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Profitability of Directional Change Based Trading Strategies: The Case of Saudi Stock Market

Author

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  • Monira Essa Aloud

    (Department of Management Information Systems, College of Business Administration, King Saud University, Saudi Arabia)

Abstract

An event-based framework of directional changes (DC) and overshoots maps financial market (FM) price time series into the so-called intrinsic time where events are the time scale of the price time series. This allows for multi-scale analysis of financial data. In the light of this, this paper formulates DC event approach into three automated trading strategies for investments in the FMs: ZI-Directional Change Trading (DCT0), DCT1, and DCT2. The main idea is to use intrinsic time scale based on DC events to learn the size and the direction of periodic patterns from the asset price historical dataset. Using simulation models of Saudi Stock Market, we evaluate the returns of the automated DC trading strategies. The analysis revealed interesting results and evidence that the proposed strategies can indeed generate effective trading for investors with a high rate of returns. The results of this study can be used further to develop decision support systems and autonomous trading agent strategies for the FM

Suggested Citation

  • Monira Essa Aloud, 2016. "Profitability of Directional Change Based Trading Strategies: The Case of Saudi Stock Market," International Journal of Economics and Financial Issues, Econjournals, vol. 6(1), pages 87-95.
  • Handle: RePEc:eco:journ1:2016-01-12
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    1. Tirole, Jean, 1982. "On the Possibility of Speculation under Rational Expectations," Econometrica, Econometric Society, vol. 50(5), pages 1163-1181, September.
    2. LeBaron, Blake, 2001. "Evolution And Time Horizons In An Agent-Based Stock Market," Macroeconomic Dynamics, Cambridge University Press, vol. 5(02), pages 225-254, April.
    3. Andrew W. Lo, A. Craig MacKinlay, 1988. "Stock Market Prices do not Follow Random Walks: Evidence from a Simple Specification Test," The Review of Financial Studies, Society for Financial Studies, vol. 1(1), pages 41-66.
    4. Peter Winker and Manfred Gilli, 2001. "Indirect Estimation of the Parameters of Agent Based Models of Financial Markets," Computing in Economics and Finance 2001 59, Society for Computational Economics.
    5. J. B. Glattfelder & A. Dupuis & R. B. Olsen, 2010. "Patterns in high-frequency FX data: discovery of 12 empirical scaling laws," Quantitative Finance, Taylor & Francis Journals, vol. 11(4), pages 599-614.
    6. Gode, Dhananjay K & Sunder, Shyam, 1993. "Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality," Journal of Political Economy, University of Chicago Press, vol. 101(1), pages 119-137, February.
    7. Lucas, Robert E, Jr, 1978. "Asset Prices in an Exchange Economy," Econometrica, Econometric Society, vol. 46(6), pages 1429-1445, November.
    8. V. Alfi & M. Cristelli & L. Pietronero & A. Zaccaria, 2009. "Minimal agent based model for financial markets II," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 67(3), pages 399-417, February.
    9. Grossman, Sanford J & Stiglitz, Joseph E, 1980. "On the Impossibility of Informationally Efficient Markets," American Economic Review, American Economic Association, vol. 70(3), pages 393-408, June.
    10. Aloud, Monira & Tsang, Edward & Olsen, Richard & Dupuis, Alexandre, 2011. "A directional-change events approach for studying financial time series," Economics Discussion Papers 2011-28, Kiel Institute for the World Economy (IfW Kiel).
    11. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
    12. Richard B. Olsen & Ulrich A. Müller & Michel M. Dacorogna & Olivier V. Pictet & Rakhal R. Davé & Dominique M. Guillaume, 1997. "From the bird's eye to the microscope: A survey of new stylized facts of the intra-daily foreign exchange markets (*)," Finance and Stochastics, Springer, vol. 1(2), pages 95-129.
    13. V. Alfi & M. Cristelli & L. Pietronero & A. Zaccaria, 2008. "Mechanisms of Self-Organization and Finite Size Effects in a Minimal Agent Based Model," Papers 0811.4256, arXiv.org.
    14. John Duffy & M. Ünver, 2006. "Asset price bubbles and crashes with near-zero-intelligence traders," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 27(3), pages 537-563, April.
    15. Szakmary, Andrew C. & Shen, Qian & Sharma, Subhash C., 2010. "Trend-following trading strategies in commodity futures: A re-examination," Journal of Banking & Finance, Elsevier, vol. 34(2), pages 409-426, February.
    16. Gençay, Ramazan & Dacorogna, Michel & Muller, Ulrich A. & Pictet, Olivier & Olsen, Richard, 2001. "An Introduction to High-Frequency Finance," Elsevier Monographs, Elsevier, edition 1, number 9780122796715.
    17. Jessica James, 2003. "Simple trend-following strategies in currency trading," Quantitative Finance, Taylor & Francis Journals, vol. 3(4), pages 75-77.
    18. LeRoy, Stephen F, 1973. "Risk Aversion and the Martingale Property of Stock Prices," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 14(2), pages 436-446, June.
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    More about this item

    Keywords

    Directional Changes; Financial Forecasting; Automated Trading; Financial Markets; Simulation;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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