Wunderwaffen

One theme I harp on relentlessly is that there’s no such thing as a magical investment strategy. By “magical strategy” I mean some asset class or system that’s inherently superior to all others. Hedge funds were once sold this way, and we’ve spent the last 10 years or so watching the ridiculous mythology built up around hedge funds die a slow and miserable death.

The unpleasant truth is that all investment strategies involve tradeoffs. In this way, investment strategies are a bit like weapons systems.

Tank design, for example, must balance three fundamental factors:

  • Firepower
  • Protection
  • Mobility

This is a Tiger tank:

Bundesarchiv_Bild_101I-299-1805-16,_Nordfrankreich,_Panzer_VI_(Tiger_I).2
Source: Bundesarchiv via Wikipedia

You might recognize it from any number of WWII movies and video games. The Tiger is often presented as a kind of superweapon (German: Wunderwaffe)–an awe inspiring feat of German engineering. In many respects, the Tiger was indeed a fearsome weapons system. Its heavy frontal armor rendered it nearly invulnerable to threats approaching head-on. Its gun could knock out an American M4 at distance of over a mile, and a Soviet T-34 at a little under a mile.

The Tiger had its weaknesses, however, and they were almost laughably mundane. It was over-engineered, expensive to produce and difficult to recover when damaged. Early models in particular struggled mightily with reliability. The Tiger was also a gas guzzler–problematic for a German panzer corps chronically short on fuel.

Viewed holistically, the Tiger was hardly a magical weapon. The balance of its strengths and weaknesses favored localized, defensive operations. Not the worst thing in the world for an army largely on the defensive when the Tiger arrived on the battlefield. But it was hardly going to alter the strategic calculus for Germany. In fact, there’s an argument to be made that German industry should have abandoned Tiger production to concentrate on churning out Panzer IV tanks and StuG III assault guns. (Thankfully, for all our sakes, it did not)

Likewise with investment strategies, the tradeoffs between certain fundamental factors must be weighed in determining which strategies to pursue:

  • Alpha Generation
  • Liquidity
  • Capacity

Alpha generation is typically inversely related to liquidity and capacity. The more liquid and higher capacity a strategy, the less likely it is to consistently deliver significant alpha. Smaller, less liquid strategies may be able to generate more alpha, but can’t support large asset bases. Investment allocations, like military doctrine, should be designed to suit the resources and capabilities at hand.

If I’m allocating capital, one of the first things I should do is evaluate my strategy in the context of these three factors.

First, do I even need to pursue alpha?

If so, am I willing and able to accept the liquidity constraints that may be necessary to generate that alpha?

If so, does my strategy for capturing alpha have enough capacity for an allocation to meaningfully impact my overall portfolio?

In many cases, the answer to all three of those questions should be a resounding “no.”

And that’s okay! Not everyone should be concerned with capturing alpha. For many of us, simply harvesting beta(s) through liquid, high-capacity strategies should get the job done over time. Identifying strategies and investment organizations capable of sustainable alpha generation ex ante is extremely difficult. And even if we can correctly identify those strategies and investment organizations, we must have enough faith to stick with them through the inevitable rough patches. These are not trivial challenges.

But even more importantly, in a diversified portfolio it’s unlikely you’ll deploy a single strategy so powerful and reliable, and in such size, that it completely alters your strategic calculus. In general, we ought to spend more time reflecting on the strategic tradeoffs facing our portfolios, and less time scouring the earth for Wunderwaffen.

Mad Scientist Futurists

“Any sufficiently advanced technology is indistinguishable from magic.” –Arthur C. Clarke.

In my line of work, I sit in a lot of meetings. I listen to a lot of conference calls. I attend a lot of conferences. Many of these are thinly disguised sales presentations. Sure, we don’t call them sales presentations. We call them “continuing education” or “maintenance due diligence” or “onsite manager visits.”

You sit through enough of these meetings and you begin to recognize different archetypes.

There’s The Brilliant Introvert.

The Streetwise Deal Guy.

The Mad Scientist Futurist.

That last fellow is most often encountered in group settings. Particularly conferences. I’d venture to say the industry conference is the Mad Scientist Futurist’s natural habitat.

Now, the guy doesn’t necessarily predict the future. He might merely describe a possible future. These days, for example, the future seems to involve a lot of e-sports and e-commerce and machine learning. A few years ago it was drones and 3D printers as far as the eye could see.

The Mad Scientist Futurist spends a lot of time on his description of the future. And it’s not just him up there talking, mind you. He’s got sales collateral. He’s got data and charts and renderings. He does Science!, remember? After about fifty slides of this, it all takes on a certain aura of inevitability.

