These days it is more or less common knowledge that our brains are not evolved for investment success. In fact, our brains are evolved to keep us from investment success. I don’t have anything profound to say on the science behind this. If you are interested in the science, I would recommend reading Thinking, Fast and Slow, by Daniel Kahneman.
Rather, I want to focus on how we can deal with our evolutionary disadvantages.
There is much investing literature insisting the solution to our cognitive and emotional biases is simply to think as dispassionately and analytically as possible. That sounds nice on paper. However, I don’t think it is realistic. I don’t believe it is possible to completely cut emotion out of the investment process. I would argue that if you think you have successfully removed emotion from your investment process what you have really succeeded in doing is deceiving yourself.
It is both healthier and more productive to openly acknowledge that investing is a difficult, emotional process. Better to channel your emotions toward productive ends than waste time and energy denying they exist.
For me, this means measuring success by the quality of your investment process and the consistency of its implementation–not the daily, weekly, monthly or even annual tape print. If you have a quality process and implement it consistently, and you strive for continuous improvement, you will do okay in the long run.
Think of this as the John Wooden approach to investing:
When Wooden arrived at UCLA for the 1948–1949 season, he inherited a little-known program that played in a cramped gym. He left it as a national powerhouse with 10 national championships—one of the most (if not the most) successful rebuilding projects in college basketball history. John Wooden ended his UCLA coaching career with a 620–147 overall record and a winning percentage of .808. These figures do not include his two-year record at Indiana State prior to taking over the duties at UCLA.
What’s more, Wooden openly shared his secret sauce:
Here are a couple of the ways I strive to channel emotion into process and not into a self-defeating obsession with outcomes:
I keep an investing journal where I document research, investment theses, thesis breaks, trades and post-mortems of exited investments. I will also write own my feelings and views of difficult situations or frustrating outcomes.
My emphasis is always on following my process. Making money on random gambles or “lottery ticket” type investments doesn’t count as success. My process does not revolve around “making money.” The goal of my process is to validate or disprove investment theses.
Thus, selling out of a bad investment is not failure. Exiting bad investments quickly allows me to redeploy that capital into better quality investments. Some of the worst investing mistakes (think Valeant) have been made by people being unwilling to admit they are wrong.
For a process-oriented investor, much of what passes for “investing” seems silly–like poker players on a tilt or dogs chasing cars.
Imagine you are a chicken. Each day a farmer comes and feeds you. After a few months of this you conclude that whenever the farmer visits, he will bring you food. All the empirical evidence supports this conclusion. Then, on an otherwise unremarkable day, instead of feeding you the farmer chops your head off.
That is a gruesome introduction to the problem of induction. (Though I have heard the chicken example many times I believe it originated with Bertrand Russell)
I have been noodling around with scientific reasoning and logic as relates to investment due diligence. What grates on me is that I have come to believe much of what people are looking to get out of a due diligence process cannot actually be achieved. For example, when we due diligence an investment manager the emphasis is on proving the manager is skilled. In reality we cannot prove this. At best we can conclude it is highly probable a manager is skilled. Alternatively, we can prove a manager is not skilled, provided we define a measure of “skill” in advance.
The average due diligence process is grounded in inductive reasoning. We make observations about the investment manager, her strategy and her firm. If the observations are favorable, we generalize that the manager is likely to be skilled and will perform well in the future. Logically this process is flawed.
The Problem of Induction
I first became aware of the problem of induction several years ago via Taleb’s Fooled By Randomness. The issue is we can only use inductive reasoning to conclude something is “likely” or “unlikely.” We cannot use inductive reasoning to prove something is true or false.
The classic example is the black swan. For a long time people believed all swans were white. They did not know all swans were white (they would have to have observed all the swans in existence to prove this). Rather, people inferred an extremely high probability for all swans being white because all the swans observed to date had been white. Then, in 1697, Willem de Vlamingh discovered cygnus atratus in Australia.
In the context of investing we struggle with the naïve extrapolation of past performance into the future. On the basis of past performance I can say, “I believe it is probable this investment manager is highly skilled.” However, I cannot use that data to prove, with certainty, that the manager will continue to outperform in the future. This WSJ article hit piece discussing Morningstar ratings is a practical exploration of the issue (although for the record I believe the WSJ badly misrepresented what Morningstar is trying to achieve with its ratings).
