Optimizing Some Popular Portfolios

Optimizing Some Popular Portfolios

In Forbes I found a post about some ‘expert’ model portfolios. I thought it would be interesting to try optimizing these portfolios using RIAengine smart portfolio optimizer, and seeing what backtests looked like. Here are the results.

Buffet 90/10 Portfolio

The first portfolio is simple, just 90% allocation to an S&P500 index fund (I chose SPY) and a 10% allocation to Bonds, I used SHY. Here is how it went:

Optimized Buffet Portfolio

What happened is the portfolio went from 90% SPY to 98% SHY. We ran a backtest on it using RIAengine backtester, and tested over 5 years including the crash of 2008. This resulted in a portfolio that returned a little less, but had dramatically less risk. It’s about what you’d expect going from mostly stocks to mostly bonds. Sharpe ratios are both negative since neither portfolio outran the risk free rate. While interesting, the optimizer is meant to handle more instruments than 2 in order to work properly.

Merriman Ultimate Buy and Hold Strategy

Let’s move on to the next portfolio in the article, the Merriman Ultimate Buy and Hold Strategy.This portfolio invests pretty much all in stocks, some large, some small, some value, some blend. Some are US and some international. Last, a little real estate gets added. Right off the bat, without some other asset class in there it will be hard to properly diversify. But let’s have a go.

Optimized Merriman Portfolio

If you want an optimal portfolio, you better have different asset classes to work with!

The interesting result is that sitting all in stocks you will have a hard time overcoming drawdowns. In this case, both portfolios had drawdowns of over 60%. To overcome this kind of drawdown your portfolio has to earn 250%! That’s why I like to talk about avoiding drawdowns, instead of maximizing returns. And sharpe ratios are negative because the portfolios didn’t earn more than the risk free rate of around 3%.

Good lesson here is get some more asset classes when optimizing a portfolio so you can take advantage of diversification.

Ivy League Endowments portfolio

Next up, the Ivy League Endowments portfolio. When optimizing a portfolio it’s good to have different asset classes to work with. This one has large cap stocks, bonds, real estate, and it should have some commodities. Since I couldn’t find commodity ETFs that have been around since 2003, I used an energy ETF, IYE. Here are the results:

Optimized Ivy League Endowments portfolio

The optimized portfolio ended up with significantly less risk, though it made half the gains. Drawdowns are lower by 38%. This will help the client hold on when things are going the wrong way. The risk of a 45% drawdown is more than just market risk. The real risk is the client can’t stomach it.

Coffeehouse Portfolio

This portfolio has a lot of equity, and add some bonds and real estate. The results:

Neither portfolio outran the risk free rate. The optimal portfolio had lower risk and drawdowns.

Bill Bernstein’s and Harry Browne’s Portfolios

The last 2 portfolios are similar in that they have 4 assets equally weighted. The results of optimizing these portfolios is a flight into lower risk, leading to much lower drawdowns and also returns.

Takeaways And Notes

All of these tests were run with no constraints or limits on asset weights. Quite often this leads to the lower risk assets getting the lion’s share of overall weight. So it’s usually best to set constraints to balance the results.

Another thing to note is that backtesting is not research. If you use the backtest to help you tweak parameters such as asset weights in optimizing a portfolio, it will almost certainly lead to misleading results. This is because you start to fit the backtest to the data. In this case I’m optimizing the portfolio from 2003 through 2004 (two years of returns is minimum) and running backtests from 2005 through 2010. If I keep tweaking the results so that the backtest performs well from 2005 through 2010, then I’m overfitting to the data.

So use the backtest to get baseline information, but don’t go too far with your tweaking!

RIAengine will be launching the smart portfolio optimizer soon to a small beta group before launching to the public. Sign up to be notified of the launch.

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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