Notes from the Diamond #1: Always Something New to Learn

Yazstremski waits for the bounce.

Don’t be afraid to take advice. There’s always something new to learn. — Babe Ruth

Trivia Question 1 of 108 — What baseball Hall of Fame catcher earned valedictorian honors while also posting a 75-3 record as a pitcher in high school? Answer furnished in main text. Ditto for an answer to the question, “Why 108?”

Boston Red Sox pitcher Brandon Workman at bat in the 9th inning of Game 3 of the 2013 World Series

The Wind Up.  Big differences in their physical demands aside, playing pro baseball and managing money for a living have much in common — a happy fact for those of us who find both endeavors engrossing and a godsend for money managers whose quarterly letters would be intolerably brief or dull absent baseball-related arcana.  Truth be told, the literature exploring parallels between baseball and investing is already so vast that Epsilon Theory (ET) faithful might reasonably pose the same question to ET management that Red Sox Faithful shouted at their TVs as the worst managerial miscue in living memory was unfolding before their eyes several years ago: “Why?!”  No, I’m not referring to Bosox manager Grady Little’s catastrophic act of omission in Game 7 of the 2003 American League Championship Series — acceding to star pitcher Pedro Martinez’s pleas that he continue pitching — but rather to the even dopier decision of Bosox manager John Farrell a decade later: letting pitcher Brandon Workman take his first-ever major league at-bat with the Sox and Cardinals tied 4-4 in the ninth inning of Game 3 of the 2013 World Series.  The Sox lost both games, of course, ending their season in 2003 and adding unnecessary angst to a stress-filled but ultimately triumphant season in 2013.  In due course, this series will explore both of the miscues just referenced plus other noteworthy hits, runs and errors in both money management and baseball, all with the aim of elevating readers’ investment games if not also their appreciation of America’s national pastime.[1]

Unwelcome Change.  I know, I know: in many folks’ eyes, football supplanted baseball as the national pastime long ago — a mutation as regrettable and seemingly irreversible as the shift toward extremism in American politics that Ben Hunt discusses so penetratingly in his multi-part note entitled Things Fall Apart.  Unable as I am to trump Ben (pun intended) in political punditry, I’ll generally avoid politics in this series, leaving it to Ben and other ET contributors to draw parallels if and as they see fit between the shifting fortunes of professional sports on the one hand and political factions on the other.  That said, I can’t resist quoting here the late political journalist Mary McGrory’s lament respecting mutually reinforcing trends she espied in the nation’s political and recreational proclivities long before POTUS 45 declined an invitation to throw out the ceremonial first pitch on Opening Day during his first year in office: “Baseball is what we were,” McGrory observed.  “Football is what we’ve become.”

The Pitch.  Shifting from wind up to pitch … ET faithful deserve an answer to this important question among others: why should they allocate a portion of their scarcest resource — time — to this note or indeed any of the 108 planned and presumably weekly missives comprising the series hereby commencing?  At least three reasons for doing so come to mind.  First, Babe Ruth had it right: there’s always something new to learn about any field of human endeavor, including the fun fact that, as the accompanying diagram confirms, baseballs have precisely 108 stitches.  Second, Ben Hunt has it right: sometimes the best way to replace bad habits with good ones in a chosen field is to look outside it for wisdom or inspiration — as Ben has done so effectively and entertainingly for us money management types with his Notes from the Field. Third, ET contributor par excellence Rusty Guinn has it right also: sometimes the best means of elevating one’s game is to take it on the road so to speak — to contemplate the origins and soundness of habits and beliefs outside one’s chosen profession or political persuasion with an eye toward assessing critically what Rusty refers to as an investor’s “priors”.  We all have ‘em, like it or not.

Anatomy of a baseball

No Guarantees.  I’ve put priors in quotes because I myself have never used that term in decades of writing about investing, nor do I expect to use jargon like it often if ever in this series.  But Rusty fancies the term; I like and respect Rusty (and Ben); and I’ve already learned much from Rusty’s series entitled Notes from the Road.  I won’t guarantee that readers will find these Notes from the Diamond comparably insightfulBut I will pledge that they’ll spawn chuckles on occasion, while also avoiding quotes from an overexposed baseball legend who’s understandably but unjustly remembered more for his malapropisms than for his central role in notching ten World Series titles for the Evil Empire (a/k/a New York Yankees).  After all, why subject readers to deja vus from Yogi Berra when the supply of edifying utterances from other baseballers is large and growing?

Superficial Stasis.  As skilled as Berra was behind the plate, the high school valedictorian referenced in the trivia question at page 1 was even more so, as well as a gifted philosopher in his own right.  Responding to a dinner companion’s jibe that the game he played for a living was intolerably slow, Hall of Fame catcher Johnny Bench intoned, “Baseball is a slow game — for slow minds.”  Rightly understood, investing as distinct from trading also entails prolonged periods of superficial stasis — superficial because effective investors must and do ponder more or less continuously whether newly arriving information necessitates portfolio changes, mindful that it seldom does.  Interestingly, the principle just flagged — favor inaction over action unless the latter is truly vital — is arguably the single most impactful insight spawned by the so-called Sabermetrics Revolution that’s transformed pro baseball in recent decades, i.e., the reshaping of what players, managers and — yes — umpires do or don’t do on the field based on advanced statistics not readily available before certain information technologies were developed.  Among many other insights these Notes will explore, Sabermetrics — a term derived from the acronym for Society for American Baseball Research or SABR — has confirmed decisively what baseball cognoscenti have long conjectured: that a base on balls or walk can be as good as a hit.   Indeed, for reasons to be explored in future notes, the “big data” revolution that’s transformed pro baseball no less than it’s transformed financial markets in recent decades has proven that walks can be better than hits for teams notching them under certain circumstances.

