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題名 利用類神經網路在台灣認購權証評價模式錯價之探討
作者 文德蘭
Wen, Te-Lan
貢獻者 蔡瑞煌<br>陳松男
文德蘭
Wen, Te-Lan
關鍵詞 類神經網路
認購權證
Neural Network
Warrant
Misprice
日期 1998
上傳時間 18-Sep-2009 19:33:11 (UTC+8)
摘要 本研究為利用人工類神經網路探討台灣認購權証之評價模式,利用人工類神經網路學習台灣認購權証之市場價格與Black-Scholes Model 的理論價格間的差異(錯價),而類神經網路在樣本內與外的表現均優於一般傳統的評價方式。此外,亦以敏感度分析來分析認購權証之市場價格與Black-Scholes Model 的理論價格間的差異可能的影響變數。
      本研究的樣本期間為包括民國八十六年九月至民國八十八年二月,樣本資料包含每日權証價格及交易量,每日權証標的股價格及每日大盤指數。檢視目前的權証之價錯,經實証後確實發現以類神經網路可以建立一個較為有效評價模式。
參考文獻 1. Aan, peter W. (1985), “How RSI Behaves”, The Magazine of Commodities & Option, v14n1, Jan p76
2. Becker (1980), “The Constant Elasticity of Variance Model and Its Implications for Option Pricing”, Journal of Finance 35 661-673
3. Black,F.,and M.Scholes (1973),"The Pricing of Warrants and Corporate Liabiltes", Journal of Political Economy,81,637-659
4. Brock, W.A., D.A. Hsieh, and B. Lebaron. (1991),”Nonlinear Dynamics, Chaos, and Instability: Statistical Theory and Economic Evidence”, Cambridge, MA:MIT press
5. Bollerslev, T. (1987), “A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return”, 59, 542-547
6. Bollerslev, T. (1986), “Generalized Autoregressive Conditional heteroskedastic-ity.” Journal of Eronometrics,31, 307-327
7. Bollerslev, T.,R.Y. Chou and K.F. Kroner. “ARCH Modeling in Finance: A Re-view of the Theory and Empirical Evidence,” Journal of Econometrics,52 5-60
8. Choi & Shastri (1989), “Bid-Ask and Volatility Estimates:The Implication for Option Pricing”, Journal of Banking and Finance 207-219
9. Cornell,B., (1990) “Volume and R2. A first look”, Journal of Financial Research 14, 1-6
10. Cox & Ross (1975),”The Valuation of Option for Alternative Stochastic Process”, Journal of financial Economics 3 145-166
11. D.Michael Long,Dennis T. Officer (1997),”The Relation Between Option Missing and Volume in the Black-Scholes Option Model”, Journal of Financial Research Volxx,No1 1-12
12. Engle, R.F. (1982) “Autoregressive Conditional Heteroscedasticity with Esti-mates of Variance of Unites Kingdom Inflation”. Econometrica 50,987 - 1007
13. Ferru,M.G.,S.B.Moore, and D.C. Schirm (1988),”Investor Expectations about Callable Warrants”,Journal of Portfolio Management, vol14 84-87
14. Hull,J., and A. White, (1987) “The Pricing of Options on Assets with Stochastic Volatilites.” Journal of Finance,2 281-300
15. Jarvis, C.H. and Atuart, N. (1996), “The sensitivity of a neural network for classi-fying remotely sensed imagery”, Computers and Geosciences, v. 22, pp. 959-967
16. Karpoff (1987), “The Relation Between Price Changes and Trading Volume A Survey”, Journal of Financial and Quantitative Analysis 109-126
17. Kon, S.J., (1984),”Models of stock returns—A comparison”, Journal of Finance 39, 147-65
18. Kremer Ronefeldt (1993), “Warrant Pricing:Jump Diffusion v.s. Black-Scholes”, Journal of Finance and Quantitative Analysis 255-272
19. Lantane,H.and R.Renefeldt (1976),”Standard Deviation of Stock Price Ratios Implied in Option Price”, Journal of Finance 369-381
20. Lauterbach & Schultz (1990),”Pricing Warrant:An Empirical study of the Black-Scholes Model and Its Alternative”, Journal of Finance 1181-1209
21. MacBeth & Merville (1979),”An Empirical Investingation of Black-Scholes Call Option Model”, Journal of Finance 1173-1186
22. Merton,R.C, (1976),”The Imact on Option Pricing of Specification Error in the Underlying Stock Price Returns”, Journal of Financial Economics vol33,331-350
23. Michael, A.E. (1994), Neural Network Time Series Forecasting of Financial Market. Naimimohasses, R., Barnett, D.M., Green, D.A., and Smith, P.R. (1995), “Sensor optimization using neural network sensitivity measures,” Measurement Science & Technology, pp. 1291-1300
24. Noreen, E. and M.Wolfson (1981),”Equilibrium Warrant Pricing Model and Ac-counting for Executive Stock Option”, Journal of accounting Research,vol19 384-398
25. Press,J.S. (1981),”A Compound Events Model of Security Prices”, Journal of Business ,317-335
26. Robin (1993),”On Improving the Performance of the Market Model”, Journal of Financial Research 367-376
27. Roll,R. (1977),”An Analytic Formula for Unprotected American Call Options on Stocks with Known Dividends”, Journal of Financial Economics vol 5 251-258
28. Rumelhart, D.E., Hinton, G.E., and Williams, R. (1986), “Learning internal repre-sentation by error propagation”, Parallel Distributed Processing, Cambridge, MA: MIT Press, Vol. 1, pp. 318-362
29. Sarkar, D. (1995),”Methods to speed up error back-propagation learning algo-rithm”, ACM Computer Surveys, Vol. 27, No. 4, pp. 519-542.
