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題名 人工股票市場的Agent-Based計算建模
On Agent-Based Computational Modeling of Artificial Stock Markets作者 廖崇智
Liao, Chung-Chih貢獻者 陳樹衡
Chen, Shu-Heng
廖崇智
Liao, Chung-Chih關鍵詞 人工股票市場
agent-based計算經濟學
複雜適應系統
遺傳規劃
演化經濟學
突現性質
社會模擬
人工經濟生命
artificial stock market
agent-based computational economics
complex adaptive system
genetic programming
evolutionary economics
emergent property
social simulation
artificial economic life日期 1999 上傳時間 26-Apr-2016 10:25:15 (UTC+8) 摘要 我們把經濟體視為一個複雜適應系統(complex adaptive system), 強調系統中異質性(heterogeneous)agent的學習適應行為與agent之間的互動性交互作用, 此時主流經濟學裡的分析架構, 如:代表性個人模型(represesentive agent model)、理性預期(rational expectation)、固定點均衡分析(fixed-point equilibrium analysis)等將不再適用, 取而代之的是演化經濟學(evolutionary economics)的研究典範, 這樣的研究架構下, 並沒有適當的數學分析工具可資運用, 因此我們改以agent-based建模(agent-based modelng)的社會模擬(social simulation)來建構一個人工的經濟體(artificial economy), 以此為主要研究方法, 這就是agent-based計算經濟學(agent-based computational economics)或稱人工經濟生命(artificial economic life)。 本文中以股票市場為主要的研究課題, 我們以遺傳規劃(genetic programming)的人工智慧(artificial intelligence)方法來模擬股市中有限理性(bounded rational)異質交易者的交易策略學習行為, 建構出一個人工股票市場(artificial stock market), 在這樣的架構下, 我們成功地產生出類似真實股票市場的股價時間序列特性, 我們同時也檢定了人工股票市場中價量的因果關係, 說明了在沒有外生因素之下, 人工股票市場的複雜系統可自發地產生出雙向的價量因果關係, 進一步地, 我們研究下層agent(交易者)行為與上層股價時間序列行為的關聯性, 我們也發現個體的行為並不能直接加總或推論出複雜適應系統的總體行為, 這就是突現性質(emergent property)的發生, 最後, 本文描述了agent-based計算經濟學研究架構的優勢與缺點, 再附帶介紹一個用以進行agent-based建模相關研究的軟體程式庫-SWARM。 參考文獻 中文部份: 高慈謙(1997), "股票價量因果關係的新檢定方法及其應用", 碩士論文, 國立台灣大學經濟學系, 台北. 英文部份: 1.Abelson, R.P. and A. Bernstein (1963), "A Computer Simulation of Community Referendum Controversies," Public Opinion Quarterly, 27, pages 93-122. 2.Arifovic, J. (1994), "Genetic Algorithm Learning and the Cobweb Model," Journal of Economic Dynamics and Control, 18, pages 3-28. 3.Arthur, W.B. (1994), "Inductive Reasoning and Bounded Rationality (The El Farol Problem)," American Economic Review, 84, pages 406-411. 4.Arthur, W.B., J. Holland, B. LeBaron, R. Palmer and R. Tayler (1997), "Asset Pricing under Endogenous Expectation in an Artificial Stock Market," in W.B. Arthur, S. Durlauf and D. Lane (eds.), The Economy as an Evolving Complex System II, Addison-Wesley, pages 15-44. 5.Brenner, T. (1998), "Can Evolutionary Algorithms Describe Learning Processes?" Journal of Evolutionary Economics, 8, pages 271-283. 6.Casti, J.L. (1997), Would-Be Worlds－How Simulation Is Changing the Frontiers of Science, John Wiley & Sons, Inc., New York. 7.Chen, S.-H. and C.-H. Yeh (1996), "Genetic Programming Learning and the Cobweb Model," Advances in Genetic Programming, Vol. 2, MIT Press, Cambridge, MA, pages 443-466. 8.Chen, S.-H. and C.-H. Yeh (1999a), "Genetic Programming in Agent-Based Artificial Stock Markets: Simulations and Analysis," in Proceedings of 1999 Congress on Evolutionary Computation, IEEE Press. 9.Chen, S.-H. and C.-H. Yeh (1999b), "On the Consequence of ‘Following the Herd’: Evidence from the Artificial Stock Market," in Proceedings of the 1999 International Conference on Artificial Intelligence, CSREA Press, pages 388-394. 10.Chen, S.-H., C.-H. Yeh and C.-C. Liao (1999a), "Testing for Granger Causality in the Stock Price-Volume Relation: A Perspective from the Agent-Based Model of Stock Markets," in Proceedings of the 1999 International Conference on Artificial Intelligence, CSREA Press, pages 374-380. 11.Chen, S.-H., C.-H. Yeh and C.-C. Liao (1999b), "Testing the Rational Expectation Hypothesis with Agent-Based Models of Stock Markets," in Proceedings of the 1999 International Conference on Artificial Intelligence, CSREA Press, pages 381-387. 