Notes on Trading Volatility: Correlation, Term Structure, and Skew

Trading Volatility: Correlation, Term Structure, and Skew
Colin Bennett

The book is a broad reference on basic option theory, dispersion, and exotic options. It includes practical insight into managing a hedged book with a focus on correlation, term structure, and skew.

In addition its appendix includes the following topics and more:

  • a taxonomy of historical vol computations including and how they rank on “bias” and “efficiency”
  • shadow greeks
  • cap structure arbitrage theory

It’s an outstanding reference so I took notes. For public sharing I re-factored them by topic and tied some back to my own investment writing.

You can find these edited notes in my public Notion page. 

Markets Will Permanently Reset Higher (My Sacrifice to the Delta Gods)

The US stock market rallied 30% in 2019. A blow-off performance punctuating a decade long bull market.

Professional money managers are pissed.

The Most Hated Rally

“Smart” money said we were in the late innings. Any bit of caution in the portfolio means you are now staring at a poor comparison to the benchmarks. I suspect the quant managers who might be evaluated on risk-adjusted returns are no happier. The rally has been steady. Low volatility. The SPX has won gold in both the absolute return and Sharpe ratio Olympics.

Relative Pain

Active managers are getting rocked. The Fidelity/Vanguard/Schwab race to the bottom on fees and the merit of indexing has been delivering brutal blows to the relative return crowd (mutual funds) and risk-adjusted return crowd (hedge funds) alike. Throw in a dose of market reflexivity and you can imagine the flight to passive strategies accelerating.

Absolute Gain

If you are an individual investor, you probably underperformed, but at least you are winning. And probably a lot more than you imagined. Your investments are an extension of your savings which you’d like to see grow to meet your future liabilities whether it’s a retirement or college fund. Measured against your realistic needs, you are sitting pretty. You would have happily locked in a guaranteed 10% return for 2019 if offered the chance on Dec 31, 2018.

Even More Expensive

Now what? If smart folks, you included, thought markets were expensive last year, you can only feel more dissonant today. We’ve all seen the CAPE charts reminding us that the stock market hasn’t been this expensive since 1999. Well, that was true one year ago as well, and look how 2019 turned out. I could compile a bunch of links showing how CAPE is a useless timing tool on any sub-10 year horizon and perhaps even longer than that.

You can drive yourself crazy and get nowhere asking how long expensive markets will march higher. No serious market observer pretends to have a high confidence answer to that question. If there was an answer it is tormenting allocators and money managers alike. Like Poe’s raven call “nevermore”.

How about the question of why are they rallying? To say more buy volume than sell volume is correct, but not especially useful. Going beyond that, you will not find a shortage of theories. The most popular, based on my state-of-the-art NLP analytics (otherwise known as browsing #fintwit), is the Fed. Central bank easing, best embodied by zero or negative interest rates in Europe and Japan, seems to be public enemy number one. Another alleged culprit has actually been passive indexing itself. This makes intuitive sense as a driver of marginal demand for shares since pulling money from active managers to allocate to say the SP500 is almost certainly going to be increasing the beta of investors’ portfolios if it is done on a dollar neutral basis. Michael Burry, of Big Short fame, has even called passive indexing a bubble.

But What If We’re Wrong

I borrowed the heading from the title of Chuck Klosterman’s book urging us to soften our attachment to the premises upon which we have built conventional wisdom. If this were easy to do he wouldn’t have needed to write a book. Blind spots are so-called for a reason.

Consider the central bank recklessness and passive indexing arguments. These appear to be reasonable explanations for how the market can be artificially or irrationally expensive. They even appear to have endpoints.

Consider these un-timeable reckonings for the central bank argument:

  • Asymmetric, short term nature of political incentives leads to hyperinflationary pressures climaxing in eventual fiat heat death. Creditors destroyed.

or perhaps…

  • A conservative central bank, inspired by the still-vibrant ghost of Volcker, tightens in response to creeping inflationary pressures. Since soft landings don’t exist, the market crashes and our record outstanding debt now teeters on a severely marked down asset base. A deflationary spiral.

How about the “bubble in passive investing” argument?

  • Eventually the inflows to passive will tip so far that active management’s price discovery process will fail to function. There won’t be enough wolves to keep the deer population in check and nature’s equilibrium will breakdown. A litany of price distortions from faulty signals will mirror how natural disasters’ can stem from unintended sequences. It’s like a climate crisis for asset pricing.

These arguments are promoted by many smart people. I’m in no position to falsify them. But I don’t think they necessarily warrant high confidence. First of all, a persistently expensive market is a complex phenomenon so there is a major burden of proof on any reductionist take that I don’t think either of these arguments has satisfied. Furthermore, the incentives of its promoters are enough to cast reasonable doubt on these arguments. To open ourselves to new reasons for the market’s relative expensiveness let’s loosen the grip on the above explanations.

Opening Our Minds

We can attack the central bank and passive indexing arguments on common ground. Both rely on a belief that the market is distorted by significant flows (whether central bank support or migration to passive). They invoke limits to liquidity and arbitrage as reasons for market inefficiency. The argument is compelling. But it’s also epistemologically diabolical in the same way that conspiracy theories recursively gnaw at the logic which allows you to dispel them in the first place.

As mathematician Jordan Ellenburg1 puts it:

“If you do happen to find yourself partially believing a crazy theory, don’t worry — probably the evidence you encounter will be inconsistent with it, driving down your degree of belief in the craziness until your beliefs come in line with everyone else’s. Unless that is, the crazy theory is designed to survive the winnowing process. That’s how conspiracy theories work.”

Those blaming passive indexing and central banks are almost certainly believers in efficient markets. Their arguments follow as so:

  • “Markets are mostly efficient.”
  • “My strategies exploit the few inefficiencies there are.”
  • “My strategies don’t work anymore.”
  • “The markets are inefficient because of X and Y”.

Well, the final conclusion is unmistakably self-serving. Building the argument in steps, the null conclusion should be, “the market, perhaps partially thanks to my work has ironed out the inefficiency I was exploiting.” The prize for this win is an incremental gift of price discovery to the world. And the checks they already cashed. But so much for their future prospects. They can ruminate a bit more on that on their yacht with all their newfound free time.

This Hurts All Investors Not Just Active Managers

If the market is indeed searching for a much higher setpoint then anyone young or who cares about someone who’s young should be concerned.

Investor Lyall Taylor 2 explains:

Most stock market investors worry incessantly about the risk of a potential market melt down. I don’t. I worry about the risk of a market meltup.

For anyone trying to grow their capital; make a living off their investments; or build a business around managing (and making money for) other investors, the absolute worst thing that could happen would be if markets everywhere were to surge and become (and remain) extremely expensive. Imagine, for instance, a world in which stocks traded at 50x earnings. If you invested, you would be offered a poultry 2% earnings yield in exchange for considerable risks.

