Study validates capital structure theories in Indian public sector banks.
problem Understanding the impact of capital structure on financial performance in Indian banks.
method Developed theoretical framework from capital structure theories, tested hypotheses using statistical techniques.
result Established relation between debt component and financial performance variables.
On June 26th, 2004, Central bank governors and the heads of bank supervisory authorities in the Group of Ten (G10) countries issued a press release and endorsed the publication of "International Convergence of Capital Measurement and Capital Standards: a Revised Framework", the new capital adequacy framework commonly k…
Bangladesh's banking sector improved through financial reforms, but challenges remain.
problem Weak asset quality, inadequate provisioning, and negative capitalization of state-owned banks.
method Two phases of reforms: private ownership promotion and gradual deregulation.
result Significant improvements in asset quality and capitalization, but challenges persist.
Derivatives impact U.S. banking sector's systemic risk, but loan and leverage ratios are more significant.
problem Systemic risk in U.S. banking sector due to derivatives and loans.
method Analysis of derivatives and loan data to assess systemic risk.
result Loan and leverage ratios are more influential in systemic risk than derivatives holdings.
Model predicts Mozambique bank failures, aiding risk management.
problem Lack of bankruptcy prediction model in Mozambique banking sector.
method Linear Discriminant Analysis method, using financial indicators.
result Model accurately predicted 84% of bank failures 1 year before Central Bank intervention.
Algorithmic stablecoins optimize monetary policy to balance price stability.
problem Persistent inflation from centralized monetary policy.
method Propose and study a rule-based monetary policy model for algorithmic stablecoins.
result Optimal trade-off between price stability and supply stability.
Geospatial framework assesses climate risks for California's banking and exposed sectors.
problem Evaluating climate risks on banking and exposed sectors in California.
method Integrates hazard mapping, exposure analysis, and scenario-based financial risk assessment.
result Framework supports portfolio monitoring and institutional readiness under new standards.
This study analyzes information flow networks in Chinese stock sectors using transfer entropy.
problem Understanding information transmission and market dynamics in Chinese stock sectors.
method Daily closing price data of 28 sectors from 2000 to 2017, transfer entropy, maximum spanning arborescence (MSA).
result The composite sector is an information source, and the non-bank financial sector is an information sink.
Study examines European banks' digital transformation strategies.
problem Lack of a common framework for open banking innovation in banking sector.
method Qualitative analysis of partnerships and API development.
result European banks are diversifying and boosting customer relationship management.
Study compares information flow between Chinese and US stock sectors.
problem Analyzing how information flows between sectors in Chinese and US stock markets.
method Daily sector indices, transfer entropy of daily returns, comparing 2000-2017.
result Most active sectors in information exchange differ between China and US, reflecting market dynamics.
Study shows how macroprudential policies affect credit growth in Israel, especially in housing and business sectors.
problem Impact of macroprudential policies on credit growth in Israel.
method Bank-level panel data analysis for Israel, 2004-2019; interaction of monetary and macroprudential policies.
result Accommodative monetary policy interacts with macroprudential policies to increase total credit growth.
Oil prices affect Russian banks' stability, with negative impacts from decreases.
problem The impact of international oil prices on Russian public banks' financial stability.
method Data from 17 Russian public banks (2008-2016), Pool Mean Group (PMG) estimator.
result An increase in international oil prices and price to book value ratio positively affects Russian public banks' stability in the long run, while negative shocks have the opposite effect.
The study examines collective behavior in banking sectors across mature and emerging markets.
problem Understanding collective behavior in banking sectors across different market types.
method Applied Random Matrix Theory (RMT) to analyze the banking sectors of 4 world stock markets.
result Mature markets exhibit higher collective behavior compared to emerging markets.
Study assesses the impact of Basel III reforms on Bangladeshi banks.
problem Impact of Basel III liquidity and capital requirements on Bangladeshi banks.
method Panel data analysis with fixed effects, including macroeconomic variables.
result Higher capital and liquidity requirements negatively affect banks' profitability but positively impact interest rates and private sector lending.
