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Information versus imitation in a real-time agent-based model of financial markets

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Abstract

This paper presents an agent based model of a financial market with a real-time engine, whose operation replicates the official time schedule of Borsa Italiana S.p.A. Simulated time series are compared with empirical data at different time scales (ticks, 1 s, 1 min, 5 min) in order to check the compliance of the model with some stylized facts. The modeled market structure is a dynamic multiplex with two layers: the first one is a star network, whose hub is the market maker (i.e., the owner of the venue holding the order book), where transactions are executed; the second one is designed according to different topologies, representing social interactions, where investors decide their behavior according to informative flows. The effect of imitation on market stability is discussed and some policy implications are provided.

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References

  • Alfi V, Coccetti F, Marotta M, Pietronero L, Takayasu M (2006) Hidden forces and fluctuations from moving averages: a test study. Physica A 370:30–37

    Article  Google Scholar 

  • Alfi V, DeMartino A, Tedeschi A, Pietronero L (2007) Detecting the traders’strategies in minority–majority games and real stock-prices. Physica A 382:1–8

    Article  Google Scholar 

  • Allen F, Gale D (2000) Financial contagion. J Polit Econ 108:1–33

    Article  Google Scholar 

  • Almgren R, Chriss N (2001) Optimal execution of portfolio transactions. J Risk 3:5–40

    Article  Google Scholar 

  • Andersen TG, Bollerslev T, Diebolt FX, Vega C (2007) Real-time price discovery in global stock, bond and foreign exchange markets. J Int Econ 73:251–277

    Article  Google Scholar 

  • Anufriev M, Panchenko V (2009) Asset prices, traders’ behavior and market design. J Econ Dyn Control 33(5):1073–1090

    Article  Google Scholar 

  • Bak P, Paczuski M, Shubik M (1997) Price variations in a stock market with many agents. Phys A Stat Mech Its Appl 246(3–4):430–453

    Article  Google Scholar 

  • Banerjee AV (1992) A simple model of herd behavior. Q J Econ 107(3):797–817

    Article  Google Scholar 

  • Banerjee AV (1993) The economics of rumours. Rev Econ Stud 60(2):309–327

    Article  Google Scholar 

  • Barabási AL, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509–512

    Article  Google Scholar 

  • Ben-Elia E, Shiftan Y (2010) Which road do I take? A learning-based model of route-choice behavior with real-time information. Transp Res Part A 44:249–264

    Google Scholar 

  • Bertsimas D, Lo AW (1998) Optimal control of execution costs. J Financ Mark 1(1):1–50

    Article  Google Scholar 

  • Bikhchandani S, Hirshleifer D, Welch I (1992) A theory of fads, fashion, custom, and cultural change as informational cascades. J Polit Econ 100(5):992–1026. https://doi.org/10.1086/261849

    Article  Google Scholar 

  • Biondo AE (2018a) Order book microstructure and policies for financial stability. Stud Econ Finance 35(1):196–218. https://doi.org/10.1108/SEF-04-2017-0087

    Article  Google Scholar 

  • Biondo AE (2018b) Learning to forecast, risk aversion, and microstructural aspects of financial stability. Economics 12(2018–20):1–21

    Google Scholar 

  • Biondo AE (2018c) Order book modeling and financial stability. J Econ Interact Coord. https://doi.org/10.1007/s11403-018-0227-6

    Article  Google Scholar 

  • Boccaletti S, Latora V, Moreno Y, Chavez M, Hwang DU (2006) Complex networks: structure and dynamics. Phys Rep 424(4):175–308

    Article  Google Scholar 

  • Boccaletti S, Bianconi G, Criado R, del Genio CI, Gómez-Gardeñes J, Romance M, Sendiña-Nadal I, Wang Z, Zanin M (2014) The structure and dynamics of multilayer networks. Phys Rep 544(1):1–122

    Article  Google Scholar 

  • Booth L, Chang B, Zhou J (2014) Which analysts lead the herd in stock recommendations? J Acc Audit Finance 29(4):464–491. https://doi.org/10.1177/0148558X14537825

    Article  Google Scholar 

  • Bouchaud JP, Farmer JD, Lillo F (2009) How markets slowly digest changes in supply and demand. In: Hens T, Schenk-Hoppe KR (eds) Handbook of financial markets: dynamics and evolution. Handbooks in Finance, North-Holland, San Diego, pp 57–160