The problem of induction is central to the validity of the scientific method. Science does not prove the truth of hypotheses, theories and laws. It merely verifies they are consistent with empirical results. However, as with inferences about the colors of swans, it only takes one false case to disprove a scientific theory. The philosopher Karl Popper therefore concluded falsifiability is the essential criteria determining whether a theory can be considered scientific.
Among his contributions to philosophy is his claim to have solved the philosophical problem of induction. He states that while there is no way to prove that the sun will rise, it is possible to formulate the theory that every day the sun will rise; if it does not rise on some particular day, the theory will be falsified and will have to be replaced by a different one. Until that day, there is no need to reject the assumption that the theory is true. Nor is it rational according to Popper to make instead the more complex assumption that the sun will rise until a given day, but will stop doing so the day after, or similar statements with additional conditions.
Such a theory would be true with higher probability, because it cannot be attacked so easily: to falsify the first one, it is sufficient to find that the sun has stopped rising; to falsify the second one, one additionally needs the assumption that the given day has not yet been reached. Popper held that it is the least likely, or most easily falsifiable, or simplest theory (attributes which he identified as all the same thing) that explains known facts that one should rationally prefer. His opposition to positivism, which held that it is the theory most likely to be true that one should prefer, here becomes very apparent. It is impossible, Popper argues, to ensure a theory to be true; it is more important that its falsity can be detected as easily as possible.
Applications To Due Diligence & Investment Analysis
This means you cannot “prove” an investment thesis is correct. At best you can gather evidence to build conviction that your investment thesis is “probably” correct. In my experience much due diligence is conducted with an inductive mindset. This leaves due diligence processes vulnerable to confirmation bias.
Should we invert the process?
In other words, you would organize due diligence with the goal of falsifying an investment thesis. If the thesis cannot be falsified, you invest. As a risk management discipline, you then establish a series of easily falsifiable statements constituting “thesis breaks” (e.g. “Company A will average double-digit revenue growth over the next 3 years”). When a thesis break is triggered, the investment is re-evaluated or removed from the portfolio.
At a high level, evaluating an investment opportunity can almost always be boiled down to the following:
People: Management has integrity and is aligned with investors.
Process: Processes are disciplined, repeatable and based on sound economic principles.
Performance: Past performance supports management’s ability to execute.
The due diligence process should not be structured to verify these statements as accurate. It should be structured to prove they are false. In practice to guide your work you would need to establish a whole series of falsifiable statements underneath these broad headings. For example, under People:
Management has never committed or been associated with securities-related offenses.
Management has no prior record of personal or business bankruptcy.
Management has never been convicted of a felony or misdemeanor offense.
Management owns >10% of shares outstanding / maintains significant personal investment.
In my view this is a more straightforward, disciplined and logically sound method of organizing a due diligence process. To a non-practitioner the distinction may seem silly. However, the structure is designed to minimize confirmation bias—a common and dangerous cognitive bias in investment research and portfolio management.
Bridgewater Associates has a reputation for being something of a cult. I do not have direct experience with Bridgewater or its founder Ray Dalio other than reading some of their research and thought pieces. So I’m not really qualified to weigh in on the cult aspect. What I do appreciate about Bridgewater’s culture is that it is fanatically process-oriented, to the point of resembling a spiritual quest. Check out these snips:
Now, this post is not meant as Bridgewater commercial. Bridgewater is simply a real-life example of firm that seeks to understand Truth, and has reaped the benefits of the process. If you understand the Truth of how the world operates, making money using that knowledge is trivial.
This might seem like a banal observation. I promise it is not. In my view a majority of investment organizations have it backward. They are focused on outcomes. The Truth of what caused those outcomes is irrelevant. It is easier from a business perspective to simply rationalize things in ways that appeal to clients. It is clients who pay the bills, after all.
The result is that investing becomes an endless performance chase. This is the finance equivalent of Buddhist samsara. It is an endless cycle of birth, suffering and death.
By now you might think I am off the deep end. Yet I am not the first person to connect practical issues in asset management to broader philosophical concepts. Patrick O’Shaughnessy has written an excellent post covering related subject matter, “Two Star Managers and the Wheel of Fortune.” In it he shares this Taoist story:
There is a Taoist story of an old farmer who had worked his crops for many years. One day his horse ran away. Upon hearing the news, his neighbors came to visit.
