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Problems with smart beta

For those that aren’t familiar, smart beta strategies involve an investor tracking an index which has been weighted differently to market-cap weightings (the traditional “passive” route). The indices usually increase weightings to factor premia, such as value stocks, small cap, momentum, low volatility, etc, in the hopes of outperforming their passive brethren.  

Smart beta has been growing rapidly in recent years, with smart beta products boasting a 5 year annualised growth in AUM of 33%, according to ETFGI. This form of investing has benefitted from 2 major trends in the industry: 1) a relentless focus on fees – smart beta is usually cheaper than traditional active management, and 2) the realisation that much of what used to be called active management’s “alpha” has now been found to be factor exposure. People have realised that many funds’ historic outperformances are less to do with their managers’ skilful stock picking, and are often due to nothing more than overweights to factors.  

To get one point clear at the beginning, whilst smart beta and factor investing are related, smart beta is not the same as factor investing. Factor investing involves investing in traditional long-short academic factors (as defined by Fama-French etc), whereas smart beta involves investing in a slightly different kind of factor exposure – one which is usually long-only, usually only in large caps, and usually with much lower tracking error. The factors are defined by the index provider.

For some background on the academic factors which smart beta products are based on, please refer to this post.

Some of the problems I’ll go through in these posts will be related to factor investing, some will be related to smart beta, some will apply to both. As there’s quite a bit of information to go through, I’ve split the posts up into more manageable chunks:

  1. Factors can go through long periods of underperformance
  2. Defining a factor is hard
  3. Factors can change
  4. Factors can decay over time
  5. It’s impossible to know when a factor stops working
  6. Factor dilution
  7. Academia isn’t real life
  8. Backtesting

Without further ado, let’s get into the first problem with smart beta.

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