Global financial crisis and early warning system of Korean housing market

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.

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Financial Early Warning System for Risk Detection and Prevention from Financial Crisis

Data Mining ◽

10.4018/978-1-4666-2455-9.ch082 ◽

2013 ◽

pp. 1559-1590

Author(s):

Nermin Ozgulbas ◽

Ali Serhan Koyuncugil

Keyword(s):

Financial Crisis ◽

Early Warning ◽

Financial Distress ◽

Business Performance ◽

Global Financial Crisis ◽

Stock Exchange ◽

Warning System ◽

Chi Square ◽

Risk Detection ◽

The Global Financial Crisis

Risk management has become a vital topic for all enterprises especially in financial crisis periods. All enterprises need systems to warn against risks, detect signs and prevent from financial distress. Before the global financial crisis that began 2008, small and medium-sized enterprises (SMEs) have already fought with important financial issues. The global financial crisis and the ensuring flight away from risk have affected SMEs more than larger enterprises When we consider these effects, besides the issues of poor business performance, insufficient information and insufficiencies of managers in finance education, it is clear that early warning systems (EWS) are vital for SMEs for detection risk and prevention from financial crisis. The aim of this study is to develop and present a financial EWS for risk detection via data mining. For this purpose, data of SMEs listed in Istanbul Stock Exchange (ISE) and Chi-Square Automatic Interaction Detector (CHAID) Decision Tree Algorithm were used. By using EWS, we determined the risk profiles and risk signals for risk detection and road maps for risk prevention from financial crisis.

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Dating Banking and Currency Crises in Turkey, 1990-2014

Advances in Finance, Accounting, and Economics - Handbook of Research on Financial and Banking Crisis Prediction through Early Warning Systems ◽

10.4018/978-1-4666-9484-2.ch012 ◽

2016 ◽

pp. 240-273

Author(s):

Ali Ari ◽

Raif Cergibozan ◽

Sedat Demir

Keyword(s):

Financial Crisis ◽

Financial Crises ◽

Early Warning System ◽

Currency Crises ◽

Global Financial Crisis ◽

Empirical Studies ◽

Banking Crisis ◽

Warning System ◽

Turkish Economy ◽

The Global Financial Crisis

The last two decades characterized by financial crisis episodes have seen a proliferation of empirical studies. These early warning system models allowed researchers to distinguish certain key determinants of financial crises, and helped predicting and preventing the occurrence of some crises. However, crises continue to arise as recently illustrated by the onset of the global financial crisis. This clarifies that there are still a lot to learn about financial crises. In this sense, this paper aimed to compare the performance of several currency and banking crisis indicators within the Turkish economy which underwent severe financial crises in the last twenty years. Different currency crisis indicators performed well by detecting the 1994, 2001 and 2008 currency crises, while banking crisis indicators had significant inconsistencies. However, two banking crisis indicators we developed stand for valuable efforts in dating banking crises by constructing aggregate indexes, and contribute significantly to the empirical crisis literature.

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Network-based Early Warning System to Predict Financial Crisis

SSRN Electronic Journal ◽

10.2139/ssrn.3314820 ◽

2019 ◽

Author(s):

Hossein Dastkhan

Keyword(s):

Financial Crisis ◽

Early Warning ◽

Early Warning System ◽

Warning System

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

Mathematical Problems in Engineering ◽

10.1155/2020/8036154 ◽

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.

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An early warning system to identify house price bubbles

Journal of European Real Estate Research ◽

10.1108/jerer-03-2019-0006 ◽

2019 ◽

Vol 12 (3) ◽

pp. 291-310

Author(s):

Daniel Hagemann ◽

Monika Wohlmann

Keyword(s):

Early Warning ◽

Early Warning System ◽

Global Financial Crisis ◽

Early Stage ◽

Explanatory Power ◽

House Price ◽

Warning System ◽

Ordered Logit ◽

Content Type ◽

Price Bubbles

Purpose The global financial and economic crisis resulting from the US housing crisis has shown that house prices can have far-reaching consequences for the real economy. For macroprudential supervision, it is, therefore, necessary to identify house price bubbles at an early stage to counteract speculative price developments and to ensure financial market stability. This paper aims to develop an early warning system to signal speculative price bubbles. Design/methodology/approach The results of explosivity tests are used to identify periods of excessive price increases in 18 industrialized countries. The early warning system is then based on a logit and an ordered logit regression, in which monetary, macroeconomic, regulatory, demographic and private factors are used as explanatory variables. Findings The empirical results show that monetary developments have the highest explanatory power for the existence of house price bubbles. Further, the study reveals currently emerging house price bubbles in Norway, Sweden and Switzerland. Practical implications The results implicate a new global housing boom, particularly in those countries that did not experience a major price correction during the global financial crisis. Originality/value The ordered logit model is an advanced approach that offers the advantage of being able to differentiate between different phases of a house price bubble, thereby allowing a multi-level assessment of the risk of speculative excesses in the housing market.

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