Mental Model: Investment Return Expectations

(This post assumes you’re familiar with the concepts outlined in the preceding mental model post on how to make money investing)

There are no shortage of people in this world selling promises. Financial advisors sell you promises. Banks sell you promises. If you’re an allocator, asset managers, consultants, third party marketers and cap intro groups all line up to sell you promises, too. Such-and-such returns over such-and-such a time period with such-and-such volatility.

Only rarely are these promises derived through anything resembling deductive logic. They’re almost always based on storytelling and data-mining.

When your financial advisor tells you she can get you 8% (god forbid, 10%) annualized on your US-biased public equity portfolio, what is that number based on? It’s almost always just a historical average. Same with your Fancy Consultant pitching private equity or middle market lending or crypto-cannabis venture capital or whatever other magical strategy happens to be selling well at the moment.

He who builds on historical averages, builds on sand.

There’s no natural law requiring US equities to return somewhere between 8% and 10% on average over 20-year rolling periods. Same with your private equity and middle market lending and crypto-cannabis venture funds. As with everything, you should build up your return expectations from first principles.

For bonds, your expected nominal return* over the bond’s tenor is equal to the starting yield.**

For stocks, your expected nominal return is equal to the starting dividend yield, plus expected growth in earnings, plus any change in valuation (price).

For the remainder of this post, we’ll focus on stock returns.

Remember the two ways to make money investing? You’ve got cash distributions and changes in investor preferences. Dividend yields and expected growth in earnings are the fundamentals of your cash distributions. Multiple expansion, as we’ve noted before, is always and everywhere a function of changes in investor preferences for different cash flow profiles.

Where people get themselves into trouble investing is extrapolating too much multiple expansion too far into the future. When you do this, you’re implicitly assuming people will pay more and more and more for a given cash flow stream over time. This kind of naive extrapolation is the foundation of all investment bubbles and manias.

If you want to be as conservative as possible when underwriting an investment strategy, you should exclude multiple expansion from the calculation all together. This is prudent but a bit draconian, even for a curmudgeon like me. I prefer a mean reversion methodology. If assets are especially cheap relative to historical averages, we can move them back up toward the average over a period of, say, 10 years. If assets are especially expensive relative to historical averages, we can do the reverse.***

Below are a couple of stylized examples to illustrate just how impactful changes in valuation can be for realized returns. Each assumes an investment is purchased for an initial price of $500, with starting cash flow of $25, equivalent to a 5% annual yield. Cash flows are assumed to grow at 5% per year, and the investment is assumed sold at the end of Year 5. Only the multiple received at exit changes.

In the Base Case, you simply get your money back at exit.

In the Upside Case, you get 2x your money back at exit.

In the Downside Case, you only get 0.25x your money back at exit.

Despite the exact same cash flow profile, your compound annual return ranges from -12% to 17.9%. I hope this conveys how important your entry price is when you invest. Because price matters. It matters a lot. At the extremes, it’s all that matters.

DOWNSIDE — Downside Case: 0.25x Exit Multiple

If you’d like to get more into the weeds on this, Research Affiliates has a fantastic primer on forecasting expected returns. Research Affiliates also offers a free, professional grade interactive asset allocation tool. It covers a wide range of asset classes in both public and private markets.

In the meantime, what I hope you take away from this post is that there are straightforward models you can use to evaluate any investment story you’re being told from a first-principles perspective. Often, you’ll find you’re being sold a bill of goods built on little more than fuzzy logic and a slick looking slide deck.

Notes

*Real returns for bonds can vary significantly depending on inflation rates. This is a significant concern for fixed income investors with long investment horizons, but lies beyond the scope of this post. Really, it’s something that needs to be addressed at the level of strategic asset allocation.

**Technically, we need to adjust this with an expected loss rate to account for defaults and recoveries. This doesn’t matter so much for government bonds and investment grade corporate issues, but it’s absolutely critical for high yield investments.

***Why is it okay to use historical averages for valuation multiples while the use of historical averages for return assumptions deserves withering snark? The former is entirely backward looking. The latter at least aspires to be forward looking.

Also, if you can establish return hurdles based on your investment objectives, you can back into the multiple you can afford to pay for a given cash flow stream. That’s a more objective point of comparison. Incidentally, the inputs for that calculation underscore the fact that valuation multiples are behaviorally driven. Mathematically, they’re inversely related to an investor’s return hurdle or assumed discount rate.

