金融复杂系统建模及动力学机制研究
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摘要
由于缺乏在可控的实验条件下重现其结论的条件,传统的经济学、金融学理论往往难以被认同为科学。以概率统计理论为基础的现代资本市场理论(MCMT),为了满足概率统计学的适用条件,提出了理性投资者、有效市场和收益率的随机游动三大假说。然而,无论是对投资者心理和行为分析,还是真实资本市场的规则与现状,以及对真实金融时间序列的实证分析,都对这三大假说提出了质疑。可控金融实验条件的缺乏,也导致了金融领域的数量化分析仍然是一种经验主义的方法。
科学计算经过长足发展已经成为继科学实验、科学理论之后的第三大科学研究方法。许多传统上难以进行理论推导或实验验证的问题可以借助计算机仿真的方法进行探索。上个世纪五十年代以来兴起的复杂性科学与复杂系统建模方法成为将实际问题转换为计算模型的强大工具。复杂性理论、复杂系统的建模方法被运用到经济学、金融学领域,为采用计算方法探索金融市场的内在动力学机制带来了曙光。特别是以圣塔菲研究院的圣塔菲人工股票市场(SFI-ASM)为代表的人工金融市场建模方法开辟了实验金融领域的全新方法。由于复杂系统建模方法的强大表达能力,行为金融学、金融心理学等新兴相关领域的假设、观念和结论可以在人工金融市场模型中得到表达和验证,从而与实验金融学相互促进。
主流的圣塔菲人工股票市场类型的人工金融市场,是以多智能体(Multi-Agent)建模技术为基础的。多智能体模型适于描述投资者群体中异质的个体投资行为,但缺乏对于投资者个体相互作用关系的表达能力。然而,资本市场中的群体行为、投资者之间的信息传播和相互影响是普遍存在的。由于元胞自动机对于复杂系统中个体间相互作用关系有着形式化的规范描述方法,弥补了多智能体技术的不足,因此也成为一些新兴的人工金融市场模型的基础。但已经出现的基于元胞自动机理论的人工金融市场,往往受限于经典元胞自动机的拓扑结构,在描述投资者个体间相互作用关系方面具有先验性。此外,这些模型在资本市场结构的表达方面尚不完备,其成熟度尚不能与圣塔菲人工股票市场类型的人工金融市场相提并论。
由于人工金融市场在金融复杂系统探索方面的巨大前景,以及现有人工金融市场模型的不足,本文作者在元胞自动机的理论基础上,从金融复杂系统理论、建模方法、实验技术、实验数据分析方法等几个方面出发,通过对真实资本市场运作机制的抽象,建立了一个比较完整的“涌现”-人工金融市场建模框架(E-AFMF)。E-AFMF可以为金融复杂系统的理论验证提供工具和方法。
首先,作者将经典元胞自动机扩展为具有网状拓扑结构和异质元胞个体的抽象元胞自动机,并给出其形式化定义。接着,在C++模板技术和微软并发运行时技术的基础上,实现了一个通用的“元胞自动机并行模板库”(CAPTL)。在模型的描述能力方面,CAPTL采用了一系列技术为建立不同类型的元胞自动机提供了最大限度的弹性。这包括通过C++模板技术将元胞状态类型参数化;实现了元胞对象的抽象基类,具体的模型中可以通过建立元胞对象的继承体系来实现异质的元胞个体;采用网状拓扑来描述元胞自动机的邻居关系结构,以获得对任意离散关系结构的描述能力。而在模型的仿真技术方面,CAPTL在微软并发运行时并行计算技术基础上,实现了元胞自动机模型的线程级并行模型,可以在共享内存体系结构的并行机上进行模型的并行仿真。
E-AFMF是在CAPTL的基础上建立的。作者将人工金融市场中的投资者群体作为一个具有网状邻居关系结构的异质元胞自动机加以实现。同时,作者针对真实资本市场的组织结构、运行机制、交易过程、价格形成机制,以及投资者群体间的信息传播方式和相互作用形式进行了合理的抽象,并实现了相应的组织框架。E-AFMF在CAPTL细粒度的并行模型基础上,增加了粗粒度并行模型来实现投资者群体之外的其他模块,最终形成了一个自我反馈迭代的复杂动力系统建模框架。在E-AFMF的支持下,只需要针对投资者行为、信息的形式和传播关系结构、交易规则和价格形成机制进行灵活定义,就可以定制出一个全新的人工金融市场模型。
建立人工金融市场模型的目的是为了检验资本市场宏观运动特征与其内在微观结构之间的关系。任何基于E-AFMF的人工金融市场模型都将产生一个与真实资本市场相似的价量时间序列,以及一系列关于人工金融市场中投资者群体内在结构的指标时间序列。人工金融市场的价量时间序列可以采用与真实资本市场相同的分析方法,与真实金融时间序列相比较;同时也可以与人工金融市场中微观结构指标时间序列进行比较研究。这样,基于E-AFMF的人工金融市场就成了一个结合金融理论实验与金融市场实证研究的工具。
E-AFMF是将复杂系统建模的理论、方法和并行仿真技术应用于实验金融领域的创造性尝试。E-AFMF的深入发展和应用将为资本市场理论带来新的研究方法;而来自基于E-AFMF的人工金融市场模型的研究成果,也将对真实金融市场的风险预警和管理带来有益的启示。
Because of the lack of the controllable experimental condition under which the conclusions could be reproduced, traditional economic and financial theories are hard to be recognized as science. In order to satisfy the applicable conditions of Probability and Statistics, the Probability and Statistics based Modern Capital Market Theory (MCMT), was built on three hypotheses: rational investor, efficient market, and the random walk of yield rate. However, there are many doubts about these hypotheses, which come from the investors’psychology and behaviors analyses, rules and realities of the real capital market, and the positive analyses about the real financial time series. Above all, the lack of financial experimental conditions causes the quantization analyses in financial field still belong to empiricism methodology.
After great development for decades, the Scientific Computation becomes the third kind of scientific research methodology, succeeding the Scientific Theory and the Scientific Experiment. Many problems, which are traditionally difficult to be proved by theoretical derivation or experimental verification, could be explored by computational simulation approaches. Since 1950s, the complexity science and the complex system modeling methods have been developed, and have become the powerful tools to transform the real problems to computational models. The application of the complexity theories and the complex system modeling methods to economic and financial fields brings a new ray of hope for the research on the internal dynamical mechanisms of financial markets. Especially, the Artificial Financial Market models, led by the Santa Fe Institute Artificial Stock Market (SFI-ASM), opened up a brand-new kind of research methodology in Experimental Finance Field. With the help of the powerful describing abilities of the complex system modeling methods, the hypotheses, concepts, and conclusions of new related fields, such as the Behavioral Finance and the Psychology of Finance, could be expressed or verified in the artificial financial market models. Then, these new theories and the experimental finance could be promoted mutually.
The mainstream artificial financial markets like SFI-ASM, are based on the Multi-Agent modeling technology. The Multi-Agent models are advanced in describing heterogeneous individual investors’behaviors. But they lack the ability to express the interactions within the investors. Herd behavior, information spreading, and individuals’interactions, however, are ubiquitous in real capital market. The Cellular Automata, benefiting from their formalized normal description method about correlations within individuals, can cover the shortage of the Multi-Agent technology. So the cellular automata become the foundation of another kind of artificial financial markets. However, because most pre-existing cellular automata based artificial financial markets.
Most pre-existing cellular automata based artificial financial markets, however, adopted classiccellular automata’s topology as their neighborhood structure. This caused the relations within individuals in these models are given apriori. Furthermore, these models described the details in capital market structure imperfectly. So, their maturity could not be compared with multi-agent based artificial financial markets like SFI-ASM.
