Transcription
(00:00) Taleb is really a big skeptic of using past data to predict future performance. He explains that he needs a lot more than just data. The rare Black Swan, or the rare event, has the potential to make all past data irrelevant. In history, it teaches us that things that have never happened before do happen. I think many people, myself included, can become duped into thinking that we can look at past data and believe that we have somehow increased our knowledge on what the future will look like, but things are just changing all the time.
(00:38) Today, I'm so excited to share what I learned from reading this wonderful book called "Fooled by Randomness" by Nim Taleb. Taleb is a well-known author and options trader, and we actually discussed his investment approach in detail with Scott Patterson back on episode 558. Taleb is also fairly well known for his books titled "The Black Swan," "Antifragile," and "Skin in the Game." Taleb, he's really essentially devoted his entire life to immersing himself in these problems of luck, uncertainty, probability, and knowledge. So,
(01:15) during this episode, I'll be touching on the role of luck in investing, biases that get investors into trouble, such as survivorship bias and the endowment effect, why we should develop a sense of skepticism and humility, the need to understand alternative histories, Taleb's personal trading strategy, distinguishing a good process from a good outcome, and much more. I really enjoyed going through this book, and I'm so excited to share these lessons with you today. Also, if you've been enjoying the show lately, I'd
(01:44) encourage you to hit the like and subscribe button below so you can get notified of our next release. This will really help support the channel so we can keep delivering these videos to you for free. A tremendous amount of time and effort goes into these videos, so I'd really appreciate it if, if you just took two seconds out of your day to click the like and subscribe button below. With that, I hope you enjoy today's episode. All right, so diving right in here, as the title suggests, "Fooled by Randomness," it really gave me an
(02:15) appreciation for the amount of randomness in financial markets and how markets might be a little bit noisier than we might realize. It's also a great read to really better understand investor psychology and how we can fool ourselves into making bad bets in the markets. Taleb has watched many traders get blown up due to overconfidence, taking excess risk, and ignoring what the market is trying to tell them. The tricky thing about financial markets is that it's really difficult, if not impossible, to distinguish luck from skill. If you have
(02:49) 100,000 people trying to invest successfully, it's no wonder that one of them will happen to turn out like Warren Buffett. Taleb isn't really trying to make the case that there are no skilled investors out there. Of course, there are, because, uh, Taleb himself has made a living from trading, you know, in the markets. The case he's really trying to make in the book is that a substantial amount of success in investing is primarily driven by luck or factors totally outside of our control. The key idea he's trying to convey in the book is that
(03:18) things are a bit more random than we tend to think, rather than things are entirely random. To be honest, it's actually pretty daunting to consider the massive role that luck plays into our lives. One common thing in the investment world is for people to turn to macro forecasters to hear their predictions, and, you know, they seem to be quite confident in these experts. They are prone to not learn from their past failures of predicting market moves, and despite being wrong time and time again, they still seem to trick themselves into
(03:48) thinking that the next call is going to be correct. This also ties into hindsight bias and how past events almost always seem obvious and look less random with the benefit of hindsight. It's easy to think that markets would, you know, skyrocket after COVID and the Fed printed all that money, but zoom back to April of 2020, very few of us would have been so certain of such an outcome. Just think about your view of markets today and how uncertain things feel. However, in the year ahead, however it plays out, it
(04:20) is going to be obvious, however it does play out, with the benefit of hindsight. Consider also the fact that people tend to underplay the role of luck when they're successful and overplay the role of luck when they fail. Taleb explains that this is largely emotions at play in the absence of deep critical thinking. A big lesson that Taleb shares in this book is to remain skeptical. He writes in the preface here, "It certainly takes bravery to remain skeptical. It takes inordinate courage to introspect, to confront oneself, to accept one's limitations."
(04:54) "Scientists are seeing more and more evidence that we are specifically designed by Mother Nature to fool ourselves." So the reason to remain skeptical is because we live in an uncertain world. There are infinite number of different scenarios that can play out in the future, and we need to be humble enough to accept that the world is fundamentally uncertain. Taleb encourages us to think probabilistically. Instead of just thinking about how things turned out in reality, we must also consider the alternative outcomes that could have
(05:26) occurred. And as investors, prevent the risk of getting blown up or taken totally out of the game. Simple ways to have this happen is doing things like taking on leverage or even over-concentrating into one or two positions. He also has this great table in the prologue that shares the key themes of the book, um, that he really wanted to address. And you have the words on the left column that are being mistaken by the words in the right column. So noise is being mistaken by signal, luck is being mistaken by skill, randomness is
(05:58) being mistaken by determinism, probability is being mistaken by certainty, a lucky idiot is being mistaken by a skilled investor, survivorship bias is being mistaken by market outperformance, among a host of other examples he lists here. It's a good reminder that the world is pretty messy and it isn't as simple as we'd like it to be. Taleb writes, "As much as you believe in the 'keep it simple, stupid,' it is the simplification that is dangerous."
