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We implement a systematic asset allocation model using the HistoricalSimulation with Flexible Probabilities (HS-FP) framework developed by Meucci.The HS-FP framework is a flexible non-parametric estimation approach thatconsiders future asset class behavior to be conditional on time and marketenvironments, and derives a forward looking distribution that is consistentwith this view while remaining close as possible to the prior distribution. Theframework derives the forward looking distribution by applying unequal time andstate conditioned probabilities to historical observations of asset classreturns. This is achieved using relative entropy to find estimates with theleast distortion to the prior distribution. Here, we use the HS-FP framework onSouth African financial market data for asset allocation purposes; byestimating expected returns, correlations and volatilities that are betterrepresented through the measured market cycle. We demonstrated a range of statevariables that can be useful towards understanding market environments.Concretely, we compare the out-of-sample performance for a specificconfiguration of the HS-FP model relative to classic Mean VarianceOptimization(MVO) and Equally Weighted (EW) benchmark models. The frameworkdisplays low probability of backtest overfitting and the out-of-sample netreturns and Sharpe ratio point estimates of the HS-FP model outperforms thebenchmark models. However, the results are inconsistent when training windowsare varied, the Sharpe ratio is seen to be inflated, and the method does notdemonstrate statistically significant out-performance on a gross and net basis.

Systematic Asset Allocation using Flexible Views for South African Markets