[00:03:08] Intro: Since 2014 and through more than 180 million downloads, we’ve studied the financial markets and read the books that influence self made billionaires the most. We keep you informed and prepared for the unexpected. Now for your host, Kyle Grieve.

[00:03:32] Kyle Grieve: Welcome to The Investor’s Podcast. I’m your host Kyle Grieve. And today I’m going to be discussing a book that was just incredibly well written and full of nearly everything that you could possibly want to know about the concept of risk. Now, Howard Marks is one of my biggest influences, both inside and outside the world of investing.

[00:03:49] Kyle Grieve: If you’ve been listening to this podcast long enough, you probably have caught up on that. And because of that, I’ve obviously listened to him a heck of a lot. And I’ve always read all of his memos. And I listened to his memos on his own podcast as well. But in one of his memos, he managed to mention that Peter Bernstein was one of the most intelligent people that he’d ever met and was one of Howard Marx’s biggest inspirations.

[00:04:11] Kyle Grieve: And so after reading that, I want to learn more about Peter Bernstein’s take on risk, which is just a fascinating subject to me. This is not only because it’s a field that I think has changed drastically as more and more research is layered on to each other, but also because I just enjoy learning about how to think about risk through the lens of investing today, I’m going to share some of the top learnings from Peter Bernstein’s incredible book, which is titled against the gods.

[00:04:34] Kyle Grieve: The remarkable story of risk. This book is a complete history of how the concept of risk has developed over the millennia to where the book ended in 1996. Bernstein points out that the earliest known form of gambling was a sort of dice game which was played with Ostragalus, or the knuckle bone from a sheep or a deer.

[00:04:54] Kyle Grieve: Egyptian tomb paintings dated 3500 BC have depicted gamblers playing with Ostrogalus. The point here is that humans have been gambling for a very, very long time, and we’re likely to continue gambling for many, many more years into the future. Over the years, the concept of gambling has produced some of the most extraordinary insights into risk.

[00:05:14] Kyle Grieve: Throughout this episode, you’ll learn about some of the characters who layered on their thoughts and research to help understand gambling better, which eventually turned into modern portfolio theory. And how risk is used in financial markets today. An interesting question that Bernstein mentions is why it took so long for the West to develop its own numerical system.

[00:05:31] Kyle Grieve: After all, the Greeks were very rational. Much of the Greek spirit was very insistent on proof. Bernstein writes, quote, Why? Matted more to them than what? The Greeks were able to reframe the ultimate questions because theirs was the first civilization in history to be free of the intellectual straitjacket imposed by an all powerful priesthood.

[00:05:51] Kyle Grieve: The Greeks were on the right path, but the number system limited the usefulness of their ability to make calculations. Now, let’s fast forward a little bit here to the beginnings of numbers in the West. This was around the year 1202, when a little book titled Liber Abaci, sorry, I probably am going to butcher that name, or the Book of Abacus appeared in Italy and was authored by a gentleman named Leonardo Pisano.

[00:06:15] Kyle Grieve: For the remainder of his life, he went by Fibonacci. Which I’m sure many listeners who have tested technical trading are going to be very familiar with now Fibonacci became interested in writing this book while visiting Algeria where his father served as a Paisan council while there He learned the wonders of the Hindu Arabic numbering system that had been introduced to the West during the Crusades This numbering system opened up numerous calculations that were impossible to do using the Roman numerals that he’d grown up with He continued learning more from other Arabic mathematicians throughout many of his different travels.

[00:06:47] Kyle Grieve: Now I’d like to touch a little bit more on the Greeks insistence on proof as it relates to the investing world. The traditional investing world of education is just insistent on using proofs to create all sorts of models in finance. Charlie Munger commented on this wonderfully saying, Warren once said to me, I’m probably misjudging academia generally in thinking so poorly of it because the people that interact with me have bonkers theories.

[00:07:10] Kyle Grieve: Charlie then elaborates, we’re trying to buy businesses with sustainable competitive advantages at a low or even a fair price. The reason professors teach such nonsense is that if they didn’t, what would they teach the rest of the semester? Teaching people formulas that don’t really work in real life is a disaster for the real world.

[00:07:28] Kyle Grieve: Now this speaks to the fact that investing can’t simply be broken down just into science. Charlie Munger, Warren Buffett, Peter Lynch, and Philip Fisher all built their careers off the backs of understanding the complex art of investing. Mathematical proofs don’t send as strong of a signal to an investor as understanding many of these qualitative aspects of investing.

[00:07:48] Kyle Grieve: Now, while it’s important to understand accounting as it is the language of business, it’s very hard to succeed in investing while using those tools exclusively. Sure, some have done it. Jim Simons being one of the best examples. However, I personally struggle to see how the average investor can take advantage of this with very limited means.

[00:08:07] Kyle Grieve: Let’s now move towards the birth of probability theory. One of the first people to ask a question that required probability theory to answer was the Franciscan monk named Luca Pacioli. Three hundred years after Fibonacci’s Libra Robocci had been published, Pacioli wrote a book we’ll call Summa. Luca’s Summa had multiplication tables to 60 by 60 and basic algebra.

[00:08:29] Kyle Grieve: But the most important part of this book was a question that he posed. A and B are playing a fair game of BALA. They agreed to continue until one has won 6 rounds. The game actually stops when A wins 5 and B wins 3. How should the stakes be divided? This question would become known as the problem of the points.

[00:08:48] Kyle Grieve: And an answer to the question would take 150 years as additional calculations were needed to answer it. Now in the meantime, a gambler named Girolamo Cardano began to formulate probability and odds. Now, the funny thing about Cardano who wrote an autobiography and a book on chance was his aims. It wasn’t known if he specifically wrote this book on chance to help gamblers to make money or to improve mathematics.

[00:09:13] Kyle Grieve: And my guess is it’s probably the former. Cardano seemed like he had a gambling addiction. He confessed to immoderate devotion to table games and dice. During many years, I have played not off and on, but as I am ashamed to say, every day. He concluded his thoughts on gambling after losing large subs. That the greatest advantage from gambling comes from not having played it at all.

[00:09:34] Kyle Grieve: Now this is such a strong message that bears thinking about, especially during times when markets are euphoric. I think right now is such a time, and if you approach investing with a gambler’s attitude, it’s going to be very, very difficult to succeed. And as Cardano said, you’re probably better off not engaging in such behavior.

