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

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Author Info

  • 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

    ()

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    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.

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    File URL: http://www.ipe.ro/rjef/rjef1_11/rjef1_2011p54-71.pdf
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    Bibliographic Info

    Article provided by Institute for Economic Forecasting in its journal Romanian Journal for Economic Forecasting.

    Volume (Year): (2011)
    Issue (Month): 1 (March)
    Pages: 54-71

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    Handle: RePEc:rjr:romjef:v::y:2011:i:1:p:54-71

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    Related research

    Keywords: financial information fraud warning models; Back Propagation Neural Networks; manufacturing industry; credit policy;

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    References

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    1. Li-Chiu Chi & Tseng-Chung Tang, 2006. "Bankruptcy Prediction: Application of Logit Analysis in Export Credit Risks," Australian Journal of Management, Australian School of Business, vol. 31(1), pages 17-27, June.
    2. Malhotra, Manoj K. & Sharma, Subhash & Nair, Satish S., 1999. "Decision making using multiple models," European Journal of Operational Research, Elsevier, vol. 114(1), pages 1-14, April.
    3. Tseng, Chih-Hsiung & Cheng, Sheng-Tzong & Wang, Yi-Hsien & Peng, Jin-Tang, 2008. "Artificial neural network model of the hybrid EGARCH volatility of the Taiwan stock index option prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(13), pages 3192-3200.
    4. Mark E. Wohar & David E. Rapach, 2005. "Valuation ratios and long-horizon stock price predictability," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(3), pages 327-344.
    5. Cochran, James J. & Darrat, Ali F. & Elkhal, Khaled, 2006. "On the bankruptcy of internet companies: An empirical inquiry," Journal of Business Research, Elsevier, vol. 59(10-11), pages 1193-1200, October.
    6. Angela J. Black & David G. McMillan, 2004. "Non-linear Predictability of Value and Growth Stocks and Economic Activity," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 31(3-4), pages 439-474.
    7. Foreman, R. Dean, 2003. "A logistic analysis of bankruptcy within the US local telecommunications industry," Journal of Economics and Business, Elsevier, vol. 55(2), pages 135-166.
    8. Vives,Xavier (ed.), 2006. "Corporate Governance," Cambridge Books, Cambridge University Press, number 9780521032032, October.
    9. Altman, Edward I. & Marco, Giancarlo & Varetto, Franco, 1994. "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," Journal of Banking & Finance, Elsevier, vol. 18(3), pages 505-529, May.
    10. Pamela K. Coats & L. Franklin Fant, 1993. "Recognizing Financial Distress Patterns Using a Neural Network Tool," Financial Management, Financial Management Association, vol. 22(3), Fall.
    11. Terry J. Ward & Benjamin P. Foster, 1997. "A Note on Selecting a Response Measure for Financial Distress," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 24(6), pages 869-879.
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