deals with uncertainty, we will, quite obviously, never be able to make a definitive statement. However, if the problem is well defined, we should be able to list all the possible outcomes. If an uncertain event is repeated often enough, the frequency of the outcomes should reflect the probability of the different possible outcomes. The difficulty arises when we are concerned with an event that happens only once.
How do we estimate the probability of passing tomorrow’s science test or the probability of the Green Bay Packers winning the Super Bowl? The problem we face is that each of these events is unique. We can look back at all the statistics on the Green Bay games, but we don’t have enough exact matchups with the exact personnel who played each other repeatedly under identical circumstances. We can recall previous science exams to get an idea of how well we test, but all tests are not identical and our knowledge is not constant.
Without repeated tests that would produce a frequency distribution, how can we calculate probability? We cannot. Instead we must rely on subjective interpretation of probabilities. We do it all the time. We might say the odds of the Packers’ winning the Lombardi trophy are two to one, or the possibility of passing that difficult science test is one in 10. These are probabilistic statements; they describe our degree of belief about the event. When it isn’t possible to do enough repetitions of a certain event to get an interpretation of probability based on frequency, we have to rely on our own good sense.
You can immediately see that many subjective interpretations of those two events would lead you in the wrong direction. In subjective probability, the burden is on you to analyze your assumptions. Stop and think them through. Are you assuming a one-in-10 chance of doing well on the science test because the material is more difficult and you haven’t adequately prepared, or because of false modesty? Is your lifelong loyalty to the Packers blinding you to the superior strength of the other team?
According to the textbooks on Bayesian analysis, if you believe that your assumptions are reasonable, it is “perfectly acceptable” to make your subjective probability of a certain event equal to a frequency probability.12 What you have to do is sift out the unreasonable and illogical and retain the reasonable. It is helpful to think about subjective probabilities as nothing more than extensions of the frequency probability method. In fact, in many cases, subjective probabilities add value because this approach allows you to take operational issues into account rather than depend on long-run statistical regularity.
Whether or not investors recognize it, virtually all the decisions they make are exercises in probability. For them to succeed, it is critical that their probability statement combines the historical record with the most recent data available. And that is Bayesian analysis in action.
“Take the probability of loss times the amount of possible loss from the probability of gain times the amount of possible gain. That is what we’re trying to do,” says Buffett. “It’s imperfect but that’s what it is all about.”13
A useful example to clarify the link between investing and probability theory is the practice of risk arbitrage. Pure arbitrage is nothing more than profiting from the discrepancy in the price of a security quoted in two different markets. For example, commodities and currencies are quoted in several different markets around the world. If two separate markets quote a different price on the same commodity, you could buy in one market, sell in another market, and pocket the difference.
Risk arbitrage, which is the form more commonly practiced today, involves announced corporate mergers or acquisitions. There are individuals who practice risk arbitrage on unannounced corporate events, but this is an area that Buffett avoids and so will we. In a speech to a group of Stanford University students, Buffett shared his views on risk arbitrage. “My job,” he said, “is to assess the probability of the events [announced mergers] actually transpiring and the gain/loss ratio.”14
Let’s preface Buffett’s next remarks to the Stanford students with this scenario. Suppose the Abbott Company began the day trading at $18 per share. Then, in midmorning, it is announced that sometime this year, perhaps in six months, Abbott will be sold to the Costello Company for $30 per share. Immediately, the share price of Abbott races to $27, where it settles in and begins to trade back and forth.
Buffett sees the announced merger and must make a decision. First of all, he tries to assess the degree of certainty. Some corporate deals don’t materialize. The board of directors could unexpectedly resist the idea of the merger or the Federal Trade Commission might voice an objection. No one ever knows with certainty whether a risk arbitrage deal will close, and that is where the risk comes in.
Buffett’s decision process is an exercise in subjective probability. He explains: “If I think there is a 90 percent chance of occurring and there is 3 points on the upside and there is a 10 percent chance that it will fall through and there is 9 points on the downside, then that’s $0.90 off of $2.70, leaving $1.80 mathematical expectation.”15
Next, said Buffett, you have to figure in the time span involved and then relate the return of the investment to other investments available to you. If you bought one share of Abbott Company at $27, there is, according to Buffett’s mathematics, a potential 6.6 percent return ($1.80/$27). If the deal is expected to close in six months, the annualized return on the investment would be 13.2 percent. Buffett would then compare the return from this risk arbitrage with other returns available to him.
Buffett freely acknowledges that risk arbitrage carries the potential for loss. “We are perfectly willing to lose money on a given transaction,” he said, “arbitrage being one example, but we’re not willing to enter into any transaction in which we think the probability of a number of mutually independent events of similar type has an expectancy of loss. We hope,” he added, “that we’re entering into transactions where our calculations of those probabilities have validity.”16
We can see quite clearly that Buffett’s risk arbitrage estimates are subjective probabilities. There is no frequency distribution in risk arbitrage. Every deal is different. Every circumstance requires different estimations. Even so, there is value to approaching the risk arbitrage deal with some rational mathematical calculation. The process, we shall learn, is no different when you invest in common stocks.
Each time you step foot inside a casino, the probability of coming out a winner is pretty low. You shouldn’t be surprised; after all, we all know the house has the best odds. But one game, if played correctly, gives you a legitimate chance to beat the house: blackjack. In a worldwide best seller called Beat the Dealer: A Winning Strategy for the Game of Twenty-One, Edward O. Thorp, a mathematician by training, outlined a process for outsmarting the casino.17
Thorp’s strategy was based on a simple concept. When the deck is rich with 10s, face cards, and aces, the player—let’s say it’s you—has a statistical advantage over the dealer. If you assign a −1 for the high cards and a +1 for the low cards, it’s quite easy to keep track of the cards dealt; just keep a running tally in your head, adding and subtracting as each card shows. When the count turns positive, you know there are more high cards yet to be played. Smart players save their biggest bets for when the card count reaches a high relative number.
Buried deep inside Thorp’s book was a notation on the Kelly betting model.18 Kelly, in turn, took his inspiration from Claude Shannon, the inventor of information theory.
A mathematician with Bell Laboratories in the 1940s, Shannon spent a good deal of his career trying to find the most optimal way to transmit information over copper lines without having the information become garbled by random molecular noise. In 1948, in an article called “A Mathematical Theory of Communication,” he described what he had discovered.19 Included in the article was the mathematical formula for the optimal amount of information that, considering the possibility of success, can be pushed through a copper wire.
