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題名 盈餘預測準確性之比較及決定性因素之實證研究--神經網路模型之應用
作者 洪振富
HONG, ZHEN-FU
貢獻者 吳安妮
LU, AN-NI
洪振富
HONG, ZHEN-FU
關鍵詞 神經網路
盈餘預測
會計
日期 1993
1992
上傳時間 2-May-2016 15:15:39 (UTC+8)
摘要 本研究之目的在嘗試將神經網路模型應用於會計盈餘預測。盈餘預測資訊可用來評估公司之獲利能力及真正價值，若能找出相當準確的盈餘預測模型，將有助於管理當局及投資大眾從事各種投資及理財決策。過去許多研究試圖以各種計量方法來找出較準確的盈餘預測模型，然因種種模型及統計上之限制，都未能獲致良好的結果。
參考文獻 一、中文部分
1 王鵲翔，以平行分散處理模式建立股市預測知識庫，國立臺灣大學商學研究所未出版碩士論文，民國七十九年
2 白晉榮，股價預測:專家系統給例學習法之研究，國立政治大學企業管理研究所未出版碩士論文，民國七十八年
3. 李順成譯，計量經濟學理論與應用(上，下冊) ，曉園出版社，民國七十八年
4. 林和譯，混沌:不測風雲的背後，天下文化出版，民國八十年
5. 林維婿，台灣上市公司盈餘預測:時間數列與公司預期之比較暨聯合效益分析，國立政治大學會計研究所未出版碩士論文，民國七十九年
6. 胡玉城，暢談類神經網路，倚天資訊公司，民國八十一年
7. 張維哲，人工神經網路，全欣資訊圖書公司，民國八十一年
8. 黃聰明，自動化知識擷取--神經網路於稅務查核之應用，國立臺灣大學商學研究所未出版碩士論文，民國八十一年。
9. 葉怡成，類神經網路模式應用與實作，儒林圖書公司，民國八十二年
10. 焦李成，神經網路系統理論，儒林圖書公司，民國八十年
11 陳燕慶及鹿浩，神經網路理論及其在控制工程中的應用，儒林圖書公司，民國八十一年
12 徐春美，期中報表預測能力之研究，國立政治大學會計研究所未出版碩士論文，民國六十七年
13.游萬淵，會計盈餘預測之準確性研究，國立政治大學會計研究所未出版碩士論文，民國七十八年
14.楊建民、張碧環、司怡平及蘇佩芬，在微平行電腦上發展類神經網路演算法以預測台灣股市行為，國科會研究計畫報告(NSC 81-0301 -H 000 4-18 ) 。
15.靳蕃、範俊波及譚永東，神經網路與神經計算機原理‧應用，民國八十一年。
16.鄭素鄉，我國上市公司季盈餘時間數列特性之研究，國立政治大學會計研究所未出版碩士論文，民國七十八年。
17.盧炳勳及曹登發合譯，類神經網理論與應用，全華科技圖書公司，民國八十一年。
二、英文部分
1 Albrecht, W.S., Lookabill, L.L. and McKeown, J.C., Time-series Properties of Annual Earnings, Journal of Accounting Research, 1977 , Vol. 15,pp. 226-244.
2 Anderson J.A., Pellionisz A. and Rosenfeld E. eds, Neurocomputing 2:Directions for Research, The MIT Press, 1990.
3. Bao, D.H., M.T. Lewis, W.T. Lin, J.G. Manegold, Applications of Time-Series Analysis in Accounting: A Review, Journal of Forecasting, 1983, Vol. 2, No.4, pp. 437-447 .
4. Blum Adam, Neural Networks in C++ - An Object-Orentied Framework for Building Connectionist Systems, John Wiley & Sons, 1992
5. Brocklebank, John C. and David A. Dickey, SAS System for Forecasting Time Series, SAS Institution Inc., 1986.
6. Brown, L.D. and Rozeff , M.S., Univariate Time-Series Models of Quarterly Accounting Earnings Per Share: A Proposed Model, Journal of Accounting Research, 1977, Vol. 15,179- 189.
7. Cadden, David T., Neural Networks and The Mathematics of Chaos - An Investigation of These Methodologies as Accurate Predictors of Corporate Bankruptcy, First International Confererce on Artifical Inteligence Applications on Wall Street, 1991, IEEE Computer Society Press.