The artificial financial markets have great prospects in exploring financial complex system, but the pre-existing cellular automata based artificial financial markets still have shortages mentioned above. On the basis of cellular automata theory, we studied the financial complex system’s theory, modeling methods, experimental technology, analysis methods of experimental data etc. By means of abstracting the operating mechanism of real capital markets, we built a complete artificial financial market modeling framework, which was named as Emergence-Artificial Financial Market Framework, E-AFMF. E-AFMF could provide tools for financial complex system theories’tests and verifications.
First of all, we extended the classic definition cellular automata to an abstract one with network topology and heterogeneous individual cells. And we promoted the formal definition of it. Next, on the basis of C++ template and Microsoft’s Concurrency Runtime technology, we realized a universal Cellular Automata Parallel Template Library, CAPTL. On the models’description ability side, a series of technologies are adopted in CAPTL to provide max flexibility for various cellular automata. These include C++ template technology to parameterize cell states’type; abstract base class of cell objects, which is used to build an inheritance hierarchy to realize heterogeneous cell individuals in a concrete model; and network topology which is used to represent neighborhoods of cellular automata, so that any kind of discrete structure could be expressed in a model. On the models’simulation technology side, a Concurrency Runtime based thread level parallel model of cellular automata has been realized in CAPTL. Any CAPTL based cellular automata model could be simulated in shared memory parallel machines.
E-AFMF is built on the basis of CAPTL. Investors in artificial financial market are realized as a heterogeneous cellular automata model with a network neighborhood. Meanwhile, we abstracted the organization structure, operation mechanism, trading process, pricing mechanism of the real capital market, the information transmission model and the interaction pattern within investors reasonably. And we realized a corresponding framework for these concepts in E-AFMF. Extended from the fine-grained parallel model in CAPTL, a coarse-grained parallel model was added into E-AFMF to realize other modules except investors in the artificial financial market. Then, the E-AFMF formed an adaptive feedback-iterated modeling framework for the complex dynamic systems. Benefiting by the E-AFMF, we can customize a brand new artificial financial market model flexibly, only if we defined the behavior rule of investors, the information’s form and transmission model.
The purpose of building an artificial financial market model is to verify the relationship between the macroscopic dynamic feature and the microcosmic inner structure of the capital market. Any E-AFMF based artificial financial market model could produce a price-volume time series, which has the same form with the price-volume time series of the real capital market, and several indicator time series, which are about the inner structure of the investors in the artificial financial market model. The price-volume time series produced by artificial financial market models could be analyzed in the same way as which produced by the real capital markets. Meanwhile, we can we can comparatively study the price-volume time series and the indicator time series of the investors inner structure. So, the E-AFMF based artificial financial market models could become powerful research tools combining financial theory experiment and empirical analyses.
The E-AFMF is a creative trial, in which the complex system modeling theories, methods, and parallel simulation technology are applied to the experimental finance field. The in-depth development of E-AFMF could bring a brand new research method for the capital market theory. Furthermore, the research achievements of the E-AFMF based artificial financial market models could be valuable for the risk warning and management of the real capital market.
科学计算经过长足发展已经成为继科学实验、科学理论之后的第三大科学研究方法。许多传统上难以进行理论推导或实验验证的问题可以借助计算机仿真的方法进行探索。上个世纪五十年代以来兴起的复杂性科学与复杂系统建模方法成为将实际问题转换为计算模型的强大工具。复杂性理论、复杂系统的建模方法被运用到经济学、金融学领域,为采用计算方法探索金融市场的内在动力学机制带来了曙光。特别是以圣塔菲研究院的圣塔菲人工股票市场(SFI-ASM)为代表的人工金融市场建模方法开辟了实验金融领域的全新方法。由于复杂系统建模方法的强大表达能力,行为金融学、金融心理学等新兴相关领域的假设、观念和结论可以在人工金融市场模型中得到表达和验证,从而与实验金融学相互促进。
主流的圣塔菲人工股票市场类型的人工金融市场,是以多智能体(Multi-Agent)建模技术为基础的。多智能体模型适于描述投资者群体中异质的个体投资行为,但缺乏对于投资者个体相互作用关系的表达能力。然而,资本市场中的群体行为、投资者之间的信息传播和相互影响是普遍存在的。由于元胞自动机对于复杂系统中个体间相互作用关系有着形式化的规范描述方法,弥补了多智能体技术的不足,因此也成为一些新兴的人工金融市场模型的基础。但已经出现的基于元胞自动机理论的人工金融市场,往往受限于经典元胞自动机的拓扑结构,在描述投资者个体间相互作用关系方面具有先验性。此外,这些模型在资本市场结构的表达方面尚不完备,其成熟度尚不能与圣塔菲人工股票市场类型的人工金融市场相提并论。
由于人工金融市场在金融复杂系统探索方面的巨大前景,以及现有人工金融市场模型的不足,本文作者在元胞自动机的理论基础上,从金融复杂系统理论、建模方法、实验技术、实验数据分析方法等几个方面出发,通过对真实资本市场运作机制的抽象,建立了一个比较完整的“涌现”-人工金融市场建模框架(E-AFMF)。E-AFMF可以为金融复杂系统的理论验证提供工具和方法。
首先,作者将经典元胞自动机扩展为具有网状拓扑结构和异质元胞个体的抽象元胞自动机,并给出其形式化定义。接着,在C++模板技术和微软并发运行时技术的基础上,实现了一个通用的“元胞自动机并行模板库”(CAPTL)。在模型的描述能力方面,CAPTL采用了一系列技术为建立不同类型的元胞自动机提供了最大限度的弹性。这包括通过C++模板技术将元胞状态类型参数化;实现了元胞对象的抽象基类,具体的模型中可以通过建立元胞对象的继承体系来实现异质的元胞个体;采用网状拓扑来描述元胞自动机的邻居关系结构,以获得对任意离散关系结构的描述能力。而在模型的仿真技术方面,CAPTL在微软并发运行时并行计算技术基础上,实现了元胞自动机模型的线程级并行模型,可以在共享内存体系结构的并行机上进行模型的并行仿真。
E-AFMF是在CAPTL的基础上建立的。作者将人工金融市场中的投资者群体作为一个具有网状邻居关系结构的异质元胞自动机加以实现。同时,作者针对真实资本市场的组织结构、运行机制、交易过程、价格形成机制,以及投资者群体间的信息传播方式和相互作用形式进行了合理的抽象,并实现了相应的组织框架。E-AFMF在CAPTL细粒度的并行模型基础上,增加了粗粒度并行模型来实现投资者群体之外的其他模块,最终形成了一个自我反馈迭代的复杂动力系统建模框架。在E-AFMF的支持下,只需要针对投资者行为、信息的形式和传播关系结构、交易规则和价格形成机制进行灵活定义,就可以定制出一个全新的人工金融市场模型。
建立人工金融市场模型的目的是为了检验资本市场宏观运动特征与其内在微观结构之间的关系。任何基于E-AFMF的人工金融市场模型都将产生一个与真实资本市场相似的价量时间序列,以及一系列关于人工金融市场中投资者群体内在结构的指标时间序列。人工金融市场的价量时间序列可以采用与真实资本市场相同的分析方法,与真实金融时间序列相比较;同时也可以与人工金融市场中微观结构指标时间序列进行比较研究。这样,基于E-AFMF的人工金融市场就成了一个结合金融理论实验与金融市场实证研究的工具。
E-AFMF是将复杂系统建模的理论、方法和并行仿真技术应用于实验金融领域的创造性尝试。E-AFMF的深入发展和应用将为资本市场理论带来新的研究方法;而来自基于E-AFMF的人工金融市场模型的研究成果,也将对真实金融市场的风险预警和管理带来有益的启示。
Because of the lack of the controllable experimental condition under which the conclusions could be reproduced, traditional economic and financial theories are hard to be recognized as science. In order to satisfy the applicable conditions of Probability and Statistics, the Probability and Statistics based Modern Capital Market Theory (MCMT), was built on three hypotheses: rational investor, efficient market, and the random walk of yield rate. However, there are many doubts about these hypotheses, which come from the investors’psychology and behaviors analyses, rules and realities of the real capital market, and the positive analyses about the real financial time series. Above all, the lack of financial experimental conditions causes the quantization analyses in financial field still belong to empiricism methodology.
