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STAR and ANN models: forecasting performance on the Spanish "Ibex-35" stock index

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  • Perez-Rodriguez, Jorge V.
  • Torra, Salvador
  • Andrada-Felix, Julian

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  • 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.
    • Handle: RePEc:eee:empfin:v:12:y:2005:i:3:p:490-509
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    21. John. Pemberton, 1987. "Exact Least Squares Multi‐Step Prediction From Nonlinear Autoregressive Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 8(4), pages 443-448, July.
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    Cited by:

    1. Jin, Xiaoye, 2022. "Performance of intraday technical trading in China’s gold market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 76(C).
    2. repec:arx:papers:1604.04044 is not listed on IDEAS
    3. Kizilaslan, Recep & Freund, Steven & Iseri, Ali, 2016. "A data analytic approach to forecasting daily stock returns in an emerging marketAuthor-Name: Oztekin, Asil," European Journal of Operational Research, Elsevier, vol. 253(3), pages 697-710.
    4. Firat Melih Yilmaz & Engin Yildiztepe, 2024. "Statistical Evaluation of Deep Learning Models for Stock Return Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 63(1), pages 221-244, January.
    5. Seyed Mehrzad Asaad Sajadi & Pouya Khodaee & Ehsan Hajizadeh & Sabri Farhadi & Sohaib Dastgoshade & Bo Du, 2022. "Deep Learning-Based Methods for Forecasting Brent Crude Oil Return Considering COVID-19 Pandemic Effect," Energies, MDPI, vol. 15(21), pages 1-23, October.
    6. Lukas Ryll & Sebastian Seidens, 2019. "Evaluating the Performance of Machine Learning Algorithms in Financial Market Forecasting: A Comprehensive Survey," Papers 1906.07786, arXiv.org, revised Jul 2019.
    7. Ciner, Cetin, 2019. "Do industry returns predict the stock market? A reprise using the random forest," The Quarterly Review of Economics and Finance, Elsevier, vol. 72(C), pages 152-158.
    8. Chong, Terence Tai-Leung & Lam, Tau-Hing & Yan, Isabel Kit-Ming, 2012. "Is the Chinese stock market really inefficient?," China Economic Review, Elsevier, vol. 23(1), pages 122-137.
    9. Ilias Lekkos & Costas Milas & Theodore Panagiotidis, 2005. "On the predictability of common risk factors in the US and UK interest rate swap markets: Evidence from non-linear and linear models," Discussion Paper Series 2005_9, Department of Economics, Loughborough University, revised Sep 2005.
    10. Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Self-organizing map analysis of agents' expectations. Different patterns of anticipation of the 2008 financial crisis”," IREA Working Papers 201511, University of Barcelona, Research Institute of Applied Economics, revised Mar 2015.
    11. Tan, Siow-Hooi & Lai, Ming-Ming & Tey, Eng-Xin & Chong, Lee-Lee, 2020. "Testing the performance of technical analysis and sentiment-TAR trading rules in the Malaysian stock market," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    12. 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.
    13. Lawrence Xaba & Ntebogang Moroke & Johnson Arkaah & Charlemagne Pooe, 2015. "A Comparative Study of Stock Price Forecasting using nonlinear models," Proceedings of International Academic Conferences 2704207, International Institute of Social and Economic Sciences.
    14. Christos Avdoulas & Stelios Bekiros & Sabri Boubaker, 2018. "Evolutionary-based return forecasting with nonlinear STAR models: evidence from the Eurozone peripheral stock markets," Annals of Operations Research, Springer, vol. 262(2), pages 307-333, March.
    15. Terence Tai-Leung Chong & Sheung Tat Chan, 2008. "Structural Change in the Efficiency of the Japanese Stock Market after the Millennium," Economics Bulletin, AccessEcon, vol. 7(7), pages 1-7.
    16. Juan Reboredo & José Matías & Raquel Garcia-Rubio, 2012. "Nonlinearity in Forecasting of High-Frequency Stock Returns," Computational Economics, Springer;Society for Computational Economics, vol. 40(3), pages 245-264, October.
    17. Kim, Sei-Wan & Mollick, André V. & Nam, Kiseok, 2008. "Common nonlinearities in long-horizon stock returns: Evidence from the G-7 stock markets," Global Finance Journal, Elsevier, vol. 19(1), pages 19-31.
    18. repec:ebl:ecbull:v:7:y:2008:i:7:p:1-7 is not listed on IDEAS
    19. Elsy Gómez-Ramos & Francisco Venegas-Martínez, 2013. "A Review of Artificial Neural Networks: How Well Do They Perform in Forecasting Time Series?," Analítika, Analítika - Revista de Análisis Estadístico/Journal of Statistical Analysis, vol. 6(2), pages 7-15, Diciembre.
    20. Jaydip Sen & Tamal Datta Chaudhuri, 2017. "A Time Series Analysis-Based Forecasting Framework for the Indian Healthcare Sector," Papers 1705.01144, arXiv.org.
    21. Jaydip SEN & Tamal DATTA CHAUDHURI, 2016. "An Alternative Framework for Time Series Decomposition and Forecastingand its Relevance for Portfolio Choice – A Comparative Study of the Indian Consumer Durable and Small Cap Sectors," Journal of Economics Library, KSP Journals, vol. 3(2), pages 303-326, June.
    22. Rico Belda, Paz, 2013. "No linealidad y asimetría en el proceso generador del Índice Ibex35/Nonlinearity and Asymmetry in the Generator Process of Ibex35 Index," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 31, pages 555-576, Septiembr.
    23. Duan, Wen-Qi & Stanley, H. Eugene, 2011. "Cross-correlation and the predictability of financial return series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(2), pages 290-296.

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