Mental Model: How To Make Money Investing

In my line of work, I see a lot of client investment portfolios. Very few of these portfolios are constructed from any kind of first principles-based examination of how financial markets work. Most client portfolios are more a reflection of differences in advisory business models.

If you work with a younger advisor who positions her value add as financial planning, you’ll get a portfolio of index funds or DFA funds.

If you work with an old-school guy (yes, they are mostly guys) who cut his teeth in the glory days of the A-share business, you’ll get an active mutual fund portfolio covering the Morningstar style box.

No matter who you work with, he or she will cherry-pick stats and white papers to “prove” his or her approach to building a fairly vanilla 60/40 equity and fixed income portfolio is superior to the competition down the street.

My goal with this post, and hopefully a series of others, is to help clarify and more thoughtfully consider the assumptions we embed in our investment decisions.

So, how do I make money investing?

There are two and only two ways to get paid when you invest in an asset. Either you take cash distributions or you sell the asset to someone for a higher price than you paid for it.

Thus, at a high level, two factors drive asset prices: 1) the cash distributions that can reasonably be expected to be paid over time, and 2) investors’ relative preferences for different cash flow profiles.

What about gold? you might wonder. Gold has no cash flows. True enough. But in a highly inflationary environment investors might prefer a non-yielding asset with a perceived stable value to risky cash flows with massively diminished purchasing power. In other words, the price of gold is driven entirely by investors’ relative preferences for different cash flow profiles. Same with Bitcoin.

So, where does risk come from?

You lose money investing when cash distributions end up being far less than you expect; when cash distributions are pushed out much further in time than you expect; or when you badly misjudge how investors’ relative preferences for different cash flow profiles will change over time.

That’s it. That’s the ball game. You lose sight of this at your peril.

There are lots of people out there who have a vested interest in taking your eye off the ball. These are the people Rusty and Ben at Epsilon Theory call Missionaries. They include politicians, central bankers and famous investors. For ~~some of them~~ almost all of them, their ability to influence the way you see the world, and yourself, is a source of edge. It allows them to influence your preferences for different cash flow profiles.

Remember your job!

If you’re in the business of analyzing securities, your job is to compare the fundamental characteristics of risky cash flow streams to market prices, and (to the best of your ability) formulate an understanding of the assumptions and preferences embedded in those prices.

If you’re in the business of buying and selling securities, your job is to take your analysts’ assessments of cash flow streams, as well as the expectations embedded in current market prices, and place bets on how those expectations will change over time.

Ultimately, as the archetypical long-only investor, you’re looking for what the late Marty Whitman called a “cash bailout”:

From the point of view of any security holder, that holder is seeking a “cash bailout,” not a “cash flow.” One really cannot understand securities’ values unless one is also aware of the three sources of cash bailouts.

A security (with the minor exception of hybrids such as convertibles) has to represent either a promise by the issuer to pay a holder cash, sooner or later; or ownership. A legally enforceable promise to pay is a credit instrument. Ownership is mostly represented by common stock.

There are three sources from which a security holder can get a cash bailout. The first mostly involves holding performing loans. The second and third mostly involve owners as well as holders of distressed credits. They are:

Payments by the company in the form of interest or dividends, repayment of principal (or share repurchases), or payment of a premium. Insofar as TAVF seeks income exclusively, it restricts its investments to corporate AAA’s, or U.S. Treasuries and other U.S. government guaranteed debt issues.

Sale to a market. There are myriad markets, not just the New York Stock Exchange or NASDAQ. There are take-over markets, Merger and Acquisition (M&A) markets, Leveraged Buyout (LBO) markets and reorganization of distressed companies markets. Historically, most of TAVF’s exits from investments have been to these other markets, especially LBO, takeover and M&A markets.

Control. TAVF is an outside passive minority investor that does not seek control of companies, even though we try to be highly influential in the reorganization process when dealing with the credit instruments of troubled companies. It is likely that a majority of funds involved in value investing are in the hands of control investors such as Warren Buffett at Berkshire Hathaway, the various LBO firms and many venture capitalists. Unlike TAVF, many control investors do not need a market out because they obtain cash bailouts, at least in part, from home office charges, tax treaties, salaries, fees and perks.

I am continually amazed by how little appreciation there is by government authorities in both the U.S. and Japan that non-control ownership of securities which do not pay cash dividends is of little or no value to an owner unless that owner obtains opportunities to sell to a market. Indeed, I have been convinced for many years now that Japan will be unable to solve the problem of bad loans held by banks unless a substantial portion of these loans are converted to ownership, and the banks are given opportunities for cash bailouts by sales of these ownership positions to a market.

