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Technical Analysis Based On Price-Volume Signals And The Power Of Trading Breaks

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  • FRANK H. WESTERHOFF

    (University of Osnabrueck, Department of Economics, Rolandstrasse 8, D-49069 Osnabrueck, Germany)

Abstract

We propose a novel stock market model and investigate the effectiveness of trading breaks. Our nonlinear model consists of two types of traders: while fundamentalists expect prices to return towards their intrinsic values, chartists extrapolate past price movements into the future. Moreover, chartists condition their orders on past trading volume. The model is able to replicate several stylized facts of stock markets such as fat tails and volatility clustering. Using the model as an artificial stock market laboratory we find that trading breaks have the power to reduce volatility and — if fundamentals do not move too strongly — also mispricing.

Suggested Citation

  • Frank H. Westerhoff, 2006. "Technical Analysis Based On Price-Volume Signals And The Power Of Trading Breaks," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 9(02), pages 227-244.
    • Handle: RePEc:wsi:ijtafx:v:09:y:2006:i:02:n:s0219024906003512
    DOI: 10.1142/S0219024906003512
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    Citations

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    Cited by:

    1. Fischer, Thomas, 2011. "News reaction in financial markets within a behavioral finance model with heterogeneous agents," Darmstadt Discussion Papers in Economics 205, Darmstadt University of Technology, Department of Law and Economics.
    2. Tut, DANIEL, 2024. "Bitcoin, speculative sentiments and crypto-assets valuation," MPRA Paper 120866, University Library of Munich, Germany.
    3. Frank H. Westerhoff, 2009. "Exchange Rate Dynamics: A Nonlinear Survey," Chapters, in: J. Barkley Rosser Jr. (ed.), Handbook of Research on Complexity, chapter 11, Edward Elgar Publishing.
    4. Leal, Sandrine Jacob & Napoletano, Mauro, 2019. "Market stability vs. market resilience: Regulatory policies experiments in an agent-based model with low- and high-frequency trading," Journal of Economic Behavior & Organization, Elsevier, vol. 157(C), pages 15-41.
    5. Noemi Schmitt & Ivonne Schwartz & Frank Westerhoff, 2022. "Heterogeneous speculators and stock market dynamics: a simple agent-based computational model," The European Journal of Finance, Taylor & Francis Journals, vol. 28(13-15), pages 1263-1282, October.
    6. 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.
    7. Changtai Li & Weihong Huang & Wei-Siang Wang & Wai-Mun Chia, 2023. "Price Change and Trading Volume: Behavioral Heterogeneity in Stock Market," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 677-713, February.
    8. Xinyue Dong & Honggang Li, 2019. "The Effect of Extremely Small Price Limits: Evidence from the Early Period of the Chinese Stock Market," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 55(7), pages 1516-1530, May.
    9. Demary, Markus, 2010. "Transaction taxes and traders with heterogeneous investment horizons in an agent-based financial market model," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 4, pages 1-44.

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