(06:31) Financial markets is one of the most prevalent places where people mistake luck for skill because it's just so difficult to distinguish between the two. Let's take basketball, for example, to try and compare that to how things work in the world or in investing. Um, I, I just absolutely love basketball, so I love using it as an example. So Steph Curry, many of you might know him if you watch basketball. He might get lucky when he makes his first or second shot against these massive defenders that are all over him. But when you see Steph Curry just make shot after shot year after
(07:04) year, and he goes on and wins multiple championships, you can say with a pretty high level of certainty that he isn't a fraud or isn't just lucky in making all these shots. But in investing, someone who has a great five or 10-year track record might just be on a lucky streak, or they might be in the right sectors in the market at the right time. For example, they might be in value during the 2000s when value was hot, or riding tech stocks throughout much of the 2010s to today. So before we get to the content here in
(07:36) chapter 1, I wanted to share one point. Taleb made in the prologue. He talks about how circumstances that are brought about primarily through luck could also be taken away by luck or randomness as well. So in the example of the investor that got lucky over a 10-year time frame, he's going to need more luck to keep that streak going. However, had his performance primarily been driven by skill, then it's much more resistant to the randomness of markets. It reminds me of the point that Morgan Housel made in his book, "Same as Ever," in the chapter titled "Too Fast, Too Soon." I'd much rather take consistently good results over a long period of time than a tremendous burst of success over a very short period of time. The success gained over long periods is much more enduring. It's resistant to outside forces and randomness.
(08:08) So in chapter one here, Taleb outlines a story of a character named Nero. Nero was a conservative trader that just wanted to consistently have good years and avoid the risk of ruin and avoid virtually any bad years. Nero made a pretty good living in the
(08:44) markets. Trading, his trading style was really risk-averse, partly because of his previous career. It wasn't nearly as lucrative, and it really wasn't nearly as fun either. Every time he thought about maximizing profits or increasing risk, he remembered what it was like in his previous career. So he's really optimizing for longevity, and he's also seen many traders get blown up or lose all their money, and he doesn't want that to be him. So this story was based in the 1990s that Taleb shares here, and he inserts
(09:18) another character named John, who actually lived across the street from Nero. So John had a larger house, and he was a high-yield trader. So from an outsider's perspective, John was much more successful than Nero. He had the bigger house, he had two top-line German cars, and many other fancy toys and things in his life. In no way did Nero want to be like John, you know, trade like him, live the life that he lived, but he couldn't help but feel the social pressure. John would just keep adding on to his house and continually let Nero know that he was
(09:55) doing quite well in life. John wasn't as well-educated, wasn't as physically fit, and likely wasn't as intelligent as Nero. Taleb points to the research that shows that most people would prefer to make $70,000 a year when others around them are making $60,000, rather than make $80,000 any year when others around them are making $90,000. Most people naturally don't care about how much they make; they care more about how much others make relative to them. So Nero had an idea of what was at play here for John.
(10:30) He didn't agree with the methods that John used to trade, and he said that it resembles taking a nap on a railway track. Taleb writes, "You make money every month for a long time, then lose a multiple of your cumulative performance in a few hours." He has seen it with option sellers in 1987, 1989, 1992, and 1998. It was in September of 1998 that it was time for John's wakeup call. Nero felt vindicated as he got up for work and he saw John out in his front yard smoking a cigarette, a sight that he probably hasn't seen
(11:08) before. When Nero saw that, he knew immediately that John had been fired from his job, and it turns out that he lost almost everything he had. And this is a great example of the emotions at play for great investors. Nero was a great investor. He had found a way to consistently make money, a method that was rational and it really made sense to him. It might not have been too hard to keep doing what worked. The hard part is watching others become richer than you are by doing really stupid things. To make this more applicable to times our
(11:42) audience might be aware of, think of those in your social group getting rich on tech stocks in 1999, in real estate in 2006, or in cryptocurrencies in profitless tech companies in 2021. Just because something is working really well doesn't mean you need to jump on board. Nero in the story is a prime example of keeping your emotions in check and sticking with a good strategy even when it's out of favor. Taleb would have referred to John as a lucky fool that got rich because he didn't understand randomness in market cycles.
(12:16) There's another lesson in the story, which is really to think probabilistically. Had Nero lived out his life a thousand times using the strategy he did, he likely would have become rich in the vast majority of those scenarios. And as for John, he was putting himself in a vastly different situation, and he likely would have been blown up in a decent amount of the thousand cases. For the vast majority of people, ending up with an above-average result is enough. You know, in the case of Nero, he lived a very comfortable life, and it offered
(12:48) him pretty much everything he could have ever needed or wanted. When you shoot for that top 1% type of life, you risk potentially exposing yourself to the rare event. When you expose yourself to the rare Black Swans enough times, it's really bound to catch up with you, and that's really what happened with John here. Taleb encourages us to think about both the observed and the unobserved outcomes. For example, say you were looking to get into a career as a trader, you might look at John and when he was doing well and
(13:21) saying that trading is a really great profession. You don't want to get led astray, though, by the outliers, thinking that you're also going to be an outlier. You'll want to also consider how well the average trader does and how long people are able to last in the profession. If your typical trader ends up exiting the field after two years, then you may have your work cut out for you. Related to the unobserved observations, consider the idea of looking at someone extremely successful. You know, you look at Elon Musk, LeBron James, Tom Brady,
(13:53) Steph Curry, you name it. We see the people at the very top, but we don't see all the people that supposedly did all the right things and didn't make it to the top. For every Elon Musk, there are countless other people out there who didn't see the fruits of their labor the way they'd like it to pan out. Taleb also uses the example of Bill Gates in the book, and he shares this idea of path dependency, so how one outcome leads to another outcome, which leads to another outcome. So it's very path-dependent. So you have the one
(14:26) outcome because of what happened before it. Computer keyboards were designed, for example, in a very suboptimal way, but we never ended up optimizing the layout of a keyboard because we started training people typing on the version that that existed, and people didn't want to have to learn a new type. So it's very similar to languages in a way. Path dependency also plays out when it comes to network effects, and this is where Bill Gates was brought into the book. Taleb writes, "While it is hard to deny that Gates is a
(14:59) man of high personal standards, work ethic, and above-average intelligence, is he the best? Does he deserve it? Clearly not. Most people are equipped with his software because other people are equipped with the software. A purely circular effect. Nobody ever claimed that it was the best software product. Most of Gates's rivals have an obsessive jealousy of his success. They're maddened by the fact that he managed to win so big while many of them are struggling to make their companies survive."