If markets were to melt up to 50x, it would feel good for a while (if you were invested). However, your future stream of dividends would not have increased, so in truth you would be no wealthier, and furthermore, you would be confronted with the reality of poor reinvestment returns on dividends and corporate stock buybacks. In the long run, this would make you worse rather than better off, despite feeling wealthier in the short run. Bond investors understand reinvestment risk, but most stock investors do not seem too. But it works the same way for stocks as it does for bonds. 

If you’re invested, you are hedged somewhat against the risk of a melt up (a risk most people don’t identify). You can lock in a reasonable return at today’s reasonable prices, and would suffer only on the reinvestment side (an unhedgeable risk). The disaster situation would be to be sitting in cash while watching markets surge all the way to 50x.

If this scenario sounds implausible, consider that we are already facing zero or negative yields in large segments of the bond and real estate markets (reports of easing cap rates in coastal US cities notwithstanding).


  • Compared to history the market is expensive.
  • The most popular explanations rely on some market- distorting mechanisms to justify valuations.
  • The implication is there should be some reversion.

But as investors, we know that markets have a habit of choosing the path which causes the maximum pain for the most people. And it’s pretty clear that valuations ripping higher from here and pushing risky yields even lower would be a world of pain for investors and owners of capital.

Towards New Explanations for Expensive Markets

Option market makers use the expression “make a sacrifice to the delta gods”. In the course of market-making, option traders, despite trying to maintain a flat delta, may end up short an underlyer. When it goes against them, in a misguided effort to not lock in a loss, they will often cover a small portion of the position hoping Mr. Market makes a fool of the most recent purchase by pushing the underlyer back down thus minimizing their loss on the entire position. “I covered a part of the short at a high price but made a lot back on the rest”. Hence, the sacrifice to the gods.

Save me the lecture on investor bias. I’m just sharing what amounts to trader gallows humor. In an effort to make a  sacrifice to Mr. Market, let’s see if there is a case for markets to revalue much higher. Even from here.

For such a justification to be considered, I suggest it:

  • Not rely on significant claims of market inefficiency.

For starters let’s interpret central bank behavior as symptomatic, not causal. It’s not a stretch to believe this.   Many believe demographic-induced secular stagnation stalks developing economies starting with Japan, China, and Europe before coming to the U.S. It’s not impossible to see accommodative policy as being correct given the perceived determinism of shrinking workforces. Taylor actually warns us that focusing on central banks may obscure what is happening. A classic red herring.

Indeed, the ability to blame central banks for any and all bubble-ish behavior may have created a blind spot in markets, and resulted in investors overlooking the other contributory factors I discuss below.

After all, rates that are ‘too low’ are supposed to end in inflation, not deflation. So far they haven’t – in almost a decade – resulted in inflation, which suggests rates may not have been too low after all.

  • Incorporate observations of current market dynamics.

For example, seeing how money managers are throwing in the towel, which is what you would actually predict as they are compared to the market’s amazing risk-adjusted returns. A process that deepens as Soros’ reflexivity sucks remaining investors into passive indexing.

Let’s try to understand what the market is telling us with these high valuations.

Expensive Markets = Cheap Capital

The flip side of lower rates of return is a low cost of capital. Instead of asking why the market is so expensive, let’s ask why is the price of capital so low? For the same reasons that prices are ever low. Some mix of weak demand and ample supply. Capital is subject to the same economic forces as anything you can touch and feel.

Taylor explains:

In short, a combination of a growing supply of savings/capital, and falling demand for the usage of those savings. The scarcity of capital is falling. Scarcity is the foundation of returns in capitalism.

Historically, capital has been scarce (sometimes more than others), and high returns/interest rates have therefore been required to ration it to its most productive uses. However, there is no guarantee that will continue. There is no rule of the universe that says capital is entitled to a decent return (or any return).

Let’s start with the weak demand for capital.

Reduced Demand For Capital

The nature of the real economy has been changing which has reduced the capital intensity of industry. The term “data economy” is often used to signify how we have shifted from moving atoms to moving bits. Back to Taylor:

The world (or the developed world at least) is heading into something of a post-industrial era, where a lot of tangible capital is no longer needed to drive growth in productivity. Innovation is instead happening in technology, software, and services, etc, while incremental consumer demand is for relatively intangible services/experiences/entertainment, rather than ‘stuff’. These two factors are together reducing the demand for new tangible capital stock.

Productivity gains are poorly accounted for justifying the valuations. The growing power of technology is all around us. The only place it is invisible is in the productivity statistics – in my opinion because rapid productivity growth is deflationary, and because new technologies are now resulting in whole industries being demonetized. However, corporations are investing less and less in hard assets because there are comparatively limited opportunities or need for them to do so vis-à-vis the past – particularly with slowing population growth. In short, the ways in which capital can be usefully deployed has been declining, and it is probably structural.

Increasing Supply Of Capital

Taylor provides some economic explanations for the surplus of capital invoking what might be expected from mature economies that have had a good run.

Meanwhile, savings are at elevated levels, and have been for quite some time. This may be for merely cyclical reasons, but it could be partly or wholly due to structural reasons as well. One of the reasons is that wealth and income inequality have been rising rapidly over the past 30 years (something that could be cyclical, structural, or both). The more one earns, the higher one’s propensity to save, and wealthy individuals seldom consume their capital (as opposed to a portion – usually small – of the returns from their capital). Consequently, rising inequality has been increasing the world’s private-sector savings stockpile. In addition, savings-heavy economies such as China have been integrated into the world economy over the past several decades, which has further added to the world’s savings surplus (which was arguably a major contributor to the build up of economic imbalances prior to the GFC).

A Lower Discount Factor

If it weren’t enough that both the supply and demand forces were coordinating to cheapen capital, a lower perception of risk is boosting investment demand. Greed and performance-chasing are timeless behaviors that we would expect a decade into a bull market. Beware. Those explanations, like the Fed excuse, can blind us from looking further. We needn’t look far. The explanation can easily hide in plain sight.


Markets are becoming more efficient. There’s a saying that information wants to be free. It wants to get out. It takes energy to keep useful information private. And if a group has an edge in information, it will be difficult to scale since achieving anything grand requires more people. More people means more leak points. So when we combine information entropy with an explosion of interconnectivity and permissionless platforms, is it any wonder that data, intelligent analysis, and best practices become table stakes?

