According to the Major League Baseball Rulebook, Rule 2.00:
“The strike zone is that area over home plate, the upper limit of which is a horizontal line at the midpoint between the top of the shoulders and the top of the uniform pants, and the lower level is a line at the hollow beneath the kneecap. The strike zone shall be determined from the batter’s stance as the batter is prepared to swing at a pitched ball.”
But what looks at least reasonably clear on paper is anything but in practice. Indeed, far from every strike called meets the above criteria (not that this is news to any baseball fan). This reality is based — in no small measure — upon how each pitch is “framed” by the catcher. Continue reading →
Value has persistently outperformed over the long-term. Why is that?
In the most general terms, growth stocks are those with growing positive attributes – like price, sales, earnings, profits, and return on equity. Value stocks, on the other hand, are stocks that are underpriced when compared to some measure of their relative value – like price to earnings, price to book, and dividend yield. Thus growth stocks trade at higher prices relative to various fundamental measures of their value because (at least in theory) the market is pricing in the potential for future earnings growth. Over relatively long periods of time, each of these investing classes can and do outperform the other. For example, growth investing dominated the 1990s while value investing has outperformed since. But value wins over the long haul.
Heraclitus is said to have been the first to assert that nothing endures but change. Yet I doubt that it was original even to him. In life and in business, a major key to success is anticipating the changes that the future holds and adapting to them.
Aaron Sorkin gets it, whatever you think of his politics. In The West Wing (I’m still a big fan – my lovely bride and I just watched a couple of episodes again this week), when President Jed Bartlet asks, often brusquely, “What’s next?,” it means that he has made up his mind and is ready to move on, even if and when his subordinates are not (see below, from Bartlet’s first presidential campaign, as he is getting acquainted with his young staff).
I have often noted (for example, here) that we generally suck at math, to our great detriment. I have also noted that we are especially poor at dealing with probabilities. If a weather forecaster says that there is an 80 percent chance of rain and it remains sunny, instead of waiting to see if it rains 80 out of 100 times when his or her forecast called for an 80 percent chance of rain, we race to conclude — perhaps based upon that single instance — that the forecaster isn’t any good. Data trumps our lyin’ eyes, but we don’t routinely see it. Continue reading →
I spent a fair amount of time yesterday watching the inaugural festivities. My interest was personal, political and even professional.
My personal interest is pretty straightforward. Two of my children and their spouses leave in the area. They attended the Inauguration Ceremony (as shown by the picture below, which my daughter took) and the Inaugural Parade.
Even better, the next picture shows my son-in-law marching with the United States Air Force Band in the Inaugural Parade. It was also taken by my daughter. Later, he was at the Commander in Chief’s Inaugural Ball, where he performed along with some lesser lights like Jennifer Hudson, Alicia Keys, Brad Paisley and Stevie Wonder.
Although I generally try to keep politics out of this blog, some of the President’s choices about what to include in his Second Inaugural Address have implications which should directly impact economic and tax policy and thus the markets. Examples include his focus on climate change, his lack of focus on debt and deficits and his suggestion that entitlements can and should be maintained at current levels without the middle class (however defined) having to pay any more for them. Sadly, it seems clear that we have consensus among virtually all of Washington (including both major parties) that entitlements should be generous and that government should be big and should continue to grow but that we the people should not have to pay for such things.
My professional interest relates primarily to the week-end’s talking head discussion on pretty much all the news shows about the President’s alleged first-term failings and the prospects of his being able to avoid the “second term jinx.”
In his terrific book, Thinking, Fast and Slow, Nobel laureate Dan Kahneman outlines what he calls the “planning fallacy.” Initially, the planning fallacy was seen as the tendency for people and organizations to underestimate how long they would need to complete a task, even when they have lots of experience. The concept was first proposed in a 1979 paper by Kahneman and his long-time collaborator, the late Amos Tversky. As with bias blindness generally, the planning fallacy only affects predictions about one’s own tasks. When uninvolved observers predict task completion times, they show a pessimistic bias, overestimating the time taken.
The planning fallacy is a corollary to optimism bias (think Lake Wobegon – where all the children are above average) and self-serving bias (where the good stuff is my doing and the bad stuff is always someone else’s fault). It’s the key reason why every building project tends to have cost overruns and why my week-end chores take at least twice as long as I expect and require three trips to Home Depot.
In 2003, Dan Lovallo and Kahneman proposed a broadened definition of the planning fallacy so as to include the tendency to underestimate the time, costs, and risks of future actions and at the same time overestimate the benefits of those same actions. It’s a kind of hubris. We thus overrate our own capacities and exaggerate our ability to shape the future. It’s largely why the results we achieve aren’t often as good as we expect and why we so routinely underestimate bad results.
Sound familiar?
Even though American presidents may well have more power than anyone else on earth, it should be no surprise that they are susceptible to behavioral biases like the planning fallacy too. Neither are any of the rest of us.