(15:35) So in the case of Bill Gates, his success is due to his own efforts in addition to just being at the right place at the right time, and all these other factors that are totally outside of his control. And in better understanding the unobserved outcomes, Taleb oftentimes refers to the example of a Monte Carlo emulation, which I essentially think of a computer program that runs through various scenarios for you, um, after you do, you know, set up your model and put these specific inputs and assumptions in. But life is even more complex than a Monte Carlo simulation. He lists two reasons for why
(16:09) this is. In the example of John, life didn't feel random to him. He saw that he could make a lot of money in the markets, but he couldn't really see the risk. It's similar to playing with a revolver where five of the chambers are empty, but one chamber contains a bullet. When all you've seen in your life are the chambers being let go, it's easy to forget that there's a bullet in one of them. It can give people a false sense of security when they fall back on experiences and only looking at the
(16:41) things that they've encountered in their own lives. To make matters even more complex, we can envision the odds, but we can't know for certain what the true odds are in markets or in many things in life. While in the revolver example, rational players can see there's a bullet in one chamber, but in life, we don't observe the barrel of reality. We can only make educated guesses, which can really lead us to becoming delusional, ignoring the risk of ruin, ignoring the potential Black Swans on the horizon, and
(17:12) this really leads to some people playing carelessly, being tricked into thinking that the game is terribly easy. Another key theme in the book is alternative histories. People tend to fixate on the outcomes and not the randomness that was associated with that outcome, and also the alternative histories. I was really trying to think of some sports examples here, and one that really came to mind was a game I watched when I was a kid. It was a game with Eli Manning and the New York Giants versus the New England Patriots during
(17:46) the Super Bowl 42 in 2008. Um, it's funny, I think about in the interview with Morgan Housel, how we can be told things when we're young, you know, we can be taught math, we just totally forget it, but it's stories that really stick with us for life. And it's funny that this was one of the first examples that came to mind. So for those of you who might not remember that game, if you watched it, with about a minute left, it was third down, and Eli Manning was in trouble. I believe they were down three or four
(18:15) points, and Manning, he evades these giant defenders, and he throws the ball deep downfield, and David Tyree jumped and caught it, and he almost lost it right after he caught it, but he ended up managing to pin it against his helmet. So he caught it, pinned it, and, uh, got the first down. And that was really a key play, and it was sort of the highlight of the game. And to me, when I go back and watch that play, the catch, it really felt quite lucky. It isn't hard to imagine these alternative histories on that just one play. You know, think about what if
(18:53) the defender reacted a split second quicker? What if Eli Manning got taken down for a sack when these massive defenders were grabbing at his jersey? What if David Tyree happened to wear a different, you know, his other style of gloves that day? What if some totally random thing caused the Giants to run a different play? There are so many alternative histories just on that one play, and randomness and luck just happened to play to the Giants' advantage, and they went down, and they ended up scoring the winning touchdown to take
(19:28) down Tom Brady and the Patriots. Now, of course, I'm not saying there was no skill involved in that catch. He's obviously a great wide receiver, great quarterback on that team, and, you know, there's obviously skill in winning that game as well. So my point is that in so many things, randomness, things we can't control, play a huge role in the ultimate outcome that ends up impacting us tremendously. When you look at the history books, it's going to show the New York Giants as the winner of Super Bowl 42, and it's not going to
(20:04) really bring into consideration how randomness, luck, or alternative histories played into that game. It's just going to show the score and the Giants as the winner. And then those who bet money on the Giants that game, they're going to be considered geniuses that hit it big. Uh, they're going to think they're really smart for making that bet. And those who bet on the Patriots, they were considered fools, and they're going to be like, "Why did I pick the Patriots? I should have picked the the Giants." Taleb writes, "Such
(20:32) tendency to make and unmake profits based on the fate of the roulette wheel is symptomatic in our ungrin inability to cope with the complex structure of randomness prevailing in the modern world." So it's so important to understand that the human brain is not wired to think in terms of probabilities and these alternative histories. And he helps drive home this point by giving an example of selling an insurance policy. If we were to go to an airport and ask travelers en route to some remote destination how much they would
(21:07) pay for an insurance policy that pays a million dollars if they died on the trip, and this is for any reason, then ask another collection of travelers how much they would pay for an insurance policy that pays the same amount in the event of death from a terrorist attack, and only for a terrorist attack. Odds are that people would pay more for the second policy, even though a terrorist threat is included in the first policy by definition. He writes, "As a derivatives trader, I noticed that people do not like to insure against something abstract. The
(21:38) risk that merits their attention is always something vivid." And Taleb is really pointing to the fact that human beings, they tend to be very emotional creatures. "It is also a scientific fact, and a shocking one, that both risk detection and risk avoidance are not mediated in the thinking part of the brain, but largely in the emotional one. The consequences are not trivial. It means that rational thinking has very little to do with risk avoidance. Much of what rational thinking seems to do is rationalize one's actions
(22:14) by fitting some logic to them." So this is why so much of the media and journalism space, they aren't trying to deliver news or deliver information that's truly helpful, but rather they're really tapping into the emotions the best they can. The media really can play into the psyche of the mindset of investors. We tend to naturally assume that down markets are more volatile than up markets. He gives an example in the book that, uh, market movements in the 18 months after 9/11 were less volatile than
(22:50) the 18 months prior. But in the minds of investors, it was very volatile. So the media really magnified the terror threats and their impact on financial markets. Unfortunately, in a world full of noise, people want the simple solution that sounds good. But following, you know, such simple adages that you hear can sometimes get you into the most trouble. Taleb has certain parts of the book here that are just hilarious. There were so many times when I was reading it where I just laughed out loud because, uh, he has quite an interesting and, uh,