Increasing Efficiency

  1. Charley Ellis3 of Greenwich advisors on the evolution of investment analysis:

The number of people involved in active investment management, best I can tell, has gone from less than 5,000 to more than 1 million over 56 years. A major securities firm might have had 10 or a dozen analysts back in 1962. What were they doing? They were looking for small-cap stocks and interesting companies that might be interesting investments for the partners of the firm. Did they send anything out to their clients? No, not anything. Goldman Sachs didn’t start sending things out until 1964 or 1965, and there was just one salesman who thought it might be an interesting idea to put out. Today, any self-respecting security firm is worldwide with analysts in London, Hong Kong, Singapore, Tokyo, Los Angeles. 400, 500, even 600 people trying to come up with insights, information, data that might be useful to clients. Anything that might be useful. Demographers, economists, political strategists, portfolio strategist and every major industry team. Every major company will have 10, 12, 15 analysts covering that company. And of course, then if you go to the specialist firms, there are all kinds of people and then there are intermediaries with access to all kinds of experts in any subject you might like. We’ve got 2000 experts. And anytime you want to talk to any one of them, just let us know. Glad to provide an unbelievable, flourishing amount of information of all kinds, all of which is organized and distributed as quickly as possible. Instantaneously, everybody.

2. Now combine this transparency with what pseudonymous writer Jesse Livermore 4 refers to as “networks of confidence.”

Valuation is a function of required rate of return to which liquidity is an input. Imagine a pre-Fed wildcat bank. You would not accept such meager real rates of return because you do not have the confidence in the liquidity of your deposit. So much of our required rates of return come down to confidence. The progress of finance has been towards greater networks levels confidence which creates downward pressure on required rates of return.

3. Finally economist Ed Yardeni 5 describes how capital is so efficiently dispersed throughout the system that distressed funds are on standby waiting to provide liquidity as quickly as opportunity emerges. This private version of plunge protection is like a Nasdaq level 2 bid below the current NBBO. He thinks that absent a broad recession, the market may be able to quarantine sector downturns. Instead of a great recession, we simply adapt to “rolling recessions”. When the US energy sector collapsed in 2015 the fallout was limited as capital was callable on relatively quick notice. If the risk of spillover from sector downturns is limited we can expect fewer recessions, which is what Yardeni attributes any sustained bear market to.

  • Ease of Diversification

First, we need a quick aside on the fact that stocks have historically been a good investment. The excess return of stocks over risk-free rate is known as the “equity risk premium”. The fact that stocks are volatile is used to justify the excess return. Academics often refer to this equity risk premium as a “puzzle” since the return has historically been in excess of what their models would predict. Breaking the Market 6 actually shows that the puzzle is simply an artifact of a false comparison. Academics use index returns as a proxy for “equity returns”. But an index is actually a weighting scheme that rebalances. It’s not the same thing as “stocks”.

“Stocks” and the “Stock Market Index” are not the same thing and never have been. One is an asset class, the other is a trading strategy of that asset class. They don’t behave the same and don’t have the same properties, return, or standard deviation. You can’t use one to replace the other.

When you compare the geometric return of stocks, not a stock index you do not find an ERP!

Ok, with that out of the way, is it now crazy to think that the passive indexing trend which became popular because of its post-fee performance (Ellis reminds us that less than 20% of active managers have beaten passive allocations) will lead to lower excess returns? If they were too high to begin with, increasing access to that strategy should lead to yet lower yields going forward. But here’s the critical point — there’s no reason to expect the yields to revert to the excess levels of yesteryear. Indexing is a  simple word for a weighting strategy that periodically rebalances. The strategy is cheap to implement AND happens to generate an excess return that academics consider excessive. So excessive they call it a risk premium.

So if it’s not stocks that have an ERP but the strategy of stock indexing that actually holds the premium, how is it persisting? The mass adoption of passive you are witnessing is the invisible hand wringing the equity index risk premium out of the market. The lower forward returns the hand leaves behind will be its proof-of-work. According to Vanguard7, in the early 1950s, 4.2% of the population held stocks, and the median number of stocks held was two. The delta from today’s level of investment adoption, especially on a cost-adjusted basis, is a degree of progress more typically associated with tech or medicine.

While democratizing indexing seems like a gift to investors its euphoria will be short-lived. Indexing, by lowering risk discounts, is a more permanent boon to companies and those who need capital. Financial innovation reduces financing friction. Livermore 8 sees this as the march of progress we expect in any other industry. It’s just that the efficiency has been accruing in the direction of those who need capital. Those who supply capital were earning an inefficiency premium. They lacked information, means to diversify, and bore high transaction costs:

The takeaway, then, is that as the market builds and popularizes increasingly cost-effective mechanisms and methodologies for diversifying away the idiosyncratic risks in risky investments, the price discounts and excess returns that those investments need to offer, in order to compensate for the costs and risks, come down.  Very few would dispute this point in other economic contexts.  Most would agree, for example, that the development of efficient methods of securitizing mortgage lending reduces the cost to lenders of diversifying and therefore provides a basis for reduced borrowing costs for homeowners–that’s its purpose. But when one tries to make the same argument in the context of stocks–that the development of efficient methods to “securitize” them provides a basis for their valuations to increase–people object.

Those who need capital ate the cost of the inefficiencies that the underwriters sought payment for via fatter WACCs. The ironing of those inefficiencies is a permanent asset to borrowers and equity issuers.

  • Privilege of Knowledge

If technology has subsidized indexing from the supply side, the “privilege of knowledge” is sparking demand. This privilege, a term coined by writer and data scientist Nick Maggiulli, recognizes that the dominant strategy of buying and holding a rebalanced index was not known until the past 30 years. As Maggiulli explains9:

From 1871-1940, the U.S. stock market grew at a rate of 6.8% a year after adjusting for dividends and inflation. No investor in 1940 could’ve known this, because the data going back to 1871 wasn’t compiled by Robert Shiller and his colleagues until 1989…[if] buy and hold might seem obvious now, that’s only because we have the benefit of hindsight, ubiquitous data, and modern computational resources.

Those same resources that lowered the costs of diversifying also helped spread the word of its efficacy. Indexing is like a technology all its own. Better and cheaper. When you put a product with those features into the world, you are not surprised when it’s pulled not pushed. 10

Expensive For A Reason

The prospect of a sustained reversion in investment yields likely extends beyond the horizon of bargain-hunters’ binoculars. We may look back at historical returns and wonder why investors ever got to have it so good. We will look back and think how inefficient it once was. Can you believe people earned 8-10% in stocks and thought it should last? We may look at equity returns for the past century the way people now look at home prices. Remember when a house only cost 3x annual income? That was cute. As you look ahead, keep Taylor’s scoffs in mind:

Indeed, when you think about it, why should an index fund holder be able to lie on a beach all day and earn 10% a year? If the world needs savings, sure, that’s fine, but if it does not, then those savings ought to earn a materially lower return, if any return at all. And that is the direction the world has been going in.

