PRAXIS
Omega Financial Group’s Unique Positioning with Praxis
Throughout the years, many of the most important advancements in investing and finance came through collaboration between academia and practice. Three prominent examples are given below. At Omega Financial Group, we have uniquely positioned our practice to be connected to top-tier academia not only for using past breakthroughs, but also for new ones. In particular, our CIO and chief strategist Dylan Minor is also a professor at UCLA (Anderson School of Management) so that he can bridge advancements in academia to Omega, for the benefit of all of our families we work with. Professor Minor was awarded the globally recognized Moskowitz prize from UC Berkeley in 2017 for outstanding investment research. The academic research repository SSRN network recently ranked the reach of his research as in the top 1% of academia.
Examples of Praxis
Many have heard the phrase Modern Portfolio Theory. This area of research revolutionized portfolio management by learning how to combine investments to create more return and less risk than they can separately. Many of the findings began with Harry Moskowitz as a graduate student at the University of Chicago. This strategic approach is used by many firms today and has become known as mean-variance optimization. Carefully used, as there have been myriad misapplications of mean-variance approaches, the strategy can help further increase return and reduce risk.
Another significant advancement in finance was provided by Eugene Fama and Kenneth French. Their so-called three-factor model helped expose the opportunity to profit from value and small stock factors. There are several concrete, low-cost ways to implement these findings from these two Nobel-prize winning economists.
Monte Carlo simulations have been increasingly used to run financial forecasts. This is a sophisticated approach to exploring many different possible outcomes to discern the likelihood of financial success, however one might define success. While this statistical modelling approach can be very helpful for making better decisions under uncertainty, it is critical to create appropriate models. Unfortunately, many, if not most, of the off-the-shelf systems being used today in the financial advisory field have some questionable settings and defaults. For example, some model life expectancy or other factors as certain, whereas other factors are probabilistic, creating results that are wrong and can lead to making the wrong financial decisions, especially in large-stakes settings. Often, to get the correct answers, these models should be created from scratch and not simply rely on simple, off-the-shelf solutions.