(23:25) unique personality and writing style. There was one amusing remark where he mentioned that because of how he is a contrarian trader that bets on people's underestimation of randomness, he needs most people in the markets to be fools of randomness, but not everybody, because he needs people to invest with him or hire his services. Because if everyone were fools, then no one would appreciate the work that he does and how he invests. And such. In chapter 3, Taleb gets into more detail on the Monte Carlo simulation that I mentioned, and I thought it was
(24:00) funny. He mentioned that he became addicted to these the minute he became a trader, and Monte Carlo simulations, they really shaped his thinking in matters related to randomness. He writes, "Mathematics is principally a tool to meditate rather than compute." So he's partly obsessed with them because they can really be programmed to simulate just about anything. And while Taleb's colleagues, they were immersed in news stories, central bank announcements, and whatnot, Taleb was just obsessed with tinkering with simulations and doing these things like
(24:37) simulating the populations of fast-mutating animals. And it's also interesting he mentions the connection between evolutionary biology and markets. And it's funny he mentions this because I'm currently reading "What I Learned About Investing from Darwin" by Pulak Prasad. And this is just an absolutely phenomenal book. I'm really happy that Kyle covered it on on the show. Pulak Prasad, he has beaten the market for more than the past decade, and, uh, Kyle covered his book back on episode 597. And we also discussed his book in
(25:10) our tip Mastermind community, as it's been one of my very favorite books of 2024. And at the end of February, um, Pulak Prasad is going to be joining us for Q&A. So if you'd like to join that Q&A and join our community, you can shoot me an email at clay@theinvestorspodcast.com. If you'd like to sit in on that discussion, because, uh, Pulak, he actually doesn't do public appearances. So with Taleb's extensive experience with Monte Carlos, he could no longer visualize a realized outcome without reference to the unrealized ones. So for everyone that is upset that they missed out investing in Tesla, remember that there are plenty of unrealized scenarios where Tesla wouldn't exist in 2024 and they ended up end up going bankrupt. And for everyone who's upset for not buying Amazon in 2001, remember that there are thousands
(26:05) of internet companies that ended up going to zero. We want to position our portfolios in a way where if we ran forward the future 1,000 times, we want to achieve sufficient investment returns in nearly all of them. We don't want luck to end up playing a major role in how life ends up panning out. Taleb also highlights the importance of studying history and how it can be quite difficult for us to learn from history. Sometimes the best lessons are learned from painful experiences, but ideally, we're able to learn from the mistakes of
(26:38) others. In studying history, investor Michael Batnick has this wonderful quote that I wanted to share here on learning from our mistakes: "Some lessons have to be experienced before they can be understood." And I can just totally resonate with that quote, where some of my biggest lessons from investing have been from the mistakes I've made along the way. And I actually reviewed Michael Batnick's book, it's titled "Big Mistakes," back on episode 579, where he really just walks through all these great investors and all the big mistakes
(27:10) they make. And it's, it's a great read, and I highly recommend tuning into that episode as well. Taleb is definitely well aware of our natural inferior ability to assess risk, and he's really watched other traders just ignore history and just get blown blown up spectacularly. It seems to be really common in the world he's in. He writes, "Characteristically, blown-up traders think they knew enough about the world to reject the possibility of the adverse event taking place. There was no courage in their taking such risks, just
(27:45) ignorance. I have noticed plenty of analogies between those who blew up in the stock market in 1987, those who blew up in the Japan meltdown in 1990, the bond market debacle in 1994, those who blew up in Russia in 1998, and those who blew up shorting NASDAQ stocks. They all made claims to the effect that their market was different and offered seemingly well-constructed intellectual arguments to justify their claims." So we either can be students of history and learn from the mistakes of others, or we probably end up learning
(28:21) the painful lesson ourselves and taking too much risk or getting caught up in a market bubble or whatnot. With many of these traders, I'm sure that greed just blinds them to so many of the risks. If you can make a killing year after year, it's just so easy to believe that you've cracked the code that no one else has found, and that the game is now easy. When investing feels easy, that's probably a good time to check in and ensure you aren't taking excess risk and you aren't letting your emotions take over. We can
(28:53) look back at the past with hindsight bias, knowing that tech stocks were far overvalued in 1999, for example. But once we're in a bubble ourselves, it's very difficult not to get carried away, especially if it's your first time experiencing such an emotional train ride. I'm speaking a bit from experience here. The same people who get caught up in a bubble are the same ones who say after the fact, "I knew it all along." Remember that the easiest person to fool is ironically ourselves. Although Taleb is a
(29:26) trader, I think there are so many great points in this book related to investing more broadly. He gives the example of a portfolio that returns 15% per year with 10% levels of volatility. So you can generally think most outcomes end up between 5% and, uh, 25% returns. So over one year, the probability of a positive return is 93%. Over any given month, the probability of a positive return is 67%. But when you zoom in to just one day, it's 54%. In one hour, it's 51%. So the more you compress the time frame, the more random your returns are, or the
(30:06) higher chance of a negative outcome. And for those of you who are aware of the loss aversion bias, naturally, losses hurt more than the gains feel good from a psychological perspective. So the more you check your portfolio in this situation, the more pain you're going to feel. So for many of you in the audience who check your stocks or portfolios daily, I am guilty as charged, just remember that you're not only looking at the returns on a day-to-day basis, but you're really looking at the volatility and the randomness of your returns. So
