IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v34y2009i2p173-193.html
   My bibliography  Save this article

Predicting EU Energy Industry Excess Returns on EU Market Index via a Constrained Genetic Algorithm

Author

Listed:
  • Massimiliano Kaucic

Abstract

No abstract is available for this item.

Suggested Citation

  • Massimiliano Kaucic, 2009. "Predicting EU Energy Industry Excess Returns on EU Market Index via a Constrained Genetic Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 34(2), pages 173-193, September.
    • Handle: RePEc:kap:compec:v:34:y:2009:i:2:p:173-193
    DOI: 10.1007/s10614-009-9176-4
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10614-009-9176-4
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10614-009-9176-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Costantini, Mauro & Pappalardo, Carmine, 2008. "Combination of Forecast Methods Using Encompassing Tests. An Algorithm-Based Procedure ; For the revised version of this paper, see Working Paper 240, Economics Series, June 2009, which includes some ," Economics Series 228, Institute for Advanced Studies.
    2. Pesaran, M Hashem & Timmermann, Allan, 2000. "A Recursive Modelling Approach to Predicting UK Stock Returns," Economic Journal, Royal Economic Society, vol. 110(460), pages 159-191, January.
    3. Ahumada, Hildegart A, 1985. "An Encompassing Test of Two Models of the Balance of Trade for Argentina," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 47(1), pages 51-70, February.
    4. Bossaerts, Peter & Hillion, Pierre, 1999. "Implementing Statistical Criteria to Select Return Forecasting Models: What Do We Learn?," The Review of Financial Studies, Society for Financial Studies, vol. 12(2), pages 405-428.
    5. Hoeting, Jennifer & Raftery, Adrian E. & Madigan, David, 1996. "A method for simultaneous variable selection and outlier identification in linear regression," Computational Statistics & Data Analysis, Elsevier, vol. 22(3), pages 251-270, July.
    6. Daniel Grenouilleau, 2006. "The Stacked Leading Indicators Dynamic Factor Model: A Sensitivity Analysis of Forecast Accuracy using Bootstrapping," European Economy - Economic Papers 2008 - 2015 249, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    7. Harvey, David I & Leybourne, Stephen J & Newbold, Paul, 1998. "Tests for Forecast Encompassing," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 254-259, April.
    8. Pesaran, M Hashem & Timmermann, Allan, 1995. "Predictability of Stock Returns: Robustness and Economic Significance," Journal of Finance, American Finance Association, vol. 50(4), pages 1201-1228, September.
    9. M. A. Kaboudan, 2000. "Genetic Programming Prediction of Stock Prices," Computational Economics, Springer;Society for Computational Economics, vol. 16(3), pages 207-236, December.
    10. Kelvin Balcombe, 2005. "Model Selection Using Information Criteria and Genetic Algorithms," Computational Economics, Springer;Society for Computational Economics, vol. 25(3), pages 207-228, June.
    11. K. J. Martijn Cremers, 2002. "Stock Return Predictability: A Bayesian Model Selection Perspective," The Review of Financial Studies, Society for Financial Studies, vol. 15(4), pages 1223-1249.
    12. Daniel Grenouilleau, 2004. "A sorted leading indicators dynamic (SLID) factor model for short-run euro-area GDP forecasting," European Economy - Economic Papers 2008 - 2015 219, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    13. David E. Rapach & Jack K. Strauss, 2008. "Forecasting US employment growth using forecast combining methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(1), pages 75-93.
    14. Dorsey, Robert E & Mayer, Walter J, 1995. "Genetic Algorithms for Estimation Problems with Multiple Optima, Nondifferentiability, and Other Irregular Features," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(1), pages 53-66, January.
    15. Julia Campos & Neil R. Ericsson & David F. Hendry, 2005. "General-to-specific modeling: an overview and selected bibliography," International Finance Discussion Papers 838, Board of Governors of the Federal Reserve System (U.S.).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ivan Savin & Peter Winker, 2012. "Heuristic Optimization Methods for Dynamic Panel Data Model Selection: Application on the Russian Innovative Performance," Computational Economics, Springer;Society for Computational Economics, vol. 39(4), pages 337-363, April.
    2. Chen, Yan & Wang, Xuancheng, 2015. "A hybrid stock trading system using genetic network programming and mean conditional value-at-risk," European Journal of Operational Research, Elsevier, vol. 240(3), pages 861-871.

