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Comparing linear and nonlinear forecasts for stock returns

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  • Kanas, Angelos
  • Yannopoulos, Andreas

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  • Kanas, Angelos & Yannopoulos, Andreas, 2001. "Comparing linear and nonlinear forecasts for stock returns," International Review of Economics & Finance, Elsevier, vol. 10(4), pages 383-398, December.
    • Handle: RePEc:eee:reveco:v:10:y:2001:i:4:p:383-398
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    7. Froot, Kenneth A & Obstfeld, Maurice, 1991. "Intrinsic Bubbles: The Case of Stock Prices," American Economic Review, American Economic Association, vol. 81(5), pages 1189-1214, December.
    8. William A. Brock, 1993. "Pathways to randomness in the economy: Emergent nonlinearity and chaos in economics and finance," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, vol. 8(1), pages 3-55.
    9. L. Kavalieris, 1989. "The Estimation Of The Order Of An Autoregression Using Recursive Residuals And Cross‐Validation," Journal of Time Series Analysis, Wiley Blackwell, vol. 10(3), pages 271-281, May.
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    11. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    12. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    13. Paul R. Krugman, 1987. "Trigger Strategies and Price Dynamics in Equity and Foreign Exchange Markets," NBER Working Papers 2459, National Bureau of Economic Research, Inc.
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    15. Donaldson, R Glen & Kamstra, Mark, 1996. "A New Dividend Forecasting Procedure That Rejects Bubbles in Asset Prices: The Case of 1929's Stock Crash," The Review of Financial Studies, Society for Financial Studies, vol. 9(2), pages 333-383.
    16. Swanson, Norman R & White, Halbert, 1995. "A Model-Selection Approach to Assessing the Information in the Term Structure Using Linear Models and Artificial Neural Networks," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 265-275, July.
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    2. Angelos Kanas, 2003. "Non-linear forecasts of stock returns," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(4), pages 299-315.
    3. Jia‐Yen Huang & Jin‐Hao Liu, 2020. "Using social media mining technology to improve stock price forecast accuracy," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 104-116, January.
    4. Amin Aminimehr & Ali Raoofi & Akbar Aminimehr & Amirhossein Aminimehr, 2022. "A Comprehensive Study of Market Prediction from Efficient Market Hypothesis up to Late Intelligent Market Prediction Approaches," Computational Economics, Springer;Society for Computational Economics, vol. 60(2), pages 781-815, August.
    5. 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.
    6. Eleni Constantinou & Robert Georgiades & Avo Kazandjian & Georgios P. Kouretas, 2006. "Regime switching and artificial neural network forecasting of the Cyprus Stock Exchange daily returns," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 11(4), pages 371-383.
    7. Takashi Matsuki & Ming-Jen Chang, 2016. "Out-of-Sample Exchange Rate Forecasting and Macroeconomic Fundamentals: The Case of Japan," Australian Economic Papers, Wiley Blackwell, vol. 55(4), pages 409-433, December.
    8. Nguyen, Dat Thanh & Phan, Dinh Hoang Bach & Anglingkusumo, Reza & Sasongko, Aryo, 2021. "US government shutdowns and Indonesian stock market," Pacific-Basin Finance Journal, Elsevier, vol. 67(C).
    9. Tseng, Chih-Hsiung & Cheng, Sheng-Tzong & Wang, Yi-Hsien & Peng, Jin-Tang, 2008. "Artificial neural network model of the hybrid EGARCH volatility of the Taiwan stock index option prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(13), pages 3192-3200.
    10. Guidi, Francesco, 2010. "Modelling and forecasting volatility of East Asian Newly Industrialized Countries and Japan stock markets with non-linear models," MPRA Paper 19851, University Library of Munich, Germany.
    11. Ariel Navon & Yosi Keller, 2017. "Financial Time Series Prediction Using Deep Learning," Papers 1711.04174, arXiv.org.
    12. Brad S. Trinkle, 2005. "Forecasting annual excess stock returns via an adaptive network‐based fuzzy inference system," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 13(3), pages 165-177, July.
    13. Kung, Ling-Ming & Yu, Shang-Wu, 2008. "Prediction of index futures returns and the analysis of financial spillovers--A comparison between GARCH and the grey theorem," European Journal of Operational Research, Elsevier, vol. 186(3), pages 1184-1200, May.
    14. Shambora, William E. & Rossiter, Rosemary, 2007. "Are there exploitable inefficiencies in the futures market for oil?," Energy Economics, Elsevier, vol. 29(1), pages 18-27, January.
    15. Eleni Constantinou & Robert Georgiades & Avo Kazandjian & George Kouretas, 2005. "Regime Switching and Artificial Neural Network Forecasting," Working Papers 0502, University of Crete, Department of Economics.

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