The World of Global Macro

This chapter is from the book

Reading Minds and Markets: Minimizing Risk and Maximizing Returns in a Volatile Global Marketplace

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This chapter is from the book

This chapter is from the book 

Reading Minds and Markets: Minimizing Risk and Maximizing Returns in a Volatile Global Marketplace

Learn More Buy

Once every 4 months or so, an invitation arrives in the mail and I find myself on a plane to one warm and sunny destination or another to deliver a speech at one of the country’s biggest investment extravaganzas, the Money Show. These presentations tend to follow a pattern. I’ll tell a roomful of eager-to-learn investors what broad trends I see taking shape. Every year, the specific questions change—as 2008 dawned, for instance, they revolved around the real estate crisis and the looming credit crunch—but inevitably someone will ask me the secret of picking those investments that are about to outperform the rest. In other words, they’re searching for the investment equivalent of the Holy Grail.

I sympathize with my listeners, who are struggling to learn everything they need to know about how to manage their own portfolios. They’ll try to cram in a dizzying array of workshops and panel discussions into the 4-day-long conference, ranging in nature from the mundane to the esoteric. How to choose among such offerings as “Investing in China through Taiwan” and “Zinc Opportunities on the Pacific Rim”? One recent show in Orlando featured an astonishing 320 workshops and 20 panel discussions. As if that wasn’t enough, the exhibition hall, with a dazzling array of 250 or so exhibitors, would have satisfied any curious investor’s need to know.

Those exhibit halls give me a chance to see, firsthand, just what the average investor is up against. Take a look at this new mutual fund, one company pleads. Put your business card in the big jar over here and win a free copy of the latest “Get Rich in 90 Days” tome, or the newest investment software, another urges. Attendees are bombarded with new products from one end of the vast exhibition hall to the other. Interested in Web 2.0? There is someone on hand to explain how he can help you identify the best stock picks in the sector. Those leaving a seminar during which pundits argued that commodity prices may be in the midst of a sustained bull market are likely to find, just outside the door, a table displaying examples of a commodity-investing newsletter. Subscribe for only $125, the sign above it promises, and you’ll get a year’s worth of tips about penny mining stocks. The trendier an investment theme and the more a market’s valuation has soared, the more dollars investment firms of all stripes devote to promoting it. What else can explain the recent prominence at Money Fairs of exhibitors devoted to Canadian energy trusts, gold-linked investment vehicles or—before the 2007/2008 market crash—real estate investment trusts (REITs)?

This phenomenon is hardly new. For the past four centuries or so, ever since men and women began investing their hard-earned capital in ventures ranging from the spice trade to the development of new windmills, there have been folks eager to advise them on just how to go about that unfamiliar process. Through the years, product offerings have expanded to accommodate the investing public. But over the past two or three decades, the array of asset classes available to the average investor has skyrocketed. At the same time, companies began to shift the burden of investing for retirement from the corporate treasurer to the individual employee by switching from defined benefit to defined contribution retirement plans. Whether or not 401(k) plan participants were fascinated by investing, they found themselves having to learn about it—or suffer the consequences. This transition occurred nearly simultaneously with the globalization of the world economy and the information technology revolution, both of which made a greater array of new investment products more viable and easier to construct and manage. These days, if you can dream of a niche investment arena (Montenegrin real estate? Scrap metal? Companies emerging from bankruptcy?), you can almost certainly find a related investment product.

Moreover, these new products—particularly the emergence of exchange-traded funds (or ETFs)—have made investing in once-inaccessible markets deceptively easy, particularly for the smaller investor. A decade or so ago, getting access to overseas stock funds might have required tens of thousands of dollars as a minimum investment. You would also have had to pay high management fees and operating costs. Now, global investing looks easy; just $1,000 or so in an emerging markets ETF and you’ve got exposure to the potential profits you believe it offers. Of course, the risks may be equally outsized, a fact that the ease of access overshadows. In 1994, investors in fledgling Latin American markets lost big; those who made hefty profits on emerging Asian economies in countries like Korea and Thailand did the same in 1997 and 1998. That rout sent emerging market indexes down as much as 40% during the 12-month period that ended in July 1998. More recently, investors in Chinese stocks have experienced equally outsize losses.

