Agent-based modeling from the perspectives of FinTech
CUI Yi'an1, XIONG Xiong1,2, WEI Lijian3, HE Shaoyi4
1. College of Management and Economics, Tianjin University, Tianjin 300072, China; 2. China Center for Social Computing and Analytics, Tianjin 300072, China; 3. Business School, Sun Yat-sen University, Guangzhou 510275, China; 4. Jack H. Brown College of Business and Public Administration, California State University, San Bernardino 92407, USA
Abstract:Agent-based modeling, with its unique "God's Perspective", is widely influencing traditional economic and financial research. With the rise of technologies such as big data and artificial intelligence in recent years, agent-based modeling and basic supporting technologies in the field of FinTech are deeply integrated to jointly promote continuous breakthroughs in FinTech. In particular, in the fields of big data ontology research, agent-based model calibration, investor behavior modeling, regulatory technology applications, etc., agent-based modeling has demonstrated its unique advantages in integrating with FinTech. From the perspectives of big data ontology, agent-based modeling is an effective method that uses financial big data ontology modeling to depict the correlation between financial entities and to study their causal relationships. From the perspectives of artificial intelligence, it provides the basis for fine modeling of individuals and behavioral evolution modeling, and agent-based modeling provides a new research framework for algorithmic trading. From the perspectives of regulatory technology, the agent-based model in the development process to promote the formation of a new ‘scenario-response’ type of risk management, thus providing supports for the ‘regulatory technology’ development. This paper holds that agent-based modeling can be integrated with the basic supporting technologies of FinTech, providing an important impetus for the development of agent-based modeling and regulatory innovation in FinTech.
崔毅安, 熊熊, 韦立坚, 何绍义. 金融科技视角下的计算实验金融建模[J]. 系统工程理论与实践, 2020, 40(2): 373-381.
CUI Yi'an, XIONG Xiong, WEI Lijian, HE Shaoyi. Agent-based modeling from the perspectives of FinTech. Systems Engineering - Theory & Practice, 2020, 40(2): 373-381.
[1] Buchanan M. Economics:Meltdown modelling[J]. Nature, 2009, 460(7256):680-682. [2] Battiston S, Farmer J D, Flache A. Complexity theory and financial regulation[J]. Science, 2016, 351(6275):818-819. [3] 张维, 张永杰, 熊熊. 计算实验金融研究[M]. 北京:科学出版社, 2010.Zhang W, Zhang Y J, Xiong X. Agent-based computational finance:An alternative way to understand the market[M]. Beijing:Science Press, 2010. [4] Lebaron B. Agent-based computational finance[J]. Handbook of Computational Economics, 2006, 2(5):1187-1233. [5] Chen S H. Varieties of agents in agent-based computational economics:A historical and an interdisciplinary perspective[J]. Journal of Economic Dynamics and Control, 2012, 36(1):1-25. [6] 张维, 武自强, 张永杰, 等. 基于复杂金融系统视角的计算实验金融:进展与展望[J]. 管理科学学报, 2013, 16(6):85-94.Zhang W, Wu Z Q, Zhang Y J, et al. Agent-based computational finance on complex financial system perspective:Progress and prospects[J]. Journal of Management Sciences in China, 2013, 16(6):85-94. [7] Iori G, Porter J. Agent-based modeling for financial markets[M]//The Oxford Handbook of Computational Economics and Finance. Oxford University Press, 2018:635-666. [8] Borrill P L, Tesfatsion L. Agent-based modeling:The right mathematics for the social sciences?