>Black Box
Manifold Learning
One of the perennial quant guessing games is speculating on RenTech (e.g. see amusing 5-year NP thread), particularly given the fascinating background of Jim Simons (see his arXiv for recent work on differential cohomology). Ignoring public commentary, whose veracity is obviously questionable, careful consideration of historical hiring trends and corresponding employee backgrounds are suggestive. While such speculation is amusing, potential relevance arises in assisting in filtering the exploration of research.
Specifically, several themes are consistent:
Infrastructure / execution: computer scientists speaks to the mundane realities of large-scale offline and online data management, risk management, multi-venue execution, and the usual collection of optimal execution concerns (particularly relevant for liquidity providing and statarb)
Applied mathematics: “natural scientists”, with an emphasis on modern physics (much of which is built upon differential geometry and statistical mechanics), seems reasonable given heavy mathematical and statistical modeling
High dimensionality: analysis and signal generation from high-dimensional spaces, which seems reasonable given many trading problems can elegantly be formulated in such a context; plus a deep well exists of both pure and applied math built by academia over the past 20 years; further, this makes obvious sense given Jim’s academic background (e.g. see Chern-Simons)
Mixing models: RenTech grew through a combination of small acquisitions and internal development, suggesting “the predictive model” (historically referred to as “Basic System”) is not one but rather a collection of heterogeneous models which are dynamically overlaid and mixed; seems reasonable, given market regimes and consistent Medallion performance over the past 20 years
Computational linguistics / NLP: numerous high-profile folks originated from speech recognition, of which numerous advancements over the past 30 years are based upon applied signal processingand statistical information theory (e.g.Mathematics of Statistical Machine Translation, by Brown, Pietra, and Mercer); a particularly consistent theme is HMM (going back to the Dragon system by Baker in 1975), which naturally support mixing via HHMM, and causal filtering(see also Berlekamp, who worked with Kelly)