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

  • Alessio Emanuele BiondoEmail author
Regular Article

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.

Keywords

Order book Imitation Agent based models Time series Networks 

JEL Classification

C63 C15 G41 E71 

Notes

References

  1. Alfi V, Coccetti F, Marotta M, Pietronero L, Takayasu M (2006) Hidden forces and fluctuations from moving averages: a test study. Physica A 370:30–37Google Scholar
  2. Alfi V, DeMartino A, Tedeschi A, Pietronero L (2007) Detecting the traders’strategies in minority–majority games and real stock-prices. Physica A 382:1–8Google Scholar
  3. Allen F, Gale D (2000) Financial contagion. J Polit Econ 108:1–33Google Scholar
  4. Almgren R, Chriss N (2001) Optimal execution of portfolio transactions. J Risk 3:5–40Google Scholar
  5. Andersen TG, Bollerslev T, Diebolt FX, Vega C (2007) Real-time price discovery in global stock, bond and foreign exchange markets. J Int Econ 73:251–277Google Scholar
  6. Anufriev M, Panchenko V (2009) Asset prices, traders’ behavior and market design. J Econ Dyn Control 33(5):1073–1090Google Scholar
  7. 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):430–453Google Scholar
  8. Banerjee AV (1992) A simple model of herd behavior. Q J Econ 107(3):797–817Google Scholar
  9. Banerjee AV (1993) The economics of rumours. Rev Econ Stud 60(2):309–327Google Scholar
  10. Barabási AL, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509–512Google Scholar
  11. 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 44:249–264Google Scholar
  12. Bertsimas D, Lo AW (1998) Optimal control of execution costs. J Financ Mark 1(1):1–50Google Scholar
  13. 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 Google Scholar
  14. 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 Google Scholar
  15. Biondo AE (2018b) Learning to forecast, risk aversion, and microstructural aspects of financial stability. Economics 12(2018–20):1–21Google Scholar
  16. Biondo AE (2018c) Order book modeling and financial stability. J Econ Interact Coord.  https://doi.org/10.1007/s11403-018-0227-6 Google Scholar
  17. Boccaletti S, Latora V, Moreno Y, Chavez M, Hwang DU (2006) Complex networks: structure and dynamics. Phys Rep 424(4):175–308Google Scholar
  18. 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):1–122Google Scholar
  19. 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 Google Scholar
  20. 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 57–160Google Scholar
  21. Chakraborti A, Toke IM, Patriarca M, Abergel F (2011) Econophysics review: I. Empirical facts. Quant Finance 11(7):991–1012Google Scholar
  22. Chakravarty S, Holden CW (1995) An integrated model of market and limit orders. J Financ Intermed 4(3):213–241Google Scholar
  23. 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 Google Scholar
  24. 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 Google Scholar
  25. Chong C, Küppelberg C (2018) Contagion in financial systems: a Bayesian network approach. SIAM J Financ Math 9(1):28–53Google Scholar
  26. 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 Google Scholar
  27. 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 Google Scholar
  28. Cont R, Bouchaud JP (2000) Herd behavior and aggregate fluctuations in financial markets. Macroecon Dyn 4(2):170–196Google Scholar
  29. Cont R, De Larrard A (2013) Price dynamics in a Markovian limit order market. SIAM J Financ Math 4(1):1–25Google Scholar
  30. 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, BerlinGoogle Scholar
  31. Cont R, Stoikov S, Talreja R (2010) A stochastic model for order book dynamics. Oper Res 58(3):549–563Google Scholar
  32. 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 Google Scholar
  33. Copeland TE, Galai D (1983) Information effects on the bid-ask spread. J Finance 38(5):1457–1469Google Scholar
  34. 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 90:108102Google Scholar
  35. Delli Gatti D, Gaffeo E, Gallegati M, Giulioni G, Palestrini A (2008) Emergent macroeconomics an agent-based approach to business fluctuations. Springer, MilanGoogle Scholar
  36. Delli Gatti D, Desiderio S, Gaffeo E, Cirillo P, Gallegati M (2011) Macroeconomics from the bottom-up. Springer, BerlinGoogle Scholar
  37. Dia H (2002) An agent-based approach to modelling driver route choice behaviour under the influence of real-time information. Transp Res Part C 10:331–349Google Scholar
  38. Elliot M, Golub B, Jackson MO (2014) Financial networks and contagion. Am Econ Rev 104(10):3115–3153Google Scholar
  39. Erdös P, Rényi A (1959) On random graphs, I. Publicationes Mathematicae, Debrecen, pp 290–297Google Scholar
  40. Evans KP (2011) Intraday jumps and US macroeconomic news announcements. J Bank Finance 35:2511–2527Google Scholar
  41. Farmer JD, Foley D (2009) The economy needs agent-based modelling. Nature 460(7256):685Google Scholar
  42. Farmer JD, Patelli P, Zovko II (2005) The predictive power of zero intelligence in financial markets. Proc Natl Acad Sci USA 102(6):2254–2259Google Scholar
  43. Foucault T (1999) Order flow composition and trading costs in a dynamic limit order market. J Financ Mark 2(2):99–134Google Scholar