30. Tauchen & Pitts (1983),”The Price Variability-Volume Relationship on Specula-tive Market”, Econometrica 485-505
31. Tauchen & Pitts (1983),”The Price Variability-Volume Relationship on Specula-tive Market”, Econometrica 485-505
32. Tsaih, R. (1997),”Reasoning network networks”, In Ellacott, S., J. Mason and Anderson, I. (Eds.), Mathematics of Neural Networks: Models, Algorithms and Applications, Kluwer Academic Publishers, London, pp. 366-371
33. Tsaih, R., W. Chen and Y. Lin (1998),”Application of reasoning neural networks to financial swaps”, Journal of Computational Intelligence in Finance, Vol. 6, No. 3, pp. 27-37
34. Whaley (1981),”On the Valuation of American Call Option Stock with Known Dividends”, Journal of Financial Economics 9 207-211
35. Yoon, Y., Guimaraes, T. and G. Swales, (1994),”Integration artificial neural net-works with rule-based expert system”, Decision Support Systems, 11, pp. 497-507
36. Yoon, Y., Swales, G., and Margavio, T.M. (1993), “A comparison of discriminant analysis versus artificial neural networks,” Journal of Operational Research Soci-ety, 44, pp. 51-60.
描述 碩士
國立政治大學
資訊管理研究所
86356025
87
資料來源 http://thesis.lib.nccu.edu.tw/record/#B2002001651
資料類型 thesis
dc.contributor.advisor 蔡瑞煌<br>陳松男zh_TW
dc.contributor.author (Authors) 文德蘭zh_TW
dc.contributor.author (Authors) Wen, Te-Lanen_US
dc.creator (作者) 文德蘭zh_TW
dc.creator (作者) Wen, Te-Lanen_US
dc.date (日期) 1998en_US
dc.date.accessioned 18-Sep-2009 19:33:11 (UTC+8)-
dc.date.available 18-Sep-2009 19:33:11 (UTC+8)-
dc.date.issued (上傳時間) 18-Sep-2009 19:33:11 (UTC+8)-
dc.identifier (Other Identifiers) B2002001651en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/36769-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理研究所zh_TW
dc.description (描述) 86356025zh_TW
dc.description (描述) 87zh_TW
dc.description.abstract (摘要) 本研究為利用人工類神經網路探討台灣認購權証之評價模式,利用人工類神經網路學習台灣認購權証之市場價格與Black-Scholes Model 的理論價格間的差異(錯價),而類神經網路在樣本內與外的表現均優於一般傳統的評價方式。此外,亦以敏感度分析來分析認購權証之市場價格與Black-Scholes Model 的理論價格間的差異可能的影響變數。
      本研究的樣本期間為包括民國八十六年九月至民國八十八年二月,樣本資料包含每日權証價格及交易量,每日權証標的股價格及每日大盤指數。檢視目前的權証之價錯,經實証後確實發現以類神經網路可以建立一個較為有效評價模式。
zh_TW
dc.description.tableofcontents CHAPTER 1 INTRODUCTION
     CHAPTER 2 LITERATURE REVIEW
     2.1 BLACK-SCHOLES PRICING MODEL
     2.2 THE WARRANT PRICING WITH BLACK-SCHOLES MODEL
     2.2.1 About riskless interest rate, dividend and expend
     2.2.2 About price-volume
     2.2.3 About Volatility
     2.3. BACK PROPAGATION NEURAL NETWORKS
     2.4. SENSITIVITY ANALYSIS
     2.5. GARCH(1,1)
     CHAPTER 3 METHODOLOGY
     3.1. EXPERIMENT DESIGNS
     3.2. PREDICTION MODELS
     3.2.1 The regression model
     3.2.2. The Neural Network model
     3.2.3 The GARCH(1,1) model
     3.3 THE ERROR MEASUREMENT
     CHAPTER 4 EMPIRICAL RESULTS
     4.1. THE PERFORMANCE ON EXP_1
     4.2 THE PERFORMANCE OF EXP_2
     4.3 THE PERFORMANCE OF EXP_3
     4.2 SENSITIVITY ANALYSIS
     CHAPTER 5 SUMMARY
     5.1. DISCUSSIONS FROM THE SIMULATIONS AND FORECASTS
     5.2. LIMITATION AND FUTURE WORK
     REFERENCE
     APPENDIX A
     APPENDIX B
zh_TW
dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#B2002001651en_US
dc.subject (關鍵詞) 類神經網路zh_TW
dc.subject (關鍵詞) 認購權證zh_TW
dc.subject (關鍵詞) Neural Networken_US
dc.subject (關鍵詞) Warranten_US