After great development for decades, the Scientific Computation becomes the third kind of scientific research methodology, succeeding the Scientific Theory and the Scientific Experiment. Many problems, which are traditionally difficult to be proved by theoretical derivation or experimental verification, could be explored by computational simulation approaches. Since 1950s, the complexity science and the complex system modeling methods have been developed, and have become the powerful tools to transform the real problems to computational models. The application of the complexity theories and the complex system modeling methods to economic and financial fields brings a new ray of hope for the research on the internal dynamical mechanisms of financial markets. Especially, the Artificial Financial Market models, led by the Santa Fe Institute Artificial Stock Market (SFI-ASM), opened up a brand-new kind of research methodology in Experimental Finance Field. With the help of the powerful describing abilities of the complex system modeling methods, the hypotheses, concepts, and conclusions of new related fields, such as the Behavioral Finance and the Psychology of Finance, could be expressed or verified in the artificial financial market models. Then, these new theories and the experimental finance could be promoted mutually.
The mainstream artificial financial markets like SFI-ASM, are based on the Multi-Agent modeling technology. The Multi-Agent models are advanced in describing heterogeneous individual investors’behaviors. But they lack the ability to express the interactions within the investors. Herd behavior, information spreading, and individuals’interactions, however, are ubiquitous in real capital market. The Cellular Automata, benefiting from their formalized normal description method about correlations within individuals, can cover the shortage of the Multi-Agent technology. So the cellular automata become the foundation of another kind of artificial financial markets. However, because most pre-existing cellular automata based artificial financial markets.
Most pre-existing cellular automata based artificial financial markets, however, adopted classiccellular automata’s topology as their neighborhood structure. This caused the relations within individuals in these models are given apriori. Furthermore, these models described the details in capital market structure imperfectly. So, their maturity could not be compared with multi-agent based artificial financial markets like SFI-ASM.
The artificial financial markets have great prospects in exploring financial complex system, but the pre-existing cellular automata based artificial financial markets still have shortages mentioned above. On the basis of cellular automata theory, we studied the financial complex system’s theory, modeling methods, experimental technology, analysis methods of experimental data etc. By means of abstracting the operating mechanism of real capital markets, we built a complete artificial financial market modeling framework, which was named as Emergence-Artificial Financial Market Framework, E-AFMF. E-AFMF could provide tools for financial complex system theories’tests and verifications.
First of all, we extended the classic definition cellular automata to an abstract one with network topology and heterogeneous individual cells. And we promoted the formal definition of it. Next, on the basis of C++ template and Microsoft’s Concurrency Runtime technology, we realized a universal Cellular Automata Parallel Template Library, CAPTL. On the models’description ability side, a series of technologies are adopted in CAPTL to provide max flexibility for various cellular automata. These include C++ template technology to parameterize cell states’type; abstract base class of cell objects, which is used to build an inheritance hierarchy to realize heterogeneous cell individuals in a concrete model; and network topology which is used to represent neighborhoods of cellular automata, so that any kind of discrete structure could be expressed in a model. On the models’simulation technology side, a Concurrency Runtime based thread level parallel model of cellular automata has been realized in CAPTL. Any CAPTL based cellular automata model could be simulated in shared memory parallel machines.
E-AFMF is built on the basis of CAPTL. Investors in artificial financial market are realized as a heterogeneous cellular automata model with a network neighborhood. Meanwhile, we abstracted the organization structure, operation mechanism, trading process, pricing mechanism of the real capital market, the information transmission model and the interaction pattern within investors reasonably. And we realized a corresponding framework for these concepts in E-AFMF. Extended from the fine-grained parallel model in CAPTL, a coarse-grained parallel model was added into E-AFMF to realize other modules except investors in the artificial financial market. Then, the E-AFMF formed an adaptive feedback-iterated modeling framework for the complex dynamic systems. Benefiting by the E-AFMF, we can customize a brand new artificial financial market model flexibly, only if we defined the behavior rule of investors, the information’s form and transmission model.
The purpose of building an artificial financial market model is to verify the relationship between the macroscopic dynamic feature and the microcosmic inner structure of the capital market. Any E-AFMF based artificial financial market model could produce a price-volume time series, which has the same form with the price-volume time series of the real capital market, and several indicator time series, which are about the inner structure of the investors in the artificial financial market model. The price-volume time series produced by artificial financial market models could be analyzed in the same way as which produced by the real capital markets. Meanwhile, we can we can comparatively study the price-volume time series and the indicator time series of the investors inner structure. So, the E-AFMF based artificial financial market models could become powerful research tools combining financial theory experiment and empirical analyses.
The E-AFMF is a creative trial, in which the complex system modeling theories, methods, and parallel simulation technology are applied to the experimental finance field. The in-depth development of E-AFMF could bring a brand new research method for the capital market theory. Furthermore, the research achievements of the E-AFMF based artificial financial market models could be valuable for the risk warning and management of the real capital market.
引文
[1]柴立和,杨战.复杂性科学的几大学派及其研究特点[J].现代物理知识,2004.06:25-30.
[2] [美]蒯因.著,江天骥译.从逻辑的观点看[M].上海:上海译文出版社,1987:19.
[3]郭元林.论复杂性科学的诞生[J].自然辩证法通讯,第27卷,总157期,2005年03期. 2005:53-70.
[4]贝塔朗菲.著,林康义,魏宏森等译.一般系统论:基础、发展和应用[M].北京:清华大学出版社, 1987: 2
[5] [美]E.N络伦兹.著,刘式达等译.周秀骥审.混沌的本质[M].北京:气象出版社,1997.
[6] Tien-Yien Li, James A.Yorke. Period Three Implies Chaos[J]. The American Mathematical Monthly, Vol.82, No. 10. 1975, pp.985~992.
[7] John Henry Holland.著,周晓牧等译.隐秩序:适应性造就复杂性[M].上海:上海科技教育出版社.2008.
[8] John Henry Holland著,陈禹等译.涌现:从混沌到有序[M].上海:上海世纪出版集团,2006.
[9] Stephen Wolfram. Computation Theory of Cellular Automata, Communications in Mathematical Physics[J]. Springer Berlin / Heidelberg, Volume 96, Number 1. Mar., 1984, pp.15~57.
[10] Stephen Wolfram. A New Kind of Science[M]. Wolfram Media, 1 edition. May 14, 2002.
[11] John N. Warfield. A Science of Generic Design: Managing Complexity Through Systems Design[M]. Iowa State Press, 2 Sub edition, January 30, 1995.
[12]宋华岭,刘丽娟,翟从敏.解读结构基础学派复杂性理论的定律、分类及指标体系[J],系统辨证学学报,第12卷,2004年04期,2004:23-30.
[13] Bastien Chopard, Michel Droz. Cellular Automata Modeling of Physical Systems[M]. Cambridge University Press, June 30, 2005.