And then, finally, once he’s painted a sufficiently fantastical image of the future, the Mad Scientist Futurist tells you what the future means.

He tells you what it means for society. He tells you what it means for the economy. He tells you what it means for your portfolio.

The Mad Scientist Futurist is a modern incarnation of the wizard! meme. We seem hardwired to respond to wizards as symbols, both in stories and in life. Merlin is a wizard. Faust is a wizard. Elon Musk and Satoshi Nakamoto are wizards.

We have an innate weakness for wizards and Mad Scientist Futurists precisely because we’re hungry for meaning and Narrative. When we see magic advanced technology in action, our tiny minds can scarcely comprehend the implications. We want someone to walk us through them. We need someone to walk us through them.

This isn’t necessarily a bad thing.

But it can be dangerous.

Because memes and archetypes are so powerful, they’re easily weaponized to sell us stuff. The Mad Scientist Futurist is typically deployed to sell what the style box classifies as “growth” investments–expensive tech stocks and venture capital and such.

Now, there’s nothing inherently wrong with owning expensive tech stocks and venture capital and the like. But you should own it because you’ve made a conscious decision about the role it plays in your portfolio–not because a Mad Scientist Futurist cast a Buy! spell on you.

A Man’s Got To Know His Limitations

Lieutenant Briggs: You just killed three police officers, Harry. And the only reason why I’m not gonna kill you, is because I’m gonna prosecute you–with your own system. It’ll be my word against yours. Who’s gonna believe you? You’re a killer, Harry. A maniac.

[Briggs starts to drive away when the car blows up]

Harry Callahan: A man’s got to know his limitations.

That’s the end of the 1973 movie Magnum Force. Briggs, a vigilante cop, has an opportunity to shoot Harry Callahan dead. But Briggs is an egomaniac convinced of his own moral superiority. He opts for a clever revenge scheme instead. He flees in a car, which, unbeknowst to him, has a live bomb in the backseat.

A man’s got to know his limitations.

I was moved to reflect on this after a recent due diligence trip. In investing, outcomes are inherently uncertain. We never have perfect information when making investment decisions. We’re lucky to have “good” information in most cases. Even then, unexpected events have a nasty habit of blowing up our plans.

Investing is an exercise in probabilistic thinking. Outcomes do not necessarily reflect the quality of decisions (good investment decisions often result in bad outcomes and vice versa).

When investing, you’ve got to know your limitations.

If you’re a typical outside minority passive investor, you have minimal control over investment outcomes. Basically, the only variable you can control is your own behavior.

You need to be realistic about what you can and can’t know, and the kinds of things you should and shouldn’t expect to get right. The more you can expect to get a decision right, the more time you should spend on that area. Don’t waste time on things that aren’t knowable, or things subject to lots of random noise.

 

Things I Will Never Get Right

Forecasts for prices and other variables. (This would seem obvious but it never ceases to amaze me how much time and energy is wasted here)

Timing, in the sense of trying to buy the bottom tick or sell the top tick.

Macroeconomics.

Intrinsic value. (It’s not observable)

 

Things I Should Get Right More Than Half The Time

The general quality of a given management team.

The general quality of a given business.

Industry dynamics, competitive forces and secular trends.

The potential range of outcomes for a given investment.

 

Things I Should Get Right Most Of The Time

The handful of key variables that will make or break an investment.

How I’ll know if I’m wrong about any of the key variables that will make or break an investment.

Assessing the major “go-to-zero” risks: leverage, liquidity, concentration, technological obsolescence and fraud.

When to average down, when to hold and when to sell out of an investment, not based on price action but on the key drivers and risks.

Mental Model: Value vs. Momentum

We’ve discussed at length how asset prices are driven by changes in investor preferences for different cash flow profiles. I’ve explored this both here and here. In this post, I suggest those preferences are grounded in two psychological profiles: mean reversion (value) and trend (momentum).

The psychology of mean reversion assumes all things revert toward long-run averages over time. Today’s winners will win a little less. Today’s losers will win a little more.

The psychology of trend assumes winners keep on winning, and losers keep on losing.

The more time I spend with investors and savers of varying sophistication levels, the more I believe people are hardwired for one or the other.

Personally, I’m hardwired for mean reversion. It’s extremely difficult for me to extrapolate strong growth, earnings, or profitability into the future. It’s painful–almost physically painful–for me to own popular stuff that’s consistently making new highs. If I happen to be winning in the markets, it invariably feels too good to be true.

