5. Identifying Metrics

In the 1920s, the great British mathematician G. H. Hardy paid a call on his ailing protégé, the self-taught prodigy Srinivasa Ramanujan. En route to Ramanujan’s home in Putney, London, he noted the number of the cab: 1729. On his arrival, he noted to Ramanujan that the number “seemed to me rather a dull one, and that I hoped it was not an unfavorable omen” for Ramanujan’s health. But the Indian mathematician—still seen as one of the world’s greatest mathematical geniuses today—countered instantly that the number had immense significance. “It is the smallest number expressible as the sum of two cubes in different ways.”

A few fortunate souls find numbers as easy to understand and manipulate as a high-school graduate finds the words in a first-grader’s reading primer. When they look at a phone number, they don’t see a series of random digits. Instead patterns combining prime and nonprime numbers take shape before their eyes. For them, conversing about numbers and their significance is as simple as chatting about the weather for the rest of us. Alas, most of us will never be able to compete in the International Mathematical Olympiad, much less win a gold medal or devise new mathematical proofs. With concentration and practice, however, you can toss your math phobia into the garbage, along with all the other bad habits that prevent you from making the best possible investment decisions, and embrace the world of metrics.

What are metrics? Really, metrics is nothing more than a fancy word used to describe numbers—specifically sets of data—to bring some kind of order to the often-chaotic investment process. Any time I sit down to analyze a prospective investment, a vast array of data competes for my attention: the stock’s price/earnings ratio, its price/sales ratio, its stock performance relative to the industry or the market, the magnitude of the company’s growth in profits and revenues in absolute and relative terms, the numbers of consumers lining up to purchase its goods or services, the economic trends that may affect those purchasing decisions. The list goes on and on, ad infinitum. And yes, these tend to be expressed in scary, jargon-filled phrases of the kind television pundits love to toss around to show how sophisticated and knowledgeable they are, and how plugged in they are to the wacky world of Wall Street.

In fact, metrics are nothing more than tools that enable investors to take raw numbers and express them in a way that tells all of us something useful about whatever it is they are studying. Without them, it’s hard to make sensible decisions, since we would be operating in an information vacuum. For instance, when I turn the ignition key in my car, I start to rely on a series of data points, or metrics. When I pull onto the road and accelerate, my reflex is to glance at the tachometer to tell me when it’s time to switch from first gear to second, and then to third. As I reach the ramp leading to the highway, I know that I can accelerate. As a sensible driver, however, I keep an eye on the speedometer to ensure that I’m not tempting fate in the shape of a traffic cop eager to nab me for speeding. And as I travel, I glance at my fuel gauge from time to time, keeping an eye open for a signal that I need to refill the gas tank.

The underlying principle behind financial metrics isn’t very different from that governing the operations of a car’s dashboard. Instead of relying on a series of data points to help me drive safely, I’m counting on different metrics to tell me what is happening in the financial markets. As I have discovered, finding the right pieces of data and understanding the signals that data sends gives me information about changing market trends (including when new trends are emerging). What kind of metrics you, the reader, find useful will vary depending on the investment decision you are trying to make. A professional options trader trying to decide whether to bet that a stock trading for $25 is likely to climb above $35 over the next 6 weeks needs to monitor data about stock price trends, volatility, and the number of days left until the various options contracts expire. Every day, quantitative analysts devise new ways of crunching through the seemingly inexhaustible amounts of data that Wall Street produces. One hedge fund manager has even found a way to analyze newspaper stories about stocks and—based on the frequency with which certain words and phrases appear—figure out whether the article is likely to have a positive or negative impact on the stock. Those calculations are run in fractions of a second on hundreds of newspaper articles every day, and then combined with more than a thousand other metrics to suggest tiny changes to the hedge fund’s portfolio. Those calculations are run every 10 or 20 minutes, 24 hours a day, 7 days a week, 52 weeks a year.

Thankfully, most investors making long-term investment and asset-allocation decisions don’t need to worry about building models that can tackle such outsize quantitative investment challenges. Rather than chasing every minute shift in the market and its individual components, you want to climb back up that decision tree. That, in turn, means identifying what pieces of data or metrics will help us make those top-level decisions: the macro investment bets. What are the building blocks that help us gain an edge in the financial markets?

I first started pondering this question when, after a brief stint as a quantitative analyst at Keystone Funds (a Boston mutual fund group that is now part of the Evergreen family of funds), I started working as a bond trader at Constitution Capital Management, the institutional investment arm of Bank of New England (now part of Bank of America). It was a big promotion, and meant I could stop transferring Keystone’s investment data from handwritten green ledger sheets onto Lotus 1-2-3 programs on one of the first-generation IBM personal computers that were then the latest word in technological innovation. Instead, I would have the chance to manage a mortgage-backed bond fund. Gulp.

