4. Exploit Your Edge; Eliminate Your Weakness II—Battling Yourself

I am my own worst enemy—but at least I know it. After 25 years in the investment world, I have gradually learned to notice the warning signs that try to alert me to an attempted takeover by my instincts. Most of the time, I am able to ensure that my hard-won professional judgment triumphs in these battles, which is why I can say that my mantra, shared with many other investment advisers, is that “my clients pay me to lose sleep at night so that they don’t have to.” And I do lose sleep, because navigating the financial markets is never simple or straightforward. Like many of you, I endure market plunges with about the same level of fortitude as I cope with severe turbulence on an airplane flight: I tighten my seatbelt while wishing I could parachute to safety. “Why didn’t I sell?” I’ll ask myself, almost angrily. But I can just as easily get caught up in the euphoria of a bull market, merrily following a trend line to the sky.

You may not need to stretch out on an analyst’s couch for an intensive round of therapy before embarking on an investment program, but it might not hurt. The truth is that our own personalities and our opinions of the way the world should work predispose us to do things that end up looking downright foolish (with the benefit of 20/20 hindsight). Competing against well-armed professional investors is difficult enough. Add your own foibles to the mix, and the odds against developing a solid decision-making process look bleak. But if you want the upside of being a successful global macro investor, you need to find a way to ignore the noise—particularly the noise from inside your own head. How many investors, watching the stock market meltdown in the autumn, reacted in panic and sold their investments? Like many other investors, your instincts probably screamed, yes, yes, YES! But the logic of global macro responds with a firm no, unless all the data agrees, reassuring you this is the right thing to do. The essence of global macro investing is making fewer but more significant investment decisions. Sure, a small-cap mutual fund manager can pick the best stocks in his or her domain. But if that manager is convinced that real estate is a better bet than all the small-cap stocks out there, there is very little he or she can do about it. It’s up to you and I to figure that out for ourselves. To manage that, all of us need to understand the ways in which our instincts will try to sabotage our better judgment.

For centuries, philosophers and economists have propounded theories of efficiency that govern both markets and men. In the case of market analysis, this peaked with efficient market theory, which I described in the first chapter. By 1970—decades before the phrase insider information became a buzzword—it was already clear that there were problems with this model. The Sage of Omaha, as Warren Buffett is known, once said, “I’d be a bum in the street with a tin cup if the markets were efficient.” So Eugene Fama, a financial markets analyst at the University of Chicago, set out to find a way to adapt the model to the market’s realities. He ended up creating three “substates” of the efficient market hypothesis. The “strong form” closely reflected the original thesis that security prices always perfectly reflect all the information that is available, both public and private, assuming a level playing field exists and that information flows readily from one group to another. Fama recognized the obvious flaws in that model. But the “weak form,” which contended past prices and returns could be used to predict those in the future, was also weak. Technical analysts loved its deterministic nature, but technical analysis and the charts its practitioners rely on have their own flaws and inconsistencies. It’s possible to link some behavioral trends (such as the visible reluctance of investors to let a stock fall below the point at which they bought it, most clearly seen when a recently public stock is hovering above its initial public offering price) to technical analysis, but charts can’t serve as the basis for any solid investment program.

There are other, real-world, limitations on the degree to which markets can ever be utterly efficient. For instance, the efficiency theory assumes that all investors have a very long time horizon and that the menu of investment options doesn’t change throughout. Both assumptions are flawed; no one’s investment horizon stretches much past a decade (our personal and financial circumstances change too rapidly), while every decade seems to bring with it a new investment option that wasn’t previously available. Those limitations help explain why the investment community now widely accepts Fama’s “semistrong” interpretation of the efficient markets theory and why it serves as the basis for most empirical economic research. This view acknowledges the reality that Buffett described: Pockets of inefficiency exist in financial markets that canny investors can identify and exploit. Increasingly, scholars who scrutinize market and investor behavior conclude that any gaps between a pure efficient market and actual price action can be attributed to human quirks. And the more volatile the market, the greater the degree to which human behavior and social psychology affect those markets. Understanding human psychology, in other words, can help investors develop an investment framework and process that stands the best chance of success. Only when we all acknowledge our own weaknesses can we rise above them.

