Century-Long Backtest Shines on a Few ETF Strategies

We’ve heard various forms of this advice before, of course, and from a range of sources, including AQR’s research. For instance, the 2013 paper that Asness co-authored on the matter of combining the value and momentum factors across asset classes—“Value and Momentum Everywhere”—is a staple for interpreting a modern view of multi-factor investing. Meanwhile, Andreas Clenow makes a persuasive case in his recent book Following the Trend for deploying a momentum strategy across asset classes to minimize the inevitable failure that harasses individual trades. And, of course, there’s a deep pool of research that’s been inspired from Meb Faber’s influential analysis on using moving averages to generate signals for tactical asset allocation–“A Quantitative Approach to Tactical Asset Allocation”.
The new article builds on the existing literature, offering deeper perspective and offering some advice on how to extend the research for perhaps superior results in real-world money management: “Other factors (for example, carry in the form of a steep yield curve for bonds, relative valuation between stocks and bonds, some macro indicators) and other forecasting methodologies may be attempted, though you risk overfitting in exchange for attempted improvement.”

For all the potential for enhancing a static asset allocation program with a dynamic approach there are some obvious things that you shouldn’t do, the authors warn. That includes going to extremes with the asset adjustments. Moving from aggressive risk-on postures to all-cash positions and vice versa in a short period is impractical and probably ill-fated. There’s a supportive research pool for TAA, but only in a modified degree. Nothing’s perfect in the realm of investing and so even the most-compelling backtested factors will suffer failure at times. This is especially true for the value factor, which can remain out of favor for years. The solution is to embrace moderation by combining factors, investing across asset classes, and never betting the house on any one signal/asset class at any one point in time.

The good news is that there’s fertile ground for developing a customized dynamic asset allocation plan that matches a particular set of investment goals, risk tolerance, etc. The logic arises directly from analyzing market history. As Asness and his co-authors remind, “when prices look cheap versus a reasonable metric, buy a bit more. When they have been trending up, buy a bit more. Of course, also do the opposite, and average both these approaches, doing the most when they agree. Do it in both stocks and bonds.”

Is that the last word on designing a robust portfolio strategy? No, but it’s a solid beginning, which is an essential ingredient for engineering successful outcomes. There are still no sure bets, but the value+momentum+asset allocation proposition has become the new standard for evaluating a prudent approach to investing. Can you do better? Perhaps, but you face a high bar for developing an alternative and convincing line of analysis by way of empirical research.