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Journal Article

Dynamic Imbalanced Financial Distress Prediction Model Based on Improved AdaBoost Algorithm

by Hamza Al-Faraj 1,* ,  Chenguang Wang 2,3 ,  Lin Yi-Jun 3  and  Chen Da-Wei 3
1
Taif University, College of Education, Saudi Arabia
2
School of Education and Social Work, University of Sydney, Camperdown, Australia
3
Faculty of Business, Lingnan University, Hong Kong, China
*
Author to whom correspondence should be addressed.
EMGBS  2023 2(1):2; https://doi.org/10.xxxx/xxxxxx
Received: 1 November 2023 / Accepted: 15 November 2023 / Published Online: 30 November 2023
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Abstract

Research Objectives: Build a dynamic imbalanced financial distress prediction model to solve concept drift I and imbalanced data distribution simultaneously. Research Methods: Based on the improved AdaBoost algorithm, ADA-CSSVM-TW model is built with cost sensitive support vector machine as the base classifier, and the data of Chinese manufacturing companies from 2010 to 2020 are used for the empirical analysis. Research Findings: The ADACSSVM-TW model I can significantly improve the prediction accuracy, with excellent performance and reliable robustness. Research Innovations: The cost sensitive support vector machine is used as the base classifier of the improved AdaBoost algorithm, and the dynamic imbalanced financial distress prediction model is built. Research Value: The research of this paper has important theoretical value and practical significance for Chinese listed companies to effectively prevent the occurrence of financial distress in practice.

Keywords: Financial Distress Prediction; Class-imbalanced Data; Cost-sensitive; Learning; Concept Drift; AdaBoost Algorithm;
Copyright: © 2023 by Al-Faraj, Wang, Yi-Jun and Da-Wei. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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APA Style
Al-Faraj, H., Wang, C., Yi-Jun, L., & Da-Wei, C. (2023). Dynamic Imbalanced Financial Distress Prediction Model Based on Improved AdaBoost Algorithm. Economic Management & Global Business Studies, 2(1), 2. doi:10.xxxx/xxxxxx
ACS Style
Al-Faraj, H.; Wang, C.; Yi-Jun, L.; Da-Wei, C. Dynamic Imbalanced Financial Distress Prediction Model Based on Improved AdaBoost Algorithm. Economic Management & Global Business Studies, 2023, 2, 2. doi:10.xxxx/xxxxxx
AMA Style
Al-Faraj H., Wang C., Yi-Jun L. et al.. Dynamic Imbalanced Financial Distress Prediction Model Based on Improved AdaBoost Algorithm. Economic Management & Global Business Studies; 2023, 2(1):2. doi:10.xxxx/xxxxxx
Chicago/Turabian Style
Al-Faraj, Hamza; Wang, Chenguang; Yi-Jun, Lin; Da-Wei, Chen 2023. "Dynamic Imbalanced Financial Distress Prediction Model Based on Improved AdaBoost Algorithm" Economic Management & Global Business Studies 2, no.1:2. doi:10.xxxx/xxxxxx
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ACS Style
Al-Faraj, H.; Wang, C.; Yi-Jun, L.; Da-Wei, C. Dynamic Imbalanced Financial Distress Prediction Model Based on Improved AdaBoost Algorithm. Economic Management & Global Business Studies, 2023, 2, 2. doi:10.xxxx/xxxxxx
AMA Style
Al-Faraj H., Wang C., Yi-Jun L. et al.. Dynamic Imbalanced Financial Distress Prediction Model Based on Improved AdaBoost Algorithm. Economic Management & Global Business Studies; 2023, 2(1):2. doi:10.xxxx/xxxxxx
Chicago/Turabian Style
Al-Faraj, Hamza; Wang, Chenguang; Yi-Jun, Lin; Da-Wei, Chen 2023. "Dynamic Imbalanced Financial Distress Prediction Model Based on Improved AdaBoost Algorithm" Economic Management & Global Business Studies 2, no.1:2. doi:10.xxxx/xxxxxx
APA style
Al-Faraj, H., Wang, C., Yi-Jun, L., & Da-Wei, C. (2023). Dynamic Imbalanced Financial Distress Prediction Model Based on Improved AdaBoost Algorithm. Economic Management & Global Business Studies, 2(1), 2. doi:10.xxxx/xxxxxx