(30:40) many days you can simply attribute stock price movements simply to randomness and not anything we should really apply sound logic to. Taleb makes the funny joke that when he sees an investor monitoring his portfolio really closely, looking at the live prices on their phone or their tablet, he smiles. Is because he knows that there are still investors out there getting caught up in the randomness and letting their emotions flow. Watching, you know, that randomness with a close eye. Taleb also recommends tuning out of the news
(31:10) as the news tends to be full of noise and information that offers you no predictive power or ability. So if the news is important enough, it's really just going to find a way to get to you without you having to check on it constantly. Taleb writes, "My problem is that I am not rational and I am extremely prone to drown in randomness and to incur emotional torture. I am aware of my needs to ruminate on park benches and in cafes, away from information, but I can only do so if I am somewhat deprived of it. My sole
(31:47) advantage in life is that I know some of my weaknesses, mostly that I am incapable of taming my emotions, facing news, and incapable of seeing a performance with a clear head. Silence is far better." So to see him stay again and again in this book, how even though he's aware of the randomness at play in the world, he's just as susceptible as anyone to fall prey to it. Jumping ahead to chapter five here, Taleb tells an interesting story about a trader that got caught on the wrong side of a trade. So he describes this trader
(32:23) as named Carlos. Carlos was an emerging markets trader, and he traded bonds from all these emerging markets. And throughout the 1990s, these bonds were really in a bull market, and he had a big tailwind at his back, and this greatly benefited traders like Carlos. So Taleb was skeptical of traders of Carlos's type. Uh, he was very well-dressed up, well-spoken, and he was really, really smart, probably had a, you know, went to the nice school, had an MBA, had all the credentials he needed. And Taleb, he jokes that he believed that true
(33:03) traders, they dress sloppily and they're really the mere opposite of the Carlos's of the world. So Carlos did exceptionally well during his career, not only because he was in the right sector riding the right trend, but also because he was buying dips along the way. So whenever there was a momentary panic, Carlos would step in with confidence during that bull market, and, uh, the trend would resume. Being right about the reversals time and time again really made Carlos feel invincible. So this strategy worked really well until the summer of
(33:39) 1998. And this is when the dip that happened didn't end up translating into a rally. That summer in 1998 would be his first bad summer. It was actually so bad that it should be considered catastrophic. Up until that point, he had earned $80 million cumulatively in those previous years, and in that one summer, he lost his firm $300 million. So he talks about how this happened. So the market dropped, and Carlos had started to average down as he'd habitually done. It's what's always worked in his experience. So, and, you know,
(34:17) he was plenty confident that the market was going to bounce, as it always did before. And, uh, Carlos knew that some other firms were going through some liquidity issues, and, and there was really forced selling at play. So this was really a common sign that bargains were available. He bought in at $52, and he stated that the bonds would never trade below $48. So he was actually at this time, he was betting big on Russian bonds, saying that Russia was just too big to fail or too big to default on their debt obligations. So he continued to double
(34:51) down as the bonds traded down to $43 in July, and he ended up wagering half of his own net worth, or around $5 million, in the Russia principal bond, thinking that the profits from it were going to have him set for life. The market had other plans for him. By the middle of August, they were trading down in the 20s. Carlos understood that there was a difference between price and value. So he continued to hold on, as he believed that the true value was substantially higher than the price that was shown on the screen. Everyone in Carlos's circle
(35:27) agreed that the sell-off was far overdone. But by the end of August, the bonds kept falling, and they dropped below $10. Now, the board members at Carlos's firm wanted to understand why in the world the company had so much exposure to a government that wasn't paying its own employees or its soldiers. Veteran trader Marty O'Connell refers to what was happening around here as the firehouse effect. And the firehouse effect is when you have a bunch of firemen with a lot of downtime, they're talking to each
(35:59) other, you know, they're doing so for far too long, and it ends up being an echo chamber where they all agree on many of the same things, and an outside observer would just deem them to be ludicrous. We can also refer to this really as an echo chamber, where the same talking points are repeated again and again by the same people that think the same way. This trader hadn't considered the possibility of the low probability event happening because he'd never seen such a thing happen in his own experience before, and
(36:30) it ended up costing Carlos a lot of money and his job. He also ended up switching careers because of the damage that it ended up doing to him. What if in 2024, you got a little bit better every day when you're learning a new language with Babel? That's exactly what you're doing. And if Babel can help you start speaking a new language in just 3 weeks, imagine what you could do in a full year. Babel's quick 10-minute lessons are designed by over 150 language experts to help you start speaking a new language
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(37:30) better. Here's a special limited-time deal for our listeners right now. Get 55% off your Babel subscription, but only for our listeners at babel.com/wsb. Get 55% off at babel.com/wsb. That's spelled B-A-B-B-L.com/wsb. Rules and restrictions may apply. What's what's also interesting is that before this collapse of Russian bonds, a trader like Carlos would have been considered the top of his field because his returns were probably really, really good relative to other traders. But Taleb wisely points out that the best
(38:12) traders, or the way I think of it, the best investors at any given point in time may actually be the worst traders or worst investors. Taleb writes here, "At a given time in the market, the most successful trader are likely to be those that are best fit to the latest cycle. This does not happen too often with dentists or pianists because these professions are more immune to randomness." So if a trader has a really hot streak, odds are it may solely be due to randomness, being at the right place at the right time, or maybe
(38:49) taking too much risk. People tend to assume that traders that had a hot streak are successful because they're skilled or they're good, good at what they do. But we must remember survivorship bias. For every successful trader, there are a host of unsuccessful ones who may be just as skilled. Over short time periods, a successful trader does not make a good trader, or a successful investor does not necessarily make for a good investor. It's also important to not get married to your positions. When something goes against
