This marks the first month (30 days) of engagement with beta financial partners. The goal is to test Sigma1 HAL0 portfolio-optimization software on real investment portfolios and get feedback from financial professionals. The beta period is free. Beta users provide tickers and expected-returns estimates via email, and Sigma1 provides portfolio results back with the best Sharpe, Sortino, or Sharpe/Sortino hybrid ratio results.
HAL0 portfolio-optimization software provides a set of optimized portfolios, often 40 to 100 “optimal” portfolios, optimized for expected return, return-variance and return-semivariance. “Generic” portfolios containing a sufficiently-diverse set of ETFs produce similar-looking graphs. A portfolio set containing SPY, VTI, BND, EFA, and BWX is sufficient to produce a prototypical graph. The contour lines on the graph clearly show a tradeoff between semi-variance and variance.
Once the set of optimized portfolios has been generated the user can select the “best” portfolio based on their selection criteria.
So far I have learned that many financial advisers and fund managers are aware of post-modern portfolio theory (PMPT) measures such as semivariance, but also a bit wary of them. At the same time, some I have spoken with acknowledge that semivariance and parts of PMPT are the likely future of investing. Portfolio managers want to be equipped for the day when one of their big investors asks, “What is the Sortino ratio of my portfolio? Can you reduce the semi-variance of my portfolio?”
I was surprised to hear that all of Sigma1 beta partners are interested exclusively in a web-based interface. This preliminary finding is encouraging because it aligns with a business model that protects Sigma1 IP from unsanctioned copying and reverse-engineering.
Another surprise has been the sizes of the asset sets supplied, ranging from 30 to 50 assets. Prior to software beta, I put significant effort into ensuring that HAL0 optimization could handle 500+ asset portfolios. My goal, which I achieved, was high-quality optimization of 500 assets in one hour and overnight deep-dive optimization (adding 8-10 basis points of additional expected-return for a given variance/semi-variance). On the portfolio assets provided to-date, deep-dive runtimes have all been under 5 minutes.
The best-testing phase has provided me with a prioritized list of software improvements. #1 is per-asset weighting limits. #2 is an easy-to-use web interface. #3 is focused optimization, such as the ability to set max variance. There have also been company-specific requests that I will strive to implement as time permits.
Financial professionals (financial advisers, wealth managers, fund managers, proprietary trade managers, risk managers, etc.) seem inclined to want to optimize and analyze risk in both old ways (mean-return variance) and new (historic worst-year loss, VAR measures, tail risk, portfolio stress tests, semivariance, etc.).
Some Sigma1 beta partners have been hesitant to provide proprietary risk measure algorithms. These partners prefer to use built-in Sigma1 optimizations, receive the resulting portfolios, and perform their own in-house analysis of risk. The downside of this is that I cannot optimize directly to proprietary risk measures. The upside is that I can further refine the HAL0 algos to solve more universal portfolio-optimization problems. Even indirect feedback is helpful.
Portfolio and fund managers are generally happy with mean-return variance optimization, but are concerned that semivariance-return measures are reasonably likely to change the financial industry in the coming years. Luckily the Sharpe ratio and Sortino ratio differ by only the denominator (σp versus σd) . By normalizing the definitions of volatility (currently called modified-return variance and modified-return semivariance) HAL0 software optimizes simultaneously for both (modified) Sharpe and Sortino ratios, or any Sharpe/Sortino hybrid ratios in-between. A variance-focused investor can use a 100% variance-optimized portfolio. An investor wanting to dabble with semi-variance can explore portfolios with, say, a 70%/30% Sharpe/Sortino ratio. And an investor, fairly bullish on semivariance minimization, could use a 20%/80% Sharpe/Sortino hybrid ratio.
I am very thankful to investment managers and other financial pros who are taking the time to explore the capabilities of HAL0 portfolio-optimization software. I am hopeful that, over time, I can persuade some beta partners to become clients as HAL0 software evolves and improves. In other cases I hope to provide Sigma1 partners with new ideas and perspectives on portfolio optimization and risk analysis. Even in one short month, every partner has helped HAL0 software become better in a variety of ways.
Sigma1 is interested in taking on 1 or 2 additional investment professionals as beta partners. If interested please submit a brief request for info on our contact page.