“Such bad luck,” they said sympathetically.
“We’ll see,” the farmer replied.
The next morning the horse returned, bringing with it three other wild horses.
“How wonderful,” the neighbors exclaimed.
“We’ll see,” replied the old man.
The following day, his son tried to ride one of the untamed horses, was thrown, and broke his leg. The neighbors again came to offer their sympathy on his misfortune.
“We’ll see,” answered the farmer.
The day after, military officials came to the village to draft young men into the army. Seeing that the son’s leg was broken, they passed him by. The neighbors congratulated the farmer on how well things had turned out.
Here is a bridge from the Taoist story to our lived experience as investors. It illustrates how something as simple as the year we were born can have a dramatic impact on our lived experiences, and by implication our worldview. If we only ever filter the world and the markets through the lens of our personal experiences (and biases), we will see things as we would like to see them rather than how they truly are.
This may blind us to opportunity. It may also blind us to risk. Even thinking of “opportunity” and “risk” as discrete concepts can be reductive and limiting. Typically where there is risk there is also opportunity, and vice versa. This is especially true of investments that are out of favor or misunderstood. To this day, skilled structured credit investors are making good money off toxic mortgage-backed securities originated prior to the financial crisis. Since they have deep, objective knowledge of the securities and the market, they are able to see past the label of “toxic” to the underlying value.
If you can understand the Truth, making money is trivial.
In my market ecosystem post I described different types of investors and the roles they might play throughout an idealized company’s life cycle. Writing that post caused me think more deeply about my own investing philosophy and the role I play in the market ecosystem (this is one of the reasons I write).
I used to think of myself primarily as a value investor: someone who is out there looking to pick up dollars for fifty cents. There is a still a part of me that is deeply attracted to these types of investments due to the margin of safety they afford.
However, another part of me is attracted to compounding machines. In order for an investment to compound over time it needs to generate high returns on capital and also offer ample opportunity to reinvest that capital for similarly high returns. This is identical to the reinvestment assumption underpinning the IRR and YTM calculations.
Traditional value investments may not compound very well. Many are maybe single digit revenue growers but with strong free cash generation. What keeps them from compounding at high rates is that the cash cannot be reinvested in growth initiatives (maybe the market is mature). Even worse, a management flush with cash may start doing stupid things to “buy” growth (such as play venture capitalist).
However, compounding machines tend to be more expensive than traditional value investments. It can be tough to find them with a fat margin of safety, particularly these days when valuations across the quality spectrum are stretched. This is mitigated somewhat by the fact that it is easier psychologically to hold a compounding machine for a very long time. A compounding machine by definition merits a richer valuation.
Therefore, what are more and more interesting to me are businesses that have reached inflection points. Ideally these are businesses that have been dumped by growth investors and are at increasing risk of being dumped by value investors but where the business nonetheless has a reasonable probability of inflecting positively. Low debt levels are important here as leverage can be catastrophic for a business in transition.
Here are some reasons I like this approach:
These businesses tend not to screen well on backward-looking quantitative measures. This makes them more likely to be overlooked and less likely to be owned by sophisticated investors facing pressure to deliver strong relative performance versus benchmarks (active mutual fund managers, pension funds, endowments). Taking a position in a stock at an inflection point introduces significant career risk into the equation for these players.
These businesses usually face significant uncertainty, which causes their market valuation to overshoot and undershoot significantly relative to intrinsic value.
If you fish for these businesses in the smaller cap segment of the market (under $1 billion and preferably $250 million or less) that is an additional constraint on large institutional investors that contributes to a structurally inefficient market niche.
If you are able to fish in the niche described above, the trading volumes for these stocks can be thin which means the price gets bounced around whenever someone buys or sells. This may present attractive buying opportunities as you build a position (but it can hurt if you need to exit the position in a hurry–another example of the advantages conferred by a permanent capital base).
The biggest risk to this investment approach is buying into a value trap. Thus, that ought to be the central focus of a risk management discipline. I am pondering how best to codify this but I think it starts with the decision to average down.
If you have ever owned an individual stock–especially a large cap stock with a lot of coverage–one thing you probably learned very quickly is to tune out day-to-day newsflow. For example, the largest cap individual stock I currently own in my portfolio is Gazprom. Hundreds, if not thousands, of news stories are published daily either about the company itself, or about energy industry or geopolitical events that could impact the stock.
