Talking To The Diamond Hands

There was a Tweet circulating recently that said if you invested $25,000 in AMZN 20 years ago instead of college you’d have $25mm today. You can replace AMZN with BTC to be more dramatic. The “should have” game is prevalent these days. It’s also a tired one. After all, we can’t just assume anyone would sit tight during the 90% drawdowns these moonshots endured en route to their epic returns. They are survivors of giant asset tournaments whose other entrants had equal or even more compelling stories at the time. Still, people cannot help but dream. It’s the same type of fantasy Lotto sells with its “dollar and a dream” slogan. It conjures images of a beachfront mansion or flipping off your boss on the way out the door.

It’s a cheap thrill…but it doesn’t need to be.

Missing a chance is still a chance to learn. It’s a prompt to think deeply about your goals and the meaning of risk. I’ve said 10 million is the new million. That means many people feel they missed out. You feel like you missed out. Stop for a moment. Don’t just waste that feeling on a daydream. You are going to use it to learn about yourself.

Let me show you how.

How Finance Brains Think Of Risk

The “should have” game is a delusion. You were not going to drop $25k into AMZN in 1999 and not also plop $25k into other internet stocks. You were not going to watch it drop 90% and then not sell as soon as it recovered back to unchanged. 

So how about a slightly less delusional game? The “could have, should have” game. This is different because it suggests AMZN made it onto a narrowed menu you actually ordered from. It’s like kicking yourself for taking a job at YHOO instead of AMZN or FB in 2004. That is a true missed encounter.

I’ll share a personal story of a “should have, could have”. It’s not an accident I’m writing this post in the wake of Coinbase going public.

The First Clue I Was Narrow-Minded

Back in 2017, I met one of my wife’s friends who had just left a VP-level bank job the prior year to join Coinbase. Work was a drag at the time, easily the most forgettable of my career (most vol traders will agree), and crypto was having its coming-out party. If you were a normie going down the rabbit hole, that was the year. And this friend facilitated my growing interest. She introduced me to another Coinbase colleague. He had also left a lucrative bank job to join the crypto party. I was paying attention.

This second Coinbaser invited me to dinner. He and one of their product managers wanted to pick my brain on futures trading GUIs. I hit it off with these guys, we were having an exciting conversation, notwithstanding Boulevard’s blue hair vibe. At some point, the product manager who was about 22 went to the restroom. The senior guy, in a hushed tone, turned to me.

“So one of the reasons [the 22-year-old] wanted to talk to you is he has a high-class problem. He’s sitting on a giant pile of ETH he’s been mining since college. At current prices, he’s rich, but doesn’t know anything about investing. He needs advice and we thought you could help.”

As I was soaking that it in, our young tycoon was returning. There was no need to tippy-toe. The whiz kid cut right to it. He explained respectfully and with great maturity his “problem”.

What did I say to him?

“I don’t have much to say. If you listened to my opinion years ago you would never be in this beautiful predicament. I would never have held on this long, so there is nothing I can say that you should listen to. But now that you are here, I can offer one way to think about it — sell an amount that makes you feel like you never have to take a job just for money. You are 22 and achieved freedom.”

I don’t even know if this is good advice. It feels like such a dowdy perspective that it can only come from someone who would never have scored that big. Just hearing his story had a deep impact on me. After that dinner, I reflected quite a bit on what it would take to truly have, what we now label, diamond hands. How did he not sell earlier? Especially as a college student?! As far as I could tell he was very smart. So I just put him in the same box as a big-wave surfer. The insane box. We’ll come back to that.

I stayed engaged with the Coinbase crew. We all liked and respected each other. My wife’s good friend urged me to get a job there. She assured me her help would increase my chances. But ultimately I didn’t take it seriously. I had a coveted PM seat in traditional finance. The lazy shield of confirmation bias was deflecting the vibrant energy I was picking up from these people that I knew were smart. Fast forward to Coinbase’s IPO. One of these friends has become close to our family and her bold bet (and 100 hour work weeks for 5 years) have paid off beyond anything including she could have imagined. We are beaming happy for her. But I would not have made the same sacrifices. I have no right to benchmark this missed opportunity to our friend’s best effort. It’s one thing to say you want to be rich, and another to actually do what it takes. 

As for thinking the 22-year-old was insane…I was wrong. It took until now to appreciate how narrow-minded I was. Not about crypto (there’s plenty of “should haves” on that front but they are the false kind. I was never going to make a giant bet on it). I was narrow-minded about how to think about risk. This essay is about the evolution of my thinking on the topic of risk. 

Let’s begin with what we know about risk.

Textbook Risk

There is no reward without risk. This is irreducible. The question is how much risk should you take given a menu of positive and negative outcomes. There are 3 embedded sub-questions that go into a sizing decision.

  1. What are the odds of each scenario?
  2. What is the payoff in each scenario?
  3. What are your constraints?

All 3 questions vary widely in how complicated they are because they depend on the context. The simple end of the spectrum is the controlled game scenario. Your objective is usually a well-defined win condition (think Monopoly) while the odds and payoffs are transparent (think casino games). On the complex end of the spectrum are real-world business/investment decisions where odds are opaque. The endgame is not towards a well-defined outcome but an evolving series of trade-offs. 

The investing world’s view of risk starts by understanding the simple, controlled setting. It then takes the math up a notch to account for more complexity. It’s the difference between deriving a “basic strategy” cheat sheet for blackjack and constructing an endowment portfolio from an infinite array of investment choices. This necessarily requires assumptions as we move from a single equation to a full model of how returns work, correlate, and feedback to each other. The degrees of freedom simply explode as we move from the first problem to the second. 

An example can show you just how far you can travel across this spectrum. Imagine being called into an economics experiment to bet on coin flips. The goal is to have the most money at the end of your limited time in the lab. This is a controlled experiment with transparent goals and odds. This is as easy as it gets. Turns out even educated people get it wrong. Quant Victor Haghani performed this experiment. Even trained economists disproportionately construct strategies that are an order of magnitude worse than optimal (I think about this a lot. Investing is difficult enough, but can the people who manage your money solve even the most basic version of these problems, the types with known solutions? If you can’t add, how can I expect you to multiply?)

The framework for solving sizing decisions in this controlled experiment comes from the literature on Kelly betting. The gist of it is, given a bet’s expected value and a bankroll, there is an optimal bet size that maximizes your long-term wealth. The framework prioritizes maximization under the constraint that we don’t want to go broke and lose our ability to play this advantageous game in the future. It does this by presuming a diminishing utility of wealth (ie the absolute joy of making a buck is less than the absolute pain of losing one).

Much of conventional thinking about risk and portfolio construction is just an extension of a Kelly framework where sizing depends on edge. Quants layer in more math and yet even more assumptions to build complex models. These models accommodate correlations, probabilities, and distributions that are unknowable.

Yet only in the controlled games, are these variables are known. So conventional finance is actually an inversion of the Haghani problem. The burden has shifted from knowing what you should do given the inputs, to “if you only knew the right value of the inputs”. In other words, a garbage-in-garbage-out problem. This is hardly a revelation. Most serious investors don’t take predictions seriously as it is. In fact, the only thing finance has is a sound framework to understand how to bet if you just knew the inputs. 

Recall our 3 sizing subquestions:

  1. What are the odds of each scenario?
  2. What is the payoff in each scenario?
  3. What are your constraints?

The simplest game world has tame answers to all three questions with the wave of a math wand. 

Investing is not simple. It is a wicked learning environment. On a daily basis, analysts furnish guesses for #1 and #2. 

#3, our constraints, are addressed by ideas like:

Risk frameworks like Kelly and its intellectual cousins like risk-parity loom so large because they are one of the only things we understand well (it’s also why portfolio construction is the lowest-hanging fruit to improve performance). They address our constraints. But notice what you inhaled from these concepts whether or not you realize it:

The assumption that you care to maximize long-term wealth.

Like Kelly’s “maximize long-term wealth” betting prescriptions, sound financial advice is entirely built on “get rich slow and steady” math. I can sense the puzzled look on your face. You’re thinking “Duh, so what?”

The reason this is interesting is because of what you are taking for granted: 

This is just one approach to risk, but it happens to garner 100% of the attention!

It’s so odorless, you did not even notice. Let’s see what we can learn if we don’t take this for granted. 

A Wide Range In How People Think Of Risk

The textbook view of risk described above is reasonable and widely used for a reason. It’s true there is plenty of debate around “garbage-in” details like samples and distributions. Investors wrangle over where diversification ends and “deworsification” begins. Still, the caveats encompassing these matters are incremental when we zoom out to the level of constraints.

Why? Because the assumptions regarding what matters are upstream of any risk management machinery.

Consider RoaringKitty of GME fame or Bill Hwang of Archegos. These gunslingers pressed risks beyond what any typical investor would consider reasonable. It’s possible they are stupid. But I find it hard to believe they are incapable of learning about textbook risk. Considering what they pulled off, it’s harder to believe they weren’t exposed to at least some risk literature. If basic risk management was hard to understand, this would be an open-and-shut case of ignorance. But “don’t put all your eggs in one basket” is something you hear at least by the time you’re in high school. These guys knowingly didn’t wear helmets.

Is there any way to defend them given that they are neither stupid nor insane?

Well, not if you need to protect you’re way of seeing the world. Defending them is to create dissonance with what you know. This is exactly why we should do it. These guys had significant amounts of their own money on the line and probably aren’t stupid (but certainly overconfident 1). 

I want to understand the mentality.

How To Ask A Normal Person How They Think About Risk

Whenever you see stories of big bets, the focus is always on why the bettor believed the world had priced some proposition incorrectly. It’s focused on the reason for the prediction. It’s a fine question and should be asked. But the more underexplored question is “why did you choose to bet the size you did?”

Press people on this question. They are not used to answering it. Most people rely on “feel” for this and if they have lots of practice making bets this is totally reasonable. They might have a tacit intuition for the volatility of the bet and the hit ratio. But with large bets, including life decisions, intuition can be a worse guide at the very least because the outcome of any large bet (money, job, spouse) influences the rest of your life in complex ways. 

So you want to answer this question rigorously. I don’t mean in a technical, quanty way. I mean in a deeper way. Giant bets are about desires. If the desire is to “maximize long-term CAGR” then you can refer them to many acceptable frameworks for coming up with a bet size. It’s a common desire with a conventional set of answers. 

Imagine 2 scripts:

  1. “Why did you put 90% of your money in SPY?”

    Because I’m young and want to retire someday.

    Ok, fine. There’s a dull but necessary conversation invoking asset allocation stuff. Send them to a robo-advisor site for some light reading and move on.

  2. “Why did you put 90% of you money in [insert turd coin]?”

    Because I want to 10x this sh*t!

    Ok, this is where things might get interesting. You need a litmus test to find out if it actually will. Your next question:

    “What if you’re wrong?”

    [They start citing a bunch of reasons for their investment. Books, podcasts, Substacks, some rich person they know…]

    You respond:

    “I understand now why you made the investment. You have high conviction. But what if you’re wrong?”

    They will almost certainly respond with more reasons for why they did the investment or repeat the ones they already gave you. That’s ok. They aren’t investors and don’t know to separate the investment process which includes research and security selection with sizing which is a risk management issue. Give them a pass on this. This is where you explain, that risk management is like a separate branch that is not affected by biases of the analysis. Like checks and balances in the government. The risk management branch is focused on survival no matter how exciting the thesis is. If your YOLO’ing friend is skeptical, remind them that the reason the best investors have risk management is that even they are often wrong. It’s this independent throttling function that keeps them in business.

    Repeat,

    “I’m asking a risk management question here. What if you’re wrong?”