So, what’s a beleaguered investor to do? It’s a bit like walking into the world’s largest supermarket without a shopping list or meal plan in your hands. In the financial supermarket, there are thousands of different investments you can make, in dozens of different countries, and as well as at least a dozen asset classes. Meanwhile, pundits tell you repeatedly to “buy what you know” and assure you that solid research and hard work will give you an edge. The temptation to believe that it’s just a matter of putting a few important analytical tools to work is irresistible; with those, the task of investment picking will become simple and straightforward. That conviction isn’t confined to the ranks of the ordinary retail investor, those who flock to events like the Money Show hoping to learn more about price/earnings ratios and other stock-picking tools. At the other end of the spectrum, every summer, tens of thousands of business school graduates take to the streets of New York, Boston, San Francisco, and Chicago. Armed with their cutting-edge HP 12-C financial calculators, which can compute an internal rate of return with the touch of a button, and an air of invincibility, they sally forth. These fledgling investment research analysts are under orders from their employers—perhaps Fidelity Investments, perhaps the newest hedge fund on the block—to seek out the best stock investment ideas in the industry they have been assigned to cover. Needless to say, quality research is critical, especially if you rely on it to help you select securities.

What’s interesting is that even as experienced market veterans and green retail investors continue to scour the landscape in search of their ideal—a pharmaceutical company that has suddenly broken through scientific roadblocks to devise a cure for cancer, say, or a technology company that has developed a new gizmo that consumers will never want to live without and that competitors can never match—a new theory of investing has been taking hold. Professor Burton Malkiel pioneered what is now called the efficient market hypothesis; the best summary is still found in his groundbreaking book A Random Walk Down Wall Street: The Time-Tested Strategy for Successful Investing. (First published in 1973, the bestseller has never been out of print.) The essence of this theory is that at any given point of time, the prices of all stocks and bonds in any market accurately reflect whatever news and analysis exists. News is unpredictable, and it is impossible to say how any given stock, bond, currency, or commodity will react to a particular piece of economic data or political catastrophe. This extreme theory suggests discarding the fundamental analysis that many professional money managers swear by, or technical analysis, in which investors try to get an edge on the market by scrutinizing historical price patterns of various securities or markets. The only way for investors to win, according to Malkiel’s thesis, is to put all their assets into a basket of securities that accurately reflects the market (an index fund) and stay put for the long haul.

Malkiel makes an interesting case. In a recent follow-up study, he reported his discovery that over periods of 10 years or more, some 80% of stock funds run by “active” managers (those who pick individual securities they believed would outperform) ended up with lower returns than stock market benchmarks or passively managed funds tied to individual indexes. The same pattern held true in international investing, where active money managers often claim stock picking is vital because markets are less efficient. Standard & Poor’s demonstrated this in a study of investment returns over a 5-year period ending in December 2006, in which analysts studied the ability of mutual fund managers to end up in the top half of their peer group in each of those years. As it turned out, only 13.2% of large-cap fund managers did so in each of the five 12-month periods. It was just as hard for a fund to follow one good year with another one: Only 17.3% of large-cap funds that posted top-quartile returns in the 5 years ended December 2001 managed to repeat that performance in the following 5-year period.¹ Not surprisingly, Malkiel sticks to his conclusion that investors fare better when they invest in low-cost index funds rather than investment products run by active stock pickers.

