IDEAS home Printed from https://ideas.repec.org/p/zbw/ifwedp/201763.html
   My bibliography  Save this paper

Fundamentals unknown: Momentum, mean-reversion and price-to-earnings trading in an artificial stock market

Author

Listed:
  • Schasfoort, Joeri
  • Stockermans, Christopher

Abstract

The use of fundamentalist traders in the stock market models is problematic since fundamental values in the real world are unknown. Yet, in the literature to date, fundamentalists are often required to replicate key stylized facts. The authors present an agent-based model of the stock market in which the fundamental value of the asset is unknown. They start with a zero intelligence stock market model with a limit-order-book. Then, the authors add technical traders which switch between a simple momentum and mean reversion strategy depending on its relative profitability. Technical traders use the price to earnings ratio as a proxy for fundamentals. If price to earnings are either too high or too low, they sell or buy, respectively.

Suggested Citation

  • Schasfoort, Joeri & Stockermans, Christopher, 2017. "Fundamentals unknown: Momentum, mean-reversion and price-to-earnings trading in an artificial stock market," Economics Discussion Papers 2017-63, Kiel Institute for the World Economy (IfW Kiel).
    • Handle: RePEc:zbw:ifwedp:201763
    as

    Download full text from publisher

    File URL: http://www.economics-ejournal.org/economics/discussionpapers/2017-63
    Download Restriction: no
    File URL: https://www.econstor.eu/bitstream/10419/169122/1/898553555.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chiarella, Carl & Iori, Giulia, 2009. "The impact of heterogeneous trading rules on the limit order book and order flows," Journal of Economic Dynamics and Control, Elsevier, vol. 33(3), pages 525-537.
    2. Martin D. Gould & Mason A. Porter & Stacy Williams & Mark McDonald & Daniel J. Fenn & Sam D. Howison, 2013. "Limit order books," Quantitative Finance, Taylor & Francis Journals, vol. 13(11), pages 1709-1742, November.
    3. Thomas Lux & Michele Marchesi, 2000. "Volatility Clustering In Financial Markets: A Microsimulation Of Interacting Agents," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 3(04), pages 675-702.
    4. Andrea Gaunersdorfer & Cars Hommes, 2007. "A Nonlinear Structural Model for Volatility Clustering," Springer Books, in: Gilles Teyssière & Alan P. Kirman (ed.), Long Memory in Economics, pages 265-288, Springer.
    5. Jean-Philippe Bouchaud & J. Doyne Farmer & Fabrizio Lillo, 2008. "How markets slowly digest changes in supply and demand," Papers 0809.0822, arXiv.org.
    6. Fischer, Thomas & Riedler, Jesper, 2014. "Prices, debt and market structure in an agent-based model of the financial market," Journal of Economic Dynamics and Control, Elsevier, vol. 48(C), pages 95-120.
    7. Cont, Rama & Bouchaud, Jean-Philipe, 2000. "Herd Behavior And Aggregate Fluctuations In Financial Markets," Macroeconomic Dynamics, Cambridge University Press, vol. 4(2), pages 170-196, June.
    8. Gu, Gao-Feng & Chen, Wei & Zhou, Wei-Xing, 2008. "Empirical distributions of Chinese stock returns at different microscopic timescales," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(2), pages 495-502.
    9. Arouri, Mohamed El Hedi & Hammoudeh, Shawkat & Lahiani, Amine & Nguyen, Duc Khuong, 2012. "Long memory and structural breaks in modeling the return and volatility dynamics of precious metals," The Quarterly Review of Economics and Finance, Elsevier, vol. 52(2), pages 207-218.
    10. Balvers, Ronald J. & Wu, Yangru, 2006. "Momentum and mean reversion across national equity markets," Journal of Empirical Finance, Elsevier, vol. 13(1), pages 24-48, January.
    11. Giulio Bottazzi & Angelo Secchi, 2003. "Common Properties and Sectoral Specificities in the Dynamics of U.S. Manufacturing Companies," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 23(3_4), pages 217-232, December.
    12. Ronald Balvers & Yangru Wu & Erik Gilliland, 2000. "Mean Reversion across National Stock Markets and Parametric Contrarian Investment Strategies," Journal of Finance, American Finance Association, vol. 55(2), pages 745-772, April.
    13. Harrison Hong & Jeremy C. Stein, 1999. "A Unified Theory of Underreaction, Momentum Trading, and Overreaction in Asset Markets," Journal of Finance, American Finance Association, vol. 54(6), pages 2143-2184, December.
    14. Martin D. Gould & Mason A. Porter & Stacy Williams & Mark McDonald & Daniel J. Fenn & Sam D. Howison, 2010. "Limit Order Books," Papers 1012.0349, arXiv.org, revised Apr 2013.
    15. Gerd Gigerenzer & Reinhard Selten (ed.), 2002. "Bounded Rationality: The Adaptive Toolbox," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262571641, April.
    16. Sandrine Jacob Leal & Mauro Napoletano, 2017. "Market Stability vs. Market Resilience: Regulatory Policies Experiments in an Agent-Based Model with Low- and High-Frequency Trading," Post-Print hal-01768876, HAL.
    17. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
    18. Franke, Reiner & Westerhoff, Frank, 2012. "Structural stochastic volatility in asset pricing dynamics: Estimation and model contest," Journal of Economic Dynamics and Control, Elsevier, vol. 36(8), pages 1193-1211.
    19. Jan C. Thiele & Winfried Kurth & Volker Grimm, 2014. "Facilitating Parameter Estimation and Sensitivity Analysis of Agent-Based Models: A Cookbook Using NetLogo and 'R'," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 17(3), pages 1-11.
    20. Carl Chiarella & Xue-Zhong He & Lei Shi & Lijian Wei, 2017. "A behavioural model of investor sentiment in limit order markets," Quantitative Finance, Taylor & Francis Journals, vol. 17(1), pages 71-86, January.