Suddenly, I found myself responsible for figuring out how consumers were likely to react to changes in interest rates; the rate at which they opted to refinance mortgages could have a dramatic impact on the value of the securities in my fund. In those days, before the 2008 collapse of Fannie Mae and Freddie Mac forced the government to nationalize the two lending agencies, I didn’t really need to worry about my investors losing their principal. Still, as early as 1989, the mortgage markets had begun to get complicated, as investment bankers chopped up the principal and cash flow of these bonds, and then divided and reassembled them into new products known as collateralized debt obligations, or CDOs. These now-infamous new structures taxed even the most elaborate valuation models, and volatility increased. Even though the value of the mortgage-backed bonds was guaranteed by government agencies, investors didn’t enjoy living through all those gyrations in the interim. They didn’t want to cope with either big swings in their portfolio values or short-term losses. They were counting on me to do more than just react to the trends that would cause gyrations in the bonds’ prices: They wanted me to anticipate those trends, and position the portfolio so that they would capture short-term profits as those trends unfolded. But that meant figuring out whether lots of homeowners would decide to renegotiate their mortgages as interest rates fell—causing high-interest loans to be repaid early. If so, no one would pay a premium for securities based on mortgages that carried above-average interest rates. The better I was at anticipating consumer behavior, the better my fund—and my career—would fare.

So I sat down and built a model, based on data sets and metrics, that anticipated mortgage-repayment behavior. I included all the available coupon and maturity combinations that were available in the mortgage market, and I updated it daily. There was so much data that the printout was about half an inch thick. Back in 1983, I had to run the program overnight because the computer’s capacity was so limited and its processor so slow. Every morning I picked up the printout and studied the results—and acted on them. I had assembled the right sets of metrics and bingo! I had an edge in navigating the complex mortgage-backed securities market. During the 4-year period I managed that fund, I trounced my mortgage-backed bond benchmark annually, without taking on any interest rate risk. All I had to do was pick out the cheapest mortgage securities in each part of the market, buy them and hold them, while making sure that the interest rate exposure on my portfolio matched that of the index. It wasn’t until an early Bloomberg data terminal introduced a similar analytical model that my edge vanished. At that point, anyone who could afford to rent a Bloomberg terminal had the same metrics.

Still, the message of how vital data can be stuck with me. That has become even more true as the amount of data available to us all has increased exponentially. As long as my analytical process is sound, the data I incorporate in that analysis is consistent, and the relationships are maintained, I know it’s still possible for me to turn to metrics to help me develop an edge in almost any kind of market. For instance, as I’ll demonstrate, if you keep track of the relationship between the yields on short-term and medium-term U.S. Treasury securities—what is known as the yield curve—you can obtain a lot of clues to the direction of the U.S. economy. For this to work consistently, you need to maintain the relationship. In other words, as you monitor the data and how it changes over time, you need to be sure that you are always using the same securities and maturities. And, as I’ll show you when I tell you more about putting all these tools to work to build your own global macro process, you need to test the data to make sure that it remains effective. All you need to do is identify which metrics are relevant for whatever investment question you want to answer, in the same way that I check my speedometer when I notice I’m moving faster than other traffic on the interstate.

The first step is figuring out what you are trying to analyze. Is it the valuation level of the stock market? The impact of the economy on the bond market? Once you’ve done that, the next stage is deciding what data will help you answer that question and what weight it should be given at each stage of the decision-making process. It’s easy, as I discovered to my chagrin when I was challenged for doing just this, to take a random statistic or piece of data and use it to produce (or rationalize) a buy or sell decision. But that’s about as helpful as using a sledgehammer to crack a peanut shell, and just as counterproductive. You need metrics that will be appropriate.

Let’s say that, because your portfolio includes some regional grocery and hardware retailers, you’re interested in what’s happening in retail stocks. You could study monthly consumer spending data. But if the companies you own do business mostly in California and the Southwest, then a recession on the East Coast that shows up in national data may not hurt your holdings. Or even if the downturn does filter through to the local economy, it might not damage purchases of staples like breakfast cereal or soup. So, you need data that is more regional, such as statewide consumer-spending data and store-specific results. You could develop metrics based on something like how many cars are parked in area shopping malls where your stores are located. How many parking spaces are available, and what percentage of them is filled? If that ratio has fallen over the past month, has a new mall been built that is siphoning off some business, and does that new mall contain the same mix of stores? You need to question any assumptions that you might be tempted to make. Perhaps the mall you are studying is new. In that case, the parking lot may be relatively empty simply because the owners built a large lot expecting to increase the size of the mall. There may be a smaller percentage of total parking capacity than at another mall. You may want to go further and study how many bags customers carry back to those cars, and what stores those customers seem to favor.