But Malkiel’s hypothesis doesn’t cover the vagaries of human behavior. Sure, over the long haul, rationality rules. But in the short term, as academics like Yale University’s Robert Schiller have argued, fear, greed, and other investor sentiments tend to drive stock markets to often-absurd extremes. One example that has become almost a cliché of how rational investment decisions morph into an irrational mania, is the seventeenth-century Dutch tulip-bulb craze. When Carolus Clusius brought a fascinating collection of new plants from Turkey when he arrived to take up his position as a botany professor at the university in Leyden in 1593, he couldn’t have expected the insanity that would follow. Tulips quickly became a staple in the gardens of prosperous Dutch burghers. Then, a few decades later, a virus caused a mutation in the flowers, producing an array of stripes or “flames” in contrasting colors that hypnotized the Dutch and made the new bulbs too valuable to be left in flowerbeds. An entire industry grew up around the new hybrid bulbs, with specialist merchants trying to predict—and stockpile—the most popular color combinations. Tulip mania was born. Speculators traded options on future tulip bulbs, and by January 1637, bulb prices soared twentyfold in a single month. Hordes of irrational investors were willing to swap real estate and jewelry for the most coveted bulbs. The next month, bulb prices collapsed, taking the Dutch economy with them.

Malkiel’s theories overlook a powerful element of human nature: the inability of most people to resist the allure of whatever “investment” has been most successful recently. (That’s exactly why regulators require mutual fund companies to spell out in any ad for their products that past performance is no guarantee of what will happen in the future.) Indeed, the bigger the recent gain, the more willing investors seem to be to toss caution to the wind. Ultimately, they become so caught up in their pursuit of riches that they can’t distinguish between a legitimate (if overpriced) investment opportunity and an illicit pyramid scheme.

Paradoxically, it’s easy to prove that winning streaks aren’t always sustainable. At Harris Private Bank, my team recently reviewed returns on large-cap stocks, long-term government and corporate bonds, and intermediate notes and Treasury bills over a 78-year period beginning in 1926 and ending in 2004. During that time frame, stocks returned an annualized 10.3%, with a standard deviation of 20.3%. (A standard deviation measures the degree of variation from the expected return, and serves as a way of expressing risk levels numerically; the higher the standard deviation, the greater the risk and the possibility of losing your investment.) A quick way to “guesstimate” the range of annual returns possible or likely given a set of return and standard deviation figures is to add 20.3%, the standard deviation, to 10.3%, the expected return to determine an upside return, then subtract 20.3% from 10.3% to determine an “expected” downside. That means the maximum possible return someone could realistically expect roughly two-thirds of the time is 30.6% (the first figure), while taking that level of risk created the possibility of a 10% loss (the second figure). Along with my analysts, I tried to determine what would have happened each year if we had constructed a portfolio built entirely of the preceding year’s top performer. The result? Annualized returns were only marginally higher than the index delivered—10.6% compared to 10.3%—but the standard deviation was significantly higher at 21.6%, meaning that an investor chasing top performers would have taken on more risk and experienced more volatility. (Losses could have hit 11%.) The best option proved to be a balanced portfolio, one with 60% invested in large-cap stocks, 30% in small-cap stocks, 5% in long-term corporate bonds and 5% in Treasury bills and that was rebalanced annually to maintain that weighting. That generated higher absolute returns—an annualized 11%—and had a lower standard deviation than the portfolio composed only of “winners.”

Recent history serves to remind us that human nature may be the biggest obstacle to structuring a diversified portfolio that stands a better chance of performing over the long haul. At the height of the dot.com boom, I was running a wealth management firm in Ponte Vedra Beach, Florida, managing portfolios of $3 million or more for wealthy individuals and their families. It was frustrating. My goal was to manage my clients’ wealth for the long haul. But every time I met with a client or even attended a dinner party, people demanded my opinion on the latest Internet innovation or a tip on the best way to play the need for greater bandwidth. Most difficult of all were the meetings with prospective clients. These folks were intelligent enough to have amassed in their own business or profession what by most standards would be considered a fortune. Now they allegedly were seeking someone to protect and manage that capital. But did they really want my expertise? Even as I presented my qualifications, reviewed my capabilities, and explained the services I could offer, I was aware that over the past year or two many of my potential clients had handily outperformed my carefully constructed model portfolios thanks to one or two lucky bets on technology stocks like Veritas or Broadcom. Why, these folks clearly wondered, should they hire me when it was obvious that I couldn’t do better for them than they were already doing?

