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Predicting Enterprise Performance by Network-Based Risk Profiling Approach

Received: 4 November 2021     Accepted: 24 November 2021     Published: 2 December 2021
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Abstract

Deriving the firms’ risk profile based on specific features has important implications in risk controlling and investment. Recently, much research demonstrates that the firms’ ownership networks substantially impact the firms’ risk profile. In this paper, we propose a framework of risk profiling approach built upon information retrieved from the firm's ownership networks. The method considers the non-linear relationships between firm fundamentals with network structures. To test the performance of the proposed method, we construct a new dataset of Chinese listed firms with their financials and network parameters in the period between 2005 and 2020. We show that the proposed method significantly outperforms traditional ones in predicting a firm's market value changes. Specifically, we first use the conventional linear method, like logistic regression and linear discriminant analysis, as the performance benchmark. Then, the more advanced technique based on information theory like Gradient Boosting is adopted and has shown remarkable performance with at least 85% area under the curve (AUC) compared with the 60% AUC of the traditional linear model. The proposed method has implications in risk management, portfolio management, and corporate finance. As a special implication example in risk management, we demonstrate that a network-based approach can effectively detect duplication of individual names in a unique dataset.

Published in International Journal of Economics, Finance and Management Sciences (Volume 9, Issue 6)
DOI 10.11648/j.ijefm.20210906.15
Page(s) 242-249
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Risk Profiling, Risk Management, Complex Network, Statistical Methodology

References
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Cite This Article
  • APA Style

    Longda Tong, Song Yin, Dongdong Hu, Zhaoyuan Li. (2021). Predicting Enterprise Performance by Network-Based Risk Profiling Approach. International Journal of Economics, Finance and Management Sciences, 9(6), 242-249. https://doi.org/10.11648/j.ijefm.20210906.15

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    ACS Style

    Longda Tong; Song Yin; Dongdong Hu; Zhaoyuan Li. Predicting Enterprise Performance by Network-Based Risk Profiling Approach. Int. J. Econ. Finance Manag. Sci. 2021, 9(6), 242-249. doi: 10.11648/j.ijefm.20210906.15

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    AMA Style

    Longda Tong, Song Yin, Dongdong Hu, Zhaoyuan Li. Predicting Enterprise Performance by Network-Based Risk Profiling Approach. Int J Econ Finance Manag Sci. 2021;9(6):242-249. doi: 10.11648/j.ijefm.20210906.15

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  • @article{10.11648/j.ijefm.20210906.15,
      author = {Longda Tong and Song Yin and Dongdong Hu and Zhaoyuan Li},
      title = {Predicting Enterprise Performance by Network-Based Risk Profiling Approach},
      journal = {International Journal of Economics, Finance and Management Sciences},
      volume = {9},
      number = {6},
      pages = {242-249},
      doi = {10.11648/j.ijefm.20210906.15},
      url = {https://doi.org/10.11648/j.ijefm.20210906.15},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijefm.20210906.15},
      abstract = {Deriving the firms’ risk profile based on specific features has important implications in risk controlling and investment. Recently, much research demonstrates that the firms’ ownership networks substantially impact the firms’ risk profile. In this paper, we propose a framework of risk profiling approach built upon information retrieved from the firm's ownership networks. The method considers the non-linear relationships between firm fundamentals with network structures. To test the performance of the proposed method, we construct a new dataset of Chinese listed firms with their financials and network parameters in the period between 2005 and 2020. We show that the proposed method significantly outperforms traditional ones in predicting a firm's market value changes. Specifically, we first use the conventional linear method, like logistic regression and linear discriminant analysis, as the performance benchmark. Then, the more advanced technique based on information theory like Gradient Boosting is adopted and has shown remarkable performance with at least 85% area under the curve (AUC) compared with the 60% AUC of the traditional linear model. The proposed method has implications in risk management, portfolio management, and corporate finance. As a special implication example in risk management, we demonstrate that a network-based approach can effectively detect duplication of individual names in a unique dataset.},
     year = {2021}
    }
    

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  • TY  - JOUR
    T1  - Predicting Enterprise Performance by Network-Based Risk Profiling Approach
    AU  - Longda Tong
    AU  - Song Yin
    AU  - Dongdong Hu
    AU  - Zhaoyuan Li
    Y1  - 2021/12/02
    PY  - 2021
    N1  - https://doi.org/10.11648/j.ijefm.20210906.15
    DO  - 10.11648/j.ijefm.20210906.15
    T2  - International Journal of Economics, Finance and Management Sciences
    JF  - International Journal of Economics, Finance and Management Sciences
    JO  - International Journal of Economics, Finance and Management Sciences
    SP  - 242
    EP  - 249
    PB  - Science Publishing Group
    SN  - 2326-9561
    UR  - https://doi.org/10.11648/j.ijefm.20210906.15
    AB  - Deriving the firms’ risk profile based on specific features has important implications in risk controlling and investment. Recently, much research demonstrates that the firms’ ownership networks substantially impact the firms’ risk profile. In this paper, we propose a framework of risk profiling approach built upon information retrieved from the firm's ownership networks. The method considers the non-linear relationships between firm fundamentals with network structures. To test the performance of the proposed method, we construct a new dataset of Chinese listed firms with their financials and network parameters in the period between 2005 and 2020. We show that the proposed method significantly outperforms traditional ones in predicting a firm's market value changes. Specifically, we first use the conventional linear method, like logistic regression and linear discriminant analysis, as the performance benchmark. Then, the more advanced technique based on information theory like Gradient Boosting is adopted and has shown remarkable performance with at least 85% area under the curve (AUC) compared with the 60% AUC of the traditional linear model. The proposed method has implications in risk management, portfolio management, and corporate finance. As a special implication example in risk management, we demonstrate that a network-based approach can effectively detect duplication of individual names in a unique dataset.
    VL  - 9
    IS  - 6
    ER  - 

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Author Information
  • School of Data Science, the Chinese University of Hong Kong, Shenzhen, China

  • WeBank Co., Ltd., Shenzhen, China

  • WeBank Co., Ltd., Shenzhen, China

  • School of Data Science, the Chinese University of Hong Kong, Shenzhen, China

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