Americans like to look on emotion-driven markets as something that we, in our collective wisdom, have left in the past. Tomes like Extraordinary Popular Delusions and the Madness of Crowds¹ remain big sellers, but most of us prefer to see phenomena like Tulipmania as something of a historical curiosity. In fact, emotions still dominate intellect when it comes to questions of value, especially the value of something that we own (from a home to an old baseball card collection). All of us believe that our possessions are worth more than the highest bidder is willing to pay. I tested that theory—proving in the process the impact on our investment behavior—at a women’s investment club luncheon in Ponte Vedra Beach, Florida. I asked the women sitting around each of the tables to compete against their neighbors. I began by asking each to guess the total market capitalization of the Standard & Poor’s 500 Index. The winner at each table—the woman who came closest to guessing the correct answer—won the floral centerpiece decorating their table. In step two, I asked each new “owner” of the centerpiece to put a value on it, and asked her seven tablemates to do the same. Without exception, the “owners” decided that the centerpiece was worth more than twice as much as the highest price offered by the seven “bidders.”

When this kind of distorted perception of value is transplanted to an investment process, it can lead to a kind of paralysis. Not surprisingly, that doesn’t tend to produce solid results! Entire markets can freeze up in this way. Consider the real estate market crisis that began in 2007: As housing values declined nationwide, the inventory of unsold homes skyrocketed. Homeowners became unwilling to sell their property for less than they thought it was worth, while skittish buyers were unwilling to pay prices that they felt were unrealistic.

We all like to believe we are infinitely more rational thinkers than our ancestors, and we draw comfort in our self-delusion from the theories developed by economic philosophers like Adam Smith. Our favorite delusion—as spelled out by Smith and cherished ever since—is that when human beings are confronted with a set of facts, they will behave in the way best calculated to maximize their future financial security, physical safety, and comfort, a belief termed unbounded rationality. But individuals make decisions en masse in much the same way as the members of the Ponte Vedra investment club did individually when it came to pinning a value on a floral arrangement: They let emotions ride roughshod over rationality. At best, argued Herbert Simon of the University of Chicago (a pioneer of the emerging field of behavioral economics) in the mid-1950s, we may be able to lay claim to “bounded rationality.” Emotions and biases constantly get in the way of making optimal investment decisions.

Back in 1999, two market analysts named Kenneth Froot and Emile Dabora decided to demonstrate how this worked in real life by studying the trading patterns of shares of two different sets of securities issued by Royal Dutch Shell. When the company was formed by a merger of Royal Dutch Petroleum in the Netherlands and Britain’s Shell Transport in 1907, both companies agreed that the securities of the two companies would trade on separate exchanges. Royal Dutch, traded in Amsterdam, would be worth 60% of the new company. Shell, traded on the London Stock Exchange, would account for the remaining 40%. In a truly efficient market, the stocks should have traded at a 6:4 ratio, after adjusting for currency fluctuations between the Dutch guilder (later the euro) and the British pound. Instead, Froot and Dabora reported that they deviated from this relationship by as much as 35%. That can only be called downright irrational. One possible reason for this is a psychological reluctance on the part of investors to abandon a market with which they were familiar and whose operations they understood, to transact business in an alien environment. The arbitrage opportunity—the possibility of buying the same thing for less money somewhere else—wasn’t enough to compensate them for the psychological stress of leaving home. Puzzled? Think of the devoted Mac computer user trying to persuade his friends to abandon their constantly crashing Windows-based personal computers in favor of a Mac. Despite the PC users’ technical difficulties, they dig in their heels; who wants to have to learn how to use a new computer system, even if it means fewer problems down the road?

Many investors have experienced firsthand the pricing anomalies that persist in closed-end mutual funds. (These funds have a finite number of shares, and an interested investor must buy those shares in the fund from a willing seller; in contrast, anyone interested in investing in an open-end mutual fund can simply send money to the fund company, which will issue new shares and put that capital to work.) Because of its structure, the unit price of an open-end mutual fund’s shares tends to mirror the net asset value of all the stocks owned in the fund’s portfolio on a per-share basis. In contrast, as described in a 1990 study for the Journal of Economic Perspectives, three researchers noted that the price of units in closed-end funds bore little relationship to the net asset value of the underlying holdings. In contrast to the flexible structure of open-end funds, issuing and redeeming shares as investors demand, the share capital of closed-end funds is fixed and finite. The researchers found the prices investors were willing to pay sellers to acquire shares in those closed-end funds had less to do with the net asset value of the holdings and more to do with their perception of the type of fund. In other words, sentiment triumphed. If small-cap stocks were out of favor with investors, closed-end funds containing small caps traded below the net asset value of their holdings—and the gap was wider than any discounts applied to other closed-end funds. Meanwhile, open-end small-cap funds continued to trade at or even above their net asset value!