For the Love of It.  What other insights from baseball of potential utility to investors will these Notes explore?  At the risk of having Ben Hunt consign me to his necessarily large nursery of raccoons — i.e., finance types who pilfer Other People’s Money by, among other means, overpromising as habitually as Ted Williams reached base safely[2]  — I’ll answer the question just posed while also wrapping up this inaugural note by providing a sneak peek at insights I plan to explore in the 107+ notes to follow.  I’ve added “+” to 107 because, more than five decades after I first laid eyes on Fenway Park’s gorgeously green grass, and more than three decades after I sank into money management, I’m as intrigued as ever by both baseball and investing.  Whether such intrigue gives me an edge in the latter pursuit is unclear, but I like to think it does, just as I like to think that major leaguers who truly enjoy their work have an edge over those who don’t.  As in finance, which comprises a regrettably large sub-population of raccoons, professional baseball comprises numerous actors motivated primarily by money.  As in finance, it’s long been thus in baseball, as perhaps the edgiest player of all time confirmed when rebuking his fellow pros as his long and distinguished playing career (1905 – 1928) was nearing its end.  “The great trouble with baseball today,” Ty Cobb scolded, “is that most of the players are in the game for the money and that’s all. Not for the love of it, the excitement of it, the thrill of it.”

Coming Attractions.  Thrilling or not, the useful insights derivable by applying ongoing advances in baseball strategies and statistics to money management are legion.  I’m excited by the prospect of pinpointing many of them in future notes, including these:

  • Why it’s not merely useful but essential for professionals to “change their stripes” — a stubbornly enduring no-no in money management whose conscious violators include not a few investment pros as successful in their evolving endeavors as Johnny Bench was in his. Why did Bench switch from pitching to catching at a crucial point in his development as a player?  Because he and those advising him deduced correctly that his foremost physical skill — a strong throwing arm — would be optimally applied as a catcher, thus permitting Bench to use his smarts as well as his physical gifts as frequently as baseball rules permit.  We’ll explore Bench’s metamorphosis and its significance for investment pros in greater detail as this series unfolds.
  • How the metrics used to assess on-field performance condition the behavior of not only players but umpires — a phenomenon with great relevance to client-manager relations in institutional funds management. As we’ll see, the fleet-footed fellow whose most celebrated achievement as a baseballer was his breaking of Ty Cobb’s all-time stolen base record understood intuitively what many capital allocators understand dimly if at all . “Show me a guy who’s afraid to look bad,” said six-time All Star and Hall of Famer Lou Brock, “and I’ll show you a guy who can be beaten.”

Not afraid to look bad: Lou Brock (#20) in action in 1964

  • What practitioners pursuing excellence must do to maintain an edge as the information revolution advances. Quite apart from rules changes already implemented that preclude future career stats as stellar as those achieved by past outliers in each domain — e.g., Wes Crawford or Bob Gibson in baseball; Michael Steinhardt or Peter Lynch in money management — the relentless and mutually reinforcing advances of technology and transparency portend continued shrinkage in the pool of dominantly successful practitioners in professional baseball no less than in professional investing.[3]  By transparency, I mean the timely collection, compilation and dissemination of essentially all available objective data germane to the aforementioned professions.  As many readers are aware, and as future notes will discuss, enhanced transparency as just defined has reduced and will continue undermining the incomes of ballplayers as well as investment pros whose “edges” entail primarily their patrons’ imperfect understanding of their true as distinct from perceived skills.  In a baseball context, “patrons” as just used is defined broadly to include team owners and managers as well as fans — all of whom can easily and inexpensively access meaningfully large chunks of the roughly seven terabytes of data per game (including but by no means not limited to video bits and bytes) that major league baseball’s Statcast system collects via cameras and radar installed in every MLB stadium. That’s a quantum of data equivalent to the contents of 700,000 copies of Webster’s Collegiate Dictionary — and literally millions times the number of data points some of us learned how to record manually on paper scorecards back in the day.

Manual recording of Red Sox labors vs. Yankees 8/18/2006

Continuous Improvement.  Imagine if fiduciaries could evaluate investment pros as quickly, cheaply and thoroughly as baseball managements can evaluate players’ every movement  (or non-movement) using Statcast.  I’m unsure such enhanced scrutiny would produce uniformly better returns, but I’m sure that it would alter managers’ as well as clients’ behaviors, just as such scrutiny has altered how pro baseball gets played, who gets to play it, and for how much.  I’m sure too that even if investment pros’ labors remain as crudely understood as pro baseballers’ were before Statcast came along, future technological advances will compel investment pros seeking sustained excellence to change their stripes on a regular if not continuous basis.  How do I square the assertion just made with Ben’s championing of repeatable processes in Things Fall Apart? I’m not sure I can, or want to, his and Rusty’s invitation to contribute to ET being rooted in their laudable desire to foster diverse viewpoints under ET’s banner.  By my lights, choiceworthy processes in money management display the same cardinal virtue that my all-time favorite player displayed when fielding caroms off Fenway’s fabled Green Monster: such processes are less “repeatable” or static than they are adaptive and ever-changing.  The player in question, of course, was Carl Yazstremski, a Long Island native whose exceptional work ethic arguably made Puritan New England (a/k/a Red Sox Nation) a fitter venue for his sporting labors than his original home turf.  “I loved the game,” Yaz said after his 23-year career came to an end in 1983.  “But I never had any fun.  All hard work, all the time.”

Carl Yazstremski awaiting a carom off Fenway’s Green Monster in the 1967 World Series

Ernie Had It Right.  Like the best opening frame this Bosox fan has ever witnessed — a 50-minute masterpiece in which the Bosox scored 14 runs on 13 hits against the visiting Florida Marlins at Fenway in June 2003 — this initial installment of Notes from the Diamond has developed proportions more expansive than might reasonably have been expected.  As noted at page 1, readers can expect future installments to be shorter — but no less replete with pearls of wisdom from wizards of the diamond, including a gentleman who changed his stripes not once qua young Johnny Bench but multiple times en route to his own induction at Cooperstown.  Nicknamed “Mr. Sunshine” for his upbeat disposition, Ernie Banks (1931 – 2015) is forever known for his catchphrase, “It’s a beautiful day for a ballgame … Let’s play two”.  With so many useful parallels between baseball and investing to be drawn — and with so many members of ET Nation including yours truly wondering what comes next for the business of investing and their own roles within it — I’m keener than ever to craft the next note in this series … and the next.  Let’s play 108, why don’t we?

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[1] Workman’s first and to date only major league at bat went poorly, with a whiff plus two called strikes producing a blindingly quick out.  If the Red Sox, for whom Workman has played on-and-off since 2013, make the World Series in 2018, the odds are good that team manager Alex Cora will call on Workman to do some relief pitching.  That said, I’d bet my family’s most prized baseball-related possession — a ball inscribed for my children by Elden Auker — that Cora doesn’t let Workman bat, ever.  The last living pitcher to have faced Babe Ruth, Auker (1910 – 2006) showed his mettle early in his 10-year major league career: Ruth was the first batter Auker faced in the pitcher’s big league debut in 1933, striking out on just four pitches.

