scholarly journals Establishment of a Financial Crisis Early Warning System for Domestic Listed Companies Based on Three Decision Tree Models

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Gang Wang ◽  
Keming Wang ◽  
Yingying Zhou ◽  
Xiaoyan Mo ◽  
Weilin Xiao
Keyword(s):  
Financial Crisis ◽  
Decision Tree ◽  
Early Warning ◽  
Financial Management ◽  
Early Warning System ◽  
Comprehensive Evaluation ◽  
Warning System ◽  
Predictive Ability ◽  
Tree Models ◽  
The Impact

The financial crisis is a realistic problem that the general enterprise must encounter in the process of financial management. Due to the impact of the COVID-19 and the Sino-US trade war, domestic companies with unsound financial conditions are at risk of shutdowns and bankruptcies. Therefore, it is urgently needed to study the financial warning of enterprises. In this study, three decision tree models are used to establish the financial crisis early warning system. These three decision tree models include C50, CART, and random forest decision trees. In addition, the ROC curve was used for comprehensive evaluation of the accuracy analysis of the model to confirm the predictive ability of each model. This result can provide reference for domestic financial departments and provide financial management basis for the investing public.

scholarly journals Measuring the effectiveness of signals approach in an early warning system for crises and its impact on textile industry: a case study for South-East Asia

2021 ◽  
Vol 72 (02) ◽  
pp. 175-190
Author(s):  
MUHAMMAD ZAHID NAEEM ◽  
CRISTI SPULBAR ◽  
ABDULLAH EJAZ ◽  
RAMONA BIRAU ◽  
TIBERIU HORAȚIU GORUN ◽  
...  
Keyword(s):  
Financial Crisis ◽  
East Asia ◽  
Early Warning ◽  
Early Warning System ◽  
Textile Industry ◽  
Global Financial Crisis ◽  
Warning System ◽  
Predictive Ability ◽  
South East Asia ◽  
The Impact

Following the work of Kaminsky, Lizondo, and Reinhart (1997), Signals Extraction Approach has been adopted with some extensions for South-East Asian (SEA) region to investigate the performance of the technique as an Early Warning System (EWS) during Asian Financial Crisis (AFC) and Global Financial Crisis (GFC). This approach is very original in the context of investigating the impact on the dynamics of the textile industry in South-East Asia. Two additional approaches namely Signal to Noise Balance (STNB) and Kuipers Score (KS) have also been utilised. Outcome suggested that variables performed well both during AFC and GFC. However, predictive ability of variables was less during GFC compared to the AFC indicating that there may exist some complex phenomenon which requires composite statistical methods.

Establishment of an early warning system for international trade tensions in the Heilongjiang province of China

2021 ◽  
Vol 95 (2) ◽  
pp. 17-28
Author(s):  
An Zhao Zhen ◽  
Keyword(s):  
International Trade ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Heilongjiang Province ◽  
Global Epidemic ◽  
General Importance ◽  
Strategic Issue ◽  
Trade Frictions ◽  
The Impact

In 2020, the global economic and trading environment has undergone major changes due to the impact of the global epidemic of the COVID-19. It is not only the world economy that has seriously suffered, protectionism in international trade is growing, and economic and trade frictions between countries with many factors have sharply worsened. Faced with a new situation and new challenges, accelerating the construction of an early warning system for international trade conflicts in Heilongjiang Province has become an important strategic issue of general importance.

Early Warning System for SMEs as a Financial Risk Detector

2009 ◽  
pp. 220-238 ◽  
Author(s):  
Ali Serhan Koyuncugil
Keyword(s):  
Data Mining ◽  
Risk Management ◽  
Early Warning ◽  
Financial Management ◽  
Early Warning System ◽  
Quantitative Data ◽  
Financial Risk ◽  
Warning System ◽  
Theoretical Background ◽  
Effect Level

This chapter introduces an early warning system for SMEs (SEWS) as a financial risk detector which is based on data mining. In this study, the objective is to compose a system in which qualitative and quantitative data about the requirements of enterprises are taken into consideration, during the development of an early warning system. Furthermore, during the formation of system; an easy to understand, easy to interpret and easy to apply utilitarian model that is far from the requirement of theoretical background is targeted by the discovery of the implicit relationships between the data and the identification of effect level of every factor. Using the system, SME managers could easily reach financial management, risk management knowledge without any prior knowledge and expertise. In other words, experts share their knowledge with the help of data mining based and automated EWS.

scholarly journals Influence of Ciawi and Sukamahi dam construction on flood early warning system in Katulampa weir

2019 ◽  
Vol 276 ◽  
pp. 04016
Author(s):  
Maulana Asrafi ◽  
Evi Anggraheni ◽  
Dwita Sutjiningsih
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Flood Control ◽  
Warning System ◽  
System Level ◽  
Dam Construction ◽  
Monitoring Point ◽  
Flood Peak ◽  
Flood Level ◽  
The Impact

Based on the Comprehensive Flood Management Plan (CFMP), one of the flood control alternatives in the Ciliwung Watershed is using the dry dam construction. Construction of Ciawi dam and Sukamahi dam who are located in Bogor District, are expected to decrease the flood peak and increasing the time concentration in order to reduce the impact caused by a flood in DKI Jakarta due to Ciliwung River. With the construction of Ciawi dam and Sukamahi dam, it is necessary to conduct a research on the effect of both dams construction in Katulampa Weir, one of monitoring point dams on the DKI Jakarta Flood Early Warning System. Hydrological analysis with Win-TR 20 and HEC-RAS was conducted to identify the changes in the flood level of Flood Early Warning System in DKI Jakarta with and without both dams are available. The results of this study indicate that the peak floods decreased by around 2.5% after the implementation of Ciawi dam and Sukamahi dams. The impacts of this reduction will not affect the Flood Early Warning System level at Katulampa Weir significantly.

scholarly journals The Design Diagram of Early Warning System for the Impact of Industrial Development Waste

2018 ◽  
Author(s):  
Veronika Nugraheni Sri Lestari ◽  
Dwi Cahyono ◽  
Sri Susilowati
Keyword(s):  
Mobile Phones ◽  
Early Warning ◽  
Early Warning System ◽  
Industrial Development ◽  
Warning System ◽  
Industrial Area ◽  
Survey Method ◽  
Industry Area ◽  
The Impact ◽  
Ph Detection

Currently the development of industrial area development is very rapid this will impact on society if the waste from this industry is not controlled and is known by the societywhose territory is passed by the river from industrial waste disposal.The rapid technological developments will also provide convenience and benefits for the community to be able to control the waste industry one of them is by utilizing android applications in the form of an early warning system application.This research uses survey method and literature study. This research is limited to 5 industry area which primary data sample is taken, 5 industrial area that is surabaya, gresik, mojokerto, lamongan and sidoarjo, research stages include sampling in the industrial area which is then analyzed by using pH detection devices after it is done testing detection devices communicated with mobile phones based on android application this is done several times intended to produce a good test devices.One of the results of this research is the design of pH detection devices communicated with android-based mobile phones, the design referred to in this paper is in the form of UML consisting of 3 diagrams that are use case diagrams, activity diagrams and sequence diagrams

Machine learning as an early warning system to predict financial crisis

2020 ◽  
Vol 71 ◽  
pp. 101507 ◽  
Author(s):  
Aristeidis Samitas ◽  
Elias Kampouris ◽  
Dimitris Kenourgios
Keyword(s):  
Machine Learning ◽  
Financial Crisis ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System
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