Pretty much since the day I wrote it, my Investors’ 10 Most Common Behavioral Biases has been the most popular post on this blog. It still gets a surprising number of hits all these months later. Due to the pioneering work of Daniel Kahneman and others, nearly everyone in the financial world acknowledges the reality of cognitive and behavioral biases and their impact on people, the markets and life in general. It’s a very popular subject.
As I have noted before, we all tend to share this foible — the bias blind spot, which is our inability to recognize that we suffer from the same cognitive distortions and behavioral biases that plague other people. As one prominent piece of research puts it:
We cannot attribute [our adversaries'] responses to the nature of the events or issues that elicited them because we deem our own different responses to be the ones dictated by the objective nature of those events or issues. Instead …. we infer that the source of their responses must be something about them.
In other words, if we believe something to be true, we quite naturally assume that those who disagree have some sort of problem. Our beliefs are deemed merely to reflect the objective facts because we think they are true. Our thought process goes something like this:
I’ve thought long and hard about it [biases leave no cognitive trace, after all] and I’m convinced I’m not a bigot. Some of my best friends are __________.
Of course, that line of thinking doesn’t convince anybody else. The research again:
We are not particularly comforted when others assure us that they have looked into their own hearts and minds and concluded that they have been fair and objective.
Of course not — they’re biased (but I’m not). It’s the same kind of thinking that allows us to smile knowingly when friends tells us about how smart, talented and attractive their children are while remaining utterly convinced with respect to the objective truth of the amazing attributes of our own kids.
The problem is even more acute when the “answer” is counter-intuitive, and good investing is often wildly counter-intuitive (it’s really hard to sell when we’re euphoric or buy when we’re terrified, for example). So what can we do to try to minimize our own behavioral biases? We can start, of course, by admitting what is obvious based upon the research and the data but so very hard to concede – we are all continually susceptible to cognitive and behavioral biases.
That’s great, so far as it goes. But even though there is a great deal of research into the reality of these biases, there isn’t a lot written about what we might do to deal with them and counteract their impact. That’s partly because there isn’t a lot we can do (as even Kahneman readily admits).
In his 1974 Cal Tech commencement address, the great physicist Richard Feynman talked about the scientific method — a careful and consistent process designed to root out error – as the best means to achieve progress. Even so, notice what he emphasizes: “The first principle is that you must not fool yourself–and you are the easiest person to fool.”
Even so, I am not eager to admit defeat. Therefore, I humbly offer the following ten suggestions to try to deal with our cognitive and behavioral biases. I hasten to emphasize that I offer them tentatively and without any assurance of success. Dealing with bias is extremely hard. Proceed with caution. But also bear in mind this insightful comment from Jeff Bezos of Amazon: people who are right a lot of the time are people who often change their minds. Much of dealing with our biases is simply being willing and able to change our minds when its appropriate. It’s hard, but the reward is huge — being right a lot.
Focus on the Data. As I have said repeatedly (and I’m not alone in this), focus on the data. As my masthead proclaims, I strive for a data-driven perspective and a data-driven process. That isn’t easy to do, sadly. We relate better to stories and are all too willing to believe and concoct narratives of various sorts to support our latest nonsense, but it’s a worthy aspiration and commitment nonetheless.
Actively Seek Out Contrary Data and Conclusions. A remarkable universe of discoveries in psychology and neuroscience demonstrate that our preexisting beliefs skew our thoughts and even color what we consider our most dispassionate and reasoned conclusions. This tendency toward so-called “motivated reasoning” helps explain why we find groups so polarized over matters about which the evidence seems so clear. In other words, expecting people (including ourselves) to be convinced by the facts is contrary to, well, the facts. Factor in behavioral biases (such as the ever popular confirmation bias, optimism bias, in-group bias and self-serving bias) and it’s easy to see (at least conceptually) why we can get it so wrong so readily. Our tendency is to look for and consider only those views that correspond to our own – which goes a long way towards explaining the popularity of Fox News and MSNBC, for example, while also explaining why the viewers of each of those networks tend to think that only the other side has it all wrong. If we are going to be able to see things a bit differently, we need to seek out and consider sources that look at things differently.
Build-In Accountability Mechanisms. We need (relative) objectivity if we are going to succeed in investing and in life unless we are extremely lucky. Having an accountability partner or (better yet) a competent and empowered team is particularly important due to our great ability to spot what’s wrong with everybody else (if not ourselves). It also means taking and dealing with criticism seriously. Even welcoming and encouraging it. It shouldn’t be surprising to see so many people who experience great investment success suffer indifferent performance or even failure subsequently (Bill Miller and John Paulson, for example). The more success and power we achieve, the easier it is to believe the hype. Accountability mechanisms that are maintained and honored can help to undercut that.
Focus on Process. Accountability is more effective when it’s part of a consistent, careful, clear and clearly defined process. We all recognize that the outcomes in many activities in life combine elements of both skill and luck. Investing is one of these. Especially troublesome is our perfectly human tendency to attribute poor results to bad luck and good results to skill. It’s a lead-pipe-lock that we’re going to err and err often in the investment world. If we are to succeed with any measure of consistency, we need carefully crafted plans with screw-up contingencies built-in together with a commitment to regular re-evaluation and a rescue plan in the event of major catastrophe.