A few years later, J. L. Kelly, another mathematician, read Shannon’s article and realized the formula could just as easily work in gambling—another human endeavor that would be enhanced by knowing the possibilities of success. In 1956, in a paper titled “A New Interpretation of Information Rate,” Kelly pointed out that Shannon’s various transmission rates and possible outcomes of a chance event are essentially the same thing—probabilities—and the same formula could optimize both.20
The Kelly optimization model, often called the optimal growth strategy, is based on the concept that if you know the probability of success, you bet the fraction of your bankroll that maximizes the growth rate. It is expressed as a formula: 2p − 1 = x where 2 times the probability of winning (p) minus 1 equals the percentage of your total bankroll that you should bet (x). For example, if the probability of beating the house is 55 percent, you should bet 10 percent of your bankroll to achieve maximum growth of your winnings. If the probability is 70 percent, bet 40 percent. And if you know the odds of winning are 100 percent, the model would say: Bet it all.
The stock market, of course, is far more complex that the game of blackjack. In a card game, there are a finite number of cards and therefore a limited number of possible outcomes. The stock market, with many thousands of stocks and millions of investors, has an almost unlimited number of various outcomes. Using the Kelly approach requires constant recalculations and adjustments throughout the investment process. Nonetheless, the underlying concept—mathematically linking the degree of probability to investment size—carries important lessons.
I believe the Kelly model is an attractive tool for focus investors. However, it will benefit only those who use it responsibly. There are risks to employing the Kelly approach, and investors would be wise to understand its three limitations:
Because the risk of overbetting far outweighs the penalties of underbetting, I believe that all investors, especially those who are just beginning to use a focus investment strategy, should use fractional-Kelly bets. Unfortunately, minimizing your bets also minimizes your potential gain. However, because the relationship in the Kelly model is parabolic, the penalty for underbetting is not severe. A half-Kelly bet, which reduces the amount you bet by 50 percent, reduces the potential growth rate by 25 percent.
In 1994, Charlie Munger accepted an invitation from Dr. Guilford Babcock to address the student investment seminar at the University of Southern California’s business school. He touched on several topics, including the “art of achieving worldly wisdom.” He also explained, as only he can, what he thinks about probabilities and optimization.
“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,” he said. “If you stop and think about it, a pari-mutuel system is a market. Everybody goes there and bets and the odds are changed based on what’s bet. That’s what happens in the stock market.”
He continued, “Any damn fool can see that a horse carrying a light weight with a wonderful win rate and good position et cetera 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. The prices have changed in such a way that it’s very hard to beat the system.”21
Charlie’s racetrack analogy is perfect for investors. Too often, investors are attracted to a long shot that pays incredible odds but, for any one of countless reasons, never finishes the race. Or sometimes they pick the sure thing without ever considering the payoff. It appears to me that the most sensible way to approach horse racing, or the stock market, is to wait until the good horse comes to the post with inviting odds.
Andrew Beyer, the author of several books on Thoroughbred racing, has spent many years watching race goers bet and has seen far too many lose money through impetuosity. At the racetrack, as elsewhere, the casino mentality—the itch to get into the action: to put down the money, toss the dice, pull the lever, do something—compels people to bet foolishly, without taking the time to think through what they are doing.
Beyer, who understands the psychological urge to get into the game, advises players to accommodate it by dividing their strategy between action bets and prime bets. Prime bets are reserved for serious players when two conditions occur: (1) confidence in the horse’s ability to win is high, and (2) payoff odds are greater than they should be. Prime bets call for serious money. Action bets, as the name implies, are reserved for the long shots and hunches that satisfy the psychological need to play. They are smaller bets and never are allowed to become a large part of the player’s betting pool.
When a horseplayer starts blurring the distinction between prime bets and action bets, says Beyer, he or she “is taking a step that will inevitably lead toward helter-skelter betting with no proper balance between . . . strong and weak selections.”22
Now, let us move away from the racetrack and put all this theory together into the reality of the stock market. The chain of thinking is the same.
Thinking about probabilities may be new to you, but it is not impossible to learn how to use them. If you are able to teach yourself to think about stocks this way, you are well on your way to being able to profit from your own lessons. Recall Buffett’s purchase of the Coca-Cola Company in 1988. He invested one-third of Berkshire’s portfolio in the company. Coca-Cola was an outstanding business with above-average economics selling at a price substantially below intrinsic value. Opportunities like this do not often occur in the stock market. But when one does occur, those who understand probabilities will recognize it and will know what to do. As Charlie Munger puts it, “The wise [investors] bet heavily when the world offers them that opportunity. They bet big when they have the odds. And the rest of the time, they don’t. It’s just that simple.”23
In 1934, during the height of the Great Depression, a remarkable book with the unremarkable title of Security Analysis was published. Its coauthors, Benjamin Graham and David Dodd, had been working on it for five years. Their writing time had been interrupted by teaching at Columbia University and helping clients cope with the aftereffects of the 1929 crash. Graham later said the delay was providential, for it allowed him to include “wisdom acquired at the cost of much suffering.”24 Security Analysis is universally acclaimed as a classic; it is still in print after five editions and 80 years. It is impossible to overstate its influence on the modern world of investing.
Fifty years after the original publication, the Columbia University Business School sponsored a seminar marking the anniversary. Warren Buffett, one of the school’s best-known alumni and the most famous modern-day proponent of Graham’s value approach, was invited to address the gathering. His speech, titled “The Superinvestors of Graham-and-Doddsville,” has become, in its own way, as much a classic as the book it honored.25
Most in the audience that day in 1984—university professors, researchers, and other academicians—still held firmly to modern portfolio theory and the validity of the efficient market hypothesis. Buffett, to no one’s surprise, firmly disagreed, and in his speech he quietly demolished the platform on which rested the efficient market theory.
He began by recapping the central argument of modern portfolio theory—that the stock market is efficient, all stocks are priced correctly, and therefore anyone who beats the market year after year is simply lucky. Maybe so, he said, but I know some folks who have done it, and their success can’t be explained away as simply random chance.
