More Than You Know

by Michael J Mauboussin

  One dollar today becomes how much when compounded over twenty years? Write the amount in the space provided.

  Starting amountCompounded at (%)Becomes how much after 20 years?

  $1 2 ________

  $1 7 ________

  $1 15 ________

  $1 20 ________

  For most of us, these calculations do not come naturally. A 2 percent compounded annual growth rate (CAGR) over twenty years turns $1 into $1.49. A 7 percent growth rate equals $3.87. A 15 percent rate—a common earnings growth goal among large companies—implies a value of $16.37. And finally, $1 compounded at a 20 percent rate becomes $38.34.

  How did you do? If you are like most people, you had difficulty properly gauging the relationship between the growth rate and the ending value. For example, it is not intuitive to most investors that an increase from 15 to 20 percent growth implies more than a doubling in value after twenty years. That’s why Albert Einstein called compounding the “eighth wonder of the world.” The trick for investors is to make the compounding work for them, not against them.

  Reality Check

  In the insightful book, Profit from the Core, Bain & Company consultant Chris Zook reveals a study of the companies that actually achieved sustained growth in the 1990s.2 The sample drew from over 1,800 companies in seven countries that had sales in excess of $500 million.

  Zook set three hurdles:• 5.5 percent real (inflation adjusted) sales growth.

  • 5.5 percent real earnings growth.

  • Total shareholder returns in excess of the cost of capital.

  Notably, these targets are well below what most strategic plans suggest. In fact, Bain found that two-thirds of the companies it examined had double-digit nominal growth rates built into their plans.

  Exhibit 27.1 shows the study results. As it turns out, only about 25 percent of all companies achieve the sales growth rate, and just one in eight meets all criteria for sustained growth. Notably, these results are against one of the most buoyant economic backdrops in a generation. The vast majority of companies seek (and plan!) to grow at a double-digit rate and the vast majority do not.

  EXHIBIT 27.1 Few Companies Achieve Sustainable Growth

  Source: Worldscope database, Bain analysis.

  How acute is the potential gap between perception and reality? To check that, I first looked at the distribution of ten-year sales growth rates (1997- 2006) for U.S. companies with base-year revenues in excess of $500 million (see exhibit 27.2). The average growth rate for that group was 6.2 percent, and less than one-third of the companies sustained double-digit nominal top-line growth. Further, these growth rates do not adjust for acquisitions, so the organic growth rate is almost surely lower.3

  Next, I layered in projected three-year earnings growth for all companies with sales in excess of $500 million (2006 base). Even though the earnings growth has been historically roughly 100 basis points higher than sales growth, the analytical point is unchanged. The average expected growth rate for this group, at 13.4 percent, is still roughly double the rates that companies achieved in the recent past (see exhibit 27.3). Also noteworthy is that the distribution of expected growth doesn’t include any negative rates.

  What is the significance of a 13 percent growth rate versus a 6 percent rate? Our compounding exercise shows that after twenty years at the 13 percent rate, the end value is nearly four times higher. As companies get larger, sustaining double-digit rates becomes very difficult. So if past is prologue, the expected growth rates for many companies will have to come down.

  EXHIBIT 27.2 Frequency Distribution of Ten-Year CAGRs in Sales, 1997-2006

  Source: FactSet and author analysis.

  EXHIBIT 27.3 Expectations Gap?

  Source: FactSet and author analysis.

  The Bigger They Are, the Slower They Grow (or Don’t Grow)

  The entire population of company sizes, like city sizes, tends to follow a distinct distribution.4 In models that replicate this distribution, scientists note that average growth rates are independent of size and that the growth rate variance declines with size. Call it the cone of growth.

  Exhibit 27.4 shows this graphically. Here I looked at the ten-year compounded annual sales growth rate for over 2,600 U.S. companies. The horizontal axis is on a log scale. The chart shows that while the average growth rate for small and large companies is approximately the same, there is less likelihood that a large company will grow or shrink rapidly. Investors often call this the law of large numbers—big companies can’t grow as fast as small companies—but it’s more accurate to say that big company growth doesn’t vary much from the average growth rate.5

  Readers who have gotten to this point may have the impression that all companies with high growth rate expectations are poor investments. Nothing could be further from the truth! The problem is that while we know that some companies will grow rapidly in the future, spurring upside revisions and attractive shareholder returns, we have no systematic way to identify those companies. Therein lies a great opportunity.

  EXHIBIT 27.4 Sales Growth CAGR

  Source: FactSet and author analysis.

  To demonstrate that growth is good but that it’s hard to take advantage of it, we turn to Jeremy Siegel’s excellent analysis of the Nifty Fifty in his investment classic Stocks for the Long Run.6 The Nifty Fifty were the leading growth stocks in the early 1970s and had high growth rate expectations and price/earnings (P/E) multiples in excess of forty. In the subsequent bear market of 1973-1974, these stocks as a group dropped sharply.

