Local and Global Balance in Financial Correlation Networks: an Application to Investment Decisions

TL;DR

This paper explores how deviations between local and global balance in financial correlation networks can identify assets that behave differently during crises, aiding investment decisions and risk management.

Contribution

It introduces the use of local-global balance deviations as a novel criterion for asset selection in financial networks during crises.

Findings

Deviations in local and global balance identify assets with unique behavior during crises.

The approach improves predictive accuracy for asset performance in financial markets.

Results support the hypothesis that local-global balance deviations are useful for portfolio optimization.

Abstract

The global balance is a well-known indicator of the behavior of a signed network. Recent literature has introduced the concept of local balance as a measure of the contribution of a single node to the overall balance of the network. In the present research, we investigate the potential of using deviations of local balance from global balance as a criterion for selecting outperforming assets. The underlying idea is that, during financial crises, most assets in the investment universe behave similarly: losses are severe and widespread, and the global balance of the correlation-based signed network reaches its maximum value. Under such circumstances, standard diversification (mainly related to portfolio size) is unable to reduce risk or limit losses. Therefore, it may be useful to concentrate portfolio exposures on the few assets - if such assets exist-that behave differently from the rest of the market. We argue that these assets are those for which the local balance strongly departs from the global balance of the underlying signed network. The paper supports this hypothesis through an application using real financial data. The results, in both descriptive and predictive contexts, confirm the proposed intuition.