[14] Tommaso Toffoli, Norman Margolus. Cellular Automata Machines: A New Environment for Modeling[M]. The MIT Press; First Edition edition (April 22, 1987) , pp.119~136.
[15] Jesse Nochella. Cellular Automata on Networks[C]. NKS 2006 Wolfram ScienceConference, June 2006.
[16] S. Bandinia, G. Mauria, R Serra. Cellular automata: From a theoretical parallel computational model to its application to complex systems[C].Parallel Computing. Cellular automata: From modeling to applications, Volume 27, Issue 5, April 2001, pp.539~553.
[17] G. Braga, G. Cattaneo., P. Flocchinib, and C. Quaranta Vogliotti. Pattern growth in elementary cellular automata[J]. Theoretical Computer Science, Volume 145, Issues 1-2, 10 July 1995, pp.1~26.
[18] G.A. Hedlund. Endomorphisims and automorphisims of the shift dynamical system[J]. Math. System Theory 3.1969, pp.320~375.
[19] D. Richardson. Tessellations with local transformations[J].Journal of Computer and System Sciences. Sci. 5, 1972,pp.373~388.
[20] Howard Gutowitz. Cellular automata: theory and experiment[M]. MIT Press. 1st edition, 1991, pp.159~180.
[21] M. Delorme., J. Mazoyer. Cellular Automata: A Parallel Model (Mathematics and Its Applications)[M], Springer, 1st edition, December 31, 1998.
[22] Domenico Talia, Lev Naumov. Parallel Cellular Programming for Emergent Computation[J], Understanding Complex Systems, 2010, Volume 0/2010, DOI: 10.1007/978-3-642-12203-3_15, pp.357~384.
[23] Anatol Rapoport. Spread of information through a population with sociostructural bias: I. Assumption of transitivity[J].Bulletin of Mathematical Biophysics, 1953, 15, pp.523~533.
[24] Linton Freeman. The Development of Social Network Analysis[M]. Vancouver: Empirical Press, 2006.
[25] Watkins, J. J. and Wilson, R. J. "A Survey of Snarks." In Graph Theory[J].Combinatorics, and Applications (Ed. Y. Alavi, G. Chartrand, O. R. Oellermann, and A. J. Schwenk). New York: Wiley, 1991, pp.1129~1144.
[26] [美]瓦茨.著,陈禹.等译.小小世界:有序与无序之间的网络动力学[M].北京:中国人民大学出版社,2006,第1版.
[27] Duncan J. Watts. The Dynamics of Networks between Order and Randomness (Princeton Studies in Complexity)[M]. Princeton University Press; illustrated edition edition, November 24, 2003.
[28] Edgar E. Peters. Chaos and Order in the Capital Markets: A New View of Cycles, Prices, and Market Volatility (Wiley Finance)[M]. Wiley; 2 edition, August 1996.
[29] N. H. Packard, J.P. Crutchfield, J. D. Farmer, R. S. Shaw. Geometry of Time Series[J]. Physical Review Letters, Volume 45, Number 9, 1980.
[30] Mandelbrot, B.B. The Fractal Geometry of Nature[M]. W.H. Freeman and Company,1982.
[31] Preis, T. et al. Accelerated fluctuation analysis by graphic cards and complex pattern formation in financial markets[J]. New J. Phys. 11 093024. 2009.
[32] Mandelbrot, Beno?t B. The (Mis)Behavior of Markets, A Fractal View of Risk, Ruin and Reward[J]. Basic Books, 2004, pp.186~195.
[33] Kristoufek, Ladislav. R/S analysis and DFA: finite sample properties and confidence intervals[EB/OL]. http://mpra.ub.uni-muenchen.de/16446/, 2009.
[34] Cencini et al., M. World Scientific. ed. Chaos From Simple models to complex systems[M]. ISBN 9814277657, 2010.
[35] X. Zeng, R. Eykholt, and R. A. Pielke. Estimating the Lyapunov-exponent spectrum from short time series of low precision[J]. Physical Review Letters 66 (25): 3229. doi:10.1103/PhysRevLett.66.3229. PMID 10043734.1991.
[36] Kirman, Alan. Economic theory and the crisis[J/OL]. Voxeu. 14 November 2009. http://www.voxeu.org/index.php?q=node/4208.
[37] Warren Buffett. Preface to the Fourth Edition[J]. The Intelligent Investor, 4 ed., Benjamin Graham, 2003.
[38] William F. Sharpe. Autobiography[M]. in The Nobel Prizes 1990, Editor Tore Fr?ngsmyr, Nobel Foundation, Stockholm, 1991.
[39] Fama, Eugene F., French, Kenneth R. Common Risk Factors in the Returns on Stocks and Bonds[J]. Journal of Financial Economics 33 (1): 3–56. doi:10.1016/0304-405X(93)90023-5,1993.
[40] Markowitz, H.M. Portfolio Selection[J]. The Journal of Finance 7 (1): 77–91. doi:10.2307/2975974, March 1952.
[41] Black, Fischer., Michael C. Jensen, and Myron Scholes. The Capital Asset Pricing Model: Some Empirical Tests[M].in M. Jensen ed., Studies in the Theory of Capital Markets, Praeger Publishers, 1972, New York, pp.79~121.
[42] Fama, Eugene F. and Kenneth French. The Cross-Section of Expected Stock Returns[J]. Journal of Finance, June 1992, pp.427-466.
[43] Khalid Alkhathlan, Sella Prabakaran. Memory Effects on Saudi Arabian Stock Market– Empirical Evidence[J].Enterprise Risk Management, ISSN 1937-7916, Vol.1, No. 2: E2, 2009.
[44] Edgar E. Peters. Fractal Market Analysis: Applying Chaos Theory to Investment and Economics[M]. Wiley; 1 edition, January 12, 1994.
[45] 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,2006.
[46] George Box, Gwilym M. Jenkins, and Gregory C. Reinsel. Time Series Analysis: Forecasting and Control[M]. third edition. Prentice-Hall, 1994.
[47] Engle, R.F., Ng, V.K. Measuring and testing the impact of news on volatility [J]. Journal of Finance 48 (5),1982,pp.1749~1778.
[48] Tim Bollerslev. Generalized Autoregressive Conditional Heteroskedasticity[J]. Journal of Econometrics, 31:307-327, 1986.
[49] Campbell, J.Y., Lo, A. W., and Mackinlay A. C. The Econometrics of Financial Markets[M]. Princeton University Press: New Jersey, 1997.
[50] Brockwell, P.J., and Davis, R.A. Introduction to Time Series and Forecasting[M]. Springer: New York, 1996.
[51] Box, G.E.P., Jenkins, G.M., and Reinsel, G. C. Time Series Analysis: Forecasting and Control[M]. 3rd edition, Prentice Hall: Englewood Cliffs, New Jersey, 1994.
[52] Danielsson, J., and De Vries, C. G. Value at Risk and extreme returns[WP].working paper, London School of Economics, London, U.K,1997.
[53] Longerstaey, J., and More, L. Introduction to RiskMetricsTM, 4th edition[M]., Morgan Guaranty Trust Company: New York,1995.
[54] Lars.Tvede著周为群译,金融心理学——掌握市场波动的真谛(修订版)[M].北京:中国人民大学出版社, 2003.
[55] Oliver, Adam. A quantitative and qualitative test of the Allais paradox using health outcomes[J]. Journal of Economic Psychology 2003, vol. 24, no 1,2003, pp.35~48.
[56] Ellsberg, Daniel. Risk, Ambiguity, and the Savage Axioms[J]. Quarterly Journal of Economics 75 (4),1961, pp.643~669.
[57] Kahneman, Daniel, and Amos Tversky. Prospect Theory: An Analysis of Decision under Risk[J]. Econometrica, XLVII,1979, pp.263~291.
