Chapter 4
Experts Tell Us Stories, Not Facts

“Just the facts, ma'am”

—Joe Friday, Dragnet

During World War II, the Allied forces set up airbases on the Melanesian Islands, which lay in a strategic zone off the northeastern coast of Australia. The Allies used the region as a staging area for troops and equipment during the war.1

At the time, these remote islands were populated by small groups of poor, indigenous peoples with limited exposure to other cultures. These isolated native populations were therefore highly curious when waves of foreign visitors from economically developed and technologically sophisticated cultures began to arrive. These nomadic armies also had an interest in the local natives, who were intimately familiar with the islands, and who could provide them with labor and other assistance as they worked to establish their military bases. In order to establish goodwill and engender the cooperation of the islanders, the foreigners began to share their commodities and supplies with them.

In this way, the natives were introduced to a huge variety of modern goods and items including jeeps, flashlights, pots and pans, knives, tents, washing machines, steel tools, tobacco, canned goods, medicine, clothing, and many other trappings of modern life. These were revolutionary technologies in the eyes of the natives, satisfying many basic needs and simplifying many daily tasks, and they dramatically enhanced the quality of life on the islands.

As time passed, the natives became increasingly enthusiastic about all the new cargo arriving on the islands, since they enjoyed the lifestyle changes and overall prosperity these modern goods generated. With these tremendous riches flowing to the local peoples, island life was good, and kept getting better. Over time, the islanders became increasingly reliant on the ongoing flow of supplies.

As the war wound down, however, the steady stream of goods onto the islands slowed to a trickle, and eventually came to an end. The airplanes stopped coming, the military bases and airstrips closed, and all the foreigners withdrew. The once spectacular flow of goods and material wealth had ceased completely. The islanders were left as they had been before.

The islanders were perplexed by the sudden change in their fortunes. What had happened to the great flow of wealth and material well-being they had enjoyed for so many years? What accounted for this unwelcome change that had occurred, and was there anything they could do to restore the material prosperity that had prevailed previously? The islanders were trying to make sense of their new world, and started identifying ways in which they might remedy their situation based on what they knew.

The Melanesian natives knew that things happen for a reason. They were not stupid. What was the nature of the connection between the foreigners, their actions, and the cargo? From the islanders' perspective, there appeared to be several possible explanations for what they had observed over time. For example, it seemed likely the foreigners were acquainted with certain methods of generating the great flow of wealth. The islanders had observed the foreigners do many different things, some of which perhaps were related to the arrival of the cargo in some way. What was the key?

The foreigners had come to the islands and built roads, bases, and airfields with towers overseeing illuminated landing strips, where airplanes filled with goods came to land, and were seen to accumulate. They conducted various marching drills, while holding weapons on their shoulders and wearing distinctive clothing. They hoisted ceremonial banners. The foreigners were very different, and did a lot of strange things.

The islanders reasoned that perhaps there was some sort of magic or deity involved in the process of wealth creation. Perhaps there was even some kind of connection to spirits or the natives' ancestors? Regardless, there appeared to be a clear connection between the foreigners and their various activities and the cargo. The natives reasoned that they were capable of doing everything they had seen the foreigners do. If some of the various rituals the islanders had observed could be recreated, these might very well activate the magical flow of materials to the island. And so they developed a strategy to get the magic, ancestors, or deities involved: They set about emulating the foreigners, and recreating their customs and rituals. They did this in various ways.

The islanders recreated the airstrips, marking them with sticks, and illuminating them with torches. They built thatched huts along the runways, outfitting these with bamboo, vines, and tin cans strung on wires to mimic control towers with radio antennae, including a place for a man to sit and wear coconut earphones to communicate with the airplanes. They stood on the landing strips making landing signals to attract the planes. They built airplanes out of sticks, leaves, and grass, in order to attract other airplanes. They made airplane sounds. They replicated complex marching drills and conducted these prescribed dances while holding makeshift firearms.

Everything was perfect. The configuration of the runways was accurate, the airplanes were highly realistic, and the huts were faithful reproductions of control towers. The drills and the marching ceremonies had been flawlessly reproduced. All was as it had been during the prosperous days. Yet, despite their elaborate preparations, the natives still lacked something crucial and fundamental, because, of course, the planes failed to land and the cargo failed to materialize.

