臺灣博碩士論文加值系統

近年來財務報表舞弊有愈來愈嚴重的趨勢，如何建立一個有效的公司財務報表舞弊偵測模式，是當前學術界與實務界相當重視的課題；再者，資料探勘應用於偵測財務報表舞弊尚缺乏一致性之實證研究。因此本研究主要目的在採用資料探勘中貝氏認知網路、類神經網路以及決策樹等方法之外，亦整合財務及非財務變數，希望能藉由更完整多元的觀點，以建構較佳之財務報表舞弊偵測模式及找出影響財務報表舞弊的重要性變數。實證結果發現，偵測財務報表舞弊模式中，貝氏認知網路偵測效果最好之外，並指出偵測財務報表舞弊之重要性變數除財務變數外，也要考量非財務變數。

In these few years, we have seen that the occurrence possibility of fraudulent financial statement (FFS) increases gradually. But however, there is no consistency using data mining to detect fraudulent financial statements in the prior study. Therefore, to build up an appropriate FFS diagnosis model has become a very important task in industry. However, in the prior studies using data mining to detect FFS lacks consistency. Thus, this paper explores the effectiveness of data mining classification techniques such as Bayesian belief networks, decision trees and neural networks in detecting firms that issue FFS and deals with the identification of factors associated to FFS. In addition to the financial indicators, the non-financial indicators are also included in the model to measure .The result shows that the Bayesian belief networks achieves better performance than any model as decision trees and neural networks. Moreover, we find out that both traditional financial indicators and non-financial indicators significantly influence the diagnostic correctness of FFS.

內容目錄
中文摘要　．．．．．．．．．．．．．．．．．．．． iii
英文摘要　．．．．．．．．．．．．．．．．．．．． iv
誌謝辭　　．．．．．．．．．．．．．．．．．．．． v
內容目錄　．．．．．．．．．．．．．．．．．．．． vi
表目錄　　．．．．．．．．．．．．．．．．．．．． viii
圖目錄　　．．．．．．．．．．．．．．．．．．．． ix
第一章　　緒論．．．．．．．．．．．．．．．．．． 1
　　第一節　　研究背景與動機．．．．．．．．．．． 1
　　第二節　　研究目的．．．．．．．．．．．．．． 4
　　第三節　　論文研究架構與流程．．．．．．．．． 4
第二章　　文獻探討．．．．．．．．．．．．．．．． 7
　　第一節　　財務報表舞弊之定義．．．．．．．．． 7
　　第二節　　財務報表舞弊之相關研究．．．．．．．． 7
　　第三節　　小結．．．．．．．．．．．．．．．． 10
第三章　　研究設計．．．．．．．．．．．．．．．． 14
　　第一節　　研究架構．．．．．．．．．．．．．． 14
　　第二節　　研究變數．．．．．．．．．．．．．． 15
　　第三節　　樣本選取與資料蒐集．．．．．．．．． 17
　　第四節　　研究方法．．．．．．．．．．．．．． 18
第四章　　實證結果與分析．．．．．．．．．．．．． 24
　　第一節　　變數篩選．．．．．．．．．．．．．． 24
　　第二節　　模式之分析．．．．．．．．．．．．． 26
　　第三節　　模式之比較分析．．．．．．．．．．． 33
第五章　　結論與建議．．．．．．．．．．．．．．． 36
　　第一節　　研究結論．．．．．．．．．．．．．． 36
　　第二節　　研究限制．．．．．．．．．．．．．． 37
　　第三節　　未來研究建議．．．．．．．．．．．． 37
參考文獻　．．．．．．．．．．．．．．．．．．．． 39
附錄Ａ　　研究樣本彙總表．．．．．．．．．．．．． 46

表 目 錄
表 2- 11 財務報表舞弊相關研究彙整表．．．．．．．． 12
表 3- 11 研究變數彙總表．．．．．．．．．．．．．． 16
表 4- 11 逐步迴歸變數篩選結果彙總表．．．．．．．．． 24
表 4- 12 10組交互驗證－貝氏認知網路．．．．．．．． 28
表 4- 13 貝氏認知網路分類結果矩陣．．．．．．．．． 29
表 4- 14 10組交互驗證－決策樹．．．．．．．．．．．．． 30
表 4- 15 決策樹分類結果矩陣．．．．．．．．．．．． 31
表 4- 16 10組交互驗證－類神經網路．．．．．．．．． 32
表 4- 17 類神經網路分類結果矩陣．．．．．．．．．． 33
表 4- 18 正確率分類結果．．．．．．．．．．．．．． 33
表 4- 19 3種模式分類結果．．．．．．．．．．．．． 34
表 4- 10 3種模式錯誤率彙整表．．．．．．．．．．． 35