And he proceeded to lay out the evidence. All the examples he presented that day were of people who had managed to beat the market over time, and who had done so not because of luck but because they followed principles learned from the same source: Ben Graham. They all reside, he said, in the “intellectual village” of Graham-and-Doddsville.
Although they may make specific decisions differently, Buffett explained, they are linked by a common approach that seeks to take advantage of discrepancies between the market price and intrinsic value. “Needless to say, our Graham and Dodd investors do not discuss beta, the capital asset pricing model or covariance of returns,” Buffett said. “These are not subjects of any interest to them. In fact, most of them would have trouble defining those terms.”
In a published article based on his 1984 speech, Buffett included tables that presented the impressive performance results of the residents of Graham-and-Doddsville.26 But it was not just the Graham approach to value investing that united these great investors. Each of these great investors—Charlie Munger, Bill Ruane, and Lou Simpson—also managed focused portfolios just like Buffett did. From their performance records there is much we can learn. But before we start this investigation, let us begin with the very first focus investor.
Most people recognize John Maynard Keynes for his contributions to economic theory. Few know that he was also a legendary investor. Proof of his investment prowess can be found in the performance record of the Chest Fund at King’s College in Cambridge, England.
Before 1920, King’s College investments were restricted to fixed income securities. When Keynes was appointed the Second Bursar in late 1919, he persuaded the trustees to begin a separate fund that would contain only common stocks, currency, and commodity futures. This separate account became the Chest Fund. From 1927, when he was named First Bursar, until his death in 1945, Keynes had sole responsibility for this account. In all that time, he kept its holdings focused on just a few companies. In 1934, the same year that Security Analysis was published, Keynes wrote to a colleague, explaining his reasoning.
It is a mistake to think one limits one’s risk by spreading too much between enterprises which one knows little and has no reason for special confidence. . . . One’s knowledge and experience are definitely limited and there are seldom more than two or three enterprises at any given time in which I personally feel myself entitled to put full confidence.27
Four years later, Keynes prepared a full policy report for the Chest Fund, outlining his principles:
Although he did not use the term, I believe it is clear from Keynes’s investment policy that he was a focus investor. He purposely limited his stocks to a select few and relied on fundamental analysis to estimate the value of his picks relative to their price. He liked to keep portfolio turnover at a very low rate. And he aimed to include “opposed risks” by introducing a variety of economic positions concentrated in high-quality, predictable businesses.
How well did Keynes perform? During his 18-year stewardship, the Chest Fund achieved an average annual return of 13.2 percent, at a time when the overall UK market remained basically flat. Considering that the time period included both the Great Depression and World War II, we would have to say that Keynes’s performance was extraordinary.
Even so, the Chest Fund endured some painful periods; in three separate years (1930, 1938, and 1940), its value dropped significantly more than that of the overall UK market. Reviewing Keynes’s performance record in 1983, two modern analysts commented, “From the large swings in the Fund’s fortune, it is obvious that the Fund must have been more volatile than the market.”29 Indeed, if we measure the standard deviation of the Chest Fund, we find it was almost two and a half times more volatile than the general market. Without a doubt, investors in the fund occasionally had a bumpy ride but, in the end, outscored the market by a significant degree.
• • •
Lest you think Keynes, with his macroeconomic background, possessed marketing timing skills, take further note of his investment policy. “We have not proved able to take much advantage of a general systematic movement out of and into ordinary shares as a whole at different phases of the trade cycle,” he wrote. “As a result of these experiences I am clear that the idea of a wholesale shift is for various reasons impracticable and indeed undesirable. Most of those who attempt to [do so] sell too late and buy too late, and do both too often, incurring heavy expenses and developing too unsettled and speculative state of mind, which if it is widespread has besides the grave social disadvantage of aggravating the scale of the fluctuations.”30
High turnover produces heavy expenses, fuels a speculative state of mind, and makes the overall market fluctuations worse. True then, true now.
Although Berkshire Hathaway’s investment performance is usually tied to its chairman, we should never forget that vice chairman Charlie Munger is an outstanding investor himself. “I ran into him in about 1960,” said Buffett, “and I told him law was a fine hobby but he could do better.”31 As you may recall from Chapter 2, Munger at the time had a thriving law practice in Los Angeles, but gradually shifted his energies to a new investment partnership bearing his name.
“His portfolio was concentrated in very few securities and, therefore, his record was much more volatile,” Buffett explained, “but it was based on the same discount-from-value approach.” In making investment decisions for his partnership, Charlie followed the Graham methodology and would only look at companies that were selling below their intrinsic value. “He was willing to accept greater peaks and valleys in performance, and he happens to be a fellow whose psyche goes toward concentration.”32
Notice that Buffett does not use the word risk in describing Charlie’s performance. Using the conventional definition of risk as found in modern portfolio theory, where risk arises from price volatility, some would say that, over its 13-year history, Charlie’s partnership was extremely risky. Its standard deviation was almost twice that of the market. But beating the average annual return of the market by 18 points over that time period was an achievement not of a risk taker but of an astute investor.
Buffett first met Bill Ruane in 1951, when both were attending Ben Graham’s security analysis class at Columbia. The two classmates stayed in contact, and Buffett watched Ruane’s investment performance over the years with admiration. When Buffett closed his investment partnership in 1969, he asked Ruane to handle the funds of some of the partners. That was the beginning of the Sequoia Fund.
Both men knew it was a difficult time to set up a mutual fund, but Ruane plunged ahead. The stock market was splitting into a two-tier market. Most of the hot money was gravitating to the new Nifty Fifty (high-flying growth companies), leaving value stocks far behind. Although, as Buffett pointed out, comparative performance for value investors in the early 1970s was difficult, “I am happy to say that my partners, to an amazing degree, not only stayed with him but added money, with happy results.”33
The Sequoia Fund was a true pioneer, the first mutual fund run on the principles of focus investing. The public record of Sequoia’s holdings demonstrates clearly that Bill Ruane and Rick Cuniff, his partner at Ruane, Cuniff & Company, managed a tightly focused, low-turnover portfolio. On average, well over 90 percent of the fund was invested in a group of six to 10 companies. Even so, the economic diversity of the portfolio was, and continues to be, broad. Ruane has often pointed out that even though Sequoia is a focused portfolio, it has always owned a variety of different businesses.
