IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1501.07504.html
   My bibliography  Save this paper

Adaptive Filter Design for Stock Market Prediction Using a Correlation-based Criterion

Author

Listed:
  • J. E. Wesen
  • V. VV. Vermehren
  • H. M. de Oliveira

Abstract

This paper presents a novel adaptive-filter approach for predicting assets on the stock markets. Concepts are introduced here, which allow understanding this method and computing of the corresponding forecast. This approach is applied, as an example, through the prediction over the actual valuation of the PETR3 shares (Petrobras ON) traded in the Brazilian Stock Market. The first-rate choices of the window length and the number of filter coefficient are evaluated. This is done by observing the correlation between the predictor signal and the actual course performed by the market in terms of both the window prevision length and filter coefficient values. It is shown that such adaptive predictors furnish, on the average, very substantial profit on the invested amount.

Suggested Citation

  • J. E. Wesen & V. VV. Vermehren & H. M. de Oliveira, 2015. "Adaptive Filter Design for Stock Market Prediction Using a Correlation-based Criterion," Papers 1501.07504, arXiv.org.
    • Handle: RePEc:arx:papers:1501.07504
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1501.07504
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Turner, Christopher M. & Startz, Richard & Nelson, Charles R., 1989. "A Markov model of heteroskedasticity, risk, and learning in the stock market," Journal of Financial Economics, Elsevier, vol. 25(1), pages 3-22, November.
    2. Pai, Ping-Feng & Lin, Chih-Sheng, 2005. "A hybrid ARIMA and support vector machines model in stock price forecasting," Omega, Elsevier, vol. 33(6), pages 497-505, December.
    3. LeBaron, Blake & Arthur, W. Brian & Palmer, Richard, 1999. "Time series properties of an artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 23(9-10), pages 1487-1516, September.
    4. Oral, Muhittin & Kettani, Ossama, 1989. "A mathematical programming model for market share prediction," International Journal of Forecasting, Elsevier, vol. 5(1), pages 59-68.
    5. Dokko, Yoon & Edelstein, Robert H, 1989. "How Well Do Economists Forecast Stock Market Prices? A Study of the Livingston Surveys," American Economic Review, American Economic Association, vol. 79(4), pages 865-871, September.
    6. Benoit Mandelbrot, 2015. "The Variation of Certain Speculative Prices," World Scientific Book Chapters, in: Anastasios G Malliaris & William T Ziemba (ed.), THE WORLD SCIENTIFIC HANDBOOK OF FUTURES MARKETS, chapter 3, pages 39-78, World Scientific Publishing Co. Pte. Ltd..
    7. Antoniou, I. & Akritas, P. & Burak, D.A. & Ivanov, V.V. & Kryanev, A.V. & Lukin, G.V., 2004. "Robust methods for stock market data analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 336(3), pages 538-548.
    Full references (including those not matched with items on IDEAS)

    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. Kamaladdin Fataliyev & Aneesh Chivukula & Mukesh Prasad & Wei Liu, 2021. "Stock Market Analysis with Text Data: A Review," Papers 2106.12985, arXiv.org, revised Jul 2021.
    2. Siddiqi, Hammad, 2007. "Rational Interacting Agents and Volatility Clustering: A New Approach," MPRA Paper 2984, University Library of Munich, Germany.
    3. E. Samanidou & E. Zschischang & D. Stauffer & T. Lux, 2001. "Microscopic Models of Financial Markets," Papers cond-mat/0110354, arXiv.org.
    4. Gaunersdorfer, A. & Hommes, C.H. & Wagener, F.O.O., 2000. "Bifurcation Routes to Volatility Clustering," CeNDEF Working Papers 00-04, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    5. J. Doyne Farmer, 2002. "Market force, ecology and evolution," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 11(5), pages 895-953, November.
    6. Baosheng Yuan & Kan Chen, 2005. "Impact of Investor's Varying Risk Aversion on the Dynamics of Asset Price Fluctuations," Papers physics/0506224, arXiv.org.
    7. Linda Ponta & Silvano Cincotti, 2018. "Traders’ Networks of Interactions and Structural Properties of Financial Markets: An Agent-Based Approach," Complexity, Hindawi, vol. 2018, pages 1-9, January.
    8. 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.
    9. 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.
    10. Paul De Grauwe & Marianna Grimaldi, 2014. "Exchange Rate Puzzles: A Tale of Switching Attractors," World Scientific Book Chapters, in: Exchange Rates and Global Financial Policies, chapter 3, pages 71-117, World Scientific Publishing Co. Pte. Ltd..
    11. Lux, Thomas & Alfarano, Simone, 2016. "Financial power laws: Empirical evidence, models, and mechanisms," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 3-18.
    12. Don U.A. Galagedera & Roland Shami, 2003. "Association between Markov regime-switching market volatility and beta risk: Evidence from Dow Jones industrial securities," Monash Econometrics and Business Statistics Working Papers 20/03, Monash University, Department of Econometrics and Business Statistics.
    13. Viktor Manahov & Mona Soufian & Robert Hudson, 2014. "The Implications Of Trader Cognitive Abilities On Stock Market Properties," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 21(1), pages 1-18, January.
    14. Baosheng Yuan & Kan Chen, 2006. "Impact of investor’s varying risk aversion on the dynamics of asset price fluctuations," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 1(2), pages 189-214, November.
    15. Sandrine Jacob Leal, 2015. "Fundamentalists, chartists and asset pricing anomalies," Quantitative Finance, Taylor & Francis Journals, vol. 15(11), pages 1837-1850, November.
    16. Aaron Wheeler & Jeffrey D. Varner, 2023. "Scalable Agent-Based Modeling for Complex Financial Market Simulations," Papers 2312.14903, arXiv.org, revised Jan 2024.
    17. Didier SORNETTE, 2014. "Physics and Financial Economics (1776-2014): Puzzles, Ising and Agent-Based Models," Swiss Finance Institute Research Paper Series 14-25, Swiss Finance Institute.
    18. Gaunersdorfer, Andrea & Hommes, Cars H. & Wagener, Florian O.O., 2008. "Bifurcation routes to volatility clustering under evolutionary learning," Journal of Economic Behavior & Organization, Elsevier, vol. 67(1), pages 27-47, July.
    19. Siddiqi, Hammad, 2006. "Belief merging and revision under social influence: An explanation for the volatility clustering puzzle," MPRA Paper 657, University Library of Munich, Germany.
    20. Makoto Nirei, 2008. "Self-organized criticality in a herd behavior model of financial markets," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 3(1), pages 89-97, June.

    More about this item

    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:arx:papers:1501.07504. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.