For you index fund investors snickering in the back row—guess what? You’re also looking for a cash bailout. Only your ownership of real world cash flow streams is abstracted (securitized) into a fund or ETF share. In fact, it’s a second order securitization. It’s a securitization of securitizations.

I’m not “for” or “against” index funds. I’m “for” the intentional use of index funds to access broad market returns (a.k.a “beta”) in a cheap and tax-efficient manner, particularly for small, unsophisticated investors who would rather get on with their lives than read lengthy meditations on the nature of financial markets. I’m “against” the idea that index funds are always and everywhere the superior choice for a portfolio.

Likewise, I’m not “for” or “against” traditional discretionary management. I’m “for” the intentional use of traditional discretionary (or systematic quant) strategies to access specific sources of investment return that can’t be accessed with low cost index funds. I’m “against” the idea that traditional discretionary (or systematic quant) strategies are always and everywhere the superior choice for a portfolio.

What sources of return are better accessed with discretionary or quant strategies?

That’s a subject for another post.

Two Kinds of People

There are two kinds of people in this world. If you drill down deep enough into someone’s psychology you will find she is hardwired psychologically for either momentum or value (a.k.a trend or mean reversion).

Some Characteristics Of Momentum People

Of the two types of people, momentum people are more sociable. They are innate trend followers. For momentum people, it’s always best to stick with what’s working.

Their business and lifestyle decisions reflect this. “Get while the getting’s good,” is what they think during an economic boom. They prefer to “cut losers and let winners run.”

Momentum people are pro-cyclical. They are fun at parties during boom times. It’s easy to be the life of the party when you are making a lot of money.

Some Characteristics Of Value People

Value people by contrast are a pain in the ass. They are often curmudgeonly and unpopular. This is no accident. Value people are innately contrarian. Mean-reversion underlies a value person’s worldview. For a value person, “things are never as good as you hope, or as bad as they seem.”

A value person’s business and lifestyle decisions reflect this. Value people pare risk and accumulate cash during boom times. They take risk and deploy cash during bear markets.

Value people are counter-cyclical. They are never much fun at parties because they’re always out of phase with the crowd.

Which Are You?

In the end it doesn’t really matter whether you are a momentum or value person. You can succeed in life and business either way (well… assuming you don’t over lever yourself).

What matters is that you recognize whether you are wired as a momentum person or a value person, and that you avoid putting yourself in positions that are a fundamental mismatch for your psychology.

For example, I think I would probably make the world’s worst venture capitalist (spoiler alert: I am a value guy). Not because I would lose money but because it would be hard for me to invest in anything in the first place.

The high base rate for failed venture investments would loom large over every decision. The incessant cash burning would haunt my nightmares.

The Trouble With Truth

A friend and I have been having a running conversation about the “post-truth era” and bias in the media. This post is an attempt to pull the ideas from those conversations together into a kind of mental model.

Essentially there are three issues in play here: epistemic uncertainty (the problem of induction), cognitive biases and incentive systems.

The first two help explain why otherwise intelligent and well-meaning people can come to inhabit echo chambers when they otherwise seek to reason objectively. Incentive systems then reinforce the sub-optimal behavior of well-meaning people and assist opportunists and charlatans in spreading outright falsehoods.

This post is not meant to address opportunists and charlatans as their motives are things like wealth, power and ideological fanaticism. For these individuals the truth is simply an inconvenient speed bump along the road to power. Rather, I am interested in how the uncertainty inherent in scientific reasoning leaves openings for multiple truths and seemingly contradictory bodies of evidence.

Epistemic Uncertainty

How can we know a thing is true in the first place? That seems like a good place to start.

Broadly speaking, we can reason deductively or inductively. Deductive reasoning is a process that arrives at a “logically certain conclusion.” Deductive reasoning is what you do in math class. The beauty of mathematics, which I did not properly appreciate as a kid, is that it is about the only discipline where you can know with certainty when you are right. Your conclusion must follow inevitably from your premises. It cannot be otherwise.

Inductive reasoning, on the other hand, takes specific observations and then infers general rules. Importantly, the scientific method is a form of inductive reasoning. All of the social sciences, including economics, utilize inductive reasoning. Inductive reasoning is subject to the so-called “problem of induction.” Namely: inferences are not “logically certain.”

The classic example involves swans. For a long time people believed all swans were white. This was an inference based on the fact that in every recorded observation of a swan, the swan had been white. Critically, this did not prove all swans were white. In order to prove all swans were white, you would have to observe every swan in existence, every swan that had ever existed, and every swan that ever would exist. That is of course impossible. And in fact, as soon as someone discovered a black swan (in Australia in 1697), the inference that all swans were white was immediately proven false.

