How To Best Evaluate A Portfolio

How To Best Evaluate A Portfolio

Recently I saw an article about Monte Carlo Analysis, which (as you know) is a way to evaluate a person’s ability to meet their future income needs. Using a portfolio of securities or asset allocations it runs through as many rolling periods as available in the data, to come up with a probability of meeting future income needs.

Certainly Monte Carlo Analysis is not great with all the input of expectations, almost certainly it will give misleading results: “OK Client, you are 87% likely to meet your future income needs based on this plan!” But is that percentage really true?.

The article says it’s better to focus on ambiguity rather than just investment risk.

So can Monte Carlo Analysis be Improved Upon?

Monte Carlo has a lot of uncertainty built into it, from all the ambiguous client expectations through the uncertainty about future markets.

One way to improve upon Monte Carlo Analysis would be to incorporate better analysis of coming market cycles: Using machine learning to look at factors such as interest rates, commodities, global events, and stock market risk, we could place higher weights on Monte Carlo periods that began when characteristics were similar to the present. Rather than looking at every period as equally likely to occur, we’d be placing higher weight on the ones that our analysis determined to be more likely to occur.

The downside would be that this would introduce yet another set of expectations.

So an even more solid approach is to tweak the portfolio such that it’s more likely to achieve needed returns without unnecessary risk. By using the Hierarchical Risk Parity approach, we can optimize the portfolio without obliterating the benefits of diversification.

RIAengine will be launching a better portfolio optimizer to a small beta group within a month.

Questions or comments? Love to hear them, please leave a comment below.

Tim Norton

Tim Norton is CEO of RIAengine. Formerly Head of Product Management at Machina, a firm building tools to apply machine learning in quantitative finance.

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