  Siegel asks a basic question: Were the Nifty Fifty overvalued in 1972 based on their subsequent total shareholder returns? Based on his analysis, the answer is no. While some stocks did much better than the market (Philip Morris, Gillette, and Coca-Cola) and others did much worse (Burroughs, Polaroid, and Black & Decker), on balance they delivered a return consistent with that of the overall market. Siegel’s point is that based on ensuing performance, the warranted P/E in 1972 was much higher for some companies and much lower for others. But on average, the P/E was just about right.

  Refuse Refuge in Castles in the Air

  That there’s a gap between expectations and reality is not new. For example, bottom-up estimates of S&P 500 earnings have consistently been more optimistic than the top-down appraisal. But today, the issue seems compounded by the earnings expectations game.7 Managers and investors engage in an expectations-bar-raising ritual. Executives work to meet or beat Wall Street’s forecasts, which encourages analysts to increase their expectations, and compels the executives to deliver even more growth—by whatever means possible. 8

  Investors and managers must have reasonable expectations. The evidence shows that sustaining rapid growth is very difficult, especially for large corporations. Furthermore, while there is nothing wrong with growth stocks, the indications are that it is very difficult to know which companies will exceed expectations and which will disappoint. Investors should continue to focus on investment ideas where the expected value is favorable—where the upside opportunity outstrips the downside risk.

  Part 4

  Science and Complexity Theory

  INTRODUCTION

  One of my first calls after the major East Coast power blackout in August 2003 was to my friend Duncan Watts, then a Columbia University sociology professor. I peppered him with questions about the failure: what might have caused it, how it progressed, and by what means could we avoid future similar events.

  Now you might ask, why would you call a sociologist to answer questions about a power failure? Watts, who has a Ph.D. in theoretical and applied mechanics, is one of the world’s experts in network theory. It so happens he practices his craft in a social science department, but he’s totally comfortable straddling the physical and social sciences. In our far-ranging discussion, he drew parallels between the blackout, Harry Potter’s success, stock market booms, and flu epidemics.

  The hard and social sciences are typically housed in different buildings o
n university campuses, but the real distance is philosophical rather than geographic. In recent years, a handful of scientists—like Duncan Watts—have shown the value of multidisciplinary thinking. Physicists, psychologists, and complexity theorists have all added to our understanding of financial markets.

  Science has much to teach investors. The essays in this part are valuable because they offer some important mechanisms that explain how markets are efficient (and inefficient), delve into important empirical results that standard finance doesn’t handle well, and show why it’s futile to make simple cause-and-effect links in markets.

  Social insects, like ants and bees, are fascinating because they show us how decentralized groups coordinate effectively to solve problems. This part looks at various forms of collective problem solving, from a honeybee waggle dance to the Hollywood Stock Exchange.

  One of the best examples of a complex adaptive system—generically, a system that emerges from the interaction of lots of heterogeneous agents—is the stock market. Research suggests that when investors err independently, markets are functionally efficient. What’s more, defining the conditions under which markets are efficient provides us with a template to consider when markets are inefficient.

  Many models in standard finance theory assume that stock price changes are normally distributed around the well-known bell curve. A normal distribution is a powerful analytical tool, because you can specify the distribution with only two variables, the mean and standard deviation.

  The model, despite its elegance, has a problem: it doesn’t describe real world results very well. In particular, the model is remiss in capturing “fat tails”: infrequent but very large price changes. The failure of risk-management models to fully account for fat tails has led to some high-profile debacles, including the 1998 demise of the hedge fund Long Term Capital Management.

  Fat tails are closely associated with power laws, a mathematical link between two variables characterized by frequent small events and infrequent large events. Power laws are fascinating, and they empirically represent relationships as diverse as city sizes, earthquakes, and income distribution. While scientists still don’t have a firm grasp on the mechanisms behind power laws, their very existence provides investors with good insight.

  Humans have a deep-seated desire to link cause and effect. Unfortunately, markets do not easily satisfy this desire. Unlike some mechanical systems, you can’t understand markets by looking at the parts. Reductionism doesn’t work. Yet we often turn to individuals to explain the workings of the market. Just as an ant relying on local information and local interaction has no clue what’s going on at the colony level, explaining all but the most mundane market moves is beyond the ability of market mavens.

  Complex adaptive systems have another feature that is difficult to grasp: the magnitude of an outcome is not necessarily proportionate to the size of the perturbation. Sometimes small perturbations lead to large changes, and vice versa. We have to let go of our conventional notions of proportionality when we study markets.

  In recent years, scientists have renewed their efforts to find connections between the hard and social sciences. Investors in the stock market can benefit from looking beyond their narrow discipline.

  28

  Diversify Your Mind

  Thoughts on Organizing for Investing Success

  The more that you read,

  the more things you will know.

  The more that you learn,

  the more places you’ll go.

