Build long-term portfolios

By Brad Steiman | May 6, 2013 | Last updated on May 6, 2013
2 min read

The emphasis on portfolio structure and asset allocation over security selection and market timing has served investors well and is supported by academic research. However, aspects of this research have been somewhat overstated.

For example, asset allocation recommendations are often accompanied by illustrations of mean-variance optimal portfolios that sit along an efficient frontier where expected returns are maximized for each level of standard deviation.

But even if you could estimate inputs with enough accuracy to identify the efficient frontier, the whole concept of optimization is still based on the faulty premise that portfolio variance is a complete measure of risk.

The empirical analysis of the relation between risk and return, in the context of market equilibrium, is known as asset pricing. The capital asset pricing model (CAPM) was the first asset pricing model, and it assumes the only risk investors are compensated for is volatility relative to the market, as measured by beta.

The model is simple, elegant, and intuitive. Unfortunately, it doesn’t work very well. All models are, by definition, false, because they are abstract. The relevant question is how good the model is at describing reality or, in this case, the relation between risk and return.

Enlarge System 1 in action

Building portfolios solely focused on the trade-off between expected return and standard deviation is not robust. When building lifetime portfolios, advisors must apply a much broader perspective by considering a host of planning and portfolio management issues.

A good starting point for the asset allocation decision is the market, which is always an efficient portfolio. For every investor overweighted in an asset class, there must be another who is underweighted.

Asking how an investor differs from others can help guide any decision to allocate assets in a way that deviates from the market portfolio. Risk preferences, tracking error, home bias, human capital, non-financial assets, consumption patterns, and other variables are some considerations that can help answer this question.

Enlarge System 1 in action

Read more: Stop playing with your optimizer >

Brad Steiman is director, head of Canadian Financial Advisor Services, and vice president of Dimensional Fund Advisors Canada ULC.

Brad Steiman