Working with individual clients, trusts, and even 401k plans inevitably leads to a discussion of asset allocation and the best way to build a portfolio for a client. Model portfolios are a great way to efficiently pre-define model allocation profiles for clients. How can you build model portfolios? Let’s explore some common techniques.
Mean-Variance Optimization
Perhaps the best known asset allocation method, mean-variance optimization was established by Harry Markowitz in his article Portfolio Selection. Mean-variance optimization is a process that tries to maximize the return and minimize the risk for a portfolio of asset classes. While return maximization techniques were not new, Markowitz understood that the risk and return relationships of asset classes are not independent when combined in a portfolio of multiple asset classes. The correlation of risk and return between asset classes became the bedrock of mean-variance optimization techniques used in many portfolio applications today. By combining the risk, return, and correlation data mean-variance optimization produces a set of portfolios that maximize return for a given level of risk. This set of portfolios can be visually defined as an efficient frontier.
Resampled Efficiency Optimization
Resampled Efficiency, created by Dr. Richard Michaud and Robert Michaud of New Frontier Advisors, builds upon Markowitz’s work of defining an optimal portfolio given a level of risk. This patented technique resamples the risk, return and correlation inputs to create a spectrum of alternative efficient frontiers. The technique then averages frontiers together to create a new efficient frontier and new set of optimal portfolios. Does this process work? According to, Harry Markowitz it does. In a simulation test, Resampled Efficiency outperformed classical mean-variance optimization in all thirty tests.
Monte Carlo Simulation
While Monte Carlo simulation isn’t a portfolio construction process, it can be used to test the probability of success of a given portfolio given the historical risk and return profiles of the asset classes. More broadly Monte Carlo simulation can also be used in the financial planning processto simulate cash flows and other income and tax considerations and how they affect a portfolio. While Monte Carlo simulation was developed in the 1950’s at Los Alamos National Labs to simulate nuclear explosions, it has only recently become a tool available to both registered representatives and registered investment advisors.
Other Model portfolios
The techniques listed above are typically available in software applications. The fi360 Toolkits, for example, uses the Resampled Efficiency process in its asset allocation optimizer. What if you don’t have the desire or time to work through these processes? You can turn to pre-built models. Many RIA and Broker/Dealer firms recommend pre-built models designed by in house staff that has the expertise and time to build the model portfolios. I recommend talking to the person who built the model to get an understanding of their process, but this is a great resource for advisors.
Another public resource is the model allocations that target-date funds disclose in their prospectus. Pick your favorite fund family and look up the prospectus for a target-date fund. By using this information, you can “borrow” the expertise of the fund company managers who build the glide path of the target date funds.
Whatever you implement, it’s important to understand that the optimal solution should reflect the needs of the client. What techniques do you use when constructing model portfolios? Let us know below.
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