Loss of structural balance in stock markets

Ferreira, Eva; Orbe, Susan; Ascorbebeitia, Jone; Pereira, Brais Álvarez; Estrada, Ernesto

doi:10.1038/s41598-021-91266-4

Cited by 3 publications

(2 citation statements)

References 51 publications

(29 reference statements)

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“…Whilst hypothesis testing and inference procedures are well established in standard Euclidean domains, there is a pressing need to develop families of inference procedures that will facilitate the development of testing frameworks when working with the topologies of graphs. This is particularly relevant as there is a growing literature that is developing graph and network-based statistical models for financial risk analysis and insurance modeling, see [ 32 , 33 ].…”

Section: Methodsmentioning

confidence: 99%

Assessing portfolio diversification via two-sample graph kernel inference. A case study on the influence of ESG screening

Gudmundarson,

Peters

2024

PLoS ONE

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In this work we seek to enhance the frameworks practitioners in asset management and wealth management may adopt to asses how different screening rules may influence the diversification benefits of portfolios. The problem arises naturally in the area of Environmental, Social, and Governance (ESG) based investing practices as practitioners often need to select subsets of the total available assets based on some ESG screening rule. Once a screening rule is identified, one constructs a dynamic portfolio which is usually compared with another dynamic portfolio to check if it satisfies or outperforms the risk and return profile set by the company. Our study proposes a novel method that tackles the problem of comparing diversification benefits of portfolios constructed under different screening rules. Each screening rule produces a sequence of graphs, where the nodes are assets and edges are partial correlations. To compare the diversification benefits of screening rules, we propose to compare the obtained graph sequences. The method proposed is based on a machine learning hypothesis testing framework called the kernel two-sample test whose objective is to determine whether the graphs come from the same distribution. If they come from the same distribution, then the risk and return profiles should be the same. The fact that the sample data points are graphs means that one needs to use graph testing frameworks. The problem is natural for kernel two-sample testing as one can use so-called graph kernels to work with samples of graphs. The null hypothesis of the two-sample graph kernel test is that the graph sequences were generated from the same distribution, while the alternative is that the distributions are different. A failure to reject the null hypothesis would indicate that ESG screening does not affect diversification while rejection would indicate that ESG screening does have an effect. The article describes the graph kernel two-sample testing framework, and further provides a brief overview of different graph kernels. We then demonstrate the power of the graph two-sample testing framework under different realistic scenarios. Finally, the proposed methodology is applied to data within the SnP500 to demonstrate the workflow one can use in asset management to test for structural differences in diversification of portfolios under different ESG screening rules.

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Section: Methodsmentioning

confidence: 99%

Assessing portfolio diversification via two-sample graph kernel inference. A case study on the influence of ESG screening

Gudmundarson,

Peters

2024

PLoS ONE

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“…The notion of SB has been extended to cycles of any length, stating that a network is maximally balanced if all cycles are balanced ( 18 ). In practice, there are often deviations from maximal balance ( 19 ), requiring the statistical analysis of the enrichment of the studied patterns versus a null model. A null model of statistical power is a randomized network that is as close to the real network as possible without capturing the actual wiring mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Proper network randomization is key to assessing social balance

Hao,

Kovács

2024

Sci. Adv.

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Social ties, either positive or negative, lead to signed network patterns, the subject of balance theory. For example, strong balance introduces cycles with even numbers of negative edges. The statistical significance of such patterns is routinely assessed by comparisons to null models. Yet, results in signed networks remain controversial. Here, we show that even if a network exhibits strong balance by construction, current null models can fail to identify it. Our results indicate that matching the signed degree preferences of the nodes is a critical step and so is the preservation of network topology in the null model. As a solution, we propose the STP null model, which integrates both constraints within a maximum entropy framework. STP randomization leads to qualitatively different results, with most social networks consistently demonstrating strong balance in three- and four-node patterns. On the basis our results, we present a potential wiring mechanism behind the observed signed patterns and outline further applications of STP randomization.

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Polarization and multiscale structural balance in signed networks

Talaga,

Stella,

Swanson

et al. 2023

Commun Phys

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Polarization, or a division into mutually hostile groups, is a common feature of social systems. It is studied in Structural Balance Theory in terms of semicycles in signed networks. However, enumerating semicycles is computationally expensive, so approximations are often needed. Here we introduce the Multiscale Semiwalk Balance approach for measuring the degree of balance (DoB) in (un)directed, (un)weighted signed networks by approximating semicycles with closed semiwalks. It allows selecting the resolution of analysis appropriate for assessing DoB motivated by the Locality Principle, which posits that patterns in shorter cycles are more important than in longer ones. Our approach overcomes several limitations affecting walk-based approximations and provides methods for assessing DoB at various scales, from graphs to individual nodes, and for clustering signed networks. We demonstrate its effectiveness by applying it to real-world social systems, which leads to explainable results for networks with expected patterns (polarization in the US Congress) and a more nuanced perspective for other systems. Our work may facilitate studying polarization and structural balance in a variety of contexts and at multiple scales.

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Loss of structural balance in stock markets

Cited by 3 publications

References 51 publications

Assessing portfolio diversification via two-sample graph kernel inference. A case study on the influence of ESG screening

Assessing portfolio diversification via two-sample graph kernel inference. A case study on the influence of ESG screening

Proper network randomization is key to assessing social balance

Polarization and multiscale structural balance in signed networks

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