    Chapter  Google Scholar 

  • Chakraborti A, Toke IM, Patriarca M, Abergel F (2011) Econophysics review: I. Empirical facts. Quant Finance 11(7):991–1012

    Article  Google Scholar 

  • Chakravarty S, Holden CW (1995) An integrated model of market and limit orders. J Financ Intermed 4(3):213–241

    Article  Google Scholar 

  • Chiarella C, Iori G (2002) A simulation analysis of the microstructure of double auction markets. Quant Finance 2(5):346–353. https://doi.org/10.1088/1469-7688/2/5/303

    Article  Google Scholar 

  • Chiarella C, Iori G, Perelló J (2009) The impact of heterogeneous trading rules on the limit order book and order flows. J Econ Dyn Control 33(3):525–537 arXiv:0711.3581

    Article  Google Scholar 

  • Chong C, Küppelberg C (2018) Contagion in financial systems: a Bayesian network approach. SIAM J Financ Math 9(1):28–53

    Article  Google Scholar 

  • Clement MB, Tse SY (2005) Financial analyst characteristics and herding behavior in forecasting. J Finance 60(1):307–341. https://doi.org/10.1111/j.1540-6261.2005.00731.x

    Article  Google Scholar 

  • Consiglio A, Lacagnina V, Russino A (2005) A simulation analysis of the microstructure of an order driven financial market with multiple securities and portfolio choices. Quant Finance 5(1):71–87. https://doi.org/10.1080/14697680500041437

    Article  Google Scholar 

  • Cont R, Bouchaud JP (2000) Herd behavior and aggregate fluctuations in financial markets. Macroecon Dyn 4(2):170–196

    Article  Google Scholar 

  • Cont R, De Larrard A (2013) Price dynamics in a Markovian limit order market. SIAM J Financ Math 4(1):1–25

    Article  Google Scholar 

  • Cont R, Potters M, Bouchaud JP (1997) Scaling in stock market data: stable laws and beyond. In: Dubrulle B, Graner F, Sornette D (eds) Scale invariance and beyond. Springer, Berlin

    Google Scholar 

  • Cont R, Stoikov S, Talreja R (2010) A stochastic model for order book dynamics. Oper Res 58(3):549–563

    Article  Google Scholar 

  • Cooper RA, Day TE, Lewis CM (2001) Following the leader: a study of individual analysts earnings forecasts. J Financ Econ 61(3):383–416. https://doi.org/10.1016/S0304-405X(01)00067-8

    Article  Google Scholar 

  • Copeland TE, Galai D (1983) Information effects on the bid-ask spread. J Finance 38(5):1457–1469

    Article  Google Scholar 

  • Daniels M, Farmer JD, Gillemot L, Iori G, Smith E (2003) Quantitative model of price diffusion and market friction based on trading as a mechanistic random process. Phys Rev Lett 90:108102

    Article  Google Scholar 

  • Delli Gatti D, Gaffeo E, Gallegati M, Giulioni G, Palestrini A (2008) Emergent macroeconomics an agent-based approach to business fluctuations. Springer, Milan

    Google Scholar 

  • Delli Gatti D, Desiderio S, Gaffeo E, Cirillo P, Gallegati M (2011) Macroeconomics from the bottom-up. Springer, Berlin

    Book  Google Scholar 

  • Dia H (2002) An agent-based approach to modelling driver route choice behaviour under the influence of real-time information. Transp Res Part C 10:331–349

    Article  Google Scholar 

  • Elliot M, Golub B, Jackson MO (2014) Financial networks and contagion. Am Econ Rev 104(10):3115–3153

    Article  Google Scholar 

  • Erdös P, Rényi A (1959) On random graphs, I. Publicationes Mathematicae, Debrecen, pp 290–297

    Google Scholar 

  • Evans KP (2011) Intraday jumps and US macroeconomic news announcements. J Bank Finance 35:2511–2527

    Article  Google Scholar 

  • Farmer JD, Foley D (2009) The economy needs agent-based modelling. Nature 460(7256):685

    Article  Google Scholar 

  • Farmer JD, Patelli P, Zovko II (2005) The predictive power of zero intelligence in financial markets. Proc Natl Acad Sci USA 102(6):2254–2259

