Mind Sprites

Inspired by Becoming a Magician by Autotransluscence

​By investing in ourselves whether our intentions are selfish or altruistic the world creates a result which looks the same on paper. The future you has more resources to share. In the massive multiplayer game of life you are leveling up:
  1. The ability to entertain or inspire
  2. Knowledge to make or heal
  3. The means to build or give
  4. A wildcard: physical or mental strength and energy to recursively feedback into the engine. Exhaust as fuel. Mechanically that’s the way a turbocharged engine actually works.
Aiming to complete a marathon is not about joy or the experience. It’s the story you tell yourself about the kind of person you can become. You want to become the kind of person that can finish a marathon. Imagine what else that person can do. Just imagine.

The quest is underway

Look at the mechanism that kicks off once you set out to train. There’s an unsaid fantasy:  I am going to be Me 2.0. The finish line will be a signal that the new you has arrived. The goal ain’t the thing, it’s a proxy for the thing. As urgently as you want to be the better thing, you will fixate on the proxy.

In your desperate pursuit, the unsaid vision of future you propels you forward. It builds an invisible audience in your mind. It cheers when you make good time on your practice run. It boos when you skip a session because you were out late the nite before. That audience is like lane assistance on your car, correcting your path when you veer.

That subconscious audience exerts a lot of influence for a bunch of nameless mind sprites. Their whispers are your self-talk. Their shouts are your self-doubt. You’re chasing a proxy and that’s ok. But beware to inspect your vision and don’t let its sprites into the game without a ticket.

Adjust your mirrors

Who does your fantasy future self serve?

What are the values of that future self?

Are you sure you want to conjure an audience for it?

The people you surround yourself in real life will also act as lane assistance. We commonly hear how we are the average of the 5 people you spend the most time with. They are your real-life audience. Their mindsets are contagious. Their optimism, pessimism, energy, and honesty are subconscious benchmarks. Think carefully about this. Are they nudging you towards the right course?

If I’m wrong about this it’s because I’m underestimating their influence.

Setting milestones

The small challenges en route should feel just slightly out of reach. The final destination should be uncomfortably ambitious. This compensates for the fact that we overestimate what we can do in the near term and underestimate what we can do in the long term.

A small change in initial conditions can result in a wide error in the point of arrival. The course correction tweaks along the way cannot overcome an angle of incidence that was wildly off the mark. Just ask a sailor.

Flirting with Models: Benn Eifert

Link: https://blog.thinknewfound.com/podcast/s2e2-benn-eifert/

About Benn: Founder of QVR Advisors specializing in option-based strategies


Selected notes from his conversation with Corey Hoffstein, co-founder of quant management firm Newfound Research

Can you maybe explain the difference between what you would consider to be more of an option strategy versus what actual volatility investing is?

  • A common option strategy is call overwriting or put writing. They are both long equity exposures. That exposure is going to be the biggest risk factor. I would contrast that with volatility strategy which tries to isolate features of the distribution of returns, but not the direction of returns.

You mentioned that you guys focus somewhat heavily on relative value strategies in the volatility space. Can you explain what constitutes a relative value strategy? What’s a trade that you might put on?

  • Create value buying cheap exposures and selling expensive exposures at the same time, trying to hedge out the main directional market risks that would dominate a traditional asset allocation.
  • Identifying trades is really an important part of the process as they move around all the time over different cycle frequencies. Imagine, for example, long volatility in large-cap energy companies versus short volatility of smaller energy companies. That might be an opportunity at a point in time driven by a series of large transactions in the equity market. A large fund may have done a bunch of overwriting in their large-cap energy names, which suddenly made them very cheap. You really have to monitor, see the prices move and understand why there are dislocations and other relative value opportunities that might cycle over time.
  • Pension fund overwriting or cash-secured put selling are types of opportunities that might last for several years while those strategies are popular. You can imagine them becoming too popular over some number of years, then the pendulum swinging the other way. I wouldn’t say that there’s risk premia that you would expect in the space to just exist in perpetuity in a relative value sense.
  • There’s a large need to provide liquidity for end-users of options and distribute risk from where options are being heavily supplying to where they’re heavily demanded. These markets really developed on the back of end-user demand and their need to transfer risk. The key thing in relative value investing and in volatility, is that the marginal price setter for the probabilities and the market prices that prevail is not a volatility investor who is thinking about nuances in implied volatility. For a specialist volatility investor, many of the best opportunities really arise from either explicitly or implicitly providing liquidity to meet the needs of end-users, and to warehouse basis risk between what they’re buying and what they’re selling.

Analogs and Differences from traditional investing?

  • Selling vol and overwriting are expressions of carry styles
  • Rather than using traditional factor language to describe volatility trading he prefers a “Star Wars” analogy: Derivative users do things in big herds. And they typically have very large size relative to the absolute return community. Those flows are very sticky and implemented in similar ways with similar benchmarks, for example selling one-month index options. This creates congestion in one segment of the overall options market creating a ‘disturbance in the force’ — this creates really steep term structures, market makers get stuffed with short term options, they don’t have the risk limits to hold. And the relative value community’s job is really to distribute that risk much more broadly, throughout the ecosystem. A nice risk-reward profile is the payment to provide the liquidity to that market. Concentrated flows need someone on the other side to warehouse and distribute that basis risk.

How do you think about identifying trades in this space? How do you think about managing trades? How do you think about exiting trades? How does the book come together? It seems like a very overwhelming landscape to try to get your arms around.

