Skip to main content
Log in

Using realistic trading strategies in an agent-based stock market model

  • Manuscript
  • Published:
Computational and Mathematical Organization Theory Aims and scope Submit manuscript

Abstract

The use of agent-based models (ABMs) has increased in the last years to simulate social systems and, in particular, financial markets. ABMs of financial markets are usually validated by checking the ability of the model to reproduce a set of empirical stylised facts. However, other common-sense evidence is available which is often not taken into account, ending with models which are valid but not sensible. In this paper we present an ABM of a stock market which incorporates this type of common-sense evidence and implements realistic trading strategies based on practitioners literature. We next validate the model using a comprehensive approach consisting of four steps: assessment of face validity, sensitivity analysis, calibration and validation of model outputs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28
Fig. 29
Fig. 30

Similar content being viewed by others

Notes

  1. Code is available at https://libraries.io/github/gitwitcho/var-agent-model. Our code is implemented in Java and uses the scheduler from Repast simphony (https://repast.github.io/repast_simphony.html).

  2. As the fundamentalist and technical agents use quite different indicators for their positions—one based on the difference between price and fundamental value, and the other one based on the difference of slope between moving averages—the model runs the risk of having one group of traders moving far major volumes and biasing the market dynamics. To give fundamentalist and technical agents the a priori opportunity to impact prices in a balanced way, we have added this normalisation factor. To calculate it, we have repeatedly run the model and calculated the ratio of average maximum fundamentalist and technical positions obtained after each run. The normalisation factor has been selected as the value for which the ratio of maximum fundamentalist/technical positions lies around 1, as this indicates that fundamentalist and technical positions take similar values and so both groups of traders have a priori a similar impact in price formation.

  3. The average price in Fig. 21a seems to oscillate, but this is only due to the averaging. In the price series obtained from individual runs, there are jumps in the price process that look like oscillations in the average price series.

  4. When the decay of the autocorrelation function of a process follows a power law with exponent β < 1, the process is said to have long-term memory (Bouchaud et al. 2009), implying that events that occurred long ago still have an impact on the current values (Thompson 2011). The Hurst exponent can be used to find out if a process has long-term memory, and is calculated as \(H = 1 - \frac{\beta }{2}\) (Rickles 2011). A process with long-term memory is characterised by having a Hurst exponent \(H \in \left( {0.5,\,1} \right)\) (empirical studies have found values \(H \in \left( {0.7,\,0.9} \right)\) for equity markets (Oh et al. 2006; Yang et al. 2013)). The exponent of a process with short-term memory (such as the series of returns) is \(H = 0.5,\) and its autocorrelation function decays faster (Bouchaud et al. 2009).

  5. The Hurst exponent reported here has been calculated with the ‘hurstSpec’ function of the ‘fractal’ package of R. There are several functions available in R to calculate the Hurst exponent, and ‘hurstSpec’ has been reported to be the most accurate one (Stroe-Kunold et al. 2009).

References

  • Alfarano S, Lux T, Wagner F (2005) Estimation of agent-based models: the case of an asymmetric herding model. Comput Econ 26:19–49

    Article  Google Scholar 

  • Arthur W, Holland J, LeBaron B, Palmer R, Tayler P (1996) Asset pricing under endogenous expectations in an artificial stock market. SFI working paper

  • Balci O (1995) Principles and techniques of simulation validation, verification, and testing. In: Proceedings of the 1995 winter simulation conference, pp 147–154

  • Barreteau O, Bousquet F, Étienne M, Souchère V, d’Aquino P (2014) Companion modelling: a method of adaptive and participatory research. In: Étienne M (ed) Companion modelling. Éditions Quae, Versailles, pp 13–40

    Chapter  Google Scholar 

  • Bianchi C, Cirillo P, Gallegati M, Vagliasindi P (2007) Validating and calibrating agent-based models: a case study. Comput Econ 30:245–264

    Article  Google Scholar 

  • Bonenkamp U (2010) Combining technical and fundamental trading strategies. Gabler, Heidelberg

    Book  Google Scholar 

  • Bouchaud P, Farmer J, Lillo F (2009) How markets slowly digest changes in supply and demand. In: Hens T, Schenk-Hoppé K (eds) Handbook of financial markets: dynamics and evolution. North Holland/Elsevier, Amsterdam, pp 57–160

    Chapter  Google Scholar 

  • Carley K (1996) Validating computational models. Working paper: social and decision sciences, Carnegie Mellon University, Pittsburgh

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

    Article  Google Scholar 

  • Clegg R (2006) A practical guide to measuring the Hurst parameter. In: 21st UK performance engineering workshop, school of computing science technical report series, CSTR-916, Newcastle, pp 43–55

  • Cont R (2001) Empirical properties of asset returns: stylized facts and statistical issues. Quant Financ 1:223–236

    Article  Google Scholar 

  • Covrig V, Ng L (2004) Volume autocorrelation, information, and investor trading. J Bank Financ 28:2155–2174

