Please use this identifier to cite or link to this item:
|標題:||Constructing the Warning Prediction Models of Corporations
|關鍵字:||Warning Prediction Models;危機預警模式;Financial Ratio Analysis Corporate Governance;Red Flags;SAS No.82;SAS No.99;Association Analysis;Data mining Neuro-Fuzzy Expert System;財務比率分析;公司治理;舞弊風險要素;SAS No.82;SAS No.99;關聯分析;資料採礦法;類神經模糊專家系統||出版社:||企業管理學系所||引用:||吳孟達，1996，審計人員內部控制判斷行為研究－類神經網路之應用，國立台灣大學會計學研究所未出版碩士論文。 呂素嬌，2001，企業財務危機與監察人結構特性之研究，國立台北大學企業管理學系碩士在職專班未出版碩士論文。 林金賢、劉沂佩、鄭育書與陳育成，2004，具學習性之模糊專家系統在財務危機預測上之應用，管理學報，第21卷第3期：291-309。 林昱成、林金賢、陳雪如與莊家豪，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，第44期：95-126。 林柄滄，1997，如何避免審計失敗，台北：作者自行出版。 林家靜，2004，公司治理、盈餘管理與投資人報酬之關連性研究，國立政治大學會計研究所未出版碩士論文。 陳文彬，2002，2001年臺灣上市上櫃公司內部高層權責劃分及內部控制狀況研析－公司治理與上市上櫃公司內部控制之探討（上），實用月刊，第326期：92-97。 陳文彬，2002，2001年臺灣上市上櫃公司內部高層權責劃分及內部控制狀況研析－公司治理與上市上櫃公司內部控制之探討（下），實用月刊，第327期：69-74。 彭金堂、張盛鴻、簡禎富與楊景晴，2005，建構關聯規則資料挖礦架構及其在台電配電事故定位之研究，資訊管理學報，第12卷第4期：121-141。 黃金火，1990，專家系統應用於審計程式規劃，國立成功大學會計研究所未出版碩士論文。 謝文馨，1998，家族企業管治機制與盈餘管理之關聯性研究，東吳大學會計學系未出版碩士論文。 簡禎富、黃三倍與翁德誠，2001，台電契約容量決策分析及某半導體廠之實證研究，管理研究學報，第1卷第1期：53-68。 Alexander, C. R., and M. A. Cohen. 1999. Why do corporations become criminals? Ownership, hidden actions, and crime as an agency cost. Journal of Corporate Finance 5:1-34. Altman, E. I. 1968. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance 23 (4):589-609. Altman, E. I., G. Marco, and F. Varetto. 1994. Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience). Journal of Banking & Finance 18 (3):505-529. Apostolou, B. A., J. M. Hassell, S. A. Webber, and G. E. Sumners. 2001. The relative importance of management fraud risk factors. Behavioral Research in Accounting 13:1-24. Ayers, S., and S. E. Kaplan. 1998. Potential differences between engagement and risk review partners and their effect on client acceptance judgments. Accounting Horizons 12 (2):139-153. Beasley, M. S. 1996. An empirical analysis of the relation between the board of director composition and financial statement fraud. The Accounting Review 71 (4):443-465. Beaver, W. H. 1966. Financial ratios as predictors of failure. Journal of Accounting Research 4:71-111. ———. 1968. Alternative accounting measures as predictors of failure. The Accounting Review 43 (1):113-122. Bell, T., S. Szykowny, and J. Willingham. 1993. Development of a decision aid for assessing the likelihood of fraudulent financial reporting. KPMB Peat Marwick. Bell, T. B., and J. V. Carcello. 2000. A decision aid for assessing the likelihood of fraudulent financial reporting. Auditing: A Journal of Practice & Theory 19 (1):169-184. Berardi, V. L., and G. P. Zhang. 1999. The effect of misclassification costs on neural network classifiers. Decision Sciences 30 (3):659-682. Berry, M., and G. Linoff. 1997. Data mining techniques for marketing, sales and customer support. New York: John Wiley and Sons. Boritz, J. E., and D. B. Kennedy. 1995. Effectiveness of neural network types for prediction of business failure. Expert Systems with Applications 9 (4):503-512. Brickley, J. A., R. C. Lease, and C. W. Smith. 1988. Ownership structure and voting on antitakeover amendments. Journal of Financial Economics 20:267-291. Calderon, T. G. 1999. Neural networks and preliminary information risk assessment in an auditing environment. Accounting Enquiries 8 (2):245-297. Calderon, T. G., and J. J. Cheh. 2002. A roadmap for future neural networks research in auditing and risk assessment. International Journal of Accounting Information Systems 3 (4):203-236. Coakley, J. R., and C. E. Brown. 1993. Artificial neural networks applied to ratio analysis in the analytical review process. International Journal of Intelligent Systems in Accounting, Finance and Management 2 (1):19-39. Coats, P. K., and L. F. Fant. 1993. Recognizing financial distress patterns using a neural network tool. Financial Management 22 (3):142-155. Colbert, J. L., M. S. Luehlfing, and C. W. Alderman. 1996. Engagement risk. The CPA Journal 66 (3):54-56. Daily, C. M., and D. R. Dalton. 1994. Corporate governance and the bankrupt firm: An empirical assessment. Strategic Management Journal 15 (8):643-654. Dalton, D. R., and I. F. Kesner. 