Yes! you need to ask such questions to your smart beta company. If they say they are smart they need to illustrate that in the monthly performance numbers. Monthly numbers are not the expected norm for Smart Beta companies. How can it be? If ‘Value’ as a style can underperform for years followed by a secular underperformance in ‘Momentum’, none of the styles will have a consistent yearly performance. So offering to discuss monthly performance numbers on an open platform or a blog is simply suicidal PR.
Smart Beta companies can’t differentiate themselves today. Even in the developed world investors don’t understand risk weighted return. It’s strange how technology is diverging from the stone age financial investment management business. You wonder how the financial services are going to handle the next crisis. Even a casual perusal of the Marxian opinion about capitalism as a sequence of crisis should be good risk management.
Ethics in Smart Beta
Take any of the big names (The V’s, the B Rocks, the Trees, the QRs and the rest), smart to dumb, sort them with products and AuM and ask them one question. “Where are the monthly numbers?” I was just commenting on a piece written by David Shrier (MIT) on ‘Ethics of Data Science’. This is what I wrote.
“The idea of fairness is utopian not only owing to its sublime nature but also because natural systems are dynamic and non-linear. There is a need for push and pull for the system to work. So we need unfairness for fairness to exist, the inefficiency of the markets is why the markets function. Human beings are emotional and poor systems, this is why markets need a crash before we react and go to the other end of regulation. And even if the lack of ethics is obvious in modern finance as it moves towards systems, challenging a 10 trillion dollar incumbent asset management company despite its visible “unfairness” needs more than a Fintech or AI. It needs more “unfairness” before the tide turns.”
Monthly Performance can show the stability of a model, strategy, methodology. It can show whether the returns are clustered. My friend Laszlo, CIO of an insurance company was sitting with me in Budapest in January investigating into the RMI performance, whether the returns showed a seasonality, whether the performance was clustered, up month – down month behavior, 2008 test etc.
The Scatter Plot
I did a small test with our RMI models listed on Bloomberg. I separated their performance into two periods, one from 2005-2016 (Fig. 1) and other for 2016 (Fig. 2). I did a two variable scatter plot; annualized excess return vs. the number of outperforming calendar months. I will be surprised to see our competition offer such numbers for the reasons I have mentioned above. So this is a unique chart, every dot represents a cartesian point of excess returns and number of months a model has outperformed in a calendar year. Every dot represents a summary of 12 months. If the dot is at 6, it means the RMI model beat the benchmark for 6 out of 12 months. This way I have put RMI under scrutiny. Now I am not just claiming that RMI delivers risk-weighted excess returns, I am also illustrating the distribution of those excess returns over the 12 month period.
Fig 2. Yearly Excess Returns (%) vs. Nr. of Outperforming Calendar Months (2016)
For the decade the average annualized excess returns were at 4.69% with an average outperforming number of calendar months at 7.79, which is almost 8 months out of the 12 months in a calendar year. If you think this is an insane risk-weighted performance, I won’t disagree. But I would like to mention that 6 out of 36 annual performances tested in the decade model produced negative annualized excess returns. And the RMI UK was the only model that took a hit and underperformed in 2008 before recovering. The UK model delivered an annualized excess return of 5.6% over FTSE 100 for the decade. For RMI models we listed in 2016, the scatter plot covers 13 annual performances. The average annualized excess returns were 3.77% with an average 6.85 score for outperforming months. This is almost 7 months out of a 12 month period. 2016 was a stellar year for us as all our RMI models outperformed their benchmarks. Below I have carried the monthly heatmap for all the 36 annual performances (Fig. 3). RMI Bloomberg listed models cover US, UK, Canada, Europe and allows for comparison with the top benchmarks like STOXX 50, S&P500, FTSE100 and TSX350.
Fig. 3. RMI Monthly Performance HeatMap for 11 Years. (2005-16)
To check out the monthly performance numbers you can visit <ORMI> on Bloomberg. I forgot to ask you if you know how much risk weighted excess returns did your hedge fund allocation deliver over the last decade. If you don’t know the answer to this question, you should shoot the question to your hedge fund manager. If he can’t deliver a 5% risk weighted excess returns on his equity models, you know what to do.
RMI Methodology. Risk Management Indices (“RMI”) methodology is based on a universal framework which considers markets as natural dynamic systems with both momentum and reversion forces acting across different holding periods. RMI captures the benefits of both growth and value factors in a dynamic, effective construct but also diversifies portfolio risk by generating a different return pattern by diversifying style factor.
Disclaimer: Past performance is no guarantee of future results. All results shown are based on simulated performance and are without fees and expenses typical of managed accounts which would reduce performance. Nothing contained in this material is intended to constitute legal, tax, securities, financial or investment advice, nor an opinion regarding the appropriateness of any investment. No part of this material may be duplicated in any form and/or redistributed without prior written consent. In so far as this report includes current or historical information, it is believed to be reliable, although its accuracy and completeness cannot be guaranteed. Risk Management Indices RMI® are Analytics, Indices and models developed and owned by Orpheus Risk Management Indices, United Kingdom, EU.
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