Smart Beta: The Next Generation

February 10, 2015

SmartbetaNextGenerationCover

With global growth projections rising and confidence returning, albeit gradually, signs remain that the worst economic crisis the world has seen since the Great Depression has left some remarkably deep scars on the psyche of players in the investment world. This has led to an increased demand for safe but innovative investment products, smart beta being one of them. This is coupled with a demand for yield in an artificially low rate environment alongside a well performing stock market. This landscape of higher returns and lower risks brings us back to the financial academics' basics of breaking down returns into factors.

Early versions of smart beta have now been launched, tested and found to be resilient by market players. Smart beta has become such a hot topic that some traditional index providers are claiming they have been providing smart beta indexes for decades. Buoyed by this initial response some feel that smart beta version 2.0 is on the horizon. But what else can smart beta bring to the table; how "smart" can it actually be? Will v 2.0 be something more akin to dumb alpha? Or will it evolve into something completely new?

Smart beta originally sprung out of academic theory, beginning with Harry Markowitz's seminal 1952 paper which established that portfolio diversification could maximise investor returns and lower risk.1 Taking its name from "beta", meaning subject to overall market performance, and "smart", meaning outperformance, beating the market is what smart beta is all about. The idea behind smart beta is not to follow market cap weight, unlike passive index funds. The stock market crash of 2008 tarnished market cap indexes because they were deemed biased to past success and risk due to excessive concentration, thus creating bubbles prior to market downfalls.2

Academics have demonstrated over the past 40 years that strategies proven to beat beta do exist. Equally weighting stocks is one method; emphasising low price-to-book ratios another. In the last few years the industry has further developed smart beta into a number of different indexes including fundamentally weighted, low volatility, maximum Sharpe ratio and momentum.3

Systematic Systemic Allocation Strategy
Today's smart beta products fit neatly into the traditional style and market cap boxes, which is a context that investment professionals can leverage. There are, however, strategic and tactical decisions regarding asset allocation that, nevertheless, still require an adviser or asset manager to decide which strategy will work, and when. While attempting to follow the numerous and complex factors within the overall global economic cycle, the ability to react in a timely manner is essential if opportunities are not to be missed. Think about it as market timing, but instead of stock picking, it is portfolio construction and style tilting. A deep value fund, for instance, can in general outperform the market. The investor, however, may be subject to wide performance swings through which growth may, at times, actually outperform value. The challenge is to then change portfolio allocation quickly to avoid the periods of value underperformance, which is fundamentally a matter of market timing - still very much an active strategy.

A systematic allocation strategy would essentially follow certain smart beta indexes and automatically shift the allocation from value to growth based on determined metrics. At a very high level, today's investor still needs to make an active decision when choosing the region in which to invest (e.g. Europe, Emerging Markets or the U.S.). True smart beta, or outperformance, can come as a result of being able to use metrics, such as economic data, to shift global equity portfolios into economies that have superior performance.

Some have already employed GDP as a weighting mechanism instead of market cap. But to capture economic growth, other indicators are more timely and independent, such as Markit's PMI surveys. Fig. 1 shows that Markit's Global PMI Output Index is a leading indicator of GDP. Coupled with Fig. 2, which shows a strong relationship between Markit's PMI and stock market performance, we can envision a systematic approach to global investing.

Another investment example is the ability to automatically increase fund allocation from equity to fixed income when the economy slows. Similarly, in the fixed income arena, allocations can systematically shift from treasuries to investment grade to high yield as the economy expands, in order to capture superior performance. This next generation of smart beta should be considered as more of a tactical product that can be applied across geographies, asset classes and sectors.

Preferred Liquidity Profile
Investment strategies show that post 2008, the ability to offer products that are truly liquid has become increasingly important. Investors now demand the flexibility to enter into or exit positions quickly, in any situation. Furthermore, a body of academic research4 shows that risk-adjusted performance for less liquid stocks is higher than expected – so pari passu less liquid stocks will outperform and investors will pay a premium for liquid products.

A preferred liquidity profile is a solution that invests in a strategy where the underlying instruments in a portfolio or index are liquid. If a portfolio is made up of cash bonds, for example, which have a naturally lower trading volume, the ability to create strategies that have a preferred liquidity profile then becomes critical. An opposing strategy, for those willing to assume additional risk, could involve a less liquid portfolio aiming for outperformance.

Likewise with equities, the ability to embed transaction cost analysis into investment decisions can allow managers to create valuable strategies offering return profiles with corresponding liquidity. Traditionally, smart beta has focused on a set of factors or micro-factors. However, while equally weighting a portfolio may look promising at first glance, when this results in an increased weight of a less liquid stock, it will generally result in an increased cost to trade, thus diminishing returns. As soon as the focus of a portfolio turns from traditional large cap stocks, such as the S&P 500, liquidity has a cost that must be factored into the analysis.

A Clear Future
The systematic allocation and liquidity strategies are new types of smart beta profiles - not seen before in either the equity or fixed income markets – that resonate with investors today. Scarred by the "credit crunch" and the poorly understood opaque products that unravelled, today's investment professionals are looking for opportunities that are transparent, replicable and comprehensive.

Recent moves by regulators show that they are of the same mindset. IOSCO (International Organisation of Securities Commissions) established a set of principles in July 2013 that index providers will need to comply with. It is likely that the European Commission will base forthcoming regulations for index provision on the IOSCO principles. At the heart of these different procedures is the pursuit of transparency in order to address the risk of conflicts of interest. Smart beta v 2.0 is consistent with these regulatory goals.

Just as the first raft of smart beta products responded to the post 2008 investment world, smart beta v 2.0 has the potential to respond to today's environment, enabling market participants to react swiftly to economic events. Given the continuing increase in passive management, smart beta is likely to grow, especially now that investors are starting to use it more systematically. In order for smart beta products to continue to succeed, however, they need to remain transparent, replicable and understandable. The industry is now well aligned around these goals. We should not lose sight of them.


Endnotes

  1. Foster, M., S. Krouse. "Smart beta is the overnight success that took decades to arrive." Financial News, May 26 2014.
  2. Legal & General Investment Management. "An alternative view of index fund management." (October 2013)
  3. Blitz, D. "How Smart is 'Smart Beta'?" Journal of Indexes, Vol. 16, No. 2, pp. 36-40.
  4. Multiple research including:
    http://www.cis.upenn.edu/~mkearns/finread/amihud.pdf
    http://people.stern.nyu.edu/lpederse/papers/liquidity_risk.pdf
    http://www.cfainstitute.org/learning/products/publications/rf/Pages/rf.v2013.n2.10.aspx

 

 

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