[R]. Staff General Research Papers 31674, Iowa State University, Department of Economics, 2010. [9] Farmer J D, Foley D. The economy needs agent-based modelling[J]. Nature, 2009, 460(7256):685-686. [10] Diebold F X. A personal perspective on the origin(s) and development of 'Big Data':The phenomenon, the term, and the discipline, Second Version[R]. PIER Working Paper Archive, 2013. [11] Laney D. 3D data management:Controlling data volume, velocity and variety[R]. META Group Research Note, 2001. [12] Viktor M S, Cukier K. Big data:A revolution that will transform how we live, work, and think[M]. Houghton Mifflin Harcourt, 2013. [13] Boyd D, Crawford K. Critical questions for big data[J]. Information Communication & Society, 2012, 15(5):662-679. [14] Marz N, Warren J. Big data:Principles and best practices of scalable real-time data systems[M]. New York:Manning Publications Co., 2015. [15] Kitchin R, Mcardle G. What makes big data, big data? Exploring the ontological characteristics of 26 datasets[J]. Big Data & Society, 2016, 3(1):1-10. [16] Chen S H, Venkatachalam R. Agent-based modelling as a foundation for big data[J]. Journal of Economic Methodology, 2017, 24(1):1-22. [17] Partridge B L. Internal dynamics and the interrelations of fish in schools[J]. Journal of Comparative Physiology, 1981, 144(3):313-325. [18] Nagy M, Akos Z, Biro D, et al. Hierarchical group dynamics in pigeon flocks[J]. Nature, 2010, 464(7290):890-893. [19] Delellis P, Polverino G, Ustuner G, et al. Collective behaviour across animal species[J]. Scientific Reports, 2014, 4:3723. [20] Reynolds C W. Flocks, herds and schools:A distributed behavioral model[J]. Computer Graphics, 1987, 21(4):25-34. [21] Gilbert N. Computational social science[M]. Thousand Oaks:SAGE Publications Inc, 2010. [22] Kirkpatrick R. A Conversation with Robert Kirkpatrick, Director of United Nations Global Pulse[J]. SAIS Review of International Affairs, 2014, 34(1):3-8. [23] Geanakoplos J, Axtell R, Farmer D J, et al. Getting at systemic risk via an agent-based model of the housing market[J]. American Economic Review, 102(3):53-58. [24] Lussange J, Belianin A, Bourgeoisgironde S, et al. A bright future for financial agent-based models[R]. Working Papers, 2018. [25] Chen S H, Chang C L, Du Y R. Agent-based economic models and econometrics[J]. The Knowledge Engineering Review, 2012, 27(2):187-219. [26] Xiong X, Cui Y A, Wei L J, et al. Adaptive asset allocation and commonality in multi order books[C]//The 23rd International Conference on Computing in Economics and Finance, June 28-30, 2017, New York, USA. [27] Xiong X, Cui Y A, Wei L J. Disposition effect & adaptive asset allocation in limit order markets[C]//The 24th International Conference on Computing in Economics and Finance, June 19-21, 2018, Milan, Italy. [28] Raberto M, Cincotti S. Modeling and simulation of a double auction artificial financial market[J]. Physica A:Statistical Mechanics & Its Applications, 2005, 355(1):34-45. [29] Gu G F, Zhou W X. Emergence of long memory in stock volatility from a modified Mike-Farmer model[J]. Europhysics Letters, 2009, 86(4):48002. [30] Brandouy O, Corelli A, Veryzhenko I, et al. A re-examination of the “zero is enough” hypothesis in the emergence of financial stylized facts[J]. Journal of Economic Interaction & Coordination, 2012, 7(2):223-248. [31] Tedeschi G, Iori G, Gallegati M. The role of communication and imitation in limit order markets[J]. European Physical Journal B, 2009, 71(4):489-497. [32] Chen J J, Tan L, Zheng B. Agent-based model with multi-level herding for complex financial systems[J]. Scientific Reports, 2015, 5:8399. [33] Gode D K, Sunder S. Allocative efficiency of markets with zero-intelligence traders:Market as a partial substitute for individual rationality[J]. Journal of Political Economy, 1993, 101(1):119-137. [34] Farmer J D, Patelli P, Zovko I I. The predictive power of zero intelligence in financial markets[J]. Proceedings of the National Academy of Sciences, 2005, 102(6):2254-2259. [35] Duffy J, Ünver M U. Asset price bubbles and crashes with near-zero-intelligence traders[J]. Economic Theory, 2006, 27(3):537-563. [36] Ladley D. Zero intelligence in economics and finance[J]. Knowledge Engineering Review, 2012, 27(2):273-286. [37] 张维, 李悦雷, 熊熊, 等. 计算实验金融的思想基础与研究范式[J]. 