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A Novel Soft Ensemble Model for Financial Distress Prediction with Different Sample Sizes

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  • Wei Xu
  • Hongyong Fu
  • Yuchen Pan

Abstract

This work presents a novel soft ensemble model (ANSEM) for financial distress prediction with different sample sizes. It integrates qualitative classifiers (expert system method, ES) and quantitative classifiers (convolutional neural network, CNN) based on the uni-int decision making method of soft set theory (UI). We introduce internet searches indices as new variables for financial distress prediction. By constructing a soft set representation of each classifier and then using the optimal decision on soft sets to identify the financial status of firms, ANSEM inherits advantages of ES, CNN, and UI. Empirical experiments with the real data set of Chinese listed firms demonstrate that the proposed ANSEM has superior predicting performance for financial distress on accuracy and stability with different sample sizes. Further discussions also show that internet searches indices can offer additional information to improve predicting performance.

Suggested Citation

  • Wei Xu & Hongyong Fu & Yuchen Pan, 2019. "A Novel Soft Ensemble Model for Financial Distress Prediction with Different Sample Sizes," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-12, April.
    • Handle: RePEc:hin:jnlmpe:3085247
    DOI: 10.1155/2019/3085247
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    Cited by:

    1. Michal Pavlicko & Marek Durica & Jaroslav Mazanec, 2021. "Ensemble Model of the Financial Distress Prediction in Visegrad Group Countries," Mathematics, MDPI, vol. 9(16), pages 1-26, August.

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