  —Dr. Seuss, I Can Read With My Eyes Shut!

  Ant Brain

  In the fall of 2000, I gathered a small group of leading investors to hear from various finance, strategy, and business luminaries. While these presenters were terrific, none got the award for creating the most buzz. That honor went to Los Alamos National Laboratory scientist Norman Johnson, who opened his talk in a seemingly inauspicious way: “I’ve been asked here to talk about what’s wrong with experts—as an expert in this area—in a subject area, finance, that I know almost nothing about.”1

  What did Johnson say to cause these smart investors to slide forward in their chairs? Simply put, he showed how diverse groups of “average” people, acting together, solve problems better than experts do. Johnson illustrated his point by discussing the behavior of social insects, including ants and bees. It was the incredible performance of these insects, above all, that sparked the imaginations of the listeners.

  Most of Johnson’s talk was at the macro level, or how the collective solves problems. This has obvious relevance for understanding how market efficiency arises.2 My focus here is on the micro level, or how investors, as individuals , should organize for investment success. While the unit of analysis is different, the message is the same: diverse information and perspectives can help improve investment performance.

  Now think carefully for a moment about your information sources. Do you read the same newspapers, talk to the same people, and review the same type of research reports over and over? Or do you allocate time to entertain new ideas, even at the risk of wasting time on intellectual cul-de-sacs? There is strong evidence to suggest that the leading thinkers in many fields—not just investing—benefit from input diversity.

  A-Mazing

  Before dwelling on the individual, I would like to show how diversity leads to better answers and how a lack of diversity can create inefficiencies. Johnson demonstrates how the collective is better than the average individual with a maze problem:• First, he asks individuals of identical capabilities to solve a maze. Because the individuals have no global sense of the problem, they simply explore until they find a solution.

  • Next, he asks the individuals to solve the problem again. With some learned information, they tend to improve.

  • Finally, he constructs a linear combination of each individual’s experiences and uses the same rules to find a collective solution.

  Because each individual’s initial search is random, a collection of individuals reflects diverse experience (maze regions), preferences (preferred paths), and performances (path lengths).3 So the collective is really just a normal individual with super information. Because of this diverse information, the collective solution is vastly more robust than the average individual solution (see exhibit 28.1).

  The power of this collective effect has not been lost on nature. This is where Johnson’s stories about ants come in. How do the ants do it? Foraging ants depart the nest with one job in mind, to find and retrieve food. They also have the ability to leave and follow chemical trails. At first, they disperse randomly. When the ants that find food come back to the nest, they leave a chemical trail that their sisters can follow. Studies show that this process allows ants to consistently find the shortest path to the food.4

  EXHIBIT 28.1 The Collective Beats the Individual

  Source: Norman L. Johnson. See http://www.ishi.lanl.gov/symintel.html.

  Once researchers understood this collective ability, they decided to play a trick on the ants. In a controlled setting, the scientists placed two food sources at identical path lengths from the nest. As it turned out, the ants ended up using just one of the paths, although they chose at random. Why? Because they follow chemical trails, a couple more ants going down one path will attract other ants, triggering a positive feedback loop. So instead of finding an optimal solution, the ants have one crowded path and an equally long empty path.

  Amazingly, though, nature anticipated this problem as well. As it turns out, ants periodically break from the main path and begin a random search process again. The ants are “programmed” to strike a balance between exploiting a known food source and exploring for the next food source (see exhibit 28.2). Johnson calls this the “wild hair” alternative. The ants are hard-wired to seek diversity.

  EXHIBIT 28.2 The Wild Hair Alternative

  Source: Sente Corporation.

  Getting a Diversity Degree

  What do mazes and ants have to do with the challenging jo
b of managing money? A lot, as it turns out. Physiologist Horace Barlow says that intelligence is all about making a guess that discovers some new underlying order. This includes solving a problem, seeing the logic of an argument, or finding an appropriate analogy.5 Where does investment intelligence come from?

  Here is where Norman Johnson’s message is so important for investors. In well-defined systems, experts are useful because they can provide rules-based solutions. But when a system becomes complex, a collection of individuals often solves a problem better than an individual—even an expert. This means that the stock market is likely to be smarter than most people most of the time, a point the empirical facts bear out.

  To be an expert in a complex system like the stock market, Johnson continues, you need two essential features. First, you must be able to create a “simulation” in your head, allowing you to conceive and select strategies.6

  A description of the legendary hedge fund manager George Soros illustrates the point:[Gary] Gladstein, who has worked closely with Soros for fifteen years, describes his boss as operating in almost mystical terms, tying Soros’s expertise to his ability to visualize the entire world’s money and credit flows. “He has the macro vision of the entire world. He consumes all this information, digests it all, and from there he can come out with his opinion as to how this is going to be sorted out. He’ll look at charts, but most of the information he’s processing is verbal, not statistical.”7