    21. Ferreira, Paulo & Dionísio, Andreia, 2016. "How long is the memory of the US stock market?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 502-506.
    22. Stanley, H. Eugene & Plerou, Vasiliki & Gabaix, Xavier, 2008. "A statistical physics view of financial fluctuations: Evidence for scaling and universality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(15), pages 3967-3981.
    23. Brock, William & Lakonishok, Josef & LeBaron, Blake, 1992. "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns," Journal of Finance, American Finance Association, vol. 47(5), pages 1731-1764, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Baumann, Michael Heinrich & Baumann, Michaela & Erler, Alexander, 2019. "Limitations of stabilizing effects of fundamentalists: Facing positive feedback traders," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 13, pages 1-26.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Blaurock, Ivonne & Schmitt, Noemi & Westerhoff, Frank, 2018. "Market entry waves and volatility outbursts in stock markets," Journal of Economic Behavior & Organization, Elsevier, vol. 153(C), pages 19-37.
    2. Alberto Ciacci & Takumi Sueshige & Hideki Takayasu & Kim Christensen & Misako Takayasu, 2020. "The microscopic relationships between triangular arbitrage and cross-currency correlations in a simple agent based model of foreign exchange markets," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-19, June.
    3. Hommes, Cars H., 2006. "Heterogeneous Agent Models in Economics and Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 23, pages 1109-1186, Elsevier.
    4. Johann Lussange & Ivan Lazarevich & Sacha Bourgeois-Gironde & Stefano Palminteri & Boris Gutkin, 2021. "Modelling Stock Markets by Multi-agent Reinforcement Learning," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 113-147, January.
    5. James Paulin & Anisoara Calinescu & Michael Wooldridge, 2018. "Understanding Flash Crash Contagion and Systemic Risk: A Micro-Macro Agent-Based Approach," Papers 1805.08454, arXiv.org.
    6. Paulin, James & Calinescu, Anisoara & Wooldridge, Michael, 2019. "Understanding flash crash contagion and systemic risk: A micro–macro agent-based approach," Journal of Economic Dynamics and Control, Elsevier, vol. 100(C), pages 200-229.
    7. Jiahua Wang & Hongliang Zhu & Dongxin Li, 2018. "Price Dynamics in an Order-Driven Market with Bayesian Learning," Complexity, Hindawi, vol. 2018, pages 1-15, November.
    8. Gao-Feng Gu & Xiong Xiong & Hai-Chuan Xu & Wei Zhang & Yongjie Zhang & Wei Chen & Wei-Xing Zhou, 2021. "An empirical behavioral order-driven model with price limit rules," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-24, December.
    9. Fa-Bin Shi & Xiao-Qian Sun & Jin-Hua Gao & Li Xu & Hua-Wei Shen & Xue-Qi Cheng, 2019. "Anomaly detection in Bitcoin market via price return analysis," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-11, June.
    10. Roberto Dieci & Xue-Zhong He, 2018. "Heterogeneous Agent Models in Finance," Research Paper Series 389, Quantitative Finance Research Centre, University of Technology, Sydney.
    11. Yamamoto, Ryuichi, 2019. "Dynamic Predictor Selection And Order Splitting In A Limit Order Market," Macroeconomic Dynamics, Cambridge University Press, vol. 23(5), pages 1757-1792, July.
    12. Adam Zaremba & Jacob Koby Shemer, 2018. "Price-Based Investment Strategies," Springer Books, Springer, number 978-3-319-91530-2, June.
    13. Lamperti, Francesco & Roventini, Andrea & Sani, Amir, 2018. "Agent-based model calibration using machine learning surrogates," Journal of Economic Dynamics and Control, Elsevier, vol. 90(C), pages 366-389.
    14. Torsten Trimborn & Philipp Otte & Simon Cramer & Maximilian Beikirch & Emma Pabich & Martin Frank, 2020. "SABCEMM: A Simulator for Agent-Based Computational Economic Market Models," Computational Economics, Springer;Society for Computational Economics, vol. 55(2), pages 707-744, February.
    15. Tubbenhauer, Tobias & Fieberg, Christian & Poddig, Thorsten, 2021. "Multi-agent-based VaR forecasting," Journal of Economic Dynamics and Control, Elsevier, vol. 131(C).
    16. Schmitt, Noemi & Westerhoff, Frank, 2021. "Trend followers, contrarians and fundamentalists: Explaining the dynamics of financial markets," Journal of Economic Behavior & Organization, Elsevier, vol. 192(C), pages 117-136.
    17. Lux, Thomas & Alfarano, Simone, 2016. "Financial power laws: Empirical evidence, models, and mechanisms," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 3-18.
    18. Simon Cramer & Torsten Trimborn, 2019. "Stylized Facts and Agent-Based Modeling," Papers 1912.02684, arXiv.org.
    19. Luis Goncalves de Faria, 2022. "An Agent-Based Model With Realistic Financial Time Series: A Method for Agent-Based Models Validation," Papers 2206.09772, arXiv.org.
    20. repec:hal:spmain:info:hdl:2441/20hflp7eqn97boh50no50tv67n is not listed on IDEAS
    21. Jovanovic, Franck & Schinckus, Christophe, 2017. "Econophysics and Financial Economics: An Emerging Dialogue," OUP Catalogue, Oxford University Press, number 9780190205034.

    More about this item

    Keywords

    Agent-based modelling; financial markets; technical and fundamental analysis; asset pricing;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D53 - Microeconomics - - General Equilibrium and Disequilibrium - - - Financial Markets
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:ifwedp:201763. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/iwkiede.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.