On the Modeling and Simulation of Portfolio Allocation Schemes: an Approach based on Network Community Detection

TL;DR

This paper introduces a novel portfolio allocation method based on network community detection, which outperforms traditional approaches in simulations and backtesting, by grouping assets into correlated communities for diversification.

Contribution

A new portfolio allocation scheme using network community detection and modularity, incorporating advanced correlation metrics, demonstrating superior performance over existing methods.

Findings

The proposed scheme outperforms traditional methods in various simulated scenarios.

Backtesting confirms the effectiveness of the community-based allocation approach.

Different correlation metrics impact portfolio diversification and performance.

Abstract

We present a study on portfolio investments in financial applications. We describe a general modeling and simulation framework and study the impact on the use of different metrics to measure the correlation among assets. In particular, besides the traditional Pearson's correlation, we employ the Detrended Cross-Correlation Analysis (DCCA) and Detrended Partial Cross-Correlation Analysis (DPCCA). Moreover, a novel portfolio allocation scheme is introduced that treats assets as a complex network and uses modularity to detect communities of correlated assets. Weights of the allocation are then distributed among different communities for the sake of diversification. Simulations compare this novel scheme against Critical Line Algorithm (CLA), Inverse Variance Portfolio (IVP), the Hierarchical Risk Parity (HRP). Synthetic times series are generated using the Gaussian model, Geometric Brownian motion, GARCH, ARFIMA and modified ARFIMA models. Results show that the proposed scheme outperforms state of the art approaches in many scenarios. We also validate simulation results via backtesting, whose results confirm the viability of the proposal.