A trend guy is just the opposite. Why own stuff that sucks? he asks. Stick with what’s working. It’ll probably get better over time. If anything, you should be shorting the losers.

A popular misconception about value and momentum guys is that value guys buy “cheap” stuff and momentum guys buy “expensive” stuff. I used to think this way. And I was wrong. For a long time I fixated on the headline valuation multiples of the stuff each personality owned, totally ignorant of what was going on under the hood.

The value guy says:

This security is pricing such-and-such a set of expectations, which reflect the naïve extrapolation of present conditions. This, too, shall pass. When expectations re-rate to properly reflect the characteristics of the underlying cash flow stream, I will exit at a profit.

The momentum guy says:

This security is pricing such-and-such a set of expectations, but those expectations aren’t high/low enough. When expectations re-rate to properly reflect the characteristics of the underlying cash flow stream, I will exit at a profit.

Of course, there’s another guy relevant to this discussion. That’s quant guy. Quant guy steps back and thinks, “gee, maybe all these mean reversion guys’ and trend guys’ psychological dispositions impact security prices in relatively predictable ways.” Quant guy decomposes the mechanics of value and momentum and builds systems for trading them. Quant guy catches a lot of flak at times, but I’ll say this for him: he tends to have a pretty clear-eyed view of how and why a given strategy works.

In closing, I want to suggest all fundamentally-oriented investment strategies, whether systematic or discretionary, are rooted in the psychology of value and momentum. Both have been shown to work over long periods of time. However, they don’t always (often?) work at the same time. Arguably, this inconsistency is directly responsible for their persistence.

Put another way: value and momentum tend to operate in regimes.

And regimes deserve a post all their own.

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.

BASE
Base Case: Constant Multiple
UPSIDE
Upside Case: 2x Exit Multiple
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.

Book Review: The Lessons Of History By Will & Ariel Durant

lessons_of_history_durantI found The Lessons of History, by Will and Ariel Durant, courtesy of Ray Dalio. (Okay, actually a Reddit ask-me-anything chat featuring Ray Dalio) The Durants are best known for their epic eleven volume history, The Story of Civilization, for which they received a Pulitzer Prize in 1968 and a Presidential Medal of Freedom in 1977.

The Lessons of History is a distillation of the key themes of the longer work. It’s the cliffs notes for The Story of Civilization.

Summary

As you read, a couple of key premises emerge: 1) history is a competitive evolutionary process, and 2) that process is cyclical.

A key driver of these cycles is the tendency for market systems to create wealth inequality over time. There isn’t anything nefarious about that. I don’t read it as a pejorative, either. It’s just the way things work. Mostly because wealth, when managed properly, compounds over time. It’s not just compound interest I’m talking about here. It’s economic opportunity more generally.

The Durants sum this up in a single, beautiful little paragraph (my favorite in the whole book):

We conclude that the concentration of wealth is natural and inevitable, and is periodically alleviated by violent or peaceable partial redistribution. In this view all economic history is the slow heartbeat of the social organism, a vast systole and diastole of concentrating wealth and compulsive recirculation.

An entire chapter on socialism follows. “[H]istory so resounds with with protests and revolts against the abuses of industrial mastery, price manipulation, business chicanery, and irresponsible wealth,” the Durants observe. “These abuses must be hoary with age, for there have been socialistic experiments in a dozen countries and centuries.”

One example, from China:

Wang Mang (r. A.D. 9-23) was an accomplished scholar, a patron of literature, a millionaire who scattered his riches among his friends and the poor. Having seized the throne, he surrounded himself with men trained in letters, science, and philosophy. He nationalized the land, divided it into equal tracts among the peasants, and put an end to slavery. Like Wu Ti, he tried to control prices by the accumulation or release of stockpiles. He made loans at low interest to private enterprise. The groups whose profits had been clipped by his legislation united to plot his fall; they were helped by drought and flood and foreign invasion. The rich Liu family put itself at the head of a general rebellion, slew Wang Mang, and repealed his legislation. Everything was as before.

“Skin in the game,” Taleb might comment.

The relationship between free market capitalism and socialism is cyclical. It’s a yin and yang type of deal. When inequality under capitalism causes enough friction, and social cohesion decays enough, people gravitate toward the utopian promises of socialism. Then, as the socialist system ossifies under the dual pressures of complexity and inefficiency, it becomes vulnerable to unexpected shocks. Eventually, people overturn the socialist system and return to free market capitalism. The cycle begins again.

The last bit of the book is devoted to the idea of “progress.” If all history is cyclical, does progress actually exist? If so, how do we measure it? I won’t spoil it for you, since this last chapter does a nice job of tying everything together.