dc.subject (關鍵詞) Mispriceen_US
dc.title (題名) 利用類神經網路在台灣認購權証評價模式錯價之探討zh_TW
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) 1. Aan, peter W. (1985), “How RSI Behaves”, The Magazine of Commodities & Option, v14n1, Jan p76zh_TW
dc.relation.reference (參考文獻) 2. Becker (1980), “The Constant Elasticity of Variance Model and Its Implications for Option Pricing”, Journal of Finance 35 661-673zh_TW
dc.relation.reference (參考文獻) 3. Black,F.,and M.Scholes (1973),"The Pricing of Warrants and Corporate Liabiltes", Journal of Political Economy,81,637-659zh_TW
dc.relation.reference (參考文獻) 4. Brock, W.A., D.A. Hsieh, and B. Lebaron. (1991),”Nonlinear Dynamics, Chaos, and Instability: Statistical Theory and Economic Evidence”, Cambridge, MA:MIT presszh_TW
dc.relation.reference (參考文獻) 5. Bollerslev, T. (1987), “A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return”, 59, 542-547zh_TW
dc.relation.reference (參考文獻) 6. Bollerslev, T. (1986), “Generalized Autoregressive Conditional heteroskedastic-ity.” Journal of Eronometrics,31, 307-327zh_TW
dc.relation.reference (參考文獻) 7. Bollerslev, T.,R.Y. Chou and K.F. Kroner. “ARCH Modeling in Finance: A Re-view of the Theory and Empirical Evidence,” Journal of Econometrics,52 5-60zh_TW
dc.relation.reference (參考文獻) 8. Choi & Shastri (1989), “Bid-Ask and Volatility Estimates:The Implication for Option Pricing”, Journal of Banking and Finance 207-219zh_TW
dc.relation.reference (參考文獻) 9. Cornell,B., (1990) “Volume and R2. A first look”, Journal of Financial Research 14, 1-6zh_TW
dc.relation.reference (參考文獻) 10. Cox & Ross (1975),”The Valuation of Option for Alternative Stochastic Process”, Journal of financial Economics 3 145-166zh_TW
dc.relation.reference (參考文獻) 11. D.Michael Long,Dennis T. Officer (1997),”The Relation Between Option Missing and Volume in the Black-Scholes Option Model”, Journal of Financial Research Volxx,No1 1-12zh_TW
dc.relation.reference (參考文獻) 12. Engle, R.F. (1982) “Autoregressive Conditional Heteroscedasticity with Esti-mates of Variance of Unites Kingdom Inflation”. Econometrica 50,987 - 1007zh_TW
dc.relation.reference (參考文獻) 13. Ferru,M.G.,S.B.Moore, and D.C. Schirm (1988),”Investor Expectations about Callable Warrants”,Journal of Portfolio Management, vol14 84-87zh_TW
dc.relation.reference (參考文獻) 14. Hull,J., and A. White, (1987) “The Pricing of Options on Assets with Stochastic Volatilites.” Journal of Finance,2 281-300zh_TW
dc.relation.reference (參考文獻) 15. Jarvis, C.H. and Atuart, N. (1996), “The sensitivity of a neural network for classi-fying remotely sensed imagery”, Computers and Geosciences, v. 22, pp. 959-967zh_TW
dc.relation.reference (參考文獻) 16. Karpoff (1987), “The Relation Between Price Changes and Trading Volume A Survey”, Journal of Financial and Quantitative Analysis 109-126zh_TW
dc.relation.reference (參考文獻) 17. Kon, S.J., (1984),”Models of stock returns—A comparison”, Journal of Finance 39, 147-65zh_TW
dc.relation.reference (參考文獻) 18. Kremer Ronefeldt (1993), “Warrant Pricing:Jump Diffusion v.s. Black-Scholes”, Journal of Finance and Quantitative Analysis 255-272zh_TW
dc.relation.reference (參考文獻) 19. Lantane,H.and R.Renefeldt (1976),”Standard Deviation of Stock Price Ratios Implied in Option Price”, Journal of Finance 369-381zh_TW
