Intelligent Database Systems Lab

Presenter : CHANG, SHIH-JIE

Authors : Peter Sarlin*

2013.PRL

Decomposing the global financial crisis: A Self-Organizing Time Map

Intelligent Database Systems Lab

Outlines

MotivationObjectivesMethodologyExperimentsConclusionsComments

Intelligent Database Systems Lab

Motivation Over the past years, modeling has oftentimes been attempted through early-warning models relying on conventional statistical methods and historical data.

Key challenge for early-warning modeling is the changing nature of crises due to, e.g.financial innovation.

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Objectives

• The SOTM performs an abstraction of temporal and cross-sectional patterns through data and

dimensionality reduction. The approach differs from traditional static exploratory analyses in that the SOTM dynamically adapts to structural changes in cross-sectional data over time, as well as visualizes the evolution of cluster structures.

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Methodology

1 . .M

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Methodology 1 . .M

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Methodology

Silhouette coefficient

K l

100+150100*150

=60*10=600

100+900100*900 =90*10=900. 10

. 10

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Experiments – dataset:14 financial indicators

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Experiments –

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Experiments– feature plan

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Experiments

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Experiments – Clustering of the SOTM

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Experiments

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Conclusions– The SOTM can identify multivariate structural

changes in data.

– This the SOTM opens the door for early identification of imbalances that expose economies to financial crises.

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Comments• Advantages

– The SOTM uses visual dynamic clustering difference from traditional statistical methods.

• Applications– Self-Organizing Time Map– Financial stability surveillance