8. California Scientific Software, BrainMaker User`s Guide and Reference Manual, California Scientific Software, 1990.
9. Caudill Maureen, The View form Now, AI Expert, June 1992, p24-31
10. Caudill Maureen and Charles Butler, Naturally Intelligent Systems, The MIT Press, 1990.
11 Crooks Ted, Care and Feeding of Neural Networks, AI Expert, July 1992, pp. 36-41
12 Cybenko, G., Continuous Valued Neural Networks with Two Hidden Layers Are Sufficient, Technical Report, 1988, Department of Computer Science, Tufts University.
13. Dopuch, N. and Watts, R., "Using Time-Series Models to Assess the Significance of Accounting Changes, Journal of Accounting Research , 1972,Vol. 10, pp. 180-194.
14. Fant, L. Franklin and Pamela K. Coats, A Neural Network Approach to Forecasting Finacial Distress, The Journal of Business Forecasting ,Winter 1991 -92, pp. 9- 12
15. Foster, G., Quarterly Accounting Data; Times-Series Properties and Predictive-Ability results, The Accounting Review, 1977, Vol. 52, pp . 1-21
16. Gorman, R.P. and T.J. Sejnowski, Learned Classification of Sonar Targets Using a Massively-Parallel Network, IEEE Transactions on Acoustics, Speech, and Signal Processing 36, 1988.
17. Griffin, P .A., The Time-Series Behavior of Quarterly Earings: Preliminary Evidence, Journal of Accounting Research, 1977, Vol. 15 ,71-83 .
18. Harmon Paul, Neural Networks: Hot Air or Hot Technology? Part I, Intelligent Software Strategies, April 1992, Vol. 8, No.4, pp. 1-12
19.-----Neural Networks: Hot Air or Hot Technology? Part II, Intelligent Software Strategies, May 1992, Vol. 8, No.5, pp. 1-18.
20.----- Neural Networks: Hot Air or Hot Technology? Part III, Intelligent Software Strategies, July 1992, Vol. 8, No.7, pp. 1-15.
21 Hawley, Delvin D., John D. Johnson and Dijjitam Raina, Arficial Neural Systems: A New Tool for Financial Decision-MAking, Financial Analysis Journal, Nov-DecI990, pp. 63-72
22 Hecht-Nielsen, R., Theory of the Backpropagation Neural Network, Proceeding of International Joint Conference on Neural Networks 1, 1989, IEEE Computer Society Press, pp. 593-611
23. HertZ John, Anders Krogh and Richard G. Planner, Introduction to the Thoery of Neural Computation, Addison-Wesley, 1991
24. Khanna Tarun, Foundations of Neural Networks, Addison-Wesley, 1990.
25. Kolmogorov, A. N., On the Representation of Continuous Functions of Many Variables by Superposition of Continuous Functions of One Variable and Addition, Dokl. Akad. Nauk, USSR 114, 953 -956.
26. Lapeds A. and R. Farber, Nonlinear Singal Processing Using Neural Networks: Prediction and System Modelling, Technical Report LA-UR -87-2662, 1987,Los Alamos National Laboratory.
27.-----How Neural Nets Work, Neural Information Systems, American Institute of Physics, 1987, pp. 442-456.
28. Leftwich, R. W. and R. L. Watts, The Time Series of Annual Accounting Earnings, Journal of Accounting Research, 1977, Vol. 15, 253-271
29. Lin, Frank C. and Mei Lin, Analysis of Financial Data Using Neural Nets, AI Expert, Feb. 1993, pp. 37-41
30. Marose, Robert A., A Financial Neural-Network Application, AI Expert, May 1990,pp. 50-53 .
31 Nelson, Marilyn McCord & W. T. Illingworth, A Practical Guide to Neural Nets, Addison-Wesley, 1991
32 Neural Ware Inc., Neural Computing, Neural Ware Inc., 1991
33. Rauch-Hindin, Wendy B., A Guide to Commercial Artificial Intelligence - Fundamentals and Real World Applications, Prectice-Hall, 1988.
34. Refenes A. N., Azema-Barac and P. C. Treleaven, Financial Modelling Using Neural Networks, in Liddell H. (ed) "Commercial Parallel Processing", Unicorn, 1993.
35. Ripley, B.D., Statistical Aspects of Neural Networks, in Chaos and Networks - Statistical and Probabilistic Aspects (edit by O.E. Barndorff-Nielsen, D.R. Cox, J.L. Jensen & W.S. kendall), Chapman & Hall, 1993, pp. 1-70.
36. Simpson, Patrick K., Artificial Neural Systems - Foundations, Paragigms, Applications, and Implementations, Pregamon Press, 1990.