    Article  Google Scholar 

  • Cristelli M (2014) Complexity in financial markets. Springer, New York

    Book  Google Scholar 

  • Ding Z, Granger C, Engle R (1993) A long memory property of stock market returns and a new model. J Empir Financ 1:83–106

    Article  Google Scholar 

  • Dosi G, Fagiolo G, Roventini A (2006) An evolutionary model of endogenous business cycles. Comput Econ 27:3–34

    Article  Google Scholar 

  • Dudukovic S (2013) Capturing stylized facts of stock market volatility and higher order cumulant function. In: Cambridge business & economics conference (CBEC), Cambridge

  • Edmonds B, Moss S (2005) From KISS to KIDS—an ‘anti-simplistic’ modelling approach. In: Davidsson P, Logan B, Takadama K (eds) Multi-agent and multi-agent-based simulation, vol 34. Springer, Heidelberg, pp 130–144

    Chapter  Google Scholar 

  • Fabretti A (2013) On the problem of calibrating an agent based model for financial markets. J Econ Interact Coord 8:277–293

    Article  Google Scholar 

  • Fagiolo G, Windrum P, Moneta A (2006) Empirical validation of agent-based models: a critical survey. LEM working paper2006/14, Pisa

  • Fagiolo G, Moneta A, Windrum P (2007) A critical guide to empirical validation of agent-based models in economics: methodologies, procedures, and open problems. Comput Econ 30:195–226

    Article  Google Scholar 

  • Farmer J, Joshi S (2002) The price dynamics of common trading strategies. J Econ Behav Organ 49:149–171

    Article  Google Scholar 

  • Feng L, Li B, Podobnik B, Preis T, Stanley E (2012) Linking agent-based models and stochastic models of financial markets. Proc Natl Acad Sci USA 109(22):8388–8393

    Article  Google Scholar 

  • Frenken K (2005) History, state and prospects of evolutionary models of technical change: a review with special emphasis on complexity theory. Utrecht University

  • Giardina I, Bouchaud J (2003) Volatility clustering in agent based market model. In: Gallegati M, Kirman A, Marsili M (eds) The complex dynamics of economic interaction. Springer, Berlin, pp 171–196

    Google Scholar 

  • Gilbert N (2004) Open problems in using agent-based models in industrial and labor dynamics. In: Leombruni R, Richiardi M (eds) Industry and labor dynamics: the agent-based computational approach. World Scientific, Singapore, pp 401–405

    Chapter  Google Scholar 

  • Gilli M, Winker P (2003) A global optimization heuristic for estimating agent based models. Comput Stat Data Anal 42:299–312

    Article  Google Scholar 

  • Hales D, Rouchier J, Edmonds B (2003) Model-to-model analysis. J Artif Soc Soc Simul 6(4):5

    Google Scholar 

  • Hommes C (2006) Heterogeneous agent models in economics and finance. In: Tesfatsion K, Judd K (eds) Handbook of computational economics, vol 2. North Holland/Elsevier, Amsterdam, pp 1109–1186

    Google Scholar 

  • Janssen M, Ostrom E (2006) Empirically based, agent-based models. Ecol Soc 11(2):37

    Article  Google Scholar 

  • Johnson N, Jefferies P, Ming Hui P (2003) Financial market complexity. Oxford University Press, New York

    Book  Google Scholar 

  • Kahneman D, Tversky A (1979) Prospect theory: an analysis of decisions under risk. Econometrica 47:313–327

    Article  Google Scholar 

  • Kestner L (2003) Quantitative trading strategies. McGraw-Hill, New York

    Google Scholar 

  • Klügl F (2008) A validation methodology for agent-based simulations. In: SAC’08, Fortaleza

  • LeBaron B (2006) Agent-based computational finance. In: Tesfatsion L, Judd K (eds) Handbook of computational economics, vol 2. North Holland/Elsevier, Amsterdam, pp 1188–1233

    Google Scholar 

  • LeBaron B, Yamamoto R (2007) Long-memory in an order-driven market. Physica A 383:85–89

    Article  Google Scholar 

  • LeBaron B, Arthur W, Palmer R (1999) Time series properties of an artificial stock market. J Econ Dynam Control 23:1487–1516

    Article  Google Scholar 

  • LiCalzi M, Pellizzari P (2006) Breeds of risk-adjusted fundamentalist strategies in an order-driven market. Physica A 359:619–633

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Madhavan A (2000) Market microstructure: a survey. Marshall School of Business, Los Angeles

    Google Scholar 

  • Malerba F, Nelson R, Orsenigo L, Winter S (1999) ‘History-friendly’ models of industry evolution: the computer industry. Ind Corp Change 8(1):3–40

    Article  Google Scholar 

  • Malkiel B (1973) A random walk down Wall Street. Norton, New York

    Google Scholar 

  • Martínez-Jaramillo S, Tsang E (2009) Evolutionary computation and artificial financial markets. In: Brabazon A, O’Neill M (eds) Natural computing in computational finance, vol 2. Springer, Berlin, pp 137–179

    Chapter  Google Scholar 

  • Menkhoff L (2010) The use of technical analysis by fund managers: international evidence. J Bank Finance 34:2573–2586