Early on, Bill Ruane was unique among money managers. Generally speaking, most investment managers put together a portfolio of stocks that is not too far different from the index they are measured against. The portfolio manager understands the industry sector weightings of the index and then goes about populating the portfolio with stocks that fit in each sector. At Ruane, Cuniff & Company, the partners begin with the idea of selecting the best possible stocks; then they let the portfolio form around those selections.
Selecting the best possible stocks, of course, requires a high level of research. The firm has built a reputation as one of the brightest shops in money management. The principals eschew Wall Street’s broker-fed research reports. Instead, they rely on their own intensive company investigations. “We don’t go in much for titles at our firm,” Ruane once said, “[but] if we did, my business card would read Bill Ruane, Research Analyst.”
Such thinking is unusual on Wall Street, he explained. “Typically, people start out their careers in an analyst function but aspire to get promoted to the more prestigious portfolio manager designation. To the contrary, we have always believed that if you are a long-term investor, the analyst function is paramount and the portfolio management follows naturally.”34
How well has this unique approach served Sequoia’s shareholders? From 1971 through 2013 the Sequoia Fund earned an average annual return of 14.46 percent, compared to 10.65 percent for the Standard & Poor’s 500 index.
Like other focus portfolios, the Sequoia Fund achieved this above-average return with a slightly bumpier ride. The standard deviation of the market during this period was 16.1 percent compared to Sequoia’s 21.2 percent. Those who adhere to the modern portfolio theory definition of risk might conclude, as with the Munger Partnership, that Sequoia took on more risk to get its excess return. But those who recognized the care and diligence that embodied the Sequoia research effort would be hard-pressed to conclude its approach was risky.
About the time Warren Buffett began acquiring the stock of the GEICO insurance company in the late 1970s, he also made an acquisition that would have a direct effect on GEICO’s financial health. His name was Lou Simpson.
Simpson, who earned a master’s degree in economics from Princeton University, worked for both Stein Roe & Farnham and Western Asset Management before Buffett lured him to GEICO in 1979. Recalling the job interview, Buffett remembers that Lou had “the ideal temperament for investing.” He was an independent thinker who was confident of his own research and “who derived no particular pleasure from operating with or against the crowd.”35
Lou developed a reputation as a voracious reader who ignored Wall Street research reports and pored over annual reports instead. His common-stock selection process was similar to Buffett’s. He purchased only high-return businesses that were run by able management and that were available at reasonable prices. Lou had something else that was in common with Buffett: He focused his portfolio on only a few stocks. GEICO’s billion-dollar equity portfolio typically owned fewer than 10 stocks.
Between 1980 and 2004, GEICO’s portfolio achieved an average annual return of 20.3 percent compared to the market’s 13.5 percent. Buffett commented, “Lou has consistently invested in undervalued common stocks that, individually, were unlikely to present him with a permanent loss and that, collectively, were close to risk-free.”36
Here again we see Buffett’s sense of risk: It has nothing to do with stock price volatility, but rather with the certainty that the individual stocks will, over time, produce a profit.
Simpson’s performance and investment style fit neatly inside Buffett’s way of thinking. “Lou takes the same conservative concentrated approach to investments that we do at Berkshire and it is an enormous plus for us to have him on board,” said Buffett. “There are very few people who[m] I will let run money and businesses that we have control over, but we are delighted in the case of Lou.”37
Buffett, Munger, Ruane, Simpson. It is clear the superinvestors of Buffettville have a common intellectual approach to investing. They are united in their belief that the way to reduce risk is to buy stocks only when the margin of safety (that is, the favorable discrepancy between the intrinsic value of the company and today’s market price) is high. They also believe that concentrating their portfolios around a limited number of these high-probability events not only reduces risk, but helps to generate returns far above the market rate of return.
Still, when we point out these successful focus investors, there are some people who remain skeptical. They wonder: Are all of these people successful because of their close professional relationships? As it turns out, these stock pickers owned different stocks. Buffett didn’t own exactly what Munger owned, and Munger didn’t own what Ruane owned; Ruane didn’t own what Simpson owned; and nobody knows what Keynes owned.
Well, that may be true, say the skeptics, but you have offered only five examples of focus investors, and five observations are not enough to draw a statistically meaningful conclusion. In an industry that has thousands of portfolio managers, these five success stories could have been completely random.
Fair enough. To eliminate any notion that the five superinvestors of Buffettville are nothing more than statistical aberrations, we need to examine a wider field. Unfortunately, the population of focus investors is very small. Among the thousands of portfolio managers who manage money, there are only a scant few who manage concentrated portfolios. Thus we are left with the same challenge.
We were faced with this same problem when I wrote The Warren Buffett Portfolio: Mastering the Power of the Focus Investing Strategy (John Wiley & Sons, 1999). There were simply not enough focus investors to study and draw a statistically meaningful conclusion. So what did we do? We went inside a statistical laboratory and designed a universe with 3,000 focus portfolios.38
Using the Compustat database of common stock returns, we isolated 1,200 companies that displayed measurable data—including revenues, earnings, and return on equity. We then asked the computer to randomly assemble, from these 1,200 companies, 12,000 portfolios of various sizes, forming four portfolio groups:
Next, we calculated the average annual return of each portfolio in each group over a 10-year period (1987–1996). Then we compared the returns of the four portfolio groups to the overall stock market (defined as the Standard & Poor’s 500 index) for the same period.
From all this, one key finding emerged: When we reduced the number of stocks in a portfolio, we began to increase the probability of generating returns that were higher than the market’s rate of return. But, not surprisingly, at the same time we also increased the probability of generating lower returns.
To reinforce the first conclusion, we found some remarkable statistics when we sorted the data:
With a 250-stock portfolio, you have a one-in-50 chance of beating the market. With a 15-stock portfolio your chances increase dramatically, to one in four.
Another important consideration: In this study, I did not factor in the effect of trading expenses. Obviously, where the portfolio turnover ratio is high, so are the costs. These costs work to lower the return for investors.
As to the second conclusion, it simply reinforces the critical importance of intelligent stock selection. It is no coincidence that the superinvestors of Buffettville are also superior stock pickers. If you run a focus portfolio and do not have good stock-picking skills, the underperformance could be striking. However, if you develop the skill set to pick the right companies, then outsized returns can be achieved by focusing your portfolio on your best ideas.