  • Our investment process as a collection of bottoms-up strategy sleeves. So an individual strategy sleeve would really be a theme that’s driven by some particular type of dislocation or some particular type of underlying flow that end-users are generating. In a cross-sectional portfolio, opportunities are more fleeting, as opposed to being structural flows that are very consistent over long periods of time. It involves building out quite a lot of infrastructure, to identify those opportunities quickly.
  • In the example of a fund running a big overwrite sale on their long, large-cap, equity names portfolio, that would feed through quickly into the prices of options within that universe, and you’d see a significant reduction in those prices, relative to the prices of the small-cap energy names. You’d see it probably wasn’t driven by underlying realized volatility dynamics, it wasn’t that the spread compressed(because the names in the short baskets started becoming very volatile, and their prices started rising). You’d have various other ways of quantitatively triangulating that which would trigger an investigation into a type of the trade to add to the portfolio.
  • Where the dislocations are potentially more persistent, it might be more a question of measuring those dislocations. How do you track the ebb and flow over time? Is it a particularly attractive opportunity set? Do you want to have maximum risk on? Is it a less attractive opportunity set? Do you only want to have 30, or 40% of risk on? The identification of those type of opportunities is a starting point in the design of the strategies.
  • Again much is driven by what end-users of derivatives are doing in really big size and affecting markets. It’s not hard to see if you are an active market participant. You spend a lot of time talking to market makers and talking to the end-users of derivatives so you see it very quickly.

How do you think about the trade off between systematic versus discretionary and volatility investing?

  • I think in terms of a spectrum between, on the one hand, fully discretionary, and gut feel based investing all the way to the other end of the spectrum of fully automated back to front, systematic trading. Most volatility managers lie somewhere in between on that spectrum. It’s really hard to get that last mile to full automation. Since options are non-linear, you need to manage the very small risk of automation failures which also makes full automation elusive.

Are there any examples that come to mind where either an opportunity was systematically identified and you had a discretionary override? How about the opposite, where you thought there was an opportunity and the systems were not flagging it?

  • Back in the early days of Abenomics, in Japan, when the Nikkei was incredibly depressed, there was an interesting dynamic showing up in skew on Japanese equity indices. So skew is the relative price and an implied volatility sense of upside, call options versus downside put options. And in Japan, it actually started to go positive, which is very unusual. In other words, upside call options, were trading at a higher implied volatility than downside put options. A lot of folks in the volatility community got really excited about how silly it was, that an upside call option would trade at a higher implied probability than a downside put option, and really aggressively sold upside call options. But the key thing to remember back then was the Japanese equity market had just been incredibly depressed for a long time. There was a tremendous macro narrative building around big structural reforms and a great unconventional monetary policy. What followed was a very volatile rally! It was really a sucker’s trap to look at skew based on the historical data set because you were selling an upside crash scenario.
  • Another example was the model not appreciating how cheap the options on VIX were when the sizing in XIV became extreme and creating a very negatively convex profile in VIX due to the size of the rebalance. If you have a fund that requires a mechanical response that has to buy volatility when vol is up, it creates a problem if the size in the market became too large. It was just a market microstructure time bomb waiting to happen. The timing of that type of event happening was uncertain. But the sizes of those positions made it almost inevitable.

When you see a very steep VIX futures curve, in your opinion, is that an expression of the markets viewpoint? Or do you think that’s just an expression of a market imbalance?

  • Typically, it’s more related to risk premium than it is some kind of unbiased forecast of future volatility. If you look academic research or practitioner research there are some fundamentals to that term structure and some expectation element but often quite a lot of element of risk premium.

If you were doing due diligence, on a volatility strategy, describe red flags (besides leverage and are they selling tail insurance) and other concerns.

  • I would want to drill into sophisticated, top-down risk systems that stress all of the main risk factors in the portfolio to very extreme levels, and see that the risks were acceptable. There is no portfolio that makes money under all circumstances which is fine. But if there’s a major risk factor in the portfolio, you should be able to take it to a very extreme unprecedented level and see that the portfolio is not going to be getting liquidated at that level.
  • It should be contained in a level that’s acceptable to the end investor.
  • I’d want to understand the assumptions they’re making in those stress tests.
  • I would really want to see the actual positions and hear them explain what other parts of the market and what other market participants are doing to understand what the squeeze risk looks like.
  • I would want to see that they had at least contemplated thoughtfully and analytically how the strategy should be expected to perform going forward. And really a thought about how the market changed in the past 20 or 30 years versus right now. Markets in general change over time but volatility and options markets have changed dramatically.

Is loneliness a downside of connectedness?

  1. Go to google.com
  2. Type “How do I learn to”. Don’t hit enter.
  3. Take note of what the top 6 or 7 autocomplete options are.

Here’s mine:

Yours probably has some differences because, you know, Google watches you undress and all. But compare a bit closer. Is anything surprising?

I actually cheated and hid a result we have in common.

The cringey one. This one. Sobering when we consider that Google’s search box might be the world’s largest mind mirror. I came across this surprising result via Adam Robinson’s 4-hour interview on the Knowledge Project podcast. The link and my notes are here.

Is Adam just another Cassandra beating the drum of dystopian despair when he reminds us that we remain sad even though “the average person today lives better than the average king a couple of centuries ago”. Several recent bestsellers, notably promoted by Bill Gates, are packed with stats and facts celebrating undeniable measures of progress. Just scan this list and you will find plenty of intellectual antibodies to the pessimism disease going around the developed world.

Whenever humanity is disappointing you just refer to that list for an injection of technophilia straight into the vein.

Unfortunately. That may backfire.

You have just learned 50 more things that you should feel good about but perhaps do not. So if you were down, you can add guilt to your baggage. No wonder we can’t love ourselves. Progress and logic are objectively improving our lives yet emotionally we cannot keep pace. Mental health, opioids, suicide have all been followed by the word “epidemic”. I’ve hinted at the loneliness amongst men in a prior letter, but just this week I saw this article titled “The Loneliness Epidemic Is So Bad, World Leaders Have Been Forced to Intervene”. And to be honest a few friends have been courageously vulnerable about the reach of loneliness. Given its prevalence, you can be certain it is an especially concealed, unaddressed form of pain. As our boomer population ages, I don’t expect the trend to halt.

Adam, a veteran of his own battle with depression and no stranger to the ethical challenges that barnacle technology, offers his thoughts.

  • He does not think it’s an accident that Palo Alto has the highest suicide rate in the US. (I have some reservations on that since it’s not fact-checked and for the same statistical reasons as to why extreme cancer rates, both high and low, are always found in smaller population states).
  • Technology is engineered to hijack your attention. Incentives are to either confirm what you already believe or enrage you.
  • He quotes Gandi, “There’s more to life than making it faster”. You may recognize the modern-retro version.