(39:22) you, putting the majority of your net worth into it probably isn't a sound practice of risk management. Taleb writes, "There's a saying that bad traders divorce their spouse sooner than abandon their positions. Loyalty to ideas is not a good thing for traders, scientists, or anyone." I just love that there's a saying that bad traders divorce their spouse sooner than they abandon their positions. This bias is also known as the endowment effect. The endowment effect is the tendency to hold on to something we
(39:58) own simply because we already own it. Taleb argues that if you buy a painting for $20,000 and the price goes up to $40,000, you should keep it only if you would acquire it at that current price. If you wouldn't acquire the painting at $40,000, then it's said that you're married to your position, and it's now become an emotional investment. This, of course, ignores the consequences of taxes, but I sort of struggle with this point by Taleb and I kind of disagree a bit because, uh, we talk a lot about letting your winners
(40:32) run on the show and not tinkering with your portfolio too much. And part of the game of achieving multi-baggers is hanging on to your winners when they approach intrinsic value or maybe even exceed intrinsic value to some degree. But the bias at play here can be a really useful mental model. Once we buy a stock, something in our brain changes about how we view it. We likely now have a positive skew towards that stock, and we're now prone to confirmation bias. And confirmation bias is essentially when we start looking for information that tells
(41:06) us we made a good decision when buying the stock, and it somewhat blinds us to anything that conflicts with our decision to buy. With that said, it's just so important to be open to the idea of changing your mind when the facts change. You shouldn't update your investment thesis just because the price goes against you, and in my opinion, you shouldn't just sell just because the price goes up. If your original investment thesis upon entering the trade doesn't pan out, then odds are that it's best to exit the position, no matter
(41:36) how much it hurts psychologically to do so. So in the case of Carlos, he was a trader that entered a position, and he morphed into a long-term investor when the environment changed. From what I understand in the story, he had updated his thesis once things changed, and that's just a big red flag and, uh, a sign that you've entered a really bad trade. So when we enter a position, we should really have a plan for what we would do in the event of losses. Carlos really wasn't aware of the possibility of losing money here, and all he had done up
(42:11) to that point was really make money. I also loved that Taleb had a chapter on skewness and asymmetry. One of my favorite lessons on asymmetry was from my previous conversation with Gotham Bade on episode 583. He explained that if you have two stocks, and one is compounding at 26% per year, um, to the upside, and the other declining at 26% per year to the downside, and then you equal weight those positions from the beginning, at the end of 10 years, your average annual return would still be 17.6%. This shows how your winners can
(42:47) really carry your portfolio, even when you have big losers that fall by the wayside. So Gotham's point during that episode was really that compound is convex to the upside, but concave to the downside. So also remember that humans are wired to think linearly, so we really have to pound these ideas into our heads that really aren't that intuitive. Asymmetry is also why Taleb is able to make money by betting on Black Swans, even though the market statistically is likely to go up year after year. It's during the downtimes when the market
(43:25) gets hammered that Taleb makes out like a bandit. He generally loses money when the market is going up, but during a month like March 2020 or Black Monday in 1987, he makes all these losses back plus some additional gains on top of it. So while others perish during chaos, he thrives in it. I, I think he referred to himself as a chaos hunter in the book. As Taleb puts it, "I try to make money infrequently, as infrequently as possible, simply because
(44:07) I believe that rare events are not fairly valued and that the rarer the event, the more undervalued it will be in price." So simply put, Taleb is capitalizing on the asymmetry of down markets. Typically, markets tend to slowly march upward, but when those surprises happen, it just sends the market down much faster than many market participants expect. Another interesting point I picked up from this piece on asymmetry is not to take stable market prices as a sign of a stable investment. Taleb is really a big skeptic of using past
(44:44) data to predict future performance. He explains that he needs a lot more than just data. The rare Black Swan, or the rare event, has the potential to make all past data irrelevant. In history, it teaches us that things that have never happened before do happen. I think many people, myself included, can become duped into thinking that we can look at past data and believe that we have somehow increased our knowledge on what the future will look like, but things are just changing all the time. There's some brilliant second-level thinking here
(45:20) from Taleb that sort of makes my head spin. He writes, "Rare events exist precisely because they are unexpected. If the fund manager or trader expected it, he and his like-minded peers would not have invested in it, and the rare event would not have taken place." So had everyone been prepared for something like the Great Financial Crisis, then there would be more people who would have been in a better position financially and not have their homes foreclosed on, for example. And since there would be less foreclosures, this would
(45:53) have helped to prevent so much of that forced selling that happened by many people and not made the crisis as bad as it was. So that's the way I sort of interpret that quote there. It's sort of a good reminder that whatever bad outcome is the worst-case scenario in your life, in your portfolio, you know, just in your head, what is the worst-case scenario? The reality is that the rare event that, uh, could happen in that worst-case scenario is likely much worse than you can even imagine or even fathom. In dealing with randomness, we have to keep
(46:28) an open mind and be open to the idea of things happening that we can't even fathom in that moment. So I want to transition here to talk about one of the most important takeaways from the book, and that's understanding survivorship bias. I've touched on this a little bit during this episode. Taleb makes the case that if you have an infinite number of monkeys in front of typewriters and you just let them type away, there's a certainty that one of them is going to come out with with an exact version of "The Iliad." The probability of this
(47:00) is ridiculously low for each particular monkey. You know, it's extremely low, but if you did this enough times, he assumes that it would eventually happen. Now, for that monkey, he asks the question of, would you be willing to bet your life savings that the monkey would be able to write "The Odyssey"?
