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Adopting Nonlinear Activated Beetle Antennae Search Algorithm for Fraud Detection of Public Trading Companies: A Computational Finance Approach

Author

Listed:
  • Bolin Liao

    (College of Information Science and Engineering, Jishou University, Jishou 416000, China)

  • Zhendai Huang

    (College of Information Science and Engineering, Jishou University, Jishou 416000, China)

  • Xinwei Cao

    (School of Management, Shanghai University, Shanghai 200000, China)

  • Jianfeng Li

    (College of Information Science and Engineering, Jishou University, Jishou 416000, China)

Abstract

With the emergence of various online trading technologies, fraudulent cases begin to occur frequently. The problem of fraud in public trading companies is a hot topic in financial field. This paper proposes a fraud detection model for public trading companies using datasets collected from SEC’s Accounting and Auditing Enforcement Releases (AAERs). At the same time, this computational finance model is solved with a nonlinear activated Beetle Antennae Search (NABAS) algorithm, which is a variant of the meta-heuristic optimization algorithm named Beetle Antennae Search (BAS) algorithm. Firstly, the fraud detection model is transformed into an optimization problem of minimizing loss function and using the NABAS algorithm to find the optimal solution. NABAS has only one search particle and explores the space under a given gradient estimation until it is less than an “Activated Threshold” and the algorithm is efficient in computation. Then, the random under-sampling with AdaBoost (RUSBoost) algorithm is employed to comprehensively evaluate the performance of NABAS. In addition, to reflect the superiority of NABAS in the fraud detection problem, it is compared with some popular methods in recent years, such as the logistic regression model and Support Vector Machine with Financial Kernel (SVM-FK) algorithm. Finally, the experimental results show that the NABAS algorithm has higher accuracy and efficiency than other methods in the fraud detection of public datasets.

Suggested Citation

  • Bolin Liao & Zhendai Huang & Xinwei Cao & Jianfeng Li, 2022. "Adopting Nonlinear Activated Beetle Antennae Search Algorithm for Fraud Detection of Public Trading Companies: A Computational Finance Approach," Mathematics, MDPI, vol. 10(13), pages 1-14, June.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:13:p:2160-:d:844009
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    References listed on IDEAS

    as
    1. Yang Bao & Bin Ke & Bin Li & Y. Julia Yu & Jie Zhang, 2020. "Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach," Journal of Accounting Research, Wiley Blackwell, vol. 58(1), pages 199-235, March.
    2. Liang Yao & Pak Kin Wong & Baoliang Zhao & Ziwen Wang & Long Lei & Xiaozheng Wang & Ying Hu, 2022. "Cost-Sensitive Broad Learning System for Imbalanced Classification and Its Medical Application," Mathematics, MDPI, vol. 10(5), pages 1-19, March.
    3. Arjan Reurink, 2018. "Financial Fraud: A Literature Review," Journal of Economic Surveys, Wiley Blackwell, vol. 32(5), pages 1292-1325, December.
    4. Patricia M. Dechow & Weili Ge & Chad R. Larson & Richard G. Sloan, 2011. "Predicting Material Accounting Misstatements," Contemporary Accounting Research, John Wiley & Sons, vol. 28(1), pages 17-82, March.
    5. Tzu-Hsuan Lin & Jehn-Ruey Jiang, 2021. "Credit Card Fraud Detection with Autoencoder and Probabilistic Random Forest," Mathematics, MDPI, vol. 9(21), pages 1-16, October.
    6. Mark Cecchini & Haldun Aytug & Gary J. Koehler & Praveen Pathak, 2010. "Detecting Management Fraud in Public Companies," Management Science, INFORMS, vol. 56(7), pages 1146-1160, July.
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    Cited by:

    1. Meichun Huang & Yunong Zhang, 2024. "Zhang Neuro-PID Control for Generalized Bi-Variable Function Projective Synchronization of Nonautonomous Nonlinear Systems with Various Perturbations," Mathematics, MDPI, vol. 12(17), pages 1-25, August.
    2. Nikola Ivković & Robert Kudelić & Matej Črepinšek, 2022. "Probability and Certainty in the Performance of Evolutionary and Swarm Optimization Algorithms," Mathematics, MDPI, vol. 10(22), pages 1-25, November.

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