So what works for him?

The paradox of life: to be happy, to find love, to be successful is to not look for these things. It’s to be fully engaged in your life. Only two places to direct your attention in life:

  1. the task at hand
  2. others
  • His metaphor is breathing. When he is home he is focused on his work. Breathing in. When he goes into the world, his focus is on others. Breathing out.
  • The problem with self-help books is they focus on yourself, but you find yourself in your value to others.
  • What does engagement in others mean to him: “Create fun and delight for others”. Lean into the moments. If this is your goal, you turn life into a fun game which holds unseen rewards for you and those around you.

My own thought:

It is possible to feel lonely around your family. You can feel lonely around old friends. You can feel lonely amongst the other parents. Don’t believe you are an outsider looking in at people who are into each other and don’t have room for another. They may not be that into each other. And they all just want the same real sense of connection you do.

Quoting George Bernard Shaw: “The single biggest problem in communication is the illusion that it has taken place.”

 

Making Friends Offline and Online

Like anything else in life, you get back what you put in. If relationships and connection are our best medicines then chances are, we under-allocate mindshare to them compared to their potential rewards. If you have never thought methodically about relationships, and I don’t mean in some slippery realtor kind of way, here’s the manual: How to Make Friends 2.0.

Nikhil Krishnan’s amazing guide is built for modernity and employs the advances in technology to facilitate our most primitive needs for social connection.

  • “Social media is not actually catching up or knowing how someone’s doing, it’s the cherry-picked positive moments of their life. We need to carve out regular time and places to be vulnerable.”
  • Friendships require 3 steps: meeting, escalation, maintenance. His guide gives truly interesting, actionable advice for each step.
  • He’s also written the definitive presentation to using Twitter to make friends (h/t Taylor) called “Why Twitter is Dope And How to Use It”.

I stand by his Twitter playbook. I have made a host of new friends in real life via Twitter. I speak to several of them in private chats on a regular basis. I have advocated for it before. It has been one of the highest-yielding experiments I’ve undertaken recently. I can probably pull a dozen articles without much effort by others who have called it the highest leverage thing they have done for their careers. And there is definitely a cohort who would argue that their handle is more important than their college degree. This isn’t Instagram. None of these people is monetizing their Twitter handle the way influencers might. In fact, being overtly commercial is a faux pas in the communities I wander (mostly “fintwit”).

It’s not about numbers. I have a tiny following, but the followers are high quality — people with shared interests, talent, willingness to share and openness. You can choose who you select for by how you conduct yourself. Have a give-first attitude and you will be rewarded. You’ve seen my advice before.

It does get real

A final note on Twitter and relationships. Very recently an anonymous member of the fintwit community passed away. It is an open secret that he was in his late-30s. He was very smart, a straight-shooter in the full sense of the word, and one of my own favorite follows as well as many others. The digital and physical worlds collide in singular ways.

  • He was a central figure in a Twitter thread with another anonymous account that led to the launch of a WisomTree fund. Bright people openly sharing commercially viable ideas is an unexpected dividend. WisdomTree clearly had some open-minded staff that was listening to Twitter.
  • After news of his passing broke, a GoFundMe for his favorite charity raised 5 figures from the fintwit community quickly after it hit Twitter.
  • Despite his anonymity, I was very saddened by his passing because his character really showed through his interactions. The loss of a good person can be felt if you have never “met” just as we can mourn a celebrity whose life improved yours.
Jason Zweig is the top dog writer at the WSJ and he wrote a fitting memorial of @nonrelatedsense this week. In the spirit of the community, he gave a list of the best Twitter follows in the financial world at the end. The list is broken down by category. I follow everyone on that list and have interacted with many of them. And I’m nobody, which tells you how gracious a group it is. You could get an MBA for free just following them. If you want to level up your thinking on investing adding those folks is a great first step.

The title:

“Financial Twitter Loses a Source of Humility and Wisdom, but Good Voices Remain
An account called @Nonrelatedsense showed that some of the smartest minds in investing are learning, and having fun, on Twitter.”

The whole article 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 or 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?

Knowledge Project: Adam Robinson

Link: https://fs.blog/adam-robinson-pt1/ and https://fs.blog/adam-robinson-pt2/

About Adam: Author, educator, founder of the Princeton Review, and hedge fund advisor


Purpose and happiness

Modernity may not be making us happier

  • Quantitative measures that suggest the “average person today lives better than the average king centuries ago”
  • Technology is engineered to hijack your attention.
    • Either confirm your biases or enrage you (reminds me of how we ended up with Ben Hunt’s “widening gyre“)
  • Gandhi: “There’s more to life than making it faster”
  • Top 5 google search: “Teach me how to love myself

Conclusions for himself after battling depression

A life paradox: to be happy, to find love, to be successful is to not look for it. It’s to be fully engaged in your life. Only two places to direct your attention in life:

  1. the task at hand
  2. others
  • His metaphor is breathing. When he is home he is focused on his work. Breathing in. When he goes into the world, his focus is on others. Breathing out.
  • The problem with self-help books is they focus on yourself, but you find yourself in your value to others.
  • Engagement in others
    • “Create fun and delight for others”. Lean into the moments. If this is your goal, you turn life into a fun game which holds unseen rewards for you and others.

2 ways to change someone’s thinking:

  1. Change their question
  2. Give a more inspired answer
  • Usually easier to supply number 2 then re-direct someone’s question.
  • Every institution is an answer to a question. Slavery is the answer to the question “how can I extract economic value with compensating a person?” The answer is horrific but the question is valid. The answer “Wikipedia” is an inspired answer to the same question.