That’s not to say the inference was a bad one. It was perfectly reasonable given the available data. You see how this presents issues for science, and any other truth-seeking endeavors. Even “good science” is often wrong.

If you have spent any time reading scientific research, you are familiar with the way hypotheses are formulated and tested. It is never a question of “true or false.” It is a question of “whether the null hypothesis can be rejected at such-and-such a confidence interval.”

What confidence interval is appropriate? The gold standard is 95% (a.k.a. within two standard deviations of the mean, assuming normally distributed results). However, there is a healthy debate over where that threshold should be set.

The probabilistic nature of induction results creates epistemic uncertainty. In that sense, there is no post-truth era. There has never really been an era of truth, either. Science has never really given us truth. It’s given us inferences, some of which have withstood many years of repeated testing (evolution, Newton’s laws, etc.), and to which we’ve assigned extremely high levels of confidence. In other words: we are pretty damn sure some things are true. Sure enough we can do things like send satellites out of our solar system. But it’s still not logical certainty.

In other areas, science has given us inferences where confidence levels are much lower, or where there is significant debate over whether the inference if of any significance at all. Many scientific studies don’t replicate.

The point of this is not to argue we should junk science or inductive reasoning. It’s to show how even if two parties use scientific reasoning in good faith and with the exact same methodology, they might arrive at different conclusions. How do you resolve the conflict?

To function properly, the scientific method requires friction. Replication of results in particular is critical. However, when we layer on cognitive biases and political and economic incentives, scientific inqiuiry and other inductive reasoning processes become distorted.

Cognitive Biases

Humans are funny creatures. Our brains evolved to deal with certain specific problems. It was not that long ago that the issues of the day were mainly things like: “can I eat this mushroom without dying?” and “that animal looks like it wants to eat me.”

Evolution did not optimize human brains for analyzing collateralized loan obligations.

I am not going to rehash the literature on cognitive biases here. If you are interested in a deep dive you should read Thinking, Fast and Slow, by Daniel Kahneman. Rather, I want to mention one bias in particular: confirmation bias.

Instead of looking for evidence that their inferences are false, people look for evidence that confirms them. The Wiki for confirmation bias calls it “a systematic error of inductive reasoning.” There is a saying among quants that if you torture data long enough it will say whatever you want it to. These days we have more data than ever at our fingertips, as well as new and exciting torture methods.

Importantly, confirmation bias does not represent a conscious decision to lie or deceive. People who consciously manipulate data to support a hypothesis they know ex ante to be false are opportunists and charlatans. We are not concerned with them here.

People aren’t evil or stupid for exhibiting confirmation bias. They just do. Intelligent people have to be especially careful about confirmation bias. They will be extra unconsciously clever about it.

You can probably see how combining this with inductive reasoning can be problematic. It creates a situation where everyone has “their” facts. What’s more, most people involved in research and reporting operate within incentive systems that encourage confirmation bias rather than mitigate it.

Incentives

If people tend to seek out information confirming their views, it is only logical that media businesses pander to that tendency. The media business is first and foremost an attention business. Either you have people’s attention or you don’t. If you don’t, the subscribers stop paying and the advertisers don’t want to be on your platform and pretty soon you are out of business. It behooves you to serve up the kinds of stories your readers like reading, and that align with their worldviews.

Likewise academics face their own pressures to conform with peers. Academic departments are subject to the same power games and politics as corporate boardrooms. Reputation matters. Particularly given the importance of tenure to young faculty. Also, if you are an academic star who has built a 40-year reputation on the back of a particular theory, how much incentive do you have to want to try and poke holes in that? If you think these dynamics don’t impact behavior, you don’t know very much about human behavior.

Closer to home for this blog, at hedge funds and mutual funds analysts often receive bonuses based on how their ideas perform once they are in a portfolio. But what if you are the analyst covering a weak opportunity set? The right thing to do is throw up your hands and say, “everything I am looking at sucks.” But if you go that route you can look forward to no bonus and possibly being fired. So instead you will sugar coat the least bad ideas and try to get them into the book.