    This can go a few ways. They might be too dense. They might need to think about this more because until now they haven’t deeply considered the possibility that they are wrong. Especially if they are already up lots of money on the trade. These will end the conversation. Some small part of the time they will retreat to think about it. More likely, they’re wondering what it’s going to be like when they are rich and you are poor. Ask them to buy drinks now while they feel flush. 

    But…if they have answers to “what if they’re wrong on this bet-of-a-lifetime”, then you are making progress. You are about to learn what makes this person tick. You are about to get on a “why” train like you are 6-years-old.

What I Learned From Asking

If you are coming from conventional finance, you are used to conventional portfolios. If you deal with individuals, you emphasize asset allocation, keeping costs low, diversification. All of this is in the context of people’s goals. Most just want to have enough money to retire at some point and something to leave their kids. They expect investing to bridge current assets with future liabilities. If you deal with institutions, it’s the same problem but the details vary. 

You are so used to seeing it, that divergence from the template is deeply unsettling.  You presume the strays are ignorant of “best practices”. Often times they are, and if they learned the basics might change their behavior.

But what if they know the conventional playbook, grok it, and actively ignore it.

Many of you might know someone with a white-knuckle concentration in crypto. Many finance types including myself see a sub 5 or 10% allocation as reasonable. (It appears to have a super-delta to our infi-liquidity regime. Losing 10% of your portfolio would suck but you’d live so it’s not the most reckless idea). Now there are people that might have started with 5% and didn’t rebalance. They could easily have a majority of their wealth in crypto. 

So here’s your homework. If you know one of them, and you are certain they understand the dangers of concentration, sit them down, and ask them about it. Be open-minded. Recite the script above. “What if you’re wrong? Why are you taking this much risk?”

If they understand conventional finance and ignore it, they see risk in a very different way than you do. It made me curious. 

The Conversation

I find having a giant chunk of your net worth in extrinsic premium insane, but everyone’s different. So I wanted to understand as best I could. I had one of the longest conversations I can remember recently with someone who has about half their net worth in various coins, including a few coins that are more under the radar. The allocation was small to start but appreciation caused its portfolio weight to skyrocket. 

Before I share what I learned, a bit of background:

  • This person was a multi-millionaire even before investing in crypto.
  • The person’s portfolio is highly diversified across asset classes and securities (albeit levered to economic growth, but that’s typical).
  • The person is in their 40s, owns a successful small business, and married to an MD.
  • The person has an extensive history of giving generously both in money and time.
  • This person keeps a countdown of how many weekends they have with their kids before they are off to college. (I actually told the person about that sobering Tim Urban essay after I heard that.)
  • This person might be the most type-A planner I have ever met. Sets goals, and tirelessly works backward to figure what they need to be doing all the time. If that’s Mario, I’m Wario. I can’t relate to the mindset but I respect it. 
  • If crypto went to zero, the person is fine. The person admits it would be upsetting, but not crippling. 

What did I learn?

I’ll just cut right to punchline. They are only interested in this for truly life-changing money. In other words, base 10 changes in wealth (See Nick Maggiulli’s Climbing the Wealth Ladder or Trung Phan’s thread about rich people).

This person’s specific logic went like this:

  • I’m confident I can secure a comfortable retirement via a thrifty lifestyle, safe employment, and a hefty financial buffer.
  • I must balance competing goals: a desire for more time with the kids while they are young and a desire to make as large a philanthropic impact as I can muster.  A 10x change in wealth would allow me to give much more money away and give me more time with my kids.
  • If my working life was shortened by a few years after the kids already went away to college, this is is much less appealing. I want to be done as fast as possible otherwise I don’t care that much.
  • I don’t see “volatility”. I see “velocity”. Velocity is good. It speeds things up! It would be riskier to my goals if there was no velocity since the possibility of 10x would be off the table. 

Reflections From The Conversation

First, let me spell out some observations from the chat.

  • The person’s preference curve regarding the retirement age is discontinuous. If they can’t retire soon, then they are indifferent between say  60 and 65. This is a hyperbolic discounting of current risk. It flattens at some rate while the kids grow, before leveling off when they leave the nest. 

  • In this person’s mind, the biggest risk is not taking a chance to achieve the ideal. This framework is not the natural framework of finance that targets the best median outcome. This is a quantized version of outcomes. Structure a tolerable worst-case scenario, lower the median outcome, fatten the upside.

Look, I still think this view is crazy. Not because of the goals actually, but because it feels static. In a dynamic situation, the right course of action is unclear and greatly changes the shape of the payoffs. Practically speaking:

  • If you average down in a sell-off, the downside risks increase.
  • A purely discretionary approach invites an unprotected tryst with Greed and Fear. Watch your wallet.
  • Nobody really knows how they will feel to lose half their net worth. Will they go on tilt? Will they become depressed?

I could go on. I’m sure you can too. But our objections are beside the point. What we discovered is how personal the logic was. 

Risk Abstracted

By actively listening and translating a thoughtful non-finance person’s views about risk, I’ve distilled a list of reference thoughts on risk.

Risk with a capital “R” is qualitative

In finance, risk is often proxied by volatility, but that is a context-based definition. Risk, in its most meaningful sense, is the possibility of not achieving your goals

Goals vary

Categorize Your Goals

You can think of goals as “need to have” and “like to have” These are deeply personal. If you have Lambos and yachts in your “need to have” you have necessarily downgraded basic shelter into “like to have”. 

The Role Of Thymos

Byrne Hobart, discussing Fukuyama’s idea of the “Last Man”:

Thymos, which can be literally translated as “spiritedness” and more figuratively and vividly means the desire for recognition and glory. Thymos describes the urge to excel, even—especially!—at great risk to oneself. Fukuyama’s ultimate source, Hegel, spends a lot of time meditating on the original thymos-inducing practice of forcing another human being to submit to one’s will; modern societies find healthier and less direct ways to harness this desire. Plato divides the soul into reason, desire, and thymos. Reason and desire are enough for a pleasurable existence—you have wants, and you figure out how to meet them. Thymos is necessary to motivate sacrifices for public service and glory.

The boldface is mine. Humans are not robots. The call to glory for its own sake is a human impulse. We climb Everest because it exists. Think of artists. Most starve. I’d never choose to hang my future security on the vagaries of such chance. But the fact that others do makes my life better. An equilibrium where everyone optimizes for the “best” median return is dull. The line between a need for an intervention and raising a glass to a visionary is not cut-and-dry in real-time. 

Regret Minimization

By now everyone has heard of Jeff Bezos’ regret minimization framework. The idea is hard to implement because it rests on you being able to predict what you will regret. I found Jim Carrey’s version similar but more compelling (speaking of a guy vibrating with thymos…next time you Netflix check Jim & Andy: The Great Beyond). His father was a great sax player who abandoned his dream and became an accountant. He was fired and crushed at age 51.  Jim internalized the fuel: “you can fail at what you don’t love”. To Jim, choosing between what you love and not love is not much of a choice.

Divergent goals demand divergent strategies

If you needed to come up with ransom money in 1 day, the riskiest thing you could do is NOT bet everything on roulette.

Things You Can Do

Talk to others about risk

Animal spirits are pulsing through Americans’ mouse-clicking fingers. More than ever, it’s important to talk to people about the risks they are taking. Use the script above. Ask “what if you’re wrong?” Coax them to separate the rationale for the trade from risk management. You may not be satisfied by what you hear, but that’s not your objective. You just want to make sure your loved ones’ actions serve their “need to haves” even if yours are different. 

Revisit your plan

I found this Julian Shapiro’s essay Life Planning to be a nice reminder that you write your own script.  

I especially like his advice: Beware the echo chamber

This is my final, high-level caution about ordering your values: beware people surrounding you who mislead you as to what you care about. Because, whether you realize it or not, you’re the average of the people you hang out with. 

He gives examples of different archetypes of people you might hang out with. Every parent already understands the lesson — you have to have intention about who you spend time with. You will absorb the values that surround you (I recount my slide into zero-sum thinking based on my environment in How I Misapplied My Trader Mindset To Investing).

Julian continues: 

Many others think “making the most out of life” means doing nothing more than finding a spouse, buying a home, securing a job, and raising kids.

I’m not passing judgment. The point is: Has exposure to certain people caused you to neglect what you value by adopting others’ values through osmosis?

To break free from this groupthink, take a moment to identify what you would truly regret not having done by the time you’re 80.

That’s the easiest way to snap yourself out of it:

Ask yourself, What would I be doing if I could start all over again?

When most people do this, they’re delighted by how many things they think they care about suddenly fall by the wayside.

Set financial goals

See my short essay in the Moontower Money Wiki: A Word About Goals 

Set your own risk management rules

Think dynamically. Every public investment including BTC that has gone up 100x, 1000x, or more has experienced massive drawdowns along the way. Looking at charts is misleading because they do not convey emotion. The doubt and pain that occur in those drawdowns don’t translate. So you need to steel yourself against them in advance. Expect to experience them and have a plan so your amygdala doesn’t take the wheel. 

Ways to do this:

  • Know Thyself

It’s never a perfect science to handicap your own risk tolerance. Some starting points to jog your brain:

    • A Way To Quantify Risk Tolerance and Determine Appropriate Equity Exposure (via Financial Samurai)

    • How Tails Constrain Investment Allocations (via Moontower)

      This posts bridges the gap between investment risk and human capital risk. It considers how much you can risk in relation to your earnings potential. It’s not a practical recipe but walking through the logic will bring more rigor to your thinking. 

  • Match Your Risk Management Strategy To Your Goals
    • Re-balancing

      Conventional finance thinking advocates for diversification and re-balancing. The primary reason for this is to avoid concentration. You are actively avoiding have a single investment dominate your portfolio. There is a major survivorship bias in pointing to anyone that has become extremely rich by concentration, whether it’s through their investment portfolio or by having the bulk of their worth in their own business. The illusion obscures 2 realities:

1. The mega-rich will get that way because of highly concentrated bets. Exceptions that come to mind are some of the top HF managers in the world. You are not them. (If you are one and you are this far into my blog post then…call me, maybe?)

2. You don’t see the rest of the tournament entrants. Remember, most stocks go to zero. Re-balancing is why stock indices do not. See Is There Actually An Equity Premium Puzzle?

Re-balancing is a convergent strategy since you sell winners and buy losers. It’s a bet on mean reversion. To understand its concave payoff profile see Ensembles and Rebalancing (via Newfound Research)

    • Trend

      If you prefer a convex payoff profile similar to a long option position, you can follow a trend strategy. A trend strategy is a set of rules that cuts your losers quickly and keeps you in your winners.  The cost of implementing the strategy is the “chop”. The options equivalent is the “theta” or time decay. 

      Trend is conceptually simple. Your stop-loss will be triggered more often than you catch a trend. So a trend strategy is a tradeoff between hit ratio and slugging percentage. If you lose on 2/3 of your bets, but your winners are 3x your losers, then you are ahead (although such a strategy trades much more than buy-and-hold incurring higher transaction and tax costs). There is infinite literature out there on trend-following but if you want to understand how trend looks like a long straddle position see Straddles and Trend Following (via Newfound Research)

    • Gorilla

      Gorilla investing is another strategy designed to look like a long option. It rebalances away from losers, into winners. It explicitly bets against mean reversion. It’s a divergent strategy that growth investors employ in winner-take-all businesses. 

      The gist of it is to invest an equal amount in a list of candidates that are competing for a giant market. As the winners start pulling away, you shed the losers and reallocate the proceeds back into the winners. This comes with its own suite of risks. Off the top of my head:
      • Umm, you overestimate the TAM
      • The eventual market winner comes from outside your original list.
      • As more entrants enter the market, you need to consider adding them to your portfolio, which will dilute your ultimate return. 