1987. Composition and CEO duality in boards of directors: an international perspective. Journal of International Business Studies 18 (3):33-42. Davalos, S., R. D. Gritta, and G. Chow. 1999. The application of a neural network approach to predicting bankruptcy risks facing the major US air carriers: 1979-1996. Journal of Air Transport Management 5 (2):81-86. Davis, J. T. 1996. Experience and auditors'' selection of relevant information for preliminary control risk assessments. Auditing: A Journal of Practice & Theory 15 (1):16-37. Dechow, P. M., R. G. Sloan, and A. P. Sweeney. 1996. Causes and consequences of earnings manipulations: An analysis of firms subject to enforcement actions by the SEC. Contemporary Accounting Research 13 (1):1-36. Deshmukh, A., and J. Romine. 1996. Assessing the risk of management fraud using red flags: a fuzzy number based spreadsheet approach. Journal of Accounting and Computers 4 (3):5-15. Deshmukh, A., and L. Talluru. 1998. A rule-based fuzzy reasoning system for assessing the risk of management fraud. International Journal of Intelligent Systems in Accounting, Finance and Management 7 (4):223-241. Füerst, O., and S.-H. Kang. 2004. Corporate governance, expected operating performance, and pricing. Corporate Ownership & Control 1 (2):13-30. Fama, E. F. 1980. Agency problems and the theory of the firm. The Journal of Political Economy 88 (2):288-307. Fama, E. F., and M. C. Jensen. 1983. Separation of ownership and control. Journal of Law and Economics 26 (2):301-325. Fanning, K., K. Cogger, and R. Srivastava. 1995. Detection of management fraud: a neural network approach. International Journal of Intelligent Systems in Accounting, Finance and Management 4 (2):113-126. Feroz, E. H., T. M. Kwon, V. S. Pastena, and K. Park. 2000. The efficacy of red flags in predicting the SEC''s targets: an artificial neural networks approach. International Journal of Intelligent Systems in Accounting, Finance & Management 9 (3):145-157. Gray, G., T. McKee, and T. Mock. 1991. The future impact of expert systems and decision support systems on auditing. Advances in Accounting 9:249-273. Hansen, J. V., J. B. McDonald, W. F. Messier, Jr., and T. B. Bell. 1996. A generalized qualitative-response model and the analysis of management fraud. Management Science 42 (7):1022-1032. Hilzenrath, D. S. 2001. Auditor Hints of ''Illegal Acts'' at Enron: Arthur Andersen CEO also says his firm made judgment error. Washington Post, December 13, 2001, E01. Huss, H. F., and F. A. Jacobs. 1991. Risk containment: exploring auditor decisions in the engagement process. Auditing: A Journal of Practice & Theory 10 (2):16-32. Jensen, M. C., and W. H. Meckling. 1976. Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics 3 (4):305-360. Jo, H., I. Han, and H. Lee. 1997. Bankruptcy prediction using case-based reasoning, neural networks, and discriminant analysis. Expert Systems with Applications 13 (2):97-108. Johnstone, K. M. 2000. Client-acceptance decisions: simultaneous effects of client business risk, audit risk, auditor business risk, and risk adaptation. Auditing: A Journal of Practice & Theory 19 (1):1-25. Kahya, E., and P. Theodossiou. 1999. Predicting corporate financial distress: a time-series CUSUM methodology. Review of Quantitative Finance and Accounting 13 (4):323-345. Kane, G. D., and F. M. Richardson. 2002. The relationship between changes in fixed plant investment and the likelihood of emergence from corporate financial distress. Review of Quantitative Finance and Accounting 18 (3):259-272. Kaplan, S., and P. M. J. Reckers. 1995. Auditor''s reporting decisions for accounting estimates: The effect of assessments of the risk of fraudulent financial reporting. Managerial Auditing Journal 10 (5):27-36. Koh, H. C., and S. S. Tan. 1999. A neural network approach to the prediction of going concern status. Accounting & Business Research 29 (3):211-216. Kreutzfeldt, R. W., and W. A. Wallace. 