Capital Sufficiency Analysis
Capital sufficiency analysis or capital needs analysis enables personal planners and wealth managers to determine the probability of their clients being able to meet their financial goals. Capital needs analysis can be performed using deterministic forecasting and Monte Carlo simulation.
On this page, we discuss deterministic forecasting and Monte Carlo analysis to perform capital sufficiency analysis in more detail. In particular, we discuss why Monte Carlo analysis is generally preferable to deterministic forecasting.
A traditional, deterministic, linear return analysis assumes that a private client’s portfolio will achieve a single compound annual growth rate across the client’s investment horizon. To be able to use deterministic forecasts, we need to collect the following set of inputs:
- the current value of the investment portfolio
- the client’s investment horizon
- an annual return assumption (this should be based on forward-looking capital market assumptions rather than the simplistic use of historical returns), this is a crucial assumption
- the contributions into the portfolio and cash flows out of the portfolio over the client’s investment horizon
- the (seizable) impact of taxes, inflation, and ongoing charges
Deterministic forecasting is easy to understand and implement. At the same time, it is overly simplistic. In particular, the use of a single return assumption is not representative of the actual market volatility. As a consequence, the approach is not frequently used by practitioners.
Monte Carlo simulation
While deterministic forecasting focuses on a single rate of return, Monte Carlo simulation allows the input variables (which are similar to the inputs used with the deterministic approach) to be given a probabilistic distribution. This takes into account real world uncertainty.
If desired, each asset class can be modeled with its own return and risk assumptions and the asset class’s correlation with the returns of other asset classes instead of using a single portfolio return assumption. Once the model is set up, the Monte Carlo simulation generates a large number of independent trails, consistent with the assumed probability distributions, with each trail showing one potential outcome at the end of the client’s investment horizon.
A wealth manager can then aggregate all the outcomes to determine the probability that a client will achieve a financial goal over the investment horizon. A key consideration when using Monte Carlo simulation is the quality of the underlying assumptions because, like any complex model, the output of a Monte Carlo simulation will only be as good as its inputs.
We discussed two commonly used approaches that can be used to perform a capital needs or capital sufficiency analysis. While Monte Carlo simulation is somewhat more complicated, it is more informative and more appropriate. For relatively simple monte carlo simulations, Excel can be used.