This kind of human irrationality can be seen throughout the financial markets. This is why I am writing a book about the benefits of global macro investing. Bottom-up stock picking and top-down market selection are not mutually exclusive. You can certainly employ stock selection as part of a global-macro approach. The issue is really a matter of time and effort because if you can get market selection right, then successful investing can be achieved with substantially fewer critical decisions. After a quarter of a century as a professional investor, I’ve come to accept that there is no way I can consistently deliver top-quality risk-adjusted returns to my investors without adhering to a rigid global macro investment discipline as part of my strategy. I have to fight this on a daily basis, as I did when I returned to Chicago after my trip to see the Canadian oil sands operations. I had to resist the stock’s siren call. I knew that I didn’t have enough insight into the company. Happily, I redirected my enthusiasm for Shell into an investment decision higher up that tree: I invested in a portfolio of companies with oil sands holdings. But every time I encounter an intriguing stock, I have to fight the same battle all over again.

The marketing hoopla that surrounds Wall Street conspires against us as investors—and if we listen to it, we will succumb to our worst instincts. On any newsstand, a dozen or more magazines offer an array of hot stocks or exciting mutual funds or ETFs. Mutual fund ads trumpet the stock-picking prowess of managers. Millions tune into CNBC, use online tools provided by Yahoo! or Google to compare stock charts, and ponder technical trading patterns. Magazine editors, television producers, and online site managers all know the truth: that their audience loves “news you can use.” The phenomenon reaches its zenith with Jim Cramer, the hedge fund manager turned CNBC market commentator. Cramer is a Wall Street veteran who joined Goldman Sachs in 1984 and began picking stocks for his own hedge fund in 1987. And wow, is he entertaining, jumping around the set, flinging stuffed bulls and bears at his delighted audience. He rants about stocks—stocks he likes, stocks he loves, and stocks he hates—but nearly every day, it’s a different stock. Cramer is capitalizing on his edge, his ability to grab your attention and turn you into an adrenaline junkie.

What would happen to Jim Cramer’s ratings if he were to change his formula to concentrate on the relationship between large- and small-cap stocks? Picture it: “For 100 consecutive trading days, large caps have looked cheap relative to small caps! On a forward P/E basis, small caps continue to trade more than one standard deviation above their relative valuation range, but small-cap momentum remains powerful! I would like to see that momentum break down before I could recommend a shift out of small caps in favor of larger stocks!” Cue the yawns.

But there’s a lot of money to be made exploiting the shifting valuations between large- and small-cap stocks. The problem? The kind of market trends that produce those returns for us can last 5 to 7 years without a major reversal. Consider the airline industry, an intensely competitive business where cash-strapped players jostle for a slightly smaller piece of a crowded market every day. When one player tries to boost fares by $25 to $50, the market watches to see whether other players match those hikes (and whether the market absorbs them). Gyrating fuel oil prices can wreak havoc on those airlines that didn’t hedge their exposure to this cost, and benefit those who did. In spite of all its uncertainties, the airline industry still stands to benefit from any signs of global economic growth, so stock pickers looking for ways to “play” the theme of synchronized global growth may want to add an airline stock to their portfolio. But the best way to balance the risks of this move is to buy the sector and spread the risks. Only then can you lessen the company-specific risks.

The best-known piece of advice delivered by Fidelity’s legendary money manager Peter Lynch is to “buy what you know.” But his most helpful tip, delivered in One Up on Wall Street, is more thoughtful. Investors, he says, shouldn’t just buy a single stock in an industry that appeals to them, but all the companies in that industry. That way, he advises, they stand the best chance of cutting volatility and reducing the risk of making a poor individual stock selection on inadequate information. That is particularly true in the example I gave you in Chapter 2, “The World of Global Macro,” when the gains of one medical device company must come at the expense of a rival. But the same pattern applies even in industries like retailing, where income levels and the health of the economy is generally believed to serve as a rising tide lifting all boats. In fact, when I studied the performance of four of the largest retailers in the period from 1993 to 2007, the degree of divergence in the results astonished me. The best performer of the four was Kohl’s, which posted an annualized 27.8% return, while Costco limped in with a mere 8.3% return. (In the middle were Wal-Mart and Target.) Every year, returns varied dramatically, with a median gap of 35.7 percentage points between the top and bottom performers—an immense difference. As ever, buying an equal-weighted portfolio of all four companies would have been the best strategy, returning an annualized 19.4% with less risk. And of course, as I’ve already pointed out, you only have to make and monitor one decision: whether the economy favors retailers in general.