An algorithm for finding opportunity

  • Appreciate limits to logic
    • Niels Bohr: “You’re not thinking, you’re just being logical”
    • Paradoxically using logic to show that logic is not the key to life’s answers: The fact that after thousands of years of pondering we still haven’t been able to use logic to reveal a universal understanding of life’s meaning
  • Insights are spontaneous
    • After the insight, we reverse-engineer the logic.
    • GK Chesterton: “You can discover truth by logic only if you have discovered it first without it”
  • A hint that you are on to something is the insight will surprise you.
    • The surprise is evidence that something is wrong with your model of how things work. It is the breadcrumbs to a learning moment.
    • Look deeply at situations that don’t make sense

Applying this algorithm to investing opportunities

  • The best opportunities occur when something seems inconsistent.
    • This reminds me of Josh Wolfe’s observation that all best investments were always polarizing at the outset. If the idea was obvious and everyone agreed it would have been fully priced or overpriced.
  • Markets are smart. When people or in disagreement with prices or confused they are in denial or are missing something from their model
    • 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”.
      • In order to think 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.
    • 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?
        • Behavioral?
        • Is there an aspect of the inefficient market that is unaccounted for and the not normalized for in the comparison to the efficient market?

    His work in education and founding the Princeton Review

    • Cracking the SAT
      • Questions get more difficult as you proceed thru the test. On a later question, if you narrow to 2 choices, pick the one that makes less sense. The lack of intuitiveness is what makes the question hard. On easy questions go with the intuition. He created a mythical first-order thinking character named “Joe Bloggs”.
        • “He asks the student what would Joe Bloggs do?”
      • Princeton Review was very advanced in using large data sets to A/B test for every possibility
        • Study of NYC school data and using custom questionnaires of teens across the world testing into Stuyvesant HS
          Cutting to the conclusion: Groups that overcame socioeconomic factors to improve on tests (test improvement is more sensitive to those factors than outright performance) had the following factors:

          • immigrant father
          • US-born mother

          Speculation to why this mattered? One parent to impart work ethic, one parent to impart language. The gender didn’t matter, it just turned out that the father typically tended to be the immigrant. This combo even outperformed both parents being born in the US.

          • Students improvement was inversely related to their confidence in their ability to improve. Want drive and counterintuitively pessimism!
      • The best lesson here was students learned they can improve! In the US we “deify” intelligence, but we should deify hard work. So when you don’t achieve something it is viewed as a choice.
        • Kipling: “If you don’t get what you want, you either didn’t really want it, or you tried to negotiate over the price”
        • Hard work is not passively learned. It’s modeled. You need to model experimentation and persistence. Need a detached, scientific process to experiment for solutions. (example of urging the kids trying to raise money on the streets to try different approaches, since their script was failing. If they find a good script they should share it with the whole organization. This is how “do you want fries with that?” became part of McDonald’s process)
        • Schools favor rote learning. LSAT selects for people who can memorize the answer to a test but not for people who can see multiple sides of an issue. GMAT interestingly favors people who can handicap the right answer fastest since getting the right answer to each question to the decimal is impossible in the time allotted. The test design favors understanding the concepts.
        • Men outperform women in both verbal and math of standardized tests because women are on average more prepared. This leads to paradoxical behavior — they are more rattled by questions which try to trip you up because they don’t expect to feel unprepared, whereas men are more used to feeling that way and simply choose an answer and move on.

        Improve Learning and Decisions

        • Distill variables that matter
          • Study of horse handicappers found that the accuracy of their predictions did not improve from the original 5 variables they desired and as they were given more variables there confidence went up (confirmation bias effect) although their accuracy did not! The handicappers with only 5 variables were well-calibrated. They were close to 2x better than chance at predicting winner 20% vs 10% and they estimated their confidence as such. When they were given more variables their accuracy remained 20% but confidence grew to 30%!
        • Importance of rehearsal
          • Re-reading rote notes is not useful for studying. You need to connect your notes to things you know, re-phrase your learnings, and practice tests and questions.
          • The closer your practice is to the desired application of the knowledge the better. Practice a language, music, writing code, writing, playing sports in the context you want to be good at. Rehearse your speech in as similar a condition as possible. Record yourself. [this has been my experience in taking music classes that force me to perform on stage]
          • Playing over chess games to copy what your idols did. Reminds me of Austin Kleon’s advice to start out copying your heroes. Not to plagiarize but to get in their heads.

Notes from EconTalk: Anja Shortland

Link: http://www.econtalk.org/anja-shortland-on-kidnap/

About Anja: Researcher and author of  Kidnap: Inside the Ransom Business


Economist Russ Roberts interviews Anja Shortland

Kidnapping for ransom as a business

The hint that kidnapping was in fact a business: 97% are resolved peacefully

How can the chance of a peaceful resolution be so high if all these things must go right:

  • Both sides must negotiate a price from a wide range
  • How to payment, typically unmarked cash, to the kidnapper?
  • Trust that the kidnapper will acknowledge payment
  • The kidnapper to trust they will not be arrested during the hand-off
  • The kidnapper must expect that the hostage will not be a witness

“The only reason for this kind of trade to go smoothly is what economists call the shadow of the future. So, people behave well this time ’round because it will help them in their business in future interactions.”

“This will only work if the kidnapper understands that he’s better off keeping the promises than breaking the promises. And that works because there must be a mechanism for information about good and bad behavior to be transmitted to future victims. So, if you have a kidnapping gang working in a city, then local gossip will probably ensure that people know whether or not they can trust the kidnappers. However, how does that work for transnational hostages? How does it work for the tourist that gets picked up in a bar late at night? How does that work for the aid-worker? How does that work for the expatriate?

Enter kidnap insurance

“There’s a very limited number of insurers, syndicates, underwrite kidnap-for-ransom, and they exchange information about trustworthy kidnappers and rogue kidnappers.”

