scholarly journals Financial distress in Brazilian banks: an early warning model,

2017 ◽  
Vol 29 (77) ◽  
pp. 312-331
Author(s):  
Paulo Sérgio Rosa ◽  
Ivan Ricardo Gartner
Keyword(s):  
Early Warning ◽  
Financial Distress ◽  
Financial Stability ◽  
Balance Sheet ◽  
Warning Signs ◽  
Capital Adequacy ◽  
Financial Indicators ◽  
Forecast Horizon ◽  
Early Warning Model ◽  
Warning Model

ABSTRACT This study aims to propose an early warning model for predicting financial distress events in Brazilian banking institutions. Initially, a set of economic-financial indicators is evaluated, suggested by the risk management literature for identifying situations of bank insolvency and exclusively taking public information into account. For this, multivariate logistic regressions are performed, using as independent variables financial indicators involving capital adequacy, asset quality, management quality, earnings, and liquidity. The empirical analysis was based on a sample of 142 financial institutions, including privately and publicly held and state-owned companies, using monthly data from 2006 to 2014, which resulted in panel data with 12,136 observations. In the sample window there were nine cases of Brazilian Central Bank intervention or mergers and acquisitions motivated by financial distress. The results were evaluated based on the estimation of the in-sample parameters, out-of-sample tests, and the early warning model signs for a 12-month forecast horizon. These obtained true positive rates of 81%, 94%, and 89%, respectively. We conclude that typical balance-sheet indicators are relevant for the early warning signs of financial distress in Brazilian banks, which contributes to the literature on financial intermediary credit risk, especially from the perspective of bank supervisory agencies acting towards financial stability.

scholarly journals Analysis of Financial Risk Early Warning Systems of High-Tech Enterprises under Big Data Framework

2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Maotao Lai
Keyword(s):  
Big Data ◽  
Financial Crisis ◽  
Early Warning ◽  
Logistic Model ◽  
High Tech ◽  
Financial Indicators ◽  
Early Warning Model ◽  
Early Warning Indicators ◽  
Warning Model ◽  
Warning Indicators

With the further development of China's market economy, the competition faced by companies in the market has become more intense, and many companies have difficulty facing pressure and risks. Among the many types of enterprises, high-tech enterprises are the riskiest. The emergence of big data technologies and concepts in recent years has provided new opportunities for financial crisis early warning. Through in-depth study of the theoretical feasibility and practical value of big data indicators, the use of big data indicators to develop an early warning system for financial crises has important theoretical value for breaking through the stagnant predicament of financial crisis early warning. As a result of the preceding context, this research focuses on the influence of big data on the financial crisis early warning model, selects and quantifies the big data indicators and financial indicators, designs the financial crisis early warning model, and verifies its accuracy. The specific research design ideas include the following: (1) We make preliminary preparations for model construction. Preliminary determination and screening of training samples and early warning indicators are carried out, the samples needed to build the model and the early warning indicator system are determined, and the principles of the model methods used are briefly described. First, we perform a significant analysis of financial indicators and screen out early warning indicators that can clearly distinguish between financial crisis companies and nonfinancial crisis companies. (2) We analyze the sentiment tendency of the stock bar comment data to obtain big data indicators. Then, we establish a logistic model based on pure financial indicators and a logistic model that introduces big data indicators. Finally, the two models are tested and compared, the changes in the model's early warning effect before and after the introduction of big data indicators are analyzed, and the optimization effect of big data indicators on financial crisis early warning is tested.

scholarly journals Adopting I<sub>3</sub>–R<sub>24</sub> rainfall index and landslide susceptibility on the establishment of early warning model for rainfall-induced shallow landslides

2017 ◽  
Author(s):  
Lun-Wei Wei ◽  
Chuen-Ming Huang ◽  
Chyi-Tyi Lee ◽  
Chun-Chi Chi ◽  
Chen-Lung Chiu
Keyword(s):  
Early Warning ◽  
Landslide Susceptibility ◽  
Regional Scale ◽  
Field Investigation ◽  
Warning Signs ◽  
Rainfall Thresholds ◽  
Essential Information ◽  
Early Warning Model ◽  
Rainfall Index ◽  
Warning Model

Abstract. Rainfall-induced landslide is one of the most devastating natural hazards in the world and the setup of early warning models is a pressing need for reducing losses and fatalities. Most part of landslide early warnings are based on rainfall thresholds defined at the regional scale, regardless of the different landslide susceptibility of each slope. Here we tried to divide slope units in southern Taiwan into three categories (high, moderate, low) according to their susceptibility. For each category, we established their rainfall thresholds separately so as to provide differentiated thresholds for different susceptibility. Logistic regression (LR) analysis was performed to evaluate the landslide susceptibility by using event based landslide inventories and predisposing factors. Through the analysis of rainfall patterns of more than 900 landslide cases gathered from field investigation, 3-hour mean rainfall intensity (I3) was recognized as a key rainfall index for short duration but high intensity rainfall; on the other hand, 24-hour accumulated rainfall (R24) was recognized as a key rainfall index for long duration but low intensity rainfall. Thus, the I3–R24 rainfall index was used for the establishment of rainfall thresholds in this study. Finally, an early warning model was proposed by setting warning signs including yellow (advisory), orange (watch) and red (warning) according to the concept of hazard matrix. These differentiated thresholds and warning signs can provide essential information for local government on evacuating decision of residents.

scholarly journals The Level of Public Acceptance to the Development of a Coastal Flooding Early Warning System in Jakarta

2021 ◽  
Vol 13 (2) ◽  
pp. 566
Author(s):  
Nelly Florida Riama ◽  
Riri Fitri Sari ◽  
Henita Rahmayanti ◽  
Widada Sulistya ◽  
Mohamad Husein Nurrahmat
Keyword(s):  
Path Analysis ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Coastal Flooding ◽  
Coastal Areas ◽  
Public Acceptance ◽  
Community Attitudes ◽  
Early Warning Model ◽  
Warning Model

Coastal flooding is a natural disaster that often occurs in coastal areas. Jakarta is an example of a location that is highly vulnerable to coastal flooding. Coastal flooding can result in economic and human life losses. Thus, there is a need for a coastal flooding early warning system in vulnerable locations to reduce the threat to the community and strengthen its resilience to coastal flooding disasters. This study aimed to measure the level of public acceptance toward the development of a coastal flooding early warning system of people who live in a coastal region in Jakarta. This knowledge is essential to ensure that the early warning system can be implemented successfully. A survey was conducted by distributing questionnaires to people in the coastal areas of Jakarta. The questionnaire results were analyzed using cross-tabulation and path analysis based on the variables of knowledge, perceptions, and community attitudes towards the development of a coastal flooding early warning system. The survey result shows that the level of public acceptance is excellent, as proven by the average score of the respondents’ attitude by 4.15 in agreeing with the establishment of an early warning system to manage coastal flooding. Thus, path analysis shows that knowledge and perception have a weak relationship with community attitudes when responding to the coastal flooding early warning model. The results show that only 23% of the community’s responses toward the coastal flooding early warning model can be explained by the community’s knowledge and perceptions. This research is expected to be useful in implementing a coastal flooding early warning system by considering the level of public acceptance.

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