What can you do about it?

If you have you participated in the re-pricing thus far, congratulations. Now what? Just as adding a 20th pound of muscle takes significantly more energy than the first, the next thousand basis points are going to require way more risk than you’ve endured until now. But to participate in the melt-up scenario the market demands you accept more risk for the same rewards. And if you abstain, Taylor reminds you of the reinvestment risk:

The disaster situation would be to be sitting in cash while watching markets surge all the way to 50x. What would you do then –particularly if the alternative was zero (or negative) rates in the bank? You’d be pretty much stuffed. If you kept holding cash, you’d have to settle for watching your capital slowly dwindle, and if you capitulated and invested, you would risk a major and permanent loss of capital if markets eventually did resettle at lower levels. I have never seen anyone worry about this risk. But they should.

If you are restricted to passive, vanilla strategies you may choose to hold your nose and stay long. I’m not qualified to advise you. But I’ll say that this is a fairly blunt hedge for a melt-up. It’s like tenting your house to get rid of ants. Consider the distribution more closely. If the market is expensive but the price of capital still has ample room to fall, it feels as though both the left and right tail are fatter. This is the solution options were built for. I don’t need to fumigate my house, I just need to shell out for some ant baits.

Evaluate Your Options

1) First the bad news. Listed financial options are probably not the answer. Why?

  • Listed call options maturities don’t match up with long term investors’ horizon (unless you consider 3 years long term). That means this type of hedge requires you get the timing right. The last thing you want is more ways to be wrong.
  • The upward-sloping volatility term structure would ensure premium pricing for the options.
  • While being long the index outright is a blunt hedge, call options, for all their extra hassle, are still not a surgically precise hedge. The right tail we are concerned with is risk premiums shrinking. This can still happen if earnings fall while multiples expand. Imagine earnings falling by 20% and the index only dropping 10%. Multiples will have actually expanded by 12.5%. I admit this sounds unlikely. But we are talking about this as a right tail event. In that context, the forces which are driving the price of capital lower may even accelerate in a recession. The financial option you actually want to buy needs to be struck on the index multiple, not the index level.

So unless a liquid market develops for the SPX 10yr 40 P/E Strike Call, I don’t see a simple financial options hedge.

2) Trend-following the index to replicate an option-like payoff. This strategy has been explored extensively with many variations incorporating momentum and dual momentum. Again, not investing advice, but these are outstanding sources to learn about trend strategies:

The strategies come in many forms but the gist is they keep you invested until the tide turns thus limiting your drawdowns.

Be warned. Trend is not a miracle-drug. You pay for this parachute in transaction costs, both explicitly and via the bid/ask spread of the signal’s entry and exit points. You can think of this whipsaw as the premium you pay for the option-like payoff. While in a financial options contract your premium is known at the outset, the trend whipsaw is a function of the asset’s future volatility and path which are unknowable. Livermore, who has also advocated for trend, makes his own disclaimers. During an interview on Invest Like the Best 11, Livermore cautioned that he is “agnostic” on trend. His creeping doubts about its future efficacy stem from his observation that in recent years there have been more whipsaws and less trend formation, possibly due to the so-called “Fed put”.

3) The last option is the most adventurous and the largest hassle. But it is the option that most directly addresses the root cause of this melt-up scenario. Start a company. If capital is cheap, the market is begging you to be an entrepreneur. I’ve written about this before in The Peace Dividend of Overvaluation.

No More Escape Velocity

So who has the most to gain from hedging the right tail?

The rich.

That it’s so difficult to hedge the right tail may even be a source of comfort for those given to schadenfreude. If the thought of a rentier class that sits back and compounds their wealth advantage for generations rubs you the wrong way then you are rooting for the melt-up. To arrive in a place in which there will be no return without substantial risk. Nassim Taleb12 has argued that the true measure of wealth inequality is the degree to which people are capable of rising or falling from classes. In a world where riskless investments yield zero or negative, nobody’s place is cemented forever. A low-yield future flattens everyone with the rich having the most to lose. Like inflation, the melt-up is a market imposed wealth tax.


In his January 2020 letter, investor Jake Taylor 13 remarks,

The return for the stock market in 2019 was quite odd. The price went up by 30%, yet earnings didn’t budge. All of the change was attributable to “valuation adjustment”.

The market is expensive by any historical measure. We talked about how painful the prospects for re-investment might be if the market marched to higher structural valuations permanently. Like you sprinted out of the gate only to discover you signed up for a marathon, not a 400m dash.

It’s popular to blame central banks, performance-chasing investors, and the rise of passive indexing. But it’s dangerous to presume that these factors are not perfectly rational. If the true equity risk premium is due to re-balance and diversification and that strategy, more commonly known as indexing, is democratized then it should reduce forward expected returns. And without any expectation of reverting to times when we didn’t know better or when that strategy was expensive to access. If capital is less scarce and in less demand it’s price must decline as capital is subject to the same economic forces that set the price of pizza or airfare. If technology cycles 14 and demographics are conspiring to suppress the cost of capital how certain are we that this is irrational?

Like Taylor15, I suspect this melt-up scenario is a tail-risk. As such the proper hedge is some very dirty combination of financial calls, equity trend exposure, and plain vanilla entrepreneurship.

I will leave you with this reminder. I am probably wrong. In fact, if this story ever took hold, sucked everyone in, and instead of the market climbing a wall of worry, ripped higher in a bull capitulation, you can thank me.

My sacrifice to the delta gods brought the rain.

Hands On Resources to Teach Kids About Business

A friend asked me if I knew of any podcasts geared towards teaching kids at middle school ages about business or money. I was surprised that while there are tons of articles online about how to teach kids there is very little directed at kids themselves. Here’s what I could find.


  • Warren Buffett’s Secret Millionaires Club (Link)

A video series inspired by value investing’s most famous practitioner.

Videos, blog, and games

  • It’s A Habit (Link)

Sam Renick’s site devoted to teaching kids about money concepts. Includes articles, newsletter, and links to many resources.

  • Hands-On Banking (Link)

Their tagline is “Money Skills for Life”. Videos appropriate at youngsters from elementary through high school.

  • Finance In The Classroom (Link)

A collection of resources from lesson plans, videos, and exercises covering K-12. The activities by grade are especially worth a look.

  • Flocabulary (Link)

Hi-quality video lessons. Seems directed towards teachers and has a paywall but there’s a free trial

  • Khan Academy (Link)

I’ve watched all the Khan Academy finance vids. It’s is a great source but probably out of reach for a middle schooler.

  • Two Cents by PBS (Link)

A weekly series about personal finance.

  • Camp Millionaire: A Money Workshop For Children (Link)

A game and activity-based financial education program for children 8 to 16 years. This site is one of my favorites for learning about value investing and mental models. The camp sounds awesome, just not sure if it will be in your area.