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References

  1. [1]Fitzpatrick P. J. , 1932, A Com parison of Ratios of Successful Industrial Enterprises with Those of Failed Firms [M], New York: Certified Public Accountant.
  2. [2]Beaver W. H. , 1966, Financial Ratios as Predictors of Failure [J], Journal of Accounting Research, 4, 71~111.
  3. [3] Altman E. I. , 1968, Financial Ratios, Discriminant Analysis and the Prediction of Cor porate Bankruptcy [J], Journal of Finance, 23 (4), 589~ 609.
  4. [4]MselmiN., Lahiani A. , Hamza т. , 2017, Financial Distress Prediction: The Case of French Small and Medium-sized Firms [J], International Review of Financial Analysis, 50, 67~ 80.
  5. [5]Kim S. Y., Upneja A. , 2014, Predicting Restaurant Financial Distress Using Decision Tree and AdaBoosted Decision Tree Models [J, Economic Modelling, 36, 354~362.
  6. [6]Gogas P. , Papadimitriou T. , Agrapetidou A. , 2018, Forecasting Bank Failures and Stress Testing: A Machine Learning Approach [J], International Journal of Forecasting, 34 (4), 440~455.
  7. [7] Moscatelli M. , Parlapiano F. , Narizzano s , Viggiano G. , 2020, Corporate Default Forecasting with Machine Learning [J], Expert Systems with Applications, 161, 113~167.
  8. [8]Chawla N. V. , Bowyer K. W. , HallL. 0. , Kegelmeyer W. P. , 2002, SMOTE: Synthetic Minority Over-sampling Technique [J], Journal of Artificial Intelligence Research, 16, 321~ 357.
  9. [9] Ganguly S. , Sadaoui S , 2017, Classification of Imbalanced Auction Fraud Data [J], Canadian Conference on Artificial Intelligence, Advances in Artificial Intelligence, 84~ 89.
  10. [10]Freund Y. , Schapire R. E. , 1997, A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting [J], Journal of Computer and System Sciences, 55 (1), 119~139.
  11. [11]Sahin Y. , Bulkan s , Duman E. , 2013, A Cost-sensitive Decision Tree Approach for Fraud Detection [J], Expert Systems with A pplications, 40 (15), 5916~ 5923.
  12. [12] Tao X. M., Li Q ,Guo W. J., Ren C., Li C. х., LiuR. , Zou J. R. , 2019, Self-adaptive Cost Weights-based Support Vector Machine Cost-sensitive Ensemble for Imbalanced Data Classification [J], Information Sciences, 487, 31~56.
  13. [13]Hulten G. , Spencer L. , Domingos P. , 2001, Mining Time-Changing Data Streams [CJ, Preceeding of the Seventh ACM SIGKDD, International Conference on Knowledge Discovery and Data Mining , 97~106.
  14. [14]Koychev L. , 2000, Gradual Forgetting for Ada ptation to Concept Drift [с], Proceeding of the ECAI 2000 Workshop Current Issues in Spatio- Temporal Reasoning, 101 ~ 106.
  15. [15] Almeida P. R. L. , OliveiraL. S. , BrittoJrA. S , Sabourin R. , 2018, Ada pting Dynamic Classifier Selection for Concept Drift [J], Expert Systems with Applications, 104, 67~ 85.
  16. [16]Tölö E., 2020, Predicting Systemic Financial Crises with Recurrent Neural Networks [J], Journal of Financial Stability, 49, 100~ 146.
  17. [17]Sun J., Li H. , Adeli H. , 2013, Concept Drift-Oriented Adaptive and Dynamic Support Vector Machine Ensemble with Time Window in Corporate Financial Risk Prediction [J], IEEE Transactions on Systems,Man, and Cybernetics. , 43 (4), 801~813.
  18. [18]Sun J., Fujita H., ChenP., Li H. , 2017, Dynamic Financial Distress Prediction with Concept Drift Based on Time Weighting Combined with Adaboost Sup port Vector Machine Ensemble [J], Knowledge-Based Systems, 120, 4~14.
  19. [19]Sun J. , Li H. , Fujita H. , Fu B. B. , Ai W. G. , 2020, Class-imbal anced Dynamic Financial Distress Prediction Based on Adaboost-SVM Ensemble Combined with SMOTE and Time Weighting [J], Information Fusion, 54, 128~144.
  20. [20]BarddalJ. P. , Enembreck F. , Gomes H. M. , Bifet A. , 2019, Boosting Decision Stumps for Dynamic Feature Selection on Data Streams [J], Information Systems, 83, 13~ 29.