[2] Most readers know that Joe DiMaggio holds the record for consecutive games with a hit: 56 in 1941.  Some may be unaware of another seemingly unbreakable record, held by the best hitter in baseball in the 1940’s or indeed any other epoch: in 1949 Ted Williams reached base safely in an astounding 84 consecutive games.

[3] The all-time career leader in triples with 309, Crawford played before “live era” or post-1910 baseballs made home runs far more frequent than triples.  Gibson notched the all-time best single season earned run average (ERA) of 1.12 in 1968, a year before pitching mounds were lowered by a third to their current height of 10 inches.  Steinhardt made big bucks for himself and his clients during the first half of his career via block trading methods that were either illegal at the time or have since been outlawed.  And Lynch turbocharged his returns via the lawful exploitation of corporate disclosure protocols benefiting big institutions like Fidelity that post-2000 securities law reforms have rendered nugatory, including especially Regulation FD.

On Deck:

What baseball’s steroid era and private equity’s salad days have in common


The Myth of Market In-Itself: Things That Matter #3, Pt. 1

That space and time are only forms of sensible intuition, and hence are only conditions of the existence of things as phenomena; that, moreover, we have no conceptions of the understanding, and, consequently, no elements for the cognition of things, except in so far as a corresponding intuition can be given to these conceptions; that, accordingly, we can have no cognition of an object, as a thing in itself, but only as an object of sensible intuition, that is, as phenomenon — all this is proved in the analytical part of the Critique; and from this the limitation of all possible speculative cognition to the mere objects of experience, follows as a necessary result.

Immanuel Kant, The Critique of Pure Reason (1781)

I know, diving right into 18th-century German metaphysics is a real crowd-pleaser. But this is just a bunch of fancy words to say that we can never know the fundamental truth of a thing independently from our perceptions and experience. It’s the realization that makes Kant probably the most indispensable of the great thinkers. Doubly so for Epsilon Theory. We desperately want to believe that with enough information and analysis, we can know the true value of something. There is an almost mythological belief in the market as the mechanism through which we uncover that truth. The rest of the world will realize that we are right, and then we will make money. But the ‘true value’ of a thing — the market in-itself — isn’t something we can know. We observe value only through price, a measure based on our collective subjective experience.

Is there any hiding of the character of an apple-tree or of a geranium, or of an ore, or of a horse, or of a man? A man is known by the books he reads, by the company he keeps, by the praise he gives, by his dress, by his tastes, by his distastes, by the stories he tells, by his gait, by the motion of his eye, by the look of his house, of his chamber; for nothing on earth is solitary, but everything hath affinities infinite.

— Journals of Ralph Waldo Emerson, June 7, 1860

Still, just because there isn’t a knowable intrinsic value to an investment, no investment in-itself, it doesn’t mean we can’t know anything about it. The people who form the market and apply their sensible tuition to these things have affinities infinite. Some of those affinities can be observed or inferred. This is the soul of the Narrative Machine writ large.

Run a BBW Tumblr blog and forget the password

I may be speaking too soon but this is a disaster

Like old people in modern sneakers

I saw Book of Mormon with a congregation of true believers

milo, “In Gaol”, a toothpaste suburb (2014)

As investors, it is very tempting to get so caught up in our own tribe of investing — our style, our philosophy — that we sit in a state of constant bemusement of other investors, sure that everyone is going to come around to our point of view on the value of something eventually. That congregation of true believers we can’t believe are watching a parody of their beliefs can stick around for a long, long time, folks. Over a sufficiently long period, being wrong about value but right about price can become indistinguishable from being right about value.

Empathy, evidently, existed only within the human community, whereas intelligence to some degree could be found throughout every phylum and order including the arachnida.

Philip K. Dick, Do Androids Dream of Electric Sheep (1968)

I’ll admit it. At any given time around our Houston office, there are four TVs tuned to CNBC. Don’t ask me why. Or at least don’t expect a satisfactory answer.

If pressed, I would tell you that it’s important to know what a voice that speaks through thousands of televisions in similar offices around the world is saying, even if it’s a meal of empty calories. After all, Epsilon Theory is about stories. Stories, those who tell them, and those who, in listening, respond. Some of those stories are powerful myths, timeless and universal, others virtuously or nefariously cultivated for a singular place in space and time. And some of them — including most of what you hear on financial television — are vapid and worthless.

A story linking six months of a presidency to the returns of a stock market at the same time. A story linking August returns to calendar years that end with the number 7. A story that “stocks slipped on the news” of a development in investigations of Russian collusion with no evidence of relationship other than that the two things happened on the same day. Oh look, oil fell on “profit-taking.” Linking a down day in U.S. markets to one of a million macro factors that moved that day. We say the stock moved “on this news” or “on that news” when, if we’re really being honest with ourselves and each other, we know that all of these stories are stupid and wrong. Deep down we know that we have no earthly clue why our investments go up and down every day, much less moment to moment, and we’re just grasping for answers. Stories. And boy, are people ready to give us some.

It’s a problem. And it’s a problem we can’t ignore, because our investment decisions communicate views about our expectations even if we don’t intend it. We don’t get to say that “it’s OK” not to understand what moves markets, because every day we are all making bets that say we do. Sure, we have all sorts of explanations for why investments should rise in value. We should be paid for taking risk. We should own something valuable if it continues to grow its earnings. We should be able to trust the fact that risky investable assets have produced positive returns over almost any long-term horizon for the last several centuries.

But the distinction between understanding why we ought to be paid for owning something and understanding how that manifests itself in changes in securities prices is not just academic. It is fundamental, and it sits squarely in Epsilon Theory’s wheelhouse. In the same way that Ben bastardized Gresham’s Law, I’m going to steal from Friedman:

Investment returns are always and everywhere a behavioral phenomenon.

That’s why Investor Behavior is #3 on our list of Things that Matter.

Knowledge and Information

I know we all know this, but from time to time it bears repeating: until it defaults, matures or is called, the price of every security in the world is ultimately driven by two — and only two — things:

  1. Who is willing to pay the most to buy the thing, and
  2. Who is willing to accept the least to sell the thing.

That’s it. A lot of applied behavioral economics IS flawed and less rigorous than it ought to be, but at the risk of giving Taleb the vapors, any argument for how prices are determined and, thus, how returns are generated that ignores investor behavior isn’t just weak. It’s objectively wrong.