  • Children’s Business Fairs (Link)

An organization that helps towns create local business fairs operated by kids. These fairs look like flea markets or science fairs. They are nationwide and you can even bring one to your town.

A note on games

I would credit a lot of my reasoning about business and money from playing games. While actually investing is the ultimate game to learn from here are some of my recommendations to get kids and teens starting to think about investing.

  • Extremely incomplete information games: Poker and Magic the Gathering

As a trader trainee, our curriculum included lots of poker. There is no better controlled environment for  learning to make decisions under uncertainty. Many fellow trainees had extensive Magic the Gathering backgrounds for similar reasons.

  • Fantasy sports and sports betting

Point spreads and draft positions are valuable early lessons in market efficiency

  • Tabletop games

Catan (Link)

Richer than Monopoly and less antagonistic. I’ve never met anyone who didn’t enjoy playing this game. Lessons in negotiation, market dynamics, odds, and planning.

Acquire (Link)

A cool intro to stocks using a real estate theme

Power Grid (Link)

A bit higher on the complexity scale. Auctions, networks, optimization, opportunity costs, replacement costs, and cutthroat market dynamics.

Your Portfolio Intuition Is Poor

Summary and takeaways from Bridge Alternatives’ Portfolio Intuition (Link)

Intuition Test


  • Your current portfolio has 5% return and 15% volatility for a Sharpe ratio of .33
  • You want to allocate 10% of your portfolio to a prospective asset
  • You want to maximize the Sharpe ratio of the resulting portfolio

Choose between A1 and A2

A1 A2
Return 4.00% 4.00%
Volatility 7.96% 46.04%
Correlation -.20 -.20

Unsurprisingly, most people prefer A1 since it has the same attributes as A2 with 1/6 the risk.

Now let’s run the numbers 

Expected return of the new portfolio is the same whether we choose A1 or A2:

Volatility of the new portfolio if we choose A1:

Sharpe ratio of original portfolio = .33

Sharpe ratio when we add A1 = .049/.13363 or .3667

The Sharpe ratio improved by about 10%

Now what is the Sharpe ratio if we add A2 instead of A1.

First, we must compute the volatility. Go ahead, plug and chug…

That’s right, the volatility is the same!

The volatility of the new portfolio is the same whether we add A1 or A2 which means the new combined portfolio has the same improvement to Sharpe whether we add A1 or A2. This is true despite A2 having a far worse Sharpe than A1! It is counterintuitive because portfolio math and the role of correlation is not intuitive.

To see why, look at the formula for portfolio volatility:

Let’s zoom in on the last 2 terms which come from adding the second asset:

Plot of change in overall portfolio volatility vs volatility of prospective asset (A1 or A2)

As we increase the asset’s risk, the first term grows exponentially, and the second term shrinks linearly (remember, the correlation is negative). It turns out that, at least temporarily, the shrinking effect from the negative correlation outweighs the exponential term.

There are 2 observations to note once you are done reeling from the bizarre impact of correlation.

  1. When adding a negatively correlated asset to a portfolio its risk must be incredibly high before it starts to degrade the Sharpe ratio of the final portfolio.
  2. Notice how, at least until we hit the vertex, if we move from left to right, representing an increase in risk, we’re actually reducing return. Put differently, if we added risk and didn’t reduce return we’d deliver more than a 10% improvement; risk has a positive payoff here, which is very cool. There is a significant range where we are reducing the prospective assets’ Sharpe and actually reducing the volatility of the new portfolio.

More Preference Tests

B1 B2 C1 C2 D1 D2
Return 10.54% 3.57% 9.33% 6.50% 6.43% -2.64%
Volatility 20.00% 20.00% 27.50% 12.50% 10.00% 40.00%
Correlation .80 -.20 .40 .40 .50 -.60

Most people agree:

  • B1 was slightly preferred to B2. For the same risk, B1 delivers much more return, though B2’s correlation is better.
  • C2 was preferred. It’s Sharpe is higher (about 0.52 versus about 0.34).
  • D1 was preferred to D2. D1’s Sharpe ratio is much higher. D2’s return is negative

The punchline, of course, is that every one of these assets improves the Sharpe of the portfolio by the same 10%. Your intuition would tell you would prefer a portfolio in the upper left green box since those assets have the best Sharpe (risk/reward), so it is probably uncomfortable to learn that the final portfolio is mathematically indifferent to all of these assets.

Correlation Is The Key

Here’s the same plot relating these equivalent portfolios by their respective correlations

As the correlation drops (corresponding to lines of “cooler” coloring), less return is required to deliver the same 10% improvement!

While Sharpe ratios are “mentally portable”, they are shockingly incomplete without being tied to correlation. To create a compact formula which links Sharpe ratios with correlation, it is helpful to view indifference curves.

Indifference Curves

RRR= Sharp Ratio of prospective asset
RRRb = Sharp Ratio of original portfolio

If Relative RRR > 1 the Sharpe of the prospective asset is greater than the Sharpe ratio of the original portfolio

The indifference curve represents an equivalent tradeoff between Sharpe ratio and correlation for various mixing weights. For example, the light green line assumes you will allocate 20% of the original portfolio to the prospective asset.


  • As the weight allocated to the asset increases (the lines move upward, from green to purple), the asset must be more performant in order to do no harm; it must be better relative to the portfolio. Put differently, as the role played by the asset increases, more is required of it, and that sounds about right.
  • A less performant asset, ie one with a worse Sharpe ratio than the original portfolio can compensate with low or negative correlations

Getting Practical

The investor’s natural question when evaluating a new asset or investment is:

“What is required from an asset (in terms of return, risk and correlation) in order to add value to my portfolio?”

With math that can be verified in the paper’s appendix we find a very handy identity:

This equality describes what’s required, in an absolute bare-minimum mathematical sense, of a prospective asset in order to do no harm. 

How to use it

For a given prospective Sharpe ratio, you very simply compute the maximum correlation the new asset can have to be accretive to the portfolio. For example, if the prospective asset has a Sharpe ratio of .10 and the original portfolio has a Sharp ratio of .40 then the prospective asset requires correlation no greater than .25 (ie .10/.40).

For a given correlation, you can compute the minimum required Sharpe ratio of the new asset to improve the portfolio. If the correlation is .80 and the original portfolio has a Sharpe ratio of .70 then the prospective asset must have a Sharpe ratio of at least .56 (ie .80 x .70).