Here are sample headlines from a quick Google news search:
Gazprom Eyes Brazil’s Natural Gas Opportunities
Gazprom—the LNG pivot?
EU says wants Russia’s Gazprom to sweeten antitrust concessions
Russia’s Gazprom Neft ‘holds its nose’ at global oil output cut
Kiev allowed to arrest Gazprom’s property: there is nothing but gas
Russian Parliament to Consider Tax Preferences for Gazprom
You get the idea.
I could spend a lot of time tracking Gazprom news. I could probably write an entire blog just about Gazprom (I am not sure it would endear me to the Russian security services). I would never want for material. But it probably wouldn’t improve my investment results much. It might even hurt my results.
Most of this daily news is just noise. It is exacerbated by the fact that Gazprom is majority owned by the Russian government — pro-Russian and anti-Russian news outlets have every incentive to put out biased coverage. If you immerse yourself in the noise, it is easy to lose track of business fundamentals. You start wondering whether you should buy or sell based on commentary from some journalist (who may or may not be able to read a balance sheet). You will be tempted to sell on every price move, either to avoid a loss or lock in a gain.
The problem is the news cycle moves very, very fast. Business cycles move slowly. The ratio of noise (meaningless information) to useful information in the news cycle is quite high. In the business cycle that ratio is lower. As a long-term fundamental investor you want to think in terms of business cycles as they are more aligned with your investment horizon.
Furthermore, when it comes to things like currency risk and geopolitical risk there is nothing about monitoring news that will give you any more control over the situation. Either the stock has properly priced the graft, administrative inefficiency, currency risk and geopolitical risk embedded in the business or it hasn’t. You deal with that up front, with the margin of safety you demand at purchase. There is no point getting worked up over day-to-day newsflow. It is not constructive.
So it is with almost all types of news. Especially political news.
We are in the midst of a particularly vicious political cycle in the US. In my view this is a cyclical phenomenon. (Bridgewater did a nice little piece on populism a while back–I don’t believe it’s a coincidence that the last time populism had this much momentum in Western countries was in the decade following The Great Depression) Anyway, the forces that shape the political climate of a given era are much larger than you or me. Much like the geopolitical risks facing Gazprom, the political environment is beyond our control.
A majority of the news we consume is meaningless noise. I am looking at the title of a news story on my phone as I write this: “Woody Harrelson’s Dinner With Trump Was So Bad He Had To Smoke A Joint.” Now if I hate Donald Trump I am laughing at this story. If I am a Trump supporter it is fake news or liberal propaganda and I am angry. I will go to Breitbart and read something uplifting about glib liberal cucks. Vice versa for “Mark Zuckerberg and liberals seek to weaken bail system that keeps us safe.”
Again, this is meaningless noise. But worse than that it is noise that gets people emotionally charged up. That is what most political news does. Indeed it is designed that way–all the better for the clicks and social shares (not to mention Facebook’s margins).
In general I try to keep my political news consumption to minimal levels. I will read the top political stories in the Financial Times. I will also read a brief set of bullets I receive each business day from another investment professional. The bullets are non-partisan and succinctly summarize major developments and strategic considerations for each political party.
Beyond that I am careful about my political news consumption. The reason? Much of it is written to drive an emotional response and precious little of it actually matters. In his book Fooled By Randomness, Nassim Taleb observes:
We just saw how the scientifically hideous George Will and his colleagues can twist arguments to sound right without being right. But there is a more general impact by information providers in biasing the representation of the world one gets from the delivered information. It is a fact our brain tends to go for superficial clues when it comes to risk and probability, these clues being largely determined by what emotions they elicit or the ease with which they come to mind […]
In that sense the description coming from journalism is certainly not just an unrealistic representation of the world but rather the one that can fool you the most by grabbing your attention via your emotional apparatus–the cheapest to deliver sensation.
If the major cable news networks and their legion of apoplectic commentators winked out of existence tomorrow, what would really go missing from your life? (Ditto the HuffPosts, Daily Beasts and Breitbarts) Would this impede your ability to earn a living? Would it diminish your relationships with family, friends and professional colleagues? Would it negatively impact your physical and psychological well-being?
So my humble suggestion is that you give the news cycle–particularly the political news cycle–the Gazprom treatment. Ignore it. Or at least ignore it a majority of the time. I suspect you will find it makes very little difference in your life. To the extent it makes any difference at all, it will probably be a positive.