I’m not suggesting you should stake out your local malls; spending all your time counting cars and shopping bags can’t be considered a great use of your time or give you any kind of ‘edge’ at all. Rather, I’m trying to give you a sense of the range of data available to you as an investor and the need to seek out what will prove most helpful. Metrics should be a tool, something that enables us to evaluate and compare one market or investment option against another in much the same way that Michelin’s star rating system helps gourmets discern which restaurant is worthy of their attention. Michelin’s reviewers don’t just describe the meals they taste at thousands of restaurants around the world as “good” or “fabulous.” They use stars as proxies for their ratings; a restaurant awarded two stars is worth going out of your way to get to, while the ultimate accolade—three stars—is awarded to a restaurant whose food is worth a trip in its own right. (When French chef Bernard Loiseau heard rumors that his restaurant, Cote d’Or, was in danger of losing its third star in 2003, he committed suicide.)

Luckily, the metrics that you need to build a robust investment process are more straightforward than is the subjective process of evaluating whether a particular restaurant’s foie gras is delectable enough to command a premium rating. But it comes down to the same idea: Gourmets have their choice of restaurants; investors, their choice of what to put in their portfolios. If you don’t invest your bonus in the precious metals mutual fund you’re evaluating today, you’re not going to stick it in your mattress. Instead, you’ll pick another investment (alternative energy stocks, perhaps, or European bonds). A decision to opt for a particular restaurant or investment product comes with an opportunity cost. Deciding to go to that gourmet restaurant 10 miles away means you won’t be able to try out the barbeque joint that has just opened down the road. You might pay more for your meal, wait longer for a table, have to change from your jeans into formal attire, and then drive through heavy traffic. Similarly, deciding to allocate part of your portfolio to gold and other precious metals means you won’t be able to capture the upside potential in European bonds or technology stocks.

Metrics can help us decide whether those tradeoffs make sense. Just as Michelin’s two-star rating may reassure diners in search of a good meal that their trip will be worth all the extra effort, so properly selected investment metrics used appropriately boost the odds of finding the best combination of potential risks and returns. In the investment world, the classic tradeoff is what is known as the risk-free rate of return. That term comes from the perception that parking capital in short-term Treasury bills is the safest investment out there; that is, free of risk, because the U.S. government guarantees those obligations (and the interest payments). That guarantee is at the heart of the credibility of the financial system; being even a day late making interest payments on Treasury securities would damage public confidence exponentially more than any number of bankruptcies or bailouts of financial institutions could do. (Let’s face it, if the U.S. government defaults on its debt obligations, most of us will have much more to worry about than how our investment portfolios are performing.) As a result, these securities typically offer a minimal yield in exchange for minimal risk. At the height of the credit market panic in 2008, 3-month Treasury bills actually traded briefly at a negative yield, meaning that investors were willing to pay to preserve their capital, an indicator of how powerful this risk-free reputation is. Any investors who opt to put their capital in anything other than short-term Treasurys by definition will be accepting a greater degree of risk in the hope or expectation of being able to capture extra return. So their actual returns may usefully be compared against this risk-free rate of return.

Whenever you make an investment decision, you are really considering this kind of tradeoff between risk and return. The first decision that you make is likely to be whether you see stocks or bonds as offering the biggest possible upside. If you opt for stocks, the next question is automatic: U.S. stocks or foreign stocks? Let’s suppose you prefer to stick with U.S. stocks. Immediately, you must make more decisions: large-cap or small-cap stocks? Growth or value? Using such a step-by-step process, you force emotion—the enemy of wise investment decisions—out of the picture altogether. You may feel intuitively that large-cap stocks, or some large-cap stocks, are attractive. But if you use the right metrics at each step in this process and end with the conclusion that stocks in general look less attractive than bonds, you would have to admit that buying large-cap stocks would be about as rational as picking investments by throwing darts at a list of stock symbols posted on a wall. Picking the right metrics and using them the right way in this kind of decision-making process boosts the odds that you minimize your weaknesses.