The suitability of this strategy requires extensive judgment, but I highlight it because it is another example of an investing algorithm (in addition to trend or rebalance), and the goal is to have you be methodical in your risk management rules regardless of whether you are trying to simply save for the future or construct a homerun-or-nothing portfolio. Your goals are your own, but your framework should give you a chance. 

If you want to learn more about this strategy see the notes for Gorilla Game or pick up the book. 

Wrapping Up

Investing is all about bridging our current assets to our future liabilities. For many of us, filling out the advisor’s template works. There are enough degrees of freedom in them to handle a possible switch from public to private school, or your spouse retiring early. But when you see what looks like insane risk by a smart person, it’s an opportunity to learn about other life scripts. In the story of the turtle and the hare, some people root for the hare. They are fine with Neil Young’s lyrics “better to burn out than fade away”.

The liability they are filling is their own definition of a life fulfilled. 

What can you learn from talking to Diamond hands? You can learn about people. 

 

 


 

How I Misapplied My Trader Mindset To Investing

I graduated in 2000. It was the height of dot-com mania. I took a job with Susquehanna, one of the largest derivatives trading market-makers in the world. My first year as a clerk was a rotation through different rings1 on the American Stock Exchange. I learned to trade ETFs, equity options, and index options.  After a year, I was off to spend 3 months at the mother ship studying theory and mock-trading in the physical pit they fashioned in a conference room. I still refer to my ragged notebook from those sessions to write my options theory blog posts.

Susquehanna is rightfully famous for its trader education program. It is an enormous source of edge to them. They ask you to sign a 3-year non-compete after the training ends to secure your job as a full-time trader with your own p/l. While I resented the non-compete at the time, I think it was a good trade in hindsight. I got an amazing experience at a young age. The firm’s way of thinking about risk and reward is recognizable to anybody that emerged from its deep-rooted culture.

But there’s a twist. A darker angle that I have been reflecting on.

The four-letter version of culture is cult. You see, Susquehanna had (I’d say “has” but I am a generation removed from the firm and this post is really about me not them) a tangible culture. Today, living near Silicon Valley, culture is discussed a lot. I’ve heard it described as “what you reward and what you punish”. Susq had a distinctly strong culture, but since I was young and impressionable, I had a cultish adherence to it. The downside of a strong culture, is a weaker mind will overinternalize it. That was me. Plenty of traders were able to take the best of the culture without overgeneralizing it to life. I missed the memo to not take it too seriously.

This post is about how I misapplied the lessons of my trading career to my investing life.

Mother’s Milk

Indoctrination started as early as the interview process. You witness Susquehanna’s preference for using games, especially poker to teach “decision-making under uncertainty.”2 Games are closed systems. Taleb warns of “ludic fallacies” in trying to transfer lessons from games to markets. Still, there are enough parallels that an elite trading firm that has trained countless recruits deems the poker table a timeless professor. 3 Using pot odds to compute risk/reward, bayesian thinking to narrow competitors hands, seating position to calibrate possible starting hands. Then there were meta lessons. Who’s the fish? The sharps’ job is not to play against one another but extract the dead money. This takes self-awareness. It requires balancing the confidence to act when you have the edge vs the risk of overconfidence when imagining yourself in the pecking order.

To a young mind, sorting the world to organize the immense amount of info that was coming at me professionally, I started to form heuristics. Taken too far, these heuristics would become oversimplifications when extended to investing. I’ll give 2 examples of how I reduced my environment.

1. Zero-Sum Edge

Strictly speaking, options trading is zero-sum. Sure, it’s possible that if you buy call options from me, and I hedge them, that we both win. But in that case, the counterparties I hedged my option deltas with lost. The p/l reflects the transfer of risk and the total risk is conserved. Markets generate prices. Prices facilitate the flow of risk from those who do not want to hold a risk to those that do in search of expected profit.

As derivatives market-makers, the most adaptive starting point to isolate the mathematical edge is to assume the underlying stocks were fairly priced. I traded MSFT options for a few years. I never had an opinion on MSFT stock price. I figured there are thousands of smart, informed investors duking it out to set its price. From my seat at the table, the point spread incorporated all substantive information. But in the small arena of vol intelligence, I could have an edge. That was our expertise.

Building on this zero-sum world, our edge was just a casino vig. Without tourists punting on earnings or hedging, there was no business. We were not interested in giving the card-counters or informed counterparties action. A large focus of the job was to discern toxic from benign flow (I’ve written about how this is the entire basis of payment for order flow). In fact, when we identified the sharps we tried to bet with them. Likewise, if we isolated benign customers we thought about how to maximize their LTV by trying to price more tightly and have them coming back to us. In this case, the casino host is a fitting if dim analogy.

The focus on who the customer is permeated every decision. If a customer quoted a risk reversal (a spread between a put and a call on different OTM strikes) we’d typically lean the market as if they were going to be a buyer of the put. Why? Think of a simple decision tree. If the customer is a buyer of the put they could be informed about an upcoming negative catalyst, but there’s also a large chance they are hedging by collaring their stock. A hedge is benign. We might not even hedge the Greeks it saddled us with (hedging is the cost of reducing risk, so if the risk was not outsize to our substantial capital AND we didn’t think the trade had any information in it, we wouldn’t hedge. The accumulated delta risks were best aggregated at the firm level for centralized hedging). However, if the customer, bought calls the probability that they were “smart” is much higher since they are probably not hedging but speculating. We might even overhedge while trying to do as little size as possible on our wrongly leaned bid/ask.

Over and over we obsessed over who we were trading with. You’d just start filing “paper” (lingo for customers) as “smart” or benign (a more colorful expression would be “donkey”). Of course, they were playing a different game and the execution edge they were giving up was the cost of doing business. But 20 years ago, you could drive a truck full of money through the wide bid/offer spreads. Any uninformed trader using the options market was facing a vig so large that their returns were doomed if they kept coming back. And many customers did go out of business as surely as our profits grew (if you are unconvinced see Understanding Edge).

I came to believe edge was only possible on short cycles. Large sample sizes and frequent trading would quickly reveal if you had one. How could you trust stock pickers with their small sample sizes and inability to validate statistical edge? I generalized a useful assumption, “the stock is fairly priced”, as gospel truth. This made me agnostic about what I was trading. Everything was just a number on a screen. A video game. Only the flows mattered. Only the patterns of buying and selling. Asset managers focused on fundamentals were tourists with elaborate stories and fancy suits.

2. Other People’s Money

Speaking of asset managers, my view of funds was especially dark. It still kinda is. I’ve written:

Asset management is the vitamin industry. It sells noise as signal. It sells placebos.  There will always be one edge that never goes out of style — marketing.

Working for a firm that had no outside capital and made profits consistently, the idea of earning single-digit returns or even losing looked idiotic. Why bother? Firms with provable edges don’t try to raise money. If it’s provable it does not need more eyeballs on it. In my view, the adverse selection of being able to invest in a fund mirrored the adversity of getting filled on a highly competitive price you made. It’s the Groucho Marx thing — “I refuse to join any club that would have me as a member.” It didn’t help that my girlfriend (now wife) would take me to her holiday parties with the Greenwich crowd where I’d always remember some overconfident hedgie craft some vision of the world. I remember one encounter with a partner who said I “looked like a trader”. WTF? (With the benefit of maturity, my insecure impulse to want to pound him was super cringe. Thanks for reading my self-therapy).

The biggest indignity came when I interviewed for an execution trader job at a famous long/short fund. I had an especially warm intro to this firm. It was the heydey of hedge funds and they had no clue how to price comp. The position would have doubled my pay. Unfortunately, I was rejected after a very strong interview with the head trader. Backchanneling, the feedback was “You would have been bored”. This single data point solidified my feeling that these overfed funds were dog-and-pony shows. (From what you can tell of my awareness skills at this point, you agree, it couldn’t have been me right? Umm, right?!)

What I Was Thinking

I’m a decade into my career.

I don’t believe anyone except the house could have an edge. I have still given no thought to personal investing. I followed the society script of saving for a condo. In the first decade of my career, I witnessed the dot-com crash, 9/11, and the Great Financial Crisis in the wake of subprime defaults. Markets were nothing but pump and dumps. Investing was astrology.

Trading was different.

Market-making was a picks and shovel business. The job was to find prices that cannot simultaneously be true. “I’m sellling this here because I can buy this here.” And those things need to have a real, almost arbitrage, relationship between each other. A short wire between them, for example, a simple call spread. Sure, real investors claim they do this on longer timelines, where the disparities are greater, but so is the length of the wire between the 2 ideas. You’d have to pull one thing so far to exhaust the slack in the wire. In the meantime, you were collecting that 2% management fee and long a performance call. This looked like a racket to me.

For my thinking to evolve, I needed a mutation.

Maturing

In 2012, I moved to SF become a portfolio manager at a vol-focused hedge fund. My journey through market-making took me into several option markets including energy, softs, ags, and precious metals. I used to bounce around trading pits as a member of all three exchanges4 in the Nymex building. Now, it was time to take a bird’s eye seat and move up the edge/capacity continuum. If you were willing to accept a worse risk/reward, you could increase capacity. And this made sense. The economics would need to flip. Instead of me earning 70% of my profits (I had a backer in my independent years after leaving Susq in 2008), I would earn a smaller cut of a bigger pie so the investors would receive most of the edge.

Can you believe it? A fund could be a win-win solution to a customer’s needs. It only took me a decade of unwavering skepticism (a total affront to my wife by the way who has been on the buy-side for the better part of 20 years).

From this seat, I learned the more formal language of investing and finance. I learned how allocators thought about correlations and diversification. I learned how they thought about liabilities and what they considered to be risk. I then started to lurk on Twitter. I learned that Michael Mauboussin has written about everything I was taught about trading. He just did it from a skyscraper, instead of on a broker’s buy/sell pad. I learned that what I called edge was called alpha. I learned how beta was a form of risk premia levered to growth. So returns didn’t occur because stock prices “just go up”. Returns come from the economy growing and earnings going up. The transmission mechanism to stocks was still very noisy. But it’s less noisy in the long term (assuming the economy is growing).

In short, while trading is a zero-sum game, investing is not!

Zero-sum Thinking Is Naturally Short Term

The major mistake of my trading mindset is how short-term it oriented me.

  • I failed to appreciate compounding 

    Trading businesses are capacity constrained. The partners at Susq were plowing money into technology and growing the business. I remember feeling betrayed when they started hiring salespeople and analysts. We were a trader-first firm but now we were being asked to cooperate with the types of people I considered showmen and dumb flow. Instead, I was missing the broad view. Susq needed to deploy their rapidly increasing capital. That would mean building out a sell-side business, but it later would mean developing fundamental views. The idea that “stocks are fairly” priced would remain useful for option traders, but was Susq going to just dump any excess money into Vanguard funds? No, they were going to figure out stock-picking. The world of trading is not investing. The bets have endpoints. You win a tournament, you take the profits off the table. You hunt for a new bet.

    But investing is about re-investing. That’s how you compound. 

  • Taxes

    I’ve lived in the highest tax domiciles in the US and have been paid on a W2 my whole career. The ratio of my earnings and lifetime p/l to my wealth is embarrassing. Part of the trading mentality is short-termism. Today I’m thinking more about bets that pay off longer in the future. Bets that build under the surface, accumulating value. Value that is not marked and therefore untaxed. Human capital works that way. Ownership often works that way.

    Sure, a mercenary mindset can be worth it. But just as a ballplayer needs to earn a giant 1 year-deal to make it appetizing relative to a longer-term contract, you must be thoughtful about how you align your rewards with your efforts. Short-term certainty creates reinvestment risk. Weigh them carefully.

Combining Influences

I’m deeply grateful for the lessons trading and Susq taught me. I’m disappointed in how I misapplied them. That’s on me. There were others who sat in on the same classes and were not as slavish or myopic.