12.Copland, T. (1976), "A Model of Asset Trading under the Assumption of Sequential Information Arrival," Journal of Finance, 31, pages 135-155. 13.Cyert, R. and J.G. March (1963), A Behavioral theory of the firm, Prentice-Hall, Englewood Cliffs, NJ. 14.DeLong, J., A. Sheleifer, L. Summers and B. Waldmann (1990), "Positive Feedback Investment Strategies and Destabilizing Speculation," Journal of Finance, 45, pages 379-395. 15.Epps, T.W. (1975), "Security Price Changes and Transactions Volumes: Theory and Evidences," American Economic Review, 65, pages 586-597. 16.Epstein, J.M. and R. Axtell (1996), Growing Artificial Societies: Social Science from the Bottom Up, Brookings Institution Press, Washington, D.C. 17.Granger, C.W.J. (1969), "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, 37, pages 424-438. 18.Harrald, P. (1998), "Economics and Evolution," the panel paper given at the Seventh International Conference on Evolutionary Programming, March 25-27, San Diego. 19.Hiemstra, C. and J.D. Jones (1994), "Testing for Linear and Nonlinear Granger Causality in the Stock Price-Volume Relation," Journal of Finance, 36, pages 143-161. 20.Holland, J.H. and J.H. Miller (1991), "Artificial Adaptive Agents in Economic Theory," American Economic Review, 81, pages 365-370. 21.Jacobs, J. (1992), Systems of survival: A Dialogue on the Moral Foundations of Commerce and Politics, Random House. 22.Jennings, R., L. Starks and J. Fellingham (1981), "An Equilibrium Model of Asset Trading with Sequential Information Arrival," Journal of Finance, 36, pages 143-161. 23.Krugman, P.R. (1996), The Self-Organizing Economy, Blackwell Publishers Inc., MA. 24.Langton, C.G. (1989), "Artificial Life," in C. G. Langton, editor, Artificial Life, Volume VI of SFI Studies in the Science of Complexity, pages 1-47, Addison-Wesley, Redwood City, CA. 25.Law, A.M. and W.D. Kelton (1991), Simulation Modeling and Analysis, 2nd ed., McGraw-Hill, New York. 26.LeBaron, B. (1995), "Experiments in Evolutionary Finance," Working Paper. 27.LeBaron, B., W.B. Arthur and R. Palmer (1999), "”Time Series Properties of an Artificial Stock Market," Journal of Economic Dynamic and Control, Forthcoming. 28.Leontif, W. (1966), Essays In Economics: Theories and Theorizing, Oxford University Press, New York. 29.Marshall, A. (1949), The Principles of Economics, 8th ed., Macmillan, London. 30.Miner, N., R. Burkhart, C. Langton, M. Askenazi (1996), "The Swarm Simulation System: A Toolkit for Building Multi-Agent Simulations," Swarm release documentation, available as 描述 碩士
國立政治大學
經濟學系
86258001資料來源 http://thesis.lib.nccu.edu.tw/record/#B2002001635 資料類型 thesis dc.contributor.advisor 陳樹衡 zh_TW dc.contributor.advisor Chen, Shu-Heng en_US dc.contributor.author (Authors) 廖崇智 zh_TW dc.contributor.author (Authors) Liao, Chung-Chih en_US dc.creator (作者) 廖崇智 zh_TW dc.creator (作者) Liao, Chung-Chih en_US dc.date (日期) 1999 en_US dc.date.accessioned 26-Apr-2016 10:25:15 (UTC+8) - dc.date.available 26-Apr-2016 10:25:15 (UTC+8) - dc.date.issued (上傳時間) 26-Apr-2016 10:25:15 (UTC+8) - dc.identifier (Other Identifiers) B2002001635 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/86166 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 經濟學系 zh_TW dc.description (描述) 86258001 zh_TW dc.description.abstract (摘要) 我們把經濟體視為一個複雜適應系統(complex adaptive system), 強調系統中異質性(heterogeneous)agent的學習適應行為與agent之間的互動性交互作用, 此時主流經濟學裡的分析架構, 如:代表性個人模型(represesentive agent model)、理性預期(rational expectation)、固定點均衡分析(fixed-point equilibrium analysis)等將不再適用, 取而代之的是演化經濟學(evolutionary economics)的研究典範, 這樣的研究架構下, 並沒有適當的數學分析工具可資運用, 因此我們改以agent-based建模(agent-based modelng)的社會模擬(social simulation)來建構一個人工的經濟體(artificial economy), 以此為主要研究方法, 這就是agent-based計算經濟學(agent-based computational economics)或稱人工經濟生命(artificial economic life)。 