Putting It All Together

So here we have it, from start to finish:

Many forms of “knowing,” including the scientific method, are forms of inductive reasoning. Inductive inferences are subject to uncertainty and potential falsification. This means there is always an opening for doubt or contradictory evidence. We accept certain scientific principles as true, but they are not actually logical certainties. Truth in the sense of logical certainty is not as common as many people think.
Due to cognitive biases, especially confirmation bias, people distort the process of scientific inquiry. Rather than seek information that could falsify their existing beliefs (the correct approach), they seek out information that confirms them. People have “their facts,” which they can back up with evidence, which in turn creates multiple, plausible versions of “the truth.”
Economically, media companies are incentivized to appeal to peoples’ cognitive biases. The economics of media incentivize a continuous feedback loop between content producers and consumers. Academics and other researchers are also incentivized to confirm their beliefs due to issues of reputation, professional advancement and compensation.

The Relative Performance Game

I wrote in a previous post that much of what passes for “investing” is in fact just an exercise in “getting market exposure.” In writing that post, and in the course of many conversations, I have come to realize the investing public is generally ignorant of the game many asset managers are playing (not what they tell you they are doing but what is really going on under the hood). In this post, I want to elaborate on this.

Broadly speaking, there are two types of return objective for an investment portfolio:

Absolute return. For example: “I want to compound capital at a rate of 10% or greater, net of fees.”

Relative return. For example: “I want to outperform the S&P 500.” Or: “I want to outperform the S&P 500, with tracking error of 1-3%.”

We will look at each in turn.

How Absolute Return Investors Play The Game

The true absolute return investor is concerned only with outperforming his established return hurdle. The return hurdle is his benchmark. When he underwrites an investment, he had better damn well be underwriting it for an IRR well in excess of the hurdle rate (build in some margin of safety as some stuff will inevitably hit the fan). He will be conscious of sector exposures for risk management purposes but he is not checking himself against the sector weights of any particular index.

I emphasize “true absolute return investor” above because there are a lot of phonies out there. These people claim to be absolute return investors but still market their ~~products~~ funds to relative return oriented investors.

Guess what? The Golden Rule applies. If your investor base is relative return oriented, your fund will be relative return oriented. I don’t care what it says in your investor presentation.

How Relative Return Investors Play The Game

The relative return investor is concerned with outperforming a benchmark such as the S&P 500. Usually managers who cater to relative return investors also have to contend with being benchmarked against a peer group of their competitors. These evaluation criteria have a significant impact on how they play the game.

Say Amazon is 2.50% of the S&P 500 trading on 100x forward earnings and you’re running a long only (no shorting) fund benchmarked to the S&P 500. If you don’t like the stock because of the valuation, you can choose not to own it or you can choose to underweight it versus the benchmark (maybe you make it 2% of your portfolio).

In practice you will almost certainly own the stock. You may underweight it but you will own it at a not-insignificant weight and here’s why: it is a popular momentum stock that is going to drive a not-insignificant portion of the benchmark return in the near term. Many of your competitors will either overweight it (if they are ~~reckless~~ aggressive) or own it near the benchmark weight. Most of them will own it at or very near benchmark weight for the same reasons as you.

Sure, if you don’t own the stock and it sells off you may look like a hero. But if it rips upward you will look like a fool. And the last thing you want to be is the idiot PM defending himself to a bunch of retail channel financial advisors who “knew” Amazon was a winner all along.

The safe way to express your view is to own Amazon a little below the benchmark weight. You will do incrementally better if the name crashes and incrementally worse if the name rips upward but the effects will not be catastrophic. When you are ranked against peers you will be less likely to fall into the dreaded third or (god forbid) fourth quartile of performance.

This is the relative performance game.

Note that the underlying merits of the stock as a business or a long-term investment get little attention. The relative performance game is about maximizing incremental return per unit of career risk (“career risk” meaning “the magnitude of relative underperformance a client will tolerate before shitcanning you”).

If you are thinking, “gee, this is kind of a prisoner’s dilemma scenario” I couldn’t agree more. In the relative performance world, you are playing a game that is rigged against you. You are handcuffed to a benchmark that has no transaction costs or management expenses. And clients expect consistent outperformance. Good luck with that.

I am absolutely not arguing that anyone who manages a strategy geared to relative return investors is a charlatan. In fact I use these types of strategies to get broad market exposure in my own portfolio.

I do, however, argue that the appropriate expectation for such strategies is broad market returns +/-, that the +/- is likely to be statistically indistinguishable from random noise over the long run*, and that this has a lot to do with the popularity of market cap weighted index funds.

Corollary: Don’t Be An Idiot

If you are one of those high net worth individuals who likes to run “horse races” between investment managers based on their absolute performance, the corollary to this is that you are an idiot.

