The Confidence Meter

If you are anywhere near as strange a person as me, you spend a lot of time thinking. And not only thinking, but thinking about thinking (whether any good ideas actually come out of this process is a discussion for another time). Over the years I’ve become more and more interested in epistemology. Is there a such thing as Truth with a capital T? If so, how would we know if we found it? How can we better manage the Bayesian updating of our priors?

Personally, as far as epistemology is concerned, I come down on the side of fallibism. Whether fallibism is, or should be, applicable to moral questions lies beyond the scope of what I write about here. But when it comes to our beliefs about economics, geopolitics, and investing, I think fallibism is an eminently sensible position.

Now, it’s important to distinguish between fallibism and nihilism.

Nihilism is extreme skepticism in the existence of Truth.

Fallibism is extreme skepticism in the provability of Truth and in the methods we use to arrive at Truth. (See also: The Problem of Induction; The Trouble With Truth)

For a fallibist, acquiring knowledge is a relentless, grinding process of formulating conjectures, challenging them, adjusting them, discarding them, and so on. It never ends. By definition, it can’t end. So if you’re bought-in on fallibism, you need to seek out people and ideas to challenge your priors.

This is not fun. In fact, we as humans pretty much evolved to do the opposite of this. For most of our history, if you were the oddball in the tribe you risked being exiled from the group to make your way in a harsh and unforgiving world, where you would likely die miserable and alone (albeit rather quickly), without the consolation of having passed along your genetic material.

The Confidence Meter is a little mental trick I use to mitigate my evolved distaste for challenging my priors, as well as my evolved distaste for being wrong. It’s something I think about when I want to judge how tightly to grip an idea (such as an investment idea). It also helps interrupt emotional thought patterns around certain ideas. For fans of Kahneman, I use it to interrupt System 1 and activate System 2.

The Confidence Meter (A Stylized Example)

the_confidence_meter

At 0% confidence, I shouldn’t even be making a conjecture. At 0% confidence, I should just be gathering information, and soaking it all in without an agenda. (Not always easy)

At a toss-up, I could make a conjecture and support it with evidence, but I wouldn’t put anything at risk. 

At 75% confidence and greater, a willingness to bet money on the outcome implies a sound grasp of the theory underlying my idea, as well as the empirical evidence. It also implies I have a sound grasp of the arguments and empirical data challenging my position.

Using this framework, how many of your beliefs do you suppose merit a >=75% confidence level?

For me, it’s a very small number. To the extent I’m >=75% confident of anything I believe, it’s elementary, almost tautological stuff, like how you make money investing.

The empirical data around the impact of the minimum wage on unemployment? Meh.

The relationship between marginal tax rates and economic growth? Meh.

That doesn’t mean I don’t have beliefs about these things. I’m just leaving an allowance for additional dimensions of nuance and complexity. Particularly when we’re inclined to look at relationships in linear, univariate terms for political reasons. The world is a more complex place than that admittedly powerful little regression model, Y = a + B(x) + e, would lead us to believe.

The Confidence Meter helps me keep that in perspective.

Mental Model: Market Regimes

Markets and economies go through cycles. We’re used to hearing about bull markets and bear markets. We’re used to hearing about economic booms and recessions. But we don’t talk quite as much about market regimes.

A regime is a particular iteration of a particular phase (or phases) of a market cycle. Understanding regimes is important because markets are adaptive systems. Investors respond dynamically to changes in the economic environment, since changes in the economic environment influence their preferences for different cash flow profiles. As I wrote here, these changing preferences are key drivers for asset prices.

What characteristics define a regime? Things like:

  • Economic growth
  • Inflation
  • Interest rates (cost of capital)
  • Credit expansion/contraction
  • Market volatility

Every market regime is a bit different, but regimes tend to influence investor behavior in relatively predictable ways (partly the intuition behind the old saw: “history doesn’t repeat, but it rhymes”). In a deflationary regime, investors sell stocks and buy long-dated Treasury bonds. In an an inflationary regime, investors sell long-dated bonds, while bidding up real assets. In a growth regime, investors will bid up stocks at the expense of long-dated bonds.

Of course, this is a massive oversimplification. Identifying and profiting from market regimes is no easy feat. That’s the goal of the top-down global macro investor, and it’s an extraordinarily complex and difficult task.

So what do us mere mortals take away from this?

We want to ensure our financial plans and investment portfolios remain robust to different market regimes. This doesn’t mean we have to become market timers or macro forecasters. It means we should be thoughtful about the bets we’re embedding in our portfolios.

Unintended Bets

Today, the consensus view is that we’re in a “lower for longer” regime. Low growth. Low inflation. Low interest rates. There are big secular drivers behind this. In developed countries, older populations need to save a lot of money to fund future liabilities. Lots of investment capital in need of a home pushes down the cost of capital. Technological advances have kept a lid on inflation in many areas of daily life.

If the regime is “lower for longer,” what you want to bet on is duration.

We can define duration in different ways. Usually we’re talking bond math. In this context, duration is the sensitivity of a bond’s price to changes in interest rates. The longer a bond’s future cash flows extend out into the future, the higher its duration. The higher the duration, the more sensitive the bond will be to changes in interest rates. The archetypical high duration asset is the zero coupon bond.

If the market regime is “lower for longer,” you have an incentive to bet on large cash flows further out into the future. Low rates and low growth mean the opportunity cost for making these bets is also low.

Duration isn’t just a bond thing. Every asset with cash flows also has duration. It’s just harder to quantify for equities and real estate because of the other variables influencing their cash flow profiles.

Your venture capital investments? They’re a duration bet.

Your small cap biotechs? They’re a duration bet.

Your cash burning large cap growth equities? They’re a duration bet.

All these things are attractive in a “lower for longer” world because they offer Growth! But they’re also sensitive to the cost of capital. In a world of cheap capital, it’s easy to convince investors to subsidize losses for the sake of Growth! If and when the regime changes, that may no longer be the case.

As much as we hate to admit it, our portfolios are products of our environment. It’s what people are talking about when they say “don’t fight the market” and “don’t fight the Fed.” They might as well be saying, “don’t fight the market regime.”

As I’ve written many times before, I’m not a fan of “all-in,” “all-out” calls. That doesn’t just go for market timing. It goes for all the unintended bets that seep into our portfolios over time.

Especially those driven by market regimes.

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.

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.