The experience on the buy-side, meeting people on Twitter, and being a better listener has been a revelation. When I combine these influences with my trading experience I have incorporated the following thoughts about investing:

  • At a high level, I focus on asset allocation.

    Risk and correlation are my primary concerns. Focus on the shape of returns and how the systemic risks you are underwriting are correlated to each other. Do this qualitatively. For example, I think of real estate as an idiosyncratic risk based on local supply/demand, but its systematic risks are interest rates and liquidity.

    Presume returns will be driven by market-implied parameters. This is the agnostic part I accept from trading. If you invest in large-cap US equities today, they offer say a 3-4% risk premia over the risk-free rate, in exchange for 20% volatility and fat tails. It’s not a great proposition, but the point is I don’t pretend I would get more out of my equity allocation. Size that proposition appropriately.

  • Taxes

    Put high turnover or income-producing investments in tax-advantaged accounts. Use ETFs over mutual funds in taxable accounts. 

  • Default to passive if not looking at a niche strategy

    Passive allocation allows you to draft on the increasing efficiency of markets and not pay too much in fees.

  • Private investments

    I appreciate that people can find an edge in their respective domains. I was spoiled by trading. Expiration cycles, large sample size, and a lack of beta meant edge, positive or negative, reveal you faster.

    Investing is a more wicked domain. My default belief is still that edge is rare and mostly unavailable to me. Storytellers can hide in the randomness and low signal-to-noise. And I’m not fully immune from them anyway. 

    Still, I believe if you filter well, the number of times you get burned will just be the cost of doing business. Any private investment has to satisfy my doubt as to why I should be invited. And once invited, I am mostly judging character and ability. This is admittedly an act of faith. I’m pattern-matching to successful traders I’ve seen. I’m comfortable betting on people. Not because I even know if I am good at this, but because I think there are more ways to fail forward. If I constrain my risks at the sizing level I can more easily enjoy the positivity that emerges from partnering, helping, and believing in one another. It’s more holistic than a spreadsheet. 

    In a recent interview with Meb Faber, Ted Seides articulated my wife and my feelings exactly:

Most of the [private] investments are actually people that I’ve known for a long time. I don’t have investments with the big brand-name people. And part of that, for me, there’s an angle on active management, and certainly, this style of active management that I think is completely lost in the active-passive debate, which is the relationship aspect of it. Because I can give money to a manager, and yes, I will get the returns that come from that, but who knows what else is going to happen, both potentially financially and also just in life, right?

There’s so much optionality that comes from having great relationships with people. It’s one of the reasons why it was easy for me to have a bias towards sticking with managers. I can’t stand ending those relationships with people I respect and think are smart. And I’ll happily, like, take a little bit of a financial hit in the short term if I think it’ll keep going for the long term. 

Wrapping Up

There are many ways you can apply the lessons of trading to life. There are adaptive ways to apply options reasoning to other domains. You can also live a totally unexciting life of collecting options5but not exercising any.

Knowing when to apply analogies is an art that requires choosing pertinent references classes and having awareness of what actually matters. 6We are at the mercy of initial conditions, frames, and contexts. You have blindspots coming from your training as surely as they come from the womb. The trading business smells so much like investing that I confused zero-sum with positive-sum because the wrapping looked the same.

Seinfeld said “Pain is knowledge rushing in to fill a gap. When you stub your toe on the foot of the bed, that was a gap in knowledge. And the pain is a lot of information really quick.” The pain of lagging a bull market made me examine my lack of emphasis on investing (As traders say, “nothing like price to change sentiment”). With more personal capital and years removed from 2 market crashes, I came to realize I should pay more attention to investing. As I examined how institutional allocators think about their goals and liabilities, I realized I should think longer-term. It’s why I came to Twitter. To plug into broader learning and more ways of thought.

Investing is a rich topic because the longer-term orientation forces you to learn more about yourself. You question your objectives and risk tolerance. You question what you know and how you came to know it. In that sense, investing is deeply about people and growth. It’s a conclusion I never could have imagined when I was bum-hunting on the trading floor.



I’m building a guide to investing. It’s a work-in-progress but currently has a full introduction, foundational beliefs, and the basics of risk. 

See the Moontower Money Wiki


Why Investing Feels Like Astrology

 Markets turn financial capital into physical capital.

Albert Wenger

That is my favorite definition of finance. I heard it years ago when Wenger was interviewed on Invest Like The Best.

The quote continues:

Finance bridges the fact that people need to borrow from the future to work on something today which will result in physical capital in the future which acts as the collateral for the loan or equity up front. 

I like this because it explains the economic purpose and mechanism of finance simply, yet abstractly. It is without prejudice to conventional objects of its abstraction like banks and funds on the supply side of capital or companies on the demand side. These objects can be substituted with other social constructs in ensuing centuries and the definition will hold.

If you listen to the interview, Wenger adds an opinion to his definition:

Markets have been the most efficient way to allocate financial capital.

I’m publishing this essay in 2021. Let me tell you that Wenger’s opinion feels pretty shaky. GME and DOGE are the tips of the absurdity spear. But since I agree with him (see Dinosaur Markets), I have felt pressure to examine my own model of how markets work. The role of finance is clear but the practice of it, investing, is far more art than science as textbooks might lure you to believe. It would be a grave omission for the next generation of textbooks to not discuss reflexivity, for example. It’s not that conventional definitions of finance are broken, it’s how two-dimensional the process is described. The returns are presented as if they mechanically spit out of a wood chipper. In reality, the wood chipper looks like it can stand up and beg more trees to jump inside it.

That sentient woodchipper is of course the market. The problem isn’t with markets. It’s with your formative beliefs about markets. Markets are an emergent system where the actors learn, act, and get feedback. Repeat loop. They all do this at the same time. That means that some of their lessons are artifacts of their own behavior. It’s like taking your pulse with your thumb, forgetting that it has a faint pulse of its own. Markets are layers of sedimentary behavior, compressing at an increasing rate, on top of a core finance function. That increasing rate scales with the modern speed of information creation and spread.

[A common criticism of crypto is that it’s just reinventing well-functioning features of conventional finance. So what? That observation is secondary to the speed at which it is doing so. It reminds me of when I moved from equities trading in 2005 to commodities. When I started on the NYMEX, the underlying was still not electronic! Once the market began to modernize, it had the advantage of leapfrogging many of the inefficiencies equity markets might have worked through as they moved to the screens.]

The layers of market sediment are sufficiently thick, and the core of the finance function is so sufficiently buried, that a focus on fundamentals will leave you feeling like markets are astrology. Prices feel governed by tides of celestial sentiment rather than waves from a named storm or measurable weather pattern. I don’t think this is new. My feeling is that it was always this way, but since there were fewer layers of learned-Vizzini sediment, the textbook illusion that prices are closely tied to fundamentals wasn’t as visibly challenged.

This post is superficially about the danger of believing the textbook-style investing delusion. But it offers a framework that extends the textbook views in ways that better align the objective (investing effectively) with what is required (focus on the correct inputs which are, sometimes, but rarely fundamental).

Regular readers know the tyranny of my trading experience means everything looks like an options nail. No judgment if you turn back, but I promise no math.

The Elements Of Value

Let’s re-purpose the concepts of intrinsic and extrinsic value. You were warned.

Intrinsic Value

The financial definition of “intrinsic value” depends on context. In options, it is the amount the contract is in-the-money. The difference between the stock price and the strike price. For an out-of-the-money option, the intrinsic value is zero.

A comparable idea in stock valuation might be book value. That’s the value of a business if you liquidated all its assets today and satisfied its liabilities. It is a simple accounting value of a company. It is unconcerned with the asset’s ability to earn money. If Elon Musk started a THC-infused lemonade stand its book value would be comprised of the street value of the THC syrup, a basket of lemons, and the Martian goblets he’d serve drinks in. Assume he’s the only employee and plans to sell 5 drinks a day on the way to the office out of the back of his cybertruck. Let’s be generous and assume the lemons are organic. The accounting value of this company is like eighty-six bucks. The equity will trade for $11 billion. So book value is $86. Equity value, $11B. If this was like options world, we’d say the intrinsic is $86. That’s what you’d get for the assets today.

This is not a helpful use of the term “intrinsic value” because it ignores the earnings power of the assets in the hands of the right operator. In reality, equity is the residual claim on a business once all liabilities have been met. The equity owner has unbounded upside in exchange for being the most subordinate in the cap structure. So equity itself looks like an option.

Luckily, there is a notion of intrinsic value that applies to stocks but is not so conservative that it would lead us to believe that anything Elon Musk tried to do would only be worth $86. We can borrow Warren Buffet’s version of intrinsic. The exact details don’t matter for our purposes (this feels irreverent to say since it’s some investors’ sacred cow, but if I’m not giving away too much, it only gets more irreverent from here. Sorry, not sorry). The gist of it is some measure of a stock’s expected future earnings discounted for risk and time value of money. More compactly, intrinsic value is a company’s earnings power times a multiple.

The key to this definition is that it smushes together a very traditional definition of intrinsic (book) value with a forward-looking estimate of earnings. But it’s a conservative estimate. Nothing that would turn your mood ring red or even get Elon out of bed in the morning.

Other assets have intrinsic values as well. Pseudonymous writer @Jesse_Livermore, on the Infinite Loops podcast:

I think the intrinsic value of an asset is what its worth in and of itself. From owning it for, for its own sake. I think a good way to test this is to just ask yourself, for any asset or anything whatever it is, what would be the most that you would pay for it if you were stuck with it forever. [Assume] you can bequeath it to other people when you die and so forth, but you’re stuck with it as is. You can’t ever translate it into cash in a market. What is the most you would pay for that thing? That is my test for what intrinsic value is.

He gives the example of a home. Even if you did not think it would go up in value, it still has consumption value since it saves you rent. You could also rent it out. In both cases, the intrinsic value will look something like Buffet’s definition. The sum of many years rent discounted for some costs.

Checkpoint

Before moving on, what have we established?

  • Stocks themselves are options. You can pretend they are zero-strike calls or calls struck at book value. It doesn’t matter, they are options. 

  • Our definition of intrinsic value is a conservative sum of future cash flows

  • Intrinsic value is a property of any asset. It can be zero if nobody is willing to pay even a scrap value for it.  [Craigslist and Ebay raised the intrinsic value of many items indirectly by lowering the cost to exchange them. It raised intrinsic by lowering the strike price.]

Extrinsic Value

Borrowing from options-land, extrinsic value is the “it could happen” premium. It’s driven mechanically by 2 inputs: time and volatility. All else equal, a 1-year option is worth more than a 1-week option. Similarly, if the underlying stock has the potential to change the world in the next year, perhaps it will cure cancer, its volatility will be high since its market is every human. Or it might fail all drug trials and be a zero. The range of possibilities gives the options more value than the options on, say, a giant widget company whose business has predictable cash flows.

But, remember we are moving from the world of financial options to thinking of the companies themselves as options. To do this, we are going to decompose extrinsic value into 2 components: imagination and liquidity premiums.

Imagination Premium

Let’s continue with the drug company.

We already assigned an intrinsic value that incorporated an expectation of future cash flows. Unfortunately, estimating any future cash flows from this drug company conservatively is impossible. No, this is a company whose value is made up solely of possibility. The probability-weighted forking paths that underpin its traded value feels…well, not like our definition of intrinsic. The discrepancy traces itself back to the fact that this particular company is less a business and more just a black box holding a far out-of-the-money call option. Yes, all equity above book value is an option but this one feels entirely comprised of time and volatility value.