    Article  Google Scholar 

  • Milton A (2016) Day trading with Donchian channels. From the balance: https://www.thebalance.com/day-trading-with-donchian-channels-1031186. Retrieved 20 Feb 2017

  • Moss S (2008) Alternative approaches to the empirical validation of agent-based models. J Artif Soc Soc Simul 11(1):5

    Google Scholar 

  • Moss S, Edmonds B (2005) Sociology and simulation: statistical and qualitative cross-validation. Am J Sociol 110(4):1095–1131

    Article  Google Scholar 

  • Murphy J (1999) Technical analysis of the financial markets. New York Institute of Finance, Paramus

    Google Scholar 

  • Ngo T, See L (2012) Calibration and validation of agent-based models of land cover change. In: Heppenstall A, Crooks A, See L, Batty M (eds) Agent-based models of geographical systems. Springer, Dordrecht, pp 181–197

    Chapter  Google Scholar 

  • North M, Macal C (2007) Managing business complexity—discovering strategic solutions with agent-based modeling and simulation. Oxford University Press, New York

    Book  Google Scholar 

  • Oh G, Kim S, Eom C (2006) Long-term memory and volatility clustering in daily and high-frequency price changes. http://www.long-memory.com/returns/OhUmKim2006.pdf. Arxiv preprint physics/0601174

  • O’Neill B (2011) Fundamentals of the stock market. McGraw-Hill, New York

    Google Scholar 

  • Pascual J, Pajares J, López-Paredes A (2006) Explaining the statistical features of the Spanish stock market from the bottom-up. In: Bruun C (ed) Advances in artificial economics: the economy as a complex dynamic system. Springer, Berlin, pp 283–294

    Chapter  Google Scholar 

  • Raberto M, Cincotti S, Focardi S, Marchesi M (2003) Traders’ long-run wealth in an artificial financial market. Comput Econ 22:255–272

    Article  Google Scholar 

  • Rickles D (2011) Econophysics and the complexity of financial markets. In: Hooker C (ed) Philosophy of complex systems. North Holland/Elsevier, Oxford, pp 531–566

    Chapter  Google Scholar 

  • Rossi E, Santucci de Magistris P (2013) Long memory and tail dependence in trading volume and volatility. J Empir Financ 22:94–112

    Article  Google Scholar 

  • Sargent R (1998) Verification and validation of simulation models. In: Proceedings of the 1998 Winter Simulation Conference, pp. 121–130

  • Shimokawa T, Suzuki K, Misawa T (2007) An agent-based approach to financial stylized facts. Physica A 379:207–225

    Article  Google Scholar 

  • Sinclair E (2013) Volatility trading, 2nd edn. Wiley, Hoboken

    Google Scholar 

  • Slanina F (2014) Essentials of econophysics modelling. Oxford University Press, Oxford

    Google Scholar 

  • Stroe-Kunold E, Stadnytska T, Werner J, Braun S (2009) Estimating long-range dependence in time series: an evaluation of estimators implemented in R. Behav Res Methods 41(3):909–923

    Article  Google Scholar 

  • Taylor S (2005) Asset price dynamics, volatility, and prediction. Princeton University Press, Princeton

    Google Scholar 

  • ten Broeke G, van Voorn G, Ligtenberg A (2016) Which sensitivity analysis method should i use for my agent-based model? J Artif Soc Soc Simul 19(1):5

    Article  Google Scholar 

  • Thompson S (2011) The stylised facts of stock price movements. N Z Rev Econ Financ 1:50–77

    Google Scholar 

  • Tsay R (2005) Analysis of financial time series, 2nd edn. Wiley, Hoboken

    Book  Google Scholar 

  • Wei J, Huang J, Hui P (2013) An agent-based model of stock markets incorporating momentum investors. Physica A 392:2728–2735

    Article  Google Scholar 

  • Werker C, Brenner T (2004) Empirical calibration of simulation models. Max Planck Institute, Papers of economics and evolution #0410

  • Westerhoff F (2010) A simple agent-based financial market model: direct interactions and comparisons of trading profits. In: Bischi G, Chiarella C, Gardini L (eds) Nonlinear dynamics in economics, finance and the social sciences. Springer, Berlin, pp 313–332

    Chapter  Google Scholar 

  • Windrum P, Fagiolo G, Moneta A (2007) Empirical validation of agent-based models: alternatives and prospects. J Artif Soc Soc Simul 10(2):8

    Google Scholar 

  • Yang C, Wang R, Hu S (2013) Modeling and analysis of an agent-based model for Chinese stock market. Phys Lett A 377:2041–2046

    Article  Google Scholar 

  • Zeigler B (1985) Theory of modelling and simulation. Krieger, Malabar

    Google Scholar 

Download references

Acknowledgements

We would like to thank the anonymous reviewers for their insightful and constructive comments.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bàrbara Llacay.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Llacay, B., Peffer, G. Using realistic trading strategies in an agent-based stock market model. Comput Math Organ Theory 24, 308–350 (2018). https://doi.org/10.1007/s10588-017-9258-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10588-017-9258-0

Keywords

JEL Classification

Navigation