[58] McDermott, Rose, James H. Fowler, and Oleg Smirnov. On the Evolutionary Origin of Prospect Theory Preferences[J]. Journal of Politics, forthcoming Paper Available at SSRN: http://www.ssrn.com/abstract=1008034, April 2008.
[59] Graham Loomes, Robert Sugden. Regret Theory: An Alternative Theory of Rational Choice under Uncertainty[J].Economic Journal, 1982, vol. 92, issue 368,1982,pp.805~24.
[60] Hersh Shefrin, Meir Statman. The Disposition to Sell Winners Too Early and Ride Losers Too Long: Theory and Evidence[J].Journal of Finance, Vol. 40, No. 3, 1985.
[61] Sébastien Michenaud, Bruno Solnik. Applying Regret Theory to Investment Choices: Currency hedging decisions[J]. Journal of International Money and Finance, Volume 27, Issue 5, September 2008, pp.677~694.
[62] Werner F. M. De Bondt, Richard Thaler. Does the Stock Market Overreact?[J].The Journal of Finance, Vol. 40, No. 3, Papers and Proceedings of the Forty-Third AnnualMeeting American Finance Association, Dallas, Texas, December 28-30, 1984. (Jul., 1985), pp.793~805.
[63] Robert J. Shiller. Irrational Exuberance[M].Princeton University Press, March 15, 2000.
[64] Simon Gervais, J. B. Heaton III, Terrance Odean, Overconfidence, Investment Policy, and Executive Stock Options[WP].Rodney L. White Center for Financial Research Working Paper No. 15-02, 2003.
[65] Griffin. Dale, Tversky. Amos. The Weighing of Evidence and the Determinants of Confidence[J].Cognitive Psychology, v24 n3, Jul 1992, pp.411~35.
[66] Simon Gervais, J. B. Heaton, Terrance Odean. The Positive Role of Overconfidence and Optimism in Investment Policy[WP].Rodney L. White Center for Financial Research Working Paper No. 15-02, July 24, 2003.
[67] Daniel, K., Hirshleifer, D., Subrahmanyam, A. Investor Psychology and Security Market Under- and Overreactions[J]. Journal of Finance 53 (6). doi:10.1111/0022-1082.00077, 1998. pp.1839~1885.
[68] Terrance Odean. Volume, Volatility, Price, and Profit When All Traders Are Above Average[J]. Journal of Finance, Vol. LIII, No. 6, December 1998, pp1887~1934.
[69] Chopra, Navin & Lakonishok, Josef & Ritter, Jay R.. Measuring abnormal performance : Do stocks overreact?[J]. Journal of Financial Economics, Elsevier, vol. 31(2), April. 1992, pp.235~268.
[70] J. Bradford DeLong, Andrei Shleifer, Lawrence H. Summers, and Robert J. Waldmann. Noise Trader Risk in Financial Markets[J]. Journal of Political Economy 98: 4, August 1990, pp. 703~738.
[71] Kyle, Albert S, Wang, F Albert. Speculation Duopoly with Agreement to Disagree: Can Overconfidence Survive the Market Test?[J].Journal of Finance, Volume (Year): 52 (1997), Issue (Month): 5 (December),1997,pp. 2073~90.
[72] W. D. Hamilton. Geometry for the selfish herd[J].Journal of Theoretical Biology, Volume 31, Issue 2, May 1971, pp.295~311.
[73] Josef Lakonishok, Andrei Shleifer and Robert W. Vishny. The impact of institutional trading on stock prices[J].Journal of Financial Economics, Volume 32, Issue 1, August 1992, pp.23~43.
[74] Vishny, Robert W. & Lakonishok, J. & Shleifer, A. The Limits of Arbitrage[J]. Journal of Finance 1997, 52(1), 1997, pp. 35~55.
[75] Russ Wermers. Mutual Fund Herding and the Impact on Stock Prices[J].Journal of Finance, April 1999.
[76] Vishny, Robert W. & Lakonishok, J. & Shleifer, A. Contrarian Investment, Extrapolation and Risk[J].Journal of Finance 1994, 49(5), 1994, pp. 1541~1578.
[77]饶育蕾,张轮.行为金融学(第二版)[M].上海:复旦大学出版社,2005.7.
[78] Masahiro Ashiya,and Takero Doi. Herd behavior of Japanese economists[J]. Journal of Economic Behavior & Organization, Volume 46, Issue 3, November 2001, pp. 343~346.
[79] Riza Demirer, Ali M. Kutan, and Chun-Da Chen. Do investors herd in emerging stock markets?: Evidence from the Taiwanese market[J].Journal of Economic Behavior & Organization, Volume 76, Issue 2, November 2010, pp. 283~295.
[80] Tonis Vaga. The Coherent Market Hypothesis[J]. Financial Analysts Journal (1990), November/December 1990, pp. 36~49.
[81] Earl Callen, Don Shapero. A theory of social imitation[J].Physics Today, Volume 27, Issue 7, ARTICLES, July 1974.
[82] K. Binder. "Ising model" in Hazewinkel[J]. Michiel, Encyclopaedia of Mathematics, Springer, ISBN 978-1556080104,2001.
[83] Benoit Mandelbrot, The Pareto-Lévy Law and the Distribution of Income[J]. International Economic Review, Vol 1, No.2, May 1960, pp.79~106.
[84]林建.大交易场:美国证券市场风云实录[M].北京:机械工业出版社,2007.10.
[85] Concurrency Runtime[EB/OL]. Visual C++ Libraries Reference, MSDN Library, http://msdn.microsoft.com/en-us/library/dd504870.aspx.
[86] Stroustrup, Bjarne. C++0x - the next ISO C++ standard[EB/OL]. AT&T. Retrieved 17 January 2011.
[87] Erich Gamma, Richard Helm, Ralph Johnson, John M. Vlissides. Design Patterns: Elements of Reusable Object-Oriented Software [M].Addison-Wesley Professional; 1 edition, November 10, 1994.
[88] Timothy G. Mattson, Beverly A. Sanders, Berna L. Massingill. Patterns for Parallel Programming [M].Addison-Wesley Professional, 2004.
[89] Steve Teixeira. Visual Studio 2010 Brings Parallelism Mainstream [EB/OL].http://www.drdobbs.com/visualstudio/224400670, April 19, 2010.
[90] Herb Sutter. The Free Lunch Is Over: A Fundamental Turn Toward Concurrency in Software[EB/OL]. http://www.gotw.ca/publications/concurrency-ddj.htm, December 19, 2007.
[91] Colin Campbell, Ralph Johnson, Ade Miller, Stephen Toub. Parallel Programming with Microsoft? .NET: Design Patterns for Decomposition and Coordination on Multicore Architectures [M].Microsoft Press, August 2010.
[92] Bill Messmer. Patterns and Practices for parallel programming in C++ [EB/OL]. http://www.microsoft.com/downloads/en/confirmation.aspx?displaylang=en&FamilyID=0e70b21e-3f10-4635-9af2-e2f7bddba4ae, 2010.
[93] R.G. Palmer and W.B.Arthur and J.H. Holland and B. LeBaron and R Tayler. Artificial Economic Life : A Simple Model of a Stockmarket [J]. Physica D, vol 75, 1994, pp. 264~274.
[94] R.G. Palmer and W.B.Arthur and J.H. Holland and B. LeBaron. An artificial stock market [J]. Artif Life Robotics (1999) 3, 1999, pp. 27~31.
[95] Yi-ming Wei, Shang-jun Ying, Ying Fan and Bing-Hong Wang. The cellular automaton model of investment behavior in the stock market [J]. Physica A: Statistical Mechanics and its Applications, Volume 325, Issues 3-4, 15 July 2003, pp. 507~516.