Test and Re-Test. No matter how good our process is, we need also to assume that we have made errors and set out actively to find them by testing and confirming everything possible. Once we have decided that a given view is correct or committed to a particular course, confirmation bias has a tendency to take over. Planning to be lucky and believing that psychological realities don’t apply to us is a lovely (if arrogant) thought. But it’s not remotely realistic. Keep testing and looking for ways that you’re wrong.
Avoid the Noise. Distinguishing signal from noise can be agonizingly difficult. Given the sheer amount of stuff competing for our attention, eliminating distractions unlikely to provide substantive benefit will improve the likelihood of our success. CNBC is fun and all, but how often does it make us smarter or better?
Take a Tip from Attorneys. I often refer to myself as a recovering attorney, and there is a great deal about the practice of law that is frustrating and silly. But one excellent technique I learned from my time in that profession is to argue the other side’s case. Understanding and even appreciating a contrary point of view is helpful to our own thinking and can provide a good check on the coherence of our own viewpoints. Understanding and seeking support for the opposition’s best arguments is a powerful learning tool. We might even decide that – gasp – mistakes were made (almost surely by someone else, of course).
Keep Track of Your Mistakes as Carefully as Your Successes. We all tend to trumpet our successes and downplay our failures. I highly suggest that, at least within your circle of influence and with those to whom you are accountable, you carefully track and analyze your failures, readily apparent or not. Sometimes these mistakes will be the result of bad luck. But often you will find correctable errors or even errors in your process. Doing so also helps with #10.
Take Your Time. The more experienced and successful we are, the easier it is to take short-cuts. Experience is what allows us to apply useful short-cuts, of course, but it’s important to remember that all behavioral biases and ideologies provide mental short-cuts of a sort too. For big decisions, at least, make sure to take the time to connect each and every dot. When I was in law school I often refereed basketball games for extra money. Many situations were repeated time and again with the next action and the right call seemingly foreordained. It was always difficult to avoid anticipating the call — blowing the whistle based upon what was highly likely (perhaps almost surely) to happen rather than waiting to see what actually happened. Surprises happen on the basketball court with remarkable frequency. They happen in investing too.
Try to Stay Humble (no matter how successful you are). Even though it takes a healthy amount of self-confidence to be an investment success, arrogance and certainty are frequent enemies of continued investment success. Your accountability partners can and should help here, of course. Spouses are especially expert at promoting humility. You will screw up and screw up often. Remind yourself of that reality often as you continue to look for where your most recent failings took place.
It’s really hard to deal with (much less overcome) our cognitive and behavioral biases. These tentative steps are offered to try to do so but I don’t promise anything like success. Yet these steps (or an ongoing commitment to implement the concepts behind them) should put you well ahead of most everyone else.
Hurricane/Superstorm Sandy rocked the eastern seaboard last week to devastating effect. In a significant instance of good planning, markets and schools were closed, states of emergency declared and mandatory evacuations begun well before the storm made landfall. Yet nearly until the storm reached land in New Jersey last Monday, I heard lots of grousing about alleged hysteria and overreaction with respect to the precautions and preparations being undertaken to mitigate potential damage (see below for a prominent example).
Some went so far as to defy evacuation orders, and some people paid for doing so with their lives. Once the storm actually hit and caused serious damage – albeit no longer officially as a hurricane, but as a “superstorm” – the complaining stopped. Fortunately, the governmental disaster preparedness organization seems to have performed well overall. You can read about these events in many venues, including here, here and here.
The pre-crisis grousing and the refusal of so many to evacuate are worth thinking about because of what is thereby revealed about us as humans and the cognitive biases that beset us. I offer three “take-away” thoughts that are broadly applicable as well as specifically applicable to the investment world.
1. We don’t deal well with probabilities. When a weather forecast says that there is a 70 percent chance of sun, we tend to think that the forecaster screwed up if it rains. But that’s not how we should evaluate probabilities. Instead, we should consider how often it rains when the forecast calls for a 70 percent chance of sun. When the forecast is spot-on perfect, it will rain 30 percent of the time when it calls for a 70 percent chance of sun. The odds favor sun, but because complex systems like the weather (and financial markets) encompass so many variables, nothing approaching certainty is possible. We don’t handle that kind of thinking very well (a very current and interesting example in a political context is examined here).
To illustrate the level of complexity I’m talking about, consider that we can construct a linear, one-dimensional chain with 10 different links in 3,628,800 different ways. For 100 different links, the possibilities total 10158. If those are the possibilities for making a simple chain, imagine the possibilities when we’re talking about complex systems where wild randomness rules.
