Ergodicity

The Idea Farm is a newsletter / research database founded by Meb Faber. If you are a long-time reader of this blog, you know how much I love Meb and if you don’t, I strongly suggest you go and check everything that he does.

I have a particularly bad relationship with newsletters: I used to subscribe and then never read them. When you spend 10+ hours a day in front of a screen for work, the last thing you dream of is to put away one laptop to open another one. I would love to read newsletters in between working tasks but I hardly ever landed a job where I could access my private email with my employer’s laptop. You see the issue, I have access to the newsletters only when I have no willingness to read them.

Luckily, the Idea Farm has also a great Twitter profile where, every week, they suggest podcast episodes worth listening to. In a world where there are more podcasts than stocks, a curated list of suggestions is invaluable.

Last week, they indicated a relatively old episode of the EconTalk podcast: Ergodicity with consultant and author Luca Dellanna. The interview was great and I wanted to share with you the passages I liked the most. I already discussed ergodicity in this post; if you do not have a clue what this weird word means, I suggest you go read it. I will not go through the basics again (as my friend Finanza Cafona says, you have to make an effort for this deal between me and you to work).

“It’s not the fastest skier who wins the race, but the fastest one of the one who finishes the race. Meaning that the skiers that we see are the ones who have escaped injury, or serious injury, and are allowed to continue their career. But it is not all about bad luck. There is also something that has to do with time horizons and risk-taking. You might optimize the way that you ski to win the race, and that will lead you to take too many risks that might actually – on one side, they are what brings you to win the race; but on the other side, they also we what can bring you to have a career-ending injury. And of course, that’s bad. And, if instead you want to win a championship, you of course need to manage your risks a bit differently.“

I do not remember who said it, but the best long-term money managers are rarely at the top of the list on any single-year ranking. To win in the long run, it is fine to be consistently in the second quintile, but more importantly, you have to avoid the bottom of the ranking. Because the bottom represents a career-ending spot…even if your performance would later bounce back, that would mean anything if all the money left your fund. Consistency lowers volatility which means higher CAGR.

In contrast, take the copy-trader wannabe. They have to be top of the rank to attract followers to generate fees. From their point of view, being at the top at the risk of ending at the bottom later is a rational strategy.

“One of the names for this is survivorship bias. And, the way to think about survivorship bias is that we don’t see the people who have lost, who have dropped out, who have been damaged, who have been harmed, who became addicted to drugs because of their obsession with winning every race, who took unhealthy risks.“

It is very important to keep survivorship bias front and centre in how we look at the world. As a teenager, I thought that being an actor was something simple: I was seeing this (seemingly) average person on TV and I thought the only thing you had to do was be there.

1. If you are a good actor, acting seems like something anyone can do because it feels so natural. You do not see a good actor next to a bad one (well, almost) to understand how hard it is. A comedian once said that at the Olympics they should allow a normal person to participate in every competition so the viewer can immediately recognise how incredible it is what the athletes are doing.
2. You do not see the group of people that wanted that role and got discarded.

You see the successful manager but you do not see the divorce, the stress, the therapist, the broken relationships, the lack of sleep. Jeez, sometimes you do not even see the belly or the lack of hair. Those are supermen. It is the same weird logic that brings you to think that if you go swimming regularly, you will grow a chest as large as those pro athletes. Only later you realise that the chest is a combination of training AND genes. Ok, this last paragraph has nothing to do with survivorship bias…

“Well, the lesson is more evident when we talk more concretely about what you should do. What’s the optimal level of risk-taking to maximize your wins? And, the answer depends: how long do you want your career to be? How long is the period that matters?“

Luca makes a great example about a skier that has a 20% of chance winning a race but also a 20% of breaking their leg. The skier can only participate in the next race if they didn’t break their leg in the race before. If their goal is to win the championship, many races in a calendar year, then that 20% of ruin is way more relevant than the 20% of winning: they should train to lower the probability of the catastrophic event more than improving their winning chances.