[96] Norman Ehrentreich. Agent-based modeling: the Santa Fe Institute artificial stock market model revisited [J]. Lecture Notes in Economics and Mathematical Systems, vol 602, Springer Berlin Heidelberg, 2008, pp.91~112.
[97] Merton, Robert K. The Matthew Effect in Science, II: Cumulative advantage and the symbolism of intellectual property [C]. ISIS 79, 1988, pp.606~623.
[98] Daron Acemoglu, Asuman Ozdaglar and Ali ParandehGheibi. Spread of (mis)information in social networks[J].Games and Economic Behavior,Volume 70, Issue 2, November 2010, pp.194~227.
[99] JoséLuis Iribarren, Esteban Moro. Affinity Paths and information diffusion in social networks [J/OL].Social Networks, In Press, Corrected Proof, Available online 16 January 2011.
[100] Luis López, Jose F.F. Mendes and Miguel A. F. Sanjuán. Hierarchical social networks and information flow [J]. Physica A: Statistical Mechanics and its Applications, Volume 316, Issues 1-4, 15 December 2002, pp.695~708.
[101] Parallel Patterns Library[EB/OL].Visual C++ Libraries Reference, MSDN Library, http://msdn.microsoft.com/en-us/library/dd492418.aspx. 2010.
[102] Asynchronous Agents Library [EB/OL].Visual C++ Libraries Reference, MSDN Library, http://msdn.microsoft.com/en-us/library/dd492627.aspx. 2010.
[103] Ben R. Marshall and Martin Young. Liquidity and stock returns in pure order-driven markets: evidence from the Australian stock market [J]. International Review of Financial Analysis, Volume 12, Issue 2, 2nd Quarter 2003, pp.173~188.
[104] Katya Malinova, Andreas Park, Liquidity. Volume and Price Efficiency: The Impact of Order vs. Quote Driven Trading [WP]. Paper provided by University of Toronto, Department of Economics in its series Working Papers with number tecipa-358, 2009.
[105] Paresh Kumar Narayan, Xinwei Zheng. Market liquidity risk factor and financial market anomalies: Evidence from the Chinese stock market [J]. Pacific-Basin Finance Journal, Volume 18, Issue 5, November 2010, pp.509~520.
[106] Harald A. Benink, JoséLuis Gordillo, Juan Pablo Pardo and Christopher R. Stephens. Market efficiency and learning in an artificial stock market: A perspective from Neo-Austrian economics [J]. Journal of Empirical Finance, Volume 17, Issue 4, September 2010, pp.668~688.
[107] Chia-Hsuan Yeh, Chun-Yi Yang. Examining the effectiveness of price limits in an artificial stock market [J]. Journal of Economic Dynamics and Control, Volume 34, Issue 10, October 2010, pp.2089~2108.
[2] [美]蒯因.著,江天骥译.从逻辑的观点看[M].上海:上海译文出版社,1987:19.
[3]郭元林.论复杂性科学的诞生[J].自然辩证法通讯,第27卷,总157期,2005年03期. 2005:53-70.
[4]贝塔朗菲.著,林康义,魏宏森等译.一般系统论:基础、发展和应用[M].北京:清华大学出版社, 1987: 2
[5] [美]E.N络伦兹.著,刘式达等译.周秀骥审.混沌的本质[M].北京:气象出版社,1997.
[6] Tien-Yien Li, James A.Yorke. Period Three Implies Chaos[J]. The American Mathematical Monthly, Vol.82, No. 10. 1975, pp.985~992.
[7] John Henry Holland.著,周晓牧等译.隐秩序:适应性造就复杂性[M].上海:上海科技教育出版社.2008.
[8] John Henry Holland著,陈禹等译.涌现:从混沌到有序[M].上海:上海世纪出版集团,2006.
[9] Stephen Wolfram. Computation Theory of Cellular Automata, Communications in Mathematical Physics[J]. Springer Berlin / Heidelberg, Volume 96, Number 1. Mar., 1984, pp.15~57.
[10] Stephen Wolfram. A New Kind of Science[M]. Wolfram Media, 1 edition. May 14, 2002.
[11] John N. Warfield. A Science of Generic Design: Managing Complexity Through Systems Design[M]. Iowa State Press, 2 Sub edition, January 30, 1995.
[12]宋华岭,刘丽娟,翟从敏.解读结构基础学派复杂性理论的定律、分类及指标体系[J],系统辨证学学报,第12卷,2004年04期,2004:23-30.
[13] Bastien Chopard, Michel Droz. Cellular Automata Modeling of Physical Systems[M]. Cambridge University Press, June 30, 2005.
[14] Tommaso Toffoli, Norman Margolus. Cellular Automata Machines: A New Environment for Modeling[M]. The MIT Press; First Edition edition (April 22, 1987) , pp.119~136.
[15] Jesse Nochella. Cellular Automata on Networks[C]. NKS 2006 Wolfram ScienceConference, June 2006.
[16] S. Bandinia, G. Mauria, R Serra. Cellular automata: From a theoretical parallel computational model to its application to complex systems[C].Parallel Computing. Cellular automata: From modeling to applications, Volume 27, Issue 5, April 2001, pp.539~553.
[17] G. Braga, G. Cattaneo., P. Flocchinib, and C. Quaranta Vogliotti. Pattern growth in elementary cellular automata[J]. Theoretical Computer Science, Volume 145, Issues 1-2, 10 July 1995, pp.1~26.
[18] G.A. Hedlund. Endomorphisims and automorphisims of the shift dynamical system[J]. Math. System Theory 3.1969, pp.320~375.
[19] D. Richardson. Tessellations with local transformations[J].Journal of Computer and System Sciences. Sci. 5, 1972,pp.373~388.
[20] Howard Gutowitz. Cellular automata: theory and experiment[M]. MIT Press. 1st edition, 1991, pp.159~180.
[21] M. Delorme., J. Mazoyer. Cellular Automata: A Parallel Model (Mathematics and Its Applications)[M], Springer, 1st edition, December 31, 1998.
[22] Domenico Talia, Lev Naumov. Parallel Cellular Programming for Emergent Computation[J], Understanding Complex Systems, 2010, Volume 0/2010, DOI: 10.1007/978-3-642-12203-3_15, pp.357~384.
[23] Anatol Rapoport. Spread of information through a population with sociostructural bias: I. Assumption of transitivity[J].Bulletin of Mathematical Biophysics, 1953, 15, pp.523~533.
[24] Linton Freeman. The Development of Social Network Analysis[M]. Vancouver: Empirical Press, 2006.
[25] Watkins, J. J. and Wilson, R. J. "A Survey of Snarks." In Graph Theory[J].Combinatorics, and Applications (Ed. Y. Alavi, G. Chartrand, O. R. Oellermann, and A. J. Schwenk). New York: Wiley, 1991, pp.1129~1144.
[26] [美]瓦茨.著,陈禹.等译.小小世界:有序与无序之间的网络动力学[M].北京:中国人民大学出版社,2006,第1版.
[27] Duncan J. Watts. The Dynamics of Networks between Order and Randomness (Princeton Studies in Complexity)[M]. Princeton University Press; illustrated edition edition, November 24, 2003.
[28] Edgar E. Peters. Chaos and Order in the Capital Markets: A New View of Cycles, Prices, and Market Volatility (Wiley Finance)[M]. Wiley; 2 edition, August 1996.
[29] N. H. Packard, J.P. Crutchfield, J. D. Farmer, R. S. Shaw. Geometry of Time Series[J]. Physical Review Letters, Volume 45, Number 9, 1980.