Each step along the way, metrics can help you shape your investment expectations and clarify your goals. Picking the right metrics tells you how attractive each investment option is from a variety of standpoints. It’s up to you to “fit” the right evaluation process and the right sets of data to the investment decision you are trying to make. Every piece of data won’t have the same importance. You must identify which ones are crucial, and which are just interesting or amusing. Suppose you were asked to develop a metrics-based method for evaluating the success of that new barbeque restaurant. Some obvious data to monitor might include how long customers have to wait for a table, how busy the restaurant is on a typical evening, how many top ratings it receives from critics, and even how often its name pops up in newspapers or on the Internet as a celebrity dining spot. But if you fail to include some metric gauging how happy diners are with their experience (Are there enough servers? Do they get the orders right?), you risk missing an early warning sign.

Of course, data is far from scarce in the Internet era. Google the phrase U.K. housing data, and hundreds of Internet websites pop up, each offering different metrics on the housing market in England, Scotland, Wales, and Northern Ireland. One even provides an easily downloadable spreadsheet containing the average prices of housing in the country dating back to 1930. The challenge? Figuring out which data set will prove helpful in your particular quest.

Once you know what that quest is—what kind of investment decision you need to make, such as whether to opt for stocks, bonds, or precious metals—the next challenge is to take a 360-degree view of that decision. What are all the metrics that might exist that could have a bearing on your decision? Sometimes the challenge is sorting through a vast amount of data in search of what is most useful. In other cases, you’ll need to assign a figure to a concept or variable, such as the level of customer satisfaction at the barbeque restaurant. Only after you’ve defined your task and identified the useful data can you begin to use it to reach a conclusion. Some decisions will be easier than others, because more data is available. If you’re trying to figure out whether Russian or U.S. energy stocks are better investment bets, the lack of extensive financial data—and questions about its reliability—on the former will be a hurdle.

After you’ve assembled all the data you can, you need to evaluate and analyze it. In that process, reliability is a key ingredient. That means being aware of where that data came from in the first place. All investors are biased when it comes to deciding which sources are most reliable, just as all of us tend to evaluate facts or opinions depending on who provides them. For instance, many studies focusing on the way in which juries reach verdicts in criminal trials have shown that jurors place great weight on eyewitness testimony despite academic studies showing that such testimony is often flawed. (Eyewitnesses are notoriously bad at cross-racial identification, for starters, and many people take away wildly different impressions of the same dramatic but fleeting events.) Similarly, you may believe that government data is inherently reliable because you choose to trust in the authority of the government, or because you believe that the government is best positioned to capture the widest array of data. Here in the United States, data measurement has reached a fine art, but big swings are routine in such key sets of data as our quarterly gross domestic product. That figure is revised at least twice before being finalized—and then can be changed once more at the end of each year, when an annual figure is calculated. Elsewhere, government data may be still less reliable. In China, official GDP growth rates throughout the first years of the decade hovered between 9% and 10%. However, economists widely agreed that this data understated what was happening to the Chinese economy. Perhaps it would reveal how much inflation actually existed, or the magnitude of China’s need for raw materials. An even more robust economy might also cause China’s trading partners to press the country’s rulers even more aggressively to allow the yuan to appreciate against the dollar and the euro (a move that would hurt China’s export-oriented economy).

Adjusting data makes it more reliable—sometimes. For instance, data providers “seasonally adjust” construction or retailing industry data, as well as the prices of commodities like gasoline or heating oil. The summer driving season peaks between Memorial Day and July 4; in that period, gasoline prices also traditionally peak. New housing construction typically declines in bad weather and during the winter, so a plunge in housing starts in December can’t be interpreted as a sign that the real estate market is in the doldrums. (It probably has more to do with the fact that builders can’t dig foundations in frozen ground.)

One of the most common ways to use metrics is by referring to how a market benchmark, or index, performs. An index is just a basket of related investments. The granddaddy of them all, the Dow Jones Industrial Average, first published in 1896, contains 30 stocks that are industry giants and collectively measure what is happening across all major market sectors. When tracked historically, an index gives investors an idea of the kinds of returns they can expect if they invest in those securities. For instance, the Russell 2000 Index’s performance will give you an idea of what you might earn—at least, on a historical basis—by buying smaller-cap stocks. It will also give you a lot of information about the nature and degree of risk you’re taking, in both relative and absolute terms. Of course, indexes and their components change over time. When the Dow Jones average was created, it included stocks such as the American Cotton Oil Company, the American Sugar Company, and National Lead. Now it includes four financial firms and three technology giants that didn’t even exist a century ago.

