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

Author

Listed:
  • 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

    as
    1. 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.
    2. 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.
    3. Patricia M. Dechow & Richard G. Sloan & Amy P. Sweeney, 1996. "Causes and Consequences of Earnings Manipulation: An Analysis of Firms Subject to Enforcement Actions by the SEC," Contemporary Accounting Research, John Wiley & Sons, vol. 13(1), pages 1-36, March.
    4. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    5. 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.
    6. 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.
    7. 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.
    8. Casey, C & Bartczak, N, 1985. "Using Operating Cash Flow Data To Predict Financial Distress - Some Extensions," Journal of Accounting Research, Wiley Blackwell, vol. 23(1), pages 384-401.
    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. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    11. 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.
    12. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    13. Chih-Hsuan Wang & Way Kuo & Hairong Qi, 2007. "An integrated approach for process monitoring using wavelet analysis and competitive neural network," International Journal of Production Research, Taylor & Francis Journals, vol. 45(1), pages 227-244, January.
    14. 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.
    15. 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, April.
    16. 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.
    17. Vives,Xavier (ed.), 2006. "Corporate Governance," Cambridge Books, Cambridge University Press, number 9780521032032.
    18. Mensah, Ym, 1984. "An Examination Of The Stationarity Of Multivariate Bankruptcy Prediction Models - A Methodological Study," Journal of Accounting Research, Wiley Blackwell, vol. 22(1), pages 380-395.
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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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