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Financial Information Fraud Risk Warning for Manufacturing Industry - Using Logistic Regression and Neural Network

Author

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  • Shih, Kuang Hsun

    (Department of Banking and Finance, Chinese Culture University, No.55, Hwa-Kang Road, Yang-Ming-Shan, Taipei, Taiwan, 11114)

  • Cheng, Ching Chan

    (Department of Food & Beverage Management, Taipei College of Maritime Technology, No.212, Sec.9, Yen Ping N, Taipei, Taiwan, 11114)

  • Wang, Yi Hsien

Abstract

This study aims to use financial variables, corporate governance variables, and cash flow variables to construct financial information fraud warning models for the manufacturing industry, and applies logistics regression and back propagation neural network (BPNN) to determine the accuracy rate of identifying normal company samples and fraudulent company samples. In a ratio of ‘1:2’, this study collects the data of 96 fraudulent company samples and 192 normal company samples, over a period of 3 years (a total of 288 samples) for prediction. The results indicate that debt ratio and shareholding ratio of board directors are two important financial variables for the identification of manufacturing industry frauds. Logistic regression has better identification capacity than BPNN in both cases of normal and fraudulent company samples. This study provides a set of correct and real-time financial information fraud warning models for the manufacturing industry, which can predict financial information frauds by observing the changes of various financial variables and shareholding ratio of the board directors in real-time. These findings can serve as a reference to financiers and the manufacturing industry for establishing credit policies.

Suggested Citation

  • Shih, Kuang Hsun & Cheng, Ching Chan & Wang, Yi Hsien, 2011. "Financial Information Fraud Risk Warning for Manufacturing Industry - Using Logistic Regression and Neural Network," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 54-71, March.
    • Handle: RePEc:rjr:romjef:v::y:2011:i:1:p:54-71
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    References listed on IDEAS

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    Cited by:

    1. Chih-Chung Yang & Yungho Leu & Chien-Pang Lee, 2014. "A Dynamic Weighted Distancedbased Fuzzy Time Series Neural Network with Bootstrap Model for Option Price Forecasting," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 115-129, June.

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    More about this item

    Keywords

    financial information fraud warning models; Back Propagation Neural Networks; manufacturing industry; credit policy;
    All these keywords.

    JEL classification:

    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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