Every 15 years or so, financial markets are rocked by the kind of violent waves of selling pressure such as that of the autumn of 2008. During that most recent wave of selling pressure, hedge funds and other investors who had relied on leverage to help them boost their returns were forced to sell whatever they could to meet margin calls or finance redemptions. Nearly all asset classes headed south in unison; conventional asset allocation, which relies on diversification as a hedge against losses, proved to be fruitless. Even investment-grade bonds—an asset class that rarely rises or falls more than a few percentage points in any given calendar year—plunged nearly 20% during the third quarter of 2008. Panicky or desperate investors tossed out the wheat with the chaff. The selloff created an array of unique buying opportunities in higher-quality asset classes such as investment-grade bonds and blue-chip stocks.

It may be this inherent optimism that leads even experienced professional investors to believe they can outsmart the market. Certainly, being a positive thinker might help get us through life with our sanity intact. Even medical research shows cancer and heart disease patients with an upbeat attitude have better outcomes or quality of life. Indeed, if we lose our confidence that the future will be better than the past, we forfeit our joie de vivre. We got a hefty dose of what a world dominated by pessimism might look like in the autumn of 2008, when Wall Street institutions began to topple like so many dominos, the credit crunch bit deep, and pundits began to draw gloomy comparisons with the 1929 stock market crash and the Great Depression. The money markets failed to function as panic gripped institutional investors. In one day alone, investors yanked more than $180 billion out of the stock market.

Conversely, an investor who is upbeat may become irrationally confident. Las Vegas’s entire existence is based on that fact. Yes, the casino may always win in the long run. But why shouldn’t I be the person who triumphs at the slot machine tonight? And if it doesn’t happen tonight, well, tomorrow I may be luckier! The same spirit of unquenchable optimism possesses the stock market “gambler”—the investor who persists in acting on limited information and relying on tips and the opinions of others. Left unchecked, that kind of over-confidence will lead an investor to buy or sell based on a hunch or “feeling in my gut” rather than on information. In their 1999 study, entitled “Unskilled and Unaware,” two Cornell University professors, Justin Kruger and David Dunning, asked a group of undergraduate students to predict their ability to recognize which jokes other people would find funny. On average, the undergrads thought they were pretty good at this; specifically, they put themselves in the 66th percentile. What’s interesting about the study isn’t whether they were correct in this assessment; it was the outsize confidence in something that wasn’t in their area of expertise. (None of them was moonlighting as a standup comic.)

Alas, in people with some kind of expertise, self-confidence teeters on the brink of hubris. It’s human nature; all of us tend to overestimate our skills. Why else would an overwhelming majority of those surveyed view themselves as “better than average” drivers? Let’s be honest: Most of us reached our current positions by making good, profitable decisions in the past. (And therein lies another human foible that afflicts investors: the tendency to take credit for past favorable outcomes, regardless of whether our decisions had anything to do with the outcome, or whether we were just very, very lucky. That’s the reason probably more than half of today’s mutual fund portfolio managers believe they’re better than the median.) Overconfidence may also make some pros overactive, buying and selling securities rapidly as their opinions fluctuate or as they respond intuitively to fresh news. The efficient market hypothesis dictates that investors should rarely trade, and yet more than a billion shares of the 30 stocks in the Dow Jones Industrial Average change hands every day. Some of the savviest veterans hold on to their stock positions for years at a time. Bill Miller, whose returns topped those of the S&P 500 Index for an astounding 15 years in a row, did so with an average annual turnover of only 13% in his fund, the Legg Mason Value Trust. That suggests that his average holding period is more than 7 years. In contrast, look at the myriad day traders who sprang up in the 1990s, making a living by reacting to each small change in a stock price by placing a buy or sell order. Few survived, and that approach to “investing” has been largely discredited.