Now, while there really aren’t any strong-form efficient market guys out there with skin in the game (i.e., outside of academia) anymore, there are still a lot who think about markets as being generally efficient, by which they mean that the market generally does a good job of pricing available information. This is actually a pretty fair point of view. To believe otherwise is to take a dim view of the value of markets as a mechanism for expressing the aggregated will of individuals. That ain’t me. I was and will always be a Hayekian at heart. Since we’ve decided it’s now acceptable to terminate employees for expressing wrongthink, I’ve started firing anyone who doesn’t see my copy of The Road to Serfdom and slam it down on the conference room table, shouting, “THIS is what we believe!” à la Maggie Thatcher.

The problem with most interpretations of information, however, is that fundamental data alone isn’t information in any real sense that matters. Facts about a company only become information when they are passed through the perceptions and preferences of the people who are participating in determining the security’s price. There is no objectively ‘right price’ for a security based on the available information about its business, its assets, its prospects or its profitability, because there is no objective sense in which changes in any of those things ought to result in changes in prices. There is only a price which reflects how that information is viewed through the collective lens of individuals or groups of individuals who participate in that market.

Now while two people functionally determine the current price of any security, the movement of prices in that security from that level are also influenced by a much larger group who are willing to buy for a little bit less than the guy setting the bid price, and those who are willing to sell for a little bit more than the guy setting the ask price. Those who’ve made those views explicit are part of the so-called order book, an actual group of people willing to buy and sell at certain prices. Greater is the group of individuals who haven’t explicitly put a line in the sand at all, but who do have a view that they have an interest in expressing. They are paying attention to the stock. Far, far larger still is the universe of investors and assets who are paying no attention to the security at all and play little to no role in its pricing, even if they own the thing. You can imagine it looking something like the below — a little bit of money is willing to trade close to the current bid-ask spread, and increasing amounts if you’re willing to sacrifice.

Source: Salient 2017. For illustrative purposes only

Again, excluding terminal events for a security — like its default, retirement, maturity or being called away — there are only two ways for a security to change in price:

  1. Someone who had an explicit or implicit view in the “order book” — a blue or red bar from the above chart — changes their mind about the price at which they would buy or sell.
  2. Someone who didn’t have a view before decides to express a view.

Many of the things we do to trade, like a market order or most common trading algorithms, cross the spread in order to find a trading partner. In other words, as the day wears on, a lot of the people who thought they’d only sell for $75.10 — but need to sell — end up saying that they’d take less. Those folks are making the price move by changing their mind about the price at which they’d buy or sell. In other situations, maybe we get a call from a client who needs money for a down payment for house. It’s a big, liquid stock, so we put in a market order. We take $74.90 for our shares despite having not expressed a view on price before that, and everyone else in the market tries to figure out why.

So why do these people change their mind? Are they, in fact, responding to the stimuli that financial TV suggest? Did a barrel of oil really just trade up by 15 cents because investors changed what they were willing to pay as a result of North Korean sabre rattling? Other than major sources of observable volatility — earnings, corporate actions and the like, and often even then — if anyone tells you they know, they are probably lying to you. All we know, because it is a tautology, is that it is absolutely a reflection of human behavior (which includes, mind you, the behaviors incorporated on the front-end of a systematic trading strategy or implicit in a trade execution algorithm). That doesn’t mean that people can’t make money off price movements over this horizon — plenty of stat arb and high-frequency trading firms do exactly that, albeit in different ways. But over the very short run, the drivers of market movement are noisy and overdetermined, meaning that there are more factors driving that noise than there is noise. They are also nearly impossible to generalize, other than to say that they are reflections of the behaviors of the individuals who caused them.

The great investor Benjamin Graham famously characterized his views on the matter in this way. “In the short run,” Graham said, “the market is a voting machine, but in the long run, it is a weighing machine.” This is a popular view. But with respect due to Mr. Graham, it is also wrong. Since I’ve bastardized and restated the words of one financial genius already, let’s make it two:

In the very short run, the market is a voting machine.

In the short run, the market is a voting machine.

In the long run, the market is a voting machine.

The Long-Run Voting Machine

There’s a contingent of people reading this who are probably saying to themselves, “Wait a minute. What about a bond? Every time I receive my coupon I book some return. Every day I get closer to maturity, and I can predict pretty accurately how a bond trading at a premium or discount is going to converge to par.” The implication of that argument is that while fundamental characteristics of an investment may only technically manifest themselves in some terminal event, they are effectively still very predictive because we can have a high degree of confidence around them for some types of investments. In other words, maybe sentiment is a bigger predictor for risky securities than it is for securities where the return is coming from predictable cash flows.

This is true.

If you intend to hold something to maturity, or if you hold an investment that is reliably paying enough cash flow to repay you over a reasonable length of time, all else being equal, the variability in the price attached to your investment and its returns, and the behaviorally driven component of those returns, ought to be lower. This is one of the reasons why we tend to buy and hold bonds in our client accounts to maturity. Yet, even here, your compound returns are going to be influenced by investor behaviors outside of that bond — you have to reinvest those coupons at rates determined by individual actors influencing prevailing interest rates, after all.

The other, more common argument — this is the Graham argument — is that these behaviorally driven features of markets are, even for investments in riskier parts of the capital structure like credit or equity, temporary noise on the path toward convergence of the investment’s price with its value. Investors have historically found comfort in the idea that the voting machine will someday converge to the weighing machine — that one day, everyone else will come to the conclusion that I have about this company and value it like I do.

This forms part of the story for a vast range of investment styles. For the investor who speaks the language of growth, it is indispensable. He is saying, explicitly or implicitly, “I believe this company will grow faster than other investors expect. When I’m right, the price will converge to the value implied by the higher earnings.” For the intrinsic value or quality investor (I’m talking to you, too, Holt, EVA, CFROI wonks), it is an even stronger impulse. He believes that a company’s ability to deploy its current assets and reinvest at higher rates than the market expects (or for a longer time than the market expects) forms a value that is essentially the stock-in-itself. That’s kind of what intrinsic means, isn’t it? Frankly, it is the multiples-driven value investor who approaches the question with the keenest awareness of behavioral influences on prices. He’s comfortable implying that investors tend to do a bad job of knowing which companies ought to be worth a lower multiple of their earnings/assets/cash flow, and that enough time will cause the outperformance of the cheap company to be recognized and rerated. Or maybe just the increase in earnings will cause investors to apply the same multiple to create a higher value. There is, at least, the self-awareness of behavior’s fickle influence.

