This paper develops an early warning system for predicting distress for large European banks. Using a novel definition of distress derived from banks' headroom above regulatory requirements, we investigate the performance of three machine learning techniques against the traditional logistic...

The study examines the stability of Indonesian Islamic Commercial Bank (ICB) and Islamic Rural Bank (IRB) by employing a Markov Switching Dynamic model of two regimes, stable (tranquil) and unstable (crisis). This study utilizes monthly data between December 2007 and April 2022. Findings show...

This paper develops an early warning system for predicting distress for large European banks. Using a novel definition of distress derived from banks' headroom above regulatory requirements, we investigate the performance of three machine learning techniques against the traditional logistic...

This paper applies an empirical model of corporate capital structure, optimal debt, and overleveraging to estimate overleveraging measured as the difference between actual and optimal debt. Estimated using a sample of the twenty largest pharmaceutical firms, covering the time span from 2000 to...

It is now a well-known fact that climate is changing globally at an unprecedented rate and agriculture is one of the most vulnerable sectors to this change. Considered as a significant threat for food security, climate change and its impact on agricultural practices are among the most prominent...

This paper provides a comprehensive early warning system (EWS) that balances the classical signaling approach with the best-realized machine learning (ML) model for predicting fiscal stress episodes. Using accumulated local effects (ALE), we compute a set of thresholds for the most informative...

In this study we develop an early warning system (EWS) to forecast currency crises in emerging countries in Asia and Latin America, using logit regression on monthly data from 1992 to 2011. We found that macroeconomic and institutional variables are valuable indicators for forecasting crises....

This report is one of the joint deliverables of Joint Research Centre on "Enhanced Situational Awareness for Crisis Management". It focuses on advancing towards integrated mid-term early warning system that will aim at anticipating disasters and crises within a 6-month window. Ability to detect...

This report is one of the joint deliverables of Joint Research Centre on "Enhanced Situational Awareness for Crisis Management". It focuses on advancing towards integrated mid-term early warning system that will aim at anticipating disasters and crises within a 6-month window. Ability to detect...

This Brief analyses the EU warning system's contribution to conflict prevention and discusses ways to strengthen it. The first section examines the factors that contribute to the success of the system. The second section suggests how the system and the EU's overall prevention approach can be...

Early warning signals (EWS) of imminent regime shifts can be identified through the observation of a system's behavior under increasing stress and before crossing a tipping point. Despite many advances in the detection of EWS in recent years, EWS are yet to find direct application in management....

Abrupt changes are a prevalent feature of financial data sets, such as prices of financial assets, returns of stocks, exchange rates, etc. An early warning system (EWS) can detect existing changes and predict possible future changes before they occur. Two important statistical models for change...

The main contribution of this paper is the construction of a cyclical systemic risk indicator from early warning indicators of banking crises (EWIs) used in Finland. Previous research has shown that combining EWIs can enhance their early warning properties. This study evaluates the indicator's...

This paper investigates the resilience of non-financial firms in Mongolia against financial distress. Utilizing firm-level financial data from 2013 to 2022, we employed a LASSO variable selection technique and logistic regression analysis to develop a distress prediction model for these firms....

This study utilizes machine learning models, including Logistic Regression, Support Vector Machine, Decision Tree, and Random Forest, in the early warning system for debt group migration in a Vietnamese commercial bank. In predicting customers' overdue debt migration (B Score), the RF model...

This paper presents a technical note on bank stress testing and climate risks analysis in Maldives. Although the Maldives' economy has rebounded strongly from the pandemic-induced contraction, macro and financial vulnerabilities remain. The stress test results broadly corroborated the identified...

Purpose Located at the confluence of the White and Blue Niles, in the core of Sudan’s capital, the small island of Tuti has been affected by flooding events throughout its history. To protect it, the Tuti people (Tawatas) developed the Taya, a traditional early warning, community-based flood...