[30] Mandelbrot, B.B. The Fractal Geometry of Nature[M]. W.H. Freeman and Company,1982.
[31] Preis, T. et al. Accelerated fluctuation analysis by graphic cards and complex pattern formation in financial markets[J]. New J. Phys. 11 093024. 2009.
[32] Mandelbrot, Beno?t B. The (Mis)Behavior of Markets, A Fractal View of Risk, Ruin and Reward[J]. Basic Books, 2004, pp.186~195.
[33] Kristoufek, Ladislav. R/S analysis and DFA: finite sample properties and confidence intervals[EB/OL]. http://mpra.ub.uni-muenchen.de/16446/, 2009.
[34] Cencini et al., M. World Scientific. ed. Chaos From Simple models to complex systems[M]. ISBN 9814277657, 2010.
[35] X. Zeng, R. Eykholt, and R. A. Pielke. Estimating the Lyapunov-exponent spectrum from short time series of low precision[J]. Physical Review Letters 66 (25): 3229. doi:10.1103/PhysRevLett.66.3229. PMID 10043734.1991.
[36] Kirman, Alan. Economic theory and the crisis[J/OL]. Voxeu. 14 November 2009. http://www.voxeu.org/index.php?q=node/4208.
[37] Warren Buffett. Preface to the Fourth Edition[J]. The Intelligent Investor, 4 ed., Benjamin Graham, 2003.
[38] William F. Sharpe. Autobiography[M]. in The Nobel Prizes 1990, Editor Tore Fr?ngsmyr, Nobel Foundation, Stockholm, 1991.
[39] Fama, Eugene F., French, Kenneth R. Common Risk Factors in the Returns on Stocks and Bonds[J]. Journal of Financial Economics 33 (1): 3–56. doi:10.1016/0304-405X(93)90023-5,1993.
[40] Markowitz, H.M. Portfolio Selection[J]. The Journal of Finance 7 (1): 77–91. doi:10.2307/2975974, March 1952.
[41] Black, Fischer., Michael C. Jensen, and Myron Scholes. The Capital Asset Pricing Model: Some Empirical Tests[M].in M. Jensen ed., Studies in the Theory of Capital Markets, Praeger Publishers, 1972, New York, pp.79~121.
[42] Fama, Eugene F. and Kenneth French. The Cross-Section of Expected Stock Returns[J]. Journal of Finance, June 1992, pp.427-466.
[43] Khalid Alkhathlan, Sella Prabakaran. Memory Effects on Saudi Arabian Stock Market– Empirical Evidence[J].Enterprise Risk Management, ISSN 1937-7916, Vol.1, No. 2: E2, 2009.
[44] Edgar E. Peters. Fractal Market Analysis: Applying Chaos Theory to Investment and Economics[M]. Wiley; 1 edition, January 12, 1994.
[45] 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,2006.
[46] George Box, Gwilym M. Jenkins, and Gregory C. Reinsel. Time Series Analysis: Forecasting and Control[M]. third edition. Prentice-Hall, 1994.
[47] Engle, R.F., Ng, V.K. Measuring and testing the impact of news on volatility [J]. Journal of Finance 48 (5),1982,pp.1749~1778.
[48] Tim Bollerslev. Generalized Autoregressive Conditional Heteroskedasticity[J]. Journal of Econometrics, 31:307-327, 1986.
[49] Campbell, J.Y., Lo, A. W., and Mackinlay A. C. The Econometrics of Financial Markets[M]. Princeton University Press: New Jersey, 1997.
[50] Brockwell, P.J., and Davis, R.A. Introduction to Time Series and Forecasting[M]. Springer: New York, 1996.
[51] Box, G.E.P., Jenkins, G.M., and Reinsel, G. C. Time Series Analysis: Forecasting and Control[M]. 3rd edition, Prentice Hall: Englewood Cliffs, New Jersey, 1994.
[52] Danielsson, J., and De Vries, C. G. Value at Risk and extreme returns[WP].working paper, London School of Economics, London, U.K,1997.
[53] Longerstaey, J., and More, L. Introduction to RiskMetricsTM, 4th edition[M]., Morgan Guaranty Trust Company: New York,1995.
[54] Lars.Tvede著周为群译,金融心理学——掌握市场波动的真谛(修订版)[M].北京:中国人民大学出版社, 2003.
[55] Oliver, Adam. A quantitative and qualitative test of the Allais paradox using health outcomes[J]. Journal of Economic Psychology 2003, vol. 24, no 1,2003, pp.35~48.
[56] Ellsberg, Daniel. Risk, Ambiguity, and the Savage Axioms[J]. Quarterly Journal of Economics 75 (4),1961, pp.643~669.
[57] Kahneman, Daniel, and Amos Tversky. Prospect Theory: An Analysis of Decision under Risk[J]. Econometrica, XLVII,1979, pp.263~291.
[58] McDermott, Rose, James H. Fowler, and Oleg Smirnov. On the Evolutionary Origin of Prospect Theory Preferences[J]. Journal of Politics, forthcoming Paper Available at SSRN: http://www.ssrn.com/abstract=1008034, April 2008.
[59] Graham Loomes, Robert Sugden. Regret Theory: An Alternative Theory of Rational Choice under Uncertainty[J].Economic Journal, 1982, vol. 92, issue 368,1982,pp.805~24.
[60] Hersh Shefrin, Meir Statman. The Disposition to Sell Winners Too Early and Ride Losers Too Long: Theory and Evidence[J].Journal of Finance, Vol. 40, No. 3, 1985.
[61] Sébastien Michenaud, Bruno Solnik. Applying Regret Theory to Investment Choices: Currency hedging decisions[J]. Journal of International Money and Finance, Volume 27, Issue 5, September 2008, pp.677~694.
[62] Werner F. M. De Bondt, Richard Thaler. Does the Stock Market Overreact?[J].The Journal of Finance, Vol. 40, No. 3, Papers and Proceedings of the Forty-Third AnnualMeeting American Finance Association, Dallas, Texas, December 28-30, 1984. (Jul., 1985), pp.793~805.
[63] Robert J. Shiller. Irrational Exuberance[M].Princeton University Press, March 15, 2000.
[64] Simon Gervais, J. B. Heaton III, Terrance Odean, Overconfidence, Investment Policy, and Executive Stock Options[WP].Rodney L. White Center for Financial Research Working Paper No. 15-02, 2003.
[65] Griffin. Dale, Tversky. Amos. The Weighing of Evidence and the Determinants of Confidence[J].Cognitive Psychology, v24 n3, Jul 1992, pp.411~35.
[66] Simon Gervais, J. B. Heaton, Terrance Odean. The Positive Role of Overconfidence and Optimism in Investment Policy[WP].Rodney L. White Center for Financial Research Working Paper No. 15-02, July 24, 2003.
[67] Daniel, K., Hirshleifer, D., Subrahmanyam, A. Investor Psychology and Security Market Under- and Overreactions[J]. Journal of Finance 53 (6). doi:10.1111/0022-1082.00077, 1998. pp.1839~1885.
[68] Terrance Odean. Volume, Volatility, Price, and Profit When All Traders Are Above Average[J]. Journal of Finance, Vol. LIII, No. 6, December 1998, pp1887~1934.
[69] Chopra, Navin & Lakonishok, Josef & Ritter, Jay R.. Measuring abnormal performance : Do stocks overreact?[J]. Journal of Financial Economics, Elsevier, vol. 31(2), April. 1992, pp.235~268.
[70] J. Bradford DeLong, Andrei Shleifer, Lawrence H. Summers, and Robert J. Waldmann. Noise Trader Risk in Financial Markets[J]. Journal of Political Economy 98: 4, August 1990, pp. 703~738.