本文中以股票市場為主要的研究課題, 我們以遺傳規劃(genetic programming)的人工智慧(artificial intelligence)方法來模擬股市中有限理性(bounded rational)異質交易者的交易策略學習行為, 建構出一個人工股票市場(artificial stock market), 在這樣的架構下, 我們成功地產生出類似真實股票市場的股價時間序列特性, 我們同時也檢定了人工股票市場中價量的因果關係, 說明了在沒有外生因素之下, 人工股票市場的複雜系統可自發地產生出雙向的價量因果關係, 進一步地, 我們研究下層agent(交易者)行為與上層股價時間序列行為的關聯性, 我們也發現個體的行為並不能直接加總或推論出複雜適應系統的總體行為, 這就是突現性質(emergent property)的發生, 最後, 本文描述了agent-based計算經濟學研究架構的優勢與缺點, 再附帶介紹一個用以進行agent-based建模相關研究的軟體程式庫-SWARM。 zh_TW dc.description.tableofcontents 謝辭…………………………………………………vi 第1章 緒論…………………………………………1 1.1 研究動機……………………………………1 1.2 Agent-Based計算建模…………………… 5 1.3 本文架構……………………………………7 第2章Agent-Based計算經濟學……………………9 2.1 建構人工經濟生命…………………………9 2.2 複雜適應系統典範…………………………13 2.3 演化經濟學…………………………………16 2.4 完全理性與有限理性………………………18 2.5 何以採用電腦模擬？………………………20 2.6 如何模擬適應行為？………………………22 2.7 ACE的研究架構…….………………………25 第3章 GP-Based人工股票市場模擬研究…………28 3.1 人工股票市場文獻回顧……………………28 3.2 GP-Based的市場結構………………………31 3.3 模型設定……………………………………35 3.4 人工股票市場時間序列……………………39 3.5 人工股票市場的價量關係檢定……………41 3.6 人工股票市場中下層agent的行為……… 43 3.7 人工股票市場理性預期的檢定……………45 3.8 小結…………………………………………46 第4章 結論與展望…………………………………59 4.1 ACE研究架構的優勢與批評……………… 59 4.2 SWARM……………………………………… 62 參考文獻與書目……………………………………66 zh_TW dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#B2002001635 en_US dc.subject (關鍵詞) 人工股票市場 zh_TW dc.subject (關鍵詞) agent-based計算經濟學 zh_TW dc.subject (關鍵詞) 複雜適應系統 zh_TW dc.subject (關鍵詞) 遺傳規劃 zh_TW dc.subject (關鍵詞) 演化經濟學 zh_TW dc.subject (關鍵詞) 突現性質 zh_TW dc.subject (關鍵詞) 社會模擬 zh_TW dc.subject (關鍵詞) 人工經濟生命 zh_TW dc.subject (關鍵詞) artificial stock market en_US dc.subject (關鍵詞) agent-based computational economics en_US dc.subject (關鍵詞) complex adaptive system en_US dc.subject (關鍵詞) genetic programming en_US dc.subject (關鍵詞) evolutionary economics en_US dc.subject (關鍵詞) emergent property en_US dc.subject (關鍵詞) social simulation en_US dc.subject (關鍵詞) artificial economic life en_US dc.title (題名) 人工股票市場的Agent-Based計算建模 zh_TW dc.title (題名) On Agent-Based Computational Modeling of Artificial Stock Markets en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 中文部份: 高慈謙(1997), "股票價量因果關係的新檢定方法及其應用", 碩士論文, 國立台灣大學經濟學系, 台北. 英文部份: 1.Abelson, R.P. and A. Bernstein (1963), "A Computer Simulation of Community Referendum Controversies," Public Opinion Quarterly, 27, pages 93-122. 2.Arifovic, J. (1994), "Genetic Algorithm Learning and the Cobweb Model," Journal of Economic Dynamics and Control, 18, pages 3-28. 3.Arthur, W.B. (1994), "Inductive Reasoning and Bounded Rationality (The El Farol Problem)," American Economic Review, 84, pages 406-411. 4.Arthur, W.B., J. Holland, B. LeBaron, R. Palmer and R. Tayler (1997), "Asset Pricing under Endogenous Expectation in an Artificial Stock Market," in W.B. Arthur, S. Durlauf and D. Lane (eds.), The Economy as an Evolving Complex System II, Addison-Wesley, pages 15-44. 5.Brenner, T. (1998), "Can Evolutionary Algorithms Describe Learning Processes?" Journal of Evolutionary Economics, 8, pages 271-283. 6.Casti, J.L. (1997), Would-Be Worlds－How Simulation Is Changing the Frontiers of Science, John Wiley & Sons, Inc., New York. 7.Chen, S.-H. and C.-H. Yeh (1996), "Genetic Programming Learning and the Cobweb Model," Advances in Genetic Programming, Vol. 2, MIT Press, Cambridge, MA, pages 443-466. 