圖 目 錄
圖 1-1 論文流程圖．．．．．．．．．．．．．．．．． 6
圖 3-1 研究架構．．．．．．．．．．．．．．．．． 15
圖 3-2 類神經網路架構圖．．．．．．．．．．．．． 23
圖 3-3 神經元之構造．．．．．．．．．．．．．． 23
圖 4-1 貝氏認知網路之結構圖．．．．．．．．．．．． 28
圖 4-2 決策樹之樹狀結構圖．．．．．．．．．．．． 30

一、中文部份
李天行，唐筱菁(2004)，整合財務比率與智慧資本於公司危機診斷模式之建構－類神經網路與多元適應性雲型迴歸之應用，資訊管理學報，11(2)，161-189。
林昱成，林金賢，陳雪如，莊家豪(2007)，類神經模糊專家系統在訴訟預警模型之應用：以公司治理觀點，會計評論，44(1)，95-126。
馬秀如(2006)，會計師揭發舞弊之責任－審計準則公報第43號導讀，會計研究月刊，253(12)，44-61。
許伯彥(2003)，財務報表舞弊風險評量模式研究，國立台灣大學會計學研究所未出版之碩士論文。
陳昭宏(2005)，以事前控制觀點應用灰色預測理論與Logit式於財務危機預警模式之研究，商管科技季刊，6(4)，655-676。
陳雅琪(2007)，董事會結構、家族控制持股、集團企業與財務報表舞弊之關聯性研究，國立成功大學會計學研究所未出版之碩士論文。
黃承龍，陳穆臻，王界人(2004)，支援向量機於信用評等之應用，計量管理期刊，1(2)，155-172。
黃郁凱(2006)，財務報表舞弊預警模式，國立政治大學會計學研究所未出版之碩士論文。
會計研究發展基金會(2006)，查核財務報表對舞弊之考量，台北：編製者發行。