    Article  Google Scholar 

  • Foucault T (1999) Order flow composition and trading costs in a dynamic limit order market. J Financ Mark 2(2):99–134

    Article  Google Scholar 

  • Frijns B, Indriawan I, Tourani-Rad A (2015) Macroeconomic news announcements and price discovery: evidence from Canadian–US cross-listed firms. J Empir Finance 32:35–48

    Article  Google Scholar 

  • Gil-Bazo J, Moreno D, Tapia M (2007) Price dynamics, informational efficiency, and wealth distribution in continuous double—auction markets. Comput Intell 23(2):176–196. https://doi.org/10.1111/j.1467-8640.2007.00301.x/abstract

    Article  Google Scholar 

  • Gilbert T, Scotti C, Strasser G, Vega C (2017) Is the intrinsic value of a macroeconomic news announcement related to its asset price impact? J Monet Econ 92:78–95

    Article  Google Scholar 

  • Glosten LR (1994) Is the electronic open limit order book inevitable? J Finance 49(4):1127–1161

    Article  Google Scholar 

  • Glosten LR, Milgrom PR (1985) Bid, ask and transaction prices in a specialist market with heterogeneously informed traders. J Financ Econ 14(1):71–100

    Article  Google Scholar 

  • Golub B, Morris SE (2017) Expectations, networks, and conventions (September 9, 2017). Available at SSRN: https://doi.org/10.2139/ssrn.2979086

  • Gopikrishnan P, Plerou V, Amaral LA, Meyer M, Stanley HE (1999) Scaling of the distribution of fluctuations of financial market indices. Phys Rev E 60:5305–5316

    Article  Google Scholar 

  • Grinblatt M, Han B (2005) Prospect theory, mental accounting, and momentum. J Financ Econ 78:311–339

    Article  Google Scholar 

  • Hirshleifer D, Hong Teoh S (2003) Herd behaviour and cascading in capital markets: a review and synthesis. Eur Financ Manag 9(1):25–66. https://doi.org/10.1111/1468-036X.00207

    Article  Google Scholar 

  • Hollifield B, Miller RA, Sandås P (2004) Empirical analysis of limit order markets. Rev Econ Stud 71(4):1027–1063

    Article  Google Scholar 

  • Hollifield B, Miller RA, Sandås P, Slive J (2006) Estimating the gains from trade in limit-order markets. J Finance 61(6):2753–2804

    Article  Google Scholar 

  • Iori G (2002) A microsimulation of traders activity in the stock market: the role of heterogeneity, agents’ interactions and trade frictions. J Econ Behav Org 49(2):269–285

    Article  Google Scholar 

  • Kao AB, Couzin ID (2014) Decision accuracy in complex environments is often maximized by small group sizes. Proc R Soc B 281 (1784). https://doi.org/10.1098/rspb.2013.3305

  • Kiyotaki N, Moore J (1997) Credit chains. In: Working paper, University of Minnesota and London School of Economics

  • Kyle AS (1985) Continuous auctions and insider trading. Econometrica 53(6):1315–1335

    Article  Google Scholar 

  • Lagunoff R, Schreft SL (2001) A model of financial fragility. J Econ Theory 99:220–264

    Article  Google Scholar 

  • Leitner Y (2005) Financial networks: contagion, commitment, and private sector bailouts. J Finance 9(6):2925–2953

    Article  Google Scholar 

  • Lorenz J, Rauhut H, Schweitzer F, Helbing D (2011) How social influence can undermine the wisdom of crowd effect. PNAS 108(22):9020–9025. https://doi.org/10.1073/pnas.1008636108

    Article  Google Scholar 

  • Lux T, Marchesi M (1999) Scaling and criticality in a stochastic multi-agent model of a financial market. Nature 397(6719):498–500

    Article  Google Scholar 

  • Lux T, Marchesi M (2000) Volatility clustering in financial markets: a microsimulation of interacting agents. Int J Theor Appl Finance 3(4):675–702

    Article  Google Scholar 

  • Mandelbrot B (1963) The variation of certain speculative prices. J Bus 36(4):394–419