Insights and Caveats

  • Correlation is best understood as a sort of performance hurdle. For assets exhibiting low correlation, less is required of their standalone performance (i.e. return over risk), all else equal.
  • Prospective assets with a Sharpe ratio greater than the original portfolio are always additive.
  • If you happen to find a truly zero-correlation asset it will be additive as long as it has positive returns. And as we saw with asset D2, a negative Sharpe Ratio asset can be additive if it has a negative correlation!
  • This cannot be used to somehow rank prospective assets. It can only serve as a binary filter: yes or no. This might feel like a real limitation. Sharpe ratios are absolutely rankable. They are measurements of the same unit (risk). But as we’ve shown in this paper, those rankings are not indicative of their true value within the context of a portfolio. Making decisions based only on return and risk is like ranking runners based on their times without asking how far they ran. It doesn’t make sense. If you take away one thing from this paper, this should be it!

My Own Conclusions

  • Correlations make portfolio math extremely unintuitive.
  • Negative and low correlations can make poor or losing stand-alone investments great additions to a portfolio. The implications for the diversifying power of low or negative-yielding assets are significant. Bonds, cash, commodities, gold.
  • Highly volatile assets with a negative correlation are tamed and even subtractive to the total risk of a portfolio.
  • While the importance of low or negatively correlated assets is well known it’s possible it remains underappreciated.

Further reading

Breaking The Market’s outstanding post Optimal Portfolios For Two Assets

You will learn:

  • How to mix assets by comparing their geometric returns.
  • Correlation’s effect on portfolio construction is not linear.
    • The closer correlations are to 1 the more they impact the recommended mix.
    • Negative correlations are deeply valuable in portfolio construction, adding to the long term return. Positive correlations are harmful, limiting the benefit of diversification.
    • The mixing range for the geometric returns is the combination of each asset’s variance, expanded or contracted based on the correlation between the two assets.
    • Negative correlation is wonderful.


You can save your own copy here

You can also play with the numbers directly below

Lesson from coin flip investing

The setup

  • You invest in 2 coins every week for the next 1000 weeks (19.2 yrs)
  • These coins pay a return each week
  • Every 4 weeks, you rebalance wealth equally between the 2 coins
  • Coins have an expected edge of 10%
  • Simulation is run 10,000x
  • Assume no transaction costs

Individual Coin Payouts

Coin Win Payout Loss Payout Expected Annual Return Expected Annual Volatility
A(Low Vol) 2.75% 2.50% 6.70% 18%
B (High Vol) 8.25% 7.50% 21.5% 54%

Results of the 2 Coin Portfolio1

Strategy CAGR Volatility Median Return Max Drawdown
Theoretical  14.1% 28.5% 10%2
Un-rebalanced simulated 17.9% 32% 6% 68%
Rebalanced simulated 13.9% 30% 9% 64%

Observations from many simulations like the one described

  1. The higher the portfolio volatility, the more the mean and median diverge
  2. Rebalancing pushes median returns closer to the theoretical mean
  3. The rebalancing benefit is positively correlated to the difference of volatility between the coins

Percents Are Tricky

Which saves more fuel?

1. Swapping a 25 mpg car for one that gets 60 mpg
2. Swapping a 10 mpg car for one that gets 20 mpg

[Jeopardy music…]

You know it’s a trap, so the answer must be #2. Here’s why:

If you travel 1,000 miles:

1. A 25mpg car uses 40 gallons. The 60 mpg vehicle uses 16.7 gallons.
2. A 10 mpg car uses 100 gallons. The 20 mpg vehicle uses 50 gallons

Even though you improved the MPG efficiency of car #1 by more than 100%, we save much more fuel by replacing less efficient cars. Go for the low hanging fruit. The illusion suggests we should switch ratings from MPG to GPM or to avoid decimals Gallons Per 1,000 Miles.

Think you got it?

Give “deflategate” a go. The Patriots controversy brought attention to a similar illusion — plays per fumble versus fumbles per play.

If you deal with data analysis you have probably come across the problem of normalizing data by percents and the pitfalls of dividing by small numbers (margins, price returns, etc).

The MPG vs GPM illusion is more clear if you are comfortable with XY plots from 8th grade math recap. Look at the slopes of x/1 versus 1000/x (in this case think of Y=M/G and the recipricol as gallons per mile. I multiplied gallons/mile by a constant 1000 to make the graph scale more legible).

The Volatility Drain

I don’t want to torment you this week, but if you trust me play along and you’ll be paid off with some non-obvious lessons.

Imagine the wish you made on your 10-year-old birthday candles comes true. You are magically given $1,000,000. But there’s a catch. You must expose it to either of the following risks:

1) You must put it all on a single spin at the roulette wheel at the Cosmo. You can choose any type of bet you want. Sprinkle the wheel, pick a color, a lucky number, whatever you want.


2) You can put all the money in play on a roulette wheel that has 70% black spaces. Place any bet you want, but you must bet it all. And one more catch…you are required to play this roulette wheel 10x in a row. Your whole bankroll including gains each time.

Think about what you want to do and why. Even if you cannot formalize your reasoning, take note of your intuition. I’ll wait.

Let’s proceed.

First of all, the correct answer for anyone without a private jet is #1. Just spread your million evenly, pay the Cosmo its $52,600 toll and try not to blow the rest of it before you get to McCarran. For many of you who computed the positive expected value of option #2 then you might feel torn.

Welcome to a constrained version of the St. Petersburg paradox.

The expected value of a single spin with a million dollars spread over the favorable blacks is $400,000 (.70 x $1,000,000 – .30 x $1,000,000). A giant 40% return.

But if you are forced to play the game 10x in a row, there is a 97% you will lose all your money (1-.70^10).

What’s going on?

This problem highlights the difference between arithmetic or simple average return vs a compounded return. If you made 100% in an investment over 10 years, the arithmetic average would be 10% per year while the compounded annual return would be 7.2%. I won’t demonstrate the math, but you can always ask me or just Google it. The mechanics are not the point. An understanding of the implications will be, so hang on.

In option #1, you will be in simple return land. In option #2, you are in compounded return land. Compounded returns are not intuitive, but they are much more important to your life. Let’s see why.

Sequencing and the geometric mean

  • Compound returns govern quantities that are sequenced such as your net worth or portfolio. If you earn 10% this year, then lose 10% next year, you are net down 1%., right? While the arithmetic average return was 0% per year, your compound return is -.50% per year (.99^2 – 1).
  • Let’s thicken the plot by increasing the volatility from 10% to 20%. If you win one and lose one, your arithmetic mean is 0, but now your compound return is -2% per year. Interesting.
  • Let’s turn to Breaking The Market  to see what happens when we tilt the odds in our favor and really ramp the vol higher. In his game, a  win earns 50%, while a loss costs you 40%.
    • The expected value of betting $1 on this game is 5%. But this is the arithmetic average. The geometric average is a loss of 5%!
    • If you played his game 20x, your mean outcome is positive but relies on the very unlikely cases in which you have an almost impossible winning streak. You usually lose money.
    • As BTM explains: Repeated games of chance have very different odds of success than single games. The odds of a series of bets – specifically a series of products (multiplication)- are driven by, and trend toward, the GEOMETRIC average. Single bets, or a group of simultaneous bets -specifically a series of sums (addition)-, are driven by the ARITHMETIC average.

