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Ensemble Neural Network Using A Small Dataset For The Prediction Of Bankruptcy : Combining Numerical And Textual Data

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  • Rasolomanana, Onjaniaina Mianin'Harizo

Abstract

This paper presents an ensemble neural network using a small data set in the context of bankruptcy prediction. The individual models of the ensemble use different data of different types. We compare the performance of three neural network models: one using a single type of data, one using a combination of both data in a single data frame, and one using ensemble learning. The results show that the ensemble model outperformed the individual model and the combined model. This suggests that with scarce training data, especially when using different types of data, ensemble neural network can improve the level of prediction accuracy.

Suggested Citation

  • Rasolomanana, Onjaniaina Mianin'Harizo, 2021. "Ensemble Neural Network Using A Small Dataset For The Prediction Of Bankruptcy : Combining Numerical And Textual Data," Discussion paper series. A 361, Graduate School of Economics and Business Administration, Hokkaido University.
    • Handle: RePEc:hok:dpaper:361
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    File URL: http://hdl.handle.net/2115/82952
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    File URL: https://eprints.lib.hokudai.ac.jp/dspace/bitstream/2115/82952/1/DPA361.pdf
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    References listed on IDEAS

    as
    1. Zopounidis, C & Pouliezos, A & Yannacopoulos, D, 1992. "Designing a DSS for the Assessment of Company Performance and Viability," Computer Science in Economics & Management, Kluwer;Society for Computational Economics, vol. 5(1), pages 41-56, February.
    2. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    3. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
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