The goal of anyone compiling an index is to provide us with a way to monitor a particular category of investment or asset class—large-cap stocks, say, or “junk” bonds issued by companies with poor credit ratings. Because each grouping is likely to react to a given set of economic data or other event in a similar fashion, because of their common characteristics, an index can serve as a benchmark. By comparing actual portfolio returns to that benchmark, investors can judge exactly how well they are doing, or how well a mutual fund manager is faring, relative to some kind of representative grouping of similar securities. As the array of possible investments has expanded, the experts of Wall Street have devised myriad new ways to slice and dice these asset classes into different categories, each one with its own index. Today it’s easy to find a small-cap value stock index, or a large-cap Southeast Asian stock index. You can even find an index measuring the performance of “frontier” markets such as Vietnam and Kazakhstan.

In many cases, this proliferation of indexes has been helpful. While rising interest rates generally cause bond prices to fall (higher rates make lower-yielding securities less valuable), not all fixed-income securities will react exactly the same way to, say, a 1 percentage point rise in interest rates. Other factors, such as the currency in which the instrument was issued and in which interest payments are made, the duration or lifespan of the bond, and the credit quality of the issuer, may all magnify (or dampen) the impact of that interest rate change. So the experts crafted narrower benchmarks against which European bonds, Latin American bonds, investment-grade corporate bonds, junk bonds, mortgage-backed securities and asset-backed securities—among others—can be measured. Each investor or money manager can pick the index against which they want performance to be measured. A large-cap stock picker usually chooses the S&P 500, but a small- or mid-cap investor is more likely to pick a Russell Index, and perhaps even a growth or value subindex. If you’re looking for some interesting reading, take a look at the quarterly fact sheet produced by a poorly performing mutual fund. In this marketing document (used to trumpet the manager’s savvy and intellect), writers go to great lengths to not only rationalize the fact the fund is lagging well behind its principal index, but to downplay this in the way the data is presented. The marketing team will present the manager’s returns for the 1-, 3- and 5-year period alongside those of the S&P 500, just as they are required to. But when that comparison makes the fund look bad, they’ll go on to include the performance of a few other indexes—the Russell 2000 Value, say—against which the fund has done much better, and explain why that is really a better way to measure the manager’s skill.

To be fair to investment managers whose investors demand that they not only keep pace with a given index but trounce it year after year, sometimes the problem does lie with the index itself. Occasionally, the metrics themselves don’t really measure what they are supposed to, or what they once did. For instance, today’s S&P 500 is a much different creature from that of 1999, when technology stocks dominated its returns and represented a whopping 33% of this common bellwether. That’s because the index is “cap weighted”: The stocks with the greatest market capitalization have the greatest percentage weight in the index. So, as the dot.com bubble inflated and tech stock valuations soared, so did the importance of technology in the S&P 500. As of the end of 2008, however, the index had only a 17% weighting in technology stocks.

Sometimes benchmarks become so flawed as to be useless. The Goldman Sachs Commodity Index, a basket of agricultural, industrial, and energy products, along with precious metals, was devised as a way to measure what’s happening to the commodity universe—the world of hard assets. But it proved an unreliable indicator of commodity prices, showing very different sets of characteristics in the 1980s, 1990s, and today. However, those characteristics don’t reflect any changes in the underlying market. Instead, the changes are attributable to the fact that the index was constructed without any provision for rebalancing it to reflect changes in the prices of individual commodities. Therefore, any outsize move in any single commodity (hardly unusual) permanently distorts the entire benchmark. There’s no way to rebalance the weightings to reflect, say, the importance of each commodity within the economy or some other impartial metric; the commodity with the biggest price gain is assumed to be the most important. As oil prices quadrupled in the 6 years beginning in early 2003, oil has become a much bigger part of the index today than it was two decades ago. If that reflects oil’s actual importance, it’s only by accident. Goldman Sachs may claim that the index is a useful economic barometer, but its flaws make it an inadequate commodities market benchmark.

In contrast, the Dow Jones-AIG Commodity index is rebalanced each January. That means that it is a reliable metric, one that can be used as a basis for analytical models, because it is consistent and represents the real changes within the broad universe of the commodities markets. Useful metrics must be not only reliable but consistent; the greater those characteristics, the more weight you can place upon those metrics before making a final investment decision. Consider this, for example: If Michelin’s analysts used their own standards to come up with a star rating, diners could never be sure that going out of their way to a three-star restaurant would always be worthwhile. So Michelin requires its reviewers worldwide to apply the same criteria to every establishment (although they keep the exact nature of that criteria as confidential as the reviewers’ identities). Similarly, you want each investment decision to be reliable, so you must keep an eye on the consistency of your investment metrics.

