Systematic Asset Allocation using Flexible Views for South African Markets

TL;DR

This paper applies the HS-FP flexible asset allocation framework to South African markets, demonstrating improved out-of-sample performance over traditional models but with some inconsistencies and statistical limitations.

Contribution

It introduces the application of the HS-FP framework to South African data for asset allocation, highlighting its flexibility and potential advantages over classic methods.

Findings

HS-FP outperforms benchmarks in net returns and Sharpe ratio in certain configurations

Framework shows low probability of backtest overfitting

Results are inconsistent across different training windows

Abstract

We implement a systematic asset allocation model using the Historical Simulation with Flexible Probabilities (HS-FP) framework developed by Meucci. The HS-FP framework is a flexible non-parametric estimation approach that considers future asset class behavior to be conditional on time and market environments, and derives a forward looking distribution that is consistent with this view while remaining close as possible to the prior distribution. The framework derives the forward looking distribution by applying unequal time and state conditioned probabilities to historical observations of asset class returns. This is achieved using relative entropy to find estimates with the least distortion to the prior distribution. Here, we use the HS-FP framework on South African financial market data for asset allocation purposes; by estimating expected returns, correlations and volatilities that are better represented through the measured market cycle. We demonstrated a range of state variables that can be useful towards understanding market environments. Concretely, we compare the out-of-sample performance for a specific configuration of the HS-FP model relative to classic Mean Variance Optimization(MVO) and Equally Weighted (EW) benchmark models. The framework displays low probability of backtest overfitting and the out-of-sample net returns and Sharpe ratio point estimates of the HS-FP model outperforms the benchmark models. However, the results are inconsistent when training windows are varied, the Sharpe ratio is seen to be inflated, and the method does not demonstrate statistically significant out-performance on a gross and net basis.