  • Insurance actually ‘orders the market’, creating moral hazard in the process.
  • Corps buy ‘kidnap for ransom’ insurance with conditions:
    • Insured cannot know about it
    • Corporation provides security
  • In some areas, kidnapping occurs because corp didn’t know who to pay protection money to
  • Lloyd’s of London brokers a market of insurance companies willing to ensure special risks (like a basketball player’s knee)
    • The market settles into a civil equilibrium
    • Small supply. Crisis responders (often ex-special forces) retained by the insurer will have specific experience with a class of kidnapper
    • Insurers share info and more coordinated than the heterogenous kidnappers which keep prices down. However, when gov’t come in splashing the pot it changes the dynamics of the game as it raises the expectations of kidnappers b/c of public pressures and gov’t large resources and because unlike insurers they are in a one-off game (France hopes the next victim is Swiss)
  • Each kidnap market has local conventions
    • Example: Pirates want money dropped in canisters next to the ship so that kidnappers can stay high enough to avoid capture himself
    • Businesses that provide secure common ground for handoffs(almost like escrow!)
    • Trustworthy middlemen — again ‘shadow of the future’; reputations and long-running exchanges (reminds me of my open-outcry trading past. In the pits, your “word was your bond”)
    • While any one transaction can go wrong on average the market hovers around a going price.
    • If kidnappers make mistakes, then they are out of business.
      • “Sometimes you have very emotional kidnappers. Sometimes you have stupid kidnappers. But stupid kidnappers will reveal information. And ultimately it is in the insurer’s interest to eliminate stupid kidnappers–well, eliminate kidnappers where possible. But if you have stupid kidnappers who make mistakes, you can remove them from the market by dropping some hints to the police.”

On the game theory of negotiation

  • Manage kidnapper’s expectation of ransom size (hide the fact that the captive is insured)
  • “Squeezing the towel” process as the concessions offered to the kidnappers turn in to a slow drip
    • Eventually, the concessions are below the kidnappers’ cost to hold the victim. For example, the longer a hostage in custody the more expensive (via bribes) to keep it secret
  • Can’t reward kidnapper’s bad behavior or threats (“parenting lesson”)
  • Negotiators help the kidnappers see things through a more rational perspective. And, they educate them. And say, ‘Yes, we don’t want you to hurt Uncle Ted.’ And, ‘You’re not going to get anything out of hurting Uncle Ted.’ And they just help the kidnappers see how that strategy is not going to be helpful.

From talented consumer to an artist

This tweet grabbed my attention this week:

Triggered.

A “talented consumer?”

A clever turn of phrase. Multiply a negative connotation word by a positive connotation word and get a negative connotation expression. I’ll pause while you check if this word arithmetic holds more generally.

Back to our gut reaction to the tweet. There’s probably a spectrum of responses amongst you. I’d bracket the range between these boundaries:

  • Didactic and pompous. Screaming on the internet. Trying to sell a book (he is)
  • I need to watch less Netflix and get off my bum

Predictably, the truth is somewhere in the middle. There’s no reason to get aggro if you thought he was too preachy after all Twitter is a 280 character playing field. Nuance and caveats come after the game. The reason this tweet resonated with me is that it surfaced a tension I’ve always had:

Analysis vs action

I took a much broader view of his use of the terms “creation” and “consuming” than the tweet probably allows for. We consume for entertainment and for learning. There’s overlap. Love Island is more the former. Your latest hardcover is more the latter. Nature documentaries are a guiltless way to watch TV. Eating at Per Se or hitting a distillery tour can be rationalized to skew more towards learning than hedonism. Different strokes, no judgment.

For me the distinction of how I use my time isn’t concentrated on the value of what I’m consuming but whether I’m consuming or doing. Am I just onboarding, or am I deploying? There’s always a balance and you may feel that you aren’t in your sweet spot. Are you moving too fast and need to stop to backfill, or are you consuming too passively and need to make more impact? The tweet is a status check. I used the discomfort as a chance to locate myself on the spectrum.

Consuming is great and necessary but it scratches a different itch than seeing your efforts play out in the wild. I don’t mean that in a ‘give back’ kind-of-way since the impact may simply be on yourself. The point is that the ‘creating’ enzyme binds to a different receptor than the ‘consuming’ enzyme.

Watching Chef’s Table vs serving your own custom Manhattan cocktail. Listening to Hendrix vs trying your hand at ProTools. Reading Wired vs writing your own automation script. Attending a lecture at your community library vs hosting your own purposeful gathering.

The get-off-your-ass starter manual

If you have ever shared my pang of being too inward, too ‘in your own head’, too much ‘analysis paralysis’ then the antidote is to see your mental energy manifested in some physical outcome in the world. Maybe it will be a piece of art. Maybe it will be a financial plan with a spreadsheet. Maybe it will exist by proxy — you have planted a seed in someone else who in turn produces something you end up consuming. A virtuous, recursive loop that you set in motion.

If you want a push I strongly recommend Austin Kleon’s short book Steal Like an Artist. I read it on New Year’s Day 6 years ago and just re-read it on vacation (twice!) last week. It takes about 30-45 minutes to read. It’s the kind of book that every reader can really take something different from because the ideas apply to creations of all kind.

I don’t want to influence your own lessons if you choose to read it but if you were curious, I did post my own.

And going back to that original tweet for a moment — I could not help responding:

Notes from Invest Like the Best: Jesse Livermore

Link: http://investorfieldguide.com/livermore/

About Jesse: Jesse Livermore is a pseudonym for the financial blogger behind philosophicaleconomics.com.


3 Methods for Drawing Meaningful Inference

  1. Intuition
    • Benefit: Low cost and readily accessible
    • Costs
      • Downside is noisy especially in ‘wicked’ learning environments
      • Not transparent
    • Traders are high in ‘cognitive reflection’ and stronger intuition
      • Careful deliberation is a hallmark. Studies have shown that people who take too long or too little to decide do worse.
      • Intuition is necessary to pull triggers, but deciding too quickly without careful deliberation leads to poorer inference
  2. Analysis
    • Benefits
      • Don’t need to gather data
      • A model of how something works can handle regime change by having a transparent mechanism from input to output
    • Costs
      • They are always incomplete and “so easy to be wrong”. The fact that we are prone to stories compounds the danger of analysis.
    • Using it responsibly
      • Leave margin for error
      • Validate

