Back-Tested Strategies: Real Or Random?

Back-Tested Strategies: Real Or Random?

Have you ever seen a bad back-test?

Investment professionals have been jokingly asking that question for years, and the answer remains the same: of course not. That is because no one will likely visit your office to discuss a new product designed to be smart beta, strategic beta, or what I’ll call factor-based whose simulated history only offers mediocre performance.

Get Our Activist Investing Case Study!

Get the entire 10-part series on our in-depth study on activist investing in PDF. Save it to your desktop, read it on your tablet, or print it out to read anywhere! Sign up below!

Why? Because few would buy it.

Ray Dalio At Robin Hood 2021: The Market Is Not In A Bubble

Fractional Shares Stock PickerAt this year's annual Robin Hood conference, which was held virtually, the founder of the world's largest hedge fund, Ray Dalio, talked about asset bubbles and how investors could detect as well as deal with bubbles in the marketplace. Q1 2021 hedge fund letters, conferences and more Dalio believes that by studying past market cycles Read More

Which brings us to how investment products are (often) made and how you can determine whether they are worth your clients’ money.

Factor-based products are often developed when asset managers examine historical data to try to determine what attributes of securities may have driven outperformance over time. Before a product is launched, a rules-based methodology may be implemented and applied to historical data as if the methodology had begun earlier, hence the term back-tested. What one person might consider research another might call data mining, and there can be a fine line between the two. But however you think about it, there is a difference between finding a random anomaly and identifying a viable rules-based strategy.

As a fun example of this, a few years ago my colleagues Joel Dickson and Chuck Thomas ran a hypothetical simulation that compared the performance of the S&P 500 Index with an equity portfolio that had an equally weighted combination of all stocks with tickers that began with S, M, A, R, or T. As the figure below shows, this simple, rules-based strategy did very well over a long period of time. However, let’s be honest, there is no sound reason to justify why it would be a good idea to pursue this strategy in the future.

Annualized return of S.M.A.R.T. beta strategy from December 31, 1994, to October 31, 2013

Source: Vanguard.

Note: The S.M.A.R.T. beta strategy is hypothetical in nature and does not represent the returns of any Index or Investment vehicle. It is constructed with equal-weighted components of all current securities in the S&P 500 whose tickers begin with the letters S.M.A.R.T. and rebalanced monthly.

While in most cases, it is hard to eliminate the risks of data mining entirely, there are a number of ways to help improve your confidence in the potential of a simple rules-based strategy to produce a return premium for a client in the future.

I invite you to download our one-page checklist for evaluating back-tested strategies, which identifies common biases that can occur when products are created. The questions in the checklist are ones you may want to ask any time you are considering a factor-based product.

Read the full article here by Doug Grim of Vanguard

No posts to display