When we isolated our 3,000 focus portfolios in the statistical laboratory 15 years ago, it was a simple and straightforward exercise. Since then, academia has delved further into the concept of focus investing, examining the behavior of different sized portfolios and studying the performance returns over much longer time periods. Most notably, K. J. Martijn Cremers and Antti Petajisto have together conducted a thorough research investigation into focus investing based on a concept they call “active share.”39
Active share represents the percentage share of a portfolio that is different from the benchmark index holdings the portfolio is measured against. It is stated as a percentage and takes into account not only security selection but also the overweighting and underweighting of the stocks in the portfolio compared to the weighting in the benchmark. According to Cremers and Petajisto, portfolio managers with an active share of 60 percent or less are closet indexers. That is, their portfolios closely resemble the index they are being measured against. A portfolio within active share of 80 percent or more is a portfolio that is truly different from the index.
Cremers and Petajisto computed the active share for domestic equity mutual funds from 1980 to 2003. They were able to relate the active share to the fund’s characteristics, including size, expenses, turnover ratios, and performance. What did they discover? Active share predicts fund performance. The mutual funds with the highest active share, funds that were the most different from the index, significantly outperformed their benchmark, whereas those with the lowest active share underperformed their benchmarks.
Interestingly, Cremers reports that in 1980, 50 percent of large-cap mutual funds had an active share score of 80 percent or more. That is, half the mutual funds had a portfolio that was significantly different from their benchmark. Today, just 25 percent of mutual funds are considered truly active. “Both investors and fund managers have become more benchmark-aware,” says Cremers. “As a manager, you want to avoid being in the bottom 20% or 40% (of your peer group). The safest way to do that, especially when you’re evaluated over the shorter time periods, is to hug the index.”40
Because investors habitually take money away from underperforming mutual funds, portfolio managers have increasingly made their portfolios more similar to indexes, thereby reducing the chance they will significantly underperform the index. Of course, as we learned, the more your portfolio resembles the index, the less likely you are to outperform it. It is important to remember that any portfolio manager who has a portfolio that is different from the benchmark, however small a difference, is an active portfolio manager. The only question that remains is: How active is your portfolio?
In that famous speech about Graham-and-Doddsville, Warren Buffett said many important things, none more profound than this: “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.”41
It is profound because of where it leads us. If we accept the idea that prices are not always rational, then we are freed from the shortsightedness of using them as the sole basis for decisions. If we accept the idea that share price is not everything, we can broaden our horizon to focus on what counts: the thoughtful research and analysis of the underlying business. Of course, we always want to be aware of prices, so we can recognize when they dip below value, but we need no longer be strangled by this one-dimensional measure that can so seriously lead us in the wrong direction.
Making this shift will not be easy. Our entire industry—money managers, institutional investors, and all manner of individual investors—is price myopic. If the price of a particular stock is going up, we assume good things are happening; if the price starts to go down, we assume something bad is happening, and we act accordingly.
It’s a poor mental habit, and it is exacerbated by another: evaluating price performance over very short periods of time. Not only are we depending solely on the wrong thing (price), Buffett would say, but we’re looking at it too often and we’re too quick to jump when we don’t like what we see.
This double-barreled foolishness—this price-based, short-term mentality—is a flawed way of thinking, and it shows up at every level in our business. It is what prompts some people to check stock quotes every day, sometimes every hour. It is why institutional investors, with responsibility for billions of dollars, are ready to buy and sell at the snap of a finger. It is the reason managers of mutual funds churn the stocks in the fund’s portfolio at dizzying rates: They think it is their job to do so.
Amazingly, those same money managers are the first to urge their clients to remain calm when things start to look shaky. They send out reassuring letters lauding the virtue of staying the course. So why don’t they practice what they preach?
It is especially easy to observe this contradiction in the handling of mutual funds because their actions are so thoroughly documented and scrutinized by the financial press. Because so much information is available, and because mutual funds are so familiar and well understood, I believe we can learn a great deal about the folly of a price-based measure by looking at how it works in mutual funds.
The financial writer Joseph Nocera has pointed out the inconsistencies between what mutual fund managers recommend shareholders do—namely, “buy and hold”—and what managers actually do with their own portfolios—namely, buy-sell, buy-sell, buy-sell. Reinforcing his own observations of this double standard, Nocera quoted Morningstar’s Don Phillips: “There is huge disconnect between what the fund industry does and what it tells investors to do.”42
The obvious question becomes: If investors are counseled to buy and hold, why do managers frenetically buy and sell stocks each year? The answer, says Nocera, “is that the internal dynamics of the fund industry make it almost impossible for fund managers to look beyond the short term.”43 Why? Because the business of mutual funds has turned into a senseless short-term game of who has the best performance, measured totally by price.
Today, there is substantial pressure on portfolio managers to generate eye-catching short-term performance numbers. These numbers attract a lot of attention. Every three months, leading publications such as the Wall Street Journal and Barron’s publish quarterly performance rankings of mutual funds. The funds that have done the best in the past three months move to the top of the list, are praised by financial commentators on television and in newspapers, rush to put out self-congratulatory advertising and promotional pieces, and attract a flurry of new deposits. Investors, who have been waiting to see which fund manager has the so-called hot hand, pounce on these rankings. Indeed, quarterly performance rankings are increasingly used to separate those managers deemed gifted from those who are mediocre.
The fixation on short-term price performance, while acutely obvious in mutual funds, is not limited to them; it dominates thinking throughout the investment industry. We are no longer in an environment where managers are measured over the long term. Even people who function as their own manager are infected by the unhealthy nuances of this environment. In many ways, we have become enslaved to a marketing machine that all but guarantees underperformance.
Caught in a vicious circle, there appears to be no way out. But of course, as we have learned, there is a way to improve investment performance. What we must do now is find a better way to measure performance. The cruel irony is that the strategy most likely to provide above-average returns over time appears to be incompatible with how we judge performance.
In 1986, V. Eugene Shahan, a Columbia University Business School alumnus and portfolio manager at U.S. Trust, wrote a follow-up article to Buffett’s “The Superinvestors of Graham-and-Doddsville.” In his piece, titled “Are Short-Term Performance and Value Investing Mutually Exclusive?,” Shahan took on the same question that we are now asking: How appropriate is it to measure a money manager’s skill on the basis of short-term performance?