二、英文部份
American Institute of Certified Public Accountants. (2002). Statement on Auditing Standards: Consideration of Fraud in a Financial Statement Audit (No.99). New York: Author.
Arminger, G., Enache, D., & Bonne, T. (1997). Analyzing credit risk data: A comparison of logistic discrimination classification tree analysis and feedforward networks. Computational Statistics, 12(2), 293-310.
Beasley, M. (1996). An empirical analysis of the relation between the board of director composition and financial statement fraud. The Accounting Review, 71(4), 443-466.
Bell, T., & Carcello, J. (2000). A decision aid for assessing the likelihood of fraudulent financial reporting. Auditing：A Journal of Practice and Theory, 9(1), 169-178.
Bierstaker, J. L., & Wright, S. (2001). A research note concerning practical problem-solving ability as a predictor of performance in auditing tasks. Behavioral Research in Accounting, 13(1), 49-62.
Cheng, J. (2001) J. Cheng’s Bayesian Belief Network Software: BN Power Predictor [Online]. Available: http://www.cs.ualberta.ca/~jcheng/bnsoft.htm [2001, October 4].
Calderon, T. G., & Cheh, J. J. (2002). A roadmap for future neural networks research in auditing and risk assessment. International Journal of Accounting Information Systems, 3(4), 203-236.
Carol, A., & Michael, C. (2001). The effects of experience and explicit fraud risk assessment in detecting fraud with analytical procedures. Accounting, Organizations and Society, 26(1), 25-37.
Chen, G. (2006). Positive research on the financial statement fraud factors of listed companies in China. Journal of Modern Accounting and Auditing, 2(6), 25-34.
Cullinan, P. G., & Sutton, G. S. (2002). Defrauding the public interest: A critical examination of reengineered audit processes and the likelihood of detecting fraud. Critical perspectives on Accounting, 13(3), 297-310.
Davies, P. C. (1994). Design issues in neural network development, Neurovest Journal, 5(1), 21-25.
Diamantaras, K. I., & Kung, S. Y. (1996). Principal component neural networks: Theory and applications, New York: Wiley.
Dunn, P. (2004). The impact of insider power on fraudulent financial reporting. Journal of Management, 30(3), 397-412.
Fanning, K., & Cogger, K. (1998). Neural network detection of management fraud using published financial data. International Journal of Intelligent Systems in Accounting, Finance and Management, 7(1), 21-24.
Farber, D. B. (2005). Restoring trust after fraud: Does corporate governance matter? The Accounting Review, 80(2), 539-561.
Green, B. P., & Choi, J. H. (1997). Assessing the risk of management fraud through neural network technology. Auditing: A Journal of Practice and Theory, 16(1), 14-27.
Kaminski, A., Kaminski, T., Wetzel, S., & Guan, L. (2004). Can financial ratios detect fraudulent financial reporting? Managerial Auditing Journal, 19(1), 15-28.
Kinney, W., & McDaniel, L. S. (1989). Characteristics of firms correcting previously reported quarterly earnings. Journal of Accounting and Economics, 11(1), 71-93.
Kirkos, S., Spathis, C., & Manolopoulos, Y. (2007). Data mining techniques for the detection of fraudulent financial statements. Expert Systems with Application, 32(4), 995-1003.
Kohavi, R. (1995). A study of cross-validation and the bootstrap for accuracy estimation and model selection, Proceeding of 14th International Joint Conference on Artificial Intelligence (pp. 1137-1145), California: San Francisco.
Kotsiantis, S., Koumanakos, E., Tzelepis, D., & Tampakas, V. (2007). Forecasting fraudulent financial statements using data mining. International Journal of Computational Intelligence, 3(2), 104-110.
Liang, L., & Wu, D. (2005). An application of pattern recognition on scoring Chinese corporations financial conditions based on back-propagation neural network. Computer and Operations Research, 32(5), 1115-1129.
Lin, J. W., Hwang, M. I., & Becker, J. D. (2003). A fuzzy neural network for assessing the risk of fraudulent financial reporting. Managerial Auditing Journal, 18(8), 657-665.
Newton, A. C., Stewart, G. B., Diaz, A., Golicher, D., & Pullin, A. S. (2007). Bayesian belief networks as a tool for evidence-based conservation management. Journal of Nature Conservation, 15(2), 144-160.
Palmrose, Z. (1991). An analysis of auditor litigation disclosures. Auditing: A Journal of Practice and Theory, 10(1), 54-71.
Persons, O. (1995). Using financial statement data to identify factors associated with fraudulent financial reporting. Journal of Applied Business Research, 11(3), 38-46.
Pudil, P., Fuka, K., Beranek, K., & Dvorak, P. (1999). Potential of artificial intelligence based feature selection methods in regression models. IEEE 3ed International Conference on Computational Intelligence and Multimedia Application (pp. 159-163), USA: Washington DC.
Quinlan, J. R. (1986). Introduction of decision trees, Machine Learning, 1(1), 81-106.
Quinlan, J. R. (1993). C4.5: Programs for machine learning. San Mateo: Morgan Kaufmann.
Ravi, V., Kurniawan, H., Thai, P. N. K., & Kumar, P. R. (2008). Soft computing system for bank performance prediction. Applied Soft Computing, 8(1), 305-315.
Rezaee, Z. (2005). Causes, consequences and deterrence of financial statement fraud. Critical Perspectives on Accounting, 16(3), 277-298.
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning internal representation by error propagation. Parallel Distributed Processing, 1(6), 318-362.
Seifert, J. W. (2004). Data mining and the search for security: Challenges for connecting the dots and databases. Government Information Quarterly, 21(4), 461-480.
Sharma, V. D. (2004). Board of director characteristics, institutional ownership and fraud-evidence from Australia. Auditing: A journal of practice and theory, 23(2), 105-117.
Spathis, C., Doumpos, M., & Zopounidis, C. (2002). Detecting falsified financial statements: A comparative study using multicriteria analysis and multivariate statistical techniques. The European Accounting Review, 11(3), 509-535.
Stamelos, I., Angelis, L., Dimou, P., & Sakellaris, D. (2003). On the use of bayesian belief networks for the prediction of software productivity. Information and Software Technology, 45(1), 51-60.
Summers, S. L., & Sweeney, J. T. (1998). Fraudulently misstated financial statements and insider trading: An empirical analysis. The Accounting Review, 73(1), 131-146.
Tang, A., Nicholson, A., Jin, Y., & Han, J. (2007). Using bayesian belief networks for change impact analysis in architecture design. The Journal of Systems and Software, 80(1), 127-148.
Viaene, S., Dedene, G., & Derrig, R. A. (2005). Auto claim fraud detection using bayesian learning neural networks. Expert Systems with Applications, 29(3), 653-666.
Welch, O. J., Reeves, T. E., & Welch, S. T. (1998). Neural network model: Bid pricing fraud. The Journal of Computer Information Systems, 38(3), 99-104.
Yen, E. C. (2007). Warning signals for potential accounting in blue chip companies - An application of adaptive resonance theory. Information Sciences, 177(20), 4515-4525.
Zhang, G., Patuwo, B. E., & Hu, M. Y. (1998). Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting, 14(1), 35-62.