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Related Experiment Video

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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
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Identification of Enterprise Financial Risk Based on Clustering Algorithm.

Bingxiang Li1, Rui Tao1, Meng Li1

  • 1School of Economics and Management, Xi'an University of Technology, Xi'an 710054, China.

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|April 22, 2022
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Summary

The K-means clustering algorithm effectively identifies high-risk companies, aiding investors and financial institutions. This method helps screen companies needing cautious investment, improving financial market stability.

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Area of Science:

  • Financial Risk Management
  • Data Mining and Machine Learning

Background:

  • Corporate financial risks pose significant threats to enterprises, credit institutions, investors, and the Chinese economy.
  • Effective screening of high-risk companies is crucial for financial stability.

Purpose of the Study:

  • To propose and evaluate clustering algorithms for identifying high-risk companies.
  • To assess the effectiveness of K-means clustering in predicting financial distress.

Main Methods:

  • Application of K-means clustering algorithm for risk screening.
  • Utilizing Gaussian mixture clustering algorithm as a comparative method.
  • Evaluating high-risk clusters based on the prediction of "special treatment" events.

Main Results:

  • K-means identified a small subset (9%) of high-risk companies.
  • This high-risk cluster contained a significant proportion (nearly 30%) of companies later designated for "special treatment".
  • The predictive accuracy of K-means for high-risk companies increases over time.

Conclusions:

  • K-means clustering is a valid and effective method for screening corporate financial risks.
  • The algorithm aids investors in identifying companies requiring cautious attention.
  • This approach contributes to mitigating the impact of financial risks on economic development.