Predicting Enterprise Performance by Network-Based Risk Profiling Approach

Longda Tong; Song Yin; Dongdong Hu; Zhaoyuan Li

doi:doi:10.11648/j.ijefm.20210906.15

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Predicting Enterprise Performance by Network-Based Risk Profiling Approach

Longda Tong, Song Yin, Dongdong Hu, Zhaoyuan Li

Published in International Journal of Economics, Finance and Management Sciences (Volume 9, Issue 6)

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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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

Longda Tong

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

WeBank Co., Ltd., Shenzhen, China
Dongdong Hu

WeBank Co., Ltd., Shenzhen, China
Zhaoyuan Li

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

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Plain Text BibTeX RIS

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

Copy | Download

@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}
}

Copy | Download

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  -

Copy | Download