This company is what AMZN was in 1998, braced on the cusp of a connected, online world. The entire value of the company was extrinsic. The implied volatility of AMZN back then was regularly in the 100-200% range, a straightforward reflection of justified qualitative uncertainty. Was it different this time? [narrator: it was]

So we can say that certain companies have option value above and beyond our modestly padded definition of intrinsic value. Is TSLA a car company, battery company, software company, self-driving car company, solar company? The market is assigning a massive premium above what any multiple-on-grounded-earnings could furnish. Not unlike AMZN over 20 years ago. This type of premium is again extrinsic. We are going to call it Imagination Extrinsic to a) honor its visionary nature and b) to distinguish it from another component of extrinsic: liquidity.

Liquidity Premium

@Jesse_Livermore’s work refers to an idea he calls “transactional value”. It is the value that permits you to pay more than intrinsic value for an asset because you know you could sell it back into a liquid market.

Here’s Jesse parsing intrinsic value from transactional value:

The intrinsic value of equities would be the cash flow stream of the equities themselves, which you can collect and they belong to you and you can spend them and do whatever you want with them.

The transactional value would be the value that comes from the fact that there’s this “network of confidence” in the market, that people have been doing this for hundreds of years and we know that when you wake up tomorrow, the S&P is not going to be at 500. It’s going to be near where it was yesterday and people are kind of anchored to where its price is…You can basically take all your money, 100% of it, and put it into the stock market and know that you’ll be able to get a lot of that out anytime you need to. That’s the transactional value, which is the premium.

The idea that liquidity commands a premium is not new. If you have any money in a savings account today, you are paying a liquidity premium in the form of negative real interest rates. The treasury market discounts off-the-run securities because they are thinly traded even though they mature to the same value as their on-the-run counterparts. But I don’t want to dismiss Jesse’s notion of transactional value because it’s not novel. His expression of it is illuminating. For example, currency is made entirely of transactional value. The fact that we can rely on it to trade warrants a premium entirely out of proportion to the value of paper that represents it.

We will call this component of premium: Liquidity Extrinsic

Checkpoint

Let’s take inventory. Asset values look like options composed of:

  • intrinsic value: a conservative sum of an asset’s future cash flows or consumption value
  • extrinsic value:  the premium in excess of intrinsic comprised of:

    • imagination extrinsic: a premium derived from hard-to-approximate, low probability states of the world that presumably benefit the asset 

    • liquidity extrinsic: a premium built on “networks of confidence” which re-assure that you can convert an asset to cash. Lacking this feature an asset would burden your liquidity profile

Asset Value = Intrinsic + (Imagination Extrinsic + Liquidity Extrinsic)

The Perspective Of The Marginal Buyer

With our elements defined, we can now discuss setting the price for an asset. Price is a prerequisite for liquidity. Think of it this way, the value of an asset, for example, shelter exists independent of price. If there is zero bid for your house, but it keeps you dry, it still has value to you. Pricing is what allows an asset to find liquidity. Price is just the bridge between buyer and seller.  A volatile market, with prices ripping in every direction without continuity, is illiquid by definition.

The question of who sets the price is really “who gives the asset liquidity?” Let’s imagine a hypothetical order book. We will fill in the bid stack with a taxonomy of players, with decreasing bid price, just like a real order book.

Mapping the bid stack to the elements of value

Let’s map the investor type to the liquidity they provide. We will start with the lowest bid and work our way to the highest.

  • Distressed investors

    Focus on the book value portion of intrinsic.  Distressed investors are using the assets comprising the book value as collateral for their liquidity.

  • Value investors

    Focus on the full intrinsic value. Value investors are using the earnings power of the company’s management + the assets as a basis for their liquidity

  • Growth investors

    Focus on imagination extrinsic. Growth investors are willing to speculate on future paths that may lie over the hills. Their liquidity underwrites the potential for an unchallenged blue ocean.

  • Magical Thinking Investors

    These investors are somewhere in between LSD levels of imagination and playing hot potato. The entire bid is contingent on the belief that someone else might pay more. It’s pure liquidity extrinsic. It’s GME. It’s DOGE. It’s intentionally buying ZOOM instead of ZM or SIGL when Signal is actually private. 

    This is not entirely ridiculous. Holding currency for its liquidity value, can be thought of as magical thinking. This is not derogatory. If you believe unpegged fiat is a “shared story” then it’s a monument to human cooperation. (Of course, trying to pay your taxes in anything other than USD will lead to knocks on your door by men in ill-fitting suits and sunglasses…so maybe I’m discounting military coercion too much.)

Liquidity Extrinsic and The Bid Stack

  • Liquidity premiums can exist anywhere and everywhere across the bid stack.

    Faith in liquidity increases the demand for an asset since it is easily convertible back to the fiat your obligations are denominated in. If you did not trust liquidity, you would need to hold more fiat in reserve just in case. This would reduce your demand for assets.

  • Liquidity extrinsic becomes a higher proportion of the extrinsic as you ascend the stack.

    We already saw that the marginal “magical thinking” bid is entirely made of liquidity premium. It can be reasonable as in the case of fiat currency, or wildly speculative like Black Lotus Magic cards. What happens at the distressed end of the stack where there is no liquidity extrinsic? 

    This is the zone where fundamentals are fully central. In a recent interview on Corey Hoffstein’s Flirting With Models, volatility manager Cem Karsan explains:



    In the very long term, all that matters is cash flows. At some point you’re gonna have a liquidity crisis and when the liquidity is not available, companies have to create their own liquidity and that’s where fundamentals matter…they matter, to the extent that they are necessary for purchasing their own stock or buying other companies.

    I’ve used this analogy before, it’s kind of hokey, but I can’t think of a better one. If you’re on an airplane, 30,000 feet off the ground, that 30,000 feet off the ground is the valuation gap. Valuations are really high, but those engines are firing. Are you worried up in that plane about the valuations or are you worried about the speed and trajectory of where you’re going, based on the engines, based on the flows? The flows are what matter for where you’re going.

    But when all of a sudden those engines go off, how far off the ground you are is all that matters. And so, [valuation] is more of a risk management tool, and ultimately it really matters when you have a liquidity crisis.
    It also matters if rates were to go back to 8, 9, 10%. Something crazy again, where nobody can borrow money, and there is no liquidity. Cash flows are all that matters again and we have a world where fundamentals are all that matters. So I want to be clear. It’s not that fundamentals don’t matter at all, it’s that they don’t matter in a world of massive liquidity. 

    Read the boldfaced words again. You should be uneasy. The textbooks, with their dogmatic models of DCF and valuation techniques, appear to be preaching a style of investing that gets lost in the liquidity. We’re going to need a closer look.

Checkpoint

  • We have defined our elements of value: 

    • Intrinsic (this is not just book value but includes a sum of reasonably visible future cash flows)
    • Imagination Extrinsic and Liquidity Extrinsic

  • We have identified our “bid stack” of distressed, value, growth, and “magical thinking” investors

  • We have mapped the bid stack to the elements of value

  • We know that as we get to higher marginal bids, liquidity premiums dominate the price.


How An Asset Flows Through Tiers Of The Bid Stack

The liquidity an asset receives depends on which class of marginal buyer is setting the price. Flows are a response to how the top bid perceives changes in the element of value they are focused on.

We’ll look at 2 examples.

1. When Intrinsic Value Goes Up, And The Stock Falls

If distressed or value investors represent a stock’s marginal liquidity there is a limit to how lofty the valuation can get. While we have allowed our version of intrinsic value to include a multiple that multiple has a speed limit. The limit isn’t a hard number on a street sign, but something on the order of the inverse of the discount rate which accounts for both risk and cost of money. Even if we use a 0% interest rate, there’s still a risk premium. If we are aggressive and say that’s only 3% we are talking about the intrinsic multiple in the realm of 33x. The exact number doesn’t matter, but subjectively speaking, this would be on the expensive side of intrinsic.

To break into a higher gear of valuations, the marginal bid has to come from imagination extrinsic focused investors.  The multiple that rests on is effectively unbounded (AMZN traded at egregious multiples in its early years and in hindsight they were not egregious enough). But this also works in reverse. If a stock’s imagination extrinsic is offensively large, its success can actually lead to poor returns.

How?

The intrinsic value increases as it makes profits, but its extrinsic value declines. The number of forked paths ahead of it evaporates faster than its fundamentals could ever make up for. It’s the same feeling you’d have if you bought a put option on a stock, and lost money even if the stock fell. You overpaid for the extrinsic which receded more than stock’s decline.

Turning to another episode of Infinite Loops, here’s @maxarb colorfully explaining:

Somebody said to me once, a smarter, older business guy, a board member that I know is like, “If you have a fast blind growth kind of company, the last thing you want to do is really get revenue. Once you get revenue, the last thing you want to do is get profit.”

I was younger then. I didn’t understand what he
was saying. And he was just like, “You don’t understand. There’s multiples that they sell at. And once there’s EBITDA, nobody cares.” And now you’re getting multiples of EBITDA.

He’s like, “I want multiples of shit. I want
multiples of magic.” And I asked, “Why?”

He goes, “Because once people have something they can poke
holes at, then you have a really big problem there. Then you have to play defense.” And everybody knows that once somebody casts a stone and there’s a crack in the window, playing defense becomes really, really difficult. If you’re throwing a stone at Space X or Tesla, it’s like, all right, well go ahead. What are you  throwing it at? There’s nothing to throw it at. It’s not there yet.

Your marginal liquidity rolled down from a price invariant buyer to one that uses Excel to justify its bid.

2. When Liquidity Is The Only Element Of Value

Suppose you think the upcoming earnings matter. Your tacit assumption is that the best bid cares about intrinsic or possibly imagination extrinsic. You must believe value and/or growth investors are in control. It’s comforting to think so. Although it’s not perfectly clear whether that’s reality. It could also be the case, that enough people think that value and growth investors are driving the flows, so it’s safe to focus on what they might focus on. That world would not look different if you just observed news and prices. But it is different. It underestimates how much liquidity itself drives price.  

Recall how DOGE and GME were being driven entirely by liquidity.  Here’s Alameda’s Sam Bankman-Fried on The Odd Lots podcast:

A beautiful moment was the moment that Robin Hood banned buying a Gamestop. [It was beautiful] because of what happened next…[when] GameStop crashed because they could only sell [and people]  couldn’t buy but they had money. Instead they bought what is, in retrospect, the only possible answer to this question, as soon as you hear it  you’re like “Oh, of course that’s what they bought”.

They bought Doge.

So, as soon as Gamestop started crashing Dogecoin 10x’d. And it’s like absolutely beautiful [because] Gamestop and Dogecoin are like very, very similar products.

As you realize that liquidity alone can drive value, it starts to feel that it totally swamps the sensible stuff that textbooks talk about. One day business considerations are driving price, the next day WSB shows up. Even if they help you it’s unnerving. Like making money on a trading error.

Other favorite examples of liquidity being the sole driver of value:

  • Archegos levered longs that imploded in March 2021. Viacom dropped >50% from its high and never recovered.
  • Amaranth blew up on a long March/April gas futures bet, which ultimately collapsed from over $3 to zero.

There is a commonality between these trades. The peak prices were built entirely on the outsize liquidity it required to drive them there. But liquidity is an unpredictable mistress. The legend I’ve heard is Brian Hunter offered to sell the spread back to Centaurus, pleading that he would offer a discount from the $3 price. 
Allegedly Centaurus founder John Arnold bid him a mere 50 cents. Offended, Hunter pleaded, “but it’s trading $3!”

Arnold replied: “Because you put it there. It’s worth $.50” 

[This is all lore from my cheap seat…if anyone knows the real story, I’d love to update this post with it!]

If liquidity is driving price, but people are talking about fundamentals, it’s like being on a plane to Calgary but everyone’s talking about how fun it’s going to be when we touch down in Vegas. Somebody packed the wrong clothes.