    Article  Google Scholar 

  • Markose SM, Alentorn A, Krause A (2004) Dynamic learning, herding and guru effects in networks. University of Essex Department of Economics Discussion Papers. http://repository.essex.ac.uk/id/eprint/3732

  • Maslov S (2000) Simple model of a limit order-driven market. Phys A Stat Mech Its Appl 278(3–4):571–578

    Article  Google Scholar 

  • Mike S, Farmer JD (2008) An empirical behavioral model of liquidity and volatility. J Econ Dyn Control 32(1):200–234

    Article  Google Scholar 

  • Morris S, Shin HS (2002) Social value of public information. Am Econ Rev 92(5):1521–1534

    Article  Google Scholar 

  • Moussaid M, Garnier S, Theraulaz G, Helbing D (2009) Collective information processing and pattern formation in swarms, flocks, and crowds. Top Cognit Sci 1(3):469–497. https://doi.org/10.1111/j.1756-8765.2009.01028.x

    Article  Google Scholar 

  • Orléan A (1995) Bayesian interactions and collective dynamics of opinion: herd behavior and mimetic contagion. J Econ Behav Org 28(2):257–274

    Article  Google Scholar 

  • Pagan A (1996) The econometrics of financial markets. J Empir Finance 3:15–102

    Article  Google Scholar 

  • Parlour CA (1998) Price dynamics in limit order markets. Rev Financ Stud 11(4):789–816

    Article  Google Scholar 

  • Parlour CA, Seppi DJ (2008) Limit order markets: a survey. Handb Financ Intermed Bank 5:63–95

    Article  Google Scholar 

  • Raberto M, Cincotti S, Focardi SM, Marchesi M (2001) Agent-based simulation of a financial market. Phys A Stat Mech Its Appl 299(1–2):319–327. https://doi.org/10.1016/S0378-4371(01)00312-0

    Article  Google Scholar 

  • Rochet J-C, Tirole J (1996) Interbank lending and systemic risk. J Money Credit Bank 28:733–762

    Article  Google Scholar 

  • Rosu I (2009) A dynamic model of the limit order book. Rev Financ Stud 22(11):4601–4641

    Article  Google Scholar 

  • Rosu I (2010) Liquidity and information in order driven markets. Chicago Booth School of Business, Chicago

    Book  Google Scholar 

  • Stauffer D, Sornette D (1999) Self-organized percolation model for stock market fluctuations. Phys A Stat Mech Its Appl 271(3–4):496–506

    Article  Google Scholar 

  • Takayasu M, Mizuno T, Takayasu H (2006) Potential force observed in market dynamics. Physica A 370:91

    Article  Google Scholar 

  • Tedeschi G, Iori G, Gallegati M (2009) The role of communication and imitation in limit order markets. Eur Phys J B 71(4):489

    Article  Google Scholar 

  • Tedeschi G, Iori G, Gallegati M (2012) Herding effects in order driven markets: the rise and fall of gurus. J Econ Behav Org 81(1):82–96

    Article  Google Scholar 

  • Wahle J, Bazzan ALC, Kugl F, Schreckenberg M (2002) The impact of real-time information in a two-route scenario using agent-based simulation. Transp Res Part C 10:399–417

    Article  Google Scholar 

  • Watts DJ, Strogatz SH (1998) Collective dynamics of ’small-world’ networks. Nature 393(6684):440

    Article  Google Scholar 

  • Yaari M (1987) The dual theory of choice under risk. Econometrica 55(1):95–115

    Article  Google Scholar 

  • Yamamoto R, Lebaron B (2010) Order-splitting and long-memory in an order-driven market. Eur Phys J B 73(1):51–57

    Article  Google Scholar 

  • Yousefi S, Moghaddam MP, Majid VJ (2011) Optimal real time pricing in an agent-based retail market using a comprehensive demand response model. Energy 9:5716–5727

    Article  Google Scholar 

  • Zhao Z, Zhang Y, Feng X, Zhang W (2014) An analysis of herding behavior in security analysts networks. Physica A 413:116–124. https://doi.org/10.1016/j.physa.2014.06.082

    Article  Google Scholar 

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Biondo, A.E. Information versus imitation in a real-time agent-based model of financial markets. J Econ Interact Coord 15, 613–631 (2020). https://doi.org/10.1007/s11403-019-00249-2

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