On the other side, if your goal is to become the next Dr Burry or John Paulson, the optimal strategy is to take the cheapest, but very levered, bet on the next recession (the bet has to be cheap because you want to repeat the experiment as many times as possible). No one would care every time you are wrong because no one knows you. But the time you hit the home run, everyone would think you are a genius. To be fair, it is not that Burry or Paulson deliberately took this strategy; but if your goal is to become famous more than rich, this is one way of doing it.

Some unknown analysts apply the same plan when submitting very “out of consensus” forecasts on the next Non-Farm Payroll print or S&P500 yearly target. No one would notice the miss but everyone would be impressed by a great out-of-the-box thinking if they hit (it is pretty trivial to build a narrative after the fact).

There are countless tutorials on how to optimise for SEO, the algorithm, key phrases but in many cases, the way to get to the top (but not to stay on the top) is doing something very different, which means something with very high chances of failure.

There is a key difference between failure and ruin. You want to fail fast, the shorter the feedback loop the better, but as harmlessly as possible. If you aim for longevity, being a free solo climber is not a great choice.

“You only get the average if you’re allowed to continue to play. Even then, you might not get the average because in a small number of plays, you just might be lucky or unlucky, but you have no chance of getting to the average reliably if you’re not allowed to play the game”

A point that I stress quite often on this blog is that stock investors get the “advertised” average return only after staying invested for a very long time (>20 years)…and sometimes not even after that!

There are many “alternative” asset classes, crypto, angel investing, even p2p lending, where this concept of ruin, or game-over, is really important. Getting the “average” is even harder because the distribution of outcomes is heavily concentrated on the tails (for p2p lending is even worse because we have a huge left tail but only moderate positive returns). Massive diversification (even within the same asset class) and rebalancing, cashing some % on your winners, are necessary ingredients to tilt the investor chance on the positive side.

“the reason we sometimes fail to remember is because, again, of survivorship bias. We look at people around us which are wildly successful, and if we aim to get the success, the reality is that we need to take risks. And, not just the kind of risk which is good, like, which only has upside and very low downside. But if we aim to be, for example, the richest person in the world, or the most successful entrepreneur in the world, we need to take risks which come with the possibility of game-over. And that, of course, means that the more we aim to be number one, the more we need to take risks that will decrease our most likely outcome. And, that is a trade-off we need to be aware of.”

Sometimes I think about drug lords as possible examples of reverse survivorship bias. The mainstream narrative is that choosing the drug lord career is akin to Russian roulette: sooner or later, you would end up killed or, if lucky?, spend your days in prison. But these are the drug lords we know. How many there are out there that we are unaware of, the Gustavo Fring-s that got away with it?

I watch too much TV?

“you might be listening at home saying, ‘Well, I don’t want to be number one: that’s too much. I don’t take those kind of risks.’ But, the fact is, is that: if you want to be successful, it’s not just number one, if you want to be successful, even not as successful as the top 10%, but top 20–forget what proportion–you just want to be successful, much of life–and this is, I think, a profound lesson–much of life has the characteristics of the kind of risks we’re talking about.”

There is a deeper lesson here and we have to go back to Taleb. Many people are fine with giving up success to achieve safety. That super boring, soul-crushing government job that makes you unfireble. They try to stay away from volatility, randomness and disorder as much as they can. Which actually makes them MORE fragile in case a Black (but in some cases even a Grey one) Swan happens. When they end up jobless, they have no employable skills at all.

Striving for a higher position, to get a better salary, to be able to SAVE more, to take calculated risks on the market is what makes us more Antifragile. The combination of career and financial risks put us in a safer position.

“for example, if you have a problem with your marriage, it’s very likely to be irreversible. And, when you have a risk of ruin which is irreversible, you cannot average them out.”

Last week I listened to Tim Ferriss interviewing Morgan Housel. Morgan told this story (he first wrote about it some years ago) of when two of his closest childhood friends died under an avalanche, a ski trip that he skipped only by chance. He then said that if each of us would review the movie of our life, we would see many of those close-miss death accidents, situations we might not even realise at the moment.