This study aims to identify the leading of inflation indicators of monetary policy in DRC. The results reveal that the most relevant inflation indicators usually come from the monetary origin than the real sector. Variance decomposition analyzes place in the foreground the rate of exchange, the money supply and the pub…
We model a network economy with three sectors: downstream firms, upstream firms, and banks. Agents are linked by productive and credit relationships so that the behavior of one agent influences the behavior of the others through network connections. Credit interlinkages among agents are a source of bankruptcy diffusion…
In this paper we consider a multivariate model-based approach to measure the dynamic evolution of tail risk interdependence among US banks, financial services and insurance sectors. To deeply investigate the risk contribution of insurers we consider separately life and non-life companies. To achieve this goal we apply …
GenAI adoption paradoxically lowers ROE for U.S. banks, with spillovers but systemic risk concerns.
problem Productivity paradox and implementation costs in U.S. banking sector with AI adoption.
method Dynamic Spatial Durbin Models (DSDM) and Synthetic Difference-in-Differences (SDID) for causal inference.
result AI adoption leads to a 428-basis-point decline in ROE for banks, with spillovers but systemic risk implications.
GARCH models predict stock volatility in Indian sectors.
problem Designing accurate models for future stock volatility.
method GARCH framework applied to ten Indian stocks.
result Asymmetric GARCH models outperform in volatility forecasting.
Framework analyzes stock price co-movement with fundamentals using big data.
problem Understanding complex relationships between stock price co-movements and fundamental characteristics.
method Advanced big data techniques, four regression models.
result Identifies leading co-movement stocks and their influencing factors.
Study reveals risk transmission channels among Chinese sectors.
problem Understanding risk transmission within Chinese economic sectors.
method Volatility spillovers analysis using VAR model and rolling window approach.
result 17 sectors are risk transmitters and 11 are risk takers.
The study predicts stock volatility using LSTM and GARCH models.
problem Accurately predicting stock price volatility is challenging.
method Multiple volatility models (GARCH, GJR-GARCH, EGARCH, LSTM) applied to three sectors.
result LSTM outperformed other models in pharma sector volatility prediction.
We present an analysis of the credit market of Japan. The analysis is performed by investigating the bipartite network of banks and firms which is obtained by setting a link between a bank and a firm when a credit relationship is present in a given time window. In our investigation we focus on a community detection alg…
The Economist recently reported that infrastructure spending is the largest it is ever been as a share of world GDP. With $22 trillion in projected investments over the next ten years in emerging economies alone, the magazine calls it the "biggest investment boom in history." The efficiency of infrastructure planning a…
Study examines market reactions and spillovers in Japanese bank mergers using multiple methods.
problem Understanding valuation and spillover effects of bank mergers in the Japanese banking sector.
method Combines event study, VAR models, IRFs, and PSM to analyze two M&A events.
result Significant positive market reaction and prolonged positive spillovers detected.
Gen AI improves document understanding but not data analysis in public sector tasks.
problem Understanding the impact of Gen AI on public sector tasks.
method Pre-registered field experiment comparing Gen AI to control group performance.
result Mixed results: Gen AI improves document understanding but not data analysis.
Online leading has disrupted the traditional consumer banking sector with more effective loan processing. Risk prediction and monitoring is critical for the success of the business model. Traditional credit score models fall short in applying big data technology in building risk model. In this manuscript, data with var…
The process of contagiousness spread modelling is well-known in epidemiology. However, the application of spread modelling to banking market is quite recent. In this work, we present a system of ordinary differential equations, simulating data from the largest European banks. Then, an optimal control problem is formula…
Paper presents an econophysics model for mixed economies.
problem Understanding mixed economies in various countries.
method Developed an econophysics model with a reduced state sector participation.
result Proposed a new model with a 10-15% state sector participation.
We propose a new methodology based on the Marshall-Olkin (MO) copula to model cross-border systemic risk. The proposed framework estimates the impact of the systematic and idiosyncratic components on systemic risk. Initially, we propose a maximum-likelihood method to estimate the parameter of the MO copula. In order to…
We investigate the macroeconomic consequences of narrow banking in the context of stock-flow consistent models. We begin with an extension of the Goodwin-Keen model incorporating time deposits, government bills, cash, and central bank reserves to the base model with loans and demand deposits and use it to describe a fr…
The Basel II internal ratings-based (IRB) approach to capital adequacy for credit risk implements an asymptotic single risk factor (ASRF) model. Measurements from the ASRF model of the prevailing state of Australia's economy and the level of capitalisation of its banking sector find general agreement with macroeconomic…
Paper proposes an adaptive modeling approach for row-type dependent predictive analysis in banking.
problem Accurate prediction of diverse row types within a single dataset.
method Adaptive modeling approach, tailored data pre-processing, feature engineering, traditional and ensemble machine learning models.
result All predictive approaches achieve a precision rate of no less than 90% for different row types.
