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Serendipity, Bridgewater and Something about Ted Lasso

We are not sure what these Two Minute Tuesdays (TMTs) should be about.

Every week a member of our team writes about an unfettered topic and the rest of us are just as surprised as you are. I did not even know what I was going to write about this week – so much so I am writing a TMT about TMTs.

In all seriousness, our objective with TMTs is to not have an objective. We simply want to spark ideas. Our intention is to cultivate a state of “effective serendipity”1 where ideas lead to deeper discussions, fresh perspectives and, potentially, decisions and actions. 

Much like the romantic beauty of our investment processes, maybe this does not sound practical. But, as the infamous dart player Ted Lasso once said: “Be curious, not judgmental.”

“Luck favors the prepared mind.”

The genesis of the idea sprang from a podcast1 where Kenneth Stanley – a machine learning engineer from OpenAI – talked about the paradox of how you can block your own ability to reach an objective by setting it. For a complex problem, we do not know the path – or set of “stepping stones” – on how to achieve it, otherwise it would not be a hard problem. Progress can be deceptive, and the right steps can seem counterintuitive at first.

Financial markets and investing are complex to say the least. Thus, we need to “proliferate” these stepping stones and, in an artificial intelligence sense, look for things that are interesting vs. just fitness. Stanley relates it to diversification in finance, but it reminds me of regimes. 

As we have highlighted in past discussions, regimes help contextualize expected returns or risks based on the state of the market. An interesting example of a risk regime is the absorption ratio.2 If the variance of underlying (group of) assets of the market can be explained by a few factors (or eigenvectors using Principal Component Analysis (PCA) then the market is increasingly “fragile” and susceptible to negative market shocks (i.e., behaving less diversified).

This signal does not predict the direction of the market – it simply expresses that the market is in a state of elevated risk. We are not always trying to make predictions, but simply increasing our absorption ratio of ideas to be in a state of elevated luck. That is effective serendipity.

Talking about luck and serendipity in money management always seems like a faux paus. It seems to undermine the rigorous knowledge, effort, and discipline it takes to be a great investor. Recently, I stumbled upon The Art of Doing Science and Engineering by Richard W. Hamming.3 And there, in the preface, is one of the most esteemed mathematicians and computer engineers refreshingly talking about art, style and luck:

“I have used the ‘story’ approach, often emphasizing the initial part of the discovery, because I firmly believe in Pasteur’s remark, ‘Luck favors the prepared mind.’ In this way I can illustrate how the individual’s preparation before encountering the problem can often lead to recognition, formulation, and solution. Great results in engineering are ‘bunched’ in the same person too often for success to be a matter of random luck.”

He describes his course as that “it covers all kinds of things that could not find their proper place in the standard curriculum.” I like to think of TMTs like that.


At one point I was going to scrap this TMT and write about the Fed, the undervalued Japanese market, or tech earnings. But then I listened to a podcast4 with Karen Karniol-Tambour – the new co-CIO at Bridgewater – where she concluded a talk ranging from the state of capital markets, geopolitical instability and asset allocation, with this:

“You grow up in this industry, and you know that there’s just so much happening in the world that at some level, curiosity is all that matters. Like being a good investor is just being really curious and learning a lot and knowing what you don’t know.”

Be curious.

Important Disclosures & Definitions

1 The Knowledge Project Podcast - Kenneth Stanley: Set The Right Objectives [The Knowledge Project Ep. #148]
2 Kritzman, Mark and Li, Yuanzhen and Page, Sebastien and Rigobon, Roberto, Principal Components as a Measure of Systemic Risk (June 30, 2010). MIT Sloan Research Paper No. 4785-10.
3 Hamming, Richard W. The Art of Doing Science and Engineering. Stripe Press, 2020.
4 Invest Like the Best Podcast - Karen Karniol-Tambour - Macro Headwinds vs. Tech Tailwinds, May 15, 2023.

AAI000582   05/30/2024

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