In each of these cases, the investors recognize that the market is a noisy, behaviorally complicated voting machine in the short run. This is why when you meet with a fund manager, they will always always always tell you that the rest of the market is looking at the next quarter’s earnings, while they stand alone at the top of the mountain, summoning the courage to weather short-term storms in favor of long-term outcomes. They’re very brave. Lots of people have been talking about it. But in each of these cases, the reality is that other than significant sources of real cash flow distributions (i.e., not stock buy-backs or debt pay-downs, for fans of the “Shareholder Yield” concept), the convergence of the voting machine to the weighing machine can take a very, very, very long time. And it may never happen, for the forces that will cause it to take place are themselves behavioral in nature! Somebody’s gotta say they’re willing to buy your stock at that price, and that somebody is either a person or a computer programmed by a person.

If the market wants to convince itself that Amazon can and will someday raise its prices to generate actual profits, and that they will then use those profits to bestow untold trillions of dollars (or maybe bitcoins, by the time this actually happens) in dividends on its loyal investor base, it can do it for a very, very, very long time. If you do not think Amazon can manage to preach this narrative to its investor base for another 10, 25 or even 50 years, you are dead wrong.

Do you think I’m arguing against value investing? Against fundamental research? Because I’m not. Not even a little bit. OK, maybe a little bit in the case of most fundamental research. What I am arguing is that when these approaches work, they still work because of the lens of preferences and experience that those who participate in the pricing of the investment bring to the table with them. ANY criticism of “behavioral” methods of investing must also be a criticism of fundamental ones, because they both include assumptions about how humans will respond to something.

This has a lot of implications:

For asset owners and allocators: How much time and effort do you spend thinking about who else owns the investment? Who else might want to own it if some bigger thing happens in the world? To that investor’s situation? To the investment or company itself? Compare that to the amount of time you spend sifting through macro data, research reports and constructing models. If you’re like most of us, you’re spending <5% of your time and resources on the former and 95% on the latter. That’s a mistake.

For fund selectors: Spend more time developing theses about managers who — through intuitive or quantitative techniques — seek to understand what drives the behaviors of other investors (or non-investor influencers of securities prices), rather than simple security-based or macroeconomic analysis.

For all investors: Always keep in mind how prices are determined when you think about how certain trends and events may impact markets and your portfolio. Think about how regulation-driven moves toward passive instruments may change price-to-value convergence. Think about how an increase in private equity dollars may influence or change price-to-value convergence in public markets. Think about what behaviors a low global growth environment could induce on the part of financial advisors, institutions and individuals as they participate in the price-setting process.

OK, so how do we do all this? If the Market In-Itself is a myth, how do we adjust our thinking?

There is no mathematical proof that solves this conundrum for us, because we can’t know people’s full motivations, preferences and exogenous influences. We do not know what investors or traders are paying attention to, except by observing the results after-the-fact and coming up with stories to attach to those analyses. Even if we could, many of these behaviors are emergent properties of the market in the aggregate, meaning that the way people behave isn’t nearly as independent of the path or state of the overall market as we’d like it to be. The market is a complex system.

What we can do is recognize what we recognize about every other aspect of society: that these motivations, preferences and exogenous influences on our behavior are reflected in the tribes we select and the language we speak. We may not be able to observe specific behaviors in action, but we can understand a lot about investors by observing, for example, the sell side. Not because they have anything useful to say (sorry), and not even because we think that they somehow reflect the consensus about a fundamental fact about a company. Because the sell side is telling us who their customers are. The feedback mechanisms of industry conventions, of style boxes, of terminology and language that our fellow investors adopt — these, too, all tell us a great deal about investor behaviors. They can also give us insight — incomplete insight, but insight nonetheless — into things like sustained low volatility, limited liquidity, the rationale for the existence of behavioral premia like momentum, value and low volatility, and how they go through sustained periods of weak or strong performance.

And that’s exactly where we’re going in Part II: the languages and tribes of investing and how they can help us understand the behavioral drivers of the Long-Run Voting Machine.

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Whom Fortune Favors: Things that Matter #1, Pt. 2

Click here to read Part 1 of Whom Fortune Favors

Fook: There really is an answer?

Deep Thought: Yes. There really is one.

Fook: Oh!

Lunkwill: Can you tell us what it is?

Deep Thought: Yes. Though I don’t think you’re going to like it.

Fook: Doesn’t matter! We must know it!

Deep Thought: You’re really not going to like it!

Fook: Tell us!

Deep Thought: Alright. The answer to the ultimate question…of Life, the Universe, and Everything…is… “42”. I checked it thoroughly. It would have been simpler, of course, to have known what the actual question was.

— Douglas Adams, Hitchhiker’s Guide to the Galaxy

As investors, our process is usually to start from the answer and work our way back to the question. Unfortunately, the answers we are provided are usually pre-baked products, vehicle types or persistent industry conventions, which means that the answers we get when we actually focus on the questions that matter may be counterintuitive and jarring. The entire point of developing a personal code for investing is knowing which questions matter and ought to be asked first, before a single product, vehicle or style box gets thrown into the mix.

The purpose you undertake is dangerous.’ Why, that’s certain. ‘Tis dangerous to take a cold, to sleep, to drink; but I tell you, my lord fool, out of this nettle, danger, we pluck this flower, safety.

William Shakespeare, Henry IV, Part 1, Act 2, Scene 3, Hotspur

Thomasina: When you stir your rice pudding, Septimus, the spoonful of jam spreads itself round making red trails like the picture of a meteor in my astronomical atlas. But if you stir backwards, the jam will not come together again. Indeed, the pudding does not notice and continues to turn pink just as before. Do you think this is odd?

Septimus: No.

Thomasina: Well, I do. You cannot stir things apart.

Septimus: No more you can, time must needs run backward, and since it will not, we must stir our way onward mixing as we go, disorder out of disorder into disorder until pink is complete, unchanging and unchangeable, and we are done with it forever. This is known as free will or self-determination.

Thomasina: Septimus, do you think God is a Newtonian?

Septimus: An Etonian? Almost certainly, I’m afraid. We must ask your brother to make it his first enquiry.

