Can Passivity Be Enhanced? Why Smart Beta Strategies Are Suddenly in Vogue
Suddenly, it seems, many investors are talking about smart beta. Earlier this year, CFA Magazine ran a feature story on the subject, noting that “pension funds around the world have increased allocations to such strategies.” In April, the EDHEC-Risk Institute published a paper titled “Smart Beta 2.0,” drawing investor attention to the risks inherent in traditional smart beta strategies and proposing a new approach. Last month, the Economist weighed in, pointing out that — despite a terrible name — “the concept is catching on.”
The Economist noted that while only about $142 billion is currently allocated to smart beta funds, compared to more than $2 trillion invested in hedge funds, smart beta funds attracted $15 billion in inflows in the first quarter of the year, up by 45% compared to the same period a year earlier, according to State Street Global Advisors.
There is no single definition of smart beta strategies, but there is one easy way to think about them: If alpha is about outperforming the market and beta is about achieving the market return, smart beta is about improving performance by passively tracking an index that is not based on a traditional market capitalization weighting. In other words, smart beta is an enhanced form of passive investing.
Why smart beta? The case for smart beta is the case against traditional market cap weighting. When you are tracking a cap-weighted benchmark in equities, your portfolio ends up having more and more of what goes up in value and less and less of what goes down. In fixed income, the stakes are even riskier, because you could be investing more and more in the debt of the most indebted companies or countries. Tracking the market in this way does sound like “beta,” but it doesn’t sound very smart (or so the argument goes).
Why now? While there is no single reason that explains the recent rise of smart beta strategies, their growth is often attributed to rising doubts about the effectiveness of active management, the ever-increasing desire to keep a lid on investment management fees, and investors’ growing awareness of the weaknesses of market cap weighting. It is no coincidence that one of the most popular approaches to smart beta strategies involves constructing an index based on fundamental measures such as book value, dividends, sales, or cash flows.
At a recent CFA Society of the UK event in London, Lionel Martellini, professor of finance at EDHEC Business School and a coauthor of the recent EDHEC paper, offered a comprehensive overview of smart beta strategies. Irina Khan, a member of CFA UK, has filed the following summary of Martellini’s talk.
Summary by Irina Khan
Why smart beta? Lionel Martellini had a simple answer for attendees: According to a recent research paper titled “An Evaluation of Alternative Equity Indexes,” by Andrew Clare, Nick Motson, and Steve Thomas, even portfolios randomly created by monkeys perform better than cap-weighted benchmarks. The underlying reason is poor diversification in cap-weighting.
For that reason, Martellini describes smart beta strategies as new approaches that “aim at adding value in the presence of possibly efficient markets but severely inefficient cap-weighted benchmarks.” He noted that investors often think of diversification as downside protection — but, rather, it should be “about generating the highest possible reward across many market conditions, including the good ones and the bad ones.”
As Clare, Motson, and Thomas noted in their paper, “One of the reasons why the randomly weighted indices rarely produce a set of weights similar to the market-cap index is that there is only a very small prospect of any stock having a weight as high as, for example, 10.0%.”
Martellini noted that a number of indices that do not use cap-weighting are available, such as those provided by MSCI, S&P, FTSE, Russell, and Stoxx. These indices use both non-optimization-based schemes, such as equal weighting, fundamentally weighting, and diversity weighting, as well as optimization-based schemes, such as minimum variance, maximum decorrelation, and risk-parity.
Martellini cautioned that such smart beta indices do not outperform all the time. He explained that although most smart beta strategies have strong probability to outperform poorly diversified cap-weighted indices over the long run, they can in some market conditions underperform for a considerable time. To prove his point, he shared a few “scary numbers” pertaining to the relative risk of various alternative beta strategies showing a maximum relative drawdown of 13.72% and a time under water of 453 days.
(If you tell your clients that monkeys can outperform cap-weighted benchmarks, and then your smart beta portfolio underperforms the cap-weighted benchmark for more than a year, “maybe things are going to get ugly,” Martellini joked.)
Moving from what he called smart beta 1.0 to smart beta 2.0, the EDHEC professor emphasized that “it is of critical importance to better understand the sources of outperformance and the associated risks, so as to assess the robustness of outperformance.” The smart beta 2.0 approach, he said, allows investors to “enjoy the benefits from smart beta investing while controlling the risks of their investments.” He outlined three main ingredients of this newer approach: measuring and managing systematic risks, specific risks, and tracking error, or ex ante control of deviations with respect to a cap-weighted reference index.
With regard to systematic risk, Martellini said that smart indices have factor bias, liquidity bias, style bias, and sector bias, all of which can result in a lower beta than a market-cap-weighted index. However, he believes these biases can be measured and managed — and smart beta methodologies can be customized to still achieve good diversification. For instance, a small-cap bias can be made to disappear if an investor performs minimum-variance optimization based on the largest cap stocks. Martellini suggested that whenever investors pursue a smart beta, they are in fact investing in a bundle of methodological choices that at times are unclear. He advised investors to make their approach and choices explicit, and use them only if they feel comfortable.
Although investors are familiar with managing tracking error, the specific risks of smart beta strategies are “a more complex problem,” Martellini contended, for a simple reason: there is a large amount. “Smart beta weights deviate from market-cap weights so as to generate more attractive risk-adjusted performance; the risk is to fail the objective, because of the use of a suboptimal scheme, and/or because of the use of the wrong parameter estimates,” he explained. Martellini decomposed total specific risk into estimation risk and optimality risk — and further broke down estimation risk into parameter sample risk and parameter model risk. He said the natural approach to managing specific risks is not through hedging but rather through diversification. “In the presence of all this specific risk, investing in more than one smart beta can make sense,” he argued. More specifically, if different portfolio construction schemes involve different types of uncertainty, Martellini suggested that asset managers should be able to add value by packaging and putting together all the smart beta in a more meaningful way in order to diversify away specific risks.
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