I started Sigma1 with $35,000 in seed capital, a Linux workstation and a domain name I acquired in auction for $760. The original plan was to create a revolutionary hedge fund with accredited investors as clients. I started studying for the Series 65 exam and all went well until I started reading about securities laws and various legal case studies. I gradually realized two things:
- U.S. Securities Law is very restrictive, even for “lightly regulated” hedge funds
- The legal start-up costs for a hedge fund were much higher than I anticipated
The first realization was the most devastating to my plans. The innovative fee structure I wished to use was likely to face serious legal challenges to implement. Without a revolutionary fee structure, more favorable to clients, the Sigma1 Fund would be hard to differentiate from the hundreds of other funds already available.
The second objective of Sigma1 has been to develop proprietary financial software. Until now the Sigma1 Proprietary Trading Fund has been constructed based on research, pencil-and-paper securities analysis and some rudimentary Excel simulations. Some quantitative analysis has been applied, but without the mathematical rigor I prefer. That is about to change.
I recently devised a way to apply techniques developed while studying Electrical Engineering and Finance in grad school. In a nutshell, I will apply evolutionary algorithms to optimize portfolio construction. The same fundamental techniques my electrical engineering colleagues and I used to explore and optimize around the random perturbations inherent in fabricated silicon circuits can be used to optimize portfolios by efficiently exploiting conventional (linear) and unconventional (non-linear) correlations between diverse assets.
I have sequestered myself in a beautiful, tranquil location while on a well-earned sabbatical from work. While evolutionary algorithms will be a significant part of the software suite I will develop, I also intend to incorporate heuristics and machine-learning techniques as well. Similarly I intend to use techniques from CAPM such as efficient-frontiers, but only as a first-order guide. Many of the limitations of CAPM (and Fama-French enhancements thereof) consist on their intrinsic reliance on Gaussian or “normal-distribution” statistical models. Such models do not properly model long-tail events, nor asymmetrical distributions, nor even log-normal distributions. Classic CAPM models even struggle with geometric-mean of expected or passed returns and generally use arithmetic means to preserve the use of linear systems analysis. Heuristic algorithms and other AI techniques need not use such assumptions as a mathematical crutch. The software I intend to develop should be able to find near-optimal solutions to financial problems that classic statistical methods “solve” only by making grossly inaccurate assumptions about probability distributions.