Smart Beta — Innovation, Meet Opportunity

Published on 11 Apr, 2017

The tech bubble wiped out an estimated $30 trillion of wealth. The credit crisis that followed soon after further shook investor confidence in the markets. As a result, investors shifted from active management and hedge funds to passive investments that were still seeing massive growth.

In an attempt to reduce costs and boost returns, ETF providers dabbled in self-indexation to beat the market curve.

Ideating, backtesting, constructing, and managing indices is a cumbersome endeavor however, demanding a lot of time and diligence if it’s to be done effectively. 

Efforts to circumvent these drawbacks have gradually spawned a new breed of innovative ETFs — known as Smart Beta — that combine aspects of both active and passive management strategies.

Ever since the first official index fund was launched in 1975, passive investments have grown significantly, accounting for transfers to the tune of half a trillion in 2016 — around 20% of the asset management industry. The gradual shift from active to passive investment options can be summarized as follows:  


The next chart from Blackrock also shows that $3.5 trillion out of the $74 trillion traded through the asset management industry were Exchange Traded Products, a fast growing segment. Most of the ETF growth has been in the equity space, but fixed income and commodities and others are receiving greater interest from the investors.

GLOBAL ETP ASSETSSource: BlackRock Advisors. 1 Data is as of March 31, 2017 for all regions. 

With ETPs accounting for more than 50% of 2016’s total inflows, and the tremendous growth they’ve enjoyed over the past five years, it’s evident that investors are gravitating toward it. 


What Limitations Among Active and Passive Led to Smart Beta Strategies?

Traditional asset management refers to the active management of portfolios. 

Active investments had a promise of creating Alpha based on the investment manager’s astuteness in selection and timing.

Continued underperformance among active managers as well as higher costs however, forced investors to opt for passive investments that mimicked a known index benchmark. 

Passive investments based on such traditional indices promised Beta from these investments. 

Index funds and ETFs based on market cap weighted indices couldn’t produce better performance however, even though their costs were lower.  This prompted fund houses to blend the best of both concepts, thus coming up with portfolios based on smart beta strategies.

The following illustration is a summary of the differences between active and passive strategies, and particularly, how smart beta investment could offset the limitations of both:

We can also summarize the characteristics of different strategies as follows:

Passive
Smart Beta
Active

Rules-based

        Yes

      Yes

        No

Factor exposure

        Low

      Medium

        Medium

Macro exposure

        High

      High

        High

Manager discretion

        Nil

      Medium

        High

Outperformance potential  

        Nil 

      Medium

        Medium to High

Transparency

        High

      High

        Low

Liquidity

        High

      Medium to High

        Low to High

Investment Capacity

        High

      High

        Low to High

Portfolio Turnover

        Low

      Low

        Medium to High

Fees & Costs

        Low

      Low to Medium

        High


Smart Beta strategies attempt to enhance performance, reduce risk — or both —  as compared to traditional market cap weighted indices. 

Some of the limitations of market-cap weighted indexes’ include:

  • Concentration.
    In most cases, more than 63% of market-cap weighted index assets are held by the top 20% biggest names.
  • Weightage.
    The ever present problem of overweighting overvalued securities while underweighting undervalued stocks. 
  • Exposure.
    Underweighted stock’s exposure to other rewarded risks like value, size, etc.

Thus, Index Funds and ETFs based on market cap weighted indices aren’t good enough for generating better risk-return tradeoffs. 

Specific index ideas had to be created in order to satisfy the investor’s needs.  For example, a simple concept like dividend paying stocks based ETF could satisfy the requirement of investors who would prefer regular income. 

Fundamentals weighted indices were thus created to offer ETFs. Single factor indices were created initially, and their success encouraged firms to bring multi-factor indices into play.  


Factor Based Smart Beta Strategies

Extensive academic and industry research over the years has identified specific factors such as size, dividend, momentum, low volatility, and Yield, which are primary drivers of investment returns. Several asset management companies have created ETFs based on these factors.

Factors that are widely used in Smart Beta ETFs right now include:

  • Value
  • Small size
  • Low volatility
  • High (dividend) yield
  • Quality
  • Momentum
  • Equal Weight
  • High beta
  • Low beta
  • Buyback
  • Growth

While these factors are essentially equity factors, some are well-known factors unique to the fixed income markets, factors such as:

  • Term - Bonds with longer maturities have earned higher returns than bonds with shorter maturities.
  • Credit - Bonds with lower credit quality have earned higher returns than bonds with higher credit quality.

Index providers such as MSCI and Standard & Poor's use these factors to construct factor indexes, which form the basis for Smart Beta ETFs. MSCI currently offers factor indexes that target six of the most commonly used factors. The next table details these factors, their "capture" objective, and some common metrics used in the construction of the factor indexes.


MSCI Factor Indexes

Factors
Capture Objective    
Selected Common Measures

Value            

Excess returns from stocks priced below

their fundamental value.                            

Book to price, earnings to price, book value, sales, earnings, dividends, cash flow.

Small Size (Small Cap)    

Excess returns from small-cap firms.

Market cap (full or free float).

Low Volatility    

Excess returns from stocks with below average volatility or beta.     

Standard deviation, beta.

High Yield

Excess returns from stocks with above-average dividend yields.     

Dividend yield.

Quality

Excess returns from high-quality stocks characterized by low debt, stable earnings growth, and so on.     

Return on equity, earnings stability, dividend growth stability, balance sheet strength, financial leverage.

Momentum 

Excess returns from stocks with strong performance in the past.               

3, 6, or 12 month relative returns, historical alpha.


Though it is proven that these factors produce excess returns in the long-run, it could produce varying returns during different economic / market regimes.  As evident in the next table from Vanguard, it is clear that no single factor outperforms traditional benchmarks consistently.  

As a result, ETF providers have started issuing multi-factor products, which is also consistent with multi-asset portfolio strategies that have gained traction recently. 

Evidently, smart beta 2.0 is nothing but multi-factor ETFs.


Issues in Smart Beta Product Offerings

Because the cost of using standard benchmarks is quite high, and is usually a percentage of the assets under management, many ETF providers have opted for self-indexation. 

Vanguard, one of the world’s largest passive fund managers, shifted from benchmark index provider MSCI in 2012, and began working with thriftier providers as well as Chicago University’s Center for Research in Security Prices (CRSP) in order to serve their 22 largest funds. 

Plenty of smaller firms have created their own specialized indices, managing the cost of their smart beta ETFs in-house or with the help of smaller firms.

Ideating, constructing, and managing these indices aren’t easy. 

It also isn’t cost-effective to use the services of existing Index Providers. 

Fund houses therefore needed to collect data, clean it up, decide on their filter rules, and backtest their idea. All corporate action adjustments need to be completed before the data can be used.  Most indices on which ETFs are introduced are calculated daily on an end of day basis, thus reducing the burden on their calculation engine, and the process in general. Such indices are usually rebalanced on a monthly or quarterly basis; benchmark indices are rebalanced at most on a quarterly basis.

There are plenty of small players who have actively engaged in the smart beta ETF business due to the standardization and scalability of this research orientation. 

Thus, the asset management industry has seen a significant democratization, but not commoditization. 

Plenty of new ETFs are based on unique index ideas, thus creating a high level of customization.  If the research and backtesting isn’t thorough however, the results could be quite counterproductive.


 


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