Perhaps the key argument of Nobel laureate Daniel Kahneman’s brilliant book, Thinking Fast and Slow, is that without careful and intentional deliberation (and often even then), we suffer from probabilistic irrationality. Remember back in 2009 when New England Patriots coach (and my former New Jersey neighbor) Bill Belichick famously decided to go for a first down on fourth-and-two in Patriots territory rather than punt while up six points late against Peyton Manning and the Indianapolis Colts? When Wes Welker was stopped just short of the first down and the Colts went on to score the winning touchdown, the criticism was overwhelming even though Belichick’s decision gave the Pats a better chance of winning. Those withering attacks simply demonstrate our difficulties with probabilities. Doing what offers the best chance of success in no way guarantees success. As analyst Bill Barnwell, who was agnostic on whether Belichick was right or wrong, wrote: “you can’t judge Belichick’s decision by the fact that it didn’t work” (bold and italics in the original). We can (and should) hope for the best while preparing for the worst.
The world is wildly random. With so many variables, even the best process (when we are able to overcome our probabilistic irrationality) can be undermined at many points, a significant number of which are utterly out of anyone’s control. As Nate Silver reports in his fine new book, The Signal and the Noise, the National Weather Service is extremely good at weather forecasting in a probabilistic sense. When the NWS says there is a 70 percent chance of sun, it’s sunny just about 70 percent of the time. Because we don’t think probabilistically (and crave certainty too), we tend to assume that the forecasts on the days it rains – 30 percent of the time – are wrong. Accordingly, when a probabilistic forecast of a dangerous hurricane is generally inconsistent with our experience (“I didn’t have a problem last time”) and isn’t what we want to hear (think confirmation bias), we can readily focus on the times we remember weather forecasts being “wrong” and discount the threat. As mathematician John Allen Paulos tweeted regarding the trouble that so many seem to have election probabilities:
Many people’s notion of probability is so impoverished that it admits of only two values: 50-50 and 99%, tossup or essentially certain.
In a fascinating research study, economists Emre Soyer and Robin Hogarth showed the results of a regression analysis to a test population of economics professors. When they presented the results in the way most commonly done in economics journals (as a single number accompanied by some error measures), the economists — whose careers are largely predicated upon doing just this sort of analysis! — did an embarrassingly poor job of answering a set of questions about the probabilities of various outcomes. When they presented the results as a scatter graph, the economists got most of the questions right. Yet when they presented the results both ways, the economists got most of the questions wrong again. As Justin Fox emphasizes, there seems to be something about a single-number probability assessment that lures our primitive brains in and leads them astray.
Due to complexity and the wild randomness it entails, the investment world — like weather forecasting — offers nothing like certainty. As every black jack player recognizes, making the “right” play (probabilistically) in does not ensure success. The very best we can hope for is favorable odds and that over a long enough period those odds will play out (and even then only after careful research to establish the odds). That we don’t deal well with probabilities makes a difficult situation far, far worse.
2. We’re prone to recency bias too. We are all prone to recency bias, meaning that we tend to extrapolate recent events into the future indefinitely. Since the recent experience of residents of the eastern seaboard (Hurricane Irene) wasn’t nearly as bad as expected (despite doing significant damage), that experience was extrapolated to the present by many. When confirmation bias (we tend to see what we want to see) and optimism bias are added to the mix, it’s no wonder so many didn’t evaluate storm risk (and don’t evaluate investment risk) very well.
3. We don’t deal well with low probability, high impact events. In the aggregate, hurricanes are low-frequency but high impact events. As I have explained before, when people calculate the risk of hurricane damage and make decisions about hurricane insurance, they consistently misread their prior experience. This conclusion comes from a paper by Wharton Professor Robert Meyer that describes and reports on a research simulation in which participants were instructed that they were owners of properties in a hurricane-prone coastal area and were given monetary incentives to make smart choices about (a) when and whether to buy insurance against hurricane losses and (b) how much insurance to buy.
Over the course of the study (three simulated hurricane “seasons”), participants would periodically watch a map that showed whether a hurricane was building as well as its strength and course. Until virtually the last second before the storm was shown to reach landfall, the participants could purchase partial insurance ($100 per 10 percent of protection, up to 50 percent) or full coverage ($2,500) on the $50,000 home they were said to own. Participants were advised how much damage each storm was likely to cause and, afterward, the financial consequences of their choices. They had an unlimited budget to buy insurance. Those who made the soundest financial decisions were eligible for a prize.
The focus of the research was to determine whether there are “inherent limits to our ability to learn from experience about the value of protection against low-probability, high-consequence events.” In other words — whether experience can help us deal with tail risk. Sadly, we don’t deal with this type of risk management very well. Moreover, as Nassim Taleb has shown, such risks — while still not anything like frequent — happen much more often than we tend to think (which explains why the 2008-09 financial crisis was deemed so highly unlikely by the vast majority of experts and their models).
The bottom line here is that participants seriously under-protected their homes. The first year, they sustained losses almost three times higher than if they had bought protection rationally. The key problem was a consistent failure to buy protection or enough protection even when serious and imminent risk was obvious (sounds like people refusing to evacuate, doesn’t it?). Moreover, most people reduced the amount of protection they bought whenever they endured no damage in the previous round, even if that lack of damage was specifically the result of having bought insurance.