The greater the trading volume and the more active the market, the more investors want to react. That’s because many of us tend to view price changes as if they were infallible signals of some fundamental change in the securities we own. If the price changes, something must be different, we reason—and we must react. Of course, if you succumb to this urge, you are just generating still more market “noise.” In 1991, Robert Shiller of Yale University surveyed the reactions of individual and institutional investors caught up in the stock market crash of October 19, 1987, when the S&P 500 plunged by more than 23% in a single day. Shiller found that investors who placed sell orders weren’t doing so in response to any news event, other than the “news” that the stock market was falling. The dramatic drop in valuations became a vicious cycle, as one investor after another sprinted for the exit without bothering to stop and think whether any fundamental events caused the apparent cataclysm. As Shiller found, there weren’t: Not a single piece of economic news existed that could have explained such a widespread selloff. For advocates of the efficient market theory, Black Monday is one of the hardest market events to explain, because there appears to be no reason for the selloff other than speculation, sentiment, and the “lemming phenomenon” of investors chasing after each other in the unwarranted assumption that the leaders of the pack must know what they are doing. Indeed, sellers on Black Monday turned out to be behaving in the same kind of self-destructive fashion as lemmings jumping off a cliff: It was investors who ran counter to the trend and bought stocks that day who emerged as the biggest winners. Any investor who had the foresight or fortitude to buy the stocks in the S&P 500 Index or an index fund on Tuesday morning, after the market had plummeted more than 20% the preceding day, would have scored a remarkable 15% return over the next 2 days (a coup by any standards).

The market crash of the fall of 2008 was, like Black Monday, one of the few occasions when investors found it easier to sell than to buy. In the midst of the incredible volatility in the Dow Jones Industrial Average and other major stock market indicators, anxious investors felt compelled to hit the “sell” button. Valuation issues had little to do with the rout; rather, the catalyst was the impact of lenders tightening their borrowing criteria on hedge funds already leveraged to the hilt. Generally speaking, however, we are financial pack rats: We buy and we hang on to our investments because we don’t like to relinquish our winners and don’t like to be forced to take a loss by selling our laggards. Psychologically, the pain of losing a dollar is greater than the joy of winning one. Suppose someone is offered the chance to flip a coin, and told that if it turned up heads, they will pocket $11, and if tails turns up, they will lose $10? Analytically, the odds are in our favor. However, most of us would pass up the opportunity to play because the chance of making $11 is offset by the potential pain of giving up our $10 bill. That might explain why, generally speaking, investors are so reluctant to realize their losses—even in the face of tax policies that allow us to use these losses to offset big realized gains elsewhere in our portfolios. It might also account for the kind of mass rush for shelter of the kind that occurred on Black Monday in 1987 and again in September and October 2008 when we collectively abandon hope that one day we will be able to show a profit on our portfolio.

The only way to protect ourselves and our portfolios from this level of irrational behavior is to establish a rational investment process that minimizes the opportunity for poor judgment, and to stick to it through thick and thin. As investors, we need to learn from Odysseus, who lashed himself to his ship’s mast and stuffed the ears of his mariners with wax to resist the Sirens who lured mariners to their doom on nearby shoals.

The key to a rational investment process is to minimize the opportunities for our weaknesses to show themselves. A global macro decision-making process accomplishes this because it requires making fewer decisions and so creates fewer opportunities to be distracted. At the same time, each decision is likely to make a bigger difference to a portfolio. To build this kind of investment process, however, you need to put inductive reasoning front and center, because that relies on making independent observations before drawing a conclusion based on those observations. Perhaps one of the most familiar pieces of inductive reasoning is the following: All the crows I have ever seen are black; therefore, every crow in the world is black. In contrast, deductive reasoning starts with a conclusion, and then identifies data that supports that view (the kind of approach that Robert Gates once criticized me for following). Deductive logic runs as follows, in contrast: All birds fly. Crows are birds. Therefore crows fly.

As you can see, deductive reasoning holds many behavioral traps. For instance, an investment strategist may believe that energy stocks will underperform the market this year and find many pieces of data to support that conclusion. These can then be assembled in such a way as to generate a compelling story to which investors will respond with enthusiasm. The problem is the strategist’s enthusiasm may cause him or her to overlook, downplay, or shun (consciously or unconsciously) contradictory information that doesn’t support that conclusion. In contrast, an inductive process would have forced the strategist to consider many facts before reaching a conclusion. Let’s go back to the crow example. Before stating conclusively that all crows are black, you might want to ponder the genetic makeup of crows to determine whether white crows are possible, or scrutinize naturalists’ reports to discover if any have been reported. Inductive logic points you toward the conclusion but forces you to consider alternative scenarios.