The Most Practical Takeaway: Know Your Game

These 2 examples highlight regime changes when the category of marginal buyer changes and with it the focus on intrinsic or extrinsic.  If you are playing the extrinsic game, your dashboard needs to be concerned with liquidity and flows. Momentum investors don’t cite fundamentals. Vol traders might not even know what the underlying even does. Their inputs match the game they are playing.

Returning to Cem Karsans interview:

[If] you’re betting on something that doesn’t, in the short term, have anything to do with the outcome, you are in a really dangerous situation…Until the liquidity situation changes, other than the fact that other participants are playing the same game and affecting those flows, [fundamentamentals are] not what’s ultimately driving price.

Ask yourself, is the asset moving from one part of the bid stack to another? You may have started shorting GME last year based on valuations but once it turned into a virus you were still using a telescope when you needed a microscope. There’s a reason famous short-sellers like Chanos or Cohodes are not known for playing the valuation game from the short side. Instead, they are hunting for frauds. The reason is there is no limit to arbitrage on the short-side (see Shorting In The Time of ShitCos) , so they are playing a game with a catalyst. With a visible horizon for price and truth to converge.

How about investors who drift into calling bubbles? If you want to profit from bubbles imploding, you must identify when the focus of an asset moves from its extrinsic to its intrinsic. From our framework, it’s clear that it is a question of liquidity. Many investors already understand this leading to an obsession with macroeconomics and central bank balance sheets. [When I read about those topics I feel as overwhelmed as when I first started learning options. As a 21-year old, I’d need to stop every few moments, “ok, so if I short a put, and puts make money when stocks go down, then I must be…long!”. Nowadays it’s, “ok, so if the Fed injects liquidity, it’s balance sheet decreases. Wait, I mean increases. Wait…ahhh, make it stop!” This hasn’t stopped a bubble in macro gurus…I guess the tourists who would have went to Vegas in 2020 found a new club to hit.] Making money from bubbles is diabolically difficult even if you become an expert in M2 or Japan. There are creative approaches that do not take them head on.

The main point is to match your tools and horizon to your game.

Wrapping Up

Between stock valuations, crypto returns, and a headlong embrace of risk within months of an economy-halting pandemic, the role of finance as manifested by investing looks like a farce. Market watchers are asking themselves:

  • Is the bulk of what we are taught about investing poorly matched for the timelines we buy and sell on?
  • Is a 5 or even 10-year track record noise if liquidity regimes delay any verdicts? It feels like the betting windows at the track have never been busier, but we never see the result of a race. 

As I’ve listened to interviews, read, and thought about what the hell is happening, I’ve come to believe that markets are not absurd. It’s our illusions about how tidy they are which is absurd. By asking ourselves meta-question about who the marginal buyer is and what their focus is we can tranche the investing world into different games. Not every tranche is driven by the same inputs. Some layers in the stack are concerned with extrinsic and others intrinsic.

Surviving within each tranche demands its own set of tools to furnish bids. To give liquidity to the assets it targets.  On the low end, cash flows matter because they create their own liquidity. On the high end, external liquidity drives most of the value. Imagine a new set of greeks for this segment. ∂ Extrinsic / ∂ Liquidity. The second derivative of that sensitivity would tell you that the sensitivity itself is changing. That’s what happens as you move from one tier of the stack to the next.

The conventional finance education omits so much of how the practice of investing manifests. Ultimately, your goal is to have the outcomes of your bets have something to do with your rationale. To do this, go ahead, read the glossary of corporate finance terms. Finance is totally real. But skip the investing sections. They present the practice of finance as being built up from the glossary. Instead, bind Matt Levine’s daily columns into a book, and work backward from the fact that markets are made of humans. Investing is about behavior and that is closer to horoscopes than balance sheets.

Darrin Johnson On Flirting With Models

Independently Shorting Volatility with Darrin Johnson (Podcast)
Corey Hoffstein’s Flirting With Models

Darrin Johnson is an options trader and the first independent trader Corey’s had on the pod. Considering Corey’s show focuses on institutional and cutting edge investment professionals, it says a lot that he had Darrin on the show. I’m not surprised, I’ve been following Darrin on Twitter for years and impressed by his understanding of options trading. I have always believed that option trading is an apprentice activity. I cannot imagine how difficult it would be to learn the game with the guidance of masters. Darrin has managed to cobble together that guidance from a variety of sources including Euan Sinclair’s books, Twitter, hiring grad students to walk him through the academic math, running countless simulations, and detailed reconstructions of financial products.

Here are some of my favorite aspects and insights from the interview (with my commentary):

  • Darrin’s entrepreneurial path before he even found his way to trading is worthy of an interview of its own.
  • The importance of building sims instead of backtesting as a way to get more samples. For those of us who trade for firms, we benefit from the collective osmosis of many traders discussing trades and situations in detail. All those morning huddles and afternoon meetings help us build a mental library of counterfactuals. Darrin did the next best thing…build simulations, knowing that a backtest is a single version of what could happen. This is crucial to get a fingertip feel for how positions behave.
  • The idea of pricing out financial products to the penny. Darrin called it “back-office” kinda stuff that retail traders don’t do. Corey said he does this too. This is exactly what you do at a mm/arb shop. As a clerk I remember building giant spreadsheets to price fair value for ETFs. This is not optional work. You will use those skills to attack new products and understand the frictions to arbitrage.
  • At around the 40:00 minute mark Darrin explains why he concentrates his selling on at-the-money or meaty options not the wings. He makes the correct insight: when you sell tails, you need to capture the entire premium. The hit ratio of selling tails is high but when you lose you lose many multiples of the premium. If you fail to collect the full premium, it will not make up for the losing trades. The difficulty of selling tails is even trickier yet. Darrin explains how betting against longshots leaves you uncertain if you have an edge in the first place. In my words: good luck differentiating between a 50-1 shot vs a 100-1 shot. That’s the difference of 1 probability point but it’s massive in payoff space. I discuss further in Tails Explained.
  • Here’s a more subtle insight from the interview. Darrin tries to find the structure that has the best payoff to his vol forecasts or thesis. Notice the subtext. If there’s a “best” there must be a “worst”. This is the basis of relative value trading — buy the best payoff and sell the worst payoff contingent on the vol forecast coming true. For example, if you thought skew was cheap in the oil complex compared to macro backdrop, you could buy the cheapest puts across the oil and products suite. You could buy some ratio of oil puts and selling RBOB or HO puts depending on how how you think the macro stress plays out. Now you might want to be outright long the vol forecast coming true so you might not want to turn this into a basis trade (the advantage of a basis style trade is you can likely do it bigger). Or you could choose to buy oil puts and say sell puts on an equity index where the stress has been priced in. Because you’d be taking an even larger basis risk than staying within the oil complex, you would size the trade smaller than the oil basis trade, but perhaps larger than an outright long oil vol position. The point is there is a lot of creativity on trade expression that balances edge and basis risk.

Since the interview was so good, it got passed around quite a bit on Twitter. In one of the ensuing discussions, I offered my down-to-the-studs view of what options trading really is:

There’s nothing magical about options trading. Paraphrasing Darrin, the intellectuals who are drawn to it prolly need a more blue collar view. Step back and think about what the market needs. What risks doesn’t it want to hold? Obsess over the who and why, not moments [of a distribution]… For years the “job to be done” in vol was be willing to pay theta The marketplace was bidding for that role and vol folks that filled it did well. The market “bids” for different roles all the time in vol-land and the job of a vol trader is to fill it. Simple not easy.

@TheSpeculator0, who trades for a firm, astutely observes: It’s not easy to catch the regime change that switches up the roles.

My response:

That’s why risk management is key. The nature of market-making, even if you don’t explicitly have that title, is you lose on the regime change. So you adjust and hope the next regime lasts long enough to pay you for the [money-losing] transitions.

If you want a fuller discussion for the raison d’etre of vol trading, you probably won’t do better than Corey’s podcast with QVR’s Benn Eifert who describes the job as “bringing balance to the force”. I took full notes for you…Flirting With Models: Benn Eifert (Link)

Interviewing Candidates

With the school year rapidly coming to a close, I was reflecting on how fortunate we were to find such an amazing woman to teach our pod comprised of our boys and two of their friends who are matched in age. I feel fortunate because this woman who we entrusted 40 hours a week of exposure to our kids was chosen on the basis of 2 hours of interviews and some reference checks. She was selected from just 4 possible candidates, sourced mostly from Care.com. There’s no way to objectively feel other than lucky.

If our odds were improved in any way, it was because of the other set of parents in our pod. I was blown away about how good they were at asking questions and reading the candidates. Yinh, who has now spent countless hours both screening and interviewing podcasts guests, felt the same. This couple is masterful in finding efficient questions to cut to the core of the candidates. It gave me so much appreciation for that skill especially when we consider the stakes.

Well, this week I came across Graham Duncan’s outstanding post about interviewing job candidates. It was one of those posts I added to my list of influential readings and re-factored for my own future reference. It’s full of practical advice but philosophically it really starts with the idea that interviewing is really a narrow application of a broader art.

From the intro:

The philosopher Kwame Appiah writes that “in life, the challenge is not so much to figure out how best to play the game; the challenge is to figure out what game you’re playing.”

When I try to figure out what game I’m playing, I see that for the last 25 years I have been playing a game of strategy applied to people, a game where over and over I try to answer the question “what’s going on here, with this human?”  In this essay, I make recommendations about candidate selection based on thousands of assessments I have made and my somewhat obsessive interest in the topic.

My goal in this essay is to help others make better decisions on a potential hire, business partner, or even life partner as quickly and as accurately as possible.  It’s made up of suggested action steps and some of the ruminations that underlie them. At the end I include my own assessment of different personality assessments and some of my go-to interview and reference questions.

My single favorite line is an idea I take seriously:

One of the greatest gifts we have for each other, for our children and spouses, for our teammates, is the positive feedback loop we can put someone into purely by believing in them, by seeing their genius and their dysfunction clearly and then helping them construct conditions for the former to flourish.

That emphasis is mine. I consider this to be one of the cheapest forms of human capital and this essay is ultimately about directing that capital.

Towards the end of the post you get a nice primer on personality tests as well as guides for conducting interviews and calling references.

Continue to my own takeaways and the link directly to Duncan’s post, Graham Duncan’s “What’s Going On Here, With This Human?” (Link)


Interviewing Engineers

I enjoyed tech founder Slava Akhmechet’s “super secret proprietary no-nonsense guide on how to interview engineers.” I am unqualified to know how valid it is, but it was fun to read. Find the full post here.

Excerpts

This guide is for interviewing very talented people.

It’s applicable if you’re building an extraordinary team at a hard technology startup. If your startup is technology-enabled, or you’re designing an interview process at a large company, or you’re hiring for well-established roles to do specialized tasks, this guide isn’t for you. You will not find references to “junior vs senior” or “front-end vs back-end” here. From the perspective of what we’re trying to accomplish, specialization is for insects.

There are three things you need to determine about a candidate: talent, judgement, and personality.

Think of hiring an engineer as if you’re buying a race car. The first thing you must look for in a race car is horsepower, because without horsepower the car is useless for racing. The horsepower of engineers is talent. Without talent, engineers are useless for building products, so it’s the first thing you must look for in a candidate. It doesn’t matter how nice the person is, or how hard-working. No horsepower, no race.Talent alone is insufficient. The world is filled with talented people who never get anywhere for a myriad reasons. Laziness, anxiety, fragility, impulsivity, egotism, victimhood, just to list a few. So once you’ve identified talent, you have to determine the shape and quality of its vessel. Where will the person direct their talent? And are they well-adapted to the demands of the external world?