Experience helped a little. Participants got better at the game as season one progressed, but they slipped back into old habits when season two began. By season three, these simulated homeowners were still suffering about twice as much damage as they should have. As Meyer’s paper reports, these research results are consistent with patterns seen in actual practice. For example, the year after Hurricane Katrina there was a 53% increase in new flood-insurance policies issued nationally. But within two years, cancellations had brought the coverage level down to pre-Katrina levels.
We simply don’t do a very good job dealing with low-probability, high-impact events, even when we have experience with them. Since those in the northeast have so little experience with hurricanes, their discounting of hurricane risk is (again) even more understandable. Given what happened to the vast majority of investment portfolios in 2008-09, the alleged market “professionals” often don’t manage tail risk very well either. That said, when a low-frequency event is treated as a certainty or near-certainty as a matter of policy, that overreaction can be disastrous and the costs too high to bear, as a Navy SEAL Commander here in San Diego once took great pains to explain to me in the context of fighting terrorism.
Taleb goes so far as to assert that we should “ban the use of probability.” I disagree, but we ought to use probabilities with care and be particularly careful about how we convey probability assessments. For example, a potential range of outcomes is better than a single number (as with the scatter graphs noted above). Similarly, an outlook that shows the weighing of probabilities together with costs and potential outcomes will also help (this discussion makes a start in that direction). Despite the risks of being perceived as “crying wolf,” we intuitively understand that when and as the potential negative outcomes are greater, lower likelihood events should generally be treated more seriously and that the progression is typically non-linear.
In virtually every endeavor, our cognitive biases are a consistent problem and provide a constant challenge. In terms of investing, they can and often do rock us like a hurricane – or at least a superstorm. As Cullen Roche points out, consistent with the research noted above, we can and should learn from our investment errors, cognitive or otherwise. Sadly, we do so far less often than we ought, as last week’s events amply demonstrate.
We all know that the outcomes in many activities in life combine both skill and luck. Investing is one of these. Understanding the relative contributions of luck and skill can help us assess past results and, more importantly, anticipate future results. It might even help our forecasting skills, but we’d probably be wise not to bet on it.
As I have noted before, in Major League Baseball, over a 162-game season the best teams win roughly 60 percent of the time. But over shorter stretches, it’s not unusual to see significant streaks. Since reversion to the mean establishes that the expected value of the whole season is roughly 50:50 (or slightly above or below that level), 60 percent being great means that there is a lot of randomness in baseball. That idea makes intuitive sense – the difference between ball four and strike three can be tantalizingly small (even if/when the umpire gets the call right); so can the difference between a hit and an out.
To look at it another way, the Tigers were big favorites in last night’s first game of this year’s World Series in large measure due to the pitching match-up. Detroit’s Justin Verlander is widely regarded as the game’s top pitcher and had been dominant through-out the post-season to that point while Barry Zito is generally thought to be one of the great busts in free agent history. Zito was even left off the World Series roster by the Giants just two years ago. But last night Zito pitched great while Verlander lasted only four difficult innings as the Giants won handily. In the words of the great Casey Stengel, Who’d-a thunk it?
Luck (randomness) is a huge factor in investment returns too, irrespective of manager. “Most of the annual variation in [one’s investment] performance is due to luck, not skill,” according to California Institute of Technology professor Bradford Cornell in a view shared by all experts (Nobel Prize winner Daniel Kahneman talks about it in this video, for example). Even more troublesome is our perfectly human tendency to attribute poor results to bad luck and good results to skill.
As a consequence, in all probabilistic fields, the best performers dwell on process. This is true for great value investors, great poker players, and great athletes. A great hitter focuses upon a good approach, his mechanics, being selective and hitting the ball hard. If he does that – maintains a good process – he will make outs sometimes (even when he hits the ball hard) but the hits will take care of themselves. Maintaining good process is really hard to do psychologically, emotionally, and organizationally. But it is absolutely imperative for investment success.
In what Kahneman calls the “planning fallacy,” our ability even to forecast the future, much less control the future, is extremely limited and is far more limited than we want to believe. In his terrific book, Thinking, Fast and Slow, Kahneman describes the “planning fallacy” as a corollary to optimism bias (think Lake Wobegon – where all the children are above average) and self-serving bias (where the good stuff is my doing and the bad stuff is always someone else’s fault). Most of us overrate our own capacities and exaggerate our abilities to shape the future. The planning fallacy is our tendency to underestimate the time, costs, and risks of future actions and at the same time overestimate the benefits thereof. It’s at least partly why we underestimate bad results. It’s why we think it won’t take us as long to accomplish something as it does. It’s why projects tend to cost more than we expect. It’s why the results we achieve aren’t as good as we expect. It’s why I take three trips to Home Depot on Saturdays. We are all susceptible – clients and financial professionals alike.