The most important insights to remember!

  • Arithmetic means are greater than geometric means; the disparity is a function of the volatility.
  • Mean returns are greater than median and modal returns (Wikipedia pic). In other words, even in positive expected value games, if the volatility is high and you bet the bulk of your bankroll, your most likely outcomes are much worse than the mean. 

Using this in real life

Step 1

Recognize compounded returns when you see them. We have already seen them in the domain of betting and investing. 

Consider these questions.

  • I want to raise the price of my product by 60%, how many customers can I lose while maintaining current revenue?
  • If CA experiences a net population outflow of 20% in the next 20 years, how much would it need to raise taxes on those that stayed behind to make up the shortfall?
  • If muscle burns 2x as much calories at fat and I lose 40% of my muscle mass, how much less calories will I burn while at rest?

After groping around with those you may have found the general formula:  X / (1-X)


If you lose 20%, you need to recover 25% to get back to even. Lose 50%, and you need 100% to get back to even. 100% volatility and you are certain to go broke. Look at the slope of that sucker as you pass 2/3.

In other words, negative volatility is a death spiral. Let the brutality of the math sink in.

Why has nearly every real estate developer you know went bust at some point? Because they are in the most cyclical business in the world and love leverage. Leverage amplifies the volatility of their returns by multiples. Compounded returns are negatively skewed. Mercifully for them, zero (aka bankruptcy) is an absorbing barrier.

Step 2

Protect Yourself

  • Diversify your bets. In the earlier casino example, if you could divide your million dollars into 10 100k bets you would now have a basket of uncorrelated bets. If you could bet 1/10th of your bankroll on 10 such wheels you’d expect to make 400k in profit (7 wins out of 10 spins). With a standard deviation of 1.45 you now have a 95% chance of getting at least 5 heads and breaking even on the bet instead of a 97% to go bust in the version where you bet everything serially.
  • When a bet is very volatile, reduce your bet size. If you put 100% of your net worth into a 20% down payment on a home you lose half your net worth if housing prices ease 10%. In investing applications, variations of Kelly criterion are good starting points for bet sizing.
  • Remember that for parallel bets to not be exposed to disastrous volatility, your investments must not be highly correlated. Having a lot of investment in the stock market and high beta SF real estate simultaneously is an illusion of diversification. Likewise, if you own 10 businesses, you will likely want them in separate LLCs. For those in finance, you will immediately recognize the divergence in interests between a portfolio manager of a multi strat fund and the gp of the fund. Izzy Englander wants his strategies to diversify each other while he gets paid on the assets, while the individual PM wants to take maximum risk. Izzy risks his net worth, the PM just her job. If you take one thing away from this paragraph: a basket of options is worth more than an option on a basket.
  • Insurance is by necessity a negative expected value purchase. You buy it because it ensures financial survival. In arithmetic return land it’s a bad deal, but if the insurance avoids ruin, it may have a profoundly positive effect on compounded returns which is what we actually care about.
  • Finally, the power of portfolio rebalancing. If you hold several uncorrelated assets, by rebalancing periodically you narrow the gap between the median and mean expected returns. This is more apparent if there is wide differences in the volatilities of your assets.
    • I ran a bunch of Monte Carlo sims on “coin flip assets” with positive drift. Some takeaways were a bit surprising.
      • If the volatility of your portfolio is about 9% per year, median returns are about 90% of the mean returns. At this level of volatility, rebalancing has little effect.
      • If the volatility of your portfolio is about 15% per year, median returns are about 50% of the mean returns if you rebalance.
      • Rebalancing actually lowers your mean returns when the volatility of the portfolio is high even though it raises the median. My intuition is by taking profits in the higher volatility assets it truncates the chance of compounding at insane rates, but it also cuts the volatility by so much that it provides a much more stable compounded return. The higher the volatility the more of the mean return is driven by highly unlikely right upside moves.
      • The impact of high volatility is stark. It is extremely destructive to compounded returns.
For finance folk and the curious
  • Compounded returns are negatively skewed. Black-Scholes option models use a lognormal distribution to incorporate that insight. The higher the volatility, the greater the distance between the mean and mode of the investment. Example pic from Quora.
    • A recollection from the dot com bubble. Market watchers like to say the market was inefficient. The options market would disagree. Stock prices and volatilities were extremely high reflecting the fact that nobody understood the ramifications of the internet. Had you looked at the option-implied distributions is was not uncommon to see that a $250 stock had a modal implied price of $50. To be hand-wavey about it, the market was saying something like “AMZN has a 10% of being $2050 and a 90% chance of being worth $50.” In other words, if you bought AMZN there was a 90% chance you were going to lose 80% of your money. If you are itching to get technical on the topic Corey Hoffstein’s paper explores how risk-neutral probabilities relate to real-world probabilities.
    • For option wonks, (assuming no carry costs) you’ll recall the concept of variance drain. The median expected stock price is S – .5 * variance. The mode is S – 1.5*variance. The higher the variance, the lower the median and mode! The distribution gets “squished to the left” as the probability the stock declines increases in exchange for a longer right tail like we saw during the dotcom days.
    • The expensive skew embedded in SPX option prices reflects 2 realities. First, the average stock in the index will see its volatility increase but more critically the cross-correlation of the basket will increase. Since index option variance is average stock variance x correlation, there is a multiplicative effect of increasing either parameter. The extra rocket fuel comes from the parameters themselves being positively correlated to each other.

Levered ETF/ETN tool

Use this tool to estimate how much a levered fund would need to buy or sell to maintain its mandated levered exposure. You should make a copy of the sheet for your own use.

A few points to consider:

  • AUM changes faster than the position size by the amount of the leverage factor
  • Inverse funds require 2x the adjustment of their long counterparts! So a levered inverse SPY fund would require 2x the adjustment of a levered long SPY fund.
  • For more detailed explanation of why funds must adjust their positions see my explanation of shorting.