Next, this is really getting to the question of how relevant is past performance in forecasting future performance. And this is a trap that many people fall into, where they drive conclusions based on a past time series. As TB puts it, "the more data we have, the more likely we are to drown in it." So instead of betting on a one-hit wonder, we like to see a long track record. A company that does well in its first year of going public, you know, it might be a great investment, but we're much more likely to have confidence in the company's ability to execute if it has a 10-year, uh, track record of solid performance, or maybe even 20, 30, 40 years. It's very hard to simply be lucky for 40 years in a row.
The predictive ability of past data depends on two factors: the randomness of the data and the number of data points there are. The world of business is full of randomness, and you know, it's just full of a ton of data points as well. If you have millions of people that are trying to start businesses and trying to make a bid, it's no wonder that we have the Elon Musks and the Jeff Bezos's of the world. Those are the people we constantly see in the headlines, um, even here on the podcast here. But the people we don't see are the countless data points that didn't get to where they are.
This is why Warren Buffett might make it look like it's so easy to beat the market, but the vast majority of fund managers don't beat the market. This really gets to the heart of survivorship bias. We can try and replicate all the tactics that Buffett uses, but for one, we simply aren't Warren Buffett, and two, there are countless people who have tried to use his approach in investing and ended up with a different result. In Buffett's early days, he was buying, you know, cigar butt type businesses, and part of his investment thesis was that the market price would eventually converge with the intrinsic value. Sometimes that might happen, and sometimes it might take a really long time for it to happen and lead to subpar returns. Or think about when he purchased Coca-Cola in 1989. He needed the company to continue to execute in all these different markets they were in globally, and he was ultimately right in his assessment of the business. But there's almost certainly an alternative scenario where the bet would have looked terrible in hindsight. Nobody would go out and say that Buffett's success was due to luck, but it certainly played some part of it.
TB uses another example of a couple that lives in one of the richest areas of Manhattan. I really, really like this story and wanted to share it. So there's a husband and a wife in Manhattan, and the husband, he worked very demanding hours, and he wanted to live close to work. So they stretched their living expenses, and they lived in a really expensive area. That way, they had close proximity to get to and from work, and then he could maximize his time at work and not spend, you know, hours commuting. So by any measure, these people were extremely successful and extremely wealthy. But when you look at the neighborhood they were in, they were actually near the bottom of it in terms of, you know, the size of where they lived, how much money they made, the things they have. And when the man's wife would look around, she felt like she had next to nothing when she compared herself to her peers. So she was surrounded by, you know, larger diamonds, larger living spaces, and she didn't get the respect that she felt she deserved. And because of this, the wife felt that her husband is a failure by comparison of those around them. But the reality is that they are wildly successful and they're actually better off than 99.5% of Americans. So when you compare the husband to his high school friends and how they turned out, he was certainly near the top. But when you compared it to his neighbors, he was certainly at the bottom. And this is a perfect example of survivorship bias at play. This chose to live in an area full of successful people, which is an area that, by definition, excludes failure. So those who failed don't show up in the sample set or show up in that neighborhood.
And this really gets to the heart of why many people never feel satisfied with their life. They get money, they move to a nice neighborhood by their standards, and then they feel poor again, or they just get used to the nice things that they're now surrounded by. And, uh, TB also calls out the book "The Millionaire Next Door." And this book explains how a bunch of people became millionaires by saving consistently, investing, among other things. And, uh, TB points out two flaws in the book. First is that the study only looked at the millionaires, which he equates to the lucky monkeys on typewriters. And the book makes no mention of the people who also invested just like these people in the study, but they happened to invest in the wrong things at the wrong time, or maybe they were in a country where their currency hyperinflated, or, you know, they were just in a situation where their assets were seized. Um, those are excluded from the study, is the point he's sort of making. And then the second flaw he points out is that there was a massive bull market in the asset values in the people they were studying. You know, if you invested from 1980 to 2020, and the book essentially assumes that these asset returns are permanent. So the US, you know, like, like I mentioned, it had that exceptional run from the 1980s, and we shouldn't assume that these level of returns are going to continue, uh, forever. And these are the sort of assumptions that led to the great crash, uh, in the Great Depression after 1929.
Another example he gives here in the book is that, uh, it's commonly said that successful people are optimistic people. So optimistic people, they tend to take more risk, and they're more confident about their odds. And then those who are successful end up showing these characteristics of being optimistic, and we give them all this praise. But we don't see the optimistic people that ended up making a bad bet. So it's a good reminder to just be careful of the assumptions we make when we're looking at a particular data set, and particularly looking at like winning stocks, for example.
I also liked a couple of the concepts here. In chapter 10, he mentions Chaos Theory, which points to how the smallest inputs can lead to a disproportionate response. Morgan Howell wrote in his book, "How the US they actually would have lost the Revolutionary War had the wind been blowing in a certain direction, preventing the opposing Army from sailing downstream to just finish the war and wipe out, uh, the US." And, you know, had the wind been blowing in the other direction, the outcome of the war would have been entirely different, and there would be no United States of America. America.
