Improved Portfolio Optimizer, and Initial Results

Improved Portfolio Optimizer, and Initial Results

I’ve been working hard on making the tools better, in particular on the optimizer, to make sure it produces great results. Keep in mind that studies that use historical returns can only be relied on so much. And we should never be using historical results to tweak the portfolio, because it leads to overfitting. If you are not aware, overfitting is a data science term that refers to making the model ‘fit’ the data. Like fitting for a suit of clothes. The problem is, that market performance won’t be duplicated, so the model might promptly fail on new ‘real’ results.

So with that said, I’ve improved the optimizer so even with smaller portfolios it produces better results. The optimizer is about reducing risk, and when it results in better returns and reduced risk, that’s a nice result. (again with the caveat I made about past results)

A couple weeks ago I made a blog post about optimizing some popular model portfolios from a Forbes article. At the time I thought it would be interesting to try optimizing these portfolios using RIAengine smart portfolio optimizer, and seeing what backtests looked like.

Now, with the improvements to the optimizer, I wanted to post updated results.

Buffet 90/10 Portfolio

This portfolio optimization had resulted in better risk and drawdowns, but returns were worse. Here are the new results:

So like with the original tests I ran, I optimized for 2003 and 2004, and then ran a backtest from 2005 through 2010. You don’t want to optimize and backtest over the same period since you’ll be overfitting. So that’s why the sequence there.

The original tests had resulted in worse performance, but improved risk and drawdowns. And the new test, with the improved optimizer, still resulted in better risk and drawdowns, but performance was higher too. This of course would be a best of both worlds scenario. Kind of a surprising result with only two assets to optimize for, but interesting too.

Merriman Ultimate Buy and Hold Strategy

The next model, the Merriman Ultimate Buy and Hold Strategy 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. As I said before, you need different asset classes to properly diversify. But let’s see how it goes.

The last time I ran these tests, the optimizer produced a worse backtest than the original. The drawdowns were worse, risk about the same, and returns worse. This time, returns still less but at least they are positive. Risk still the same and drawdowns worse.

As I said before, with an almost 100% stock portfolio, and only real estate to diversify things, it’s hard to figure out an asset mix that will perform better than any other. Ideally you’d reduce the drawdowns primarily.

Just for kicks, I decided to try increasing the real estate by a lot and see if it improves the drawdowns. So I increased them to 30% of the overall portfolio.

As you can see, it’s not really an improvement. Drawdowns still over 60%. If drawdowns are 60%, you need returns of 150% to get back to even! So, probably need to avoid this portfolio. Sorry Merriman.

Once again, these are hypothetical results, based on historical returns. And future performance won’t ever be exactly like any past period.

Ivy League Endowments portfolio

Next up, the Ivy League Endowments. You are probably getting used to this, so let’s jump into the results:

So the result is that the improved optimizer resulted in better returns, while risk is still better than the original portfolio, and drawdowns as well. So a good result.

Generally speaking, things that improve returns will probably result in more risk, but not always. We’re shooting for both, but if we have to choose, lower risk is preferable.

As I’ve said before, the risk of the client losing their stomach in the market is worse than just drawdowns!

Coffeehouse Portfolio

The next portfolio was called Coffeehouse and is a good mix of assets. Let’s see what happens with the comparison:

The improved optimizer gave a much better result with this model and backtest. Returns are much better, though a little less than the original. But risk, sharpe, drawdowns, all much better. While risk, sharpe and drawdowns are not as good as the first time we optimized, overall the result is more balanced.

Bill Bernstein’s No Brainer Portfolio

This portfolio is just 4 assets, equally weighted. The optimizer pushed the bonds higher than stocks, and lowered the real estate allocation. Let’s see how it turned out.

As expected, with more bonds, returns should be less normally, while risk should also be less. But since the improved optimizer didn’t allocate so much to bonds as the first time we ran this, the effect was less. This means we have higher returns than before, with still improvement in risk measures. On to the last portfolio.

Harry Browne’s Permanent Portfolio

Like the Bernstein portfolio, this one allocates equally to 4 securities, but it includes gold rather than real estate. The results:

The results are similar to the Bernstein portfolio, and similar compared to the last test. Returns are improved over the last test, though still not as good as the original models. However, once again a nice balanced result. Still reducing the risk of the original portfolio, and less returns but not dramatically so. Overall the client would sleep better at night.

Takeaways And Notes

This time around, results are more balanced, so the improvements are looking promising. Once again, past performance won’t be repeated. With portfolio optimization, we’re looking for diversification that results in lower risk portfolios, and trying not to reduce returns. But we hate drawdowns, since they make it harder to recover. A portfolio that’s underwater for a long time results in an unhappy client!

RIAengine has launched the smart portfolio optimizer to a small beta group and will be launching to the public during the next month. 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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