Talent

  • Talent is a combination of speed, working memory, taste, knowledge of the toolchain, understanding how computers work, and ability to program. It’s IQ, but specialized for engineers. IQ is 50-80% heritable, impossible to improve, normally distributed, and strongly correlated with success in fields like science and engineering.
  • This matters for candidate selection because someone can improve within their talent band, but they can’t jump talent bands. A person with IQ of 145 (σ=3) is dramatically better than a person with IQ of 115 (σ=1). If you watch both people work, it’s like they’re from different galaxies. So your job as an interviewer is to find out the candidate’s talent band.

Judgement

  • Judgement tends to be weakly correlated with talent, and comes down to this: there is a difference between a tinkerer and an engineer. They’re close, but they aren’t the same thing. Tinkering is building a Rube Goldberg machine for the sheer delight of building it. Engineering is discovering and satisfying (often unintuitive) constraints. The tinkerer works for the machine. The engineer makes the machine work for him.
  • Most engineers aren’t tinkerers, they’re in it for the money. Don’t hire those because they have no soul and hanging around them will slowly poison your own soul by osmosis. Conversely, many tinkerers aren’t engineers. Don’t hire those either, because they’ll build beautifully complex structures that serve no purpose other than their own existence. You want people who take great delight in building Rude Goldberg machines, but balance it with a broader sense of what they’re trying to accomplish.
  • Another way of thinking about it is that talent is a combination of general aptitude and programming tactics. Judgement is programming strategy.

Personality

  • The easiest way to think about it is in terms of the big five personality traits. These are kind of like Myers Briggs, except real. The three traits you especially care about are conscientiousness, agreeableness and neuroticism. Psychologists have precise technical definitions for these terms, but in plain language you’re trying to find out (a) whether the candidate is lazy or hard working, (b) are they an asshole, and (c) are they going to be stuck in analysis paralysis and invent life emergencies for themselves all the time instead of working.
  • The bad news is that you can’t find any of this information out until after you’ve hired the candidate. You can set up a low pass filter that might trap a few bad apples, but almost everyone is on their best behavior during interviews. Personal flaws rarely come out until long after the person is working for you. My sixth sense for picking out talent and judgement is pretty good. But for personality it’s only slightly better than random. So unless you have magic powers in this area, set up a low pass filter and later fire bad personalities as they reveal themselves.

Random observation

I’ve now noticed the respect for the Big 5 Personality Traits in multiple places. They’re beloved in tech world.  Slava said the Big 5 “are kind of like Myers Briggs, except real.” Marc Andreesen focuses on ‘conscientiousness’ trait in his interview on education (my notes here). And as we saw earlier, Duncan wrote “Within psychology, it’s the equivalent of gravity, and at this point, nearly everyone in academia finds it a useful mental model for personality.”

Personally, I scored around average for neuroticism, extraversion, and openness. I scored over the 90th percentile in conscientiousness (yay, allegedly) and agreeableness. Of course, as a trader I find agreeableness to be a backhanded compliment (see my post Being a Disagreeable Investor).

At any rate, when I hear the words “Big Five” I still think of going on safari.


More useful posts on interviewing:

  • Cedric Chin’s Using Head Fake Questions To Achieve Your Career Goals (Link)
  • First Round’s 40 Favorite Interview Questions from Some of the Sharpest Folks We Know (Link)

Bubbles: Knowing You’re In One Is Not Even Half The Battle

Select excerpts from Aaron Brown and Richard Dewey’s paper:

Toil and Trouble, Don’t Get Burned Shorting Bubbles (SSRN)

It was not a mystery that there was a bubble in subprime from 2005-2008. That did not mean shorting it was an easy trade. With the benefit of hindsight, we can learn about the risks of shorting frothy assets that may even be a bubble.


From the abstract:

Bubbles are among the most puzzling and controversial phenomena of financial markets. Although rare, their cumulative impact on both investor returns and the broader economy can be great. One particular question that has motivated research is why shrewd short sellers don’t prevent excessive price increases. The “limits to arbitrage” idea argues that correcting inefficient market prices is neither easy, cheap nor riskless. The “rational bubble” literature identifies situations in which being long the bubble is a better trade than being short, even if investors know for certain the bubble will pop.

We examine the “short subprime” trade from 2005 to 2008 to evaluate these and other explanations. We argue that the short subprime trades had more risk than is commonly appreciated. We discuss how the opaque and illiquid nature of subprime mortgages deterred some investors from purchasing CDS contracts and note that other investors assessed the risk of counterparty failure, government intervention and unknown time horizon to be sufficient enough not to purchase CDS contracts.

Talking to investors who saw the bubble and passed on shorting it, instead opting for alternative strategies:

We interviewed and analyzed the internal research of several investors who evaluated the short subprime mortgage trade and decided not to purchase CDS contracts and present some of their reasoning below.
    • The Basis Trade: Magnetar Capital in Chicago.

      Magnetar did not cooperate with the media, so their story has not been widely told. Magnetar reportedly employed a strategy whereby they purchased the riskiest equity tranche in many CDOs which often offered double-digit returns. They used this positive carry to pay for protection on the AAA tranches that most investors assumed were safe. Magnetar appreciated that the correlation between the safest AAA tranche and the lowest quality equity tranche would be close to one in a crisis due to the way these securities were constructed.

    • Picking-up-the pieces trade: Soros and Tepper

      Soros

      Perhaps the safest way to profit from the subprime mortgage meltdown was the time-honored method of picking up the pieces at the bottom. George Soros and his Chief Investment Officer Keith Anderson smelled opportunity and hired two ex-Salomon Brothersmortgage experts, Mason Haupt and Howie Rubin.

      Tepper

      David Tepper purchased shares of Citi and Bank of America near the bottom, helping his Appaloosa fund return 120% in 2009 on $12 billion in capital.

    • Convex-listed hedges correlated with a downturn: Talpins and Dalio

      Ray Dalio at Bridgewater and Jeff Talpins at Element Capital are rumored to have purchased futures or options on government bonds that would rise in value if the Fed aggressively cut interest rates. Many on Wall Street believe that Element purchased Eurodollar options in 2007 that helped his firm generate returns of 26.4% in 2007 and 34.9% in 20083. Talpins has posted annualized returns north of 20%, without a single losing year in the decade that followed. And simply being long volatility in equity markets, fixed income or currency markets paid off nicely for many traders. The key to these trades is that they removed some of the unattractive aspects of the subprime trade, by using more liquid instruments, waiting for the crisis to materialize or constructing more nuanced expressions.

The collection of people that did these trades: George Soros, David Tepper and the team at PIMCO are investors with long-term track records. They made their money quietly, in sensible trades over several years, and were also able to put large amounts of capital to work.

The sobering difficulty of the short subprime trade:

Betting against subprime mortgages worked, but it was somewhat of a Goldilocks trade: it required default rates to get high enough to generate profits on your insurance, but low enough that the banking system survived to pay you and that the government didn’t help out borrowers at your expense.

Current backdrop:

As we write this analysis in the first quarter of 2021, financial market pundits are calling bubbles in everything from cryptocurrencies and TSLA to SPACs, high-end real estate and, most recently, stocks hyped on Reddit.

My takeaways:

    • Shorting subprime looked like a hero trade but the path was painful and uncertain. You needed to weather the negative carry and margin calls on bilateral trades with banks for years. And you needed to bet against on institutions that were not bailed out.
    • Taking a bubble on straight ahead looks like foolish risk reward, especially if the bubble is “rational”1 and has no clear correcting catalyst.
    • Need to think about the risks and incentives of the system. For example, if you believe bonds are currently overpriced and shorting them, even with their low-yield and therefore relatively small negative carry, you cannot ignore the possibility that the rules can be tampered with in the name of the system. Just as banks were bailed out, it’s not impossible to imagine a yield curve control policy (YCC) similar to post-WWII would cap any upside on a short Treasury trade.
    • The paper describes what you are up against eloquently:

      The reality is that structuring good trades is often every bit as difficult as forecasting. This is particularly true if a trade is contingent on a crisis materializing, when pricing is less reliable, liquidity dries up and contractual obligations are sometimes not honored. In these instances, trade construction is everything. Even if an asset price bubble can be confidently identified ex-ante (no easy task), making money from the bubble is perhaps equally challenging….

      This separates opinion havers from risk-takers. Getting the right odds on the right contingent payoff in the state of the world that matched that payoff. And then actually being able to collect. Having an opinion on markets is like have a business idea but no ability to execute.


Great Grandmother’s Day

We hosted Yinh’s family for a bbq pool party. Yinh’s mother is one of 12. Most of them live in the Bay Area so we had 40+ family members here yesterday. I was the tallest. By a lot. I am also the hairiest.

Yinh’s grandmother (mom’s side) also visited. This woman had 12 live births (no twins). She has been pregnant for much of her adult life. I seriously feel moved just being around her. She embodies peacefulness, a living treasure.

With such an unusually large family, you get some unusual, um, facts I guess. Here’s two:

  • The grand-matron has grandchildren older than her own children
  • Some of the grandchildren were nursed by their mother and their grandmother because they had babies near the same time.

With that, Happy Mother’s, Grandmothers, and Great-Grandmothers Day (I love you mom, thanks for maybe reading this)!

Thinking About Doing

Last week, I talked about the trap of confusing what you do with your meaning. I admit I don’t have too much insight on meaning:

I suspect we all have to create our own meaning… I don’t have an answer for the “why”. I just know that it’s not necessary to confuse “what you are doing” as your “why”. I think we can figure out what we should be doing without a “why”.

Marrying meaning and what you are doing is hard for many of us including me. One of the ways it can manifest is if you fall on an extreme end of the thinker-doer continuum.

  1. At one extreme, you are paralyzed by a lack of purpose and many choices. You spend so much time in your head trying to introspect your way out of the mud and clouds, instead of just setting even an arbitrary goal and seeing it through.
  2. On the other end, you are a whirlwind of activity, maybe even accomplishments, and find yourself in a crisis of self-rationalization because you can’t tie your output back to any sense of meaning.

Maybe the grass is greener, but I think #2 is a better place to be since purpose and meaning are elusive no matter where you are. I have made it clear that I’m in the camp of “get out of your head”. Instead, I recommend get unstuck and move.

So putting the “why” aside, and accepting that you need to get moving, let’s examine perspectives on what you should be doing.

Ways To Fail

How to spend your time is a question that many grapple with (if you don’t, consider yourself extremely lucky). The reason is that we must all balance our goals with what we enjoy doing. You’ve all heard the “follow your passion” advice and the backlash to it — “you become more interested in things you’re good at”. There’s also the parable of the old man who gets the kids to stop playing on his lawn by offering to pay them a dollar every day that they do play on the lawn. As human nature would have it — they predictably stop.

Ultimately, neither view is complete. The first advice fails to acknowledge the role of ability. The backlash underestimates the virtuous loop of working on things that interest you even while you are struggling. Eventually, everything gets hard so it’s beneficial if what you are doing naturally appeals to you.

When it comes to meta-questions about what you should do, there’s rarely a cut and dry answer so it’s more useful to identify the failure modes.

  1. Too Much Grit

    I’ve warned you about taking the idea of “grit” too seriously. Grit is important but at some point it’s counterproductive. If you have amusia, save yourself the grief of auditioning for American Idol. Sometimes “hard equal wrong”. One of the best takeaways from David Epstein’s Range is the importance of “match quality” and avoiding “premature optimization”. Cedric Chin illuminates that work in his passage The Trouble With Too Much Grit.

    If you bang your head against a wall long enough you might be doing the wrong thing. The need to be encouraging but realistic is tense. A recent warning I gave to aspirational option traders starts “if your goal is to become rich, the vol career is a bad strategy”. Sorry, I can’t make reality go away.