It was true that there was a connection between the rituals of the foreigners and the arrival of the cargo. The natives were right about that. The rituals did accompany the arrival of the cargo. And in some sense the rituals were also required as a condition for the arrival of the cargo. But they did not independently cause the arrival of the cargo. This was the flaw in the story. The real reasons the cargo came were much more subtle and complex, and related to hidden factors that were completely removed from the experience of the islanders and their explanations. The true nature of the rituals was misinterpreted by the natives as being causative, when the reality was totally different.2

And while we might consider the natives naïve, we also need to recognize these natives are only human, and behave just as other humans behave. They created a plausible explanation, essentially out of thin air, and believed it.

Humans believe in stories at the expense of evidence all the time—even in our modern society. And these stories abound. Below we list a sampling of “stories” that aren't grounded in facts:3

People, in general, are wired to try to understand their world. When we lack a cogent interpretation of events, this generates cognitive strain, and we struggle to make sense of things. Humans thus crave a coherent narrative; the human mind likes to resolve conflicts that are not understood or cause cognitive dissonance and create disharmony. In other words, humans like stories to explain things!

For instance, loathing ambiguity, the mind is predisposed, after the fact, to invent stories that fit unexpected outcomes. Once we have created a story that seems to fit our specific circumstances particularly well, then we tend to believe that story, and it becomes our reality. The problem is that our stories sometimes simply do not reflect reality.

Clearly, we can sometimes be swayed by a good story, despite a lack of evidence or even contravening evidence. In the investing context, we see a similar phenomenon: story-based investing is everywhere, but evidence-based investing is scarce. But, why? One would expect that financial decisions, which can have a large influence on our well-being, would be grounded in evidence and not vulnerable to our innate belief in storytelling. This would seem not to be the case. Throughout the balance of this chapter, we will explore story bias and how this bias can be reflected in the financial marketplace.

Story-Based Investing

The three of us all have young daughters who always ask, “Daddy, how did we get these presents under the Christmas tree?” We respond with, “Oh, Santa dropped them off.” Our daughters then say, “How did Santa bring them here?” We reply, “On his sleigh, guided by his reindeers. Of course.” Our daughters consider this, and then respond, “Oh, yeah, that makes sense. He even ate the cookies we left by the fireplace and his reindeers ate the carrots we left outside.” The circle is complete, and the story is airtight.

Human beings have strong preferences for coherent stories and often build powerful narratives to help interpret complex situations. In our daughters' cases, the impossible physics of the Santa story will cause the story to break down over time, but the powerful Santa Claus narrative will likely extend well beyond what many would consider its “rational life.” The Santa Claus story is one that appeals to many young children,4 but it is not just children who suffer from a strong belief in stories; adults are highly susceptible as well.

The basis for our persistent belief in stories, in spite of evidence suggesting a story is unbelievable, has perplexed researchers for many years. Consider the range of fantastic human superstitions, which are pervasive in society. The behavioral psychologist B. F. Skinner and several colleagues demonstrated that our innate need for superstition is deeply ingrained in our primal brains.5 To make the point, Skinner studied one of the more powerful brains in the animal kingdom—the pigeon.

Skinner put hungry pigeons in a cage and dispensed food pellets to them every five seconds. Now, as we all know from our urban sidewalk experiences, pigeons will naturally wander around any space looking for food, and will do so in predictably pigeon-like ways. One pigeon might step to the left and then step to the right; another pigeon might jump, land, and then jump again. In the experiment, following these random movements a food pellet will appear, consistent with a five-second release pattern. After a few rounds of engaging in the same random activities and earning a series of food pellets, each pigeon develops an internal story: Some particular deliberate action it took in the cage is causing food pellets to pop out of the feeder. Pigeons that randomly kick to the right start to believe that kicking to the right releases the pellet. Pigeons that kick to the left continue the same pattern with the opposite foot, and so on.

Amazingly, once a pigeon establishes a superstition, it is exceedingly difficult to train the pigeon out of the story. Skinner attempts to give the pigeons evidence that their superstition is worthless (e.g., only releasing pellets when the right foot is NOT kicked), but the pigeons continue with their story-based ways. Evidence has a hard time entering the decision-making process once a behavior has been established.