    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. Robert Sollis, 2005. "Predicting returns and volatility with macroeconomic variables: evidence from tests of encompassing," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(3), pages 221-231.
    2. Rapach, David E. & Wohar, Mark E., 2006. "In-sample vs. out-of-sample tests of stock return predictability in the context of data mining," Journal of Empirical Finance, Elsevier, vol. 13(2), pages 231-247, March.
    3. Rossi, Barbara, 2013. "Advances in Forecasting under Instability," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1203-1324, Elsevier.
    4. Hartmann, Daniel & Kempa, Bernd & Pierdzioch, Christian, 2008. "Economic and financial crises and the predictability of U.S. stock returns," Journal of Empirical Finance, Elsevier, vol. 15(3), pages 468-480, June.
    5. Cem Cakmakli & Dick van Dijk, 2010. "Getting the Most out of Macroeconomic Information for Predicting Stock Returns and Volatility," Tinbergen Institute Discussion Papers 10-115/4, Tinbergen Institute.
    6. Barnhart, Scott W. & Giannetti, Antoine, 2009. "Negative earnings, positive earnings and stock return predictability: An empirical examination of market timing," Journal of Empirical Finance, Elsevier, vol. 16(1), pages 70-86, January.
    7. João M. Sousa & Ricardo M. Sousa, 2019. "Asset Returns Under Model Uncertainty: Evidence from the Euro Area, the US and the UK," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 139-176, June.
    8. Michael Cooper & Huseyin Gulen, 2006. "Is Time-Series-Based Predictability Evident in Real Time?," The Journal of Business, University of Chicago Press, vol. 79(3), pages 1263-1292, May.
    9. Pesaran, M. Hashem & Timmermann, Allan, 2002. "Market timing and return prediction under model instability," Journal of Empirical Finance, Elsevier, vol. 9(5), pages 495-510, December.
    10. Pierdzioch, Christian & Risse, Marian & Rohloff, Sebastian, 2014. "The international business cycle and gold-price fluctuations," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(2), pages 292-305.
    11. Shamsi Zamenjani, Azam, 2021. "Do financial variables help predict the conditional distribution of the market portfolio?," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 327-345.
    12. Hanan Naser & Fatema Alaali, 2018. "Can oil prices help predict US stock market returns? Evidence using a dynamic model averaging (DMA) approach," Empirical Economics, Springer, vol. 55(4), pages 1757-1777, December.
    13. Dangl, Thomas & Halling, Michael, 2012. "Predictive regressions with time-varying coefficients," Journal of Financial Economics, Elsevier, vol. 106(1), pages 157-181.
    14. Döpke, Jörg & Hartmann, Daniel & Pierdzioch, Christian, 2008. "Real-time macroeconomic data and ex ante stock return predictability," International Review of Financial Analysis, Elsevier, vol. 17(2), pages 274-290.
    15. Christian Pierdzioch & Marian Risse & Sebastian Rohloff, 2016. "Fluctuations of the real exchange rate, real interest rates, and the dynamics of the price of gold in a small open economy," Empirical Economics, Springer, vol. 51(4), pages 1481-1499, December.
    16. Lucio Sarno & Giorgio Valente, 2009. "Exchange Rates and Fundamentals: Footloose or Evolving Relationship?," Journal of the European Economic Association, MIT Press, vol. 7(4), pages 786-830, June.
    17. Amit Goyal & Ivo Welch, 2003. "Predicting the Equity Premium with Dividend Ratios," Management Science, INFORMS, vol. 49(5), pages 639-654, May.
    18. Marco Aiolfi & Carlo Ambrogio Favero, "undated". "Model Uncertainty, Thick Modelling and the predictability of Stock Returns," Working Papers 221, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    19. Yufeng Han, 2010. "On the Economic Value of Return Predictability," Annals of Economics and Finance, Society for AEF, vol. 11(1), pages 1-33, May.
    20. Todd E. Clark, 2004. "Can out-of-sample forecast comparisons help prevent overfitting?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(2), pages 115-139.

    More about this item

    Keywords

    Genetic algorithm; Penalty function method; Model selection; Excess return; Information criteria; C32; C52; C53; C61; C63;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

    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:kap:compec:v:34:y:2009:i:2:p:173-193. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.