[00:09:50] Kyle Grieve: Let’s look at two of Cardano’s most significant additions to risk. This was probability and odds. Cardano said the probability of an outcome is the ratio of a favorable outcome to the total opportunity set. The odds of an outcome are the ratio of a favorable outcome to unfavorable outcomes. Now, let’s look at this through the investing lens.

[00:10:09] Kyle Grieve: We’ll use a simple hypothetical example where one is either right or one is wrong. Since the outcome that we want is right, we’re going to say that represents 1 over 2 or 0. 5 or 50 percent probability of a favorable outcome. Now, when looking at investing, I personally like using a bear bull and base case.

[00:10:27] Kyle Grieve: In this case, the chances of my base case would be a third or 33 percent probability of that outcome. Now, odds are a little bit different. In the example of the bear, base, and bull case, the odds are 2 to 1. We have two favourable outcomes, the base and the bull case, and one unfavourable one, which is the bear case.

[00:10:43] Kyle Grieve: If we convert those odds into probability, we have about a 66. 7 percent chance of a favourable outcome. This is obviously overly simplified and doesn’t take into account how much we win when we win versus how much we lose when we lose. But you can still see the relationship in investing. This was part of what Munger was saying when he wanted to tilt the odds in his favour.

[00:11:00] Kyle Grieve: If you have 99 favorable outcomes to just one negative outcome, well, that’s probably a bet that you want to put a lot of money behind. Now let’s move to three guys who helped move probability theory along substantially. Bernstein writes, the first, Blaise Pascal was a brilliant young dissolute who subsequently became a religious zealot and ended up rejecting the use of reason.

[00:11:21] Kyle Grieve: The second, Pierre de Fermat, was a successful lawyer for whom mathematics was a sideline. The third member of the group was a nobleman, the Chevalier de Mer, who combined his taste for mathematics with an irresistible urge to play games of chance. His fame rests simply on his having posed a question that set the other two on the road to discovery.

[00:11:41] Kyle Grieve: The question that the Chevalier de Mer posed was to divide the stakes of an unfinished game of chance between two players when one of them is ahead. Pascal and Fermat came up with the combination to try and solve the problem. Peter Bernstein changes the question and makes it into a baseball reference that helps explain the problem and solution.

[00:11:59] Kyle Grieve: So the way he poses it is to take two baseball teams, and let’s say they’re in the World Series of baseball. One team wins the first game. After that game, what is their probability of winning the World Series now? There are 64 possible outcomes. 42 of these outcomes favour the team that already won one game, and only needs three more to win the series.

[00:12:19] Kyle Grieve: There are then 22 possible combinations where the team that’s down 0 1 can come back and win. As a result, there’s about a 1 in 3 odds, or 22 over 64, that your team, down 0 to 1, will come back. And with that, Pascal and Fermat had solved the problem that the Chevalier Demers had posed, and had been unanswered for a few centuries.

[00:12:39] Kyle Grieve: While this doesn’t seem like a big deal, people like Charlie Munger came to understand the importance of what they discovered. In his speech titled The Lesson on Elementary Worldly Wisdom, Munger said, First, there’s mathematics. Obviously, you’ve got to be able to handle numbers and quantities, basic arithmetic.

[00:12:55] Kyle Grieve: And the great useful model after compound interest is the elementary math of permutations and combinations. And that was what was taught in my day, in the sophomore year of high school. I suppose by now in great private schools, it’s probably down to 8th grade or so. It’s elementary algebra. It was all worked out in the course of about one year between Pascal and Fermat.

[00:13:16] Kyle Grieve: They worked it out casually in a series of letters. It’s not that hard to learn. What is hard is to get to so you use it routinely almost every day of your life. The Fermat Pascal system is dramatically consonant with the way the world works, and it’s a fundamental truth. So you simply have to have the technique.

[00:13:34] Kyle Grieve: Now Pascal later gave up his pursuits in mathematics to focus on his religion. During this time, he came up with an interesting question, which was called Pascal’s Wager. The question is, God is or he is not? Which way should we incline? Now, I’m not going to discuss anything to do with God on the show, but the question was very important because it was one of the first questions posed in relation to decision theory, which is deciding what to do when the result is uncertain.

[00:14:01] Kyle Grieve: A great modern example of this is our fear of, say, being hit by lightning. It doesn’t appear that this was a fear any less impactful 400 years ago. A book was written that briefly discussed uncertainty. Fear of harm ought to be proportional not merely to the gravity of the harm, but also the probability of the event.

[00:14:19] Kyle Grieve: I’ve seen this first hand with someone I know who is, you know, fearful of being bit by a shark. The chances of being bit by a shark are about 1 in 4. 3 million. But some people are much more fearful of that event versus something like drowning, where the odds are actually significantly higher at about 1 in 1000.

[00:14:35] Kyle Grieve: Bernstein points out that Pascal and Fermat bred the science of forecasting the probability of future events. This is an area that all investors are interested in or should be interested in. Many investors attempt to base their decisions on the probability of future events. It might be something on a smaller scale, such as estimating the probability of a positive outcome for a specific business that you maybe own or you’re researching.

[00:14:57] Kyle Grieve: Thank you. I do this regularly on businesses that I own and businesses that I’m researching. Or maybe you have other investors who seek to base their investing on large macroeconomic events such as interest rates or election results. I want to transition here to the next character in the history of risk, which is going to be Daniel Bernoulli.

[00:15:15] Kyle Grieve: Now, Bernoulli was integral in bringing intuition and measurement together. Bernstein writes, Bernoulli introduces us to the risk taker, the player who chooses how to bet or whether to bet at all. While probability theory sets the choices, Bernoulli defines the motivations of the person who does the choosing.

[00:15:35] Kyle Grieve: This is an entirely new area of study and body of theory. Bernoulli laid the intellectual groundwork for much of what was to follow, not just in economics, but in theories about how people make decision and choices in every aspect of life. Now, if you’ve done any research into financial psychology, you’ll recognize this.

[00:15:54] Kyle Grieve: This is pretty much as an early form of utility theory where people’s motivation to make a financial decision are impacted by more than just probability theory and odds. We newly discovered things like how the utility of an additional dollar becomes less and less valuable as someone gets more and more wealthier.

[00:16:12] Kyle Grieve: Now this is a very interesting point for me, and I thought about it pretty often. in. I thought about it, especially in relation to taking financial risks in my own life. I know that because I’m still in the early innings of my own path towards financial freedom. I’m probably chasing a higher hurdle rate than someone who’s, you know, a couple decades in the future from where I am now.