37. Stanley Jeannette, Introduction to Neural Networks, California Scientific Software, 1990.
38. Stein Roger, Selecting Data for Neural Networks, AI Expert, Feb. 1993, pp. 42-47.
39. Preprocessing Data for Neural Networks, AI Expert, March 1993,pp. 32-37
40. Surkan, Alvin J. and 1 Clay Singleton, Neural Network Performance in Emulations of Professional Bond Rating Judgements, Proceeding of International Neural Networks Conference, 1990, IEEE Computer Society Press, p394.
41 Tang Zaiyong, Chrys de Almeida, Paul A. Fishwick, Times Series Forecasting Using Neural Networks vs. Box-Jenkins Methodology, Simulation, Nov. 1991,pp. 303 -31 0.
42 Utans Joachim and John Moody, Selecting Neural Network Architectures via the Predictiion Risk: Application to Corporate Bond Rating Prediction, First International Confererce on Artifical Inteligence Applications on Wall Street, 1991, IEEE Computer
Society Press.
43. Varfis A. and C. Versino, Univariate Economic Time Series Forecasting by Connectionist Methods, Proceeding of International Neural Networks Conference, 1990, IEEE Computer Society Press, pp . 342-345.
44. Wasserman, Philip D., Neural Computing - Theory and Practice, Van Nostrand Reinhold, 1989.
45. Watts, R.L., The Times-Series Behavior of Quarterly Earnings, Research Paper, New South Wales: Department of Commerce, Univerity Of Newcastle,1975.
48. Weiss, Sholom M. and Casimir A. Kulikowski, Computer Systems That Learn - Classfication and Prediction Methods from Statistics, Neural Nets, Machine Learning, and Expert Systems, Morgan Kaufmann Publishers, 1991
47. White, Halbert, Neural-Network Learning and Stastics, AI Expert, December 1989, pp. 48-52
48. Wong, Francis and PanYong Tan, Neural Networks and Genetic Algorithm for Economic Forecasting, Technical Report, 1992, Institute of Systems Science,National University of Singapore.
49. Wu Berlin, How to Use Neural Networks in Nonlinear Time Series Forecasting, The National Chengchi University Journal, Vo1 66.
50. Wu, Fred. Y. and Kang K. Yen, Applications of Neural Network in Regression Analysis, Computer and Industrial Engineering, 1992, Vol. 23, pp. 93-95.
描述 碩士
國立政治大學
會計學系
資料來源 http://thesis.lib.nccu.edu.tw/record/#B2002004334
資料類型 thesis
dc.contributor.advisor 吳安妮zh_TW
dc.contributor.advisor LU, AN-NIen_US
dc.contributor.author (Authors) 洪振富zh_TW
dc.contributor.author (Authors) HONG, ZHEN-FUen_US
dc.creator (作者) 洪振富zh_TW
dc.creator (作者) HONG, ZHEN-FUen_US
dc.date (日期) 1993en_US
dc.date (日期) 1992en_US
dc.date.accessioned 2-May-2016 15:15:39 (UTC+8)-
dc.date.available 2-May-2016 15:15:39 (UTC+8)-
dc.date.issued (上傳時間) 2-May-2016 15:15:39 (UTC+8)-
dc.identifier (Other Identifiers) B2002004334en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/89194-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 會計學系zh_TW
dc.description.abstract (摘要) 本研究之目的在嘗試將神經網路模型應用於會計盈餘預測。盈餘預測資訊可用來評估公司之獲利能力及真正價值，若能找出相當準確的盈餘預測模型，將有助於管理當局及投資大眾從事各種投資及理財決策。過去許多研究試圖以各種計量方法來找出較準確的盈餘預測模型，然因種種模型及統計上之限制，都未能獲致良好的結果。zh_TW
dc.description.tableofcontents 論文提要..........i
圖目次..........v
表目次..........vii
第一章 緒論..........1
第一節 研究動機..........1
第二節 研究問題..........3
第三節 研究方法..........3
第四節 研究貢獻..........4
第五節 論文架構..........4
第二節 神經網路簡介..........6
第一節 神經網路的意義..........6
第二節 神經網路的發展..........8
第三節 神經網路的特性..........11
第四節 神經網路的架構..........12