Thomasina: No, Septimus, a Newtonian. Septimus! Am I the first person to have thought of this?

Septimus: No.

Thomasina: I have not said yet.

Septimus: “If everything from the furthest planet to the smallest atom of our brain acts according to Newton’s law of motion, what becomes of free will?”

Thomasina: No.

Septimus: God’s will.

Thomasina: No

Septimus: Sin.

Thomasina (derisively): No!

Septimus: Very well.

Thomasina: If you could stop every atom in its position and direction, and if your mind could comprehend all the actions thus suspended, then if you were really, really good at algebra you could write the formula for all the future; and although nobody can be so clever as to do it, the formula must exist just as if one could.

Septimus (after a pause): Yes. Yes, as far as I know, you are the first person to have thought of this.

— Tom Stoppard, Arcadia, (1993)

On this most important question of risk, we and our advisors often default to approaches which rely on the expectation that the past and present give us profound and utterly reliable insights into what we ought to expect going forward. As a result, we end up with portfolios and, more importantly, portfolio construction frameworks which don’t respect the way in which capital actually grows over time and can’t adapt to changing environments. That’s not good enough.

Most of these notes tend to stand on their own, but this one (being a Part 2) borrows a lot from the thinking in Part 1. If you’re going to get the most out of this note, I recommend you start there. But if you’re pressed for time or just lazy, I wanted you to take away two basic ideas:

  • That the risk decision dominates all other decisions you make.
  • That the risk decision is not exactly the same as the asset class decision.

Children of a Lazier God

Before I dive into the weeds on those ideas, however, I want to tell you about a dream I have. It’s a recurring dream. In this dream, I have discovered the secret to making the most possible money with the least possible effort.

Hey, I never said it was a unique dream.

It is, however, a unique investing case. Imagine for a moment that we had perfect omniscience into returns, but also that we were profoundly lazy – a sort of Jeffersonian version of God. We live in a world of stocks, bonds and commodities, and we want to set a fixed proportion of our wealth to invest in each of those assets. We want to hold that portfolio for 50+ years, sit on a beach watching dolphins or whatever it is people do on beach vacations, and maximize our returns. What do we hold? The portfolio only needs to satisfy one explicit and one implicit objective. The explicit objective is to maximize how much money we have at the end of the period. The implicit objective is the small matter of not going bankrupt in the process.

This rather curious portfolio is noteworthy for another reason, too: it is a static and rather cheeky case of an optimal portfolio under the Kelly Criterion. Named after John Kelly, Jr., a Bell Labs researcher in the 1950s, the eponymous criterion was formally proposed in 1956 before being expanded and given its name by Edward O. Thorp in the 1960s. As applied by Thorp and many others, the Kelly Criterion is a mechanism for translating assessments about risk and edge into both trading and betting decisions.

Thorp himself has written several must-reads for any investor. Beat the Dealer, Beat the Market and A Man for All Markets are all on my team’s mandatory reading list. His story and that of the Kelly Criterion were updated and expanded in William Poundstone’s similarly excellent 2005 book, Fortune’s Formula: The Untold Story of the Scientific Betting System that Beat the Casinos and Wall Street.  The criterion itself has long been part of the parlance of the professional and would-be professional gambler, and has also been the subject of various finance papers for the better part of 60 years. For the less prone to the twin vices of gambling and authoring finance papers, Kelly translates those assessments about risk and edge into position sizes. In other words, it’s a guide to sizing bets. The objective is to maximize the geometric growth rate of your bankroll — or the expected value of your final bankroll — but with zero probability of going broke along the way. It is popular because it is simple and because, when applied to games with known payoffs, it works.

When we moonlight as non-deities and seek to determine how much we ought to bet/invest, Kelly requires knowing only three facts: the size of your bankroll, your odds of winning and the payout of a winning and losing bet. For the simplest kind of friendly bet, where a wager of $1 wins $1, the calculation is simple: Kelly says that you should bet the difference between your odds of winning and your odds of losing. If you have a 55-to-45 edge against your friend, you should bet 10% of your bankroll. Your expected compounded return of doing so is provably optimal once you have bet against him enough to prove out the stated edge — although should you manage to reach this point, you are a provably suboptimal friend.

Most of the finance papers that apply this thinking to markets have focused on individual trades that look more or less like bets we’d make at a casino. These are usually things with at least a kinda-sorta knowable payoff and a discrete event where that payoff is determined: a single hand of blackjack, an exercise of an option, or a predicted corporate action taking place (or not taking place). It’s a lot harder to get your head around what “bet” we’re making and what “edge” we have when we, say, buy an S&P 500 ETF instead of holding cash. Unless you really are omniscient or carry around a copy of Grays Sports Almanac, you’re going to find estimating the range of potential outcomes for an investment or portfolio of investments pretty tricky. Not that it stops anyone from trying.

Since I don’t want to assume that any of us is quite so good at algebra as to write the formula for all the future, at a minimum what I’m trying to do is get us to think about risk unanchored to the arbitrarily determined characteristics and traits of asset classes. In other words, I want to establish an outside bound on the amount of risk a person could theoretically take in a portfolio if his only goal was maximizing return. Doing that requires us to think in geometric space, which is just a fancy way of saying that we want to know how the realization of returns over time ends up differing from a more abstract return assumption. It’s easy enough to get a feel for this yourself by opening Excel and calculating what the return would be if your portfolio went up 5% in one year and down 5% in the next (works for any such pair of numbers). Your simple average will always be zero, but your geometric mean will always be less than zero, by an increasing amount as the volatility increases.

So, if we knew exactly what stocks, bond and commodities would do between 1961 and 2016, what portfolio would we have bought? The blend of assets if we went Full Kelly would have looked like this:

Source: Salient 2017. For illustrative purposes only.

Only there’s a catch. Yes, we would have bought this portfolio, but we would have bought it more than six times. With perfect information about odds and payoffs, the optimal bet would have been to buy a portfolio with 634% (!) exposure, consisting of $2.00 in stocks, $3.21 in bonds and $1.13 in commodities for every dollar in capital we had. After all was said and done, if we looked back on the annualized volatility of this portfolio over those 50 years, what would we have found? What was the answer to life, the universe and everything?