Strategists, by nature, are confident professionals, most of whom have spent decades toiling in the investment business. Without incorporating an inductive process, there is no certainty that their future calls will be as successful; after all, those recommendations could be little more than emotion-based hunches bolstered by carefully selected and presented data points. And given the immense quantities of data available about anything you can imagine, it’s all too easy these days for sloppy thinkers to start out with a hunch and find facts that support their theory. Nothing can illustrate the power of conviction more dramatically than looking at the mysterious case of the Hummer aficionados. A vehicle originally developed for military use, Hummers are gas guzzlers that hog the road, are hard to park, and aren’t that useful. Still, Hummer drivers are die-hard fans of their vehicles and treat them almost as patriotic symbols. Even as the conflict in Iraq intensified and as gasoline prices soared, Hummer fans flaunted their vehicles. Eventually, the Hummer became exceedingly expensive to keep on the streets, and it wasn’t until their pride rapidly eroded that some Hummer owners began to swap their prizes for environmentally friendly hybrids.

Inductive reasoning isn’t infallible. After all, just because the only crows I have ever seen are black doesn’t mean that somewhere in the world there isn’t a unique breed of black-and-white striped crows flying around. I may never see one, or they may not exist at all, but it would be irrational to scour the globe in search of an exception to what has been an observable phenomenon throughout my life and the lives of everyone I know. Therefore, if the evidence supports it, I can reasonably conclude that it is safe to act as if all crows are black. Equally, it is reasonable to base investment decisions on data, or metrics, with a demonstrated ability over time to forecast a certain outcome. Adopting this kind of “bottom-up” approach helps me to remove raw emotion from the process. I can then make sound investment decisions by turning to indicators that have previously demonstrated their reliability. By so doing, I minimize my human urge to dig for data that supports a predetermined investment decision (for instance, the urge to dump my stock investments during a stock market rout like that of 2008 in response to what others are doing, instead of looking to see what the fundamental signals are telling me to do).

When you are in the midst of a massive market decline, the events are almost too traumatic to process immediately. That was certainly the case in September 2008 when the House of Representatives unexpectedly rejected a proposed rescue package for the banking industry in the wake of the Lehman Brothers bankruptcy. Instead of Congress resigning themselves to a $700 billion bailout package, investors had to suffer a $1 trillion loss in market capitalization, as the Dow Jones average plunged 8%. The move caught me completely off guard. I had been sitting in my office, writing my monthly newsletter to clients, when I answered the phone to a journalist asking me what would happen next. Next? Confused, I glanced at the stock quote terminal on my desk and was transfixed by what I saw: The Dow Jones benchmark had fallen 700 points. My first reaction was anger. Didn’t Congress understand the gravity of the situation; the fact that if left unchecked, the credit crunch could cause the collapse of our financial system? I was angry, but also scared. A week later, however, I used the forced and panicky selling of the good, the bad, and the ugly in the stock market as a buying opportunity. The “good” stocks had been hammered so indiscriminately that they were trading at the same valuations as their “bad” and “ugly” counterparts.

To develop a process that will help you function in the midst of an emotional storm of this kind, you need to identify metrics that will help you single out the best opportunities in the financial markets while avoiding as many sources of risk as possible. Back in the final months of 1999, for instance, the stock market was in the final innings of the dot.com bubble. Many investors had drunk the Kool-Aid and waxed rhapsodic about a “new paradigm,” while strategists who fretted about lofty market valuations found themselves fearing losing their jobs for becoming too bearish too soon. But investors who had kept an eye on the Federal Reserve valuation model (which measures the market’s earnings yield against the prevailing 10-year Treasury rate) throughout the late 1990s would have noticed that by the fourth quarter of 1999 this critical fundamental signal suggested stocks were trading about 70% above their fair value. Although the degree to which that model signals the precise degree of over- or undervaluation might be up for debate, the sheer magnitude of the extent to which the market was out of whack made the model a valuable indicator. Indeed, it turned out to be a rather accurate tool: Over the next 3 years, the S&P 500 Index tumbled 33%, while the Nasdaq Composite Index plunged 56%.

You may find an investment approach that is based on numbers and sets of data feels boring at first. But it’s far safer than chasing the “buzz” surrounding a single stock. Learn to be comfortable working with numbers, because building a solid investment process is all about finding the right sets of reliable metrics.

Endnotes

¹ Mackay, Charles. Extraordinary Popular Delusions and the Madness of Crowds. First published 1841; there are many reissues.