We use data from 1999 to 2015 for 45 different assets. We compare risk-return characteristics in an out-of-sample framework of all network-based asset allocation strategies with their respective benchmark models. Within our sample, we show that utilizing information on the interconnectedness of various asset returns given the topological structure of a network improves the risk-return characteristics of standard benchmark portfolios. It is quite clear that the constructed network takes into account complex relationships between assets beyond those measured by correlations.

We propose the so-called ρ-dependent strategy and test its performances against the extremely simple yet effective 1/N naïve rule and two Markowitz-related policies. Our out-of-sample results show that the ρ-dependent strategy tends to present significant higher portfolio Sharpe ratios and lower portfolio variance relative to these well-known benchmarks. Additionally, this enhanced performance is not explained by large exposures to traditional risk factors as indicated by the reported positive and statistically significant Carhart's alphas. More importantly, our results are robust to several portfolio configurations, time periods and markets even after accounting for transaction costs.

The MVO algorithm treats all variables as interrelated, assuming a complete cluster. In other words, traditional asset allocation does not recognize the complexity immerse in the data. This work presents some novel, robust and flexible methods with visual interpretations to construct risk-adjusted portfolios. Clustering methods showed a better trade-off between return and risk than MVO algorithm. The empirical results indicate that hierarchical algorithms have a better performance when building diversified portfolios measured by the Omega ratio. One of the most important results is the stable behavior of clustering-based portfolios addressing a special issue in financial markets, the volatility.

Convert partition table from MBR to GPT

Converting Ubuntu into UEFI or Legacy mode