3. Data Analysis

    • Benefit: It is rooted in reality
    • Costs
      • Without context can be misleading
      • It is more costly
      • Requires sufficient “trial size” not just a naively high sample size
        • If your samples are highly correlated than your effective trial size is much smaller than you think. For example, all financial data drawn from a single regime or independent coin flips with an unfair coin
      • Data mining and multiple comparison
        • Patterns emerge randomly so this can occur in subtle ways, not necessarily because of fraud or nefarious incentives
          • Suggestions:
            • “Call your shot”
            • Out of sample test
            • Avoid overfitting by testing outcomes against variables that you know should not matter (for example, changing the day of the week an investing strategy occurs on should not change the result meaningfully)

Earnings are a distorted measure

  • Current strict accounting standards around depreciation understate earnings relative to history
    • Old accounting standards did not adjust depreciation for inflation effectively understating inflation and overstating earnings. The market is wise and understood that earnings were overstated and assigned lower multiples during a period of excessive inflation
    • Difficult to compare multiples over time because of this change in standards
    • Depreciation is not just about physical decay of an asset but the competitiveness of an asset. E.g: Inventory of Kodak cameras become obsolete much faster than their physical decay when digital cameras emerged. Any typical depreciation formula would have vastly understated the depreciation of the assets and overestimated the book value.
  • Inflation overstates earnings
    • He calculated the book value of the entire market and keeps track of retained earnings
    • The earnings being overstated means that the retained earnings that remain to actually be either re-invested or paid out to shareholders are understated once adjusted for inflation and compared to history. This means that published return on equity is likely understated because the money being re-invested is actually understated.
    • This is a known issue
      • Studied by prior economists
      • Big corps like Sears in mid-1900s argued for inflation adjusting depreciation because the overstated earnings were weakening their position in labor negotiations
  • Free cash flow handles many of these distortions more accurately
    • Free cash flow “plunges” during high inflation periods validating the distortions caused by inflation on earnings
  • P/IE Ratio (price to ‘integrated equity’)adjusts for all these shortcomings
    • outperforms all measures of valuation including many permutations of CAPE in correlating to future returns.
    • Highly correlated itself to CAPE and tells us that market is on the higher end of valuation which Jesse thinks is structurally justifiable
  • If you want to  dig deeper, OSAM published their joint findings

Why is it plausible that markets get permanently more expensive?

  1. Valuation is a function of the 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. The Fed put is an example of this.
  2. With low growth and inflation (demographics follow Japan, Europe), volatility will be to the downside but the Fed can also act more aggressively without fear of inflation. Higher structural valuations may be reflecting this market understanding.
  • Implications
    • Trend: we are seeing less trend formation and more whipsaws. Speculative but possibly due to Fed put. This has led Jesse to try to restrict his trend strategy to when it is most likely to work (ie fewer whipsaws). Historically, trend’s alpha has come from times of large market drawdowns. So he uses the trend strategy when it coincides with fundamental recession indicators. He admits the sample size is small so the research is thin and probably overfit. Best recession indicators:
      • Retail sales
      • Earnings
      • Unemployment trend
      • Housing starts
      • Industrial Production
    • He is agnostic on trend. Thinks it works but is worried about it.

Valuing the Market

  • CAPE and other statistical attempts to correlate valuation with future returns suffer from small trial sizes. Markets cycles so multiple years in succession are really just a draw from the same regime (overlapping data sets)
  • An alternative method of using the relative supply of assets to predict future returns. Derived from his work. My own notes on his full post are here.
  • Interesting inefficiency which hints at the validity of this: There are some egregiously overpriced preferred stocks carrying low yields, are callable, and sit in inferior positions in the cap structure. The only reasonable explanation is they are in relatively short supply. It’s a “rare baseball card”. The explanation issuance of preferred stocks has declined faster than investment demand for yielding securities. In other words, the demand for asset allocation in relative proportions has not changed as much as the composition of supply has changed.
  • Being biased towards flow-based explanations of pricing myself, I find this idea very compelling
  • His conclusions without proof: supply matters and there are inefficiencies. The presence of the inefficiency doesn’t surprise me since constrained supply means fear of squeezes and lack of scalability. Arbitrage or relative value trading is less likely to close the mispricing.

How using OSAM data, he tried to gain insight into how factors work

  • Value and momentum work very differently
  • His “Factors From Scratch” work with OSAM (O’Shaugnessey Asset Management)
  • My own notes on his post as well as the related OSAM work on “Alpha Within Factors”

Notes on OSAM’s Factors from Scratch

http://osam.com/Commentary/factors-from-scratch

How does the ‘value’ factor generate excess return?

  • Method for decomposing the return into earnings growth and multiple expansion
    • Limit to large caps (conservative, since the factor is weakest here)
    • June 1964 – Oct 2017
    • Cheapest quintile of stocks rebalanced annually at the end of June
      • Rebalance averages about 38% turnover
      • Turnover requires technical adjustments to normalize the decomposition: “rebalancing growth” and “unrebalanced valuation change” [details in the paper]
  • Findings
    • Value stock fundamentals do deteriorate in the holding period. This is reasonably expected since these are the cheapest stocks. But the prices turn out to be overly pessimistic, as the multiple expands during the holding period more than enough to compensate for the decline in earnings.
    • The excess return becomes highly diluted after 1 year as the bulk of the market’s re-rating of the stock occurs within the first year. 1 year maximizes the “gain to time invested ratio” and also strikes a reasonable balance with the costs to rebalance
    • The market’s re-rating of the stock higher proves to be vindicated as fundamentals do stabilize. The value factor is capturing the ‘overreaction’ discount and the rebalance sells the re-rated names into the market’s stabilizing bid.
    • Value is difficult or impossible to time since it only correlates reliably with future returns at market extremes (ie dot com era).
  • ‘Value’ in recent context
    • Values has underperformed since the financial crisis although the underperformance is not unprecedented
      • Value is even cheaper today on relative basis compared to the overall market but both value and the market are about 50% more expensive than historical averages
      • Value is not currently stretched despite underperformance
      • Value may be underperfroming since cheap stock’s implied underperformance turned out to be even higher than their subsequent realized underperformance.
      • Likely that this is bad luck as opposed to the market having become better at handicapping future performance.
      • From Asness: ‘In stock selection, value is still not super cheap (i.e., super-cheap would be if the cheap stocks were way cheaper versus the expensive ones than normal).78 It would be fair to wonder why not, especially given the poor long-term value returns. Well, with any strategy, you can lose because either prices or fundamentals move against you. Unfortunately, more of this current drawdown has been about fundamentals. Value, at least using the behavioral. explanations, is a bet that prices over-extrapolate current prospects. The better companies deserve to be priced higher versus fundamentals, but even so, they’re priced too high (and vice versa). However, sometimes prices are correctly reflecting this information, and sometimes they are actually underreacting to it (meaning what looks expensive is actually an ex ante good deal). Prices may over-extrapolate on average, that’s why value works long-term, but not all of the time. Value wins more than it loses, but when price differentials underdo it (meaning, unlike most of the time, cheap companies aren’t actually cheap enough versus the expensive ones) is a time that value fails.79 Importantly, we find no signal from this analysis for timing value going forward. Value is not predictably bad or good following periods where fundamentals move against it.’