[00:16:32] Kyle Grieve: My goal is to double my capital every five years, which comes out to about a 15 percent compound or annual growth rate. Now, a couple of points on this. I’m still in my thirties and I’m still in the capital accumulation phase. I’m When I get into my sixties, am I going to still be chasing the exact same goal?

[00:16:48] Kyle Grieve: Probably not. At that point, I may be more interested in capital preservation. And this speaks to utility theory, because as I gain more capital, I may become less interested in the types of investments that I am making today. Now it’s a really interesting question to ask yourself. Bernoulli and his kin introduced many interesting theories to the world.

[00:17:08] Kyle Grieve: In his definition of wealth, he defined wealth as. Anything that can contribute to the adequate satisfaction of any sort of want. There is then nobody who can be said to possess nothing at all, in this sense, unless he starves to death. Now what he was discussing here was actually human capital, which was a novel concept at the time.

[00:17:25] Kyle Grieve: But we now know that human capital is just integral to the growth of pretty much everything around us. The concept of human capital makes me think of my interview that I had with Hamilton Helmer back on TIP 600. One of his seven powers covered in his book is known as a cornered resource. While this resource can be intellectual property, it can also be a human resource.

[00:17:45] Kyle Grieve: Hamilton’s case study was on the early days of Pixar. During this time, they had three primary people that he considered a corner resource. The first one was John Lasseter. The second one was Ed Catmull. And the third was Steve Jobs. Now, these guys could have joined other studios and basically instantly added value wherever they went.

[00:18:03] Kyle Grieve: They themselves were the value add. It wasn’t the surrounding video equipment, the studios, or other personnel that were surrounding them. They were human capital at the highest level. Now let’s go back to the 1600s where insurance companies began popping up that were insuring pretty much all sorts of type of events.

[00:18:20] Kyle Grieve: The creation of insurance was due in large part to the work of John Grant, who discovered sampling. Now you can think of sampling to help you kind of guess an average. So Grant showed a number of survivors in different age cohorts. Now in Grant’s time during the 1600s, obviously people lived a lot shorter than they do on average today.

[00:18:41] Kyle Grieve: So he created his table, and it showed that as people got older, the cohorts became smaller and smaller. For instance, the age cohort of the age 6 to 64 was 64 percent in his day, and the cohort for the 76 plus age was only 1%. Now, this probably doesn’t seem overly useful, but it had far ranging consequences on insurance, as I just mentioned.

[00:19:02] Kyle Grieve: So knowing what is normal would help build the entire insurance industry. Lloyd’s of London was one of the largest insurance houses in the world for over two centuries, and many of Grant’s lessons were the reason that insurance was formed in the first place. Bernstein writes, insurance is a business that is totally dependent on the process of sampling, averages, independence of observations, and the notion of normal that motivated Grant’s research.

[00:19:28] Kyle Grieve: In the late 1600s, the English government utilized annuities to help fund the English national debt. Now the concept of annuities lined up with Grant’s research. For those unfamiliar with annuities, it’s a financial product that provides you with a guaranteed regular income. The gist of it is that the provider of the annuity can use sampling to understand how long the average person will survive.

[00:19:48] Kyle Grieve: So if they sell some of these to a large group of people, they know they’re going to make money on those who pass away early as they won’t have to pay out those monthly payments for a very long period of time. But then there’s going to be a smaller group of people who do survive and live longer and end up gaining more than they paid for for the annuity.

[00:20:04] Kyle Grieve: Since the sellers of the annuities have samplings of how long the average person will live, they can structure them so they end up making money on them as a total, even though they will end up losing money on some of the people who do tend to survive longer. Additionally, the insurance company can invest the principal until it has to pay it out to the annuitant.

[00:20:22] Kyle Grieve: The next person I want to chat about in this book is Thomas Bayes. Now, you may have heard us speaking of Bayes theorem on the podcast before, as it’s just a central tenet in using probability in investing terms. Now, in Bayes essays in Philosophical Transactions, he mentioned a problem that he was trying to solve.

[00:20:39] Kyle Grieve: Determine the probability that an unknown event’s likelihood of occurring in a single trial falls between any two specified probability values. Given the number of times it has happened and failed, Bernstein points out that the primary application of the Bayesian system is the ability to utilize new information to revise probabilities based on old information.

[00:21:00] Kyle Grieve: Let’s circle this back to investing. So I mentioned earlier in this episode that I like to look at a bear, base and bull probabilities on my investments to try and incorporate risk into my evaluation and my decision making. Bayes theorem takes it one step further and allows us to revise our numbers when the information changes.

[00:21:17] Kyle Grieve: John Maynard Keynes once said, when the facts change, I change my mind. And this is exactly what Bayes theorem does, but in mathematical terms. Now let’s say I apply a 33 percent probability to my bear case, my base case, and my bull case. Let’s just say something bad happens with the business that I applied these probabilities to.

[00:21:36] Kyle Grieve: There’s a business right now, for instance, where gross profit margins have been compressing down from 50 percent about a year ago to about 30 percent today. Management has noted that the normalized range is likely to be in the middle of these two numbers. Now, when I update my numbers knowing this new information, I might increase the probability of my bear case and decrease the probability of my bull case.

[00:21:55] Kyle Grieve: Maybe I move the bear case to 40 percent probability, keep the base case at about 33%, and reduce the bull case to 26%. Obviously this is inexact, as it’s hard to quantify precisely how likely a lousy outcome is. But I think this is more useful than just doing nothing. I know one of my personal problems in investing was holding on to ideas for too long.

[00:22:15] Kyle Grieve: If I had focused more on using Bayes theorem, I think I might have realized that the bear case was becoming more and more likely, and I would have been able to let go of an idea faster and therefore lose less money. Now, all investors should attempt to think probabilistically about their businesses.

[00:22:29] Kyle Grieve: During bull markets, it’s just incredibly easy to forget the potential downside of businesses that you own. You’ll often see investors giving the rosiest possible forecast on businesses forgetting the base rates for businesses were much lower. Now, it’s important to remember that even the best businesses go through some degree of cyclicality.

[00:22:48] Kyle Grieve: So, modeling for the best possible numbers when you’re at the top of a cycle is only going to be a disservice to you once the cycle turns. This is why having conservative forward numbers is such an intelligent strategy. It builds in an extra safety net. And any surprise to the upside is obviously going to be very welcome.

[00:23:05] Kyle Grieve: It’s easy when looking at a new business to assume only the best case scenario is the one that’s going to play out. Nobel laureate Kenneth Arrow said, Our knowledge of the way things work, in society or in nature, comes trailing clouds of vagueness. Vast ills have followed a belief in certainty. And when investor certainty is at the highest, is usually a period where vast ills is likely in the very near future.

[00:23:28] Kyle Grieve: Now let’s discuss Carl Friedrich Gauss and Francis Galton. First, Gauss. Gauss brilliance came in a pretty unlikely form. He was tasked with conducting a geodistic survey of Bavaria. Now, the geodistic measurements were used to estimate distances between sample areas in the study. As Goss analyzed the distribution of these estimates, he found that the results varied.

[00:23:49] Kyle Grieve: But as he collected more and more data, got more and more data points, the numbers seemed to cluster around a central point. Bernstein writes, That central point was the mean statistical language for the average of all observations. The observation also distributed themselves into a symmetrical array on either side of the mean.

[00:24:08] Kyle Grieve: The more measurements Gauss took, the clearer the picture became, and the more it resembled a bell curve that a gentleman named Desmoivre had come up with 83 years earlier. And the primary use case I’ve seen for Goss’s distributions are an average returns. You may have come across the graph that shows the average returns over a variety of time periods.

[00:24:28] Kyle Grieve: So that graph will display that during short time periods, if we use Goss’s example, the numbers cluster a lot less. And as anyone in the stock market knows, the short term is a bit of a dart throw. And so because those clusters are a lot less, there’s a lot more volatility and there’s a lot more potential outcomes.

[00:24:45] Kyle Grieve: So the chances of you being up 20 percent are about the same as you say, being down 20%. But then when we look at shorter time periods, such as a 10 year time period, the data begins clustering towards making more and more money. If we look at that chart that I just discussed over a 10 year period, our range of outcomes is essentially always profitable and in the positive eight to 9 percent range.

[00:25:06] Kyle Grieve: Now, another brilliant scientist, Francis Galton, who was also Charles Darwin’s cousin, continued on with Gauss’s theories about distribution. Galton suggested two conditions are necessary for observations to be distributed around their average. First, there must be a large number of observations, and second, the observations must be independent.

[00:25:25] Kyle Grieve: Now the point here about independent observations is very important. Bernstein gives a great example of this magazine that sent out returnable ballots in the form of postcards to names selected from both telephone directories and automobile registrations in 1936. Now, they essentially were asking who was going to win the election.

[00:25:44] Kyle Grieve: So 59 percent of the ballot favored Landon while only 41 percent favored Roosevelt. When the real ballots came in, Roosevelt won with 61 percent of the votes compared to only 39 percent for Landon. And the point here was that the recipients of the postcards were not independent enough to represent an independent observation and therefore it was invalid.

[00:26:03] Kyle Grieve: Now, Galton was interested in distribution, not just in the sense of furthering science, but also for practical purposes. He wanted to find out how many people in a sample size could achieve eminence. And this led to his findings on peapods. He analyzed thousands of peapods, specifically looking at the size of the parent seeds and the size of their offspring.

[00:26:26] Kyle Grieve: What he found out was that small parents generally had larger offspring, and the larger parents generally had smaller offspring. As a result, regression to the mean was born. Galton wrote, Regression to the mean, or reversion, is the tendency of the ideal mean filial type version to depart from the parental type reverting to what may be roughly and perhaps fairly described as the average ancestral type.

[00:26:51] Kyle Grieve: Bernstein added that if this narrowing process didn’t exist, nature would become freakier with each generation, creating dwarfs and giants with nothing in between. My favorite example of regression is looking at a business’s returns on invested capital. Michael Mauboussin has done tons of research in this area.

[00:27:06] Kyle Grieve: And, really, no matter the industry, as time goes by, the returns on invested capital decreases over time. In a business sense, it’s pretty easy to see why, right? As more capital is added to the industry, competition then goes up. And since many businesses have fixed costs, as the industry becomes more competitive and products become more commoditized, the only way to differentiate yourself from competitors is to decrease pricing.

[00:27:29] Kyle Grieve: And over time, if you decrease pricing, while capital costs remain the same, your returns on invested capital will eventually decrease. This is also important to think about when looking at investors single year track records. A good year just doesn’t say that much about an investor’s level of talent.

[00:27:43] Kyle Grieve: They may have one stock pick that produced outsized rewards during one specific year. It’s only when you zoom out and look at a multi year time period that an asset manager’s talent becomes more visible. This is why most managers with multi decade track records tend to hug the results of the index over time.

[00:27:59] Kyle Grieve: There are some outliers, but most of them will be around average or even below average as well. In any one year, they may be much better or worse than the index, but it’s important not to get swept up by the short term. Bernstein gives another excellent example of regression in the mean. Economists Richard Thaler and Werner de Bont did a study lasting between 1926 and 1982, where they studied three year returns of over 1, 000 stocks.

[00:28:23] Kyle Grieve: Now, each of these stocks was classified. Winners were classified as stocks that had gone up or had fallen less than the market average in each three year period. Losers went up less or fell more than the market average. Then they just calculated the performance of each class over the last half century.

[00:28:40] Kyle Grieve: Loser portfolios outperformed the market by an average 19. 6 percent 36 months after portfolio formation. Winner portfolios, on the other hand, produce returns about 5 percent less than the market. If an investor were inclined to hire a manager to manage their money, this means the logical step would be to look for a manager that currently has a short term losing track record.

[00:29:02] Kyle Grieve: Now, this is extremely counterintuitive, believe me, I understand, but this is why so few investors do it. I could maybe see myself doing it if I had someone that I wanted to invest in, if they had a very, you know, long term track record of being in the market, but maybe over the short term, we’re going over some turmoil.

[00:29:19] Kyle Grieve: Now, a great example of this would be Scott Barbie, who I got the chance to interview back on TIP 651 Scott’s fund, the ages of value fund had been in the market for. A good amount of time, well over a decade. But during the great financial crisis, his fund decreased by 72 percent over a two year period.

[00:29:35] Kyle Grieve: In terms of understanding intrinsic value, Scott was unfazed. He knew his businesses simply had gotten very cheap. They had not decreased in value. Then in one of the best examples of regression to the mean that I’ve ever seen, he returned 91 percent in 2009 and made it onto the cover of the Wall Street Journal.

[00:29:52] Kyle Grieve: Scott told me that he didn’t really do anything special to earn that 91%. He simply held on to his shares and once the selling stopped and capital eventually came back into the market, they bought his highly undervalued stocks and they re rated them higher. Now I’ll end this discussion on reversion to the mean with a very thought provoking statement that Bernstein wrote.

[00:30:10] Kyle Grieve: Dependence on reversion to the mean for forecasting the future tends to be perilous. When the market itself is in flux. Now, the next area I want to touch on is luck, which plays a massive role in decision making, and what I view as even more important than decision making, which is process and outcome.

[00:30:26] Kyle Grieve: Bernstein makes some crucial notes in his books about luck and cause and effect. Many intelligent mathematicians and the characters that we’ve covered believed in cause and effect. But how do we face issues that arise that don’t have one cause for the effect? October 1987 is a great example that Bernstein gives.

[00:30:44] Kyle Grieve: Thanks. In that month, the market fell more than 20%. Bernstein adds, there is no agreement on what caused it, though theories abound. It could have occurred without a cause, and yet, that cause is obscure. Despite its extraordinary character, no one could come up with strong proofs of its origin. My thoughts on luck have evolved over time, and I now settle on the fact that I will have lucky and unlucky decisions.

[00:31:08] Kyle Grieve: There’s no way to be lucky all the time. If that were possible, we could subscribe to the cause and effect theories in investing and decision making, but we do not have that luxury. And since investing is such a problematic puzzle full of information gaps that we will always have, the best we can do is just to play the odds.

[00:31:25] Kyle Grieve: And this is why investing is so difficult. Let’s say I make a decision to buy a stock. Let’s use one I bought in the past, but no longer own, which is Bank Ozk. I bought this in 2020 at around 23 and sold it about a year later at 47. When I first purchased the shares of the business, I believe they were undervalued.

[00:31:42] Kyle Grieve: There was a short report out. And when you mix that with COVID and the fact that they finance real estate, there was a lot of uncertainty surrounding the business, but I decided to buy it thinking the returns would be somewhere around my hurdle rates. Nor did I theorize that I would double my money in a year.

[00:31:58] Kyle Grieve: The fact that happened was luck, but I think that luck was built upon decent decision making that the business would get better over some period of time. Earnings per share EPS had been depressed in 2020, dropping from about 3. 30 in 2019 to 2. 30 in 2020. That would likely mean that there would be some regression to the mean as COVID fears went away, and the business could release some of its provisions for credit losses back into the income statement.

[00:32:22] Kyle Grieve: And that itself provided a massive boost in 2021 with EPS going all the way back up to 4. 50. Now, the point here is that luck will have an impact on all of our decisions. It’s essential to accept that sometimes it will be in our favor, and unfortunately, sometimes it will not be. The best we can do is to look at our prior decision making and try to make adjustments for future decision making.

[00:32:44] Kyle Grieve: This reminds me of what Charlie Munger said about good versus bad businesses. The difference between a good business and a bad business is that a good business throws up one easy decision after another. The bad business throws up painful decisions time after time. Now this relates to luck because I think good businesses also have more exposure to being lucky than a lousy business does.

[00:33:04] Kyle Grieve: I prefer to take part in those businesses who create potential tailwinds over businesses that expose themselves to headwinds. Now, let’s transition to some of the more modern areas of risk that are still in use today. For that, we’re going to start by covering two brilliant economists. They are Frank Knight and John Maynard Keynes.

[00:33:22] Kyle Grieve: Now, Frank Knight began picking away at utility theory, saying that humans default to acting irrationally. He said, there is much question as to how far the world is intelligible at all. It is only in the very special and crucial cases that anything like a mathematical study can be made. Frank Knight believed that the real world had too many surprises, and impacted future decision making to a very large extent.

[00:33:45] Kyle Grieve: In a business sense, you have to look at all the parties involved, such as buyers. Sellers, workers, and capitalists. These people will never have all the information they need to make perfect decisions. Frank Knight believed that classical economics didn’t take this uncertainty into account and therefore made it less applicable to real life.

[00:34:04] Kyle Grieve: Now John Maynard Keynes also Had issues with the classical methods to measure decision making. He said, Most of our decisions to do something positive can only be taken as a result of animal spirits and not as the outcome of a weighted average of quantitative benefit multiplied by quantitative probabilities.

[00:34:23] Kyle Grieve: Now this concept of animal spirits is essential because it altered people like Galton’s peapod analogy. Now while Keynes agreed with his theory on peapods in nature, he disagreed with it being relevant to human beings. For instance, he rejected analysis based on events, choosing to make predictions based on propositions.

[00:34:43] Kyle Grieve: Additionally, he was scornful of the rules of large numbers. Just because a large amount of prior events happened in the past, didn’t mean they would continue happening in the future. This was an incredibly powerful notion. Keynes points were that all mathematical concepts on decision making were less valuable because events changed all the time.

[00:35:02] Kyle Grieve: That would alter human decision making. Bernstein points out that Keynes preferred to think in degrees of belief. This also allowed Keynes to change his belief system based on events. If a new event happened, then his degrees of belief would change based on that event. Now before World War II, Keynes said the characteristics assumed by the classical theory happened not to be those of the economic society in which we actually live.

[00:35:27] Kyle Grieve: With the result that its teaching is misleading and disastrous if we attempt to apply it to the facts of experience. Now, building on Knight and Kane’s importance of uncertainty came Game Theory. Game Theory states that the true source of uncertainty lies in the intentions of others. John von Neumann, a brilliant scientist who had his hands in multiple scientific discoveries, invented Game Theory.

[00:35:50] Kyle Grieve: Now, he first discussed game theory in one of his papers, where he discusses a childhood game that he called Match Penny. So here’s how it worked. It had two players. Each of these players would have a coin that was either heads or tails, and they would put it down on the table however they wanted. They weren’t flipping it or anything.

[00:36:07] Kyle Grieve: It wasn’t by chance. Now, both the players would then turn their hands up and display their coin to the other at the exact same time. If both the coins were heads, or if both of them were tails, then player A wins. And if different sides showed up, then player B would win. Now, where game theory comes into play, is what side of the coins each player decides to play.

[00:36:27] Kyle Grieve: Von Neumann concluded that the trick in playing matchpenny lies not in trying to guess the intentions of the opponent, so much as in not revealing your own intentions. So, if one player, let’s say, decides to play heads every single time, then his opponent is eventually going to figure that out, and adjust.

[00:36:44] Kyle Grieve: Both players would eventually adjust to randomly playing heads and tails. At random sequences and each player is going to win approximately 50 percent of the time. Now, the mathematical contribution that Von Neumann made was that the outcome was a product of rational decision making by the players and not probability.

[00:37:01] Kyle Grieve: It is the player themselves who caused the 50 50 payoff in this game. Now, one of the most fascinating developments of this theory was what became known as the Nash Equilibrium. The Nash Equilibrium is a situation in a game where no player wants to change their strategy because they are already making the best possible choice given what others are doing.

[00:37:20] Kyle Grieve: If you’ve seen the graph of how capital efficiency reduces over time for a given industry, I think that is a great example of Nash Equilibrium. In other words, it can adjust. As more capital enters an industry, more capital needs to be spent to capture small market share. Over time, this means the entire industry suffers.

[00:37:37] Kyle Grieve: Eventually, it becomes uneconomic for new entrants into the market and it reaches a Nash equilibrium where the whole sector barely makes a profit. Now we’re at a point in against the gods where we’re going to start talking about a lot of theories that are still very deeply embedded into the investing industry today.

[00:37:52] Kyle Grieve: Let’s start with Harry Markowitz, who’s the grandfather of portfolio management theory. His most significant premise was that most investors desire returns that minimize variance. For him, risk and variance were the exact same thing. His aim was to construct portfolios that had low variance because that was what he assumed most investors wanted.

[00:38:11] Kyle Grieve: Now, I haven’t spent too much time studying portfolio management theory, which I’ll call PMT because people like Buffett and Munger always said, you’re probably better off ignoring it. And I think you’ll do just fine. But over the course of my education as an investor, you can’t help but pick up a few things.

[00:38:25] Kyle Grieve: So first off, I think I’m going to give Mark with some credit here because I think he was actually right that most investors would prefer returns that are less volatile. The problem is that the market is so volatile anyways, that even if your portfolio were constructed to have minimal variance with the market, you’ll still end up making all the classic investor blunders because the market itself has a lot of variance from year to year.

[00:38:47] Kyle Grieve: Another one of Markowitz’s theories was that he rejected John Burr Williams premise that investing is a process focused on investing. Identifying the best possible investment at the best price. Instead of that, Markowitz thought that investors should diversify their investments because diversification was simply the best weapon against the variation of return.

[00:39:07] Kyle Grieve: Again, he was onto something here. Most investors are best with widely diversified investments. It protects them from losing too much on one investment, and it allows them to reap the rewards of a few outsized winners. But you have to remember that the average person Finds reading a 10k to be about as attractive as shoveling their walkway to take out the trash It’s just not something they want to do or derive enjoyment out of But for the few people who do enjoy doing things like this like myself and probably our listeners Diversification can be seen as a handicap to getting better results Robert Hagsen pointed out in the Warren Buffett portfolio that random portfolios with fewer positions had a better chance to both outperform and underperform in the market So, for investors who have an edge in picking great businesses or investing in great investing opportunities, there are advantages to making more considerable, concentrated investments.

[00:39:58] Kyle Grieve: Another problem with Markowitz’s theories is in understanding what you own. The average person, let alone a professional money manager, cannot possibly understand 100 plus businesses at a very deep level. This is why Buffett said, Portfolio concentration may well decrease risk. If it raises as it should, both the intensity with which an investor thinks about a business and the comfort level he must feel with its economic characteristics before buying into it.

[00:40:24] Kyle Grieve: And as Tobias Carlyle pointed out in his book, Concentrated Investing, some of the best investing legends have come to the same conclusion about concentration. With Warren Buffett, Charlie Munger, and Seth Klarman preferring say 10 to 15 positions. Tobias also points out that research from Patrick O’Shaughnessy found that about 25 positions offered the best volatility adjusted returns.

[00:40:46] Kyle Grieve: Now, the last point I’d like to make on Markowitz was that his theories always feel so academic to me. You know, while much of investing definitely does rely on academic theories, I think a lot of successful investing also relies just simply on common sense and that is the ingredient that seems to be missing in some of Mark Witz’s theories and let’s go over an example here.

[00:41:07] Kyle Grieve: Let’s say I’m offered two different portfolios. One has high variance, but on average earns 50 percent per year. The other has low variance, but on average earns 10 percent per year. According to MPT, I should take the second portfolio. But personally, I don’t care about variance and I’d instantly choose the high variance portfolio with a better long term return.

[00:41:26] Kyle Grieve: Another theory in conjunction with Mark with his theories on the market was created by Bill Sharpe. And this was called the capital asset pricing model or CAPM. Which use the term beta to describe the average volatility of individual stocks or other assets relative to the market as a whole over some specific period of time so that you know what beta means.

[00:41:45] Kyle Grieve: In reality, let’s say a portfolio has a beta of 1.5. It means that when the index goes up 1%, it goes up 1.5%, and let’s say the market falls 10%, it would go down 15%. The goal was to have a low beta stock so that your results wouldn’t defer much from the index. Now, let’s take the same example I just gave, but instead of a portfolio, I now look at a single stock.

[00:42:06] Kyle Grieve: Let’s say I’m offered stock A or stock B. A has a beta of 1. 5 and B has a beta of 1. I think A will return 14 percent in the next 5 years and I think B will return 9 percent over the next 5 years. Now, modern portfolio theory would have me choose a lower beta position to put into my portfolio, even though I prefer the higher returns with the accompanying roller coaster ride on the higher variance option.

[00:42:27] Kyle Grieve: Now for investors with specific risk appetites, MPT is simply nonsensical. I also think MPT plays into what Buffett and Munger used to call helpers who are paid by other companies to provide help. When in reality, they’re just collecting fees for providing very little to zero or negative value in some cases.

[00:42:46] Kyle Grieve: When you can slap on numbers and terms like beta, you simply just look smarter and have more pages that you can write reports on to sell to potential customers, but it doesn’t necessarily help anyone other than the helpers themselves. Now, I’m super excited here to discuss the next topic of the book, which is based around prospect theory and many of the remarkable findings that Amos Tversky and Daniel Kahneman discovered together.

[00:43:09] Kyle Grieve: Their work layered on Markowitz’s work, so Peter Bernstein named the chapter The Failure of Invariance. Bernstein writes, The classical models of rationality, the model on which game theory and most of Markowitz’s concepts are based, specifies how people should make decisions in the face of risk, and what the world would look like if people did in fact behave as specified.

[00:43:30] Kyle Grieve: Extensive research and experimentation, however, reveal that departures from the model occur more frequently than most of us would admit. This is where Kahneman and Tversky came in. They wanted to understand how people manage risk and uncertainty in real life and not just in terms of how utility theory stated, which assumed that people should act a certain way.

[00:43:49] Kyle Grieve: Prospect theory discovered specific behavior patterns that hadn’t been recognized by proponents of rational decision making. They noted two significant shortcomings in human behavior. 1. Emotions destroy self control that is necessary for rational behavior. And 2. People tend to be unable to understand what they’re dealing with.

[00:44:07] Kyle Grieve: Or, in other words, they succumb to all sorts of cognitive biases. Now, one of the most significant discoveries of prospect theory is the asymmetry between decisions involving gains versus decisions involving losses. For instance, when discussing larger sums of money, people will reject a fair gamble in favor of a certain gain.

[00:44:27] Kyle Grieve: For example, a certain gain of 100, 000 is preferable to a 50 50 possibility of winning 200k versus winning zero. But the expected value of each scenario is the same. It’s 100, 000. If we look at another example, they gave people options, again with smaller sums of money here. This time they were offered an 80 percent chance to win 4, 000, or a 20 percent chance to win 0, or they could make a certain gain of 3, 000.

[00:44:52] Kyle Grieve: In this case, they took the 3, 000, even though the expected value of the former option is actually 3, 200. And this means that a rational person would take the first option. But 80 percent of subjects chose the certain things. We are risk averse. But how do we act when our choice involves losses? In a follow up experiment, they offered a choice between taking the risk of an 80 percent chance of losing 4, 000 and a 20 percent chance of breaking even versus a 100 percent chance of losing 3, 000.

[00:45:22] Kyle Grieve: Now, 92 percent of respondents chose the gamble, even though its mathematical expectation of a loss of 3, 200 was once again larger than the certain loss of 3, 000. So this shows us that when the choice involves losses, we are actually risk seekers, not risk averse. Now, let’s loop this into investing and see how this plays out in real life.

[00:45:41] Kyle Grieve: Risk aversion is rife in the stock market, especially with how investors treat gains. Remember, when we are dealing with larger sums of money, we become more risk averse. This means that when we have a position that goes up in price, we become more and more likely to sell our position and lock in a profit.

[00:45:57] Kyle Grieve: The problem with doing that consistently is that you miss many of the large run ups and businesses that can continue growing for long periods of time, which are often much longer than investors believe they can grow for. So even if we lock in our gains, we are now ignoring the fact that there is a chance that we could make more in the future.

[00:46:12] Kyle Grieve: Of course, we could also be wrong on the idea and we could risk it going below our purchase price or worst case scenario is going to zero. Now, if we look at risk seeking with losses, you see this all the time as well. So I had these businesses that were all in the red, but by continuing holding them, hoping that someday they would rebound, then I eventually just cut ties with the three of them.

[00:46:30] Kyle Grieve: And since then, the capital has been put to other use. But I can admit that I held them hoping to recoup some of my losses. And this is the same as the experiment above where we become risk seeking when faced with the potential of a certain loss. I simply could have cut my losses sooner and have lost less money.

[00:46:47] Kyle Grieve: Another problem that is rife in the value investing community regarding losses and risk seeking is adjacent to the example above, which is adding to our losers or, you know, averaging down. Now I did this on one of the businesses in the bucket that I mentioned, which was Alibaba. Now, averaging down, Is a fantastic opportunity.

[00:47:04] Kyle Grieve: If you’re correct that the business will once again, pick up and at the market will once again, assign it a reasonable multiple. Now I’m just speaking from my own experiences here, as I’m sure there’s tons of people who have opposite experiences to me, but I’ve rarely had a positive experience when I averaged down.

[00:47:20] Kyle Grieve: Averaging up has actually been much more lucrative for me. Now, this goes a little bit outside the realm of prospect theory, so I won’t go into any more detail, but needless to say, I much prefer averaging up. I mentioned earlier that there was a chapter in Bernstein’s book titled The Failure of Invariance.

[00:47:34] Kyle Grieve: But what exactly is the failure of invariance? The failure of invariance is pretty similar to mental accounting, where we change our decisions based on how the question is framed. Let’s imagine that you’re buying a jacket, and let’s say that this jacket usually costs a hundred bucks. Now you find out that there’s a store that’s five minutes away from the one that you saw at a hundred dollars, and that they’re, they’re selling it for $75, a $25 discount.

[00:47:56] Kyle Grieve: Most people are happily going to take the five minutes and enjoy the $25 discount. But now let’s take another situation. So now let’s say we’re at some store and there’s a laptop that we want. It’s $2,000. You then hear that the same laptop is available for 1, 975 in a store, once again, five minutes away.

[00:48:14] Kyle Grieve: And again, that’s a 25 discount. This time, most people won’t bother making the five minute journey to save 25. In both cases, you’re saving 25. But in the first case of the jacket, it obviously feels like a significant percentage of the total price as it’s 25 percent off. Well, the second case, a laptop is small, it’s just 1.25 percent off.

[00:48:31] Kyle Grieve: Now, if we’re looking at utility theory and from a rational perspective, 25 is still 25 no matter what you’re buying. But our brains don’t see it that way. We are influenced by how the choice is framed rather than focusing purely on the actual dollars that are saved. Bernstein covers this feeling in Investing Exceptionally Well, writing, One of the most familiar manifestations of the failure invariance is in the old Wall Street saying you never got poor by taking a profit.

[00:48:58] Kyle Grieve: It would follow that cutting your losses is also a good idea. But investors hate to take losses. Because tax considerations aside, a loss taken is an acknowledgement of error. Loss aversion combined with ego leads investors to gamble by clinging to their mistakes in the fond hope that So that someday the market will vindicate their judgment and make them whole.

[00:49:18] Kyle Grieve: Another point worth mentioning is in regard to how we use information. We often believe that more information will help us make better decisions and be more rational. Daniel Ellsberg published a paper which discussed a concept known as ambiguity aversion. That cognitive bias states that we prefer taking risks on the basis of known versus unknown probabilities.

[00:49:39] Kyle Grieve: This is why I think investors can often make bad investment decisions after spending a large amount of time researching an investment. I know I’ve done this before. So, you know, put yourself in this position. Sometimes You find a stock, you start liking it more and more. You put more and more time researching.

[00:49:56] Kyle Grieve: Let’s say you spend, you know, 10, hours learning about it, researching competitors, maybe speaking with other investors or maybe company insiders. And after this time, you know, you, you, you begin to develop an attachment to the business. You know, you learn more about it, you consume more information and let’s say maybe you eventually purchase shares in the business, even though maybe when you try to look at the numbers of the returns that you’re going to get, they’re kind of unattractive.