dc.relation.reference (參考文獻) 20. Lauterbach & Schultz (1990),”Pricing Warrant:An Empirical study of the Black-Scholes Model and Its Alternative”, Journal of Finance 1181-1209zh_TW
dc.relation.reference (參考文獻) 21. MacBeth & Merville (1979),”An Empirical Investingation of Black-Scholes Call Option Model”, Journal of Finance 1173-1186zh_TW
dc.relation.reference (參考文獻) 22. Merton,R.C, (1976),”The Imact on Option Pricing of Specification Error in the Underlying Stock Price Returns”, Journal of Financial Economics vol33,331-350zh_TW
dc.relation.reference (參考文獻) 23. Michael, A.E. (1994), Neural Network Time Series Forecasting of Financial Market. Naimimohasses, R., Barnett, D.M., Green, D.A., and Smith, P.R. (1995), “Sensor optimization using neural network sensitivity measures,” Measurement Science & Technology, pp. 1291-1300zh_TW
dc.relation.reference (參考文獻) 24. Noreen, E. and M.Wolfson (1981),”Equilibrium Warrant Pricing Model and Ac-counting for Executive Stock Option”, Journal of accounting Research,vol19 384-398zh_TW
dc.relation.reference (參考文獻) 25. Press,J.S. (1981),”A Compound Events Model of Security Prices”, Journal of Business ,317-335zh_TW
dc.relation.reference (參考文獻) 26. Robin (1993),”On Improving the Performance of the Market Model”, Journal of Financial Research 367-376zh_TW
dc.relation.reference (參考文獻) 27. Roll,R. (1977),”An Analytic Formula for Unprotected American Call Options on Stocks with Known Dividends”, Journal of Financial Economics vol 5 251-258zh_TW
dc.relation.reference (參考文獻) 28. Rumelhart, D.E., Hinton, G.E., and Williams, R. (1986), “Learning internal repre-sentation by error propagation”, Parallel Distributed Processing, Cambridge, MA: MIT Press, Vol. 1, pp. 318-362zh_TW
dc.relation.reference (參考文獻) 29. Sarkar, D. (1995),”Methods to speed up error back-propagation learning algo-rithm”, ACM Computer Surveys, Vol. 27, No. 4, pp. 519-542.zh_TW
dc.relation.reference (參考文獻) 30. Tauchen & Pitts (1983),”The Price Variability-Volume Relationship on Specula-tive Market”, Econometrica 485-505zh_TW
dc.relation.reference (參考文獻) 31. Tauchen & Pitts (1983),”The Price Variability-Volume Relationship on Specula-tive Market”, Econometrica 485-505zh_TW
dc.relation.reference (參考文獻) 32. Tsaih, R. (1997),”Reasoning network networks”, In Ellacott, S., J. Mason and Anderson, I. (Eds.), Mathematics of Neural Networks: Models, Algorithms and Applications, Kluwer Academic Publishers, London, pp. 366-371zh_TW
dc.relation.reference (參考文獻) 33. Tsaih, R., W. Chen and Y. Lin (1998),”Application of reasoning neural networks to financial swaps”, Journal of Computational Intelligence in Finance, Vol. 6, No. 3, pp. 27-37zh_TW
dc.relation.reference (參考文獻) 34. Whaley (1981),”On the Valuation of American Call Option Stock with Known Dividends”, Journal of Financial Economics 9 207-211zh_TW
dc.relation.reference (參考文獻) 35. Yoon, Y., Guimaraes, T. and G. Swales, (1994),”Integration artificial neural net-works with rule-based expert system”, Decision Support Systems, 11, pp. 497-507zh_TW
dc.relation.reference (參考文獻) 36. Yoon, Y., Swales, G., and Margavio, T.M. (1993), “A comparison of discriminant analysis versus artificial neural networks,” Journal of Operational Research Soci-ety, 44, pp. 51-60.zh_TW