Evidence-Based Investing

Think you are different from a pigeon? Pigeons aren't the only animals suffering from story bias. Wes's uncle is convinced that a Dallas Cowboys victory during the Thanksgiving Day football game is a great signal for the stock market. The logic is as follows: The Cowboys are “America's Team” and if America's Team is doing well, people are happier and they spend more money. Wes's uncle really believes in this story, despite the consistent reminder he receives from all of our wives, who are life-long Eagles fans, and who highlight the evidence: Since the turn of the century, the Cowboys prediction measure is batting 9/14, or 64 percent. This means that over the past 14 years, when the Cowboys win the Thanksgiving game, 9 times out of 14 the market was positive the following year (see Table 4.1). This sounds pretty good until you consider that over the past 87 years (1927 to 2013) there is a 72.4 percent chance the market is positive. Clearly, the Cowboys indicator is bunk.

Table 4.1 Performance of Dallas Cowboy Victory Signal (2000 to 2013)

Year S&P 500 Return Cowboy Outcome Year Ahead Return Correct Prediction?
2000 −8.34% Loss −11.88% Yes
2001 −11.88% Loss −21.78% Yes
2002 −21.78% Win 28.72% Yes
2003 28.72% Loss 10.98% No
2004 10.98% Win 5.23% Yes
2005 5.23% Loss 15.69% No
2006 15.69% Win 5.76% Yes
2007 5.76% Win −36.46% No
2008 −36.46% Win 26.49% Yes
2009 26.49% Win 15.35% Yes
2010 15.35% Loss 2.11% No
2011 2.11% Win 16.00% Yes
2012 16.00% Loss 32.39% No
2013 32.39% Win 13.69% Yes

So Wes's uncle might have told himself a great story, but it certainly isn't backed by robust empirical evidence. And even if it were backed by evidence, you would be hard-pressed to raise investment capital to invest in the strategy. Or maybe you wouldn't. We would hope, however, that the “Dallas Cowboys” indicator is a bit far-fetched for most. How about the “52-week low” stock screen? Many of our stock-picking friends love this screen, thinking that 52-week-low stocks are “cheap,” on average, and therefore must offer the potential for great return relative to other stocks in the investment universe. Unfortunately, “52-week-low stocks” are virtually synonymous with what academic researchers call “low-momentum stocks.” Low-momentum stocks, for those who shy away from reading academic finance journals, have been shown to be one of the worst-performing groups of stocks one can choose from.

There are many other stock market superstitions—sell in May and go away; let your winners run, but cut your losses; head and shoulders patterns; this is a stock-pickers' market; the Santa Claus rally; invest in what you know; do your homework; buy with a margin of safety; and so forth. Some of these stories are backed by evidence; others are not. We should not believe these stories indiscriminately, just because they seem to make sense or some ostensible authority figure insists they are true. The main point is that one's investment process should not be based on a story, but rather, on an evidence-based process that demonstrates robustness over time. Below, we outline three common and compelling market stories where empirical evidence is lacking (there are many more).

Myth #1 Warren Buffett Beats Ben Graham

Ben Graham, Warren Buffett's mentor and original employer, had a strict focus on margin of safety. Graham's investment philosophy was to always buy cheap and never stray from a low price strategy. The essence of Ben Graham is captured in two of his recommended investment approaches:

  1. Purchase stocks at less than their net current asset value, a strategy Graham considered “almost unfailingly dependable and satisfactory.”6
  2. Create a portfolio of stocks a minimum of 30 stocks meeting specific price-to-earnings criteria (below 10) and specific debt-to-equity criteria (below 50 percent).7

Both of these investment approaches maintain an overarching theme involving paying a low price, independent of quality.

When Buffett came in to the spotlight, he suggested a wrinkle in Graham's original approach. Buffet's own words capture the flavor of his investment approach:

“It's far better to buy a wonderful company at a fair price than a fair company at a wonderful price.”8

In a Buffett world, Coke at a price-to-earnings ratio of 20 might be a value stock, but the textile firm Berkshire Hathaway may be overpriced at a P/E of 5. In a Graham world, Berkshire Hathaway is always the better bet. Anecdotally, it is easy to claim that Buffett was the clear winner in the horse race against Graham. But are we suffering from availability bias or story bias when we make this conjecture?9

What does the actual evidence have to say on the subject?