系统工程理论与实践, 2012, 32(3):495-507.Zhang W, Li Y L, Xiong X, et al. Ideological foundation and research paradigm in agent-based computational finance[J]. Systems Engineering-Theory & Practice, 2012, 32(3):495-507. [38] Hommes C. Heterogeneous agent models in economics and finance[M]//Handbook of Computational Economics, 2006, 2:1109-1186. [39] Chen S H, Kuo T W, Hoi K M. Genetic programming and financial trading:How much about “what we know”[M]//Handbook of Financial Engineering. Boston, MA:Springer, 2008:99-154. [40] Terna P. Cognitive agents behaving in a simple stock market structure[M]//Agent-Based Methods in Economics and Finance. Boston, MA:Springer, 2002:187-227. [41] Lebaron B, Yamamoto R. Long-memory in an order-driven market[J]. Physica A:Statistical Mechanics & Its Applications, 2006, 383(1):85-89. [42] Chiarella C, Iori G. A simulation analysis of the microstructure of double auction markets[J]. Quantitative Finance, 2002, 2(5):346-353. [43] Brock W A, Hommes C H, Wagener F O O. More hedging instruments may destabilize markets[J]. Journal of Economic Dynamics and Control, 2009, 33(11):1912-1928. [44] Chiarella C, He X Z, Wei L. Learning, information processing and order submission in limit order markets[J]. Journal of Economic Dynamics and Control, 2015, 61:245-268. [45] Wei L J, Xiong X, Zhang W, et al. The effect of genetic algorithm learning with a classifier system in limit order markets[J]. Engineering Applications of Artificial Intelligence, 2017, 65:436-448. [46] Lamperti F, Roventini A, Sani A. Agent-based model calibration using machine learning surrogates[J]. Journal of Economic Dynamics and Control, 2018, 90:366-389. [47] Van Der Hoog S. Deep learning in agent-based models:A prospectus[R]. Bielefeld Working Papers in Economics and Management, 2016. [48] Hu R, Watt S M. An agent-based financial market simulator for evaluation of algorithmic trading strategies[C]//6th International Conference on Advances in System Simulation, 2014:221-227. [49] Muehlhauser L, Hibbard B. Exploratory engineering in artificial intelligence[J]. Communications of the ACM, 2014, 57(9):32-34. [50] Gsell M. Assessing the impact of algorithmic trading on markets:A simulation approach[R]. CFS Working Paper, 2008. [51] 王宇超, 李心丹, 刘海飞. 算法交易的市场影响研究[J]. 管理科学学报, 2014, 17(1):57-71.Wang Y C, Li X D, Liu H F. Market impact of algorithmic trading[J]. Journal of Management Sciences in China, 2014, 17(1):57-71. [52] 吴晓灵, 李剑阁, 王忠民. 高频交易对市场的影响[J]. 清华金融评论, 2016(2):16-24.Wu X L, Li J G, Wang Z M. The impact of high frequency trading on the market[J]. Tsinghua Financial Review, 2016(2):16-24. [53] Arifovic J, He X Z, Wei L J. !!! High frequency trading in FinTech age: AI with speed[R]. !!! SSRN Working Paper, !!! 2019.} [54] Waldrop M M. Modeling Surprise[J]. Technology Review, 2008, 111(2):67. [55] 张维. 金融风险管理的新视角:计算实验金融思想与探索[R]. 双清论坛, 北京, 2011-11-29.Zhang W. A new perspective of financial risk management:Thoughts and exploration on agent-based computational finance[R]. Shuangqing Forum, Beijing, 2011-11-29. [56] 韦立坚, 张维, 熊熊. 股市流动性踩踏危机的形成机理与应对机制[J]. 管理科学学报, 2017, 20(3):1-23.Wei L J, Zhang W, Xiong X. The mechanism and solution for the liquidity stampede crisis in stock markets[J]. Journal of Management Sciences in China, 2017, 20(3):1-23. [57] Darley V, Outkin A V. A NASDAQ market simulation:Insights on a major market from the science of complex adaptive systems[M]. World Scientific, 2007. [58] 李悦雷, 张维, 熊熊. 最小报价单位对市场流动性影响的计算实验研究[J]. 管理科学, 2012, 25(1):92-98.Li Y L, Zhang W, Xiong X. Impact of tick size on market liquidity by agent-based modeling approach[J]. Journal of Management Science, 2012, 25(1):92-98. [59] Wei L J, Zhang W, Xiong X, et al. Position limit for the CSI 300 stock index futures market[J]. Economic Systems, 2015, 39(3):369-389. [60] 韦立坚. T+0交易制度的计算实验研究[J]. 管理科学学报, 2016, 19(11):90-102.Wei L J. An agent-based model for the impact of the T+0 trading mechanism on market quality[J]. Journal of Management Sciences in China, 2016, 19(11):90-102. [61] Xiong X, Liang J, Cui Y A, et al. Analysis of the spot market's T+1 trading system effects on the stock index futures market[J]. Eurasia Journal of Mathematics, Science and Technology Education, 2017, 13(12):7679-7693. [62] Erlingsson E J, Raberto M, Stefánsson H, et al. Integrating the housing market into an agent-based economic model[J]. Lecture Notes in Economics & Mathematical Systems, 2012, 662:65-76.