Intelligent Database Systems Lab

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Predicting corporate bankruptcy using a self-organizing map: An empirical study to improve the forecasting horizon of a financial failure model

Presenter: Jun-Yi Wu　Authors: Philippe du Jardin, Eric Séverin

2011 DSS

國立雲林科技大學National Yunlin University of Science and Technology

Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.Outline

Motivation Objective Methodology Experiments Conclusion Comments

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Intelligent Database Systems Lab

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I. M.Motivation

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Most prediction models fail to forecast accurately the occurrence of failure beyond 1 year, and their accuracy tends to fall as the prediction horizon recedes.

Prediction rates are rather good one year before failure, but less so as the horizon recedes to two and three years.

Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.Objective

To use what some researchers have called the “trajectory of corporate collapse” to examine another way of estimating the changes in firms' financial health.

To propose a new way of using a Kohonen map to improve model reliability.

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Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.Methodology

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Intelligent Database Systems Lab

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I. M.Experiments

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Intelligent Database Systems Lab

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I. M.Experiments

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Intelligent Database Systems Lab

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I. M.Experiments

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Intelligent Database Systems Lab

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I. M.Experiments

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I. M.Experiments

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Intelligent Database Systems Lab

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I. M.Experiments

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Intelligent Database Systems Lab

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I. M.Conclusion

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To propose a new way of assessing a company’s financial health.

To use what we called “trajectories”, and a Kohonen map to quantize such trajectories, to measure it over time, rather than at a given moment in time.

To compared the predictive ability of these trajectories to that of modeling methods traditionally used to design financial failure models.

Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.Comments

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Advantage Many experiments

Application Forecasting horizon Financial failure prediction