Of course, Wall Street is anything but static. It wasn’t until after the First World War that stocks joined bonds as a “mainstream” investment. Even then, it was only in the 1970s and 1980s that the popularization of mutual funds, combined with the shift from defined-benefit pension plans to defined-contribution retirement plans, made stock investing a preoccupation of ordinary Americans beyond Wall Street. As the size of these markets has increased along with the number of participants, so the diversity has grown. And the array of possible investments has exploded. An asset-allocation model that a decade ago might have been restricted to stocks, bonds, and cash could today include commodities, real estate, and private equity. Some of those markets didn’t exist 25 years ago; others have undergone sweeping changes. As a result, metrics that were once useful may now be outdated.

The evolution of the emerging markets provides an excellent example of what happens to metrics as markets evolve. A decade ago, these countries had more in common with each other—their state of development relative to the rest of the global free market economy—than they do today. Back in 1996, for instance, the average correlation was 15%, meaning that for every 1% change in U.S. or European markets in any given direction, the emerging markets would react with a move of only 0.15%. Today, developed and emerging markets are more tightly linked; the correlation has hit 44%. That means it’s harder for investors to seek shelter from the stock market meltdown in the United States in the emerging markets, even if the financial stocks in the latter remain relatively healthy. Moreover, that once-homogeneous universe of emerging markets is starting to fragment. Markets in Korea, Mexico, and Taiwan, for instance, no longer battle the lack of liquidity, the widespread corruption, and serious fundamental risks they did a decade ago. Rather, these markets have seen a dramatic improvement in credit quality, and many of their largest companies boast investment-grade ratings even as their national governments are becoming net creditors rather than debtors.

As the emerging markets category has evolved, new ways have to be found to reflect that a single broad index no longer accurately reflects what is going on within the asset class itself. For instance, one of the most heavily marketed global investing trends around is the BRIC phenomenon, sparked by a forecast by Goldman Sachs market guru Jim O’Neill when he forecast in 2001 that the BRIC countries (Brazil, Russia, India, and China) would dominate the global economy by mid-century. Standard & Poor’s jumped on the concept, launching a new BRIC index, and investors have allocated billions of dollars to a host of new BRIC-specific funds worldwide. The market has acknowledged that the economic growth potential of these four economies has distinct characteristics from that of the rest of the emerging markets universe. Meanwhile, latecomers to BRIC investing are gravitating to the concept of frontier markets that remain full of perils for outsiders, ranging from Nigeria to Vietnam. Again, the argument is that these countries have more in common with each other than they do with others.

The bottom line for investors is that as the composition of the “emerging markets” universe changes and the correlation between it and “developed” markets increases, metrics once used to make decisions about whether to allocate capital to the emerging markets have become less consistent, less reliable, and thus less helpful. This isn’t because of any inherent flaw in the construction of global indexes, however, but because the evolution of the markets themselves have made a broad global benchmark less useful. Standard & Poor’s has pointed out that markets such as Korea and Taiwan dominate the MSCI Index (the equivalent of the S&P 500 for emerging markets investors, used to make decisions about the absolute and relative attractions of the emerging markets as an asset class). But the gross domestic products of these countries is tiny compared to countries such as China, and that gap is likely to widen. So perhaps the MSCI, which places more emphasis on the size of a country’s financial market than the size of its economy, is no longer the most reliable metric for an investor trying to capture outsize returns as the next generation of emerging markets becomes mainstream. Maybe it’s time to look around for a BRIC index. That is the kind of question investors must be prepared to ask themselves, in every market. Similarly, they need to question their automatic assumptions or biases about data. Anyone assuming that all emerging markets included in the MSCI benchmark must offer, by definition, the same combination of low correlations, high risk, and the potential for high returns would risk missing the reality. In fact, Korea’s stock market, for instance, has much more in common with the S&P 500 than it does with that of Vietnam. The more I am prepared to question assumptions like this, the better I will fare in deciding what weight to give any given metric in a decision-making process.