We can empirically verify whether a Buffett or Graham philosophy has been more effective over the past 37 years. To do so, we need to quantify Warren Buffett and Ben Graham's strategies in a systematic way. Joel Greenblatt, famous for his book, The Little Book that Beats the Market, tells a story about a systematic investment approach that encapsulates the Warren Buffet mantra of trying to “buy a wonderful business at a fair price.” Greenblatt's formula is straightforward: Rank all stocks on their earnings before interest and taxes relative to their total enterprise value (EBIT/TEV). EBIT/TEV serves as the “cheapness” indicator for a given security (labeled “Graham” in Table 4.2). Next, measure the “quality” of a firm by calculating the ratio of EBIT to capital (labeled “Quality” in Table 4.2), which satisfies Buffett's own criteria that the “more appropriate measure of managerial economic performance is return on equity capital.”10 To generate the Warren Buffett clone strategy, we simply average the EBIT/TEV and EBIT/CAPITAL ranks and then purchase the top-ranked stocks based on the combined “cheapness” and “quality” ranking (labeled “Buffett” in Table 4.2).

Table 4.2 Performances of the Three Strategies

Buffett Graham Quality S&P 500 TR
CAGR 13.94% 15.95% 10.37% 10.46%
Standard Deviation 16.93% 17.28% 17.04% 15.84%
Downside Deviation 12.02% 11.88% 11.35% 11.16%
Sharpe Ratio 0.55 0.64 0.35 0.37
Sortino Ratio (MAR = 5%) 0.80 0.96 0.56 0.56
Worst Drawdown −36.85% −37.25% −47.15% −50.21%

The performance metrics in Table 4.2 are calculated over the 1974 to 2011 timeframe and the universe consists only of investable firms (we eliminate small/micro caps). The far left column is the performance of Warren Buffett stocks as captured by Greenblatt's combined cheapness and quality measure. The second column represents the Graham cheap-stock strategy using only EBIT/TEV as the sorting variable. The third column is the stand-alone quality measure. The fourth column is the S&P 500 total return index. Each active strategy ranks stocks on the respective metric every June 30th and rebalances annually. The results reported represent the performance of the top decile of stocks for a given measure.

The performance for the Buffett formula (average of cheapness and quality) is admirable over the time period analyzed. Annual growth rates are almost 3.5 percent higher per year than the S&P 500 benchmark, and the Sharpe and Sortino risk-reward calculations are also stronger. But the Graham strategy (cheapness) outperforms on nearly every metric. The Graham strategy beats the market by over 5 percent a year, on average, and risk-reward metrics are much stronger than both the benchmark and the Buffett strategy. The evidence supports the argument that the original Graham value-investment philosophy is superior to the updated Buffet value-investment philosophy.

How is it possible that Graham beats Buffett? The answer lies in the quality component of the Buffett philosophy. If we examine the quality strategy's stand-alone performance we notice that the results are slightly weaker than the benchmark, suggesting that any strategy that, based on a quality metric, moves out of cheap stocks, and into expensive stocks, will correspondingly dilute overall performance. We see this borne out in the Buffett results, which represent a mix of a quality component and a low-price component. As we summarize in Quantitative Value, “[an equally weighted combination of quality and price algorithm] systematically overpays for quality. It is structurally flawed, leading us to fish in the wrong pond.” The lesson from the evidence is that Graham was correct, on average. And yet, the story of “value investing” has slowly evolved away from strictly buying cheap stocks to buying stocks across the price spectrum based on quality attributes that are not useful if a stock is not cheap. This is effectively the idea behind “growth at a reasonable price” investing. Unfortunately, this revamped value-investing story is not backed by robust empirical evidence. Warren Buffett is merely an anecdote associated with a great story, but the tale told by Graham (buy cheap stocks) is backed by evidence and therefore should maintain its status as the “golden rule of value investing.”

