“Efficient market theory is correct in that there are no gross inefficiencies, but we look at anomalies that may be small in size and brief in time.” – Jim Simons, Founder, Renaissance Technologies
Somewhere along the serendipitous journey, I had the good fortune to spend a flash of time with Jim Simons and a few key members of his team at Renaissance – see Jim Simons, Godfather of the Quants: Hiding in Plain Sight. He is certainly one of the great minds of the modern financial markets era – even if he might confess himself to be as or more lucky than smart – and among a short list of the most successful pioneers of the quantitative trading revolution.
Now, one of the most amazing aspects about being in the presence of great minds is that they can shed significant pearls of wisdom so effortlessly, as if in passing. There’s no drum roll or squadron of trumpets that precedes the statement of insight. It simply comes out – mixed with all the other word salad – and it’s up to the witnesses to make sense of the gifts. Or, maybe not.
The video below, created in late 2015 with TED’s Chris Anderson, is not new. However, some of the key insights in this interview continue to age well – and therefore, I believe they are worth memorializing in the Alphacution Feed. Simons’ comments on data and strategy capacity are fleeting, but since they are so relevant to so much of our current research, I figure they are more impactful being said by a legend than by me. (The discussion on Renaissance begins around 8:35 into the video.)
Here are the key passages from the video that avid readers of the Feed should reflect on:
- “We stayed ahead of the pack by finding other approaches; shorter term approaches to a certain extent. But, the real thing was to gather a tremendous amount of data. And, we had to gather it by hand in the early days. We went down to the Federal Reserve and copied interest rate histories and stuff like that because it didn’t exist on computers. We got a lot of data and very smart people. That was the key.”
In the early going, Simons’ team literally built some of their dataset by hand. Is there a scenario today, in the era of AI and machine learning, where this old-fashioned scenario might be replicated? Under what circumstances could there still be a cache of data valuable enough to need to be assembled by hand? (Hint. hint.)
2. “At some point, we bought out all the investors because there’s a capacity…”
What’s the relationship between market liquidity, strategy capacity, volatility and expected performance? It turns out, average position sizing may bring us closer to the answer to this question…
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