Of course, because all kinds of data you and I use in our investment decisions is collected, compiled, and analyzed by human beings, it’s hard to escape bias. Sometimes it’s institutional. For instance, many indexes automatically weed out stocks that delist from an exchange, a process referred to as survivor bias. Others are either the unconscious byproduct of wishful thinking produced by an analyst deliberately donning rose-tinted spectacles. Did anyone really expect an association of real estate brokers to give you early warning that the bottom was about to fall out of the real estate market in 2006? Anyone who did was almost certainly late in recognizing what was happening. I have already discussed the most blatant kind of bias, the outright horse trading on the part of Wall Street investment banks during the 1990s in which analysts swapped favorable stock ratings for investment banking mandates. As a result, metrics based on a survey of the ratio of buy to sell ratings, or upgrade to downgrade ratings, or even analysts’ earnings estimates during the 90s couldn’t be seen as reliable, much less objective. Hopefully, the lasting legacy of the analysts’ scandal will be the need for a greater degree of skepticism on the part of investors with respect to the potential for personal bias or conflict of interest in all the data they receive.

Yes, I demand a lot from the metrics I use. I have to. My investment track record depends on them. They must be an objective, consistent, and reliable way of measuring certain phenomena. And they have to be valid, in both an absolute and relative sense. To understand what I mean by this, imagine that I describe some one as “tall.” In a vacuum (that is, without context), you have no way of determining whether my assessment is valid. Is that person a man or a woman, or maybe even a child? How tall am I? How tall are most other people in the room? To take that into the world of investing, consider one of the most widely used metrics in the investment universe: the price/earnings (or P/E) ratio. As its name implies, the P/E ratio tells us the relationship between a stock’s price and its earnings by dividing the share price by the earnings per share. But the P/E ratio is a much fuzzier metric than that description implies. For instance, what earnings figure is used to compute the ratio? Some analysts use historic earnings per share (the profits the company posted the previous year), whereas others prefer to use their subjective estimates of what the company will earn this year or next. Let’s assume that can be solved by insisting that P/E ratios be calculated in a standard fashion. Another hurdle appears: How is it possible to use P/E ratios in isolation and expect them to be valid? If Intel trades at 16 times earnings, is that good or bad? Unless you know the P/E ratios of other semiconductor companies, other large-cap technology companies, and the S&P 500 Index, you can’t claim that is a bullish or bearish valuation.

Lots of metrics-related traps lie in wait for unwary investors. Sometimes relationships between a metric and an investment decision are casual, not causal, and deserve to be discarded or, at best, viewed as amusing but irrelevant. Figuring out which is which requires no mathematical skill whatsoever, but only a dose of common sense. For instance, we’re all aware (thanks to Al Gore and An Inconvenient Truth) that global warming is heating up our planet and that the melting polar ice caps are triggering a rise in sea levels around the world, to the point where low-lying islands and nations such as Bangladesh find their long-term survival threatened. That’s a causal relationship. Now, let’s turn to the stock market, where index levels also rise and fall in any given year, but where, over a decade or two, the trend is for the value of the index to rise, notwithstanding the painful plunge in stock values of 2008. Hmm, interesting, isn’t it? In fact, if you plot the level of the market and the level of the Atlantic Ocean on the same chart, you might even see a remarkably high correlation.

Whoa! You have just ventured from the territory of causal relationships into that of the casual relationship. Just because some kind of correlation may exist doesn’t mean that the level of the Atlantic Ocean has anything to do with the level of the stock market (or vice versa) any more than global warming explains a multiyear bull market. Of course, this is an extreme example of the kind of silliness that follows if an investor starts treating casual relationships as causal ones. Alas, it’s far from an isolated one. Every winter, for instance, as the world of football gets ready for the Super Bowl, CNBC is certain to start pondering, only half in jest, about what it will mean for the stock market if one team rather than another wins the game. That’s because over four decades of Super Bowl championships, football fans doubling as stock market pundits have observed a pattern taking shape: Years in which a National Football Conference division champion has triumphed have generally been bullish years for stock investors. (The only exceptions were 1990, 2000, and 2008, when the Dow Jones Industrial Average plunged despite an NFC team’s victory. With the victory of the AFC’s Pittsburgh Steelers in early 2009, the stock market outlook seemed bearish for the year.) Add some common sense to this analysis, and you’ll quickly conclude that football doesn’t have a lot to do with the stock market. Certainly, any manager who admits relying on such casual indicators rather than sensible metrics is likely to see investors flee his or her fund as fast as possible!