第五節 倒傳遞演算法..........16
第六節 神經網路與統計方法間的關係..........20
第七節 應用神經網路須注意的問題..........23
第三章 文獻探討..........26
第一節 盈餘預測準確性相關研究回顧..........26
第二節 神經網路與統計方法比較之研究..........28
第三節 研究的延申..........37
第四章 研究方法..........40
第一節 觀念性架構..........40
第二節 研究設計..........41
第三節 研究工具..........42
第四節 資料蒐集,43
第五節 變數衡量..........44
第六節 資料分析方法..........45
第五章 實證結果..........47
第一節 影響預測準確性的因素..........48
第二節 盈餘預測準確性的比較..........66
第三節 最佳模式的設定是否為通解..........68
第四節 結論與解釋..........69
第六章 結論與建議..........72
第一節 結論..........72
第二節 研究限制..........73
第三節 建議..........74
參考文獻..........76
附錄：神經網路的詳細結果..........82
..........		zh_TW
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#B2002004334en_US
dc.subject (關鍵詞) 神經網路zh_TW
dc.subject (關鍵詞) 盈餘預測zh_TW
dc.subject (關鍵詞) 會計zh_TW
dc.title (題名) 盈餘預測準確性之比較及決定性因素之實證研究--神經網路模型之應用zh_TW
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 一、中文部分
1 王鵲翔，以平行分散處理模式建立股市預測知識庫，國立臺灣大學商學研究所未出版碩士論文，民國七十九年
2 白晉榮，股價預測:專家系統給例學習法之研究，國立政治大學企業管理研究所未出版碩士論文，民國七十八年
3. 李順成譯，計量經濟學理論與應用(上，下冊) ，曉園出版社，民國七十八年
4. 林和譯，混沌:不測風雲的背後，天下文化出版，民國八十年
5. 林維婿，台灣上市公司盈餘預測:時間數列與公司預期之比較暨聯合效益分析，國立政治大學會計研究所未出版碩士論文，民國七十九年
6. 胡玉城，暢談類神經網路，倚天資訊公司，民國八十一年
7. 張維哲，人工神經網路，全欣資訊圖書公司，民國八十一年
8. 黃聰明，自動化知識擷取--神經網路於稅務查核之應用，國立臺灣大學商學研究所未出版碩士論文，民國八十一年。
9. 葉怡成，類神經網路模式應用與實作，儒林圖書公司，民國八十二年
10. 焦李成，神經網路系統理論，儒林圖書公司，民國八十年
11 陳燕慶及鹿浩，神經網路理論及其在控制工程中的應用，儒林圖書公司，民國八十一年
12 徐春美，期中報表預測能力之研究，國立政治大學會計研究所未出版碩士論文，民國六十七年
13.游萬淵，會計盈餘預測之準確性研究，國立政治大學會計研究所未出版碩士論文，民國七十八年
14.楊建民、張碧環、司怡平及蘇佩芬，在微平行電腦上發展類神經網路演算法以預測台灣股市行為，國科會研究計畫報告(NSC 81-0301 -H 000 4-18 ) 。
15.靳蕃、範俊波及譚永東，神經網路與神經計算機原理‧應用，民國八十一年。
16.鄭素鄉，我國上市公司季盈餘時間數列特性之研究，國立政治大學會計研究所未出版碩士論文，民國七十八年。
17.盧炳勳及曹登發合譯，類神經網理論與應用，全華科技圖書公司，民國八十一年。
二、英文部分
1 Albrecht, W.S., Lookabill, L.L. and McKeown, J.C., Time-series Properties of Annual Earnings, Journal of Accounting Research, 1977 , Vol. 15,pp. 226-244.
2 Anderson J.A., Pellionisz A. and Rosenfeld E. eds, Neurocomputing 2:Directions for Research, The MIT Press, 1990.
3. Bao, D.H., M.T. Lewis, W.T. Lin, J.G. Manegold, Applications of Time-Series Analysis in Accounting: A Review, Journal of Forecasting, 1983, Vol. 2, No.4, pp. 437-447 .
4. Blum Adam, Neural Networks in C++ - An Object-Orentied Framework for Building Connectionist Systems, John Wiley & Sons, 1992
5. Brocklebank, John C. and David A. Dickey, SAS System for Forecasting Time Series, SAS Institution Inc., 1986.
6. Brown, L.D. and Rozeff , M.S., Univariate Time-Series Models of Quarterly Accounting Earnings Per Share: A Proposed Model, Journal of Accounting Research, 1977, Vol. 15,179- 189.
7. Cadden, David T., Neural Networks and The Mathematics of Chaos - An Investigation of These Methodologies as Accurate Predictors of Corporate Bankruptcy, First International Confererce on Artifical Inteligence Applications on Wall Street, 1991, IEEE Computer Society Press.
8. California Scientific Software, BrainMaker User`s Guide and Reference Manual, California Scientific Software, 1990.
9. Caudill Maureen, The View form Now, AI Expert, June 1992, p24-31
10. Caudill Maureen and Charles Butler, Naturally Intelligent Systems, The MIT Press, 1990.
11 Crooks Ted, Care and Feeding of Neural Networks, AI Expert, July 1992, pp. 36-41
12 Cybenko, G., Continuous Valued Neural Networks with Two Hidden Layers Are Sufficient, Technical Report, 1988, Department of Computer Science, Tufts University.
