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.

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.

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.