1986. Error characteristics in audit populations: their profile and relationship to environmental factors. Auditing: A Journal of Practice & Theory 6 (1):20-43. Kumar, N., R. Krovi, and B. Rajagopala. 1997. Financial decision support with hybrid genetic and neural based modeling tools. European Journal of Operational Research 103 (2):339-349. La Porta, R., F. Lopez-de-Silanes, and A. Shleifer. 1999. Corporate ownership around the world. The Journal of Finance 54 (2):471-517. Lee, T.-S., and Y.-H. Yeh. 2004. Corporate governance and financial distress: evidence from Taiwan. Corporate Governance 12 (3):378-388. Leech, S. A., and A. J. A. Sangster. 2002. Expert systems in accounting research: a design science perspective. In Researching Accounting as an Information Systems Discipline, edited by V. Arnold and S. Sutton: American Accounting Association(Sarasota), 65–79. Lenard, M. J., P. Alam, and D. Booth. 2000. An analysis of fuzzy clustering and a hybrid model for the auditor''s going concern assessment. Decision Sciences 31 (4):861-884. Lenard, M. J., P. Alam, and G. R. Madey. 1995. The application of neural networks and a qualitative response model to the auditor''s going concern uncertainty decision. Decision Sciences 26 (2):209-227. Lipton, M., and J. W. Lorsch. 1992. A modest proposal for improved corporate governance. The Business Lawyer 48 (1):59-77. Loebbecke, J. K., M. M. Eining, and J. J. Willingham. 1989. Auditors'' experience with material irregularities: frequency, nature, and detectability. Auditing: A Journal of Practice & Theory 9 (1):1-28. Loebbecke, J. K., and J. J. Willingham. 1988. Review of SEC accounting and auditing enforcement releases. University of Utah. Lord, A. T., and G. D. Zeune. 1998. The impact of the new Fraud Standard on changes in the audit practices of local CPA firms. Pennsylvania CPA Journal 68 (4):32-37. McMullen, D. A. 1996. Audit committee performance: an investigation of the consequences associated with audit committees. Auditing: A Journal of Practice & Theory 15 (1):87-103. Odom, M. D., and R. Sharda. 1990. A neural network model for bankruptcy prediction. Paper read at International Joint Conference on Neural Networks, at San Diego. Ohlson, J. A. 1980. Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research 18 (1):109-131. Osyk, B. A., and B. S. Vijayaraman. 1995. Integrating expert systems and neural nets exploring the boundaries of AI. Information Systems Management 12 (2):47-54. Oviatt, B. M. 1988. Agency and transaction cost perspectives on the managers-shareholder relationship: incentives for congruent interests. Academy of Management Review 13 (2):214-225. Patton, A., and J. C. Baker. 1987. Why won''t directors rock the boat? Harvard Business Review 65 (6):10-14. Pound, J. 1988. Proxy contests and the efficiency of shareholder oversight. Journal of Financial Economics 20 (1,2):237-265. Ramamoorti, S., J. Andrew D. Bailey, and R. O. Traver. 1999. Risk assessment in internal auditing: a neural network approach. International Journal of Intelligent Systems in Accounting, Finance and Management 8 (3):159-180. Rechner, P. L., and D. R. Dalton. 1991. CEO duality and organizational performance: a longitudinal analysis. Strategic Management Journal 12 (2):155-160. Romney, M. B., W. S. Albrecht, and D. J. Cherrington. 1980. Auditors and the detection of fraud. Journal of Accountancy 149 (5):63-69. Salchenberger, L. M., E. M. Cinar, and N. A. Lash. 1992. Neural networks: a new tool for predicting thrift failures. Decision Sciences 23 (4):899-916. Sharda, R., and R. Wilson. 1996. Neural network experiments in business failures prediction: a review of predictive performance issues. Journal of Computational Intelligence and Organizations 1 (2):107-117. Shleifer, A., and R. W. Vishny. 1986. Large shareholders and corporate control. The Journal of Political Economy 94 (3):461-488. Sorenson, J. E., H. D. Grove, and F. H. Selto. 1983. Detecting management fraud: an empirical approach. Paper read at Symposium on Auditing Research, at Department of Accountancy, University of Illinois. Sutton, S. G., R. Young, and P. Mckenzie. 