44. Sorry, Deep Thought, you were off by two.

Perhaps the only characteristic of this portfolio more prominent than its rather remarkable level of exposure and leverage, is its hale and hearty annualized volatility of 44.1%. This result means if all you cared about was having the most money over a 50+ year period that ended last year, you would have bought a portfolio of stocks, bonds and commodities that had annualized volatility of 44.1%, roughly three times the long-term average for most equity markets[1], and probably five times that of the typical HNW investor’s portfolio.

And before you go running off to tell my lovely, charming, well-dressed and distressingly unsusceptible-to-flattery compliance officer that I told you to buy a 44% volatility super-portfolio, allow me to acknowledge that this requires some… uh… qualification. Most of these qualifications are pretty self-explanatory, since the whole exercise isn’t intended to tell you what you should buy going forward, or even the right amount of risk for you. This portfolio, this leverage and that level of risk worked over the last 50 years. Would they be optimal over the next 50?

Of course not. In real life, we’re not omniscient. Whereas a skilled card counter can estimate his mathematical edge fairly readily, it’s a lot harder for those of us in markets who are deciding what our asset allocation ought to look like. Largely for this reason, even Thorp himself advised betting “half-Kelly” or less, whether at the blackjack table or in the market. When asked why, Thorp told Jack Schwager in Hedge Fund Market Wizards, “We are not able to calculate exact probabilities… there are things that are going on that are not part of one’s knowledge at the time that affect the probabilities. So you need to scale back to a certain extent.”

Said another way, going Full Kelly on a presumption of precise certainty about outcomes in markets is a surefire way to over-bet, potentially leading to a complete loss of capital. Now, scaling back is easy if we are starting from an explicit calculation of our edge as in a game of blackjack. It’s not as easy to think about scaling down to, say, a Half Kelly portfolio. There is, however, another fascinating (but intuitive) feature of the Kelly Optimal Portfolio that allows us to scale back this portfolio in a way that may be more familiar: the Kelly Optimal Portfolio can be generalized as the highest return case of a set of portfolios generating geometric returns that are most efficient relative to the risk they take[2].

This may sound familiar. In a way, it’s very much like a presentation of Markowitz’s efficient frontier. Markowitz plots the portfolios that generate the most return for a given unit of risk, but his is a single-period calculation. It isn’t a geometric approach like Kelly, but rather reflects a return expectation that doesn’t incorporate how volatility and non-linearities impact the path and the resulting compound return. There have been a variety of academic pieces over the years covering the application of geometric returns to this framework, but most have focused on either identifying a single optimal geometric portfolio or on utility. Bernstein and Wilkinson went a bit further, developing a geometric efficient frontier.

All of these analyses are instructive and useful to the investor who wants to take path into account, but because the efficient frontier is heavily constrained by the assumed constraint on leverage, it’s not as useful for us. What we want is to take the most efficient portfolio in geometric terms, and take up or down the risk of that portfolio to reflect our tolerance for capital loss. In other words, we want a geometric capital market line. The intuitive outcome of doing this is that we can plot the highest point on this line as the Full Kelly portfolio. The second, and perhaps more satisfying outcome, is that we can retrospectively identify that scaling back from Full Kelly just looks like delevering on this geometric capital market line.

The below figure plots each of these items, including a Half Kelly portfolio that defines ruin as any scenario in the path in which losses exceed 50%, rather than full bankruptcy. The Half Kelly portfolio delivers the highest total return over this period without ever experiencing a drawdown of 50%.

Source: Salient, as of December 31, 2016. For illustrative purposes only.

When we de-lever from the Full Kelly to Half Kelly portfolio, we drop from a terrifying 44% annualized volatility number (which experiences an 80% drawdown at one point) to 18.5%, closer to but still materially higher in risk than most aggressive portfolios available from financial advisors or institutional investors.

This can be thought of in drawdown space as well for investors or advisors who have difficulty thinking in more arcane volatility terms. The below exhibit maps annualized volatility to maximum loss of capital over the analysis period. As mentioned, the 50% maximum drawdown portfolio historically looks like about 18.5% in volatility units.

Source: Salient, as of December 31, 2016. For illustrative purposes only.

For many investors, their true risk tolerance and investment horizon makes this whole discussion irrelevant. Traditional methods of thinking about risk and return are probably serving more conservative investors quite well. And there are some realities that anyone thinking about taking more risk needs to come to terms with, a lot of which I’m going to talk about in a moment — there’s a reason we wanted to talk about this in geometric terms, and it’s all about risk. But for those with a 30, 40 or 50-year horizon, for the permanent institutions with limited cash flow needs, it’s reasonable to ask the question: is the amount of risk in the S&P 500 Index or in a blend of that with the Bloomberg Barclays Aggregate Bond Index the right amount of risk to take? Or can we be taking more? Should we be taking more?

Did you think that was rhetorical? Nope.

Many investors can – and if they are acting as fiduciaries probably ought to — take more risk.

If every hedge fund manager jumped off a bridge…

This may not be a message you hear every day, but I’m not telling you anything novel. Don’t just listen to what your advisors, fund managers and institutional peers are telling you. They’re as motivated and influenced by career risk concerns as the rest of us. Instead, look at what they’re doing.

The next time you have a conversation with a sophisticated money manager you work with, ask them where they typically put their money. Yes, many of them will invest alongside you because that is right and appropriate (and also expected of them). But many more, when they are being honest, will tell you that they have a personal account or an internal-only strategy operated for staff, that operates at a significantly higher level of risk than almost anything they offer to clients. Vehicles with 20%, 25% or even 30% volatility are not uncommon. Yes, some of this is hubris, but some of it is also the realization on the part of professional investors that maximizing portfolio returns — if that is indeed your objective — can only be done if we strip back the conventions that tell us that the natural amount of risk in an unlevered investment in broad asset classes is always the right amount of risk.

Same thing with the widely admired investors, entrepreneurs and business operators. The individual stocks that represent their wealth are risky in a way that dwarfs most of what we would be willing to tolerate in individual portfolios. We explain it away with the notion that they are very skilled, or that they have control over the outcomes of the company — which may be true in doses — but in reality, they are typically equally subject to many of the uncontrollable whims that drive broader macroeconomic and financial market outcomes.

Then observe your institutional peers who are increasing their allocations to private equity and private real estate. They’re not just increasing because hedge funds have had lower absolute returns in a strong equity environment, although that is one very stupid reason why this is happening. It’s also happening because institutions are increasingly aware that they have limited alternatives to meet their target returns. While few will admit it explicitly, they use private equity because it’s the easiest way to lever their portfolios in a way that won’t look like leverage. In a true sense of uncertainty or portfolio level risk, when the risk of private portfolios is appropriately accounted for, I believe many pools of institutional capital are taking risk well beyond that of traditional equity benchmarks.