8.Chen, S.-H. and C.-H. Yeh (1999a), "Genetic Programming in Agent-Based Artificial Stock Markets: Simulations and Analysis," in Proceedings of 1999 Congress on Evolutionary Computation, IEEE Press. 9.Chen, S.-H. and C.-H. Yeh (1999b), "On the Consequence of ‘Following the Herd’: Evidence from the Artificial Stock Market," in Proceedings of the 1999 International Conference on Artificial Intelligence, CSREA Press, pages 388-394. 10.Chen, S.-H., C.-H. Yeh and C.-C. Liao (1999a), "Testing for Granger Causality in the Stock Price-Volume Relation: A Perspective from the Agent-Based Model of Stock Markets," in Proceedings of the 1999 International Conference on Artificial Intelligence, CSREA Press, pages 374-380. 11.Chen, S.-H., C.-H. Yeh and C.-C. Liao (1999b), "Testing the Rational Expectation Hypothesis with Agent-Based Models of Stock Markets," in Proceedings of the 1999 International Conference on Artificial Intelligence, CSREA Press, pages 381-387. 12.Copland, T. (1976), "A Model of Asset Trading under the Assumption of Sequential Information Arrival," Journal of Finance, 31, pages 135-155. 13.Cyert, R. and J.G. March (1963), A Behavioral theory of the firm, Prentice-Hall, Englewood Cliffs, NJ. 14.DeLong, J., A. Sheleifer, L. Summers and B. Waldmann (1990), "Positive Feedback Investment Strategies and Destabilizing Speculation," Journal of Finance, 45, pages 379-395. 15.Epps, T.W. (1975), "Security Price Changes and Transactions Volumes: Theory and Evidences," American Economic Review, 65, pages 586-597. 16.Epstein, J.M. and R. Axtell (1996), Growing Artificial Societies: Social Science from the Bottom Up, Brookings Institution Press, Washington, D.C. 17.Granger, C.W.J. (1969), "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, 37, pages 424-438. 18.Harrald, P. (1998), "Economics and Evolution," the panel paper given at the Seventh International Conference on Evolutionary Programming, March 25-27, San Diego. 19.Hiemstra, C. and J.D. Jones (1994), "Testing for Linear and Nonlinear Granger Causality in the Stock Price-Volume Relation," Journal of Finance, 36, pages 143-161. 20.Holland, J.H. and J.H. Miller (1991), "Artificial Adaptive Agents in Economic Theory," American Economic Review, 81, pages 365-370. 21.Jacobs, J. (1992), Systems of survival: A Dialogue on the Moral Foundations of Commerce and Politics, Random House. 22.Jennings, R., L. Starks and J. Fellingham (1981), "An Equilibrium Model of Asset Trading with Sequential Information Arrival," Journal of Finance, 36, pages 143-161. 23.Krugman, P.R. (1996), The Self-Organizing Economy, Blackwell Publishers Inc., MA. 24.Langton, C.G. (1989), "Artificial Life," in C. G. Langton, editor, Artificial Life, Volume VI of SFI Studies in the Science of Complexity, pages 1-47, Addison-Wesley, Redwood City, CA. 25.Law, A.M. and W.D. Kelton (1991), Simulation Modeling and Analysis, 2nd ed., McGraw-Hill, New York. 26.LeBaron, B. (1995), "Experiments in Evolutionary Finance," Working Paper. 27.LeBaron, B., W.B. Arthur and R. Palmer (1999), "”Time Series Properties of an Artificial Stock Market," Journal of Economic Dynamic and Control, Forthcoming. 28.Leontif, W. (1966), Essays In Economics: Theories and Theorizing, Oxford University Press, New York. 29.Marshall, A. (1949), The Principles of Economics, 8th ed., Macmillan, London. 30.Miner, N., R. Burkhart, C. Langton, M. Askenazi (1996), "The Swarm Simulation System: A Toolkit for Building Multi-Agent Simulations," Swarm release documentation, available as zh_TW
