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Self-Organizing Time Map: A Novel Approach to Understanding Financial Crises

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bessie
15 diapositives · 278 Views · Jul 24, 2014

This paper presents the Self-Organizing Time Map (SOTM) methodology, designed to identify and analyze temporal and cross-sectional patterns in financial data. Traditional early-warning models often rely on static historical data, making it difficult to adapt to the evolving nature of financial crises. The SOTM dynamically adjusts to structural changes in multidimensional datasets, allowing for better visualization and detection of financial imbalances. Experimental results demonstrate that the SOTM effectively identifies critical shifts, paving the way for improved financial stability surveillance.

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Self-Organizing Time Map: A Novel Approach to Understanding Financial Crises
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  1. Decomposing the global financial crisis: A Self-Organizing Time Map Presenter : CHANG, SHIH-JIE Authors : Peter Sarlin*2013.PRL

  2. Outlines • Motivation • Objectives • Methodology • Experiments • Conclusions • Comments

  3. 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.

  4. 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.

  5. Methodology 1 . . M

  6. Methodology 1 . . M

  7. Methodology K l 100*900 . 10 =90*10=900 Silhouette coefficient 100+900 100*150 . 10 =60*10=600 100+150

  8. Experiments – dataset:14 financial indicators

  9. Experiments –

  10. Experiments– feature plan

  11. Experiments

  12. Experiments – Clustering of the SOTM

  13. Experiments

  14. 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 financialcrises.

  15. Comments • Advantages • The SOTM uses visual dynamic clustering difference from traditional statistical methods. • Applications • Self-Organizing Time Map • Financial stability surveillance