Smart beta exchange-traded fund (ETF) strategies outperform traditional capitalization-weighted ETF strategies because of factor exposures. Some argue that this outperformance is simply the result of size and value exposure and that these strategies would outperform their cap-weighted counterparts with any mix of stocks, even if they were inverted. The authors call these portfolios “monkey portfolios” because their performance is similar to randomly generated portfolios. They argue that smart beta strategies that overweight particular factors, such as momentum, profitability, low investment, low risk, and possibly others, add discernible value that provides outperformance. They test this argument by building portfolios with factor exposures compared with cap-weighted indexes and by inverting these strategies.

They conclude that smart beta strategies do add value by explicitly overweighting factors, and when inverted, performance declines. The monkey portfolio proponents also claim that the outperformance is attributable to rebalancing. The authors find that rebalancing is mostly affected by when the rebalancing took place and that the outperformance could be simply the result of luck. They examine an equal-weighted portfolio with a large weighting in financials that was rebalanced during the financial crisis and performed very well when the financial sector rebounded during the market recovery. They conduct a theoretical rebalancing on this portfolio at different dates and find large differences in performance.

The authors examine a developing trend with smart beta strategies that are now offered by most ETF providers and gaining in popularity. Smart beta is a term that can describe many different strategies that are used to beat cap-weighted indexes. One of the earliest smart beta strategies was to create an equal-weighted ETF, which outperformed because of an inadvertent overweighting in value and size factors. Some investors assumed that all smart beta strategies outperformed because of size and value exposures and that these strategies would outperform equally as well if inverted.

It is true that an equal-weighted strategy would outperform if inverted, but the authors reexamine the research by looking at the newer ETFs whose performance is derived from specific exposure to such factors as momentum, profitability, low investment, and low risk. Portfolios with specific factor tilts outperform the indexes and performance declines if the tilts are lowered, as well as if the portfolios are inverted. This knowledge can help practitioners build diversified portfolios using the various factor exposures. The research is also useful for ETF providers.

The authors examine explicit factor-tilted smart beta strategies that have been developed relatively recently. They build specific diversified portfolios that overweight securities with high factor exposures to momentum, profitability, low investment, low risk, small cap, and value. They compare the performance of these portfolios with the monkey portfolio equivalents as well as with inverse strategies. They find that portfolios with explicit factor tilts outperform the comparable benchmarks, but they do not sustain this performance when constructed differently, especially with inverse strategies. They find that the monkey portfolios and upside-down strategies have lower Sharpe ratios and lower information ratios.

ETFs are very popular vehicles that are used by a broad array of investors. ETF providers have been creating newer investments that use relatively recent academic research that highlights various factors that have proven to be the source of outperformance versus the standard cap-weighted indexes. As the knowledge and acceptance of factor investing grows, profitable opportunities will arise for ETF providers. The authors add an interesting perspective on some of these newer ETF developments.