  44. Frijns B, Indriawan I, Tourani-Rad A (2015) Macroeconomic news announcements and price discovery: evidence from Canadian–US cross-listed firms. J Empir Finance 32:35–48Google Scholar
  45. 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 Google Scholar
  46. 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 92:78–95Google Scholar
  47. Glosten LR (1994) Is the electronic open limit order book inevitable? J Finance 49(4):1127–1161Google Scholar
  48. Glosten LR, Milgrom PR (1985) Bid, ask and transaction prices in a specialist market with heterogeneously informed traders. J Financ Econ 14(1):71–100Google Scholar
  49. Golub B, Morris SE (2017) Expectations, networks, and conventions (September 9, 2017). Available at SSRN:  https://doi.org/10.2139/ssrn.2979086
  50. Gopikrishnan P, Plerou V, Amaral LA, Meyer M, Stanley HE (1999) Scaling of the distribution of fluctuations of financial market indices. Phys Rev E 60:5305–5316Google Scholar
  51. Grinblatt M, Han B (2005) Prospect theory, mental accounting, and momentum. J Financ Econ 78:311–339Google Scholar
  52. 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 Google Scholar
  53. Hollifield B, Miller RA, Sandås P (2004) Empirical analysis of limit order markets. Rev Econ Stud 71(4):1027–1063Google Scholar
  54. Hollifield B, Miller RA, Sandås P, Slive J (2006) Estimating the gains from trade in limit-order markets. J Finance 61(6):2753–2804Google Scholar
  55. 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):269–285Google Scholar
  56. 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
  57. Kiyotaki N, Moore J (1997) Credit chains. In: Working paper, University of Minnesota and London School of EconomicsGoogle Scholar
  58. Kyle AS (1985) Continuous auctions and insider trading. Econometrica 53(6):1315–1335Google Scholar
  59. Lagunoff R, Schreft SL (2001) A model of financial fragility. J Econ Theory 99:220–264Google Scholar
  60. Leitner Y (2005) Financial networks: contagion, commitment, and private sector bailouts. J Finance 9(6):2925–2953Google Scholar
  61. 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 Google Scholar
  62. Lux T, Marchesi M (1999) Scaling and criticality in a stochastic multi-agent model of a financial market. Nature 397(6719):498–500Google Scholar
  63. Lux T, Marchesi M (2000) Volatility clustering in financial markets: a microsimulation of interacting agents. Int J Theor Appl Finance 3(4):675–702Google Scholar
  64. Mandelbrot B (1963) The variation of certain speculative prices. J Bus 36(4):394–419Google Scholar
  65. 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
  66. Maslov S (2000) Simple model of a limit order-driven market. Phys A Stat Mech Its Appl 278(3–4):571–578Google Scholar
  67. Mike S, Farmer JD (2008) An empirical behavioral model of liquidity and volatility. J Econ Dyn Control 32(1):200–234Google Scholar
  68. Morris S, Shin HS (2002) Social value of public information. Am Econ Rev 92(5):1521–1534Google Scholar
  69. 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 Google Scholar
  70. Orléan A (1995) Bayesian interactions and collective dynamics of opinion: herd behavior and mimetic contagion. J Econ Behav Org 28(2):257–274Google Scholar
  71. Pagan A (1996) The econometrics of financial markets. J Empir Finance 3:15–102Google Scholar
  72. Parlour CA (1998) Price dynamics in limit order markets. Rev Financ Stud 11(4):789–816Google Scholar
  73. Parlour CA, Seppi DJ (2008) Limit order markets: a survey. Handb Financ Intermed Bank 5:63–95Google Scholar
  74. 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 Google Scholar
  75. Rochet J-C, Tirole J (1996) Interbank lending and systemic risk. J Money Credit Bank 28:733–762Google Scholar
  76. Rosu I (2009) A dynamic model of the limit order book. Rev Financ Stud 22(11):4601–4641Google Scholar
  77. Rosu I (2010) Liquidity and information in order driven markets. Chicago Booth School of Business, ChicagoGoogle Scholar
  78. Stauffer D, Sornette D (1999) Self-organized percolation model for stock market fluctuations. Phys A Stat Mech Its Appl 271(3–4):496–506Google Scholar
  79. Takayasu M, Mizuno T, Takayasu H (2006) Potential force observed in market dynamics. Physica A 370:91Google Scholar
  80. Tedeschi G, Iori G, Gallegati M (2009) The role of communication and imitation in limit order markets. Eur Phys J B 71(4):489Google Scholar
  81. 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):82–96Google Scholar
  82. 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 10:399–417Google Scholar
  83. Watts DJ, Strogatz SH (1998) Collective dynamics of ’small-world’ networks. Nature 393(6684):440Google Scholar
  84. Yaari M (1987) The dual theory of choice under risk. Econometrica 55(1):95–115Google Scholar
  85. Yamamoto R, Lebaron B (2010) Order-splitting and long-memory in an order-driven market. Eur Phys J B 73(1):51–57Google Scholar
  86. Yousefi S, Moghaddam MP, Majid VJ (2011) Optimal real time pricing in an agent-based retail market using a comprehensive demand response model. Energy 9:5716–5727Google Scholar
  87. 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 Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Dipartimento di Economia e ImpresaUniversità degli Studi di CataniaCataniaItaly

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