The guys at Ritholtz Wealth Management (see my Recommended Reading page) have written and spoken extensively about the problems with such an incentive system. It is nonetheless worth re-hashing the idiocy inherent in such a system to close out this discussion. It will further illustrate how economic incentives impact portfolio construction.

If you say to three guys, “I will give each of you 33% of my net worth and whoever has the best performance one year from now gets all the money to manage,” you will end up with a big winner, a big loser and one middle of the road performer. You will choose the the big winner who will go on to be a loser in a year or two. Except the losses will be extra painful because now he is managing all your money.

Here’s why. You have created an incentive system that encourages the prospective managers to bet as aggressively as possible. This is exacerbated by the fact that your selection process is biased toward aggressive managers to begin with. No self-respecting fiduciary would waste his time with you. People like you make for terrible clients and anyway a self-respecting fiduciary’s portfolio is not likely to win your ill-conceived contest. Your prospect pool will self-select for gamblers and charlatans.

In Closing

Incentive systems matter. Knowing what game you are playing matters. There is a name for people who play games without really understanding the nature of the games.

They’re called suckers.

*Yes, I know it is trivial to cherry pick someone ex post who has generated statistically significant levels of alpha. I can point to plenty of examples of this myself. Whether it is possible to do this reliably ex ante is what I care about and I have yet to see evidence such a thing is possible. Also defining an appropriate threshold for “statistical significance” is a dicey proposition at best. If you feel differently, please email me as I would love to compare notes.

A Mental Model For Politics

Politics is the process by which tribal groups negotiate the distribution of power and resources in a society. A tribal group may identify strongly with a particular philosophy. However, conflating politics with philosophy (“values”) is a muddy way of viewing the underlying drivers of political conflict. It took me a long time (about 15 years) to realize this.

I now realize there are two dimensions to tribal politics:

The competitive dimension (the political process itself). This is essentially a strategy game. Because it is a strategy game, effective political operatives (Lee Atwater) needn’t actually concern themselves with “correct” policy or philosophy. Their role is simply to “win”–that is, secure power and resources for the tribal group they serve.
The philosophical dimension (the inner lives of tribal group members). This is the process by which tribal group members construct their identities, bond with one another and develop a shared vision of how power and resources should be allocated across society. Tribal group members may or may not develop their identities through a rigorous process of reasoning from first principles. That depends largely on the mental complexity of each individual.

Thus, mental complexity is a key input to this model:

Socialized minds simply adopt an identity consistent with their surroundings.
Self-authoring minds go a step further and build their own identity.
Self-transforming minds go a step even further and work to develop a meta-understanding of tribal group dynamics, in order to integrate that into a more “complete” mental model of how the world works.

To make that more concrete:

The socialized mind says: “Everyone in my town and my workplace supports Political Party A. Political Party A is the place to be. I am A.”
The self-authoring mind says: “I identify with aspects of Political Party A, but also Political Party B. Furthermore, I believe in X, Y and Z based on my education, life experience and vision for what I want to achieve in life. I combine these inputs to formulate my own identity, views and goals. I am a C.”
The self-transforming mind says: “I am a C, but it is possible (in fact likely) that my views as a C are incomplete, inaccurate, or oversimplified. I must leave room to modify these views over time. Over the years I will likely transform from a C to a D, to an E, and so on as I constantly integrate new learnings into my mental model of the world.”

Morgan Housel provides a good example of how a self-transforming mind views tribal politics:

Everyone belongs to a tribe and underestimates how influential that tribe is on their thinking. There is little correlation between climate change denial and scientific literacy. But there is a strong correlation between climate change denial and political affiliation. That’s an extreme example, but everyone has views persuaded by identity over pure analysis. There’s four parts to this:

Tribes are everywhere: Countries, states, parties, companies, industries, departments, investment styles, economic philosophies, religions, families, schools, majors, credentials, Twitter communities.

People are drawn to tribes because there’s comfort in knowing others understand your background and goals.

Tribes reduce the ability to challenge ideas or diversify your views because no one wants to lose support of the tribe.

Tribes are as self-interested as people, encouraging ideas and narratives that promote their survival. But they’re exponentially more influential than any single person. So tribes are very effective at promoting views that aren’t analytical or rational, and people loyal to their tribes are very poor at realizing it.

Psychologist Geoffrey Cohen once showed Democratic voters supported Republican proposals when they were attributed to fellow Democrats more than they supported Democratic proposals attributed to Republicans (and the opposite for Republican voters). This kind of stuff happens everywhere, in every field, if you look for it.

