Cary,
Thank you for making me aware of the book.
Kernel regression is mentioned multiple times in the book. This is trivially implemented in R (with whatever data you may posses). I have not tried to use kernel regression after switching to Python but I am sure some type of kernel regression can be done in Python.
For anyone interested in exploring Simons’ methods in R, I would recommend using LOESS (local regression). It worked well for what I was using it for. There are other Kernel Regression methods that can be found in R. One can also find programs that use “Splines” which will accomplish the same thing.
According to the book:
“The firm began incorporating higher dimensional kernel regression approaches, which seemed to work best for trending models, or those predicting how long certain investments would keep moving in a trend.”
Most of the programs in R and Python are “higher dimensional” and this is used here to add drama and make it seem difficult, as I am sure you know.
I also find this interesting:
“…as long as they had p-values, or probability values, under 0.01—meaning they appeared statistically significant, with a low probability of being statistical mirages—they were added to the system.”
Yep. The dreaded statistics. Sadly, it has been well established in the forum that this only works for RT. The laws of science and mathematics are just different for multi-billion dollar firms. Either that or we, at P123, just are not smart enough to use this—it takes a genius like Simons to get a p-value. In any case, you should forget about the idea of using this in the small- and micro-cap space where RT might not be playing, we are told.
The above is demonstrably false with what Simons has done being adequate proof. Unless, of course, any edge we could have is already lost because of multiple institutions already using similar methods.
Note that–as established above in this thread–Simon’s and others at RT had no “Domain Knowledge” in Finance. However, using established Financial principles may have potential, Cary suggests.
Cary is spot-on with this and this has great potential, I think. Cary notes that the book gives no evidence that, RT at least, would be competing with regard to Financial Fundamental factors as predictors. I believe this is an idea we are already exploring at P123 with our sims, rank performance optimization and spreadsheets.
I strongly agree with Marc (and Cary I think) that “domain knowledge” in Finance will benefit—especially here on the P123 platform. But there is no reason we cannot use domain knowledge in Finance and learn a lesson (or two) from Simons at the same time, I think.
-Jim