As Nate Silver’s outstanding new book emphasizes, forecasting is really hard. There are simply too many variables and too much uncertainty (Donald Rumsfeld’s infamous – but accurate – “unknown unknowns”) for forecasting to be anything like easy. As I keep repeating, information is cheap; meaning is expensive. For example (per Leonard Mlodinow), we are tricked into thinking that random patterns are meaningful, we build models that are far more sensitive to our initial assumptions than we realize, we make approximations that are cruder than we realize, we focus on what is easiest to measure rather than on what is really important, we build models that rely too heavily on statistics without enough theoretical understanding, and we unconsciously let biases based on expectation or self-interest affect our analysis.
Accordingly, consider the following.
No less an authority than Milton Friedman called Irving Fisher “the greatest economist the United States has ever produced.” However, in 1929 (just three days before the notorious Wall Street crash) Fisher forecast that “stocks have reached what looks like a permanently high plateau.”
Many of you may remember a book published in late 2000 by James Glassman and Kevin Hassett entitled Dow 36,000. Its introduction states as follows. “If you are worried about missing the market’s big move upward, you will discover that it is not too late. Stocks are now in the midst of a one-time-only rise to much higher ground – to the neighborhood of 36,000 on the Dow Jones Industrial Average.”
Also back in 2000, Fortune magazine picked a group of ten stocks designed to last the then-forthcoming decade and promoted them as a “buy and forget” portfolio of their best ideas. Unfortunately, anyone who purchased that portfolio would want to forget it. An investment in an equally weighted portfolio of these stocks back then would have suffered a 70% loss over the next decade.
For perhaps the most pertinent example of all, Pundit Tracker checked up on this year’s pre-season World Series predictions of 58 pundits from ESPN and Sports Illustrated. These guys are all paid experts who pontificate for a living. Yet even though the Tigers and the Giants were among the favorites to win their respective pennants (Vegas handicappers had the Tigers at 3-to-1 odds and the Giants at 7-to-1), not a single one of these “experts” picked the Tigers and Giants to meet in the World Series. That’s a lot of randomness.
The take-away here is pretty obvious. If your investment approach requires or even includes a relevant forecast of future events, be very careful. And the more specific the forecast, the more careful you should be.
My series on risk has sought to outline and categorize many of the risks faced by investors. As I have tried carefully to point out, risk is difficult if not impossible to define fully, much less quantify in any comprehensive way. It is surely not the same thing as volatility. Indeed, taking no risk or too little risk are huge risks, albeit of different sorts. But one thing we know for sure is that risk is risky.
Yet to this point in the series I have only alluded to the greatest risk of all. In the immortal words of Pogo, we have met the enemy and he is us.
Fortunately, behavioral economics has done a terrific job at the beginnings of an outline as to what these behavioral and cognitive risks look like. My post entitled Investors’ 10 Most Common Behavioral Biases from back in July describes 10 key problem areas (and has consistently been the most popular post overall on this site).
Unfortunately for us, #10 on that list — the bias blind spot – is not just true, it’s really true, reeking of truthiness, true in spades. It is our overarching problem.
We all tend to share this foible — the inability to recognize that we suffer from the same cognitive distortions and behavioral biases that plague other people. As one prominent piece of research puts it:
We cannot attribute [our adversaries'] responses to the nature of the events or issues that elicited them because we deem our own different responses to be the ones dictated by the objective nature of those events or issues. Instead …. we infer that the source of their responses must be something about them.
In other words, if we believe something to be true, we quite naturally assume those who disagree have some sort of problem. Our beliefs are deemed merely to reflect the objective facts because we think they are true. Duh. Our thought process goes something like this:
I’ve thought long and hard about it [biases leave no cognitive trace, after all] and I’m convinced I’m not a bigot. Some of my best friends are __________.
Of course, that line of thinking doesn’t convince anybody else. The research again:
We are not particularly comforted when others assure us that they have looked into their own hearts and minds and concluded that they have been fair and objective.
Of course not — they’re biased (but I’m not). It’s the same kind of thinking that allows us to smile knowingly when friends tells us about how smart, talented and attractive their children are while remaining utterly convinced as to the objective truth of the amazing attributes of our own kids.
We can only hope to deal with the bias blind spot by constantly remaining on the look-out for it. I suggest routinely consulting people with whom you disagree. Spouses can be particularly helpful here. They will, almost surely, be able to point out your biases and other faults with perfect clarity.
The existence of behavioral minefields is difficult enough. That we don’t think they apply to us is often fatal to our judgment.
So what are these biases? A set of reminders follow.
Confirmation bias means that instead of the impartial judges of information that we like to think we are, we’re much more like attorneys looking for any argument we think we can exploit without much regard for whether it’s true. It’s why Fox News and MSNBC viewers tend to see each other as some version of stupid, delusional and evil, no matter the situation or circumstances.
