In finance, there is a modern tendency to make investing decisions at a very coarse grain of granularity. At Basepoint, we believe that valuable information and protection can be gained by digging deeper.
“For want of a nail the shoe was lost.
For want of a shoe the horse was lost.
For want of a horse the rider was lost.
For want of a rider the message was lost.
For want of a message the battle was lost.
For want of a battle the kingdom was lost.
And all for the want of a horseshoe nail.”
-Proverb
Imagine traveling back in time 60 million years for the thrill of hunting one of the largest land predators to ever exist, the Tyrannosaurus rex. What precautions would be necessary to ensure that your safari would not change the course of history? In the sci-fi short story “A Sound of Thunder”, Ray Bradbury presents this dilemma as Eckels hires Time Safari, Inc to take him on such a journey. Before they reach their destination, Safari Guide Travis explains to Eckels the importance of not touching anything except the T. rex that was destined to be killed by a falling tree not more than 60 seconds after their shots:
"All right," Travis continued, "say we accidentally kill one mouse here. That means all the future families of this one particular mouse are destroyed, right?"
"Right"
"And all the families of the families of the families of that one mouse! With a stamp of your foot,
you annihilate first one, then a dozen, then a thousand, a million, a billion possible mice!"
"So, they're dead," said Eckels. "So what?"
"So what?" Travis snorted quietly. "Well, what about the foxes that'll need those mice to
survive? For want of ten mice, a fox dies. For want of ten foxes a lion starves. For want of a lion, all manner of insects, vultures, and infinite billions of life forms are thrown into chaos and destruction. Eventually, it all boils down to this: fifty-nine million years later, a caveman, one of a dozen in the entire world, goes hunting wild boar or sabertoothed tiger for food. But you, friend, have stepped on all the tigers in that region. By stepping on one single mouse. So the caveman starves. And the caveman, please note, is not just any expendable man, no! He is an entire future nation. From his loins would have sprung ten sons. From their loins one hundred sons, and thus onward to a civilization. Destroy this one man, and you destroy a race, a people, an entire history of life. It is comparable to slaying some of Adam's grandchildren. The stomp of your foot, on one mouse, could start an earthquake, the effects of which could shake our earth and destinies down through Time, to their very foundations. With the death of that one caveman, a billion others yet unborn are throttled in the womb. Perhaps Rome never rises on its seven hills. Perhaps Europe is forever a dark forest, and only Asia waxes healthy and teeming. Step on a mouse and you crush the Pyramids. Step on a mouse and you leave your print, like a Grand Canyon, across Eternity. Queen Elizabeth might never be born, Washington might not cross the Delaware, there might never be a United States at all. So be careful. Stay on the Path. Never step off!"
It is sometimes easy to lose sight of how unpredictable the world that we live in is, especially over time. When we focus on our own lives, the daily grind can seem consistent, almost to the point of being bland. We wake up at the same time every day, we eat lunch at the same place, poker night is on Thursday, and on weekends in the fall we watch football. When we are trying to make decisions about our financial future, it feels like the financial professionals that we see on television have things all figured out. Then, when the markets crumble, and our asset prices plummet, we feel betrayed by the talking heads since they should have seen this coming. This article is an attempt to explain the complexity that we face when making financial decisions, and to give perspective on why it is so hard to make consistently good investing decisions. We will explore the hard sciences as they relate to social sciences like economics to explain the vast amount of uncertainty that we are up against when trying to make your money last the rest of your life.
Granularity:
“The man who moves a mountain begins by carrying away small stones."
-Confucius
Granularity refers to the level of detail that we consider when analyzing a system or problem. A coarse-grained analysis is comprised of large chunks of information, and a fine-grained analysis is broken down into even more divisible components. In Sociology we analyze groups of humans and how they interact; our base level of measure is a society or group of individuals. In Psychology, we study individuals, and how they develop mental capabilities and disabilities and how they interact with other humans and themselves.
Moving from the broad social sciences to the natural sciences we find Biology, where we examine groups of cells organized into living organisms, both large and small, classify them into species, and examine the way each individual organism functions. In addition, we study ecosystems and how these organisms interact with one another. In Chemistry, we study chemical substances, and how they react and bond with one another to form more complex structures like cells and molecules. Finally, in physics, the current base level of scientific granularity, we examine the laws and forces of matter and energy, the stuff that atoms are made of.
All this knowledge is cumulative, and each step up in the granularity ladder increases complexity. The coarser-grained sciences are necessarily bound by the laws and discoveries of finer-grained disciplines. The rungs on the ladder are not equidistant. It is a much larger leap to climb from understanding particles to people, and an even greater leap from people to societies. The boundaries are not as stark as we would like to believe, especially when a finer-grained discipline disagrees with a necessary premise of a coarser-grained discipline. It would be a mistake to limit our study to a particular discipline when we are dealing with cumulative knowledge.
When we study markets and economies, we climb even higher up this ladder, and introduce significantly more complexity. Dependence on our starting conditions becomes even more critical. A few successful hedge fund managers have PhDs in physics. They use concepts from physics to study price formation, market instabilities, ergodicity, and market dynamics. We still believe these strategies are speculative, no matter how grounded in hard sciences they may be because they are studying price movements as if they were natural systems. Although some have found success, we have no way to determine if this is from skill or luck.
Each of these areas of study is continuous. We are studying all the rungs simultaneously, and advances in technology deepen our understanding of each layer independently. The microscope was a large advance in biology, and the particle collider increased our understanding of particle physics. As technology improves, the internal granularity of each rung continues to deepen. Granularity is both horizontal and vertical, and each dimension is very important.
In finance, there is a modern tendency to make investing decisions at a very a coarse grain of granularity. Investors buy ETFs with hundreds of stocks and meant to represent entire national markets. Aspersion is cast upon those attempting to know what they own, or to invest in portfolios that do not represent an entire market. Finance academics tell us that fundamental analysis is a waste of time, and that buying an overpriced portfolio that is cheap is better than buying a reasonably priced portfolio that has a larger fee. At Basepoint, we believe that valuable information and protection can be gained by digging deeper.
Reductionism:
"The displacement of a single electron by a billionth of a centimeter at one moment might make the difference between a man being killed by an avalanche a year later or escaping."
-Alan Turing
Reductionism is a philosophical belief arguing that to understand a coarser-grained system or process, you should break the components or concepts into their simplest constituent parts. Turing’s quote above encapsulates the importance of things that are beyond our current ability to calculate or observe. Although this statement could be hyperbole, if we had the gift of omniscience and unlimited computational ability, and we were able to analyze each individual snowflake on the top of a mountain, incorporate wind speed and temperature, monitor the fluctuating heat provided by the moving sun, and determine the exact weight pressing down on the mountain, continuously over time, we may be able to compute the exact moment of an avalanche. It is possible that the difference in having snow on the mountain versus at the bottom of the mountain really could come down to a single electron.
The roots of reductionism go back to Ancient Greece, where Democritus first proposed that all matter is comprised of invisible, indivisible units called atoms. While Democritus was correct that matter is made of atoms, he was wrong in that atoms are now known to be even further divisible into subatomic particles like protons, neutrons and electrons, which can be further reduced into quarks, gluons, and muons. Perhaps someday, physics will be replaced at base granularity with some unknown science that will descend into the basement of our current level of granularity.
René Descartes believed in mechanistic universe. In Part V of his “Discourses”, written in 1637, he suggested that the entire world was like a clock, and that taking it apart into pieces, studying the pieces, and putting it back together would give us an understanding of all physical processes. This clockmaker view of the Universe solidified his belief in the existence of God. He used logic to build proofs for the existence of a divine and perfect creator.
Newton and Darwin were also reductionists. Newton’s laws of motion, and Darwin’s theory of evolution both rely on reducing complexity by describing phenomenon in terms of fundamental principles. Reductionism made its way into Chemistry when Dmitri Mendeleev organized the periodic table of elements. Finally, physics reached the limit of our current ability to reduce with Quantum Mechanics and the study of fundamental particles and the four forces.
As science and technology continue to evolve, our fundamental understanding of our universe will continue to improve. In 1874 physics professor Philipp von Jolly told a young Max Planck that “in this field, almost everything is already discovered, and all that remains is to fill a few holes.” 30 years later Einstein came along with his Special Theory of Relativity and opened a whole new level of granularity.
Reductionism is not without criticism. Many suggest that reductionism fails to quantify emergent properties that are impossible to understand by examining components in isolation. Emergent properties were best defined by Aristotle, when he said, “the whole is greater than the sum of its parts”. It is possible that our inability to identify emergent properties is a consequence of fundamental misunderstandings of interactions, or non-linear interactions that have yet to be identified.
Even if finance were able to be fully reduced, we still would not be able to predict future investment prices, and we would likely never be able to predict, with accuracy, how much money you will have on your 100th birthday. Many times, clients ask me “What’s the stock market going to do this week?” I always answer the same way, “it will either go up, or go down, and there is a very small chance it will remain exactly the same”. But why can’t I predict the stock market? After all, I spend most of my time reading and thinking about it. It is complexity, chaos, emergent properties, non-linear dynamics, uncertainty, and randomness that are the culprits. Should the fact that we can’t predict the future cause us to throw up our hands and stop digging deeper to the finest level of granularity that increases our probability of success?
Complexity and Chaos:
“Ideas thus made up of several simple ones put together, I call complex; such as are Beauty, Gratitude, a Man, an Army, the Universe.”
-John Locke, An Essay Concerning Human Understanding
What happens when we reach the edge of our ability to reduce, find ourselves at the finest relevant level of granularity we can observe, and still are unable to make predictions about outcomes? We have entered the world of complexity, and complex adaptive systems. A complex system is an organization or structure characterized by many small, simple parts, arranging themselves without a central organizer, into a collective whole that creates patterns, uses information, and in some cases evolves and learns.1
We are surrounded by complex systems and interact with them and participate in them daily. Insect colonies, our immune system, our brain, and our entire economy are all systems that exhibit the characteristics of complex adaptive systems. There are several commonalities present in a complex system:
Complex Collective Behavior- Simple individual components following simple rules, with no central leader, and give rise to hard-to-predict, changing patterns of behavior.
Signaling and Information Processing- Produce and use information and signals from both internal and external environments.
Adaptation- Constituent parts change behavior to improve outcomes through learning or evolution.
The science of studying complexity is relatively new. The Santa Fe Institute in New Mexico was organized for just this goal in 1984 by a group of interdisciplinary scientists. Biologists, Chemists, Physicists, Mathematicians, and many other classes of scientists gather here to collectively study the scientific aspects of what creates complexity. 30 resident professors and postdoctoral fellows, along with over 100 external faculty members work together here on problems such as artificial life, chaos theory, genetic algorithms, complexity economics, econophysics, complex networks, and systems biology.
While financial markets exhibit many characteristics of a complex system, there are times when the market seems to devolve into chaos. Chaos exists in a deterministic system that exhibits extreme sensitivity to starting conditions. This differs from complex systems, in that the system itself is predictable, but the outcome is difficult to guess because of non-linear dynamics that make the system more variable as time passes.
The sources of unpredictability differ in complex and chaotic systems. A complex system is unpredictable because of the many interactions between individual components, and the emergent properties that are not predictable based on their discrete properties. A complex system evolves or adapts over time to maximize outcomes. The unpredictable outcomes could be mitigated by a better understanding of the system dynamics. In a chaotic system, unpredictability stems from extreme sensitivity to starting conditions, making long-term conditions nearly impossible to predict. Unpredictability is inherent to chaotic systems, even though the system itself is deterministic in nature. In a chaotic system we may be able to make reliable predictions for a short period of time, but long-term predictions may be very unreliable. This is apparent in some trading strategies that seem to work for a short period of time, and then descend into chaos, and losses.
The more components we add to a portfolio, the more chaotic it becomes. When we have portfolios with thousands of positions in dozens of asset classes with leverage and short positions, future outcomes become wildly unpredictable. This is not to say that the returns necessarily have more variance over short periods of time. Widely diversified portfolios exhibit “negative skewness”, which is a fancy way to say that they have consistent lower returns with rare large losses caused by convergence of correlation. This just means when the panic sets in, everything goes down at once. 2022 was a prime example of this type of dynamic. In many cases, Basepoint was able to avoid large losses because instead of diversifying into every class of bond, we limited our credit risk and interest rate risk, and focused on short-term, high-quality credit, even though it gave us lower short-term returns.
During times of market dislocation and volatility, the market seems more chaotic than complex, and emergent properties break down and appear to be random even though the underlying system is governed by deterministic laws. Chaotic systems included weather, climate, and a double pendulum. But the market itself is not a deterministic system. Even if we had perfect information, we would not be able to predict the future, regardless of starting conditions, this is because of non-linear dynamics, uncertainty, and randomness.
Non-linear dynamics:
"We are all agreed that your theory is crazy. The question which calls for a reply is whether it is crazy enough to be true."
-Niels Bohr
Non-linear dynamics exist when relationships between individual actions is not proportional. This means that a change in one variable may lead to an unpredictable change in a related variable. In a non-linear system, a very small change in starting conditions can lead to a very large difference in outcome. These systems are also subject to feedback loops. This means that outcomes from the system change the dynamics of the system itself. This can increase or decrease changes in unpredictable ways. Non-linear systems can also exhibit bifurcation, which means that small changes in variables can lead to large shifts in outputs. These small changes can change the entire structure of the system dramatically.
Think about the dynamics of an entire world economy. It is a continuous system that has been in operation for over 5,000 years. The system is comprised of billions of individuals, trying to reach their independent goals, while interacting with other individuals doing the same. These individuals form corporations and governments, and information is not uniformly distributed. These corporations and governments compete with each other, make rules that shift incentives and opportunities, and money, interest rates, and taxes are all shifted between political regimes speaking different languages, operating in different cultures, and with differing views of historical context.
Interest rates and inflation are two variables that impact investment returns in a non-linear way. When inflation began to heat up and interest rates rose in response in late 2021, it was a prime example of non-linear dynamics, and we all saw the results. Having a passive portfolio allocated based on past correlations and expected returns gave chaotic results. Non-linear systems in finance include, but are not limited to stock price movements, options trading, dividend reinvestment, credit card debt, interest rates and yield curves, inflation, investor behavior and sentiment, and economic variables.
Uncertainty and Randomness:
"There is nothing more uncertain than the fact that you are certain."
-Friedrich Nietzsche
Once we have understood the deepest layer components of a system, and evaluated the dynamics of the system, meaning how the components interact with each other, and the rules they follow, we still have instances where the outcome is unknowable. This may be due to our inability to measure, the extent of the required calculations, or it may be simply because the outcome is somehow not determinable in advance because psychology is involved. This introduces the concepts of uncertainty and randomness.
Uncertainty was known in science before Heisenberg popularized its usage in 1927. Early Greek philosopher Heraclites spoke of concepts like flux and change, which posited that certainty was elusive. Thomas Aquinas struggled with integrating certainty into the studies of faith and reason. In the 16th Century, Pascal and de Fermat formalized the calculations underlying uncertainty as it applied to gambling, and this early and rudimentary form of probability analysis has evolved into our modern financial calculations. Uncertainty is not just about the outcome of rolling dice or flipping coins. Wittgenstein wrestled with the implications of uncertainty in the core of our language and knowledge.
Uncertainty, as a broad concept, simply tells us that the future is unknown, and as such, is the major contributor to risk, as defined in modern finance. Elroy Dimson of the London School of Economics has defined risk as “more things can happen than will happen”. Modern Finance, and specifically Modern Portfolio Theory, attempt to help us “make decisions under uncertainty” using mathematical tools to calculate probable outcomes.
Randomness is a cousin to uncertainty, but they are not the exact same. Randomness applies to an outcome with a normal distribution where all possible outcomes are equally likely, whereas true uncertainty applies to outcomes that not only include randomness, but also includes situations where a non-normal distribution is possible, where we may be lacking complete information, where we may have made errors in calculations, or where it is impossible to measure at the level of granularity needed to fully predict the likely outcomes.
Economic systems are comprised of individual human participants. Economics tells us that if we assume that all humans are rational and make choices to maximize utility and efficiency, that we can use mathematics to determine outcomes. The problem is that these individuals are not rational. They make random decisions, and more importantly, mistakes. They sell at the bottom and buy at the top, they make quick reactions to unverified news, they exhibit herding behavior, they buy a boat they cannot afford, they utilize variable rate mortgages when 30 year rates are under 3%, they call in sick to work to go fishing, and some of them even lie, cheat, and steal. This makes predicting the outcome of trillions of independent decisions impossible without knowing the psychology of individual participants.
Under normal circumstances, market returns exhibit the characteristics of randomness in the very short-term and evolve into uncertainty as time passes. The longer our period of measurement, the more our ability to account for extraneous factors erodes, and the more vicissitudes are cast upon us. This is especially true when we have exposure to thousands of securities that we are unfamiliar with.
Probability and Statistics:
"With four parameters, I can fit an elephant. With five, I can make him wiggle his trunk."
-John von Neumann
Probability and Statistics are a branch of mathematics that attempts to tame the unknowable future. The roots of these tools probably go back as far as Mesopotamia and ancient China where gamblers would utilize informal logic, or gut instincts, to decide if a certain bet was worth taking. The more one could determine a likely payout through intuition, the more likely he was to win at chance. Early games revolved around astragalus, or knuckle bones, that were rudimentary forms of dice.
As mentioned in this article and in others that we have written, the genesis of financial mathematics came from Pascal and de Fermat trying to solve an unfinished game of balla, where they outlined the “problem of points”. In letters that Pascal and de Fermat wrote to each other, they devised the concept of expected value and maximization of returns. This work is still the foundation of modern probability theory and has been expanded through time by mathematicians, scientists, and economists.
In the 18th Century, Bernoulli introduced the law of large numbers, which states that a sample of a large set of independent trials converges on the true value as the number of trials increases. If you flip a coin 10 times the mix of heads and tales is random; if you flip a coin a billion times it will be very, very close to 50/50. Later in the same century, Pierre-Simon Laplace developed the Uniform Probability Distribution, and the Central Limit Theorem.
Statistics as a distinct discipline of mathematics emerged in the 19th century with Quetelet, Pearson, Fisher, and Neyman contributing concepts like utilizing averages, correlation coefficients, chi-squared tests, hypothesis testing, and estimation. Fisher’s book “Statistical Methods for Research Workers” contains many concepts still being taught to modern researchers.
In the early 20th century, Louis Bachelier introduced the concept of random walks in financial markets, which cast the cloud of uncertainty over the field of investing. 50 years later, Harry Markowitz integrated all of these concepts into a cohesive theory that attempted to maximize the utility of a rational man by balancing mean-variance using correlations to build a portfolio on the efficient frontier. These are the foundations of those pie charts on the internet showing you how to allocate your assets.
As our computers have grown in power, these concepts have been developed in an attempt to put a saddle on risk and to maximize risk-adjusted return in portfolios. This “mathematization of investing” gives investors a sense of simplicity in a world of chaos by utilizing all the concepts discussed in this article, we need to make a judgement on if the returns they promise are as certain as practitioners suggest.
The premise of utilizing probability and statistics in finance is that even though the individuals comprising the system are irrational, their net cumulative behavior is rational. As if somehow, all of them acting in unison causes the mistakes to cancel each other out netting a rational output. But as we discussed in non-linear dynamics, their cumulative economic performance may create emergent properties, or bifurcation. The worst part is that irrational decision makers communicate with each other and convince otherwise rational participants to follow their lead.
Tying it all together:
“You may accumulate a vast amount of knowledge, but it will be of far less value to you than a much smaller amount if you have not thought it over for yourself; because only through ordering what you know by comparing every truth with every other truth can you take complete possession of your knowledge and get it into your power."
- Arthur Schopenhauer
When you sit down in front of a professional truly versed in finance, all the concepts laying menacingly and boringly above underly the impenetrable verbiage used to lull you into a sense of certainty. Very few of the people who espouse the jargon of finance have taken the time to view its origin under a microscope. The important thing now is to describe how we translate our understanding of these concepts into an implementable investment strategy.
Knowledge can be thought of in two dimensions, depth and breadth. Depth describes the level of granularity of your knowledge, and breadth describes how many different topics you are aware of. In a topic that we ourselves are unfamiliar with, it is very difficult to determine whether we are talking to a master of a trade, or a jack of many trades. Knowing a little about a lot of things can get us into trouble when we are dealing with complexity and emergent properties with non-linear dynamics, and it is impossible to use reductionism to the base level of granularity when we only have a surface understanding of many things.
The usage of probability and statistics in finance encourages us to abandon the use of reductionism in investing and to simply utilize average returns, deviations, and correlations to build a portfolio. It assumes that maximizing average annual risk-adjusted returns, in the form of an unmeasurable concept called utility is our goal. It further utilizes a normative framework, that assumes that we are both rational and know all possible outcomes and their respective probabilities in advance. The hope is that the law of large numbers will remove all unsystematic uncertainty (risk), and that we will simply be left with the emergent risk of the entire system to deal with, which they say it is impossible to remove. Is that really what we want- being left with the residual risks of an entire world economy, blindly following the herd even if it runs off a cliff?
A portfolio is simply a collection of cash flows that we buy in the hopes that those cash flows will grow over time. We buy stocks for their appreciation in price and dividends, and we buy bonds for their consistency and to generate current income. We may hold cash and other things like precious metals or commodities. We may buy real estate, or we may buy odd things like toll bridges, catastrophe risk, or timber. These can all be boiled down to an estimated series of cash flows, some unknown, and some known.
When we evaluate stocks we buy based on the current value in relation to the price. We have revenue, earnings, and dividends to measure the possible future cash flows. We can either buy things that are currently priced at a value to provide a good return, or we can buy things that require a lot of future success to determine our outcome. When we buy stocks for their growth, we must make assumptions about the future. Growth stocks require a much higher granularity view, and they face considerable uncertainty. When we buy common stocks based on current earnings, we still face uncertainty, but it is in the form of possible things that could go wrong, instead of depending on things to go right.
When we buy bonds, we buy for income and stability. With government bonds we will still face fluctuations based on interest rates, but if we hold to maturity, we know exactly what we will receive over the life of the bonds. When we add corporate bonds, we introduce credit risk, which changes the dynamics of our portfolio due to possible defaults. As we move down in credit quality, defaults become more likely, and we begin to calculate things like “recovery given default” and need to ensure that our income is above our estimated default risk. When we introduce asset-backed securities, we add another layer of risk called prepayment risk. Prepayment changes the dynamics of our stream of income because as rates rise our bond is likely to continue paying below market rates of interest, and when rates fall, instead of rising in price, the bond is likely to be called or paid off. We expect a higher rate on these bonds due to this additional layer of risk; it is a little bit like pricing an option. Other bonds have risks due to their liquidity that we have to price and factor into our stream of income calculations.
When we buy real estate, we hope to collect rent in a sufficient amount to cover our expenses. Because real estate is more likely to be valued based on comparable transactions, we can utilize loans to finance our purchase, which adds to the costs associated with holding the property. Loans magnify both our gains and our losses. In addition, we have costs associated with managing the property, and we get to depreciate the property over time which reduces the taxable income associated with it. We also must make sure that our property has a low vacancy rate because empty properties generate no income; this is an additional layer of risk that needs to be taken into account.
When we buy commodities, like gold and silver we only have two major cash flows. The cash leaving our account when we buy, and the cash returning to our account when we sell. Precious metals have small negative cash flows during the period that we hold them due to storage and insurance. This makes valuation very difficult, and there is no way to determine future value except that we believe that over time the value of precious metals will increase with the rise of inflation. It is said that in 562 B.C., during the reign of King Nebuchadnezzar of Babylon, an ounce of Gold was worth 350 loaves of bread. Other popular stories say the buying power of an ounce of gold should be one fine men’s suit, or 38 ounces should equal an army captain’s (Centurion in Roman times) annual salary. So right now, it appears that gold may be a little overvalued in terms of bread, and a captain’s salary, or that inflation has not quite been tamed.
When we make an active decision to hold cash we compare the interest that we receive to the opportunity cost of holding other securities. It is very important to remember that the expected short-term return of some securities may be negative, and that taking a small, guaranteed rate of return may be vastly superior to a highly probable loss over a couple of years. Cash also gives us an option on future security prices. When bond interest rates were close to zero, we were not receiving much reward for taking on the risk of negative fluctuations in bond prices, and cash, even at 0%, was an improvement on our future prospects. We manage your cash position as an active part of your portfolio, not just a placeholder waiting for action.