It should be obvious by now why most political debates among individuals go nowhere:

Most individuals debating politics do not clearly distinguish between the strategy game dimension and the philosophical dimension. This is important. A genuine philosophical debate is a complex and mentally taxing endeavor requiring concentration and a high level of openness. The goal of a philosophical debate is to pursue Truth, not to “win.” What we call political “debate” is almost always strategy and tactics masquerading as philosophy.
Socialized minds are simply not capable of engaging in genuine philosophical debate. They do not possess the requisite level of mental complexity (though they certainly can develop it). You will never change a socialized mind with evidence and argument. You need look no further than the comments section of a website for evidence of this.
Self-authoring minds are more than capable of engaging in lively philosophical debate. However, they tend to grasp their mental models rather tightly (after all, these are intelligent, highly motivated individuals we are talking about). This can be perceived as either stubborn, obnoxious or even courageous, depending on the observer. As mentioned above, genuine philosophical debate is exhausting. Most people do not want to put the energy into engaging in genuine philosophical debate. Don’t waste your time trying to debate political philosophy with people who aren’t interested in working hard at the process!
From a distance, self-transforming minds can seem devoid of logical consistency. This is especially true from the perspective of a socialized mind, for which maintaining an identity consistent with the tribal group is of paramount importance. This is because self-transforming minds are explicitly aware not only of the need to develop mental models, but of the need to adjust them. This can make it difficult for them to relate to the more static worldviews of self-authoring and socialized minds (and vice versa). Others may view a self-transforming mind as an untrustworthy waffler.

To conclude, here is a little checklist for thinking about politics:

Be explicit about the dimension you are analyzing:
- Strategic Dimension?
- Philosophical Dimension?
When analyzing the strategic dimension, do not conflate “values” with strategy and tactics. This will allow you to reason more clearly.
If you are analyzing the philosophical dimension, account for mental complexity.
If you are involved in a political discussion, try to understand the level of mental complexity the other part(ies) are operating at. This will lead to richer, more fulfilling conversations. On the other end of the spectrum, it will clue you in on when it might make sense to simply disengage.
If you want to do deep, truthful, political analysis, you need to integrate both the strategic and philosophical dimensions.

First Principles

I have three great posts I would like to share. All deal with the subject of mental models and reasoning from first principles:

“Speculation In A Truth Chamber” (Philosophical Economics)

First, the idea behind the exercise is not for you to literally walk through it, in full detail, every time you are confronted with a question that you want to think more truthfully about. Rather, the idea is simply for you to use it to get a sense of what it feels like to be genuinely truthful about something, to genuinely try to describe something correctly, as it is, without pretenses or ulterior motivations. If you know what that state of mind feels like, if you are familiar with it, then you will be able to stop and return yourself to it as needed in your trading and investment deliberations and in your everyday life, without having to actually step through the details of the scenario.

Second, the exercise is intended to be used in situations where you actually want to get yourself to think more truthfully about a topic and where you would stand to actually benefit from doing so. Crucially, that situation does not describe all situations in life, or even most situations. There are many situations in life where extreme truthfulness can be counterproductive, creating unnecessary problems both for you and for others.

Third, all that the exercise can tell you is what you believe the most likely answer to a question is, along with your level of confidence in that belief. It cannot tell you whether you are actually correct in having that belief. You might believe that the answer to a question is X when it’s in fact Y; you might have a lot of confidence in your belief when you should only have a little. Your understanding of the subject matter could be mistaken. You could lack the needed familiarity or experience with it to have a reliable opinion. Your judgment could be distorted by cognitive biases. These are always possibilities, and the exercise cannot protect you from them. However, what it can do is make you more careful and humble as a thinker, more open to looking inward and assessing the strength and reliability of your evidence and your reasoning processes, more willing to update your priors in the face of new information–all of which will increase your odds of getting things right.

“Thinking From First Principles” (Safal Niveshak)

Practicing first principles thinking is not as easy as explaining it. As Musk said, it’s mentally taxing. Thinking from first principles is devilishly hard to practice.

The first part, i.e., deconstruction, demands asking intelligent questions and having a deep understanding of the fundamental principles from various fields. And that’s why building a latticework of mental models is so important. These mental models are the fundamental principles, the big ideas, from different fields of human knowledge.

The best way to achieve wisdom, said Charlie Munger, “is to learn the big ideas that underlie reality.”

The second step is the recombination of the pieces which were identified in the first step. This is again a skill which can only be developed by deliberate practice. Any idea as an isolated piece of information doesn’t stay in the human brain for long. To be sticky, it needs to be connected with other ideas. A latticework is essentially a grid of ideas connected to each other. These connections are the glue which holds those ideas together.

