AI summaryⓘ
The authors studied how stock prices in the Indonesian market move together by comparing simple linear methods (Pearson correlation) and more complex methods that capture non-linear relationships (Mutual Information). They tested different ways to build networks from these relationships and found that the classic combination of Pearson correlation with a simple network and community detection best matches known industry sectors. However, more flexible methods (PMFG with Mutual Information) reveal smaller hidden groups that don't align with traditional sectors but show interesting economic patterns. Their work suggests that while linear methods remain useful, exploring non-linear connections helps uncover subtle structures in the market.
Pearson correlationMutual InformationMinimum Spanning TreePlanar Maximally Filtered GraphCommunity detectionStock market networksNon-linear dependencySector classificationSpectral dynamicsMarket topology
Abstract
The collective movement of stock prices harbors complex interdependencies that are conventionally simplified only through a linear lens. This paper explores computed structural network representations in the Indonesian capital market by testing the limits of Pearson correlation and Mutual Information (MI) in unveiling the spectral dynamics of the market. Across 2,328 rolling observation windows from 2015 to 2025, we examine 24 methodological configurations that combine three dependency estimators (Pearson, MI adaptive binning, and MI-kNN), two graph filtering schemes (Minimum Spanning Tree/MST and Planar Maximally Filtered Graph/PMFG), and four community decoders. The empirical results unveil a fundamental reality: topological richness does not always resonate with sectoral classification precision. The Pearson, MST, and Infomap configuration is shown to remain the most robust foundation for recovering conventional sectoral taxonomy. Nevertheless, when deeper observation demands the exposition of local structures and the weave of heterogeneous communities, the architectural relaxation through PMFG demonstrates its superiority. In the realm of residual information detection, MI adaptive binning appears far more proportional than kNN; histogram-based regularization successfully tames empirical noise without sweeping away traces of non-linear dependency. Ultimately, the synergy of MI and PMFG is not positioned to dethrone the dominance of linear correlation, but rather to provide an essential analytical lens for excavating hidden economic sub-structures -- such as the cohesion of commodity regimes -- that have long transcended the rigid boundaries of the market's formal sectors.