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Awards - June 16, 2015

Sivagaminathan Sivasubramanian, Quantitative Research Analyst at EDHEC-Risk Institute, awarded research prize by EDHEC Business School for best research work on smart beta

Sivagaminathan Sivasubramanian, Quantitative Research Analyst at EDHEC-Risk Institute, was awarded the research prize for the best research work on smart beta at the EDHEC Master graduation ceremony that was held on June 6, 2015 at EDHEC Business School’s campus in Lille for his thesis entitled, "Active Allocation of Smart Beta Indices based on Factor Timing and Regime Switching".

Scope of the winning thesis

Smart beta strategies have generated considerable and growing interest among investors. Smart beta strategies aim to achieve better returns than capitalisation-weighted indices using simple portfolio construction methodologies that are rule-based, transparent and have lower management fees than traditional active strategies. Performance analysis suggests that the outperformance of smart beta strategies represents reward to exposures to the value, size, momentum and low volatility factors.

Smart beta indices overcome the drawbacks of the cap-weighted benchmark which is heavily concentrated on large-cap and growth stocks which are unrewarded risk exposures. Smart beta indices on the other hand can be customised to tilt toward a particular rewarded risk factor according to the investor preferences. Thus a value investor can get exposure to the value factor by holding a smart beta portfolio that tilts towards the value factor and he can do so by holding a well-diversified portfolio.

However, there is significant evidence in the literature that factors are cyclical in nature, i.e. they do provide significant risk premia in the long run but they also have periods of short term losses. This led to the concept of allocating across multiple factors with the aim to take advantage of imperfect correlation among the risk factors. This study finds that further value can be added to the portfolio by combining different factor tilts by timing the factor cycles.

The study analyses the long term historical track records of ERI Scientific Beta USA smart factor indices using the Markov Regime switching model to categorise each factor index into high and low volatility regimes. Two portfolios with allocations across smart factor indices are constructed: one for the low volatility regime and one for the high volatility regime. Forecasts of future regimes are used to switch between the two portfolios accordingly. Regime-dependent portfolios produce better return to risk trade-offs (as measured by the Sharpe and Information Ratios) than static portfolios both in and out of sample. Implementation of the proposed regime-based factor allocation strategy is possible with reasonably low turnover and hence manageable transaction costs.

For further information, please contact research@edhec-risk.com.