Unfortunately, financial markets are chock-full of casual relationships. To make matters even more confusing, a relationship that once seemed to be causal can become less reliable and more casual with the passage of time. Counting the help wanted ads in major metropolitan newspapers was once a great way to gauge the labor market’s strength because the data (the number of ads) bore a strong relationship to demand for workers and, by extension, wages, inflation trends, and corporate earnings trends. But the advent of the Internet changed everything. Employers and jobseekers alike turned to websites like Monster.com; fewer employers posted ads in the newspapers, and some stopped altogether. Relying on what those newspapers said, in isolation, would have produced a distorted picture of reality. That made the Conference Board’s Help Wanted Index, based on those newspaper ads, less useful in anticipating key labor market data. The result? Today’s traders, trying to “read” an early warning signal about a surprise change in unemployment or payroll data (which in turn might trigger big moves in stock and bond prices), have had to develop new metrics.

A more subtle consideration in selecting which metrics to rely on to make investment decisions is your time horizon. If you have a long-term investment timeframe—and as I’ll explain, you shouldn’t count on your decisions to translate into portfolio profits in less than 12 months—you don’t want to be distracted by short-term data points. Over the shorter term, markets are driven by unpredictable, transient, and intangible elements that have nothing to do with fundamentals. Emotions kick in. A big investor might wake up one morning after a nightmare in which half of his portfolio was annihilated in the wake of an outbreak of nuclear war in the Middle East. Even though he realizes on a rational level that there is little likelihood of his nightmare happening, he believes he’ll feel better if he sells 20% of his stock holdings and turns them into cash. The market reacts to his selling. Momentum takes over. Before you know it, you have a giant selloff. Massive, short-lived market movements have often tended to be unreliable indicators of long-term trends, although big slumps like those of September 1987 and the autumn of 2008 are painful to experience. In late February 2007, for instance, the Shanghai Stock Exchange’s index plunged nearly 10% in a single day. Within five months, however, the market had not only regained all of that loss but was up nearly 80% for the year. There may well have been fundamental reasons for concern about the outsize valuations and profits in the Chinese stock market in 2006 and 2007, but a single day’s trading isn’t the metric that will tell you what those are, or how to position yourself in response to them.

So an investor looking for compatible metrics needs to identify those that will be useful in making investment decisions over the next 12 to 18 months. Let’s say you believe that investor sentiment is an important ingredient in deciding whether the U.S. stock market is a good place to be for that period of time. (As I’ll demonstrate in Chapter 10, “The Fourth Factor: Psychology—Greed Versus Fear,” metrics measuring investor psychology can prove useful in gauging confidence and enthusiasm and thus in making investment decisions.) The next step is to find the right way to measure levels of both fear and greed. Alas, many of the metrics most often used to determine investor attitudes to the market are too short term to be useful. Studying the weekly ratio of put options to call options, for instance, gives you information that is too volatile to be meaningful. Take a glance at a graph that shows the weekly change in the relationship between put and call options purchased on the Standard & Poor’s 500 Index (options that give their holders the right to sell or buy the index, respectively, at a predetermined value within a defined period of time) and you’ll see a sawtooth pattern. If you try to follow it that narrowly, your chosen metric will be sending dramatically different signals every 10 days or so! Any profit you might capture chasing these data signals is likely to be offset by your trading costs, not to mention the unquantifiable risks associated with sleepless nights. You need to find a way to look for bigger behavioral swings using the same basic data, ones that are aligned with our 12- to 18-month time horizon.

It takes a lot of work to figure out which metrics can be trusted to be consistently useful and reliable in any decision-making process. But the effort doesn’t stop there. You can never take for granted that the metrics you have settled on as the building blocks for your investment process will remain as reliable as they appear today. Just as every homeowner should periodically ask a surveyor to look for early warning signs that termites might be snacking on the wooden joists in the basement, so, too, investors must periodically step back and question the foundations on which their investment process has been constructed. Are the metrics still the right ones to use? Do they still send reliable signals about the same phenomena? As I have shown, correlations that have endured for decades may fade. For many years, investors who wanted insight into the course of inflation could look at the price of crude oil—an essential business input. Between 1986 and 2000, when crude oil prices rose, consumer and producer prices followed suit. But since 2001, the two sets of data have parted ways. Is this a permanent divergence or an anomaly? The answer remains unclear, but what is certain is that anyone counting on oil prices to predict inflation would have been drawing incorrect conclusions from that raw data and possibly have invested in securities and asset classes they expected to outperform in an inflationary environment.

In the words of Lord Kelvin, a nineteenth-century mathematician, “To measure is to know.” Once you understand that the process of measuring involves the use of reliable and consistent metrics, it’s time to start figuring out what to measure. What information is going to give you an edge? An investor’s job is to determine which metrics work best at each step along the path toward an investment decision. The next chapters guide you through the process of breaking down seemingly complex investment decisions into bite-sized pieces and applying metrics to help you make those decisions successfully.