13. Dopuch, N. and Watts, R., "Using Time-Series Models to Assess the Significance of Accounting Changes, Journal of Accounting Research , 1972,Vol. 10, pp. 180-194.
14. Fant, L. Franklin and Pamela K. Coats, A Neural Network Approach to Forecasting Finacial Distress, The Journal of Business Forecasting ,Winter 1991 -92, pp. 9- 12
15. Foster, G., Quarterly Accounting Data; Times-Series Properties and Predictive-Ability results, The Accounting Review, 1977, Vol. 52, pp . 1-21
16. Gorman, R.P. and T.J. Sejnowski, Learned Classification of Sonar Targets Using a Massively-Parallel Network, IEEE Transactions on Acoustics, Speech, and Signal Processing 36, 1988.
17. Griffin, P .A., The Time-Series Behavior of Quarterly Earings: Preliminary Evidence, Journal of Accounting Research, 1977, Vol. 15 ,71-83 .
18. Harmon Paul, Neural Networks: Hot Air or Hot Technology? Part I, Intelligent Software Strategies, April 1992, Vol. 8, No.4, pp. 1-12
19.-----Neural Networks: Hot Air or Hot Technology? Part II, Intelligent Software Strategies, May 1992, Vol. 8, No.5, pp. 1-18.
20.----- Neural Networks: Hot Air or Hot Technology? Part III, Intelligent Software Strategies, July 1992, Vol. 8, No.7, pp. 1-15.
21 Hawley, Delvin D., John D. Johnson and Dijjitam Raina, Arficial Neural Systems: A New Tool for Financial Decision-MAking, Financial Analysis Journal, Nov-DecI990, pp. 63-72
22 Hecht-Nielsen, R., Theory of the Backpropagation Neural Network, Proceeding of International Joint Conference on Neural Networks 1, 1989, IEEE Computer Society Press, pp. 593-611
23. HertZ John, Anders Krogh and Richard G. Planner, Introduction to the Thoery of Neural Computation, Addison-Wesley, 1991
24. Khanna Tarun, Foundations of Neural Networks, Addison-Wesley, 1990.
25. Kolmogorov, A. N., On the Representation of Continuous Functions of Many Variables by Superposition of Continuous Functions of One Variable and Addition, Dokl. Akad. Nauk, USSR 114, 953 -956.
26. Lapeds A. and R. Farber, Nonlinear Singal Processing Using Neural Networks: Prediction and System Modelling, Technical Report LA-UR -87-2662, 1987,Los Alamos National Laboratory.
27.-----How Neural Nets Work, Neural Information Systems, American Institute of Physics, 1987, pp. 442-456.
28. Leftwich, R. W. and R. L. Watts, The Time Series of Annual Accounting Earnings, Journal of Accounting Research, 1977, Vol. 15, 253-271
29. Lin, Frank C. and Mei Lin, Analysis of Financial Data Using Neural Nets, AI Expert, Feb. 1993, pp. 37-41
30. Marose, Robert A., A Financial Neural-Network Application, AI Expert, May 1990,pp. 50-53 .
31 Nelson, Marilyn McCord & W. T. Illingworth, A Practical Guide to Neural Nets, Addison-Wesley, 1991
32 Neural Ware Inc., Neural Computing, Neural Ware Inc., 1991
33. Rauch-Hindin, Wendy B., A Guide to Commercial Artificial Intelligence - Fundamentals and Real World Applications, Prectice-Hall, 1988.
34. Refenes A. N., Azema-Barac and P. C. Treleaven, Financial Modelling Using Neural Networks, in Liddell H. (ed) "Commercial Parallel Processing", Unicorn, 1993.
35. Ripley, B.D., Statistical Aspects of Neural Networks, in Chaos and Networks - Statistical and Probabilistic Aspects (edit by O.E. Barndorff-Nielsen, D.R. Cox, J.L. Jensen & W.S. kendall), Chapman & Hall, 1993, pp. 1-70.
36. Simpson, Patrick K., Artificial Neural Systems - Foundations, Paragigms, Applications, and Implementations, Pregamon Press, 1990.
37. Stanley Jeannette, Introduction to Neural Networks, California Scientific Software, 1990.
38. Stein Roger, Selecting Data for Neural Networks, AI Expert, Feb. 1993, pp. 42-47.
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