Systemic Financial Risk Early Warning System Based on House Price Fluctuation Factor

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.

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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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Research on the Early Warning Mechanism of Financial Risk by Computer Technology

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.687-691.4922 ◽

2014 ◽

Vol 687-691 ◽

pp. 4922-4925

Author(s):

Liang Liu ◽

Chun Ling Li

Keyword(s):

Financial Markets ◽

Empirical Analysis ◽

Early Warning ◽

Early Warning System ◽

Financial Risk ◽

Warning System ◽

Warning Signal ◽

Financial Risks ◽

Early Warning Mechanism ◽

Starting Point

The briefly review and the development of the financial risk early warning theory is first discussed in this study and the domestic and foreign research is analyzed as a brief summary. Secondly, the concept of financial risks, financial crisis and the financial early warning is defined. Financial fragility as a starting point is used to establish the rationality model of the financial risk early warning system. The early warning indicators is selected on the basis of the 12 indicators of macro-financial risks, 15 net financial indicators is selected to represent the financial markets according to the characteristics of China's financial markets. In the empirical part, the previous empirical analysis method is chosen to build the financial risk early warning signal system. In order to display China's financial risk profile, the proper model for the calculation is made on the basis of empirical analysis. Thus, in order to minimize the local financial risk, the early warning system should be established by the local government, together with some other necessary measures.

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A Fish Swarm Algorithm for Financial Risk Early Warning

International Journal of Enterprise Information Systems ◽

10.4018/ijeis.2018100104 ◽

2018 ◽

Vol 14 (4) ◽

pp. 54-63

Author(s):

Liu Yunshan

Keyword(s):

Early Warning ◽

Early Warning System ◽

Financial Risk ◽

Simulation Analysis ◽

Warning System ◽

The Sustainable Development ◽

Artificial Fish Swarm Algorithm ◽

Artificial Fish Swarm ◽

The Index System ◽

Swarm Algorithm

The financial risk early warning system is important for promoting the sustainable development of enterprise, and in this article, the fish swarm algorithm is applied to it. First, the main financial risk factors of enterprise are summarized. Second, the index system of the financial risk early warning system is constructed based on relating theory. Third, the basic theory of artificial fish swarm algorithm is studied, and the mathematical models are constructed. Then, the wavelet neutral network is improved based on the fish swarm algorithm, and the algorithm procedure is designed. Finally, a simulation analysis is carried out, and the predicting correctness of samples is 100%, and results show that the fish swarm algorithm is an effective method for improving the financial risk early warning system.

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