Preview below:

The difficulty with shorting and inverse positions

Shorting is hard

Shorting assets is intrinsically difficult because
  1. while your position goes against you it gets bigger
  2. and when you win your position is getting smaller
Consider the impact of a $1mm fund that is designed to mimic a $1mm short in stock X.
  • X down 50% scenario

    • The fund earns 50% return. So now the fund has $1.5mm aum and the short is only $500k. For the fund to match the return of X going forward the fund must now triple its position.
      • Note this requires selling into a declining market (negative gamma)
      • The fund must keep its initial Position/AUM ratio constant. So initially this was 1:1 but then became 1/3 which is why it needs to triple the position
  • X up 50% scenario

    • If the stock increased 50% the fund loses 50%. Its AUM is $500k and the short is $1.5mm. The fund must cover much of the short.
      • The fund must buy in a rallying market (negative gamma).
      • The new position/AUM ratio is 3:1 so the fund must buy back $1mm or 2/3 of its position so that its AUM is $500k and its position is $500k. In this case the fund is insolvent.

Inverse ETFs and ETNs

The above dynamic is also how an inverse ETF or ETN work. The ETN must match the inverse return of a reference asset. So if all the AUM is exposed to the asset then we calculate the fund PositionSize/AUM.
  • NAV = AUM / Shares Outstanding
  • The down case

    • As the reference asset moves lower the fund must sell more of it to maintain the PositionNotional/AUM ratio. In this case, as the reference asset moved lower, the fund AUM increased due to profits while its position size decreased as the price of the reference asset declined. The fund must sell enough of the asset to rebalance the initial PositionNotional/AUM ratio. Selling into a declining market. This ensures the ensuing percentage move in the reference asset corresponds to the percentage change in NAV.
    • Redemptions are stabilizing as they require the position rebalance to be smaller as the AUM declines and the reference asset is purchased
  • The up case

    • As the reference asset rallies the fund must cover its notionally increasing short. PositionSize is increasing while AUM declines, so the reference asset must be purchased to reduce the position size and again normalize the notional/AUM ratio.
    • In this case, redemptions are de-stabilizing as they reduce AUM which further moves away from its initial value and the redemption also prompts an in-kind purchase of the already appreciated reference asset.

In sum:

  • For inverse etfs to maintain a constant exposure in return space to their reference asset they must rebalance such that the dollar size of the underlying position is a fixed ratio to the AUM.
  • The inverse nature means that the AUM and position size are always moving in opposite directions requiring constant rebalance (negative gamma). This creates a downward drift to the product NAVs.
  • As the reference asset rallies, position size gets bigger and AUM drops due to losses. As reference asset falls, position size shrinks while AUM increase due to profits.
  • Redemptions can stabilize rebalance requirements in declines and exacerbate rebalance quantities in rallies as redemptions reduce shares outstanding and in turn AUM while in both cases triggering the fund’s need to buy the reference asset which again is stabilizing after declines but not after rallies. In other words, profit-taking is stabilizing while puking is de-stabilizing.
  • I extend this explanation to levered funds here.

Adam Robinson’s Game Theory Approach to Markets

Distilled from his interview with Shane Parrish on the Knowledge Project

Markets are smart

When people are in disagreement with prices or confused they are in denial or are missing something from their model

Dunking on fundamental value investing

  • Relies on Ben Graham’s undefined notion of “intrinsic value”
  • It is defined by “the value justified by the facts”. This is a meaningless definition. Like “gravity is when things go down”.
  • Thinking fundamental investing works is hubris. You must believe:
    1. There is a true value
    2. You can ascertain it
    3. Others will come around to your view in a reasonable timeframe
  • What about Buffet and Munger?
    • They hold things forever.
    • They are geniuses.
    • It is a stretch to attribute their success to this idea of fundamental investing.

Dunking on technical analysis

  • Exercise in confirmation bias and data mining

Adam’s approach: Game theory

  • He doesn’t try to predict market prices. He follows the smart money
  • The market is a predatory ecosystem. Books like Peter Lynch “One Up on Wall Street” give retail the illusion they can win in what is a ‘gladiatorial pit’
  • Keynes who was also a great investor described investing: “How do we anticipate the anticipation of others?”
  • What pattern of behavior have you seen that correlates with a different future?
    • People placing bets are wagers on a view of the future
    • His favorite investing book is not an investing book: 1962’s Everett Roger’s “The Diffusion of Innovation”
      • A trend at its core is the spread of ideas
      • Roger’s decomposes the lifecycle of an idea. Early adopters are ridiculed, the masses begin to come around, the idea is enshrined and seen as ‘self-evident’
  • His ordering of traders and how they express their views. Traders near the top of the order will be “right” on a lagged basis. The giant caveat is that these orderings may not have applied as strongly before the 2000s because he claims the world was different (different investment flows, presence of EU, etc). But he makes the case they still held. He looks for strongly divergent views between asset classes to make probabilistic bets on the future. He prefers this because it is the expression of bets vs say using economic statistics. You don’t trade statistics, you trade assets.
    1. Metals traders sentiment is proxied by the copper/gold ratio. They are the “Forrest Gumps” of the investing world —simplistic. They are the closest to economic activity. They are very far-sighted because of mine timelines. They have never been wrong in the past 18 years on the direction of interest rates. In September 2018, during this conversation, the copper/gold ratio implied that interest rates should be at 1-year lows instead they were at 1-year highs. He thinks the metal traders will again be right, they are just early. (9 months later as I write this, interest rates have gone back to 1-year lows!)
    2. Bond traders sentiment proxied by the ratio of LQD/IEF. Basically, credit spreads
      • When they disagree with equity traders, the bond traders tend to be right and early
    3. Equity traders
    4. Oil traders sentiment reflected in XLE vs SP500 spread. The price of oil is less reliable because of sovereign intervention
    5. FX traders sentiment reflected in commodity currency crosses
    6. Economists: Always wrong as a group
    7. Central Bankers: not in touch with the real economy; rely on models only. And economists
  • 3 Ways a Trend Can Form
    1. A stock very sharply reverses a long-standing trend. The trend needs to have been in place for a long time (long is ambiguous; he says ‘months’ or ‘years’). The stock will retrace after its sharp move but if it runs out of gas then the early adopter of the new direction are starting to win converts
    2. Parabolic moves precede a change in direction and a new trend in the opposite direction (reminds me of dynamics of a squeeze)
    3. An asset in a long-established, tight range starts to break out. The less patient hands have been transferring their position to hands that have more conviction evidenced by them willing to wade into a dead name.

His ranking model jives with how I think about trading

  • The science part of trading is the constant measuring of market prices and implied parameters.
    1. Rank which markets are the most efficient
    2. Find the parameters which are in conflict with one another
    3. The parameters in the less efficient markets that conflict with more efficient markets represent an opportunity set
  • The art part is to then investigate why those parameters are priced “inefficiently”.
    • Flow-based? Who’s the sucker? Who’s better capitalized?
    • Behavioral? Confirmation, anchoring, recency biases? Others?
    • Is there an aspect of the inefficient market that is unaccounted for and therefore not normalized for in the comparison to the efficient market?