The Basel II internal ratings-based (IRB) approach to capital adequacy for credit risk plays an important role in protecting the Australian banking sector against insolvency. We outline the mathematical foundations of regulatory capital for credit risk, and extend the model specification of the IRB approach to a more g…
The paper models systemic risk in European and U.S. banks using factor copulas.
problem Modeling the joint and conditional distress probabilities of banks across Europe and the U.S.
method Employing Credit Default Swaps (CDS) and factor copulas, the paper proposes multi-factor, structured factor, and factor-vine models.
result Systematic contagion channel drives distress probabilities in the banking system as a whole, while regional factors are important within each region.
We present a network-based framework for simulating systemic risk that considers shock propagation in banking systems. In particular, the framework allows the modeller to reflect a top-down framework where a shock to one bank in the system affects the solvency and liquidity position of other banks, through systemic mar…
TDA detects stock market crashes across continents.
problem Detecting extreme events in multiple stock indices simultaneously.
method Topological Data Analysis (TDA) to analyze stock market crashes.
result TDA identifies stock market crashes and their duration.
We develop the first basic Operational Risk perspective on key risk management issues associated with the development of new forms of electronic currency in the real economy. In particular, we focus on understanding the development of new risks types and the evolution of current risk types as new components of financia…
A new framework for optimizing interventions with limited data.
problem Small data, default intervention data, unmodeled objectives, unforeseen consequences.
method Bandit data-driven optimization combining online bandit learning and offline predictive analytics.
result PROOF algorithm achieves no-regret and superior performance in simulations and real-world application.
Study examines financial performance determinants of Kenyan microfinance banks.
problem Competition from commercial banks threatens microfinance banks' financial performance.
method Descriptive research design with secondary data analysis.
result Operational efficiency, capital adequacy, and firm size positively correlate with financial performance.
This research quantifies cross-sectoral inequalities using latent class analysis.
problem Addressing multiple and intersecting forms of inequality in various sectors.
method Innovative latent class analysis approach to quantify discrepancies.
result Significant discrepancies found among minority ethnic groups and between them and non-minority groups.
Private equity deals predict public market returns with up to 70% accuracy.
problem Predicting public market behavior from private equity transactions.
method Logit model using detailed analysis of private equity diligence process.
result Model predicts public market returns with up to 70% accuracy in specific sectors.
Model predicts internal fraud in retail banking is cyclical and influenced by corruption.
problem Predicting and mitigating internal fraud losses in retail banking.
method Developed a dynamic model considering internal factors and macroeconomic indicators.
result Internal fraud losses are pro-cyclical and positively affected by corruption perceptions.
The study designs inherently interpretable machine learning models for high-risk sectors.
problem The need for transparent and explainable machine learning models in regulated industries.
method Qualitative template based on feature effects and model architecture constraints for assessing inherent interpretability.
result Demonstrates the design and evaluation of an interpretable ReLU DNN model for predicting credit default.
Study shows how missing data from certain groups can unfairly bias risk models.
problem Data missingness without indicators of missingness can unfairly bias risk models.
method Developed an analytically tractable model of differential feature under-reporting and proposed new methods to mitigate bias.
result Under-reporting typically leads to increasing disparities in risk models.
Paper introduces a specialized text classification system for French Open Banking transactions.
problem Classifying specialized banking text data with high accuracy and efficiency.
method Data collection, labeling, preprocessing, modeling, and evaluation stages with language-specific techniques.
result Enhanced performance and efficiency compared to generic approaches.
Knowledge distillation boosts simple models in banking without complexity.
problem Complexity and performance constraints in banking models.
method Soft Targets, Sample Selection, Data Augmentation.
result Improved model performance without altering model simplicity.
This paper reviews bank performance determinants, highlighting future research areas.
problem Understanding bank performance factors to improve sector efficiency and knowledge.
method Analysis of 54 studies in peer-reviewed journals.
result Bank performance factors remain largely unexplored, especially post-COVID-19.