Myth #2 Economic Growth Drives Stock Returns

Should investors favor strong economic growth? Of course they should if they want to earn high returns. Strong growth drives profits, which drives returns. This practically goes without saying in the investing world. If economic vitality didn't matter, all the time spent pontificating over economic figures and developing growth forecasts associated with these estimates would be a complete waste of time, right? And that's obviously wrong, because astute professional investors are too smart to waste time on activity that doesn't matter or add value.

Not so fast.

We're going to let you in on a secret: Investors focused on economic growth are wasting their time. Jay Ritter tells a compelling evidence-based story that economic growth doesn't benefit stockholders.11 If anything, the evidence suggests a negative correlation between equity returns and GDP growth.12 Figure 4.1 shows the relation between real equity returns and real per capita GDP growth for 16 countries over the 1900 to 2002 period—over a 100-year testing period!

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Figure 4.1 Real Equity and Real GDP Per Capita Growth

The figure highlights the fact there is virtually no relationship between stock returns and GDP growth. And yet, investors are so focused on the powerful narrative that GDP growth increases corporate profits, they forget to review the underlying theory or evidence sustaining this bogus story. From a theory perspective, the only way a firm increases stockholder value is by investing firm capital in positive net present value projects. And it is unclear why strong economic growth will contribute to a firm's ability to identify more, or higher yielding, investment projects in a competitive economy (such as which projects the CEO decides to undertake). In some cases, economic growth could make this more difficult.

Buffett made this point painfully clear in his famous 1999 article in Fortune magazine. First, the Oracle of Omaha rattles off a handful of transformative high-growth industries that translated into terrible investments (e.g., airlines, automobiles, radios, and televisions). He then leaves us with a profound statement that lays out a logical case that investors shouldn't fall in love with growth for growth's sake: “The key to investing is not assessing how much an industry is going to affect society, or how much it will grow, but rather determining the competitive advantage of any given company and, above all, the durability of that advantage. The products or services that have wide, sustainable moats around them are the ones that deliver rewards to investors.”13

Buffet reminds investors why they shouldn't cling to macroeconomic growth stories. So, on which area should investors focus? As Ritter says quite succinctly: “current earnings yields.” Translated for non-finance geeks, this simply means price. And as any intelligent investor will tell you, the price you pay has everything to do with the returns one will receive. If investors pay a high price for a given asset, they can expect low returns; if the same investors pay a low price for a given asset, they can expect high returns. The real story here is that high equity returns are earned by investors who focus on paying low prices for firms with strong abilities to invest in positive net present value projects. It may be that the best prices can be had in times of low economic growth, whereas we tend to overpay in a growing economy. The idea that strong economic growth translates into strong stock returns is a superstition, and is not backed by evidence.

Myth #3 The Payout Superstition

Every quarter, boards across America wrestle with the complex question of dividend policy. Perhaps the company has excess cash that should be paid out as a dividend? Or perhaps cash should be directed to high net-present-value projects? It's a nuanced and sophisticated debate, which makes it the perfect breeding ground for generating investor superstitions.

Quant heavyweights Cliff Asness (AQR) and Rob Arnott (Research Affiliates) have noted that market observers often predict that low-dividend payout ratios imply higher earnings growth in the future.14 Conversely, when dividend payout ratios are high, commentators suggest that earnings growth will slow in subsequent years. We call this story the payout superstition.

Again, the logic seems to make sense: If companies retain earnings (i.e., low dividend payout) and plow them back into promising projects, earnings growth should be higher in the future; conversely, if companies don't see any growth opportunities, they will push cash back to shareholders (i.e., high dividend payout) and future earnings shouldn't experience robust growth.

The payout superstition is a great story, but is this really how the world works from an empirical standpoint?

Arnott and Asness looked at historical payout ratios and earnings growth of stocks broadly representative of the market. Figure 4.2 is a scatterplot showing payout ratios and subsequent 10-year real earnings growth from 1946 to 2001.

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Figure 4.2 Scatterplot of Payout Ratio vs. Subsequent 10-Year Real Earnings Growth, 1946–2001 Data

The evidence definitely indicates that there is a relationship between the payout ratio and future earnings growth. But it is not the relationship we expected. Instead, it is higher dividend-payout ratios that predict higher growth, while lower payout ratios predict lower growth—the opposite of the payout superstition. As a two-year-old says after dropping food from the table: “Uh-oh.” The results are completely backward—it's precisely the opposite of what we expect.