I pre-ordered this book on Amazon after seeing it mentioned on Josh Brown’s blog, The Reformed Broker. I was intrigued because it purported to be a rigorous treatment of cryptocurrency and cryptoassets written from the perspective of a relatively sophisticated investor.
Burniske and Tatar state their goal was to produce a book that is the equivalent of Benjamin Graham’s Intelligent Investor for cryptoassets. That is kind of like Dennis Rodman saying he wanted to do for rebounds what Michael Jordan did for dunks. To the authors’ credit I think they have done an admirable job of approaching a fast-evolving space in a balanced and rigorous way.
The book is well-organized. It is segmented into three parts: What, Why and How.
What: Discusses the theoretical underpinnings of cryptoassets and provides background information on the history and evolution of several major cryptocurrencies: Bitcoin, Ethereum, Ripple, Monero, Zcash and Dash (I may have omitted a couple). Burniske and Tatar take pains to distinguish between cryptocurrencies, cryptocommodities and cryptotokens.
Why: This section provides an overview of Modern Portfolio Theory (MPT) and the use of mean-variance optimization in constructing an investment portfolio. The authors argue for the inclusion of cryptoassets in an investor portfolio based on their potential to improve overall portfolio efficiency, similar to more “traditional” alternative investments such as hedge funds, private real estate and commodities. I skipped most of this section as I am very familiar with MPT.
How: This section was really what attracted me to the book as it lays out a framework for performing due diligence on a prospective cryptocurrency investment. The authors address issues of custody, valuation and trading, as well as some of the nuances of trading in fragmented markets with the potential for wide fluctuations in trading volumes. The valuation model they float for cryptoassets is more or less the Equation of Exchange (MV = PY or in this case P = MV/Y). One issue I don’t think they adequately address is the issue of reflexivity in the “velocity” of crypto transactions (speculative trading activity drives up network activity which in my view creates a kind of feedback loop).
Who Should Read This Book
Anyone looking for a comprehensive introduction to cryptoassets would benefit from reading this book. It would be particularly useful financial advisors looking to educate themselves in order to address client questions or advisors considering cryptoassets for inclusion in client portfolios. The book is very much written in the language of the financial professional.
Who Should Not Read This Book
This book does not contain any secret sauce for getting rich quick. People who are looking for “hot tips” or “hacks” will be disappointed. While the authors are clearly bullish on the long-terms prospects for cryptoassets, they emphasize the need for investors to educate themselves, conduct thorough due diligence and develop an investment discipline. The due diligence concepts outlined in the book are applicable to any asset class or investment opportunity.
My comments on this book should in no way be taken as a recommendation to buy or sell any cryptoasset. If you are wondering whether you should own cryptoassets as part of your investment portfolio you should consult with a financial advisor who can advise you based on your unique financial circumstances.
In my previous post on valuing Bitcoin I settled on supply/demand balance as the “least-bad” valuation model. I have been thinking more on how one might actual implement this in practice. The supply side is fairly straightforward. There are lots of free calculators that allow you to play with cost assumptions for Bitcoin miners. Now, there are probably going to be places in the world where an astute Bitcoin miner can arbitrage differences in electricity costs. But for now that’s splitting hairs.
The far trickier part is the demand side.
The reason is that while there are lots of use cases for Bitcoin, far and away the most prevalent is speculative trading. Therefore, if you take network activity at face value you are probably missing the fact that there is some reflexivity in those statistics. It’s basically a circular error problem. Speculative trading activity drives up network activity which drives up miner’s costs which causes the equilibrium price to rise. BUT, if speculative trading activity slackens (e.g. Bitcoin is in an asset bubble that deflates in the future) then the reverse will occur on the way down.
So in my view what you need to do is account for potential increases or decreases in speculative trading activity (and other kinds of activity) in your model. To do this you would need data that segments different transaction types.
The trick is finding that data.
As always this is not an investment recommendation. It is written for entertainment purposes only. As my thorough disclosure states very clearly, you should never make any investment decision based on something some random dude writes on the internet. Everything I am saying here could be wrong. In fact it is likely wrong. If you are looking for a recommendation on whether to own Bitcoin or any other cryptocurrency you should consult with a trusted financial advisor.