The #1 Question about Sigma1 Financial Software

The question that potential investors and clients inevitably ask me is, “With thousands of very smart people working in finance, what makes you think Sigma1 Portfolio-Optimization Software is better?”   Others are more blunt, asking, “Do you think you are smarter than all the quants working at, say, Goldman Sachs?”

Let me start by saying that there are people in the industry that could understand Sigma1’s HAL0 algorithms.  In order to do so, it would be very helpful to have an electrical engineering background, a computer science background and understanding of CAPM.  Because of this fact, it is very important that Sigma1 keep these proprietary algorithms and trade secrets secret.  Sigma1 has also started evaluating patent attorneys and exploring patent protection.

The seeds of HAL0 methods and algos were born out of my early experiences in electrical engineering, specifically the area of VLSI design.  In the late 90’s and early 00’s it was becoming apparent to leading-edge VLSI design companies that silicon physical design could not continue to keep up with Moore’s Law unless there was a paradigm shift in our industry.  You see, VLSI design had already been revolutionized by a technology called logic synthesis.  But synthesis was breaking down for large designs due to it’s reliance on statistical wireload models (WLMs).    It turned out that actual wireloads did not follow normal probability distributions.  They had long, fat tails on the side of bad variance.

In 2001, my college and I wrote an article that became the July cover story for an industry magazine.  One of the two main points of the article was that the statistical models for wire delay were sub-optimal to the point of being broken.   My co-author and I were suggesting that companies that continued to follow the statistical methods of the past were going to either adopt new methods or become obsolete.  More than ten years later, the VLSI industry has shown this to be a true prediction.  Today, statistical WLMs are virtually obsolete.

A couple years later, while still working in VLSI design, I started grad school, taking graduate-level classes in finance and electrical engineering.  I worked with some really smart folks working on algorithms to solve some difficult circuit optimization problems.  Meanwhile I was learning about CAPM and MPT, as well as Fama French factors.  As I applied myself to both disciplines, little did I suspect that, years later, I would connect them both together.  As I learned more about finance, I saw how the prices of fixed income investments made mathematical sense.  Meanwhile I was stuck with the nagging idea that the theories of stock portfolio optimization were incomplete, especially because variance was a sub-optimal risk model.

I was on sabbatical, driving across New Mexico, thinking about my favorite theoretical problems to pass the time.  I was mentally alternating between Fourier transforms, semivariance portfolio-optimization, and heuristic optimization algorithms.   I was thinking back to when my undergrad colleagues and I predicted that class-D audio amplifiers would replace class A, A/B, and B amplifiers, especially for sub-woofers.  [I now own a superb class-D subwoofer that I bought on the web.]   In a flash it occurred to me that the same principles of superposition that make Fourier transforms and class-D amplifiers work, also apply to investment portfolios.  I suddenly knew how to solve semivariance and variance simultaneously and efficiently.  In the next 5 minutes I know what I was going to do for the rest of my sabbatical:  develop and test software to optimize for 3 variables — variance, semivariance, and total return.

A bit like Alexander Fleming, who “accidentally” discovered penicillin from observing a discarded Petri dish, I discovered something new my “accident” while driving on that New Mexico highway.  The Petri dish was a thought experiment in my mind.  Bits of knowledge of VLSI and computer science fell into the Petri dish of semivariance, and I saw it flourish.  All the previous semivariance dishes had languished, but suddenly I had found a glimmer of success.

In short, I have been thinking about semivariance for almost 10 years.  I have seen the limitations of variance-based models fail in my line of work in VLSI, and seen how improved models transformed the industry, despite early doubters.  The financial industry has been  happy to hire Ph.D.s in physics, a field in which variance-based models have proven extremely successful, from quantum physics, to thermodynamics, to PV=nRT.  Where I saw the breakdown of variance-based models, physicists have seen near universal success in applications tied to physics.

When your only tool is a hammer, nails appear everywhere.   I happened to have the right tools at the right time, and a spark of innovation on a stormy day driving across New Mexico.  I was lucky and sufficiently smart.  That is why I believe Sigma1’s portfolio-optimization solution is perhaps the best solution currently on the planet.  It is possible others have found similarly-effective solutions, but I my reasonably rigorous search I have found no evidence of such yet.

There are two primary factors that set Sigma1 portfolio-optimization software apart:  1)  Efficient solutions to semivariance-based portfolio optimization that scale to 1000+ assets, 2) 3-objective (3-D) models that concurrently optimize for, say, variance, semi-variance and expected return simultaneously.  I have seen a handful of software offerings that support semivariance and expected return in addition to variance and expected return, but independently (in a 2-D objective space).  Superimposing two 2-D models on the same chart is not the same as HAL0’s 3-D optimization.  For exampled, HAL0’s 3-D surface models (of the efficient frontier in 3 dimensions) allow exploration of the the variance/semivariance trade offs over any desired common expected return contour.  In the same way that topo maps graph the topology of the Earth’s terrain, Sigma1 technology can map the objective space of portfolio optimization in 3-D.

In summary, I was in the right place at the right time with the right knowledge to create revolutionary portfolio-optimization software.





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