How does the ‘momentum’ factor generate excess return?

  • Momentum factor constructs equal weighted basked of top quintile of names based on prior 6 month returns
  • Findings
    • Recent returns are a better predictor of earnings growth than simple expensiveness
    • Decomposing returns we find that the resultant earnings growth exceeds the size of the multiple contraction
    • Unlike ‘value’ which leads to excess returns for many years (albeit at a declining rate after year 1, the momentum strategy is mean reverting. The excess return actually overshoots in year one and subsequent years actually show underperformance.

Combining ‘value’ and ‘momentum’

  • Value converges to fair value after initial overreaction which leaves them overly offered, momentum diverges above fair value at the tail end of the holding period as shares become overly bid.
    • This makes them complementary timing-wise over the 1 year holding period
    • They work best in opposite market environments

Digging further into factors

https://www.osam.com/Commentary/alpha-within-factors

The above describes how, in general, these factors work (distilled to their essence on average you are betting against overdone price declines in companies facing headwinds or trouble). [The strategy is innately convergent and supplies liquidity]. However, when digging into this general dynamic further, OSAM finds lots of dispersion under the hood. Since that is the case, it makes sense look for what differentiates the names which are favorably re-priced vs those which continue to underperform their price outlook.

To illustrate they show AAPL vs IBM from 2014-2018. Both stocks were in the cheapest quintile of P/E in 2014.

IBM earnings declined, AAPL’s grew and the stocks were predictably punished and rewarded respectively. They validate this is not an anomaly by looking at names historically that are priced similarly and then looking at their performance as a function of earnings growth over the next year. The names with faster-growing earnings outperform those with slower growing or declining earnings and the effects increase are amplified by the degree of growth (fastest growing, perform better than just faster growing on average etc). The paper’s appendix goes further by decomposing the stock’s returns into contributions from multiple expansion and earnings growth.

These findings unsurprisingly apply to the stock market in general — companies whose earnings grow faster, have share prices that grow faster. However, they find this dynamic to be much stronger in cheap (aka value) stocks meaning the rewards for being able to predict earnings growth are higher in the value arena.

This chart shows the ‘excess return vs historical average’ binned by rank of earnings growth. While the names with fastest growing earnings are the relative best performers, we can see how much volatility there is in the entire value factor with periods like the most recent 5 years and periods in the late 1980s being notably poor for the factor.

Zooming in:

How to capture this return with info available at the time?

“Is it possible to reliably identify the top-growing stocks in the value factor using presently-available information? The answer is surely no, especially if presently-available information is limited to price and financial statement data. The forces that determine future earnings outcomes in businesses arise out of complex, idiosyncratic chains of causality that are not fully captured in that data.”

A more reasonable and still very valuable goal is to “tilt” exposure towards the names which are more likely to be indicative of real value vs the ‘value traps’ which bring the value factor’s average down.

  1. Minimize selection bias by using a composite of measures to identify value
    • For example, P/E will be understated if a company takes a one time gain (for example if it sold a balance sheet item for much higher than its accounting value). Measures of value are vulnerable to any accounting variable which is not reflective of the ongoing business. By using a composite of measure the risk of a single accounting aberration having undue influence is mitigated.
  2. Addition by subtraction by removing the value-traps
    • Momentum: a measure of trailing total return; higher is better.
    • Growth: a measure of trailing change in earnings; higher is better.
    • Earnings Quality: a measure of accruals; lower is better.
    • Financial Strength: a measure of leverage; lower is better.

Because low scores in these indices have a disproportionately large impacts they choose to cut of say the bottom 10% as the best trade-off between the desire to avoid the worse outcomes reliably and the desire to maintain a large enough universe of names and diversification. Asymmetrical filter: Scoring poorly on those measures is a better predictor of poor performance than good scores are predictors of positive performance

  1. Create an equal weighted portfolio of the remaining top half of names: “value leaders portfolio”

Summary

The results as the portfolio becomes tilted to higher quality value names:

When the process above is used to filter value-traps and we further narrow the universe to the ‘value leaders’ we find that our equally weighted portfolio had much higher exposure to the faster-growing names than a strategy ranked according to simply cheapness (ie “Value:Top Quintile”)

This is the excess return to the traditional value factor in different historical periods:

This table shows how the tilt to the top quintile exceeded 20% in every period

This table shows the returns to strategies decomposed to multiple expansion (ie the point spread re-rating) vs earnings growth

The leaders strategy improves the generic value strategy by eliminating the names which drag on EPS growth (at the lesser expense of having less pronounced average multiple expansion)

The outperformance of the value leaders strategy is notable for three reasons:

  • First, it requires only a modest amount of intervention. The percentage of original value stocks retained in the final strategy–38%–is relatively large. Moreover, the strategy is rebalanced annually, rather than quarterly or monthly. These characteristics suggest that the strategy is able to accomplish more with less.
  • Second, it’s occurring entirely in the large cap space, a space in which factor signals are comparatively weak and, according to some, non-existent.
  • Third, it’s associated with a significant shift in allocation towards the value factor’s top growth bins, a shift that we know is efficacious, given the extreme levels of outperformance produced by stocks in those bins.”