“Human was the music,
natural was the static.”
—John Updike
Building a portfolio is like composing a symphony. There are many instruments playing together, and they sound differently when playing in unison than when played alone. Timing and key both matter, as well. Stripping out the oboes because you don’t like their sound, and adding more timpani because you like bass can completely change the sound that emerges. Some of our worst results come when clients like some of our ideas, don’t like others and want us to add a few things that their neighbor has been talking about. It changes the entire sound of the piece. It is also important to remember that all instruments are not meant to be playing at the same time. Some things will be up for a while, while others are down. When system dynamics change, this very frequently reverses course. The goal is achieving consistent returns over long periods of time, and that necessarily means that some securities will show losses in the short-term.
The problem with buying indexes and using mean-variance analysis is that we consolidate many variant streams of income and strip away the identifying data and hope that they cancel each other out. At Basepoint, we strive to dig deeper and utilize the coarse-grained granularity to decide on how we allocate assets, and the finer-grained granularity to determine where we allocate. We utilize lower security diversification because we do not want to have a portfolio that is simply the systematic risk of the market. The problem with striving for “average” returns is that there is no guarantee that average will not be significantly negative.
The funds that we use typically hold around 30 stocks. The average mutual fund holds closer to 100, and many ETFs hold significantly more. The problem with constructing a portfolio with such coarsely grained building blocks is that you have a lot of emergent properties built into your materials, and little flexibility in controlling which risk exposures you have taken on. We try to look at each stream of income as close to the source as we can, and make sure that we do not have too much exposure to any single source. If you look at the S&P 500 right now, you will find that almost 35% of the assets are concentrated in 10 stocks. Most of these stocks are highly dependent on AI not only being successful but generating significant cash flows over the next 20 years. While this is possible, the consequences of this not happening could be catastrophic, and the prices being paid right now assume that this is a certainty.
While I can’t tell you with any confidence what the stock or bond markets will do over the next 2 years, I can tell you that we have done our best to build portfolios for our clients that do not require a perfect future to perform up to our expectations. Our goal is not utility maximization, it is maximization of the probability that your money will last for the rest of your life. While this strategy may underperform during periods of excitement and glee, it has been our experience that avoiding mistakes has a much bigger impact on your success than riding waves of euphoria and despair that rely on statistics and psychology to emerge.
We will continue to deal with uncertainty the way we always have. We will not let down our guard when things look too good to be true because we are afraid of missing out on short-term security spikes. We have been through periods like this before, and up to this point being disciplined has always paid off in the long run. We do not want to add so many variant securities that we have purposely entered the world of chaos and created a symphony too complex to play.
I could summarize this entire article in 4 words: know what you own. The importance of the above discussion is to document and illustrate the level of thought that has gone into making sure that we are logically consistent in our process, so that you can see if we are missing anything. As always, if you have questions about your portfolio, please do not hesitate to reach out to your advisor, or me personally.
Warm Regards,
Allen
1. Complexity: A Guided Tour, Melanie Mitchell
This article is for informational and educational purposes only and is not an offer to sell or a solicitation of an offer to buy the securities or instruments named or described in this report. The charts, graphs, and formulas included are not intended to be used by themselves to determine which securities to buy or sell, or when to buy or sell them. Such charts and graphs offer limited information and should not be used on their own to make investment decisions. Decisions to buy or sell a position should be based on an investor's investment objectives and risk tolerance and should not rely solely on this report. Investments involve risk and unless otherwise stated, are not guaranteed. Be sure to first consult a qualified financial adviser before implementing any strategy discussed. Supporting information related to the recommendation, if any, made in the research report is available upon request. Past performance may not be indicative of future performance.
The information in this report has been obtained from sources believed by Basepoint Wealth, LLC to be reliable and accurate. We cannot guarantee its accuracy, completeness, and validity and cannot be held liable for any errors or omissions. Any opinions or estimates contained in this report represent the judgment of Basepoint Wealth, LLC at this time and are subject to change without notice.
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