[71] Kyle, Albert S, Wang, F Albert. Speculation Duopoly with Agreement to Disagree: Can Overconfidence Survive the Market Test?[J].Journal of Finance, Volume (Year): 52 (1997), Issue (Month): 5 (December),1997,pp. 2073~90.
[72] W. D. Hamilton. Geometry for the selfish herd[J].Journal of Theoretical Biology, Volume 31, Issue 2, May 1971, pp.295~311.
[73] Josef Lakonishok, Andrei Shleifer and Robert W. Vishny. The impact of institutional trading on stock prices[J].Journal of Financial Economics, Volume 32, Issue 1, August 1992, pp.23~43.
[74] Vishny, Robert W. & Lakonishok, J. & Shleifer, A. The Limits of Arbitrage[J]. Journal of Finance 1997, 52(1), 1997, pp. 35~55.
[75] Russ Wermers. Mutual Fund Herding and the Impact on Stock Prices[J].Journal of Finance, April 1999.
[76] Vishny, Robert W. & Lakonishok, J. & Shleifer, A. Contrarian Investment, Extrapolation and Risk[J].Journal of Finance 1994, 49(5), 1994, pp. 1541~1578.
[77]饶育蕾,张轮.行为金融学(第二版)[M].上海:复旦大学出版社,2005.7.
[78] Masahiro Ashiya,and Takero Doi. Herd behavior of Japanese economists[J]. Journal of Economic Behavior & Organization, Volume 46, Issue 3, November 2001, pp. 343~346.
[79] Riza Demirer, Ali M. Kutan, and Chun-Da Chen. Do investors herd in emerging stock markets?: Evidence from the Taiwanese market[J].Journal of Economic Behavior & Organization, Volume 76, Issue 2, November 2010, pp. 283~295.
[80] Tonis Vaga. The Coherent Market Hypothesis[J]. Financial Analysts Journal (1990), November/December 1990, pp. 36~49.
[81] Earl Callen, Don Shapero. A theory of social imitation[J].Physics Today, Volume 27, Issue 7, ARTICLES, July 1974.
[82] K. Binder. "Ising model" in Hazewinkel[J]. Michiel, Encyclopaedia of Mathematics, Springer, ISBN 978-1556080104,2001.
[83] Benoit Mandelbrot, The Pareto-Lévy Law and the Distribution of Income[J]. International Economic Review, Vol 1, No.2, May 1960, pp.79~106.
[84]林建.大交易场:美国证券市场风云实录[M].北京:机械工业出版社,2007.10.
[85] Concurrency Runtime[EB/OL]. Visual C++ Libraries Reference, MSDN Library, http://msdn.microsoft.com/en-us/library/dd504870.aspx.
[86] Stroustrup, Bjarne. C++0x - the next ISO C++ standard[EB/OL]. AT&T. Retrieved 17 January 2011.
[87] Erich Gamma, Richard Helm, Ralph Johnson, John M. Vlissides. Design Patterns: Elements of Reusable Object-Oriented Software [M].Addison-Wesley Professional; 1 edition, November 10, 1994.
[88] Timothy G. Mattson, Beverly A. Sanders, Berna L. Massingill. Patterns for Parallel Programming [M].Addison-Wesley Professional, 2004.
[89] Steve Teixeira. Visual Studio 2010 Brings Parallelism Mainstream [EB/OL].http://www.drdobbs.com/visualstudio/224400670, April 19, 2010.
[90] Herb Sutter. The Free Lunch Is Over: A Fundamental Turn Toward Concurrency in Software[EB/OL]. http://www.gotw.ca/publications/concurrency-ddj.htm, December 19, 2007.
[91] Colin Campbell, Ralph Johnson, Ade Miller, Stephen Toub. Parallel Programming with Microsoft? .NET: Design Patterns for Decomposition and Coordination on Multicore Architectures [M].Microsoft Press, August 2010.
[92] Bill Messmer. Patterns and Practices for parallel programming in C++ [EB/OL]. http://www.microsoft.com/downloads/en/confirmation.aspx?displaylang=en&FamilyID=0e70b21e-3f10-4635-9af2-e2f7bddba4ae, 2010.
[93] R.G. Palmer and W.B.Arthur and J.H. Holland and B. LeBaron and R Tayler. Artificial Economic Life : A Simple Model of a Stockmarket [J]. Physica D, vol 75, 1994, pp. 264~274.
[94] R.G. Palmer and W.B.Arthur and J.H. Holland and B. LeBaron. An artificial stock market [J]. Artif Life Robotics (1999) 3, 1999, pp. 27~31.
[95] Yi-ming Wei, Shang-jun Ying, Ying Fan and Bing-Hong Wang. The cellular automaton model of investment behavior in the stock market [J]. Physica A: Statistical Mechanics and its Applications, Volume 325, Issues 3-4, 15 July 2003, pp. 507~516.
[96] Norman Ehrentreich. Agent-based modeling: the Santa Fe Institute artificial stock market model revisited [J]. Lecture Notes in Economics and Mathematical Systems, vol 602, Springer Berlin Heidelberg, 2008, pp.91~112.
[97] Merton, Robert K. The Matthew Effect in Science, II: Cumulative advantage and the symbolism of intellectual property [C]. ISIS 79, 1988, pp.606~623.
[98] Daron Acemoglu, Asuman Ozdaglar and Ali ParandehGheibi. Spread of (mis)information in social networks[J].Games and Economic Behavior,Volume 70, Issue 2, November 2010, pp.194~227.
[99] JoséLuis Iribarren, Esteban Moro. Affinity Paths and information diffusion in social networks [J/OL].Social Networks, In Press, Corrected Proof, Available online 16 January 2011.
[100] Luis López, Jose F.F. Mendes and Miguel A. F. Sanjuán. Hierarchical social networks and information flow [J]. Physica A: Statistical Mechanics and its Applications, Volume 316, Issues 1-4, 15 December 2002, pp.695~708.
[101] Parallel Patterns Library[EB/OL].Visual C++ Libraries Reference, MSDN Library, http://msdn.microsoft.com/en-us/library/dd492418.aspx. 2010.
[102] Asynchronous Agents Library [EB/OL].Visual C++ Libraries Reference, MSDN Library, http://msdn.microsoft.com/en-us/library/dd492627.aspx. 2010.
[103] Ben R. Marshall and Martin Young. Liquidity and stock returns in pure order-driven markets: evidence from the Australian stock market [J]. International Review of Financial Analysis, Volume 12, Issue 2, 2nd Quarter 2003, pp.173~188.
[104] Katya Malinova, Andreas Park, Liquidity. Volume and Price Efficiency: The Impact of Order vs. Quote Driven Trading [WP]. Paper provided by University of Toronto, Department of Economics in its series Working Papers with number tecipa-358, 2009.
[105] Paresh Kumar Narayan, Xinwei Zheng. Market liquidity risk factor and financial market anomalies: Evidence from the Chinese stock market [J]. Pacific-Basin Finance Journal, Volume 18, Issue 5, November 2010, pp.509~520.
[106] Harald A. Benink, JoséLuis Gordillo, Juan Pablo Pardo and Christopher R. Stephens. Market efficiency and learning in an artificial stock market: A perspective from Neo-Austrian economics [J]. Journal of Empirical Finance, Volume 17, Issue 4, September 2010, pp.668~688.
[107] Chia-Hsuan Yeh, Chun-Yi Yang. Examining the effectiveness of price limits in an artificial stock market [J]. Journal of Economic Dynamics and Control, Volume 34, Issue 10, October 2010, pp.2089~2108.
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