He noted that, with the exception of Buffett himself, many of the people Buffett described as “Superinvestors”—undeniably skilled, undeniably successful—faced periods of short-term underperformance. In a money-management version of the tortoise and the hare, Shahan commented: “It may be another of life’s ironies that investors principally concerned with short-term performance may very well achieve it, but at the expense of long-term results. The outstanding records of Superinvestors of Graham-and-Doddsville were compiled with the apparent indifference to short-term performance.”44 In today’s mutual fund performance derby, Shahan explains, the Superinvestors of Graham-and-Doddsville would have been overlooked. The same is also true of the Superinvestors of Buffettville.
John Maynard Keynes, who managed the Chest Fund for 18 years, underperformed the market one-third of the time. Indeed, he underperformed the market the first three years he managed the fund, which put him behind the market by 18 percentage points.
The story is similar at the Sequoia Fund. In the marking period, Sequoia underperformed 37 percent of the time. Like Keynes, Ruane also had difficulty coming of age. “Over the years,” he confessed, “we have periodically qualified to be the Kings of Underperformance. We had the blurred vision to start the Sequoia Fund in mid-1970 and suffered the Chinese water torture of underperforming the S&P four straight years.” By the end of 1974, Sequoia was a whopping 36 percentage points behind the market. “We hid under the desk, didn’t answer the phones and wondered if the storm would ever clear.”45 But of course the storm did clear. Seven years after inception, Sequoia had gained 220 percent, versus a gain of 60 percent for the Standard & Poor’s 500 index.
Even Charlie Munger couldn’t escape the inevitable bumps of focus investing. Over 14 years, Charlie underperformed 36 percent of the time. Like other focus investors, he had a string of short-term bad luck. From 1972 through 1974, Munger fell behind the market by 37 percentage points. Over 17 years, Lou Simpson underperformed the market 24 percent of the time. His worst relative performance, which occurred in a one-year period, put him 15 percentage points behind the market.
Incidentally, we saw the same performance trends when we analyzed our laboratory of 3,000 focus portfolios. Of the 808 portfolios that beat the market over the 10-year period, an astonishing 95 percent endured some prolonged period of underperformance—three, four, five, or even six years out of 10.
What do you think would have happened to Keynes, Munger, Ruane, or Simpson if they were rookie managers starting their careers in today’s environment, which can see only the value of one year’s performance?
Yet, following the argument that using a focus strategy will likely result in periods of underperformance, how can we tell, just using price performance as our measure, whether we are looking at a very bright investor who is having a poor year (or even a poor three years) but will do well over the long haul, or a manager who perhaps lacks the skills to be a good focus investor?
We can well imagine what Warren Buffett might say. For him, the moral of the story is clear: We have to drop our insistence on price as the only measuring stick, and we have to break ourselves of the counterproductive habit of making short-term judgments.
But if price is not the measuring stick, what are we to use instead? “Nothing” is not a good answer. Even buy-and-hold strategists don’t recommend keeping your eyes shut. We just have to find another benchmark for measuring performance. Fortunately, there is one, and it is the cornerstone of how Buffett judges his performance and the performance of his operating units at Berkshire Hathaway.
Warren Buffett once said he “wouldn’t care if the stock market closed for a year or two. After all, it closes on Saturday and Sunday and that hasn’t bothered me yet.”46 It is true that “an actively trading market is useful, since it periodically presents us with mouth-watering opportunities,” said Buffett. “But by no means is it essential.”47
To fully appreciate this statement, you need to think carefully about what Buffett said next. “A prolonged suspension of trading in securities we hold would not bother us any more than does the lack of daily quotations for [Berkshire’s wholly owned subsidiaries]. Eventually our economic fate will be determined by the economic fate of the business we own, whether our ownership is partial [in the form of shares of stock] or total.”48
If you owned a business and there were no daily quotes to measure its performance, how would you determine your progress? Likely you would measure the growth in earnings, the increase in return on capital, or the improvement in operating margins. You simply would let the economics of the business dictate whether you were increasing or decreasing the value of your investment. In Buffett’s mind, the litmus test for measuring the performance of a publicly traded company is no different. “Charlie and I let our marketable equities tell us by their operating results—not by their daily, or evenly yearly, price quotations—whether our investments are successful,” he explains. “The market may ignore a business success for a while, but it eventually will confirm it.”49
But can we count on the market to reward us for picking the right companies? Can we draw a significantly strong correlation between the operating earnings of a company and its share price? The answer appears to be yes, if we are given the appropriate time horizon.
When we set out to determine how closely price and earnings are connected, using our laboratory group of 12,000 companies, we learned that the longer the time period, the stronger the correlation. With stocks held three years, the degree of correlation between stock price and operating earnings ranged from .131 to .360. (A correlation of .360 means that 36 percent of the variance in the price was explained by the variance in earnings.) With stocks held for five years, the correlation ranged from .374 to .599. In the 10-year holding period, the correlation between earnings and stock price increased to a range of .593 to .695.
This bears out Buffett’s thesis that, given enough time, the price of a business will align with the company’s economics. He cautions, though, that translation of earnings into share price is both “uneven” and “unpredictable.” Although the relationship between earnings and price strengthens over time, it is not always prescient. “While market values track business values quite well over long periods,” Buffett notes, “in any given year the relationship can gyrate capriciously.”50 Ben Graham gave us the same lesson: “In the short run the market is a voting machine but in the long run it is a weighing machine.”51
It is clear that Buffett is in no hurry to have the market affirm what he already believes is true. “The speed at which a business’s success is recognized, furthermore, is not that important as long as the company’s intrinsic value is increasing at a satisfactory rate,” he says. “In fact, delayed recognition can be an advantage: It may give you a chance to buy more of a good thing at a bargain price.”52
To help shareholders appreciate the value of Berkshire Hathaway’s large common stock investments, Buffett coined the term look-through earnings. Berkshire’s look-through earnings are made up of the operating earnings of its consolidated businesses (its subsidiaries), the retained earnings of its large common stock investments, and allowance for the tax that Berkshire would have to pay if the retained earnings were actually paid out.
