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Early prediction of Ibex 35 movements

Author

Listed:
  • I. Marta Miranda García
  • María‐Jesús Segovia‐Vargas
  • Usue Mori
  • José A. Lozano
Abstract
In this paper, we examine the early predictability of the market's directional movement using intraday high‐frequency data (695,764 observations) from an stock index (Ibex 35 Index) to provide, either private or institutional investors, an early warning system based on an “early indicator” of the financial market fluctuations with an optimal combination of the two more relevant variables for this strategy, accuracy, and earliness. A novel supervised machine learning early classification technique (Artificial Intelligence) has been applied, for the first time, to the high‐frequency time series of both price and certain technical indicators. The results obtained allow us to assert that the intraday movement of the Ibex 35 can be predicted with acceptable levels of accuracy 24 min after the start of the session and to establish certain informative intraday hourly patterns. Consequently, different indicators of precision and earliness in the session are generated, obtaining that, after a certain point in the session, no gains in precision are generated.

Suggested Citation

  • I. Marta Miranda García & María‐Jesús Segovia‐Vargas & Usue Mori & José A. Lozano, 2023. "Early prediction of Ibex 35 movements," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1150-1166, August.
  • Handle: RePEc:wly:jforec:v:42:y:2023:i:5:p:1150-1166
    DOI: 10.1002/for.2933
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    References listed on IDEAS

    as
    1. Guglielmo Caporale & Luis Gil-Alana & Alex Plastun & Inna Makarenko, 2016. "Intraday Anomalies and Market Efficiency: A Trading Robot Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 47(2), pages 275-295, February.
    2. Dimitri Vayanos & Paul Woolley, 2013. "An Institutional Theory of Momentum and Reversal," The Review of Financial Studies, Society for Financial Studies, vol. 26(5), pages 1087-1145.
    3. Hasbrouck, Joel, 1991. "Measuring the Information Content of Stock Trades," Journal of Finance, American Finance Association, vol. 46(1), pages 179-207, March.
    4. 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.
    5. Wood, Robert A & McInish, Thomas H & Ord, J Keith, 1985. "An Investigation of Transactions Data for NYSE Stocks," Journal of Finance, American Finance Association, vol. 40(3), pages 723-739, July.
    6. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
    7. Chris Brooks & Melvin. J. Hinich & Douglas M. Patterson, 2003. "Intra-day Patterns in the Returns, Bidask Spereads, and Trading Volume of Stocks Traded on the New York Stock Exchange," ICMA Centre Discussion Papers in Finance icma-dp2003-14, Henley Business School, University of Reading.
    8. Gao, Lei & Han, Yufeng & Zhengzi Li, Sophia & Zhou, Guofu, 2018. "Market intraday momentum," Journal of Financial Economics, Elsevier, vol. 129(2), pages 394-414.
    9. Mingyue Qiu & Yu Song, 2016. "Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-11, May.
    10. Timmermann, Allan, 2008. "Reply to the discussion of Elusive Return Predictability," International Journal of Forecasting, Elsevier, vol. 24(1), pages 29-30.
    11. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    12. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Journal of Economic Perspectives, American Economic Association, vol. 17(1), pages 59-82, Winter.
    13. Mark Buchanan, 2015. "Physics in finance: Trading at the speed of light," Nature, Nature, vol. 518(7538), pages 161-163, February.
    14. Barberis, Nicholas & Shleifer, Andrei & Vishny, Robert, 1998. "A model of investor sentiment," Journal of Financial Economics, Elsevier, vol. 49(3), pages 307-343, September.
    15. Anat R. Admati, Paul Pfleiderer, 1988. "A Theory of Intraday Patterns: Volume and Price Variability," The Review of Financial Studies, Society for Financial Studies, vol. 1(1), pages 3-40.
    16. Schulmeister, Stephan, 2009. "Profitability of technical stock trading: Has it moved from daily to intraday data?," Review of Financial Economics, Elsevier, vol. 18(4), pages 190-201, October.
    17. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Working Papers 111, Princeton University, Department of Economics, Center for Economic Policy Studies..
    18. Perez-Rodriguez, Jorge V. & Torra, Salvador & Andrada-Felix, Julian, 2005. "STAR and ANN models: forecasting performance on the Spanish "Ibex-35" stock index," Journal of Empirical Finance, Elsevier, vol. 12(3), pages 490-509, June.
    19. Fuwei Jiang & Guoshi Tong & Guokai Song, 2019. "Technical Analysis Profitability Without Data Snooping Bias: Evidence from Chinese Stock Market," International Review of Finance, International Review of Finance Ltd., vol. 19(1), pages 191-206, March.
    20. Leung, Mark T. & Daouk, Hazem & Chen, An-Sing, 2000. "Forecasting stock indices: a comparison of classification and level estimation models," International Journal of Forecasting, Elsevier, vol. 16(2), pages 173-190.
    21. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Working Papers 111, Princeton University, Department of Economics, Center for Economic Policy Studies..
    22. Cristina Ortiz & Jos� Mar�a Ortiz de Z�rate & Luis Vicente, 2015. "New evidence of quarterly return patterns in the Spanish stock market," Applied Economics Letters, Taylor & Francis Journals, vol. 22(13), pages 1025-1029, September.
    23. De Bondt, Werner F M & Thaler, Richard, 1985. "Does the Stock Market Overreact?," Journal of Finance, American Finance Association, vol. 40(3), pages 793-805, July.
    24. repec:pri:cepsud:91malkiel is not listed on IDEAS
    25. Pfeifer, Phillip E., 1985. "Market Timing and Risk Reduction," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 20(4), pages 451-459, December.
    26. Qianwei Ying & Tahir Yousaf & Qurat ul Ain & Yasmeen Akhtar & Muhammad Shahid Rasheed, 2019. "Stock Investment and Excess Returns: A Critical Review in the Light of the Efficient Market Hypothesis," JRFM, MDPI, vol. 12(2), pages 1-22, June.
    27. Matteo Rossi, 2016. "The capital asset pricing model: a critical literature review," Global Business and Economics Review, Inderscience Enterprises Ltd, vol. 18(5), pages 604-617.
    28. Chen, Chun-nan, 2013. "The predictability of opening returns for the returns of the trading day: Evidence from Taiwan futures market," International Review of Economics & Finance, Elsevier, vol. 25(C), pages 272-281.
    29. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    30. Tay, Francis E. H. & Cao, Lijuan, 2001. "Application of support vector machines in financial time series forecasting," Omega, Elsevier, vol. 29(4), pages 309-317, August.
    31. Narasimhan Jegadeesh & Sheridan Titman, 2001. "Profitability of Momentum Strategies: An Evaluation of Alternative Explanations," Journal of Finance, American Finance Association, vol. 56(2), pages 699-720, April.
    32. Batten, Jonathan A. & Lucey, Brian M. & McGroarty, Frank & Peat, Maurice & Urquhart, Andrew, 2018. "Does intraday technical trading have predictive power in precious metal markets?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 52(C), pages 102-113.
    33. Harris, Lawrence, 1986. "A transaction data study of weekly and intradaily patterns in stock returns," Journal of Financial Economics, Elsevier, vol. 16(1), pages 99-117, May.
    34. Timmermann, Allan, 2008. "Elusive return predictability," International Journal of Forecasting, Elsevier, vol. 24(1), pages 1-18.
    35. Jegadeesh, Narasimhan & Titman, Sheridan, 1993. "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, vol. 48(1), pages 65-91, March.
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