Limiting Drawdowns Rather Than Maximizing Returns

Limiting Drawdowns Rather Than Maximizing Returns

Prominent asset manager Mary Meeker once said her primary goal is limiting drawdowns! While most portfolio construction is explicitly or implicitly focused on returns, she knew that minimizing events that can decimate the portfolio is more important for long term gains than trying to maximize growth periods. Famously, a portfolio needs to double to make up for a 50% drawdown.

So how to minimize drawdowns?

Portfolio diversification will naturally make a difference in drawdowns, as long as it’s actually diversified. The problem with diversification is in order to try to come to the best asset weights, you have to subject it to some process of optimization. And many optimization methods are known to reduce the benefits of diversification either because the asset mix isn’t linked or due to estimation errors on the front end.

So using robust optimization methods is key.

These robust methods lead to a portfolio with less risk and volatility, and therefore less risk of drawdowns.

For example, taking a portfolio made up of securities randomly selected from 2000-2010 and run through typical covariance/correlation matrices, here is what a graph of the correlation matrix looks like:

The yellow diagonal is a correlation of 1, or every asset with itself. Then the blocks move from yellow to dark blue as the correlation between them is less or negative.

Now let’s re-sort the assets by their correlations so that the highest are on one end and lowest on the other. This results in a graph with a much more orderly appearance:

In this graph, the assets are now clustered and it looks like a heat map, with hot being high correlation.

So with the assets being more closely linked to those that are ‘corr-a-likes’, when we perform our optimization we end up with a set of weights that will have lower risk due to diversification having been preserved.

This lower risk portfolio results in lower drawdowns. And often higher returns, particularly during more challenging stretches in the market.

RIAengine will be launching a machine learning portfolio optimizer to a small beta group within a couple weeks.

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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