OSAM notes that “In practice, we therefore use methods that are more focused and refined. We also take advantage of the benefits of concentration and size: factor investing is more powerful when applied in a concentrated manner and when used outside of the large cap space.”

Notes on Philosophical Economics: Predicting equity returns from supply/demand not valuation

http://www.philosophicaleconomics.com/2013/12/the-single-greatest-predictor-of-future-stock-market-returns/

Premise

If there is too much supply of a given asset relative to the amount that investors want to hold in their portfolios, then the market price of the asset will fall, and therefore the supply will fall. If there is too little supply of a given asset relative to the amount that investors want to hold in their portfolios, then the market price will rise, and therefore the supply will rise. Obviously, since the market price of cash is always unity, $1 for $1, its supply can only change in relative terms, relative to the supply of other assets.

Aggregate investor allocation to equities is the best predictor of future returns. This equals:

Market Value of All Stocks / (Market Value of All Stocks + Total Liabilities of All Real Economic Borrowers)

A description of various economic data used to calculate the total liabilities

His constructed chart

Assumptions

  • The rest of the world holdings of domestic assets cancels out US investors holdings of foreign assets.
  • The supply of cash and bonds that investors in an economy must hold perpetually increases with the economy’s growth. The cash and bonds in investor portfolios are literally “made from” the liabilities that real economic borrowers take on to fund investment–the fuel of growth. Chart shows how this grows at 5-15% per year.
  • For investors’ allocation to equities as a proportion of total assets, equity prices must rise commensurately or new share issuance must fill the gap. Equity issuance has actually been declining since the 1980s. Thus stock prices must levitate if the investor preference for equity is unchanged while the economy grows (which can only happen via cash and debt growth)

Price is a supremely important determinant of return!

Price balances supply/demand of allocators and “there’s absolutely nothing that says that this process has to equilibrate at any specific valuation. History confirms that it can equilibrate at a wide range of different valuations. For perspective, the average value of the P/E ratio for the U.S. stock market going back to 1871 is 15.50. But the standard deviation of that average is a whopping 8.4, more than 50% of the mean. One standard deviation in each direction is worth 243% in total return, or 13% per year over 10 years.”

There is no explicit link which mandates price and value must be sensibly related which highlights the risk of owning equities.

“Consider the classic buy-and-hold allocation recommendation: 60% to stocks, 40% to bonds (or cash). What rule says that there has to be a sufficient supply of equity, at a “fair” or “reasonable” valuation, for everyone to be able to allocate their portfolios in this ratio? There is no rule.”

Markets contain both ‘mechanical’ and ‘active allocators’ with active allocators varying allocations based on perceived risk and expected returns whereas ‘mechanical’ allocators are systematic investors who simply allocate on a pre-defined or regular basis typically without regard to price. They are a minority but significant part of the market.

Decomposing drivers of return:

Mostly price change, not dividends. The price change is a function of multiple changes and earnings changes.

Return = Change in price + dividend return

…but decomposing the price return:

Change in price = Price Return from Change in Aggregate Investor Allocation to Stocks + Price Return from Increase in Cash-Bond Supply (Realized if Aggregate Investor Allocation to Stocks Were to Stay Constant)

So the mechanism of return conceptually is reframed:

“In the previous way of thinking, the earnings grow normally as the economy grows. If the multiple stays the same, the price has to rise–this price rise produces a return. When the multiple increases alongside the process, the return is boosted. When it decreases, the return is attenuated. The multiple is said to be mean-reverting, and therefore when you buy at a low multiple, you tend to get higher returns (because of the boost of subsequent multiple expansion), and when you buy at a high multiple, you tend to get lower returns (because of the drag of subsequent multiple contractions).

In this new way of thinking, the supply of cash and bonds grows normally as the economy grows. If the preferred allocation to stocks stays the same, the price has to rise (that is the only way for the supply of stocks to keep up with the rising supply of cash and bonds–recall that the corporate sector is not issuing sufficient new shares of equity to help out). That price rise produces a return. When the preferred allocation to equities increases alongside this process, it boosts the return (price has to rise to keep the supply equal to the rising portfolio demand). When the preferred allocation to equities falls, it subtracts from the return (price has to fall to keep the supply equal to the falling portfolio demand)”

“If you buy in periods where the investor allocation to equities is high, you will get the dividend return plus the price return necessary to keep the portfolio equity allocation constant in the presence of a rising supply of cash and bonds, but then you will have to subtract the negative price return that will occur when equity allocation preferences fall back to more normal levels. This is what happened to investors in the 2001-2003 bear market. This way of thinking about stock market returns accounts for relevant supply-demand dynamics that pure valuation models leave out. That may be one of the reasons why it better correlates with actual historical outcomes than pure valuation models. ”

How can this explain the earningless bull market of the 1980s

It can explain, for example, the earningless bull market of the 1980s. Unbeknownst to many, earnings were not rising in the 1980s bull market. They actually fell slightly over the period–which is unusual. But prices didn’t care–they skyrocketed. The P/E ratio ended up rising well above 20, despite interest rates near 10%–a then unprecedented valuation disparity. Valuation purists can’t explain this move–they have to postulate that the “common sense” rules of valuation were temporarily suspended in favor of investor craziness.

But if we look at what investor allocations were back then, we will see that investors were already dramatically underinvested in equities. If prices hadn’t risen, if investors had instead respected the rules of “valuation” and refrained from jacking up the P/E multiple, the extreme underallocation to equities would have had to have grown even more extreme. It would have had to have fallen from a record low of 25% to an absurd 13% (see blue line in the chart below, which shows how the allocation would have evolved if the P/E multiple had not risen). Obviously, investors were not about to cut their equity allocations in half in the middle of a healthy, vibrant, inflation-free economic expansion–a period when things were clearly on the up. And so the multiple exploded.

This framework has a much higher correlation with future returns than any of the popular valuation based models

This table shows the R-squared stats for different methods