Optimism bias means that one’s subjective confidence in his judgment is reliably greater than his objective accuracy – we think we’re right far more often than we are. It’s why (together with confirmation bias) Little League bleachers all over America are full of parents just sure that Johnny-boy is a future Major Leaguer (or a least a prospective college scholarship winner — all evidence to the contrary) and why venture capitalists are wildly overconfident in their estimations of how likely their potential ventures are to succeed.
Our self-serving bias pushes us to see the world such that the good stuff that happens is my doing (like the coach who says “We had a great week of practice, worked hard and executed today”) while the bad stuff is always someone else’s fault (“It just wasn’t our night” or “We would have won if the refereeing hadn’t been so awful” or “We couldn’t have foreseen that 100-year flood market crisis”).
Our loss aversion means that we feel losses between two and two-and-a-half times as strongly as gains. It favors inaction over action and the status quo over any alternative. It’s one reason why football coaches are so frustratingly cautious and “go for it” far less often than the data says they should.
The planning fallacy is our tendency to underestimate the time, costs, and risks of future actions and at the same time to overestimate the benefits thereof. It’s why we overrate our own capacities and exaggerate our abilities to shape the future. It’s one reason why every building project tends to have cost overruns and why my week-end chores take at least twice as long as I expect and require three trips to Home Depot.
Intuitively, we think the more choices we have the better. However, the sad truth is that too many choices can lead to decision paralysis due to information overload. It’s why participation in 401(k) plans among employees decreases as the number of investable funds offered increases and why New Jersey diner menus (this one, for example) can be so frustrating. It’s also why we so readily rely upon heuristics (rules of thumb) rather than getting down and dirty with the data.
We routinely run in herds — large or small, bullish or bearish. Investment institutions herd even more than individuals in that investments chosen by one institution predict the investment choices of other institutions by a remarkable degree. It’s why market bubbles occur – in tulips, baseball cards, internet stocks and real estate. It’s why the NFL is a copycat league.
We inherently prefer narrative to data — often to the detriment of our understanding – even though stories are crucial to how we make sense of reality. That’s why ridiculous conspiracy theories abound, even among otherwise smart and intelligent people, without a bit of good evidence. It’s also why we will tend to distrust data unless and until a good story is attached to it.
We are all prone to recency bias, meaning that we tend to extrapolate recent events into the future indefinitely. That’s why most NFL pre-season play-off predictions look like the previous year’s play-off pool even though there is typically a 50 percent change-over in play-off teams year-to-year. And as reported by Bespoke, Bloomberg surveys market strategists on a weekly basis and asks for their recommended portfolio weightings of stocks, bonds and cash. Even though they are the supposed “experts,” their collective views are a great contra-indicator. The peak recommended stock weighting came just after the peak of the internet bubble in early 2001 while the lowest recommended weighting came just after the lows of the financial crisis. That’s recency bias.
Again, the existence of these behavioral and cognitive deficiencies is difficult enough. That we tend to think they’re other people’s problems and not our own – the bias blind spot – is the icing on the cake. As I have tried to point out repeatedly, risk is risky. Because of our behavioral biases and our tendency to think they don’t apply to us, the odds are overwhelming that we’re going to miss or ignore many of the risks that plague us. The greatest risk of all is staring us in the face when we look into the mirror. At least, the greatest risk of all is staring you in the face when you look into the mirror.
Reckoning with Risk (3) shows our failings at dealing with low-probability, high-impact events.
Reckoning with Risk (4) looks at what the Yale Endowment experience can teach us about risk.
Reckoning with Risk (5) explains that professional managers face different risks than those for whom they manage money and that those differences matter.
In what is now a ubiquitous concept, a “black swan” is an extreme event that lies beyond the realm of our normal expectations and which has enormous consequences (e.g., Donald Rumsfeld’s “unknown unknowns”). It is by definition an outlier. Examples include the rise of Hitler, winning the lottery, the fall of the Berlin Wall and the ultimate demise of the Soviet bloc, the development of Viagra (which was originally designed to treat hypertension before a surprising side effect was discovered) and of course the 9.11 atrocities.
As Nassim Taleb famously pointed out in his terrific book outlining the idea, most people (at least in the northern hemisphere) expect all swans to be white because that is consistent with their personal experience. Thus a black swan (native to Australia) is necessarily a surprise. Yet, once discovered, we tend to concoct explanations for black swans which make them appear more predictable and less random than they actually are. This tendency is called the “narrative fallacy.” Our minds are designed to retain, for efficient storage, past information that fits into a compressed narrative. This distortion, the “hindsight bias,” prevents us from adequately learning from what has gone on before.
Black swans also have extreme effects, both positive and negative. Even though I think that Taleb overstates their overall significance somewhat, just a few explain a surprising amount of our history, from the success of some ideas and discoveries to events in our personal lives. Moreover, their influence seems to have grown beginning in the 20th century (on account of globalization and growing interconnectedness), while ordinary events — the ones we typically study, discuss and learn about in history books or from the news — seem increasingly inconsequential. A fascinating discussion of these ideas within the context of the 2008-09 financial crisis between Taleb and Nobel laureate Daniel Kahneman is available on video here.