But when individual investors believe it’s easy to build a top-performing portfolio using a few individual stock picks, it’s time to head for the hills. Indeed, within 2 years, the dot.com bubble had exploded, leaving a trail of wreckage in its place. Those of us who valued process over pizzazz suddenly looked wise again; the speculators lived to regret their foolhardiness in betting their wealth on stocks such as Pets.com. Many will never see their portfolios recover, and some retirees were even forced back into the workplace to compensate for their losses. Building a portfolio is not easy; it requires sorting through a mountain of data and a vast array of investment options. It also means being especially alert when markets are irrational.

Just how hard is it to evaluate a single stock? A dedicated stock picker will tell you it’s easy—just study the company’s annual report or financial statements and look at its price and financial ratios. But many of the factors that dictate a company’s share price never show up in these documents, as I came to realize. That’s why most of us rely on professional research analysts to evaluate individual issues. Back in 1997, when Pepsico spun off its fast-food chain operator, Tricon Global (later to be renamed Yum Brands), as an independent, publicly traded company, I decided that the cash flow they generated selling pepperoni pizzas and buckets full of extra-crispy chicken at food courts nationwide was attractive enough to snap up some shares in the aftermath of the initial public offering. After all, I rationalized, if there’s one thing I can count on, it’s that the American consumer’s passion for tasty high-fat treats will triumph over likely future heartburn any day of the week. And with brands like KFC, Pizza Hut, and Taco Bell under their roof, Yum Brands was well positioned to keep benefiting from that trend. Of course, I scrutinized the company’s public reports and other publicly available information, looking for anything that would signal a flaw in my logic. It turned out that a crucial piece of information—that some genetically modified corn, not approved for human consumption, had snuck its way into the production line of Taco Bell brand tortilla chips—was nowhere to be found until it was reported in the media in July 2000. The reason? The chips weren’t made by Yum Brands and were sold in grocery stores rather than the fast-food outlets. But all that mattered was the brand association. Shares of Yum Brands plunged 24% within 3 weeks, erasing every penny of the profit I had made since the initial public stock offering 3 years earlier.

Any time an investor buys an individual stock, he or she could be signing up for a rollercoaster ride of this kind. So Malkiel’s thinking needs to be combined with other analytical tools, especially given the fact that entire markets or asset classes can be pricey or cheap on both an absolute and relative basis at different points in time and generate wildly divergent returns over the course of any given year. All of us have seen, for instance, how between 2004 and 2007, stock markets struggled to eke out modest single-digit returns as commodity investment funds soared. In that kind of investment environment, advising someone to go for a random walk or only focus on pure valuation levels of individual companies isn’t good enough. What is needed is an investment approach that understands valuations for market indexes, each of which contain hundreds or sometimes even thousands of different stocks, bonds, or other securities. This kind of big-picture approach, or “macro” investment strategy, is at once more inviting and more statistically robust for investors trying to capture some incremental returns.

So, what’s the secret sauce—the vital ingredient for an investor who wants to maximize his or her returns? Macro investing is all about finding a way to evaluate markets, rather than focusing just on individual stocks. Investors have limited time at their disposal, even as the array of investment options that they are asked to pass judgment on continues to expand rapidly. Whenever someone makes an investment decision, consciously or unconsciously they are working through a series of decisions like the one in Figure 2.1, what I refer to as a decision matrix.

Figure 2.1 The decision tree.