The notion of look-through earnings was originally devised for Berkshire’s shareholders, but it also represents an important lesson for focus investors who seek a way to understand the value of their portfolio when, as will happen from time to time, share prices disengage from underlying economics. “The goal of each investor,” says Buffett, “should be to create a portfolio (in effect, a company), that will deliver him or her the highest possible look-through earnings a decade or so from now.”53
According to Buffett, since 1965 (the year Buffett took control of Berkshire Hathaway), the company’s look-through earnings have grown at almost the identical rate of the market value of its securities. However, the two have not always moved in lockstep. On many occasions, earnings moved ahead of prices; at other times, prices moved far ahead of earnings. What is important to remember is that the relationship works over time. “An approach of this kind,” counsels Buffett, “will force the investor to think about long-term business prospects rather than short-term market prospects, a perspective that will likely improve results.”54
When Buffett considers adding an investment, he first looks at what he already owns to see whether the new purchase is any better. What Berkshire owns today is an economic measuring stick used to compare possible acquisitions. “What Buffett is saying is something very useful to practically any investor,” Charlie Munger stresses. “For an ordinary individual, the best thing you already have should be your measuring stick.” What happens next is one of the most critical but widely overlooked secrets to increasing the value of a portfolio. “If the new thing you are considering purchasing is not better than what you already know is available,” says Charlie, “then it hasn’t met your threshold. This screens out 99 percent of what you see.”55
You already have at your disposal, with what you now own, an economic benchmark—a measuring stick. You can define your own personal economic benchmark in several different ways: look-through earnings, return on equity, or margin of safety, for example. When you buy or sell stock of a company in your portfolio, you have either raised or lowered your economic benchmark. The job of a portfolio manager who is a long-term owner of securities, and who believes future stock prices eventually will match with underlying economics, is to find ways to raise your benchmark.
If you step back and think for a moment, the Standard & Poor’s 500 index is a measuring stick. It is made up of 500 companies and each has its own economic return. To outperform the S&P 500 index over time—to raise that benchmark—we have to assemble and manage a portfolio of companies with economics that are superior to the average weighted economics of the index.
“If my universe of business possibilities was limited, say, to private companies in Omaha, I would, first, try to assess the long-term economic characteristics of each business,” says Buffett. “Second, assess the quality of the people in charge of running it; and third, try to buy into a few of the best operations at a sensible price. I certainly would not wish to own an equal part of every business in town. Why, then, should Berkshire take a different tack when dealing with the larger universe of public companies? And since finding great businesses and outstanding managers is so difficult, why should we discard proven products? Our motto is: If at first you do succeed, quit trying.”56
Focus investing is necessarily a long-term approach to investing. If we were to ask Buffett what he considers an ideal holding period, he would answer, “Forever”—so long as the company continues to generate above-average economics and management allocates the earnings of the company in a rational manner. “Inactivity strikes us as an intelligent behavior,” he explains. “Neither we nor most business managers would dream of feverishly trading highly profitable subsidiaries because a small move in the Federal Reserve’s discount rate was predicted or because some Wall Street pundit has reversed his views on the market. Why, then, should we behave differently with our minority positions in wonderful businesses?”57
Of course, if you own a lousy company, you require turnover. Otherwise, you end up owning the economics of a subpar business for a long time. But if you own a superior company, the last thing you want to do is sell it.
This slothlike approach to portfolio management may appear quirky to those accustomed to actively buying and selling stocks on a regular basis, but it does have two important economic benefits, in addition to growing capital at an above-average rate. It works to reduce transaction costs and to increase after-tax returns. Each advantage by itself is extremely valuable; their combined benefit is enormous.
In a review of 3,650 domestic stock funds, Morningstar, the Chicago-based researcher of mutual funds, discovered that funds with low turnover ratios generated superior returns compared to funds with higher turnover ratios. The Morningstar study found that, over a 10-year period, funds with turnover ratios of less than 20 percent were able to achieve returns 14 percent higher than funds with turnover rates of more than 100 percent.
This is one of those commonsense dynamics that is so obvious it is easily overlooked. The problem with high turnover is that all that trading adds brokerage costs to the fund, which works to lower your net returns.
Except in the case of nontaxable accounts, taxes are the biggest expense that investors face—higher than brokerage commissions and often higher than the expense ratio of running a fund. In fact, taxes have become one of the principal reasons why funds generate poor returns. “That is the bad news,” according to money managers Robert Jeffrey and Robert Arnott. They are the authors of “Is Your Alpha Big Enough to Cover Its Taxes?” which appeared in the well-respected Journal of Portfolio Management. “The good news,” they write, “is that there are trading strategies that can minimize these typically overlooked tax consequences.”58
In a nutshell, the key strategy involves another of those commonsense notions that is often underappreciated: the enormous value of the unrealized gain. When a stock appreciates in price but is not sold, the increase in value is an unrealized gain. No capital gains tax is owed until the stock is sold. If you leave the gain in place, your money compounds more forcefully.
Overall, investors have too often underestimated the enormous value of this unrealized gain—what Buffett calls an “interest-free loan from the Treasury.” To make his point, Buffett asks us to imagine what happens if you buy a $1 investment that doubles in price each year. If you sell the investment at the end of the first year, you would have a net gain of $0.66 (assuming you’re in the 34 percent tax bracket). Let’s say you reinvest the $1.66 and it doubles in value by the second year-end. If the investment continues to double each year, and you continue to sell, pay the tax, and reinvest the proceeds, at the end of 20 years you have a net gain of $25,200 after paying taxes of $13,000. If, instead, you purchase a $1 investment that doubles each year and is not sold until the end of 20 years, you would gain $692,000 after paying taxes of approximately $356,000.
The Jeffrey-Arnott study concluded that to achieve high after-tax returns, investors need to keep their average annual portfolio ratio somewhere between 0 and 20 percent. What strategies lend themselves best to low turnover rates? One possible approach is a low-turnover index fund. Another is a focus portfolio. “It sounds like premarital counseling advice,” say Jeffrey and Arnott, “namely, to try to build a portfolio that you can live with for a long, long time.”59
Before we end this chapter, it is critically important that you think seriously about what exactly a focus investing approach entails:
As a focus investor, your goal is to reach a level of understanding about your business that is unmatched on Wall Street. You may protest that this is unrealistic, but, considering what Wall Street promotes, it may not be as hard as you think. Wall Street sells short-term performance, emphasizing what may happen from quarter to quarter. Business owners, in contrast, are more interested in the long-term competitive advantages of the companies they own. If you are willing to work hard at studying businesses, you will likely get to know, over time, more about the companies whose stocks you own than the average investor, and that is all you need to gain an advantage.