Asness and Arnott suggest a few hypotheses for why this might be true:

  • Since managers don't like to cut dividends, if they are concerned about the sustainability of earnings in the future they would not offer a higher dividend today; a higher payout ratio is a signal that they think future prospects look poor.
  • When earnings are not paid out, cash is used to finance poor investments (malinvestment), leading to reduced earnings growth.
  • When managers hold cash, it may signal “empire building,” where managers try to increase their power, rather than act to benefit shareholders.

Arnott and Assness's analysis suggests that if a firm has extra cash, there are reasonable arguments why they should pay out cash as dividends, rather than hold it or invest it in disastrous projects that could destroy value. In a world where malinvestment and empire building are pervasive, dividends might provide a valuable signal about a firm's shareholder policies. And perhaps mischievous corporate managers are exploiting the payout superstition for their own benefit?

The Moral of the Stories

The number of fairytales, rules of thumb, and magic charms sold in the financial markets are too numerous to list. We've highlighted three of the more coherent and believable stories in the marketplace that are called into question by empirical facts. The lesson is clear for all of us who enjoy a great investment pitch: In order to be good investors, we need to appreciate our natural preference for coherent stories over evidence that conflicts with those stories. Don't be the pigeon doing a “pellet voodoo dance,” when it has already been shown that the pellet voodoo dance doesn't work.

The Recap: Why Experts Fail

Once again, the expert's hypothesis is based on the following flawed assumptions:

  • Qualitative information increases forecast accuracy.
  • More information increases forecast accuracy.
  • Experience and intuition enhance forecast accuracy.

As we have shown thus far in this book, the assumptions underlying the expert's hypothesis are empirically invalid: “Soft,” or qualitative, information doesn't enhance forecasting ability; more information doesn't enhance forecasting ability; and experience doesn't enhance forecasting ability.

Systematic models work because the human mind is reliably unreliable.

Let us push crushing reality aside for a moment, and make the claim that most of us can be truly evidence-based decision makers who are not influenced by stories that capture our imagination and impact our decision-making ability. If we are truly empirical-based individuals, the evidence overwhelmingly suggests that we should all be using models and other algorithms to implement and execute decisions, rather than relying on experts.

But who is ready to concede that a machine is better at making decisions? You are probably like us: The idea of scrapping our years of hard-won experience is awfully hard to swallow.

Humans naturally seek to fulfill what Maslow—famous for developing the human hierarchy of needs15—calls our innate need for esteem and self-actualization. We want to feel that our opinions and judgment matter. Recognizing the fact that simple models outperform experts directly challenges our self-directed desire to achieve goals, gain confidence, and feel a sense of achievement. We want to feel like our efforts are worthwhile, but we often devote little effort to understanding if our frenetic activity actually adds value.

Consider the act of banging one's head against the wall for 10 hours a day, seven days a week. Banging your head against the wall involves a lot of activity, but because the outcome of this activity is clearly “bad,” it is easy to know that this focused effort is a waste of time. However, what if we are spending 10 hours a day reading SEC filings of companies? Is this intense activity valuable? Are we learning anything that is actually helping us to make better stock picks? A lot of investors assume it is, but have they ever systematically reviewed this assumption? Maybe this, or other, so-called “value-add” activities performed by experts is equivalent to banging one's head against the wall? Perhaps these activities are not contributing to value at all, but are actually detracting from value?

We can't say with certainty, but based on the bevy of tests previously cited, we can conjecture that while the analyst reading SEC documents all day is clearly collecting more information, the information may do nothing to enhance the analyst's forecasting ability. In fact, it is quite likely that the additional information detracts from his ability, as the analyst becomes systematically overconfident in his forecast of the future. Overall, any potential information edge that may exist can easily be overwhelmed by costs associated with cognitive bias issues.

Why Not Use Models?