Higher levels of complexity lead to systems that are increasingly fragile and susceptible to sudden, spectacular collapse. Indeed, John Casti’s X-Events argues that today’s highly advanced and overly complex societies have grown highly vulnerable to extreme events that may ultimately result in the collapse of our civilization. Examples could include a global internet or technological collapse, transnational economic meltdown or even robot uprisings.
Per Andrew Zolli in the Harvard Business Review, CalTech system scientist John Doyle calls such systems Robust-Yet-Fragile. While they are good at dealing with anticipated threats, they are quite poor at dealing with unanticipated ones. Accordingly, as the complexity of these systems grows, both the sources and severity of possible disruptions increases. Meanwhile, the size required for potential ‘triggering events’ decreases. Thus it may only take a tiny event, at the wrong place or at the wrong time, to spark a calamity. While the chances of any of these possibilities actually happening is individually remote, our general susceptibility to that type of catastrophe is surprisingly real.
Taleb applies these concepts to the investment world by trying to be extremely risk-averse where the risks are high and the potential gains are small and extremely aggressive where the costs are low and the potential gains are high. He thus worries less about small failures and more about large, potentially terminal ones. He worries more about conventional investments and less about the truly speculative ones. In essence, Taleb wants to gain exposure to positive Black Swans — when a failure would be of small moment — and avoid those situations where he is under threat from a negative Black Swan. Taleb’s forthcoming book will seek to examine these ideas in more detail.
To be sure, it can be hard to distinguish between black swan “positioning” and long-range forecasting. Current long-term investment approaches focusing on food, farmland and timber probably fall into this category. Moreover, any number of potential extreme events are simply too extreme to deal with in more than a rudimentary way. For example, if the entire economic system melts down, it won’t likely matter how short the market you are at the time. Even so, there are lessons to be learned and actions that can be considered and taken to mitigate these (seemingly increasing) risks.
Some tentative conclusions follow.
Minimize downside risk exposure efficiently to the extent possible. That may mean buying puts and/or other types of insurance. It may mean keeping a cash cushion. It may also mean reducing one’s reliance on a key supplier despite significant added cost. The “efficiently” qualifier simply means that we should be careful how much we pay for protection. Taleb argues persuasively that long-term protection from tail risk is often underpriced. But our general risk aversion can readily push us to pay too much to avoid certain or general risks.
Plan, test, evaluate, adjust and plan some more (and frequently), both near, intermediate and long-term. The inherent difficulty in planning – our dreadful track record in trying to make predictions about the future – has always been with us, but good planning remains good business (investment or otherwise). In a highly uncertain environment, that means that we should be using scenario planning. Since no one base case or even one set of cases can be regarded as highly probable or comprehensive much of the time, it is necessary to develop robust plans based upon the assumption that multiple futures are possible and therefore to focus attention on the underlying drivers of uncertainty.
Develop a clear and multi-sourced pipeline of pertinent information flow. This approach should include obtaining and evaluating information and ideas from sources with different objectives and outlooks. Within organizations that means trying to foster what Kahneman calls “adversarial collaboration” and making sure that everyone can be challenged without fear of reprisal and that everyone (and especially anyone in charge) is accountable.
Stay flexible in terms of outlook, approach and action. It’s easy to get caught up in ideological thinking rather than data-driven analysis. Confirmation bias makes this problem much worse. As I have noted repeatedly, we like to think that we’re like judges, carefully evaluating the facts before coming to a rational and impartial decision. Instead, we’re much more like attorneys, searching for alleged facts and arguments that support our preconceived positions and ideas. It is crucial that one remain willing, based upon sufficient data, to change course, perhaps quickly (both in terms of emergency response and in terms of fixing the problem). It also means encouraging innovation at every level of an organization, including innovative and (pardon the cliché) outside-the-box thinking (because we all tend toward tunnel vision). Finally, it means dealing with the psychological impact of being shocked by what may be an inconceivable event of staggering proportions. People who are that wrong often have trouble adjusting to a new reality. It’s one reason we’re lousy at dealing with new and different situations generally but really good at gearing up to fight the last war.
Don’t get lost in the details, suffering from what Taleb calls the error of excessive and naïve specificity. Future black swans are necessarily abstract and elusive. Therefore, detailed knowledge of previous black swans isn’t likely to help much except in broad generalities.
Maintain adequate alert systems and contingency plans. Even great planning will often be wrong. But how those failings are dealt with is crucial to future success.
Being surprised by a true black swan is not the same thing as being surprised in general. The 2008-09 financial crisis was not a black swan – it was readily foreseeable, even though the timing of it was not. Whenever we screw up we want to claim “I couldn’t have known. It wasn’t my fault.” But if we’re going to get better, we’re going to have to “read the signs” more carefully and more effectively and we’re also going to have to do more and better planning for real black swans.
According to the Major League Baseball Rulebook, Rule 2.00:
