基于ACE的金融市场建模关键技术研究
|
摘要
现代金融市场理论是新古典经济学在金融学领域的延伸,其理论基础为“随机游走模型”和“有效市场假说”。虽然这一理论体系在形式上十分精巧,但随着众多学者对现代金融市场理论和现实金融市场的深入研究,出现了许多质疑的声音。与此同时,学者们提出了许多新的研究方法和理论,解释了一些令人困惑的金融市场的异常现象,并得出许多有别于现代金融市场理论的结论。基于ACE的金融市场建模方法是全新的研究金融市场的重要方法之一。因此,它对于我们了解金融市场的每一个细节,透彻地分析金融市场的异常现象,发现底层规则和高层涌现特征之间的联系,最终建立更加完备的金融市场理论体系具有十分重要的理论价值和实际意义。
本文以人工股票市场模型(Artificial Stock Market Model,简称ASMM)为例对基于ACE的金融市场建模关键技术进行了深入研究。论文的主要工作和创新性成果如下:
(1)建立了基于人元的Agent模型。这一模型将Agent的客观属性和主观属性有效的分开使得Agent建模更加清晰;同时,运用模糊控制技术建立了具有模糊决策能力的主元模型,提高了Agent模型的决策能力。这两种Agent建模技术都具有一定的普遍性,可以在其他Agent模型中推广应用。
(2)ASMM不仅再现了现实股票市场的典型统计特征,包括收益分布的尖峰厚尾特征、股价波动的线性和非线性相关性、股价与交易量的关系和波动聚集性等特征。而且产生了与现实股市十分相似的分形结构和混沌现象。因此,可以说,ASMM不仅能产生与现实股市极为相似的股价走势和特征,而且其分形结构、混沌特征与现实股市具有深刻的一致性。因此,通过对ASMM的深入研究可以揭示现实股票市场的演化规律、运作机理、政策影响以及更好的投资策略。
(3)最后对ASMM的涌现结果进行了分析。首先,分析了学习与股票价格波动的演化关系,发现Agent们的不断学习是导致股票价格波动的主要成因之一;其次,分析了股票市场中财富分布状态的“二八现象”,发现产生这种现象的主要原因是市场中仅有少数Agent可以战胜市场,进而解释了现实股票市场中为什么好的投资策略往往是秘而不宣的;最后,分析了个体行为对政策效果的影响,发现市场中个体的行为模式是政策能否起到预期效果的重要因素之一。
总之,基于ACE的金融市场建模方法不仅验证、支持现有的金融市场理论,而且可以作为各种新理论、新政策、新机制的试验平台和工具。
The modern financial market theory is the extension of the neoclassical economics in the field of finance,the theoretical basis of which is the "Random Walk Model" and "Efficient Market Hypothesis".Although this system info is very delicate in the formality,many interrogative voices has appeared along with research to the modern financial market theories and the realistic financial market by many literates.At the same time,many literates,who explained some financial anomalies of the financial market and get many conclusions which are different from the modern financial market theories,put forward many new research methods and theories.Modeling methodology in financial market based on ACE is one of the most important methods in all of the new research method.Therefore,it has very important theories value and actual meaning,which can make us understand the detail of the financial market,and analyze the financial anomalies,and discover the interaction rule, and build up more complete financial market theories.
Artificial stock market model(ASMM) is developed to illustrate the ACE modeling key techniques in financial market.As a result,several innovative points and practical meanings are as follows.
(1) Firstly,we build up the human-element-based agent model,which separates the agent into the mental part and the physical part,and which can make the agent modeling easier and clearer.At the same time,we build up the main dollar model based on fuzzy control technology,which possess the fuzzy decision ability and improve the decision ability of the Agent model.Furthermore,theses two techniques can be generalized to other models of ACE.
(2) ASMM not only can reproduce many typical facts of the real market,such as the Peak and Fat-tailed character of the returm distribution,linear correlation and nonlinear correlation between the price fluctuations,price-volume characteristic, volatility clustering of the price,but also can generate the fractal structure and chaos phenomena similar with real stock market.Comparing with the real stock market, ASMM can not only generate stock price trends and properties rather similar to the real, but also show the fractal structure and the chaotic behavior in deep consistency with the real stock market.So,research on ACE modeling approach and technique can reveal evolution rule of real stock market,operate mechanism,policy influence and better investment strategy.
(3) Finally,we analyze the results of the emergent of the ASMMM.Firstly,the phenomena of stock price fluctuation under the different market condition is analyzed,. We find that study and evolution of Agent is one reason of the stock price fluctuation; Secondly,we analyses the "20/80" phenomenon of the wealth distribution and find that the reason of this phenomenon is the forecasting rule of a handful of agents in the market can win the stock market,so we can better explain why the good investment strategy usually keeps secret in the real stock market;Finally,we analyses how individual action impact on policy effect and find that individual action is one reason of whether effectiveness of policy limits can get expectation.
As a result,ACE modeling approach in financial market is not only a tool to support and verify the modern financial market theories,but also a test-bed to experiment various new theories,new policy,and new mechanism.
本文以人工股票市场模型(Artificial Stock Market Model,简称ASMM)为例对基于ACE的金融市场建模关键技术进行了深入研究。论文的主要工作和创新性成果如下:
(1)建立了基于人元的Agent模型。这一模型将Agent的客观属性和主观属性有效的分开使得Agent建模更加清晰;同时,运用模糊控制技术建立了具有模糊决策能力的主元模型,提高了Agent模型的决策能力。这两种Agent建模技术都具有一定的普遍性,可以在其他Agent模型中推广应用。
(2)ASMM不仅再现了现实股票市场的典型统计特征,包括收益分布的尖峰厚尾特征、股价波动的线性和非线性相关性、股价与交易量的关系和波动聚集性等特征。而且产生了与现实股市十分相似的分形结构和混沌现象。因此,可以说,ASMM不仅能产生与现实股市极为相似的股价走势和特征,而且其分形结构、混沌特征与现实股市具有深刻的一致性。因此,通过对ASMM的深入研究可以揭示现实股票市场的演化规律、运作机理、政策影响以及更好的投资策略。
(3)最后对ASMM的涌现结果进行了分析。首先,分析了学习与股票价格波动的演化关系,发现Agent们的不断学习是导致股票价格波动的主要成因之一;其次,分析了股票市场中财富分布状态的“二八现象”,发现产生这种现象的主要原因是市场中仅有少数Agent可以战胜市场,进而解释了现实股票市场中为什么好的投资策略往往是秘而不宣的;最后,分析了个体行为对政策效果的影响,发现市场中个体的行为模式是政策能否起到预期效果的重要因素之一。
总之,基于ACE的金融市场建模方法不仅验证、支持现有的金融市场理论,而且可以作为各种新理论、新政策、新机制的试验平台和工具。
The modern financial market theory is the extension of the neoclassical economics in the field of finance,the theoretical basis of which is the "Random Walk Model" and "Efficient Market Hypothesis".Although this system info is very delicate in the formality,many interrogative voices has appeared along with research to the modern financial market theories and the realistic financial market by many literates.At the same time,many literates,who explained some financial anomalies of the financial market and get many conclusions which are different from the modern financial market theories,put forward many new research methods and theories.Modeling methodology in financial market based on ACE is one of the most important methods in all of the new research method.Therefore,it has very important theories value and actual meaning,which can make us understand the detail of the financial market,and analyze the financial anomalies,and discover the interaction rule, and build up more complete financial market theories.
Artificial stock market model(ASMM) is developed to illustrate the ACE modeling key techniques in financial market.As a result,several innovative points and practical meanings are as follows.
(1) Firstly,we build up the human-element-based agent model,which separates the agent into the mental part and the physical part,and which can make the agent modeling easier and clearer.At the same time,we build up the main dollar model based on fuzzy control technology,which possess the fuzzy decision ability and improve the decision ability of the Agent model.Furthermore,theses two techniques can be generalized to other models of ACE.
(2) ASMM not only can reproduce many typical facts of the real market,such as the Peak and Fat-tailed character of the returm distribution,linear correlation and nonlinear correlation between the price fluctuations,price-volume characteristic, volatility clustering of the price,but also can generate the fractal structure and chaos phenomena similar with real stock market.Comparing with the real stock market, ASMM can not only generate stock price trends and properties rather similar to the real, but also show the fractal structure and the chaotic behavior in deep consistency with the real stock market.So,research on ACE modeling approach and technique can reveal evolution rule of real stock market,operate mechanism,policy influence and better investment strategy.
(3) Finally,we analyze the results of the emergent of the ASMMM.Firstly,the phenomena of stock price fluctuation under the different market condition is analyzed,. We find that study and evolution of Agent is one reason of the stock price fluctuation; Secondly,we analyses the "20/80" phenomenon of the wealth distribution and find that the reason of this phenomenon is the forecasting rule of a handful of agents in the market can win the stock market,so we can better explain why the good investment strategy usually keeps secret in the real stock market;Finally,we analyses how individual action impact on policy effect and find that individual action is one reason of whether effectiveness of policy limits can get expectation.
As a result,ACE modeling approach in financial market is not only a tool to support and verify the modern financial market theories,but also a test-bed to experiment various new theories,new policy,and new mechanism.
引文
[1]Anderson M.R.,Sandholm T.W.Leveled Commitment Contracts with Myopic and Strategic Agents[M]Journal of Economic Dynamics and Control,2001:615～640.
[2]Antoniewicz R.,A causal relationship between stock returns and volume[J],Board of Governors of the Federal Reserve system Finance and Economics Discussion Series,1992,8:50.
[3]Arthur W.B.,Holland J.,Lebaron B.et al.Asset Pricing Endogenous Expectations in an Artificial Stock Market[A].In Arthur W.B.,Durlauf S.,Lane D.The Economy as an Evolving Comples System Ⅱ[C]Bostom:Addison-Wesley,1997,15-44.
[4]Axelrod R.The Complexity of Cooperation[MJ.Princeton University Press,1997.
[5]Barberis N.,Shleifer A.and Vishny R.,A Model of Investor Seniment[J],Journal of Financial Economics,1998,49(3),307-343.
[6]Barberis N.,Huang M.and Santos T.,Prospect Theory and Asset Prices[J],the Quarterly Journal of Economics,2001,116:1-53.
[7]Bollerslev.T,Generalized Autoregressive Conditional Heteroskedasticity[J],Jouranl of Econometrics,1986,31:307-327.
[8]Bondt W and Thaler R,Do Security Analysts Overreact?[J].American Economic Review,1990,80:52-57.
[9]Bourgine P.,Nadal J.P.Cognitive Economics:An Interdisciplinary Approach[M].Springer,2004.
[10]Brad B.and Terrance O.,Boys will be Boys:Gender,Overreaction,and Common Stock Investment[J],The Quarterly Journal of Economics,2001,116,261-292.
[11]Bray M.Learning,Estimation and the Stability of Expectations[J].Journal of Economic Theory,1982,26:318-339.
[12]Brock,Potter S.M.,diagnostic testing for nonlinearity,chaos,and general dependence in time series data[J],in M.Casdagli and S.Eubank(eds.),Nonlinear Modeling and Forecasting:Proceedings of a Workshop on Nonlinear Modeling and Forecasting held September 1990 in Santa Fe,New Mexico(Redwood City,Calif.,Addison-Wesley),1992,137-159.
[13]Carley K.M.,Prietula M.J.Computational Organization Theory[M].Lawrence Erlbaum Associates,1994.
[14]Chen Shu-Heng,Yeh Chia-Hsuan.Evolving Traders and the Business School with Genetic Programming:A New Architecture of the Agent-based Artificial Stock Market[J].Journal of Economic Dynamic& Control,2001,25:363-393.
[15]Claudia P.W.,Ebenoth E.An Adaptive Toolbox Model:a pluralistic modeling approach for human Behaviour based on observation[J].Journal of Artificial Societies and Social Simulation,2004,7(1):66-74.
[16]Cohen K.J.,Maier S.R,Schwartz R.A.A Simulation Model of Stock Exchange Trading[J],Simulation,1983,41:181-191.
[17]Cootner,R,The random character of stock market price[M],Cambridge:Massachusetts Institute of Technology press,1964.
[18]Cutler D,Poterba J and Summers L.Speculative Dynamics[J].Review of Economic Studies,1991,58:529-546.
[19]Daniel K,Hirshleifer D.and Subrahmanyam A.,Investor Psychology and Security Market Under and Overreactions[J],Journal of Finance,1998,53(6),1839-1886.
[20]De Grauwe P.,Dewachter H.,Embrechts M.Exchange Rate Theory:Chaotic Models of Foreign Exchange Markets[M].Blackwell,Oxford,1993.
[21]Errunza V,Hogan K.,Kini O.Conditional Heteroskedasticity and Global Stock Return distributions[J].Financial Review,1994,29(3):187-203.
[22]Edmonds B.,Moss S.From KISS to KIDS-an “anti-simplistic” modeling approach[J],2005,Springer:130～144.
[23]Elton E.J.,Gruber M.J.On the Optimality of Some Multi-period Portfolio Selection Criteria[J].Journal of Business,1974,7:231-243.
[24]Engle,R.F,Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflations[J],Econometrica,1982,50:987-1007.
[25]Fama,E.Efficient Capital Markets:A Reviews of Theory and Empirical Work[J],Journal of Finance,1970,25,p280-294.
[26]Fleischmann A.:A Model for a Simple Luhmann Economy[J].Journal of Artificial Societies and Social Simulation,2005,8(2):281-312.
[27]Frankel J.A.,Froot K.A.Explaining the Demand for Dollars:International of Return and the Expectations of Chartists and Fundamentalists[M].Westview Press,Boulder,CO.,1988.
[28]Gallant A.R.,Rossi P.E.,Tauchen G,Stock prices and volume[J],Review of Financial Studies,1992,5(2):199-242.
[29]Galluccio S.,Caldarelli G,Marsili M.,Scaling in currency exchange[J],Physica A,1997,245:423-436.
[30]Gopikrishnan P.,Plerou V.,Amaral N.,Scaling of the distribution of fluctuations of financial market indices[J],Phys.Rev.E,1999,60(5):5305-5316.
[31]Granger C.W.J.,Investingating causal relations by econometric models and cross-spectral methods[J],Econometrica,1969,37:424-438.
[32]Grossman S.,Stiglitz F.On the Impossibility of Informational Efficient Markets[J].American Economic Review,1980,70:393-408.
[33]Hersh S.and Meir S.,The Disposition to Sell Winners too early and Rids Losers too long:Theory and Evidence[J],Journal of Finance,1984,40,777-790.
[34]Hiemstra C,Jones J.D.,Testing for linear and nonlinear Granger causality in the stock price-volume relation[J],Journal of Finance,1994,49:1639-1664.
[35]Hong H.,Stein J.C.,A Unified Theory of Underreaction,Monentum Trading and Overreaction in Asset Markets[J],Journal of finance,1999,54(6),2143-2184
[36]Hsieh D.A.Testing for nonlinear dependence in daily foreign exchange rates[J],Journal of Business,1989,62:339-368.
[37]Hsieh D.A.Chaos and nonlinear dynamics:applications to financial markets[J],Journal of Finance,1991,46:1839-1877.
[38]Hurst H.Long Term Storage Capacity of Reservoirs[J].Transactions of the American Society of Civil Engineers,1951,116:770-799.
[39]Karpoff J.M.,The relationship between price changes and trading volume:a survey[J],Journal of Financial and Quantitative Analysis,1987,22(1):109-126.
[40]Kahneman D.and Tversky A.,Prospect Theory:an Analysis of Decision under Risk[J],Econometrica,1979,47(2),263-291.
[41]Kim G,Markowitz H.Investment Rules,Margin and Market Volatility[J],Journal of Portfolio Management,1989,16(1):45-52.
[42]LeBaron B.,Arthur W.B.,Palmer R.Times Series Properties of an Artificial Stock Market[J].Journal of Economic Dynamic& Control,1999,23:1487-1516.
[43]Lebaron B.,A builder's guide to agent-based financial markets[J],Quantitative Finance 1(2),254-261.
[44]LeBaron B.Financial Market Efficiency in a Co-evolutionary Environment[C].Proceedings of the Workshop on Simulation of Social Agents:Architectures and Institutions,Argonne National Laboratories,2001,33-51.
[45]LeBaron B.Short-memory Traders and Their Impact on Group Learning in Financial Markets[C].Proceedings of the National Academy of Science:Colloquium 99,2002,7201-7206
[46]Leydesdorff L.Anticipatory Systems and the Processing of Meaning:a Simulation Study Inspired by Luhmann's Theory of Social Systems[J].Journal of Artificial Societies and Social Simulation,2005,8(2):258-280.
[47]Li T.Y.,York J.A.Period Three means Chaos[J].American Mathematical Monthly,1975,(82):985-992.
[48]Lintner J.The Valuation of Risk Assets and the Selection of Risky Investment in Stock Portfolio and Capital Budgets[J].Review of Economic and Statistics,1965,47:13-37.
[49]Liu Y.,Cizeau P.,Meyer M.,Correlations in economic time series[J],Physica A,1997,245:437-440.
[50]Luhmann N.Ecological Communication[M].The University of Chicago Press,1989.
[51]Mantegna R.N.,Stanley H.E.,Scaling behaviour in the dynamics of an economic index[J],Nature,1995,376:46-49.
[52]Markowitz H.Portfolio Selection[J].The Journal of Finance,1952,7(1):77-91.
[53]MarkOWitz H.著,朱菁,欧阳向军译,资产组合选择和资本市场的均值[J],上海三联书店,1999.
[54]Maturana H.R.,Verela F.I.Autopoiesis and cognition[M].D.Reidel Publishing Company,1980.
[55]Mehra,R.and E.C.Prescott,the Equity premium,A Puzzle.Journal of Monetary Economics,1985,15,145-161.
[56]Merton R.C.Continuous-time Finance[M].Cambridge,MA:Basil Blackwell,1990.
[57]Mossin J.Optimal Muti-period Portfolio Policies[J].The Journal of Business,1968,41:215-229.
[58]Nelson,D.,Conditional Heteroskedasticity in asset returns:A new approach[J],Econometrica,1991,59:347-370.
[59]Peters E.,Chaos and Order in the Capital Markets[M].New York:John Wiley & Sons,1991.
[60]Peters E.,A Chaotic Attractor for the S&P500[J].Financial Analysts Journal,March/April 1991.
[61]Peters E.,R/S Analysis using Logarithmic Returns:A Technical Note[J].Financial Analysts Journal,November/December 1992.
[62]Pingle M.,Tesfatsion L.Non-employment Benefits and the Evolution of Worker-employer Cooperation:Experiments with Real and Computational Agents[J/OL].< http:// www.econ.iastate.edu / tesfatsi / sce2001.pdf>,2001.
[63]Robert J.and Shiller,Human Behavior and the Efficiency of the Financial System[M],NBER Working Paper Series,1998.
[64]Ruey S.Tsay著,潘家柱译.金融时间序列分析[M],北京,机械工业出版社,2007.
[65]Samuelson P.A.Lifetime Portfolio Selection by Dynamic Stochastic Programming[J].The Review of Economics and Statistics,1969,50:239-246.
[66]Scheinkman J.A.,Lebaron B.Nonlinear dynamic and stock returns[J],Journal of Business,1989,62:311-317-331-7.
[67]Sewell S.P.,Stansell S.R.,Lee I.Nonlinearities in Emerging Foreign Capital Markets[J]. Journal of Business Finance and Accounting,1993,20(2):1583-l594.
[68]Sharpe W.Capital Asset Prices:A Theory of Market Equilibrium under Conditions of Risk[J].Journal of Finance,1964,19:425-438.
[69]Shiller R.Do Stock Prices Move Too Much to be justified by Subsequent changes in Dividends[J].American Economic Review,1981,71:421-436.
[70]Shu-Heng Chen,Chia-Hsuan Yeh,on the emergent properties of artificial stock markets:the efficient market hypothesis and the rational expectations hypothesis[J],Journal of Economic Behavior & Organization,2002,49:217-239.
[71]Shu-Heng Chen,Chia-Hsuan Yeh,evolving traders and the business school with genetic programming:a new architecture of the agent-based artificial stock market[J],Journal of Economic Dynamics & Control,2001,25:363-393.
[72]Shu-Heng Chen,Chia-Hsuan Yeh,agent-based computational modeling of the stock price-volume relation[J],Information Sciences,2005,170:75-100.
[73]Siegel J,stocks for the long run[M].3rd edition,New York:McGraw-Hill,2002,158-180.
[74]Simaan Y.Estimation Risk in Portfolio Selection:the Mean Variance Model Versus the Mean Absolute Deviation Model[J].Management Science,1997,43:1437-1446.
[75]Smith K.V.A Transition Model for Portfolio Revision[J].Journal of Finance,1967,22:425-439.
[76]Skjeltorp J.A.Scaling in the Norwegian Stock Market[J].Physical A,2000,283:486-528.
[77]Staller A.,Petta P.Introducing Emotions into the Computational Study of Social Norms:A First Evaluation[J].Journal of Artificial Societies and Social Simulation,2001,4(1):599-630.
[78]Stuzer,M.J,Chaotic Dynamic & Bifurcation in a Macro Model[J],Journal of Economic Dynamic &Control,1980,2.
[79]Sun R.,Naveh I.Simulating Organizational Decision-Making Using a Cognitively Realistic Agent Model[J].Journal of Artificial Societies and Social Simulation,2004,7(3):48-68.
[80]Tesfatsion L.Agent-based Computational Economics[Z].ISU Economics Working Paper No.1,Revised August,2003.
[81]Tversky,A.and Kahneman,D.,Belief in the law of small numbeis[J],Psychological Bulletin,1971,76:105-110.
[82]Tversky,A.and Kahneman,D.,Judgment under uncertainty:Heuristics and biases[J],Science,1974,185:1124-1130.
[83]Tversky,A.and Kahneman,D.,The framing of decisions and the psychology of choice[J],Science,1981,211:453-458.
[84]Tversky,A.and Kahneman,D.,Extensional versus intuitive reasoning:The conjunction fallacy in probability judgment[J],Psychological Review,1983,90:293-315.
[85]Ying C.C.,Stock market prices ans volumes of sales[J],Econometrica,1966,34(3):676-685.
[86]Vriend N.J.:An illustration of the essential difference between individual and social learning,and its consequences for computational analyses.Journal of Economic Dynamics & Control,vol.0(24):1-19,2000.
[87]Von Neumann J,Morgenstern O:Theory of Games and Economic Behavior.Princeton:Princeton Univ.Press,1944.
[88]Wang B.H.,Hui P.M.,The distribution and scaling of fluctuations for Hang Seng index in Hong Kong stock market[J],Eur.Phys.J.B,2001,20:573-579.
[89]Watts D.J.Small Worlds:The Dynamics of Networks between Order and Randomness [M].Princeton University Press,2003.
[90]Wolf J.B.,H.L.Swinney and J.A.Vastano,Determining Lyapunov exponents from a time series [J],Physica 16D,1985,p285-317.
[91]彼得斯著,储海林,殷勤译.分形市场分析-将混沌理论应用到投资与管理理论上[M],北京,经济科学出版社,2002.
[92]蔡文,杨春燕著.《可拓逻辑初步》[M],科学出版社,2003.
[93]陈禹,丁浩.实验经济学、CAS理论与经济建模--复杂性科学研究进展[M].北京:科学出版社,2004,p85-106.
[94]邓凯.基于Agent的金融市场仿真研究[D],上海,上海交通大学,2002.
[95]樊智,张世英.金融市场的效率与分形市场理论[J],系统工程理论与实践,2002,21(3),p13-19.
[96]高宝俊,戴辉,宣慧玉.基于Agent的股票市场仿真:个体行为对市场、政策效果的影响[J],系统工程理论方法应用,2005,14(6),p497-501.
[97]高铁梅(编).计量经济分析方法与建模[M],北京,清华大学出版社,2006.
[98]韩文秀,郁俊莉,王其文.我国资本市场混沌特性研究[J],系统工程理论与实践,2002,10,p1043-48.
[99]霍兰,陈禹等(译).涌现-从混沌到秩序[M],上海科学技术出版社,2001.
[100]霍兰,周晓牧(译).隐秩序--适应性造就复杂性[M],上海科技教育出版社,2000.
[101]蒋泽军编著.模糊数学教程[M],北京,国防工业出版社,2004.
[102]李立文,周佩玲,夏懿.虚拟股市中股民的学习对股市运动的影响,计算机仿真,2003,20(11),p95-97.
[103]李仲飞,王寿阳.投资组合优化与无套利分析[M].北京:科学出版社,2001.
[104]刘力.行为金融理论对效率市场假说的挑战[J],经济科学,1993,p63-71.
[105]刘颖,陈禹.复杂适应系统理论对控制SARS疫情的模拟分析[J].复杂系统与复杂性科 学,2004,1(2),p74-80.
[106]刘文财,刘豹,王启文,张维.基于Agent的金融市场模型研究进展综述[J].系统工程学报,2003,18(2),p135-141.
[107]刘海啸.行为金融理论及其对标准金融市场理论的挑战[J],燕山大学学报(哲学社会科学版),2004,5(2),p53-55.
[108]刘晓光,刘晓峰.计算经济学研究新进展--基于Agent的计算经济学透视[J],经济学研究动态,2003,0(11),p58-62.
[109]吕金虎等编著.混沌时间序列分析及应用[M],湖北,武汉大学出版社,2002.
[110]罗弈,黄诒蓉.上海股市非线性结构的BDS检验[J],华北金融,2007,6,p7-9.
[111]马宁.校企联盟的理论与实践研究[D],北京,北京交通大学,2006.
[112]彭作祥著.金融时间序列建模分析[M],四川,西南财经大学出版社,2005.
[113]薛求知,黄佩燕,鲁直等.行为经济学----理论与应用[D],上海,复旦大学,2003.
[114]西蒙.管理行为[M],北京,北京经济学院出版社,1988.
[115]徐龙炳,陆蓉.R/S分析探索中国股票市场的非线性[J],预测,1999,2,p59-62.
[116]熊辉.基于agent的经济模拟研究[D].北京,国家图书馆,2001.
[117]许启发,蒋翠侠.我国股票市场的非线性结构[J],统计与决策,2002,146(2):10-11.
[118]斯科特,施俊琦,王星译.决策与判断[M],北京,人民邮电出版社,2004.
[119]孙广振,王劲松.深圳股市混沌现象的辨识及其讨论[J],数量经济与技术经济,1994,1,p64-67.
[120]盛昭瀚,蒋德鹏.演化经济学[M],上海,上海三联书店,2002.
[121]翁学东.西方行为金融学理论的进展[J],中央财经大学学报,2003,1,p49-52.
[122]伍海华.论证券市场的复杂性:以上海股票市场为例[J],系统仿真学报,2002,14(11),p1486-1490.
[123]闫志刚.上海证券市场GARCH效应检验和模型选择[J],统计与信息论坛,2005,1,p66-69.
[124]杨春霞,王杰,周涛,刘隽,许曼,周佩玲,汪秉宏.基于自组织逾渗的金融市场模型[J],科学通报,2005,20,p127-131。
[125]杨春霞,周佩玲,刘隽,郭立,周涛.基于虚拟股市的演化复杂性度量与分析[J],中国科学技术大学学报,2006,5,p98-103.
[126]易宪容,赵春明.行为金融学[M].北京:社会科学文献出版社,2004.
[127]王明涛.基于R/S法分析中国股票市场的非线性特征[J].预测,2002,3,p42-45.
[128]王春峰,张庆翠,李刚.中国股票市场收益的长期记忆性研究[J],系统工程,2003(1),p 22-28.
[129]王飞跃,Lansing J.S.从人工生命到人工社会--复杂社会系统研究的现状和展望[J].复杂 系统与复杂性科学,2004,1(1),p33-42.
[130]王德河著.资本市场非线性行为研究[M].北京,中国金融出版社,2006.
[131]王德佳.证券市场中的混沌分形现象与分形走动[J],东北大学学报,2001,22(5),p501-504.
[132]张江.基于agent的计算机经济学建模方法及关键技术研究[D],北京,北京交通大学,2006.
[133]张维,黄兴.沪深股市的R/S实证分析[J],系统工程,2001,1,p1-5.
[134]张维,刘文财,王启文,刘豹.面向资本市场复杂性建模:基于Agent计算试验金融学[J].现代财经,2003,23,p4-7.
[135]周洪涛.资本市场的非线性复杂性与投资组合优化[D],武汉,华中科技大学,2004.
[136]周佩玲,夏懿,李立文.虚拟股市的建模与仿真[J],计算机仿真,2002,19(6),p69-72.
[137]周佩玲,杨春霞,周涛,李立文.虚拟股市建模与混沌分析[J],中国科学技术大学学报,2004,4,p61-67。
[138]周涛,周佩玲,江秉宏等.元胞自动机用于金融市场建模[J],复杂系统与复杂性科学,2005,2(4),p10-15.
[139]周炜星.金融物理学导论[M],上海,海财经大学出版社,2007.
[140]周忠英.股票市场价格行为的仿真研究[D],天津,天津商学院,2003.
[141]郑黎明.基于CAS理论构建中国的消费模型[J].复杂性科学研究进展,2004,p177-182.
[142]张晓峒编.计量经济学基础[M],天津,南开大学出版社,2007.
[143]张树人,方美琪.复杂系统建模与仿真[M],中国人民大学出版社,2003.
[144]庄新田.资本市场分形结构及其复杂性[M],辽宁,东北大学出版社,2003.
[145]史永东,赵永刚.中国证券市场非线性特征的实证分析[J],中国管理科学,2006,14,p251-254.
[2]Antoniewicz R.,A causal relationship between stock returns and volume[J],Board of Governors of the Federal Reserve system Finance and Economics Discussion Series,1992,8:50.
[3]Arthur W.B.,Holland J.,Lebaron B.et al.Asset Pricing Endogenous Expectations in an Artificial Stock Market[A].In Arthur W.B.,Durlauf S.,Lane D.The Economy as an Evolving Comples System Ⅱ[C]Bostom:Addison-Wesley,1997,15-44.
[4]Axelrod R.The Complexity of Cooperation[MJ.Princeton University Press,1997.
[5]Barberis N.,Shleifer A.and Vishny R.,A Model of Investor Seniment[J],Journal of Financial Economics,1998,49(3),307-343.
[6]Barberis N.,Huang M.and Santos T.,Prospect Theory and Asset Prices[J],the Quarterly Journal of Economics,2001,116:1-53.
[7]Bollerslev.T,Generalized Autoregressive Conditional Heteroskedasticity[J],Jouranl of Econometrics,1986,31:307-327.
[8]Bondt W and Thaler R,Do Security Analysts Overreact?[J].American Economic Review,1990,80:52-57.
[9]Bourgine P.,Nadal J.P.Cognitive Economics:An Interdisciplinary Approach[M].Springer,2004.
[10]Brad B.and Terrance O.,Boys will be Boys:Gender,Overreaction,and Common Stock Investment[J],The Quarterly Journal of Economics,2001,116,261-292.
[11]Bray M.Learning,Estimation and the Stability of Expectations[J].Journal of Economic Theory,1982,26:318-339.
[12]Brock,Potter S.M.,diagnostic testing for nonlinearity,chaos,and general dependence in time series data[J],in M.Casdagli and S.Eubank(eds.),Nonlinear Modeling and Forecasting:Proceedings of a Workshop on Nonlinear Modeling and Forecasting held September 1990 in Santa Fe,New Mexico(Redwood City,Calif.,Addison-Wesley),1992,137-159.
[13]Carley K.M.,Prietula M.J.Computational Organization Theory[M].Lawrence Erlbaum Associates,1994.
[14]Chen Shu-Heng,Yeh Chia-Hsuan.Evolving Traders and the Business School with Genetic Programming:A New Architecture of the Agent-based Artificial Stock Market[J].Journal of Economic Dynamic& Control,2001,25:363-393.
[15]Claudia P.W.,Ebenoth E.An Adaptive Toolbox Model:a pluralistic modeling approach for human Behaviour based on observation[J].Journal of Artificial Societies and Social Simulation,2004,7(1):66-74.
[16]Cohen K.J.,Maier S.R,Schwartz R.A.A Simulation Model of Stock Exchange Trading[J],Simulation,1983,41:181-191.
[17]Cootner,R,The random character of stock market price[M],Cambridge:Massachusetts Institute of Technology press,1964.
[18]Cutler D,Poterba J and Summers L.Speculative Dynamics[J].Review of Economic Studies,1991,58:529-546.
[19]Daniel K,Hirshleifer D.and Subrahmanyam A.,Investor Psychology and Security Market Under and Overreactions[J],Journal of Finance,1998,53(6),1839-1886.
[20]De Grauwe P.,Dewachter H.,Embrechts M.Exchange Rate Theory:Chaotic Models of Foreign Exchange Markets[M].Blackwell,Oxford,1993.
[21]Errunza V,Hogan K.,Kini O.Conditional Heteroskedasticity and Global Stock Return distributions[J].Financial Review,1994,29(3):187-203.
[22]Edmonds B.,Moss S.From KISS to KIDS-an “anti-simplistic” modeling approach[J],2005,Springer:130～144.
[23]Elton E.J.,Gruber M.J.On the Optimality of Some Multi-period Portfolio Selection Criteria[J].Journal of Business,1974,7:231-243.
[24]Engle,R.F,Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflations[J],Econometrica,1982,50:987-1007.
[25]Fama,E.Efficient Capital Markets:A Reviews of Theory and Empirical Work[J],Journal of Finance,1970,25,p280-294.
[26]Fleischmann A.:A Model for a Simple Luhmann Economy[J].Journal of Artificial Societies and Social Simulation,2005,8(2):281-312.
[27]Frankel J.A.,Froot K.A.Explaining the Demand for Dollars:International of Return and the Expectations of Chartists and Fundamentalists[M].Westview Press,Boulder,CO.,1988.
[28]Gallant A.R.,Rossi P.E.,Tauchen G,Stock prices and volume[J],Review of Financial Studies,1992,5(2):199-242.
[29]Galluccio S.,Caldarelli G,Marsili M.,Scaling in currency exchange[J],Physica A,1997,245:423-436.
[30]Gopikrishnan P.,Plerou V.,Amaral N.,Scaling of the distribution of fluctuations of financial market indices[J],Phys.Rev.E,1999,60(5):5305-5316.
[31]Granger C.W.J.,Investingating causal relations by econometric models and cross-spectral methods[J],Econometrica,1969,37:424-438.
[32]Grossman S.,Stiglitz F.On the Impossibility of Informational Efficient Markets[J].American Economic Review,1980,70:393-408.
[33]Hersh S.and Meir S.,The Disposition to Sell Winners too early and Rids Losers too long:Theory and Evidence[J],Journal of Finance,1984,40,777-790.
[34]Hiemstra C,Jones J.D.,Testing for linear and nonlinear Granger causality in the stock price-volume relation[J],Journal of Finance,1994,49:1639-1664.
[35]Hong H.,Stein J.C.,A Unified Theory of Underreaction,Monentum Trading and Overreaction in Asset Markets[J],Journal of finance,1999,54(6),2143-2184
[36]Hsieh D.A.Testing for nonlinear dependence in daily foreign exchange rates[J],Journal of Business,1989,62:339-368.
[37]Hsieh D.A.Chaos and nonlinear dynamics:applications to financial markets[J],Journal of Finance,1991,46:1839-1877.
[38]Hurst H.Long Term Storage Capacity of Reservoirs[J].Transactions of the American Society of Civil Engineers,1951,116:770-799.
[39]Karpoff J.M.,The relationship between price changes and trading volume:a survey[J],Journal of Financial and Quantitative Analysis,1987,22(1):109-126.
[40]Kahneman D.and Tversky A.,Prospect Theory:an Analysis of Decision under Risk[J],Econometrica,1979,47(2),263-291.
[41]Kim G,Markowitz H.Investment Rules,Margin and Market Volatility[J],Journal of Portfolio Management,1989,16(1):45-52.
[42]LeBaron B.,Arthur W.B.,Palmer R.Times Series Properties of an Artificial Stock Market[J].Journal of Economic Dynamic& Control,1999,23:1487-1516.
[43]Lebaron B.,A builder's guide to agent-based financial markets[J],Quantitative Finance 1(2),254-261.
[44]LeBaron B.Financial Market Efficiency in a Co-evolutionary Environment[C].Proceedings of the Workshop on Simulation of Social Agents:Architectures and Institutions,Argonne National Laboratories,2001,33-51.
[45]LeBaron B.Short-memory Traders and Their Impact on Group Learning in Financial Markets[C].Proceedings of the National Academy of Science:Colloquium 99,2002,7201-7206
[46]Leydesdorff L.Anticipatory Systems and the Processing of Meaning:a Simulation Study Inspired by Luhmann's Theory of Social Systems[J].Journal of Artificial Societies and Social Simulation,2005,8(2):258-280.
[47]Li T.Y.,York J.A.Period Three means Chaos[J].American Mathematical Monthly,1975,(82):985-992.
[48]Lintner J.The Valuation of Risk Assets and the Selection of Risky Investment in Stock Portfolio and Capital Budgets[J].Review of Economic and Statistics,1965,47:13-37.
[49]Liu Y.,Cizeau P.,Meyer M.,Correlations in economic time series[J],Physica A,1997,245:437-440.
[50]Luhmann N.Ecological Communication[M].The University of Chicago Press,1989.
[51]Mantegna R.N.,Stanley H.E.,Scaling behaviour in the dynamics of an economic index[J],Nature,1995,376:46-49.
[52]Markowitz H.Portfolio Selection[J].The Journal of Finance,1952,7(1):77-91.
[53]MarkOWitz H.著,朱菁,欧阳向军译,资产组合选择和资本市场的均值[J],上海三联书店,1999.
[54]Maturana H.R.,Verela F.I.Autopoiesis and cognition[M].D.Reidel Publishing Company,1980.
[55]Mehra,R.and E.C.Prescott,the Equity premium,A Puzzle.Journal of Monetary Economics,1985,15,145-161.
[56]Merton R.C.Continuous-time Finance[M].Cambridge,MA:Basil Blackwell,1990.
[57]Mossin J.Optimal Muti-period Portfolio Policies[J].The Journal of Business,1968,41:215-229.
[58]Nelson,D.,Conditional Heteroskedasticity in asset returns:A new approach[J],Econometrica,1991,59:347-370.
[59]Peters E.,Chaos and Order in the Capital Markets[M].New York:John Wiley & Sons,1991.
[60]Peters E.,A Chaotic Attractor for the S&P500[J].Financial Analysts Journal,March/April 1991.
[61]Peters E.,R/S Analysis using Logarithmic Returns:A Technical Note[J].Financial Analysts Journal,November/December 1992.
[62]Pingle M.,Tesfatsion L.Non-employment Benefits and the Evolution of Worker-employer Cooperation:Experiments with Real and Computational Agents[J/OL].< http:// www.econ.iastate.edu / tesfatsi / sce2001.pdf>,2001.
[63]Robert J.and Shiller,Human Behavior and the Efficiency of the Financial System[M],NBER Working Paper Series,1998.
[64]Ruey S.Tsay著,潘家柱译.金融时间序列分析[M],北京,机械工业出版社,2007.
[65]Samuelson P.A.Lifetime Portfolio Selection by Dynamic Stochastic Programming[J].The Review of Economics and Statistics,1969,50:239-246.
[66]Scheinkman J.A.,Lebaron B.Nonlinear dynamic and stock returns[J],Journal of Business,1989,62:311-317-331-7.
[67]Sewell S.P.,Stansell S.R.,Lee I.Nonlinearities in Emerging Foreign Capital Markets[J]. Journal of Business Finance and Accounting,1993,20(2):1583-l594.
[68]Sharpe W.Capital Asset Prices:A Theory of Market Equilibrium under Conditions of Risk[J].Journal of Finance,1964,19:425-438.
[69]Shiller R.Do Stock Prices Move Too Much to be justified by Subsequent changes in Dividends[J].American Economic Review,1981,71:421-436.
[70]Shu-Heng Chen,Chia-Hsuan Yeh,on the emergent properties of artificial stock markets:the efficient market hypothesis and the rational expectations hypothesis[J],Journal of Economic Behavior & Organization,2002,49:217-239.
[71]Shu-Heng Chen,Chia-Hsuan Yeh,evolving traders and the business school with genetic programming:a new architecture of the agent-based artificial stock market[J],Journal of Economic Dynamics & Control,2001,25:363-393.
[72]Shu-Heng Chen,Chia-Hsuan Yeh,agent-based computational modeling of the stock price-volume relation[J],Information Sciences,2005,170:75-100.
[73]Siegel J,stocks for the long run[M].3rd edition,New York:McGraw-Hill,2002,158-180.
[74]Simaan Y.Estimation Risk in Portfolio Selection:the Mean Variance Model Versus the Mean Absolute Deviation Model[J].Management Science,1997,43:1437-1446.
[75]Smith K.V.A Transition Model for Portfolio Revision[J].Journal of Finance,1967,22:425-439.
[76]Skjeltorp J.A.Scaling in the Norwegian Stock Market[J].Physical A,2000,283:486-528.
[77]Staller A.,Petta P.Introducing Emotions into the Computational Study of Social Norms:A First Evaluation[J].Journal of Artificial Societies and Social Simulation,2001,4(1):599-630.
[78]Stuzer,M.J,Chaotic Dynamic & Bifurcation in a Macro Model[J],Journal of Economic Dynamic &Control,1980,2.
[79]Sun R.,Naveh I.Simulating Organizational Decision-Making Using a Cognitively Realistic Agent Model[J].Journal of Artificial Societies and Social Simulation,2004,7(3):48-68.
[80]Tesfatsion L.Agent-based Computational Economics[Z].ISU Economics Working Paper No.1,Revised August,2003.
[81]Tversky,A.and Kahneman,D.,Belief in the law of small numbeis[J],Psychological Bulletin,1971,76:105-110.
[82]Tversky,A.and Kahneman,D.,Judgment under uncertainty:Heuristics and biases[J],Science,1974,185:1124-1130.
[83]Tversky,A.and Kahneman,D.,The framing of decisions and the psychology of choice[J],Science,1981,211:453-458.
[84]Tversky,A.and Kahneman,D.,Extensional versus intuitive reasoning:The conjunction fallacy in probability judgment[J],Psychological Review,1983,90:293-315.
[85]Ying C.C.,Stock market prices ans volumes of sales[J],Econometrica,1966,34(3):676-685.
[86]Vriend N.J.:An illustration of the essential difference between individual and social learning,and its consequences for computational analyses.Journal of Economic Dynamics & Control,vol.0(24):1-19,2000.
[87]Von Neumann J,Morgenstern O:Theory of Games and Economic Behavior.Princeton:Princeton Univ.Press,1944.
[88]Wang B.H.,Hui P.M.,The distribution and scaling of fluctuations for Hang Seng index in Hong Kong stock market[J],Eur.Phys.J.B,2001,20:573-579.
[89]Watts D.J.Small Worlds:The Dynamics of Networks between Order and Randomness [M].Princeton University Press,2003.
[90]Wolf J.B.,H.L.Swinney and J.A.Vastano,Determining Lyapunov exponents from a time series [J],Physica 16D,1985,p285-317.
[91]彼得斯著,储海林,殷勤译.分形市场分析-将混沌理论应用到投资与管理理论上[M],北京,经济科学出版社,2002.
[92]蔡文,杨春燕著.《可拓逻辑初步》[M],科学出版社,2003.
[93]陈禹,丁浩.实验经济学、CAS理论与经济建模--复杂性科学研究进展[M].北京:科学出版社,2004,p85-106.
[94]邓凯.基于Agent的金融市场仿真研究[D],上海,上海交通大学,2002.
[95]樊智,张世英.金融市场的效率与分形市场理论[J],系统工程理论与实践,2002,21(3),p13-19.
[96]高宝俊,戴辉,宣慧玉.基于Agent的股票市场仿真:个体行为对市场、政策效果的影响[J],系统工程理论方法应用,2005,14(6),p497-501.
[97]高铁梅(编).计量经济分析方法与建模[M],北京,清华大学出版社,2006.
[98]韩文秀,郁俊莉,王其文.我国资本市场混沌特性研究[J],系统工程理论与实践,2002,10,p1043-48.
[99]霍兰,陈禹等(译).涌现-从混沌到秩序[M],上海科学技术出版社,2001.
[100]霍兰,周晓牧(译).隐秩序--适应性造就复杂性[M],上海科技教育出版社,2000.
[101]蒋泽军编著.模糊数学教程[M],北京,国防工业出版社,2004.
[102]李立文,周佩玲,夏懿.虚拟股市中股民的学习对股市运动的影响,计算机仿真,2003,20(11),p95-97.
[103]李仲飞,王寿阳.投资组合优化与无套利分析[M].北京:科学出版社,2001.
[104]刘力.行为金融理论对效率市场假说的挑战[J],经济科学,1993,p63-71.
[105]刘颖,陈禹.复杂适应系统理论对控制SARS疫情的模拟分析[J].复杂系统与复杂性科 学,2004,1(2),p74-80.
[106]刘文财,刘豹,王启文,张维.基于Agent的金融市场模型研究进展综述[J].系统工程学报,2003,18(2),p135-141.
[107]刘海啸.行为金融理论及其对标准金融市场理论的挑战[J],燕山大学学报(哲学社会科学版),2004,5(2),p53-55.
[108]刘晓光,刘晓峰.计算经济学研究新进展--基于Agent的计算经济学透视[J],经济学研究动态,2003,0(11),p58-62.
[109]吕金虎等编著.混沌时间序列分析及应用[M],湖北,武汉大学出版社,2002.
[110]罗弈,黄诒蓉.上海股市非线性结构的BDS检验[J],华北金融,2007,6,p7-9.
[111]马宁.校企联盟的理论与实践研究[D],北京,北京交通大学,2006.
[112]彭作祥著.金融时间序列建模分析[M],四川,西南财经大学出版社,2005.
[113]薛求知,黄佩燕,鲁直等.行为经济学----理论与应用[D],上海,复旦大学,2003.
[114]西蒙.管理行为[M],北京,北京经济学院出版社,1988.
[115]徐龙炳,陆蓉.R/S分析探索中国股票市场的非线性[J],预测,1999,2,p59-62.
[116]熊辉.基于agent的经济模拟研究[D].北京,国家图书馆,2001.
[117]许启发,蒋翠侠.我国股票市场的非线性结构[J],统计与决策,2002,146(2):10-11.
[118]斯科特,施俊琦,王星译.决策与判断[M],北京,人民邮电出版社,2004.
[119]孙广振,王劲松.深圳股市混沌现象的辨识及其讨论[J],数量经济与技术经济,1994,1,p64-67.
[120]盛昭瀚,蒋德鹏.演化经济学[M],上海,上海三联书店,2002.
[121]翁学东.西方行为金融学理论的进展[J],中央财经大学学报,2003,1,p49-52.
[122]伍海华.论证券市场的复杂性:以上海股票市场为例[J],系统仿真学报,2002,14(11),p1486-1490.
[123]闫志刚.上海证券市场GARCH效应检验和模型选择[J],统计与信息论坛,2005,1,p66-69.
[124]杨春霞,王杰,周涛,刘隽,许曼,周佩玲,汪秉宏.基于自组织逾渗的金融市场模型[J],科学通报,2005,20,p127-131。
[125]杨春霞,周佩玲,刘隽,郭立,周涛.基于虚拟股市的演化复杂性度量与分析[J],中国科学技术大学学报,2006,5,p98-103.
[126]易宪容,赵春明.行为金融学[M].北京:社会科学文献出版社,2004.
[127]王明涛.基于R/S法分析中国股票市场的非线性特征[J].预测,2002,3,p42-45.
[128]王春峰,张庆翠,李刚.中国股票市场收益的长期记忆性研究[J],系统工程,2003(1),p 22-28.
[129]王飞跃,Lansing J.S.从人工生命到人工社会--复杂社会系统研究的现状和展望[J].复杂 系统与复杂性科学,2004,1(1),p33-42.
[130]王德河著.资本市场非线性行为研究[M].北京,中国金融出版社,2006.
[131]王德佳.证券市场中的混沌分形现象与分形走动[J],东北大学学报,2001,22(5),p501-504.
[132]张江.基于agent的计算机经济学建模方法及关键技术研究[D],北京,北京交通大学,2006.
[133]张维,黄兴.沪深股市的R/S实证分析[J],系统工程,2001,1,p1-5.
[134]张维,刘文财,王启文,刘豹.面向资本市场复杂性建模:基于Agent计算试验金融学[J].现代财经,2003,23,p4-7.
[135]周洪涛.资本市场的非线性复杂性与投资组合优化[D],武汉,华中科技大学,2004.
[136]周佩玲,夏懿,李立文.虚拟股市的建模与仿真[J],计算机仿真,2002,19(6),p69-72.
[137]周佩玲,杨春霞,周涛,李立文.虚拟股市建模与混沌分析[J],中国科学技术大学学报,2004,4,p61-67。
[138]周涛,周佩玲,江秉宏等.元胞自动机用于金融市场建模[J],复杂系统与复杂性科学,2005,2(4),p10-15.
[139]周炜星.金融物理学导论[M],上海,海财经大学出版社,2007.
[140]周忠英.股票市场价格行为的仿真研究[D],天津,天津商学院,2003.
[141]郑黎明.基于CAS理论构建中国的消费模型[J].复杂性科学研究进展,2004,p177-182.
[142]张晓峒编.计量经济学基础[M],天津,南开大学出版社,2007.
[143]张树人,方美琪.复杂系统建模与仿真[M],中国人民大学出版社,2003.
[144]庄新田.资本市场分形结构及其复杂性[M],辽宁,东北大学出版社,2003.
[145]史永东,赵永刚.中国证券市场非线性特征的实证分析[J],中国管理科学,2006,14,p251-254.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |