A proposal of a methodological framework with experimental guidelines to investigate clustering stability on financial time series

We present in this paper an empirical framework motivated by the practitioner point of view on stability. The goal is to both assess clustering validity and yield market insights by providing through the data perturbations we propose a multi-view of the assets' clustering behaviour. The perturbation framework is illustrated on an extensive credit default swap time series database available online at www.datagrapple.com.Comment: Accepted at ICMLA 201

Community detection for correlation matrices

A challenging problem in the study of complex systems is that of resolving, without prior information, the emergent, mesoscopic organization determined by groups of units whose dynamical activity is more strongly correlated internally than with the rest of the system. The existing techniques to filter correlations are not explicitly oriented towards identifying such modules and can suffer from an unavoidable information loss. A promising alternative is that of employing community detection techniques developed in network theory. Unfortunately, this approach has focused predominantly on replacing network data with correlation matrices, a procedure that tends to be intrinsically biased due to its inconsistency with the null hypotheses underlying the existing algorithms. Here we introduce, via a consistent redefinition of null models based on random matrix theory, the appropriate correlation-based counterparts of the most popular community detection techniques. Our methods can filter out both unit-specific noise and system-wide dependencies, and the resulting communities are internally correlated and mutually anti-correlated. We also implement multiresolution and multifrequency approaches revealing hierarchically nested sub-communities with `hard' cores and `soft' peripheries. We apply our techniques to several financial time series and identify mesoscopic groups of stocks which are irreducible to a standard, sectorial taxonomy, detect `soft stocks' that alternate between communities, and discuss implications for portfolio optimization and risk management.Comment: Final version, accepted for publication on PR

Strategic Groups and Banks’ Performance

The theory of strategic groups predicts the existence of stable groups of companies that adopt similar business strategies. The theory also predicts that groups will differ in performance and in their reaction to external shocks. We use cluster analysis to identify strategic groups in the Polish banking sector. We find stable groups in the Polish banking sector constituted after the year 2000 following the major privatisation and ownership changes connected with transition to the mostly-privately-owned banking sector in the late 90s. Using panel regression methods we show that the allocation of banks to groups is statistically significant in explaining the profitability of banks. Thus, breaking down the banks into strategic groups and allowing for the different reaction of the groups to external shocks helps in a more accurate explanation of profits of the banking sector as a whole. Therefore, a more precise ex ante assessment of the loss absorption capabilities of banks is possible, which is crucial for an analysis of banking sector stability. However, we did not find evidence of the usefulness of strategic groups in explaining the quality of bank portfolios as measured by irregular loans over total loans, which is a more direct way to assess risks to financial stability.strategic groups, financial stability, clustering, Ward algorithm, panel regression