In this study, we explore the impact of contemporary bank run incidents on stock market performance, taking into consideration insured deposit concentration. Specifically, we use data from the recent downfall of the Silicon Valley Bank (SVB). By employing event study methods with the mean-adjusted return model and market models, we evaluate the cumulative abnormal returns (CARs). Our findings reveal a substantial negative CAR for all the listed companies in our sample, suggesting that the SVB crisis adversely affected stock returns. Further analysis shows an even more pronounced effect on the banking sector and that banks with a high concentration of insured deposits experienced economically and statistically less negative CARs. We also find that the response by the Treasury Department, the Federal Reserve, the Federal Deposit Insurance Corporation, and other agencies—aimed at fully safeguard all depositors—led a rebound in CARs. Our results highlight the importance of deposit insurance policy and regulatory responses in protecting the financial system during panic events.

This study aims to identify the risk factors causing the delay in the completion schedule and to determine an optimization strategy for more accurate completion schedule prediction. A validated questionnaire has been used to calculate a risk rating using the analytical hierarchy process (AHP) method, and a Monte Carlo simulation on @RISK 8.2 software was employed to obtain a more accurate prediction of project completion schedules. The study revealed that the dominant risk factors causing project delays are coordination with stakeholders and changes in the scope of work/design review. In addition, the project completion date was determined with a confidence level of 95%. All data used in this study were obtained directly from the case study of the Double-Double Track Development Project (Package A). The key result of this study is the optimization of a risk-based schedule forecast with a 95% confidence level, applicable directly to the scheduling of the Double-Double Track Development Project (Package A). This paper demonstrates the application of Monte Carlo Simulation using @RISK 8.2 software as a project management tool for predicting risk-based-project completion schedules.

This research analyzes disaster risk financing within the framework of the disaster management policy in Indonesia as the implementation of the Disaster Management Law, Number 24 of 2007, by examining recent issues, challenges, and opportunities in disaster financing. Utilizing a qualitative approach, the research systematically reviews various studies, reports, and existing regulations and policies to understand the current landscape comprehensively. Recent developments in disaster risk financing in Indonesia highlight the need for a nuanced exploration of the existing policy framework. Fiscal constraints, evolving risk landscapes, and the increasing frequency of disasters underscore the urgency of effective disaster risk financing strategies. Through a qualitative examination, this study identifies challenges while illuminating opportunities for innovation and improvement within the current policy framework. The contribution of this research extends to both theoretical and practical levels. Theoretically, it enriches the academic discourse on disaster risk financing by offering a nuanced understanding of the complexities involved. On a practical level, the findings derived from the examination provide actionable recommendations for policymakers and practitioners engaged in disaster management in Indonesia. The insights aim to inform the refinement of disaster management policies and practices, fostering resilience and adaptability in the face of evolving disaster scenarios.

Malaria is a mosquito-borne infectious disease that affects humans and poses a severe public health problem. Nigeria has the highest number of global cases. Geospatial technology has been widely used to study the risks and factors associated with malaria hazards. The present study is conducted in Ibadan, Oyo State, Nigeria. The objective of this study is to map out areas that are at high risk of the prevalence of malaria by considering a good number of factors as criteria that determine the spread of malaria within Ibadan using open-source and Landsat remote sensing data and further analysis in GIS-based multi-criteria evaluation (MCE). This study considered factors like climate, environmental, socio-economic, and proximity to health centers as criteria for mapping malaria risk. The MCE used a weighted overlay of the factors to produce an element at-risk map, a malaria hazard map, and a vulnerability map. These maps were overlaid to produce the final malaria risk map, which showed that 72% of Ibadan has a risk of malaria prevalence. Identification and delineation of risk areas in Ibadan would help policymakers and decision-makers mitigate the hazards and improve the health status of the state.

The goal of this work was to create and assess machine-learning models for estimating the risk of budget overruns in developed projects. Finding the best model for risk forecasting required evaluating the performance of several models. Using a dataset of 177 projects took into account variables like environmental risks employee skill level safety incidents and project complexity. In our experiments, we analyzed the application of different machine learning models to analyze the risk for the management decision policies of developed organizations. The performance of the chosen model Neural Network (MLP) was improved after applying the tuning process which increased the Test R² from −0.37686 before tuning to 0.195637 after tuning. The Support Vector Machine (SVM), Ridge Regression, Lasso Regression, and Random Forest (Tuned) models did not improve, as seen when Test R² is compared to the experiments. No changes in Test R²’s were observed on GBM and XGBoost, which retained same Test R² across different tuning attempts. Stacking Regressor was used only during the hyperparameter tuning phase and brought a Test R² of 0. 022219.Decision Tree was again the worst model among all throughout the experiments, with no signs of improvement in its Test R²; it was −1.4669 for Decision Tree in all experiments arranged on the basis of Gender. These results indicate that although, models such as the Neural Network (MLP) sees improvements due to hyperparameter tuning, there are minimal improvements for most models. This works does highlight some of the weaknesses in specific types of models, as well as identifies areas where additional work can be expected to deliver incremental benefits to the structured applied process of risk assessment in organizational policies.