Some investors would rather chatter about “what the market is doing” than bother to read an annual report. But a cocktail conversation about the future direction of the stock market and interest rates will be far less profitable than spending 30 minutes reading the last communication provided by the company in which you are invested.
Does that seem like too much effort? It’s easier than you might think. There are no computer programs to learn or two-inch-thick investment banking manuals to decipher. You do not have to become an MBA-level authority on business valuation to profit from the focus approach. There is nothing scientific about valuing a business and then paying a price that is below this business value.
“You don’t need to be a rocket scientist,” confesses Buffett. “Investing is not a game where the 160 IQ guy beats the guy with the 130 IQ. The size of an investor’s brain is less important than his ability to detach the brain from the emotions.”60 Changing the way you approach investing, including how you will, going forward, interact with the stock market, will involve some emotional and psychological adjustments. Even though you may fully accept the mathematical arguments for focus investing, and even though you see that other very smart people have been successful with it, you may still feel some hesitation—emotionally speaking.
The key is to keep the emotions in appropriate perspective, and that is much easier if you understand something of the basic psychology involved in applying the Warren Buffett Way—which brings us to the next chapter.
Notes
1. Conversation with Warren Buffett, August 1994.
2. Dan Callaghan, Legg Mason Capital Management/Morningstar Mutual Funds.
3. Berkshire Hathaway Annual Report, 1993, 15.
4. Ibid.
5. Conversation with Warren Buffett, August 1994.
6. Outstanding Investor Digest, August 10, 1995, 63.
7. Ibid.
8. Peter L. Bernstein, Against the Gods (New York: John Wiley & Sons, 1996), 63.
9. Ibid.
10. Ibid.
11. Outstanding Investor Digest, May 5, 1995, 49.
12. Robert L. Winkler, An Introduction to Bayesian Inference and Decision (New York: Holt, Rinehart & Winston, 1972), 17.
13. Andrew Kilpatrick, Of Permanent Value: The Story of Warren Buffett (Birmingham, AL: AKPE, 1998), 800.
14. Outstanding Investor Digest, April 18, 1990, 16.
15. Ibid.
16. Outstanding Investor Digest, June 23, 1994, 19.
17. Edward O. Thorp, Beat the Dealer: A Winning Strategy for the Game of Twenty-One (New York: Vintage Books, 1962).
18. I am indebted to Bill Miller for pointing out the J. L. Kelly growth model.
19. C. E. Shannon, “A Mathematical Theory of Communication,” Bell System Technical Journal 27, no. 3 (July 1948).
20. J. L. Kelly Jr., “A New Interpretation of Information Rate,” Bell System Technical Journal 35, no. 3 (July 1956).
21. Outstanding Investor Digest, May 5, 1995, 57.
22. Andrew Beyer, Picking Winners: A Horse Player’s Guide (New York: Houghton Mifflin, 1994), 178.
23. Outstanding Investor Digest, May 5, 1995, 58.
24. Benjamin Graham, The Memoirs of the Dean of Wall Street (New York: McGraw-Hill, 1996), 239.
25. The speech was adapted as an article in the Columbia Business School’s publication Hermes (Fall 1984), with the same title. The remarks directly quoted here are from that article.
26. Warren Buffett, “The Superinvestors of Graham-and-Doddsville,” Hermes, Fall 1984. The superinvestors Buffett presented in the article included Walter Schloss, who worked at Graham-Newman Corporation in the mid-1950s, along with Buffett; Tom Knapp, another Graham-Newman alumnus, who later formed Tweedy, Browne Partners with Ed Anderson, also a Graham follower; Bill Ruane, a former Graham student who went on to establish the Sequoia Fund with Rick Cuniff; Buffett’s partner Charlie Munger; Rick Guerin of Pacific Partners; and Stan Perlmeter of Perlmeter Investments.
27. Berkshire Hathaway Annual Report, 1991, 15.
28. Jess H. Chua and Richard S. Woodward, “J. M. Keynes’s Investment Performance: A Note,” Journal of Finance 38, no. 1 (March 1983).
29. Ibid.
30. Ibid.
31. Buffett, “Superinvestors.”
32. Ibid.
33. Ibid.
34. Sequoia Fund Annual Report, 1996.
35. Solveig Jansson, “GEICO Sticks to Its Last,” Institutional Investor, July 1986, 130.
36. Berkshire Hathaway Annual Report, 1986, 15.
37. Berkshire Hathaway Annual Report, 1995, 10.
38. The research described here was conducted with Joan Lamm-Tennant, PhD, at Villanova University.
39. K. J. Martijn Cremers and Antti Petajisto, “How Active Is Your Fund Manager? A New Measure That Predicts Performance,” Yale ICF Working Paper No. 06-14, March 31, 2009.
40. “Active Funds Come out of the Closet,” Barron’s, November 17, 2012.
41. Buffett, “Superinvestors.”
42. Joseph Nocera, “Who’s Got the Answers?,” Fortune, November 24, 1997, 329.
43. Ibid.
44. V. Eugene Shahan, “Are Short-Term Performance and Value Investing Mutually Exclusive?,” Hermes, Spring 1986.
45. Sequoia Fund, Quarterly Report, March 31, 1996.
46. A Warren Buffett widely quoted remark.
47. Berkshire Hathaway Annual Report, 1987, 14.
48. Ibid.
49. Ibid.
50. Berkshire Hathaway Annual Report, 1981, 39.
51. Benjamin Graham and David Dodd, Security Analysis, 3rd ed. (New York: McGraw-Hill, 1951).
52. Berkshire Hathaway Annual Report, 1987, 15.
53. Berkshire Hathaway Annual Report, 1991, 8.
54. Ibid.
55. Outstanding Investor Digest, August 10, 1995, 10.
56. Berkshire Hathaway Annual Report, 1991, 15.
57. Berkshire Hathaway Annual Report, 1996.
58. Robert Jeffrey and Robert Arnott, “Is Your Alpha Big Enough to Cover Its Taxes?,” Journal of Portfolio Management, Spring 1993.
59. Ibid.
60. Brett Duval Fromson, “Are These the New Warren Buffetts?,” Fortune, October 30, 1989, from Carol Loomis, Tap Dancing to Work: Warren Buffett on Practically Everything, 1966–2012 (New York: Time Inc., 2012), 101.