Who Should Read This Book?

Literally everyone should read this book. It is a short read, easy to follow and relevant to every human being on the planet. This is the type of “Big Idea” book that helps you see the world as it is, rather than how you want to see it.

Book Review: Every Shot Must Have A Purpose

every_shot_must_have_a_purposeGolf is a weird game. Playing well is actually fairly demanding physically (assuming you are walking). It requires core strength and good hand-eye coordination. But what makes golf truly weird is the mental dimension. Sure, all sports have a mental dimension. But golf is especially mental. If your head is not right, you will play terribly.

Every Shot Must Have A Purpose, by Pia Nilsson and Lynn Marriott, is described early on as “a life philosophy, not merely a golf instruction book.” It is therefore relevant for anyone engaged in any complex and mentally demanding endeavor (read: investing). Given the nature of this blog, I’m going to focus on the broader relevance of the ideas in the book.

Summary

There are a handful  of Big Ideas in this book:

  • Focus on process, not outcome
  • Learn to bring yourself from heightened emotional states back to neutral
  • Trust your swing. It is your signature.

All of this is relevant for investors. Even the part about trusting your swing. I’ll take them in reverse order.

Trust Your Swing

On trusting your swing, Nilsson and Marriott write:

If you can hit the shots you want under pressure, your swing is working. What is important is to make up your mind what swing you believe in, and to have the discipline not to abandon that belief because of a bad round or two. To be in “search-and-scan” mode never works over time. Find your swing, trust it, and stay committed to it.

For the investor, your “swing” is your investing discipline. It is the value creation mechanism(s) that will compound the value of your capital over time.

Classical Ben Graham value investing is a swing form. Munger and Buffett-style value investing is a swing form. Momentum investing is a swing form. All of these swing forms “work” because they are fundamentally sound in terms of economic principles and investor behavior. Just like the golf swing “works” because it is grounded in the laws of physics.

What does not work very well is trying to time different styles to chase “what’s working” at a given point in time. This is the equivalent of trying to rebuild your golf swing from scratch after every round where you score poorly. Both are a recipe for poor future performance.

Bring Yourself Back To Neutral

It is fun to take a pitching wedge from 90 yards out and land a perfect strike six feet from the pin. When you hit a shot like that, you literally get high. But when you chunk a five iron thirty-five yards from a perfect lie in the middle of the fairway, you crash.

Experiencing wild emotional swings is not a recipe for consistent golf.

Likewise in investing, you get high when a stock doubles in three months. You crash when a name halves on some seemingly random exogenous event.

How many times have you hit your tee shot into the trees and then, in a fit of anger, tried to do too much with your second shot and ended up making a triple bogey? The disappointment with the drive leads you to attempt to erase the poor shot with one swing. And we all know how that works out. More often than not, a gamble is greeted with a ball clunking off a tree or remaining in the rough.

The frustrating thing is that on many of those occasions, when you looked back at the round you wondered why you didn’t just pitch back to the fairway and settle for a bogey–or maybe a one-putt par. Anger opens the door to a variety of mistakes: bad decisions, hesitant swings, rushed tempo, and even not seeing the line to the target clearly.

Consistent performance starts internally, with how you regulate your emotions. The goal isn’t to become a robot impervious to emotion. I don’t think such a thing is possible. And even if it is, it’s certainly not healthy. The goal is that whether you hit a good shot or a bad shot (whether an investment is a winner or a loser) you are able to bring yourself back to a neutral state of focus, where your attention is on executing the shot in front of you.

Focus On Process, Not Outcome

One of the reasons golfers–professionals as well as recreational players–can’t take their games from the range to the course is that, in the current practice culture, they are two different experiences. Just as we try to unify the mental with the mechanical aspects of the game, we also must try to erase the line between practice and playing. We want to teach you to play when you practice and practice when you play. In the end, it all has to be about executing golf shots with total commitment when it matters most. To do this you have to learn that playing needs to be a process focus and not score focus.

It’s not that different in investing. Particularly in situations where you have to make a buy/sell/hold decision under pressure. Thinking about the score (returns) doesn’t do any good here. If anything, you’ll fall victim to the disposition effect.

Who Should Read This Book

Anyone trying to improve her golf game should read this book. Investors and other professionals who golf (regardless of skill level–I think I am a 25 handicap) can also benefit from applying these concepts to areas outside the game. I would not recommend the book to non-golfers, as it’s hard to relate if you haven’t struggled through learning the game or fought through some difficult rounds.

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.