Advisors face an increasing urgency to provide more comprehensive planning services to their clients, but in a way that facilitates scalable practice growth. The advisor’s role in planning clients’ portfolios, managing and monitoring their progress and forecasting possible outcomes has become more complex in today’s environment. Recent market volatility, greater service level demands on an advisor’s practice and the increasing impact of “black swan” events – such as the subprime mortgage crisis and COVID-19 – on the long-term performance of portfolios are all headwinds that an advisor needs to contend with.

A better toolkit is in order. Traditional models of portfolio planning and projection are not enough in these market conditions. Moreover, the models often thought of as an improvement, based on simplistic Monte Carlo simulations, also fall short.

The use of Monte Carlo simulations in investment planning is viewed as an enhancement over older and simplistic straight-line return assumptions, to generate a more realistic range of scenarios and possible outcomes for advisors and clients. However, Monte Carlo simulations themselves are driven by an erroneous assumption of a normal distribution of returns, and as a result often generate a range of probabilities not unlike those of straight-line assumptions.

Interest rates and weather

We can use two examples to demonstrate Monte Carlo’s limitations. In an investment context, if a fixed income investor in 2020 were to apply a Monte Carlo simulation when analyzing the past 15 years of U.S. government bond returns, the model would assume a 4% annual return for the entire simulation of clients’ financial plans. This would ignore the reality of Treasury yields being at all-time lows in 2020 due to COVID-19.

For a more stark example of Monte Carlo’s limitations, we can look at a non-investment analogy: weather forecasting. A forecast based on a simplistic Monte Carlo simulation would just generate thousands of simulations based on broad assumptions, like average daily temperature and standard deviation. When Monte Carlo is used to forecast August temperatures in the city of Toronto, for instance, it provides an impractical range of outcomes from -25°C (-13°F) to 53°C (127°F), with an expected temperature of 13°C (55°F)! Far from useful. That’s because the Monte Carlo simulation does not account for factors like the most recent daily temperature and seasonality.

Advisors need a more sophisticated approach to scenario analysis in long-term wealth planning. In our next article, we will discuss how a more advanced portfolio projection model can give advisors and clients the superior insights they need in today’s uncertain markets.

For more information on truly advanced client portfolio monitoring and goals-based planning, visit the OPAL Wealth page or download the product brochure via the button below. For any other questions or demo request, please contact Neil Greenbaum below.

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