To answer basic questions, fundamental indexing firm Research Affiliates’ co-founder Jason Hsu presents some of the key factors that cause a growing segment of professional investors to view smart beta as a clever investing strategy.
Hsu, who co-founded Research Affiliates with the firm’s chairman, Rob Arnott, is considered a thought leader in the smart beta field, having authored several award-winning papers published in journals of academic finance in addition to his teaching at UCLA’s Anderson School of Management.
With a background in physics and finance, Hsu is clearly at home with the complexity involved in smart beta. ThinkAdvisor distills Hsu’s exploration of seven smart-beta issues into bite-size nuggets for advisors, who can immerse themselves more fully through a paper found on Research Affiliates’ site.
How Did Smart Beta Start?
Hsu explains smart beta as an evolutionary step forward from traditional, or market-cap-based, indexing.
In a market-cap-weighted index, a rising share price increases that stock’s weight in the index, and vice versa.
This product design has its origins in what is known as the Capital Asset Pricing Model (CAPM), once the cutting edge of finance theory.
But while still taught because of its conceptual utility, Hsu says CAPM has been superseded by the Arbitrage Pricing Model (APT), a multi-factor approach that includes multiple sources of equity return premia: market returns — the source of traditional indexing returns — is just one factor; but APT also factors in value, momentum and low volatility.
How Does Smart Beta Differ From Quant Strategies?
OK, so you want to capture sources of return other than just the market returns on which traditional indexing is based. Why not turn to a hedge fund using quantitative strategies?
Hsu explains that strategy indexing falls in two camps — an alpha camp following active, but rules-based strategies and a beta camp using rules-based strategy to passively capture excess return.
The numerous differences between the two approaches include the high turnover (and associated costs) of the active quants versus the relatively low turnover of smart beta; the opacity of the former versus the transparency of the latter; and the portfolio concentration of the former versus smart beta’s broad exposure to the economy.
Are Smart-Beta Portfolios Optimal or Just Good Enough?
Financial theory is laden with quests for portfolio optimization, be it Modern Portfolio Theory exponent Harry Markowitz’s mean-variance optimization or his fellow Nobel laureate William Sharpe’s eponymous Sharpe ratios, designed to optimize risk-adjusted return.
Hsu argues that smart beta strategies are more mean-variance-efficient (note the word “efficient” rather than “optimized”) than cap-weighted indexes. But this attribute does not stem from portfolio optimization, a theoretical goal that is difficult to apply in actual practice; rather, smart beta has outperformed based on mean reversion in stock prices and contrarian rebalancing.
The inclusion of other risk/return factors beyond just market beta is likely to enhance risk-adjusted return, Hsu argues, adding that investors should not let the quest for optimization get in the way of “good enough.”
How Should Investors Measure Smart Beta Risk?
Measures such as tracking error and information ratios that are used to examine portfolio manager performance relative to benchmarks apply poorly to smart beta indexes, says Hsu.
Tracking error, when applied to active managers, generally distinguishes between high-conviction managers versus closet indexers; while information ratios take that a step further by quantifying the value-added return per unit of tracking error.