Smart beta evolves

With their blend of high theory and automated rebalancing, smart beta strategies have staked out the middle ground between active and passive investment management – and have made it their ow


With their blend of high theory and automated rebalancing, smart beta strategies have staked out the middle ground between active and passive investment management – and have made it their own. 

According to data from Morningstar, $616 billion in global assets under management (AUM) were tied to the performance of smart beta indexes as of 2015 – up from a mere $103 billion in 2008. Roughly one-fifth of the $3 trillion ploughed into exchange-traded funds (ETFs) is linked to smart beta. 

Unlike traditional benchmark indexes like the Standard & Poor’s 500, whose weightings are determined by the market capitalisation of their constituents – or very occasionally their price, as in the case of the Dow Jones Industrial Average – smart beta indexes use rules-based systematic weightings to track factors such as volatility or momentum, rebalancing at set intervals to refresh the stocks they offer exposure to. 

Investors like to use the strategies as a simple, cost-effective way of gaining diversification from traditional long-only equities investments – tracking only undervalued stocks as a putative way of avoiding market bubbles, for instance – or as naked plays on particular strategies, such as tracking low-volatility stocks. 

While the sector has grown at a terrific pace, many still see headroom for further significant growth. In 2015, many large and midsize US public pension funds began making sizeable allocations to smart beta strategies – a trend that smaller funds, many of which still have large AUM, may look to emulate.

Banks and index providers that construct the benchmarks expend vast quantitative finance resources – both brainpower and processor power – on getting the blend just right. The prize for those that stand out from the market is a lucrative revenue stream from providers that license the methodology for use in investment products such as ETFs or annuities. 

Some clients, such as more sophisticated pension funds, even like to play an active role in designing the algorithms that track the various factors they are seeking exposure to – while allowing banks and index providers to take on the labour-intensive process of actually running the strategy on a day-to-day basis. 

The quantitative theory applied to the exposures and rebalancings grows ever more sophisticated, and yet factor investing traces its roots back a long way. In the 1930s, Columbia Business School academics David Dodd and Benjamin Graham harnessed data on price movements to establish the idea of investing in stocks that traded below book value. This was the birth of the theory that we know today as value investing, one of the most popular smart beta plays. 

Today, new flavours of smart beta often seek to offer investors exposure to multiple factors at once. For instance, a multi-factor index might track stocks that appear undervalued and also exhibit low volatility.

While some fear certain strategies may be reaching saturation point, others see ample room for further expansion and diversification. Many equities subsectors, emerging markets and large parts of the fixed-income markets all lack smart beta offerings – with prodigious amounts of investor money still up for grabs.


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