And TB also takes a stab at Bill Gates here, saying that he clearly doesn't deserve the level of success he's had to date. And, uh, I talked about path dependence earlier, and it's really interesting how in the information age, path dependence and network effects are extremely important. So in the case of Bill Gates and Microsoft, many people were onboarded on a Microsoft, and it created that feedback loop of, since others were on Microsoft, you now had an incentive to use that program. He writes, you know, "sometimes it's just better to be lucky than to be good." There's that saying.
Another interesting part about network effects and these networks is the tipping point at which the network takes off, and it offers this sort of asymmetric upside. In Malcolm Gladwell's book, "The Tipping Point," he shows some of the behaviors of variables such as epidemics that spread extremely fast beyond some unspecified critical level. And it's really the same with book sales. Um, book sales tend to explode once they reach some sort of tipping point, and the word of mouth just takes it everywhere. And these nonlinearities are practically impossible to model or predict, and it's just really due to the randomness that's involved.
This again links to one of the beauties of investing. For my own portfolio, I want to spread myself out across bets that I think have limited downside but have a lot of potential upside far down the line. And I don't know which bets exactly are going to be the big winners, but I want to expose myself to companies with a lot of room to grow, they're bought at a fair price that I deem to be sensible, and I size it enough to be a large enough part of my portfolio where it really makes a difference. And then I just want to leave it alone for many years. Since we can't predict the significance of these nonlinearities, they really aren't going to be obvious beforehand, but it's going to look obvious, uh, with the benefit of hindsight. Like I've talked about today, just think about most big winners in the markets. I'm sure absolutely nobody 10 years ago envisioned them being as big as they are today in terms of their size. Our brains really can't fully comprehend the power of these nonlinearities. And if we find ourselves with a winner, we generally want to let it run. At least that's my personal approach. And then we'll have the Nim TBs of the world call us lucky after the fact.
It's so interesting. He also mentions the idea of "when it rains, it pours." Some people just really seem to be inherently lucky, and other people just seem to be inherently unlucky. An author either has a wildly successful book, or the author can hardly make a sale at all. Especially with the information age, successful people or books or whatever else, it spreads like wildfire, and then a lot of the others are just left, left in the dust and can hardly attract a single eyeball. Because there can be so much variability in our world, it can make it quite difficult to forecast anything. For example, people might want to know where the S&P 500 is going to be trading one year from now. And since people want that, many firms are happy to try and forecast it.
What's maybe more important that I wanted to mention here, uh, more important than the forecast, at least, is the variability in the forecast or the degree of confidence in that forecast. So it's one thing if you estimate a 10% increase with a, say, a 5% variability in either direction. And it's a whole different story if you estimate a 10% increase where returns might be as high as 40 or 50%, or as low as negative 40%. So, you know, if you have a high degree of certainty within a narrow band, that's totally different than that second scenario I mentioned. So the variation in the return matters a lot. But no one really asks about, you know, the variation or the degree of confidence when someone makes a forecast. They usually just share their forecast.
One other important takeaway I wanted to mention here is differentiating process versus outcome. And so much in the stock market and in price movements is just noise. And we need to be mindful of the degree of randomness in markets. So underperforming the market in one year doesn't necessarily make one a bad investor, and outperforming the market in one year doesn't necessarily make one a good investor. Sometimes terrible stocks skyrocket in price, and sometimes great stocks decline in price, and that's just the way markets work. What is more important is honing in on a process that meshes well with your skillset and your temperament. The best investors focus on a process that is repeatable, and they aren't quite as focused as much on the outcome, at least in the short term, because they recognize the role of luck and randomness at play into that short-term outcome, and it's often times just simply full of noise. Investors who are focused on the outcome tend to chase things as well. This investment went up recently, XYZ investment went up a lot recently, so they're going to chase that fad or chase that trend. And this is really an outcome-based approach without considering the underlying process and the probability of success with that approach. When your process isn't based on luck, and it's based on an approach that is sound, logical, and rational, hopefully this will lead to results that you are satisfied with over the long run.
All right, so we're getting close to wrapping up this discussion. And in light of all this talk on luck and randomness, TB says that he has no conclusion on figuring out when something doesn't have an element of luck in it, which honestly isn't all that helpful. Um, there are, of course, investors who are very skillful, and their performance is largely driven by skill and not primarily by luck. And the sort of catch-22 here is that many of the best investors are going to fly under the radar, in my opinion at least, because they're going to be implementing high levels of risk management. So an investor who's far outpacing the market either has luck going their way, or they're taking too much risk. They're going out and marketing themselves, trying to get more investors to invest with them. But eventually, you know, the bad investors, their luck is going to turn. And I'm not going to name any names here, but I'm sure many in the audience can come up with somebody that's been in the headlines.
TB writes, "I am unable to answer the question of who's lucky or unlucky. I can tell person A seems less lucky than person B, but the confidence in such knowledge can be so weak as to be meaningless. I prefer to remain a skeptic. I never said that every rich man is an idiot and every unsuccessful person is unlucky, only that in absence of much additional information, it is preferable to reserve one's judgment, as it is safer." End quote.
All right, that's all I have for today's episode. If you enjoyed this episode, be sure to let me know what you think of it in the comments. And if you haven't already, please just take two seconds out of your day, click the like button below, and subscribe to the video if you haven't already to support the show. Thanks for tuning in, and I hope to see you again next time.
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We don't know what is going to change in the future in terms of when's the next recession, what's the next big technology, who's going to win the next election. We've never been able to get those right. But the behaviors that have always been enduring and always been with us, regardless of what happens in the future, regardless of what is the next technology or the next recession, we know how people are going to respond to it, regardless of what it is.