  2. The Competence Trap

    If you focus too much on what you are good at you can find yourself in what Scott Young calls a “competence trap”:

    If we see our engagement in professions and hobbies as a way of getting rewards (money, respect, achievement or just fun) for the time we invest, we can see how this can create a trap. As you get better at some things, the opportunity cost to learn something else increases. This funnels you into a narrower set of hobbies, passions and work than you might otherwise be capable of.

    Now there is nothing wrong with specialization. But here’s what I find interesting:

    One flaw of a positive feedback loop, is that it tends to exaggerate small differences. Have a really discouraging math teacher may push you off the path to learning more math permanently.

    Thus, I think our interests are artificially narrower than they could be. We could have more interesting hobbies, more diverse skills and more varied professional lives. True, some of the obstacles we face are cognitive—our talents lie elsewhere and so we build on our strengths—but much of the obstacles seem to be affective as well. We stay inside our boxes, not because we can’t climb out, but because we lose the curiosity to see what’s outside.

In Between The Failure Modes

In between those extreme mistakes of:

a) trying to be Michael Phelps when you’re built like Shrek

and

b) avoiding writing because you happen to be really good at coding

there’s a wide range of endeavors that we are suited for. Even though we have natural cognitive differences in intelligence and therefore learning rates, the larger barriers are what Young called affective. The word affective encompasses moodinterests, and motivation.

We are going to focus on the big muscle movement here: motivation.

I know what you’re thinking. “You said we were going to figure out what to do without a why or purpose”. Here Young offers us a view of motivation that sidesteps metaphysical questions. Motivation is driven by 3 basic psychological needs just like water satisfies the physical need of thirst.

Those needs are:

  • competence
  • autonomy
  • relatedness

They are best demonstrated by Young’s example:

So even if you had a natural interest in math, if you feel like you’re no good at it (competence), that doing poorly will result in others thinking less of you (relatedness) or you feel forced to do it for school (autonomy), it’s unsurprising that motivation plummets.

In contrast, if you felt like you were pretty good at math you might get reinforcement from the teacher and peers (relatedness), feelings of skill as you take on new challenges (competence), and you might even seek out to understand things yourself (autonomy). Your interest in math grows, just as it withers for the people who weren’t as lucky.

Why These Ideas Resonated With Me

I’ve been confused about what I’m doing for as long as I can remember. There are pockets of clarity, usually about the 2 feet in front of my face. But I just don’t own any religious or VC-influencer top-down visions of the world. So I think about our needs to belong (and I think our need to feel like we are contributing is part of that even if it’s not the ends in itself). So of course these topics, which make belonging easier, resonate.

  1. Match Quality

    It goes back to the idea of matching your talents to what you do. You will go further for the same level of effort if you get this right. The guidance counselor is looked down upon as a joke profession. If it is then it’s because of the people who occupy it not because of its function. Guiding children and teens’ minds strikes me as one of the highest leverage objectives of a society. The guidance counselor might be an impotent channel for this but the objective of matching potential to opportunity is both useful and merciful.

  2. Premature optimization

    This is the other side of the “match quality” coin. Parents should be careful how they label children because they listen and internalize — “he’s an athlete”, “she’s pretty”. Try to encourage children in ways where they don’t subtly hear another door closing. Like adults, children are prone to think in false binaries. You are a good writer OR good at math. You are good at drawing OR good at sports. The tradeoffs in how we treat kids are everywhere. Redshirt your child in kindergarten so they can be the oldest in the class, but then what happens when they are bored?

Think of all the possible things you could do with your time. When you come home from work, how small is that menu compared to the full conceivable set? It’s a hypothetical question, and it gnaws at my thinker side. It’s been helpful to keep in mind the idea that our motivations are a function of competence, autonomy, and relatedness. The competence trap reminds us that our choices are wider than we think and as we debate what to do, our aptitude is only one consideration. There’s plenty of area under our natural limits to explore regardless of how low those limits might appear. If the prospective action fills up the buckets of autonomy and relatedness it can still be extremely worthy to pursue.

Ok, this got pretty long. Hopefully, this has expanded your horizon without ever needing to invoke purpose. If you have an answer to that one, well, you know where to reach me.

Understanding Vega Risk

In a chat with an options novice, they told me they didn’t want to take vol (vega) risk so they only traded short-dated options. This post will explain why that logic doesn’t work.

Here’s the gist:

It’s true that the near-term option’s vega is not large. That is counterbalanced by the fact that near-term implied vols move faster (ie are more volatile) than longer-term vols.

The goal of this post is to:

  • demonstrate that near-term vols are more volatile both intuitively and with napkin math
  • show the practical implications for measuring risk

Near Term Vols Are More Volatile

An Intuitive Understanding

Think of the standard deviation of returns that a stock can realize over the course of a week. If there is a holiday in that week the realized volatility will likely be dampened since there are 4 days of trading instead of 5. If Independence Day falls on Friday, Thursday might see even lower volatility than a typical trading day as fund managers chopper to the Hamptons early. On the other extreme, if a stock misses earnings and drops 25%, then we have a Lenin-esque week where a year happens. The range of realized volatilities is extremely wide. This requires the range of implied volatilities to be similarly wide for a 1-week option. Those large single-day moves are diluted when they are part of a computation for 1-year realized volatility (there are 253 trading days in a year).

This concept is easily shown with a “volatility cone” (credit: OptionsUniversity)

Here we can see the standard deviation of realized volatility itself declines as the sampling period lengthens.

The Napkin Math Understanding

The intuition for why the range of short-dated volatility is wider than long-dated volatility is easy to grasp. To cement the intuition let’s look at a numerical example.

Consider:

A weekly option [5 days til expiry]

Assume the stock’s daily vol is expected to be 1% per day. The fair implied vol can be computed as follows:

IV = sqrt(.01² x 5 days x 52 weeks) = 16.1%1

Remember variances are additive not standard deviations so we must square daily vols before annualizing them. We take a square root of the expression to bring it back into vols or standard deviation terms.

Ok say 1 of those days is an earnings day and is expected to be 3% daily vol.

IV = sqrt([.01² x 4 days + .03² x 1 day] x 52 weeks) = 26%

Look what happened.

The single-day expected vol jumping from 1% to 3% means there is more variance in that single day than the remaining 4 days!

.01² x 4 days < .03²

How did this earnings day affect the fair IV of a longer-dated options?

A 2-week option [10 days til expiry]

 IV =  sqrt([.01² x 9 days + .03² x 1 day] x 26 bi-weeks) = 21.6%

A 1-month option [21 days til expiry]

IV = sqrt([.01² x 21 days + .03² x 1 day] x 12 months) = 19%

The increased vol from a single day is clearly diluted as we extend the time til expiry. When we inserted a single day of 3% vol:

  • The 1-week option vol went from 16% to 26%. 10 vol point increase.
  • The 1-month option went from 16% to 19%. 3 vol point increase.

To understand why this matters look at the effect on P/L:

Remember, the vega of the 1-month straddle is 2x the vega of the 1-week option.

    • The 1-week straddle increased by 10 vol points x the vega.
    • The 1-month straddle increased by 3 vol points x 2 x the vega of the 1-week straddle

      10x > 6x

      The 1-week straddle increased in price 10/6 (ie 66%) more than the 1-month straddle!

      (This is why event pricing is so important. The astute novice’s head will now explode as they realize how this works in reverse. You cannot know what a clean implied vol even is unless you can back out the market’s event pricing)

Practical Implications For Measuring Vega Risk

Comparing Risk

So while a 1- month ATM option has 1/2 the vega of a 4-month option2, if the 1 month IV is twice as volatile it’s the same vega risk in practice. You need to consider both the vega and the vol of vol!

In practice, if I tell you that I’m long 100k vega, that means if volatility increases [decreases] 1 point my position makes [loses] $100k. But this risk doesn’t mean much without context. A 100k vega position means something very different in a 1-week option versus a 1-year option. Looking at a vol cone, we might see that 1-week implied vol has an inter-quartile range of 30 points while 1-year vol might only have a 3 point range. You have 10x the risk if the vega is in the weekly vs the yearly!

Another way of thinking about this is how many contracts you would need to have to hold 100k vega. Since vega scales by sqrt(time) we know that a 1-year option has √52x or 7.2x as much vega. So to have the equivalent amount of vega in a 1-week option as a 1-year option you must be holding 7x as many contracts in the near-dated.

Normalizing Vegas

It’s common for traders and risk managers to normalize vega risk to a specific tenor. The assumption embedded in this summary is that volatility changes are proportional to root(time). So if 1-week volatility increased by 7 points, we expect 1-year vol to increase by 1 point.

This is an example of normalizing risk to a 6-month tenor:

Observations:

  • Your headline raw vega is long, but normalized vega is short
  • Your 2,000 vega in a weekly option is more vol risk than your 10,000 vega in the 6-month
  • You want the belly of the curve to decline faster than the long end. This is a flattening of the curve in a rising vol environment and a steepening in a declining vol environment.
  • If the entire vol curve were to parallel shift lower, you’d lose as you are net-long raw vega.
  • If we choose to normalize to a different tenor than 180 days, we would end up with a different normalized vega. The longer the tenor we choose, the shorter our normalized vega becomes (test for yourself).

Critically, we must remember that this summary of net vega while likely better than a simple sum of raw vega is embedding an assumption of sqrt(time). If you presume that vol changes across the curve move in proportion to 1/sqrt(t), the value of calendar straddle spreads stays constant. At this point, you should be able to test that for yourself using the straddle approximation in the footnotes. This would imply that as long as your total normalized vega is 0, you are truly vega neutral (your p/l is not sensitive to changes in implied vol).

As you might expect, that assumption of sqrt(time) vol changes across the curve is just a useful summary assumption, not gospel. In fact, on any given day you can expect the curve changes would deviate from that model. As we saw above, the bottoms-up approach of adding/subtracting volatility with a calendar has uneven effects that won’t match up to sqrt(time) rule. Your actual p/l attributed to changes in volatility will depend on how the curve shifts and twists. Perhaps the decay rate in a vol cone could provide a basis for a more accurate scaling factor. It does require more work plus scaling to time allows us to normalize across assets and securities more understandably rather than using some empirical or idiosyncratic functions.

Conclusion

Just because the vega of a longer-dated option is larger doesn’t necessarily mean it has more vol risk.

  • We need to consider how wide the vol range is per tenor. We looked at realized vol cones, but implied vol cones can also be used to approximate vol risk.
  • We need to recognize that a steepening or flattening of vol curves means the price of straddle spreads is changing. That means a vega-neutral position can still generate volatility profits and losses.
  • Changing straddle spreads, by definition, means that vol changes are not happening at the simple rate of sqrt(time).
  • Measuring and normalizing vols (or any parameter really) always presents trade-offs between ease, legibility/intuition, and accuracy.

Maxen Turns 5

Normalcy is snapping back quickly.
  • We got a sitter Friday night and went to a dive bar (Retro Junkie in Walnut Creek). Fireball was involved.
  • I joked on Twitter about the need for some overfunded startup to tackle the scheduling Tetris that comes with trying to book summer camps. Since nobody responded with “bitcoin fixes this” I presume it’s hopeless.
  • Maxen turns 5 today. Here’s to hoping the super-soaker event doesn’t turn into a super-spreader event.

When Zak turned 5 I wrote him a long letter that I will give him when he moves out one day. I’ve been thinking about what I’m going to write for Max lately. The nurses in the maternity ward warned us. The way he screamed when he was hungry was unusually intense based on the sample they had seen in SF baby factory known as CPMC.

Max is brooding and extremely hot-blooded. Nothing like his older brother or his parents. He keeps us all on our toes. He’s the family clown. He’s also the one to grab you around the neck at random, kiss you on the lips, and declare his love to you. I used the Chris Joke last year on Max’s birthday…I don’t know if we are saving college money or bail money.

5 years ago:

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