ERRATUM: Cascading Failures in Bi-partite Graphs: Model for Systemic Risk Propagation

This article analyses the impact of Economic Policy Uncertainty (EPU) on the credit risk of US commercial banks considering their size, profitability and solvency. To achieve this goal, a sample of 2994 US commercial banks was selected for the period 2017–2019. Using panel data, the results reveal a statistically significant positive relationship between EPU and credit risk of US commercial banks. Banks of less profitability and less solvency were found to be more vulnerable to the effect of EPU on credit risk. No conclusive results were found regarding the impact of bank size on vulnerability to EPU. Overall, our evidence suggests that policy makers and bank managers should consider the effect of EPU on their decisions.

How to explain non-performing loans by many corporate governance variables simultaneously? A corporate governance index is built to US commercial banks

Similar to the Internet, which created a revolution in the ability to communicate quickly and cheaply, FinTech companies are using blockchain and digital wallets to create a revolution in the transfer of value across the world. The literature and blog space to date have indicated that digital wallets could overtake the need for traditional banking. The purpose of this article is to provide further testing on whether FinTech banking companies could overtake the need for traditional banking by evaluating whether the number of deposits at various US commercial banks and the growth of digital wallets have impacted US commercial bank market values over the past two to three years. Results indicate a strong positive relationship between the number of deposits and US commercial bank market values and a strong negative relationship between digital wallet proxy measures and US commercial bank market values from 2017 to 2019. The trends indicate a stable amount of deposit in the US banking system to a slight decline versus a large growth in the digital wallet sphere. Results indicate that digital wallets could create a large decrease in the market value of commercial banks in the future. Such a result would indicate the need to consider carefully how FinTech companies should be included in financial sector indices. TOPICS:Real assets/alternative investments/private equity, currency Key Findings • The growth in digital wallets in the past several years has been rapid. In comparison, the growth in bank deposit accounts has been rather stagnant. • The question is whether digital wallets are a complement to banks or a competitor. Regression results indicate that the number of deposit accounts has a strong positive relationship with bank market value and that digital wallet proxies have a negative relationship. This means the FinTech companies are taking away market value and may be viewed as a competitor. • Assuming that digital wallet growth takes away deposits from banks, results indicate that market values of bank may be on the decline. If such a case exists, it would be wise for exchange-traded funds and index companies to incorporate FinTech companies in their funds sooner rather than later. This will help offset the possible withdrawal from pure traditional banking funds.

Climate risk, ESG performance, and ESG sentiment in US commercial banks

We address a fundamental problem that is systematically encountered when modeling real-world complex systems of societal relevance: the limitedness of the information available. In the case of economic and financial networks, privacy issues severely limit the information that can be accessed and, as a consequence, the possibility of correctly estimating the resilience of these systems to events such as financial shocks, crises and cascade failures. Here we present an innovative method to reconstruct the structure of such partially-accessible systems, based on the knowledge of intrinsic node-specific properties and of the number of connections of only a limited subset of nodes. This information is used to calibrate an inference procedure based on fundamental concepts derived from statistical physics, which allows to generate ensembles of directed weighted networks intended to represent the real system—so that the real network properties can be estimated as their average values within the ensemble. We test the method both on synthetic and empirical networks, focusing on the properties that are commonly used to measure systemic risk. Indeed, the method shows a remarkable robustness with respect to the limitedness of the information available, thus representing a valuable tool for gaining insights on privacy-protected economic and financial systems.

Bipartite networks that characterize complex relationships among data arise in various domains. The existing bipartite network models are mainly based on a type of relationship between objects, and cannot effectively describe multiple relationships in the real world. In this paper, we propose a multi-relationship bipartite network (MBN) model, which can describe multiple relationships between two types of objects, and realizes simple weighted bipartite network reconstruction. Our model contains three major modules, namely multi-relationship bipartite network modeling (MBNM), multi-relationship aggregation module (MAM) and network reconstruction module (NRM). In MBNM, a multi-relationship bipartite network is proposed to describe multiple relationships between two types of objects. In the MAM, considering that different relationships have different information for the model, we introduce a novel relationship-level attention mechanism, and the aggregation of multiple relationships is carried out through the importance of each relationship. Based on the learning framework, the NRM can learn the potential representations of nodes after multi-relationship aggregation, and design a nonlinear fusion mechanism to reconstruct weighted bipartite network. We conducted extensive experiments on three real-world datasets and the results show that multi-relationship aggregation can effectively improve the performance of the model. In addition, experiments also show that our model can outperform existing competitive baseline method.

An Advantage of Newness: Learning by Entrants and Incumbents in US Commercial Banking

Using GMM framework on the data of the US commercial banks spanning over 2002 to 2018, this study shows that banks adjust their regulatory capital ratios faster than traditional capital ratios. Our results show that the speed of adjustment of regulatory capital ratios and traditional capital ratios increases in bank capital adequacy and bank liquidity, respectively. We also find that the speed of adjustment of regulatory capital ratios of too-big-to-fail banks is lower than well-capitalized, adequately-capitalized, nationally-chartered, and state-chartered banks. In addition, the speed of adjustment of regulatory capital ratios of commercial banks is higher in the post-crisis period than the pre-crisis era. Although scholars suggest that adjustment of capital ratios through rebalancing liabilities is more beneficial to the banks, our findings show that banks also use their assets side of balance sheet to rebalance their capital ratios.

Economics of Capital Adjustment in the Us Commercial Banks: Empirical Analysis

Financial risk contagion based on dynamic multi-layer network between banks and firms

Leverage and Risk in US Commercial Banking in the Light of the Current Financial Crisis

The Financial Industry's Plan for Resolving Failed Megabanks Will Ensure Future Bailouts for Wall Street

The role of loan portfolio losses and bank capital for Asian financial system resilience

Shadow Banks and Systemic Risks

Boolean networks have been widely used to model gene networks. However, such models are coarse-grained to an extent that they abstract away molecular specificities of gene regulation. Alternatively, bipartite Boolean network models of gene regulation explicitly distinguish genes from transcription factors (TFs). In such bipartite models, multiple TFs may simultaneously contribute to gene regulation by forming heteromeric complexes, thus giving rise to composition structures. Since bipartite Boolean models are relatively recent, an empirical investigation of their biological plausibility is lacking. Here, we estimate the prevalence of composition structures arising through heteromeric complexes. Moreover, we present an additional mechanism where composition structures may arise as a result of multiple TFs binding to cis-regulatory regions and provide empirical support for this mechanism. Next, we compare the restriction in BFs imposed by composition structures and by biologically meaningful properties. We find that though composition structures can severely restrict the number of Boolean functions (BFs) driving a gene, the two types of minimally complex BFs, namely nested canalyzing functions (NCFs) and read-once functions (RoFs), are comparatively more restrictive. Finally, we find that composition structures are highly enriched in real networks, but this enrichment most likely comes from NCFs and RoFs.