Ruslan Grytsenko

Type of the article: Research Article

Abstract
Under stress, information about counterparties can change willingness to interact faster than balance-sheet indicators register it, yet financial stability analysis still treats trust mainly as an aggregate variable. The study aims to develop a conceptual framework explaining how information signals update trust in specific relationships and thereby change the effective topology of the financial network. The framework synthesizes information asymmetry, financial intermediation, institutional, and financial network theories; the neural network of trust serves only as a functional analogy. Trust is defined as a directed, time-varying relational state variable, distinct from perceived default risk, with willingness to transact treated as its consequence. A generic mechanism updates trust from its previous level, information signals, and feedback from realized interactions. The framework separates trust, potential channel capacity, effective link strength, and realized flow, with effective link strength serving as the edge weight; its general form allows positive link strength at low trust where collateral, guarantees, or legal enforcement substitute for trust. A network matrix of effective link strengths connects bilateral dynamics to systemic outcomes through centrality, concentration, redundancy, and dependence on critical nodes. Four propositions follow: signals alter trust before funds move; trust updating reshapes link strength and network topology; systemic consequences depend on the network position of affected links; and, although each link responds monotonically to trust, trust concentrated in systemically important links may increase intermediation efficiency while reducing systemic resilience under particular network configurations. The framework shifts financial stability analysis from the level of trust to its distribution across relationships.