Quant Research Debunks the Hype Behind Smart-Beta Investing

Given the amount of criticism smart-beta investing has faced lately, new research confirming its outperformance should be a welcome change. Unfortunately even that comes with a sting.

A new paper has demonstrated the superiority of these strategies, which attempt to blend the best of active and passive with a quant approach known as factor investing, usually wrapped in exchange-traded funds.

However, researchers concluded the outperformance has next-to-nothing to do with those quantitative methods, which weight shares based on characteristics like measures of cheapness or the dividends they pay. Instead, excess returns stem entirely from how funds periodically adjust their holdings.

“The outperformance of these strategies is completely explained by the diversification returns embedded in the portfolio rebalancing inherent in all such strategies,” wrote academics Wenguang Lin from Western Connecticut State University and Gary C. Sanger from Louisiana State University. “Efficient factor tilts explain none of the outperformance.”

In other words, smart beta’s edge over a strategy of simply weighting shares based on their market capitalization comes not from being smarter, but from making more changes.

Quant Research Debunks the Hype Behind Smart-Beta Investing

The study is the latest broadside against the booming $1 trillion smart-beta industry, which accounts for more than a fifth of U.S. ETF assets. Its promise of delivering sophisticated quant strategies at a lower price continues to lure investors, even amid swirling doubts about the effectiveness of factor investing and performance that has lately disappointed.

The new paper surveyed the returns of 1,000 U.S. stocks over four decades, built portfolios based on fundamental metrics including value, sales, dividends and cash flow and tested various weighting approaches.

The researchers then used the difference between each share’s individual volatility and the portfolio’s volatility to add a parameter known as diversification return.

The idea is that the act of reshuffling the portfolio to maintain certain exposures -- as opposed to a passive buy-and-hold-the-index approach -- increases diversification. That reduces the risk of the portfolio, meaning its compound returns are greater than the sum of its parts.

“We propose that all alternative weighting schemes, through their embedded periodic rebalancing, generate significant diversification returns relative to the traditional cap-weighted benchmark,” Lin and Sanger wrote. “In fact, we show that all of the outperformance of these alternative investing strategies is due to the diversification return.”

The research comes with caveats. In an email, Sanger highlighted that it’s based on simulated results using backtesting. That’s a practice which some have suggested creates the “mirage” of smart-beta outperformance in the first place.

Meanwhile, there are reasons to be cautious about extrapolating the findings to the wider factor-investing universe. The rebalancing mechanism cannot explain most of the factors that are studied rigorously by academics and quant investors, according to Ashley Lester, global head of multi asset research and systematic investments at Schroder Investment.

“Most actual quants test factors by building ‘good’ and ‘bad’ portfolios of the factor, in such a way that both sides should benefit from the rebalancing mechanism,” he said. “That way, if the ‘good’ side does better than the ‘bad,’ the result cannot be driven by rebalancing.”

It goes beyond testing. A true factor approach would involve taking both a long position on the stocks that fit most closely to the factor, and a short position in the least compliant. Smart-beta ETFs are usually long-only for simplicity and cost reasons, meaning they -- and the research -- don’t fully reflect the efficacy of factors.

Quant Research Debunks the Hype Behind Smart-Beta Investing

Nonetheless, Olivier d’Assier, head of applied research for Asia-Pacific at Qontigo, said the paper’s findings confirm some of the market analytics company’s past studies. They have found strategies targeting a particular factor can often be overwhelmed by others if no effort is made to reduce their influence.

“The portfolios used in this study do not represent ‘pure’ factor portfolios but long-only ones designed to tilt the broad market portfolio towards a certain smart beta without neutralizing others,” he said. “It has also been our experience that these types of portfolio construction methodologies introduce a lot of noise in the performance attribution from incidental exposures to non-target factors.”

©2020 Bloomberg L.P.

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