Imagine you are watching Gary Kasparov, Russia's preeminent chess master, taking on IBM's Deep Blue, a cold, calculating box designed by a bunch of geeks. During the match, Gary is sweating it out, smiling when he makes a nice move, and cringing in pain when Deep Blue takes his queen. We see that Gary is like us. He is familiar; the machine is just an inhuman metal box. The machine has no emotion, no feelings, no empathy. Who do we want to win the match? We want Gary. He's like us, and we have a preference for the familiar (yes, another bias, we know). Nobody wants a computer to win.

And so what if the machine is actually better at chess than a human? We get it: Deep Blue with its ability to analyze 200 million positions per second, can best a human opponent. Does that mean we want a chess-playing computer mainframe making our life-and-death medical decisions—even if the evidence suggests it should? Probably not. Humans might be willing to put up with a flawed, but familiar human, because we empathize with flesh and blood. If the heart surgeon kills my aunt because he accidently tied the tubes the wrong way, that's unfortunate, and I'm angry, but “people make mistakes, we're all human.” But imagine if a robot performs surgery on my aunt and she dies because the robot tied the tubes the wrong way. My immediate reaction: “Who in the heck thought it was a good idea to have a robot perform heart surgery—Where's my lawyer!?!” However, the truth is, increasingly in medicine the robot is much less likely to make such a mistake, on average. We should be rooting for machines that make fewer errors, not excusing human error.

Even if one buys the argument that models can be useful, one might object that models are too limited and cannot be applied in sophisticated contexts like investment decision-making. What, for example, is the algorithm going to say when we face a unique situation the world has never seen? This time is actually different. The story is that the human expert can adapt and create on-the-fly modifications to the model that creates value. This well-trodden, but empirically busted, rebuttal against algorithms is deemed the broken-leg theory and relies on the false premise that humans don't suffer from System 1 flaws.

Under the broken leg theory, if we are competing against a model to judge whether someone will, say, go to the movies, and we happen to know this person has a broken leg and the model does not account for this, then we can use this unique, incremental knowledge to make a better judgment than the machine. One problem with this view is that it is difficult to know whether a given information signal (which may be more nuanced than a broken leg) is dispositive and reliable. Thus, we continue to extend and apply our judgment over and over again in different cases, and override the model when we shouldn't.

Sure, knowledge of the broken leg helps us beat the model. Discretionary decision makers are often able to identify the value-enhancing modifications that can theoretically outperform a simple model in specific cases. However, they simultaneously identify value-destroying modifications that cause them to underperform in other cases. Discretionary experts' inclination to “modify” simple models resembles a bag of Lay's Potato Chips—the experts “can't eat just one” modification. They might add a positive “broken leg” modification, but they also add negative modifications. And as the evidence suggests, allowing a human to engage in ad-hoc “gut” decision-making is not a good idea.

Of course, the great irony is that an expert can read and understand the evidence on systematic versus ad-hoc decision-making and agree that decisions should be made systematically with a simple model. However, overconfidence, arguably the bias that hurts experts the most, causes these “special” experts to believe they are the exception to the rule. While others can't beat the simple model, they can.

The problem is that we all believe we are better than average. The crushing reality is, “You are less beautiful than you think.”16

Relegating your decision-making processes to systems requires a massive dose of humble pie. Most—if not all—experts are unable to consume this dish. But to be a better decision maker, we must eat our humble pie. As we have shown in the previous chapters, in order for decision-making to be effective, it must be systematic. And the only systematic thing about humans is our flaws. Therefore, it is best to leave the stock picking to a simple model, which over time, will most likely outperform experts.

Summary

Chapter 4 is about how stories sometimes do not reflect reality. We began with a description of a story created by Melanesian Islanders that failed to bring cargo back to their islands. Next, we discussed how children believe Santa's story (although grownups don't), and how B. F. Skinner's pigeons also created stories around meaningless rituals that preceded the delivery of food pellets. We also discussed meaningless rituals that humans imbue with predictive value, such as the Dallas Cowboys indicator. Even Warren Buffett seems to be selling a story about paying more for high-quality companies that doesn't seem to hold up based on the evidence. We also reviewed other finance stories that seem to make sense, but just aren't true, including the stories that economic growth and lower payout ratios drive higher future stock returns. Because stories are so compelling, we must be on constant guard to prevent ourselves from believing in something that isn't based on evidence.

Notes