Michael Recce – Tim Cook’s Dashboard

**Patrick O'Shaughnessy** (0:00)
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Hello and welcome everyone. I'm Patrick O'Shaughnessy and this is Invest Like the Best. This show is an open-ended exploration of markets, ideas, methods, stories, and of strategies that will help you better invest both your time and your money. You can learn more and stay up to date at investorfieldguide.com.

**SPEAKER_2** (0:59)
Patrick O'Shaughnessy is the CEO of O'Shaughnessy Asset Management. All opinions expressed by Patrick and podcast guests are solely their own opinions and do not reflect the opinion of O'Shaughnessy Asset Management. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions.
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**Patrick O'Shaughnessy** (1:22)
My guest this week is Michael Recce, the Chief Data Scientist for Neuberger Berman. The topic of our conversation is the use of data in the investment process to help cultivate what is commonly referred to as an information edge. I call the episode Tim Cook's Dashboard because of an interesting question that Michael poses. If you are the best Apple analyst in the world with Tim Cook's private business dashboard, telling him everything going on in the Apple universe that day, what might that be worth? Effectively, Michael's goal is to recreate the equivalent of a company dashboard for many businesses, helping analysts understand the fundamental health and direction of companies a bit better than the market does, and in so doing, creating an actionable edge.
This is a daunting task and you will hear why. It requires both a fundamental understanding of business and of data, statistics, and methods like machine learning. In our own work, we found machine learning to be useless for predicting future stock prices, but extremely useful for other things, like extracting and classifying data. This conversation can get wonky at times, but as listeners know, that is the best kind of conversation even if it requires a second slower listen. I hope you enjoy this talk with Michael Reece.
Afterward, I highly recommend you invest the time to read a series of posts called Machine Learning for Humans, which I will link to in the show notes. It helps demystify the buzzwords and explain how these new technologies are being used. Now on to my conversation with Michael Reece.
So Michael, we will begin by maybe using your own history in the industry as a means of describing the changes that have happened in data science and how it has been applied to different investing processes. So maybe give us each of your stops and maybe alongside each stop, the major kind of changes and developments and exciting things that have happened in this space.

**Michael Recce** (3:05)
Okay, sure. Thanks. Great to be here. So I think it's probably better to start before the investing industry because by happenstance I ended up doing things which turned out to be very useful. So my initial background was in math and physics. I did graduate work in physics. I abandoned the PhD to go work for Intel.
After five years at Intel, I could tick the box. I could earn a living and didn't really want my boss's job. I was in my late 20s. So I thought, well, what do I want to do? I want to be an AI researcher. Knew enough computer science and engineering, but I didn't know anything about biology. So I did a PhD in neuroscience so I could learn some biology and became a professor teaching medical students about the brain, teaching computer scientists about machine learning.
Graduated about 12 PhDs, but I missed the impact of being in industry. So I helped my students start a couple of businesses. The first one, we were analyzing bank transactions to find white collar crimes. So we were looking for anti-money laundering and we're looking for doing some trade surveillance and things like that. And we had 18 of the top 25 international banks as customers when we sold the business to Wilbur Pincus in 2005 So the second business was analyzing people's online activity to figure out what ad to show them. So in that business, about a million times a second, someone goes to a web page and you have a tenth of a second to decide what ad to show them based upon their history of their clickstream activity and how much to bid for it. And so the reason why that's relevant is you'll see that those types of data actually end up being exactly the types of data you need to look at and machine learning background turned out to be pretty helpful too. But for about 15 years before being recruited from that second business by Steve Cohen to join Point72, I was working essentially for my student running these firms. And the second firm, we were about 1,000 people when I left, I was running engineering. I used to say at conferences, if you have really good students, they employ you. But then I went to work for Steve Cohen as Chief Data Scientist there. We were on the discretionary side of the business, essentially trying to use data to help predict the direction of earning surprise. So I was there for 15 months. The second employer was GIC, Singapore Sovereign Wealth Fund, and I was Chief Data Scientist there. And there we're looking across all asset classes, not just equities and certainly not just events, and they're a long term investor.