At each level of this matrix, decisions have a certain payoff or return. The more specific the decision, the lower the likely payoff. Let’s say that you’re trying to decide whether to invest in large- or small-cap stocks, and are thinking of adding an extra 10% of your portfolio to whichever you decide. Given that the average difference of the return on the Standard & Poor’s 500 Index (the large-stock benchmark) and the Russell 2000 Index (the small-cap bellwether) hovers around 10 percentage points at any given point in time, this gives you the chance to boost your returns by a full percentage point—from 8% a year to 9%, say. Over the years, given the magic of compound returns, that translates into enough extra money to spell the difference between retiring early in your 50s to your dream home and still working in your 70s to make ends meet. The more diligently you study these macro-level valuation discrepancies, the more accurately you will be able to predict the times when you need to make asset-allocation shifts. (At times, the performance gap between the two benchmarks has reached an astonishing 30 percentage points!) The harsh reality is that not only do more macro-level decisions further up that “decision tree” take less time to make, but they also offer you the biggest potential returns with the least risk. Face it: If you were in the middle of a minefield, wouldn’t you get out of it more rapidly and with greater confidence if you knew there were only 3 mines in a 3-acre area, instead of 300?

Macro-level investing means investors focusing on industry groups, sectors, or asset classes. Take a look at the medical devices arena—a compelling but ultra-volatile part of the healthcare industry. Here, picking the “winner” can spell the difference between success and failure, as is obvious by the jostling for position in just one area, the field of pacemakers and coronary stents. Medtronic and St. Jude Medical wage a fierce battle for preeminence, and investment analysts track their successes and failures on a quarterly basis and scramble to predict which has the edge at any given second. It’s downright exhausting—but seems crucial, because although Medtronic’s stock rose twice as much as that of St. Jude between 1997 and 2001, their fortunes reversed. Over the next 5 years, St. Jude outperformed Medtronic by a whopping 400%. But just a minute. Why should you, the investor, back one of these two companies at the expense of the other, just because they choose to compete so ferociously?

In fact, the simplest and lowest-risk solution in that 10-year period would have been to buy and hold stock in both companies. Medtronic delivered an annualized return of 12.5% and St. Jude an annualized 16% over the course of that decade, with standard deviations of 29.7% and 36.9%, respectively. So, for each “unit” of risk taken, Medtronic returned 0.42%, while St. Jude delivered 0.44%. An investor deciding to invest equally in both competitors would have captured an annualized 14.4% return, with a 26.8% standard deviation, meaning he would have taken less risk than he would have by betting on just one of the two rivals. Even better, he would have eliminated the biggest part of his research headache—the need to keep track of the perpetual battle between the two rivals and to guess the point at which one will take over the lead. Add in a third player, Boston Scientific, and the risk-return scenario becomes still more attractive; an equal-weighted portfolio generated 0.54% of return for each “unit” of risk taken. The result? An annualized return and risk level that are both better than two out of the three stocks (see Table 2.1).

Table 2.1. Building a Risk-Adjusted Healthcare Portfolio. Source: Bloomberg

What is true of industry sectors applies all the way up the decision tree. Any time that an investor can make fewer decisions at the upper levels of that tree, the odds improve that he or she can do so with the help of more and more reliable information. So, too, do the odds that these decisions will help to boost investment returns and curb risk levels. The term for this approach, global macro, sounds fancy enough to require a small fleet of Gulfstream IV jets and offices in 37 countries. But all it means in practice is taking this kind of approach to every sector and market in which you invest. Rather than analyze each stock or bond in isolation, this macro approach places nearly all markets on a level playing field. Are foreign stocks more attractive than their domestic counterparts, or is the battered real estate market a better option than either?

The goal is twofold. First, global macro investing simplifies the investment process because it means you don’t have to spend time following every little hiccup in the performance of a few hundred stocks in your portfolio and outside it. At the same time, you can inject more certainty into the decision-making process. In essence, by pushing up the decision-making tree to the topmost levels, global macro strategies permit you to minimize your exposure to risky and overvalued segments and increase your allocation to those asset classes that offer the most compelling risk/reward tradeoff.

