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7 Smart Beta Questions, Answered

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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?

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But while still taught because of its conceptual utility, Hsu says CAPM has been superseded by the Arbitrage Pricing Model (APT), a multi-factor approach that includes multiple sources of equity return premia: market returns — the source of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?

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?

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?

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.

Hsu says tracking error measures appear to show smart beta indexes as low in conviction, but what it really measures is the extent of the portfolio’s non-market-beta sources of return; the information ratio of smart beta portfolios similarly reflect the factor premia alongside the market premium in the portfolio’s stocks.

Hsu recommends Sharpe ratios as a more effective means of comparing the risk-adjusted return of smart-beta indexes versus those of traditional passive index alternatives.

How Does Smart Beta Stack Up Against Value Indexes?

How Does Smart Beta Stack Up Against Value Indexes?

Since smart beta incorporates value as a key source of excess return, investors might try to capture value through a fund from one of the Morningstar style boxes, say, small-value.

Hsu argues that traditional indexes suffer from two drawbacks: one is the previously discussed problem of market-price-weighted indexes’ overemphasis on stocks that have risen in value.

But another flaw involves index construction methodologies that tend to overrepresent industries where low price-to-book ratios are typical, such as finance and energy.

In contrast, smart beta looks for value stocks across the economy, and it favors stocks with value characteristics within an industry.

For example, financial and energy sector stocks made up 27.4% and 11%, respectively, of the Russell 1000 Value Index compared with 20.2% and 10.4% of the smart beta FTSE RAFI 1000 Value Index as of the end of last year.

And within, say, the automotive industry, FTSE RAFI is looking for relative value, preferring Ford to Tesla rather than just a value-weighted slice of that particular industry.

Hsu believes this different approach explains smart beta’s 200 basis point simulated return advantage. Over the period from Dec. 31, 1978, to September 30, 2013, the FTSE RAFI 1000 Index produced average annual returns of 14.09% versus 12.52% for the Russell 1000 Value Index and 11.98% for the S&P 500 Value Index.

If Smart Beta is So Smart, Who’s On the Other Side of the Trade?

If Smart Beta is So Smart, Who’s On the Other Side of the Trade?

When smart beta indexes are rebalancing, selling the expensive stocks and buying the cheap ones, who is foolish enough be buying the expensive stocks and selling the cheap ones?

Hsu didn’t need much space to explain this one. The nature of human beings, with our deep-seated instincts for greed and fear, make the other side of the trade the natural one; it is contrarian, smart beta-type value investing that is highly uncomfortable.

Investors expect the rapidly rising stock to be the next Google or Apple, while the sputtering stock is viewed as the proverbial falling knife set to further weaken in its fundamentals.

But while Googles are born and value stocks do meet catastrophe, the reality is that the majority of growth stocks fail to live up to investors’ hopes and most value stocks do eventually recover.

How Can Investors Best Capture the Return Premium From Value Investing?

How Can Investors Best Capture the Return Premium From Value Investing?

Investors generally understand there is mean reversion in stocks: for example, the subsequent three-year returns following the tech boom averaged -14.5% per year while the subsequent three-year returns following the global financial crisis averaged 23.5% per year.

But Hsu says there is growing evidence that the value premium is mean reverting as well. So the ratio of the price-to-book ratio for growth stocks over the price-to-book for value stocks rose as high as 14.65 in July 2000 at the peak of the tech boom. In the subsequent three years, value cumulatively beat growth by 60.3%. When in March 2009 the growth stock P/B was 11.5 times the value stock P/B, value cumulatively outperformed growth by 33.1%.

Investing in low P/B (i.e. value) stocks captures some of the value premium. But smart beta’s emphasis on regular rebalancing effectively imposes dollar-cost averaging on contrarian market bets, capturing more of the value premium by buying low P/B stocks when the spread between them and high P/B stocks is greatest.

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