you would borrow money from a bank, and leverage your bet up 10 times and bet $1,000,000 on this bet. Why not, right? It is a guarantee.
We know how a human approaches this question, but how does a computer think about this question? A computer identifies the red-green-blue (RGB) values for a pixel in box A and the RGB values for a pixel in B. Next, the computer tabulates the results: 120-120-120 for box A; 120-120-120 for box B. Finally, the computer compares the RGB values of the pixel in A and the pixel in B, identifies a match, and concludes that box A and box B are the exact same color. The results are clear to the computer.
So which is it? After taking into consideration the results from the computer algorithm, would you still consider A darker than B? We don't know about you, but we still think A looks darker than B – call us crazy. But then that's what makes us human – we aren't perfect.
The sad reality is the computer is correct, and our perception is wrong. Our mind is being fooled by an illusion created by a vision scientist at MIT, Professor Ed Adelson. Dr. Adelson exploits local contrast between neighboring checker squares, and the mind's perception of the pillar as casting a shadow. The combination creates a powerful illusion that tricks every human mind. The human mind is, as succinctly stated by Duke Psychology Professor Dan Ariely, “predictably irrational.”
That may seem to be a strong statement. Perhaps the illusion as revealed in Figure 1.2 has convinced you that our minds may not be perfect in certain isolated settings (yes, the parallel bars are the same color from top to bottom). Or perhaps it has only persuaded you to believe that while a subset of the population may be flawed, you still possess a perfectly rational and logical mind. Don't be too sure, as a well-established body of academic literature in psychology demonstrates conclusively that humans are prone to poor decision-making across a broad range of situations.
Figure 1.2 Ed Adelson Checkerboard Illusion Answer
But what about experts? Surely, experts are beyond the grip of such cognitive bias? We often assume that professionals with years of experience and expertise in a particular field are better equipped and incentivized to make unbiased decisions. Unfortunately for experts, and for those who rely on them, the academic evidence is unequivocal: systematic decision-making, which relies on models, outperforms discretionary decision-making, or experts. We will come back to this point in a moment, but first let's discuss some other reasons experts might not always provide flawless advice.
When paying a financial expert to manage your money, a good question to ask is the following: What are the experts' incentives? This is important to know, because even if the expert has true knowledge about financial markets, misaligned incentives can destroy an edge the expert has, or make the expert look better than he really is. Here are a few examples of when experts' incentives might not be properly aligned:
• Focusing on short-term vs. long-term results. Consider a financial expert creating a value strategy with an assumed “edge,” or ability to beat the market in the long run. This expert can decide to invest in 200 of her best stock ideas or 50 of her best stock ideas. The expert faces a trade-off between these two approaches. On one hand, the expert knows that, over the long-haul, buying the cheapest 50 value stocks will be a better risk-adjusted bet than the 200-stock portfolio, since the larger portfolio would be dilutive to performance in the long run. On the other hand, the expert also understands that the 50-stock portfolio has a higher chance of losing to a standard benchmark in a given year, which will could cause her to lose clients in that year. The expert, who assumes, correctly, that most investors focus on short-term results, will opt for a 200-stock portfolio in order to minimize downside risk (and retain clients), and thus, will create a suboptimal product that doesn't fully leverage her expertise. In effect, the expert is indeed an expert, but there is an incentive alignment problem between the expert and investors that negates the benefits of her expertise.
• Exploiting authority to generate business. Let's say we have two financial experts. One expert shows up in a pair of jeans and a sweatshirt and states that simply investing in the S&P 500 from 1927 to 2013 has a return of 9.91 percent on average. The second expert shows up in an Armani suit, with his research team of PhDs (also in suits) behind him, and tells you that with his investment technique, $100 would have grown to $371,452 from 1927 to 2013. “Wow,” you would say, and then ask, “So what are the details of the strategy?” Our straight-talking sweatshirt and jeans expert might say, “Well, you simply buy and hold the S&P 500 Index and reinvest dividends to achieve the 9.91 percent return.” However, our Armani-suited PhD squad may respond with the following: “Our strategy is proprietary, is built off of 30 years of research by 15 PhDs, and seeks to dynamically allocate to certain sectors of the market, with more weight going toward better-performing securities.” Sounds impressive, but the strategy is the same: Buy and hold the market! Sadly, that is the expert's power over the layman. If you are unable to fully interpret the advice of an expert, you may be beguiled by his overblown rhetoric masquerading as skill. Overall, an investor needs to be aware of experts' incentives to leverage their position of authority. If an expert cannot explain his strategy to you in a simple, understandable way, we recommend walking in the other direction.
• Favoring complexity over simplicity. All else equal, a financial expert prefers a more complex model to a simple one. Why? Because complex models allow them to charge higher fees! As we will show later in the book, simple models beat complex models, and they certainly beat human experts. Why would an expert, many of whom are informed of this fact, recommend a complex solution other than for an increased fee? Consider two asset-allocation alternatives: The first option is an “optimized, time-varying, strategic allocation approach, based on years of research,” whereas, the second option is a 50/50 split between stocks and bonds, buy and hold forever. Also consider that both approaches sell for a 1 percent management fee and you have to choose one of the options. Your instinct probably suggests the more advanced version. But why? What if the simpler option is actually superior to the more complex one?
Overall, there are some true experts in the field. We recommend focusing on those experts who have long-term goals, are transparent about their investment strategy, and have an ability to explain their approach in one sentence.
To be clear: We are not making the claim that human experts are worthless across all aspects of the decision-making process. Dentists are great at filling cavities, surgeons are quite handy at repairing ACLs, and the right financial advisor can protect us from making expensive mistakes. Experts are critical, but only for certain elements of the decision-making process. To better frame the decision-making problem, we break the decision-making process into three components (see Figure 1.3):
• Research and development (build systems)
• Systematic implementation (implement systems)
• Evidence-based assessment (assess systems)
Figure 1.3 The Decision-Making Process
We would argue that human experts are required for the first and third phases of a decision-making process, which are the research and development phase and the assessment phase, respectively. The crux of our argument is that human experts should not be involved in the second phase of decision-making, or the implementation phase.
During the research and development phase of decision-making, experts build and test new ideas. In this phase, experts are required to create a sensible model. In the second phase – implementation – one should eliminate human involvement and rely on systematic execution. Finally, during the assessment phase of decision-making, one should once again rely on human experts to analyze and assess model performance to
