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Forecasting Performance of Nonlinear Models for Intraday Stock Returns

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  • José M. Matías
  • Juan C. Reboredo

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  • José M. Matías & Juan C. Reboredo, 2012. "Forecasting Performance of Nonlinear Models for Intraday Stock Returns," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 31(2), pages 172-188, March.
    • Handle: RePEc:wly:jforec:v:31:y:2012:i:2:p:172-188
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    Citations

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    Cited by:

    1. Christian L Dunis & Spiros D Likothanassis & Andreas S Karathanasopoulos & Georgios S Sermpinis & Konstantinos A Theofilatos, 2013. "A hybrid genetic algorithm–support vector machine approach in the task of forecasting and trading," Journal of Asset Management, Palgrave Macmillan, vol. 14(1), pages 52-71, February.
    2. Chang Liu & Raja Nassar & Min Guo, 2015. "A Method of Retail Mortgage Stress Testing: Based on Time‐Frame and Magnitude Analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(4), pages 261-274, July.
    3. Dendramis, Yiannis & Kapetanios, George & Tzavalis, Elias, 2014. "Level shifts in stock returns driven by large shocks," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 41-51.
    4. Vitor Azevedo & Christopher Hoegner, 2023. "Enhancing stock market anomalies with machine learning," Review of Quantitative Finance and Accounting, Springer, vol. 60(1), pages 195-230, January.
    5. Flavio Barboza & Geraldo Nunes Silva & José Augusto Fiorucci, 2023. "A review of artificial intelligence quality in forecasting asset prices," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1708-1728, November.
    6. Rounaghi, Mohammad Mahdi & Nassir Zadeh, Farzaneh, 2016. "Investigation of market efficiency and Financial Stability between S&P 500 and London Stock Exchange: Monthly and yearly Forecasting of Time Series Stock Returns using ARMA model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 10-21.
    7. Rodriguez, E. & Aguilar-Cornejo, M. & Femat, R. & Alvarez-Ramirez, J., 2014. "US stock market efficiency over weekly, monthly, quarterly and yearly time scales," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 554-564.
    8. Nahida Akter & Ashadun Nobi, 2018. "Investigation of the Financial Stability of S&P 500 Using Realized Volatility and Stock Returns Distribution," JRFM, MDPI, vol. 11(2), pages 1-10, April.
    9. Sermpinis, Georgios & Theofilatos, Konstantinos & Karathanasopoulos, Andreas & Georgopoulos, Efstratios F. & Dunis, Christian, 2013. "Forecasting foreign exchange rates with adaptive neural networks using radial-basis functions and Particle Swarm Optimization," European Journal of Operational Research, Elsevier, vol. 225(3), pages 528-540.
    10. Mahdi Moradi & Mehdi Jabbari Nooghabi & Mohammad Mahdi Rounaghi, 2021. "Investigation of fractal market hypothesis and forecasting time series stock returns for Tehran Stock Exchange and London Stock Exchange," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 662-678, January.
    11. Huiwen Wang & Shan Lu & Jichang Zhao, 2018. "Aggregating multiple types of complex data in stock market prediction: A model-independent framework," Papers 1805.05617, arXiv.org.
    12. Georgios Sermpinis & Andreas Karathanasopoulos & Rafael Rosillo & David Fuente, 2021. "Neural networks in financial trading," Annals of Operations Research, Springer, vol. 297(1), pages 293-308, February.
    13. Reboredo, Juan C. & Wen, Xiaoqian, 2015. "Are China’s new energy stock prices driven by new energy policies?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 624-636.
    14. Reboredo, Juan C. & Rivera-Castro, Miguel A. & Miranda, José G.V. & García-Rubio, Raquel, 2013. "How fast do stock prices adjust to market efficiency? Evidence from a detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(7), pages 1631-1637.

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