It all boils down to active asset allocation: the ability to identify when it is time to increase your exposure to stocks, bonds, commodities, or real estate. The key to success is devising an investment process that regularly evaluates markets in an attempt to identify areas that are overpriced and risky as well as those that contain hidden value. Over an adequate time horizon (let’s say 1 to 3 years), a global macro investor can use this process to add significant incremental return. In contrast, between markets or asset classes it is not only easier to predict the range of returns you can capture but also where you are likely to get the biggest bang for your buck. That’s because the best-performing market can outperform the worst by as much as 70 percentage points in any given year. If you make your investment decisions at the asset-allocation level, you almost immediately boost the odds that your portfolio will outperform the market. That’s because asset classes, as a rule, contain fewer surprises than individual stocks—just ask thousands of Lehman Brothers stockholders left lamenting the evaporation of their investment in the firm! Of course, they would have suffered losses in the broader financial sector in those turbulent months in the autumn of 2008, but would have fared better had they diversified across a sector or, better yet, the asset class of stocks as a whole. And because asset classes are less volatile than individual stocks, it is not only simpler but for smaller investors, less expensive to construct a portfolio: Sometimes as few as five or six holdings can offer enough diversification, thanks to the magic of ETFs.

The key to detecting market mispricings of asset classes and industry sectors is data—the right data, used the right way. My own conviction that being able to measure market dynamics would help me become a better investor dates back to a brief conversation with then-CIA director Robert Gates in 1991. At the time, I was working in Boston for Eagle Investment Associates, a division of Bank of Boston. Some of the world’s most sophisticated investment professionals, such as Fidelity Investments’s guru, Peter Lynch, were trustees for entities that had entrusted their assets to us. It was downright intimidating to have to stand up in front of experts like Lynch and bring them up-to-date on how their portfolios had fared under my management.

My epiphany occurred in the boardroom of Draper Laboratories, an extremely low-profile joint venture between the Massachusetts Institute of Technology and the U.S. military, for whom I managed part of their endowment. In that pre-9/11 era, the security seemed enormous; before I could talk to the trustees about what the markets were doing, I had to pass a background check. After that kind of introduction, the rather austere meeting room, furnished with Formica tables and metal chairs, seemed a bit anticlimactic. My self-confidence returned as I presented my investment outlook. I was rather bullish; the economy was emerging from a recession, and the yield curve suggested that economic growth would accelerate. I noted that the price/earnings ratio on stocks was low—another bullish signal, as was the fact that credit spreads (the extra interest income lenders demand in return for loans) were tightening. That was a hint that lenders were becoming more optimistic. After my upbeat presentation, I asked whether anyone had questions. A pause; then a hand went up. It belonged to a rather laconic but self-confident board member, who introduced himself as Robert Gates. He wasn’t an investment person, he admitted, but he asked whether the data I had just presented as a reason for my bullish outlook was data that I had selected because it supported my theory. Or was it data that I consistently tracked, hoping to detect signs of change? Sheepishly, I had to respond that it, in this case, was more the former than the latter.

Gates might not have known much about investing, but whenever the director of the Central Intelligence Agency talks about how to use and interpret data, you listen. He suggested that if I could identify a set of data that, over a period of time, provided reliable information about the market and the economy, then I could track that data over time to pick up subtle shifts. Those incremental changes could, in turn, shape and modify my investment outlook and provide me with an edge in detecting inflection points in a wide array of asset classes. Initially, I saw his suggestion as a professional challenge. Thinking it through, however, I realized that finding a way to measure market dynamics would be vital to my long-term success as an investor and money manager.

It took me years to develop such a process, and I continue to refine and adapt it today, but it has already proven its value. Beginning with the recognition that human weaknesses play a great role in our investment decisions, this “global macro” and inductive decision-making process is one that I am convinced spells the difference between building a modest retirement nest egg and turning those savings into a level of affluence that spells true wealth. And the first step to build that decision-making process is understanding the need to insulate yourself from the human flaw that is most dangerous of all to any investor: You must find a way to be sure that you don’t let emotions like greed and fear replace cold, hard logic in that investment model.