Financial Information Fraud Risk Warning for Manufacturing Industry - Using Logistic Regression and Neural Network
AbstractThis 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by Institute for Economic Forecasting in its journal Romanian Journal for Economic Forecasting.
Volume (Year): (2011)
Issue (Month): 1 (March)
Contact details of provider:
Postal: Casa Academiei, Calea 13, Septembrie nr.13, sector 5, Bucureşti 761172
Phone: 004 021 3188148
Fax: 004 021 3188148
Web page: http://www.ipe.ro/
More information through EDIRC
financial information fraud warning models; Back Propagation Neural Networks; manufacturing industry; credit policy;
Find related papers by 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
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Vives,Xavier (ed.), 2006. "Corporate Governance," Cambridge Books, Cambridge University Press, number 9780521032032, April.
- 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.
- 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.
- 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.
- 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.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Corina Saman).
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.