(Stock Market Stability Index for Early Warning System of Financial Crisis)

2007 ◽  
Author(s):  
Kyong Oh ◽  
Tae Yoon Kim
Keyword(s):  
Financial Crisis ◽  
Stock Market ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Stability Index ◽  
Market Stability

An early warning system for financial crisis using a stock market instability index

2009 ◽  
Vol 26 (3) ◽  
pp. 260-273 ◽  
Author(s):  
Dong Ha Kim ◽  
Suk Jun Lee ◽  
Kyong Joo Oh ◽  
Tae Yoon Kim
Keyword(s):  
Financial Crisis ◽  
Stock Market ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Instability Index ◽  
Market Instability

Designing an Early Warning System for Stock Market Crashes by Using ANFIS

2011 ◽  
pp. 109-127
Author(s):  
Murat Acar ◽  
Dilek Karahoca ◽  
Adem Karahoca
Keyword(s):  
Stock Market ◽  
Early Warning ◽  
Fuzzy Inference System ◽  
Early Warning System ◽  
Fuzzy Inference ◽  
Warning System ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Inference System ◽  
Market Crashes

This chapter focuses on building a financial early warning system (EWS) to predict stock market crashes by using stock market volatility and rising stock prices. The relation of stock market volatility with stock market crashes is analyzed empirically. Also, Istanbul Stock Exchange (ISE) national 100 index data used to achieve better results from the view point of modeling purpose. A risk indicator of stock market crash is computed to predict crashes and to give an early warning signal. Various data mining classifiers are compared to obtain the best practical solution for the financial early warning system. Adaptive neuro fuzzy inference system (ANFIS) model was proposed to forecast stock market crashes efficiently. Also, ANFIS was explained in detail as a training tool for the EWS. The empirical results show that the fuzzy inference system has advantages to gain successful results for financial crashes.

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.

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

Early Warning System for Financial Crises

2018 ◽  
pp. 1-25 ◽  
Author(s):  
Amir Manzoor
Keyword(s):  
Financial Crisis ◽  
Early Warning ◽  
Financial Crises ◽  
Early Warning System ◽  
Financial Stability ◽  
Time Horizon ◽  
Warning System ◽  
False Alarms ◽  
Financial Risks ◽  
Policy Makers

To maintain financial stability, prevention of financial crisis is very important. This prevention is especially is especially important for developing countries where we need robust instruments for prediction of financial crises. One such instrument is Early Warning System (EWS). An EWS provided signals that could reflect the likelihood of a financial crisis over a given time horizon. Changing nature of financial risks due to liberalization of economies has increased the importance of an effective EWS. This chapter explores the state of the art of EWS. It is suggested that policy makers should take into account their objectives and related thresholds of various while developing an EWS since there exists a sharp trade-off between correctly calling crises and false alarms.

Designing an Early Warning System for Stock Market Crashes by Using ANFIS

Data Mining ◽  
2013 ◽  
pp. 2250-2268
Author(s):  
Murat Acar ◽  
Dilek Karahoca ◽  
Adem Karahoca
Keyword(s):  
Stock Market ◽  
Early Warning ◽  
Fuzzy Inference System ◽  
Early Warning System ◽  
Fuzzy Inference ◽  
Warning System ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Inference System ◽  
Market Crashes

This chapter focuses on building a financial early warning system (EWS) to predict stock market crashes by using stock market volatility and rising stock prices. The relation of stock market volatility with stock market crashes is analyzed empirically. Also, Istanbul Stock Exchange (ISE) national 100 index data used to achieve better results from the view point of modeling purpose. A risk indicator of stock market crash is computed to predict crashes and to give an early warning signal. Various data mining classifiers are compared to obtain the best practical solution for the financial early warning system. Adaptive neuro fuzzy inference system (ANFIS) model was proposed to forecast stock market crashes efficiently. Also, ANFIS was explained in detail as a training tool for the EWS. The empirical results show that the fuzzy inference system has advantages to gain successful results for financial crashes.

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