Between skiing and snowboarding, surfing, horse riding, jaywalking, drunk driving, skin cancer, exploded appendicitis…I have my own stories. And these are the ones I know.

Almost everything in life is non-ergodic, starting from life itself.

“this irreversibility is what differentiates risks that you can take and risks that you be very must be very careful from taking.”

“Irreversibility means you can’t repair it by relying on the law of large numbers.”

“The idea is that the average return is not all that counts. It matters whether you can continue to play.”

The concept of irreversibility is important when dealing with investments that can go to zero but it is also crucial in retirement: the stock market goes up on average but certain paths can lead a retiree to ruin. You know, the Dave Ramsey nonsense of “if your portfolio earns 12% on average, you can safely withdraw 8%”.

“So, for example, Russian roulette, if played by an individual, has this problem of irreversible losses that move the expected average win from, let’s say, $5,000 to $0, over long-term. But, if you see it from the point of view of an hypothetical company that employs Russian roulette players, and they can just hire new people when someone dies, for them Russian roulette will still almost always have an expected win of $5,000.”

The conversation goes on touching on relevant conflicts that can arise between employer and employee. I would not explain again the Russian roulette example in the context of ergodicity, if you are not familiar with it just do a quick research. Here Luca explains well the business model of gambling joints and those brokers that offer high leverage to traders. Their main issue is to find a constant stream of, construction, losers to take their cash. A very high churn is intrinsic in the business model, therefore the a need to find and engage influencers that can bring new meat into the grinder.

“When you’re talking about low-risk activities–low-risk in terms of probability but not in terms of outcome–again, it’s hard to keep these distinct: low chance of a bad outcome but when the outcome does occur, it’s very bad.”

As Taleb says, you should keep some redundancies in your life, do not optimise everything to the max. For example, if you are retired and depend on your portfolio to live, do not keep all of it with a single broker. The chances of IBKR going under tomorrow are very small but not zero, especially if you consider some out-of-the-ordinary risk like a hack or a rogue employee. The bankruptcy caretaker of IBKR might retrieve all your funds after a catastrophic event, but what if it takes them a year? How would you buy groceries next month? I am not thinking of buying a nuclear shelter (actually…living in Switzerland I have one) and filling it with canned food but you know, relatively inexpensive and mildly annoying Plan Bs. Vanguard is great but at a certain point, you might want to use some BlackRock ETFs, just in case. Unfortunately, illegal shit happens, the cheater at the poker table type of stuff.

“One of the main points that Taleb teaches is that, because in the tail you have very few data points, you also have an incredible uncertainty about how the tail looks like. And, that’s why I usually advise people not to think about what the tails may look like, but to assume that the tail looks very bad and think, ‘What about it? How can I prevent it?’ or, ‘How can I mitigate the risk assuming that it happens?“

To materially improve the results of a portfolio that invests 100% in stocks you need to be out of the market in just a few instances. You know, the infamous fact that if you remove the worst 10 days in a year, returns like double or something. So many participants create these ‘risk off signals’ that should indicate when to move from equities to cash. The issue is that the data points are so few, and some so far in the past that you question if the circumstances can even be valid today, that you end up solving for the last crisis and not for the next one (or sacrifice so many returns, from false positives, that you completely give up and revert back to buy&hold).

“Sometimes you do want to expose yourself to risk, and do it in a way that ensures that you’ll keep trying to succeed when things are going to be challenging.“

This is a great example of how to use risk for your own benefit. Same as putting yourself in a situation of mild discomfort at work, because that’s the point where learning happens. Obviously by discomfort I mean giving a public presentation if you never did it, or taking a new task in a field adjacent to what you normally do, not finding a jerk as a boss.

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They touch on many more important topics like: diversification and rebalancing in investing, why the Kelly Criterion is too aggressive (I would say stupid because it gives you false confidence…but it might be indeed better than your finger in the air alone), spotting near-misses, when nonergodicity leads to positive outcomes.

To sum it up

“You don’t just care about the average, you care about the variance–another concept in statistics that measures variability.“

Or as Taleb said, do not be the 6ft tall man who drowned crossing the 4ft deep on the average river.

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