1995. An analysis of potential legal liability incurred through audit expert systems. International Journal of Intelligent Systems in Accounting, Finance and Management 4 (4):191-204. Tam, K. Y., and M. Y. Kiang. 1992. Managerial applications of neural networks: the case of bank failure predictions. Management Science 38 (7):926-947. Tong, R. M., and P. P. Bonissone. 1984. Linguistic solutions to fuzzy decision problem in Zimmermann. In Fuzzy sets and Decision Analysis, edited by L. A. Zadeh and B. R. Gaines. Tung, A. K. H., L. Hongjun, H. Jiawei, and F. Ling. 2003. Efficient mining of intertransaction association rules. Transactions on Knowledge and Data Engineering 15 (1):43-56. Turetsky, H. F., and R. A. McEwen. 2001. An empirical investigation of firm longevity: a model of the ex ante predictors of financial distress. Review of Quantitative Finance and Accounting 16 (4):323-343. Von Altrock, C. 1996. Fuzzy logic and neuroFuzzy applications in business and finance. New Jersey: Prentice Hall. Wilkins, M. S. 1997. Technical default, auditors'' decisions and future financial distress. Accounting Horizons 11 (4):40-48. Wilson, R., and R. Sharda. 1994. Bankruptcy prediction using neural networks. Decision Support Systems 11 (5):545-557. Wright, D. W. 1996. Evidence on the relation between corporate governance characteristics and the quality of financial reporting: University of Michigan. Wu, R. C.-F. 1994. Integrating neurocomputing and auditing expertise. Management Audit Journal 9 (3):20-26. Yeh, Y.-h., T.-s. Lee, and T. Woidtke. 2001. Family control and corporate governance: evidence from Taiwan. International Review of Finance 2 (1/2):21-48. Yoon, Y., T. Guimaraes, and G. Swales. 1994. Integrating artificial neural networks with rule-based expert systems. Decision Support Systems 11 (5):497-507. Zmijewski, M. E., and J. R. Dietrich. 1984. Methodological issues related to the estimation of financial distress prediction models/discussion. Journal of Accounting Research 22:59-82.||摘要:||
This thesis consists of three essays related to the problems a company may encounter: financial distress, litigation, and fraud. These three issues have been discussed for a long time in financial and accounting field. Due to its importance for both the academia and practice, three new tries are made in hope to provide new insights into this area in this thesis.
In the first part, instead of constructing a warning system to predict the possibility of financial distress, association analysis is adopted to explore how the financial ratios are deteriorated, and how the relationships among the financial ratios are changed before the financial distress. Different from most of the other studies related to financial distress to predict the financial distress based on the cross sectional data, this research is trying to provide some guidelines for a corporation to avoid a financial distress from a longitudinal point of view.
In the second part, a litigation warning model is constructed based on the governance factors by using neuron fuzzy which is a hybrid technique combining the functionality of fuzzy logic and the learning ability of neural network. In this paper, a comparison is made between the traditional statistical technique, logic, and the proposed technique, neuro fuzzy. In addition to providing a litigation warning model with higher accuracy, the proposed neuron fuzzy model can also provide the auditor the knowledge about the relationship among the variables obtained from the past data.
In the third part, a comparison is made between the prediction results based on the different categorization of the risk factors according to SAS No. 82 and SAS No. 99. With the empirical results, this research is trying to show what the difference is between these two statements, which has not been discussed in the past. These results can be referred to before a new statement is advanced in the future.
在第三部份，本文比較舞弊風險要素在SAS No.82與SAS No.99不同分類下的預測結果。並嘗試藉由實證結果來闡訴過去研究未曾探討的公報間優劣之比較，此結果將有助於未來新公報擬訂時之參酌。
|Appears in Collections:||企業管理學系所|
Show full item record
TAIR Related Article
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.