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Nonlinear index prediction

Author

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  • Zemke, Stefan

Abstract

Neural network, K-nearest neighbor, naive Bayesian classifier and genetic algorithm evolving classification rules are compared for their prediction accuracies on stock exchange index data. The method yielding the best result, nearest neighbor, is then refined and incorporated into a simple trading system achieving returns above index growth. The success of the method hints the plausibility of nonlinearities present in the index series and, as such, the scope for nonlinear modeling/prediction.

Suggested Citation

  • Zemke, Stefan, 1999. "Nonlinear index prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 269(1), pages 177-183.
    • Handle: RePEc:eee:phsmap:v:269:y:1999:i:1:p:177-183
    DOI: 10.1016/S0378-4371(99)00091-6
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    Citations

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    Cited by:

    1. Minjae Park & Mi Lim Lee & Jinpyo Lee, 2019. "Predicting Stock Market Indices Using Classification Tools," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 9(2), pages 243-256, February.
    2. Blazejewski, Adam & Coggins, Richard, 2005. "A local non-parametric model for trade sign inference," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 348(C), pages 481-495.
    3. Chen, Wenjin & Szeto, K.Y., 2012. "Mixed time scale strategy in portfolio management," International Review of Financial Analysis, Elsevier, vol. 23(C), pages 35-40.
    4. Adam Blazejewski & Richard Coggins, 2004. "A local non-parametric model for trade sign inference," Finance 0408009, University Library of Munich, Germany.
    5. Jerić Silvija Vlah, 2020. "Comparing classification algorithms for prediction on CROBEX data," Croatian Review of Economic, Business and Social Statistics, Sciendo, vol. 6(2), pages 4-11, December.
    6. Qifeng Qiao & Peter A. Beling, 2016. "Decision analytics and machine learning in economic and financial systems," Environment Systems and Decisions, Springer, vol. 36(2), pages 109-113, June.

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