If the new knowledge doesn’t find any connection or relevance to the old knowledge, it will soon be forgotten. New ideas can’t just be “stored” like files in a cabinet. They have to connect with what’s already there like pieces of a jigsaw puzzle. As you become better in finding connections between seemingly disconnected ideas, your recombination-muscle becomes stronger. Someone with a strong recombination-muscle will find it easy to practice the second step of first principles thinking.

“Playing Socratic Solitaire” (Fundoo Professor)

I am going to play a game based on ideas derived from Socrates and Charlie Munger. We will start with “Socratic Questioning” which is described as

disciplined questioning that can be used to pursue thought in many directions and for many purposes, including: to explore complex ideas, to get to the truth of things, to open up issues and problems, to uncover assumptions, to analyze concepts, to distinguish what we know from what we don’t know, to follow out logical implications of thought, or to control the discussion.

Socratic Questioning relates to “Socratic Method,” which is:

a form of inquiry and debate between individuals with opposing viewpoints based on asking and answering questions to stimulate critical thinking and to illuminate ideas.

Charlie Munger started using these two Socratic devices in a variation he called Socratic Solitaire, because, instead of a dialogue with someone else, his method involves solitary play.

Munger used to display Socratic Solitaire at shareholder meetings of Wesco Corporation. He would start by asking a series of questions. Then he would answer them himself. Back and forth. Question and Answer. He would do this for a while. And he would enthral the audience by displaying the breadth and the depth of his multidisciplinary mind.

I am going to play this game. Or at least, I am going to try. Watch me play.

If you are seriously interested in finance and investing, there is nothing more important to your development than accumulating a robust inventory of mental models. What mental models and reasoning from first principles allow you to do is see through to the true drivers of a situation, where it is often easy to get bogged down in unimportant details.

For example, if you are viewing a business through the lens of discounted cash flow valuation, here are the drivers of intrinsic value:

Operating margin
Asset turnover
Maintenance capex needs
Growth capex/reinvestment opportunities
Discount rate

Operating margin and asset turnover are quantitative measures reflecting the strength of your competitive advantage and, perhaps more importantly, the source of your competitive advantage.

Maintenance capex tells you how much cash the business needs to spend to keep running.

Growth capex/reinvestment opportunities give you an idea of growth potential over time.

When you combine operating margins and asset turnover (technically NOPAT x Sales/Invested Capital) you get a figure for return on capital. Return on capital is an excellent quantitative proxy for management’s skill allocating capital. Thus, it is also an excellent proxy for quality of management (though it is certainly not a be-all, end-all measure). When you combine return on capital with reinvestment opportunities you get an idea of what sustainable growth in operating income might look like.

There are lots of ways to handle the discount rate. Over time I have come to prefer an implied IRR method, where you simply “solve for” the discount rate that sets your cash flow model equal to the current stock price. You can then compare this to your hurdle rate for new investments.

DCF is one of the most important models in finance because it works with any investment that produces (or is expected to produce) cash flows in the future. At the end of the day, even an exercise as complicated as valuing a mortgage-backed security is just a variation on discounting cash flows.

All great mental models have two defining characteristics:

(1) They are robust. That is, they are applicable to a broad set of opportunities.

(2) They are parsimonious. That is, that is they demonstrate “economy of explanation.”

In my humble opinion, the most important mental models you need to understand in investing are:

Time Value of Money/Discounted Cash Flows
Capital Structure
Expected Value/Probabilistic Thinking
Optionality
Convexity/Linear Vs. Non-Linear Rates Of Change (e.g. compounding)
Investor Psychology

Conceptually that is really what it all boils down to (though the permutations are endless–for instance, a mortgage can be viewed as the combination of discounted cash flows and a call option). Now, you could of course write dozens of volumes on the nuances and applications of each of these models. That is part of what makes them robust. They are adaptable to an almost inconceivable range of circumstances.

This is something I don’t think most candidates in the CFA Program think about (they are too preoccupied with passing the exams!). The curriculum is designed to comprehensively introduce you to the most robust mental models in finance, and then to test your ability to apply those models to specific cases. Level I tests whether you understand the basic “tools” you have available to you; Level II tests more advanced uses of those tools (in exhausting detail, one might add); Level III tests your ability to apply all your tools to “real world” situations.

Maybe some day I will write up how I think about these super important mental models. In the meantime, enjoy the above linked posts!