Many of the investors we all respect the most are already taking more risk than they let on, but explain it away because it’s not considered “right thinking.”

To Whom Much is Given

When we make the decision to take more risk, however, our tools and frameworks for managing uncertainty must occupy more of the stage. This isn’t only about our inability to build accurate forecasts, or even our inability to build mostly accurate stochastic frameworks based on return and volatility, like the Monte Carlo simulations many of us build for clients to simulate their growth in wealth over time. It’s also because the kinds of portfolios that a Full Kelly framework will lead you to are usually pretty risky. Their risk constraint is avoiding complete bankruptcy, and that’s not a very high bar. The things we have to do to capture such a high level of risk and return also usually disproportionately increase our exposure to big, unpredictable events. If you increase the risk of a portfolio by 20%, most of the ways you would do so will increase the exposure to these kinds of events by a lot more than 20%.

Taken together, all these things create that famous gap between our realized experience and what we expected going in. This is a because most financial and economic models assume that the world is ergodic. And it ain’t. I know that’s a ten-dollar word, but it’s important. My favorite explanation of ergodicity comes from Nassim Nicholas Taleb, who claims to have stolen it from mathematician Yakov Sinai, who in turns claims to have stolen it from Israel Gelfand:

Suppose you want to buy a pair of shoes and you live in a house that has a shoe store. There are two different strategies: one is that you go to the store in your house every day to check out the shoes and eventually you find the best pair; another is to take your car and to spend a whole day searching for footwear all over town to find a place where they have the best shoes and you buy them immediately. The system is ergodic if the result of these two strategies is the same.

There are infinite examples of investors making this mistake. My mind wanders to the fund manager who offers up the fashionable but not-very-practical “permanent loss of capital” definition of risk, a stupid definition that is the last refuge of the fund manager with lousy long-term performance. “Sure, it’s down 65%, but that’s a non-permanent impairment!” Invariably, the PM will grumble and call this a 7-standard deviation event because he assumed a world of ergodicity. Because of the impact of a loss like this on the path of our wealth, we’ll now have to vastly exceed the average expected return we put in our scenario models in Excel just to break even on it.

“It’s not a permanent impairment of capital!”

It matters what path our portfolios follow through time. It matters that our big gains and losses may come all at once. It matters to how we should bet and it matters to how we invest. You cannot stir things apart!

So if you’ve decided to take risk as an investor, how we do avoid this pitfall? Consider again the case of the entrepreneur.

The entrepreneur’s portfolio is concentrated, which means that much of his risk has not been diversified away. A lot of that is going to be reflected in the risk and return measures we would use if we were to plot him on the efficient frontier. That doesn’t necessarily mean his risk of ruin will appear high, and his analysis might, in fact, inform the entrepreneur that he ought to borrow and hold this business as his sole investment. He’s done the work, performed business plan SWOT analyses, competitor analyses, etc., and concluded that he has a pretty good grasp of what his range of outcomes and risks look like.

In an ergodic world, this makes us feel all warm and fuzzy, and we give ourselves due diligence gold stars for asking all the right questions. In a non-ergodic world, the guy dies using his own product. A competitor comes out of nowhere with a product that immediately invalidates his business model. A bigger player in a related industry decides they want to dominate his industry, too. And these are just your usual tail events, not even caused the complexity of a system we can’t understand but by sheer happenstance. For the entrepreneur, all sorts of non-tail events over time may materially and permanently change any probabilistic assessment going forward. How do we address this?

The first line of defense as we take more risk must be diversification. After all, there is a reason why the Kelly Portfolios distribute the risk fairly evenly across the constituent asset classes.[3]

Even that isn’t enough. Consider also the case of the leveraged investor in multiple investments with some measure of diversification, for example a risk parity investor, Berkshire Hathaway[4], or the guy who went Full Kelly per our earlier example, but without the whole perfect information thing. This investor has taken the opposite approach, which is to diversify heavily across different asset classes and/or company investments. His return expectation is driven not so much by his ability to create an outcome but by the exploitation of diversification. As he increases his leverage, his sensitivity to the correctness of his point-in-time probabilistic estimates of risk, return and correlations between his holdings will increase as well. In an ergodic world, this is fine and dandy. In a non-ergodic world, while he has largely mitigated the risk of idiosyncratic tails, he is relying on relationships which are based on a complex system and human behaviors that can change rapidly.

Thus, the second line of defense as we take more risk must be adaptive investing. Sometimes the only answer to a complex system is not to play the game, or at least to play less of it. Frameworks which adapt to changing relationships between markets and changing levels of risk are critical. But even they can only do so much.

Liquidity, leverage and concentration limits are your rearguard. These three things are also the only three ways you’ll be able to take more risk than asset classes give you. They are also the three horsemen of the apocalypse. They must be monitored and tightly managed if you want to have an investment program that takes more risk.

It’s not my intent to end on a fearful note, because that isn’t the point at all. More than asset class selection, more than diversification, more than fees, more than any source of alpha you believe in, nothing will matter to your portfolio and the returns it generates more than risk. And the more you take, the more it must occupy your attention. That doesn’t mean that we as investors ought to cower in fear.

On the contrary, my friends, fortune favors the bold.

[1] Back in 1989, Grauer and Hakansson undertook a somewhat similar analysis on a finite, pre-determined set of weightings among different assets with directionally similar results. Over most windows the optimal backward-looking levered portfolio tends to come out with a mid-30s level of annualized volatility.

[2] For this and the other exhibits and simulations presented here, I’m very grateful to my brilliant colleague and our head of quantitative strategies at Salient, Dr. Roberto Croce.

[3] And that reason isn’t just “we’re at the end of a 30-year bond rally,” if you’re thinking about being that guy.

[4] One suspects Mr. Buffett would be less than thrilled by the company we’re assigning him, but to misquote Milton Friedman, we are all levered derivatives users now.

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Information, Please.

On episode 19 of the Epsilon Theory podcast, Dr. Ben Hunt is joined by Rusty Guinn, Salient’s executive vice president of asset management. Picking up from their last conversation on fake news, Ben and Rusty consider the kinds of information that we have at our disposal and if we are asking the right questions in our analysis — or just searching for the answers we want.

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