[00:50:23] Kyle Grieve: Now, I think there’s some degree of ambiguity aversion at play here because we’ve now obviously increased the amount of information we know about this business. We’re actually taking A larger risk based on buying that business because we know more about it when it comes to a name that we barely understand or haven’t spend any time researching on.

[00:50:43] Kyle Grieve: We have no problem taking a pass. So, let’s look at some of the other behavioral finance points that burn scene discusses. One of the most significant contributors to behavioral finance is a man named Richard Thaler. Behavioral finance studies how investors balance logic and emotions in decision making, leading to market behavior that often defies theoretical models.

[00:51:04] Kyle Grieve: Many of which are the models we have discussed already in this episode. Now, Bernstein points out that Thaler was a member of what he called the theory police. And the theory police were people who constantly checked to see whether investors were obeying or disobeying the laws of rational behavior that we’ve covered from the Bernoulli’s.

[00:51:20] Kyle Grieve: to Markowitz. Thaler ran an experiment where he asked two questions. 1. How much would you be willing to pay to eliminate a 1 in 1000 chance of immediate death? 2. How much would you have to be paid to accept a 1 in 1000 chance of immediate death? The results were simply fascinating. A typical answer was, I wouldn’t pay more than 200, but I wouldn’t accept an extra risk for 50,000.

[00:51:46] Kyle Grieve: Thaler concluded that the disparity between buying and selling prices was very interesting. He considered this to be called anomalous behavior, which was behavior that violated the predictions of standard rational theory. Some of his other anomalous behaviors included large differences in what people would be willing to buy an item and sell the exact same item, as well as the failure to recognize sunk costs.

[00:52:09] Kyle Grieve: Thaler then connected with some of Tversky and Kahneman’s converts and realized that his anomalous behavior was really normal behavior, and that the rational behavior was the exception. Now, remember back when I was talking about Bayes and Keynes, I said that investors can and should update their probabilities based on new information.

[00:52:26] Kyle Grieve: Thaler and DeBondt found that investors don’t actually update their beliefs the way they should. Instead of following Bayes rule, they put too much weight on new information and they ignore past trends. Basically, they make decisions based on gut reactions rather than historical probabilities. And the result is that stock prices swing too far in either direction, creating predictable reversals, regardless of what’s actually happening with earnings, dividends, or other fundamentals in the business.

[00:52:54] Kyle Grieve: Now, this is a super powerful message, and I think does just a really good job of explaining why markets just in generally swing so widely, but this also shows why individual stocks can be so volatile in any one year. One thing you can do is just look at some of your recent buys and sells and identify where maybe your gut prompted you to make a mistake.

[00:53:13] Kyle Grieve: You may find there’s some very particular errors in the fact that you’re selling businesses, maybe just because the price is going down. And obviously that error is going to be quite obvious if you’re selling it, even though the business’s fundamentals are continuing to improve. In that case, you know, you’re succumbing to bias and you can try to create some strategies to try and deal with them as best as you can.

[00:53:35] Kyle Grieve: One strategy that I like to talk about pretty often is just limiting the frequency that you allow yourself to look at stock prices. Now, as we’ve explored in this episode, risk is a concept that has evolved over centuries, from the ancient gamblers of Egypt to the mathematicians who laid out the groundwork for probability theory, and ultimately to the modern frameworks that are used in investing today.

[00:53:56] Kyle Grieve: The journey through Bernstein’s against the gods highlights one central theme, which is our understanding of risk is still evolving, and it will likely never be a solved equation for investors. The key takeaway here is that while models theories and probabilities provide some very valuable tools.

[00:54:13] Kyle Grieve: They’re never a substitute for things like judgment, adaptability, and experience. Risk isn’t just about numbers. It’s about uncertainty, human behavior, and decision making under pressure. Behavioral biases shape markets just as much as any mathematical model, and understanding those biases can be just as important, if not more, as understanding the underlying businesses that we invest in.

[00:54:34] Kyle Grieve: So I want to finish this episode just talking about how we can tilt the odds in our favor. First, we need to think probabilistically. The best investors don’t just assign a single outcome to an investment. They consider a range of possibilities and assign probabilities to each of them. Instead of thinking, you know, this stock is going to double, a more helpful approach is this stock has a 30 percent chance of doubling, a 50 percent chance compounding at, say, market average rates, and a 20 percent chance of underperforming or declining.

[00:55:01] Kyle Grieve: This way, you build a more realistic framework for decision making. Secondly here, embrace Bayes theorem, and you can do this by updating your probabilities as new information emerges. Investors who just stubbornly cling to their original theses ignore negative signals and often get burned. Conversely, those who react impulsively to every piece of news are just as likely to make costly mistakes.

[00:55:24] Kyle Grieve: The right balance is learning to weigh new data appropriately without overreacting. Third, structure your portfolio to take advantage of asymmetry. Buffett and Munger talk about betting big when the odds are overwhelmingly in your favor. This doesn’t mean reckless concentration, but it does mean allocating more capital to opportunities where you have a high conviction and where the risk reward profile is disproportionately in your favor.

[00:55:47] Kyle Grieve: The best investors aim for situations where the upside potential far outweighs the downside risk. This is how you compound capital effectively over multiple decades. Fourth, always consider the role of psychology. Investors love certainty, but markets don’t provide it, unfortunately. Understanding how fear and greed manifest in market cycles, I think, allows you to act rationally when others are acting more emotionally.

[00:56:11] Kyle Grieve: Having an investment checklist can help you remain disciplined and help you avoid making impulsive decisions that are based on short term noise. And finally, recognize reversion to the mean is one of the most powerful forces in investing periods of extreme optimism or extreme pessimism don’t last forever.

[00:56:29] Kyle Grieve: Overvalued markets eventually correct. And beaten down stocks often rebound when sentiment shifts understanding these cycles and having the patience to wait for favorable opportunities gives you a significant edge over those who chase momentum or panic during the first signs of volatility investing is not about eliminating risk, but learning how to navigate it intelligently.

[00:56:50] Kyle Grieve: Whether through Bayesian updating, strategic concentration, or understanding market psychology, the best investors consistently look for ways to stack the deck in their favor rather than just seeking certainty where none exists. That’s all I have for you for today. If you want to interact with me on Twitter, please follow me @IrrationalMrkts or on LinkedIn under Kyle Grieve. If you enjoy my episodes, please feel free to let me know how I can make your listening experience even better. Thanks again for tuning in. Bye bye.

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