For those of you who may have been following the Sigma 1 blog for the past few years, you may be surprised by the sudden rebranding and name change. A lot has happened in 2016 that has precipitated these changes. Firstly, I am sad to inform you that the founder of Sigma 1, Dave Balhiser, passed away in January of 2016. Secondly, I would like to introduce myself. I am Gabi Endress-Balhiser, and I was Dave's wife and partner. I have decided to not let the work my husband put into his portfolio optimization product go to waste, so I have partnered with some of his team and we are working on continuing to develop the tool and bring it to market, hopefully sometime in 2017. Admittedly it has been a difficult year. Dave's brilliant mind and kind heart are missed by many. I watched him put hundreds of hours into developing this software. It was his passion and he truly believed it was revolutionary. The back-end of the product was rock solid before he passed, and the only missing element was a front-end to make it user-friendly. I have a background in usability and I have managed teams of developers in the past, so I am hoping I can help my husband's dream come to fruition. I am admittedly not a Quant by any stretch of the imagination, but if there is an interest I may invite guest bloggers to contribute to this site as we continue to move forward. Thank you to everyone who supported my husband in his quest to build a revolutionary portfolio optimization software. I hope I will have your support along the way as well.
Latest Blog Posts
August 6, 2015
Building a Better Robo AdvisorThe more we learned about the current crop of robo advisory firms, the more we realized we could do better. This brief blog post hits the high points of that thinking.
Not Just the Same Robo Advisory TechnologyIt appears that all major robo advisory companies use 50+ year-old MPT (modern portfolio theory). At Sigma1 we use so-called post-modern portfolio theory (PMPT) that is much more current. At the heart of PMPT is optimizing return versus semivariance. The details are not important to most people, but the takeaway is the PMPT, in theory, allows greater downside risk mitigation and does not penalize portfolios that have sharp upward jumps. Robo advisors, we infer, must use some sort of Monte Carlo analysis to estimate "poor market condition" returns. We believe we have superior technology in this area too. Finally, while most robo advisory firms offer tax loss harvesting, we believe we can 1) set up portfolios that do it better, 2) go beyond just tax loss harvesting to achieve greater portfolio tax efficiency.
August 1, 2015