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 modular… Show more
“…Providing less risky portfolios out of sample compared to traditional risk parity methods, the hierarchical risk parity approach (de Prado, 2016 ) also presents a better risk-adjusted performance than the equal risk contribution strategy (Jaeger et al, 2021 ). More recently, Ferretti ( 2022 ) introduces the naive network modularity-based allocation showing a generally good performance. However, these innovative methods lack integrating ESG risks.…”
Ahead of the new asset management era that calls for sustainable investments, the limitations of the traditional bi-objective mean–variance framework need to be resolved, to accommodate responsible investment objectives. In this paper, we propose a multi-objective minimax-based portfolio optimization model, attempting to simultaneously maximize the risk performance of the three typical ESG investment objectives. Also, apart from the systematic risk, the underlying formulation incorporates the controversy dimension, associated with each company participating in the optimal ESG portfolio. The validity of the proposed model is assessed through an extensive empirical testing on the EURO STOXX 50, the DAX, the CAC 40 and the DJIA, for a 5-year period. The results are considered as highly satisfactory, since the optimal ESG portfolios produced by the model provide consistently higher risk-adjusted returns, in comparison to their respective market benchmarks.
“…Providing less risky portfolios out of sample compared to traditional risk parity methods, the hierarchical risk parity approach (de Prado, 2016 ) also presents a better risk-adjusted performance than the equal risk contribution strategy (Jaeger et al, 2021 ). More recently, Ferretti ( 2022 ) introduces the naive network modularity-based allocation showing a generally good performance. However, these innovative methods lack integrating ESG risks.…”
Ahead of the new asset management era that calls for sustainable investments, the limitations of the traditional bi-objective mean–variance framework need to be resolved, to accommodate responsible investment objectives. In this paper, we propose a multi-objective minimax-based portfolio optimization model, attempting to simultaneously maximize the risk performance of the three typical ESG investment objectives. Also, apart from the systematic risk, the underlying formulation incorporates the controversy dimension, associated with each company participating in the optimal ESG portfolio. The validity of the proposed model is assessed through an extensive empirical testing on the EURO STOXX 50, the DAX, the CAC 40 and the DJIA, for a 5-year period. The results are considered as highly satisfactory, since the optimal ESG portfolios produced by the model provide consistently higher risk-adjusted returns, in comparison to their respective market benchmarks.
The Normalized Mutual Information (NMI) metric is widely utilized in the evaluation of clustering and community detection algorithms. This study explores the performance of NMI, specifically examining its performance in relation to the quantity of communities, and uncovers a significant drawback associated with the metric's behavior as the number of communities increases. Our findings reveal a pronounced bias in the NMI as the number of communities escalates. While previous studies have noted this biased behavior, they have not provided a formal proof and have not addressed the causation of this problem, leaving a gap in the existing literature. In this study, we fill this gap by employing a mathematical approach to formally demonstrate why NMI exhibits biased behavior, thereby establishing its unsuitability as a metric for evaluating clustering and community detection algorithms. Crucially, our study exposes the vulnerability of entropy-based metrics that employ logarithmic functions to similar bias.
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