IDEAS home Printed from https://ideas.repec.org/p/zbw/sfb649/sfb649dp2008-009.html

Recursive portfolio selection with decision trees

Author

Listed:
  • Andriyashin, Anton
  • Härdle, Wolfgang Karl
  • Timofeev, Roman

Abstract

A great proportion of stock dynamics can be explained using publicly available information. The relationship between dynamics and public information may be of nonlinear character. In this paper we offer an approach to stock picking by employing so-called decision trees and applying them to XETRA DAX stocks. Using a set of fundamental and technical variables, stocks are classified into three groups according to the proposed position: long, short or neutral. More precisely, by assessing the current state of a company, which is represented by fundamental variables and current market situation, well reflected by technical variables, it is possible to suggest if the current market value of a company is underestimated, overestimated or the stock is fairly priced. The performance of the model over the observed period suggests that XETRA DAX stock returns can adequately be predicted by publicly available economic data. Another conclusion of this study is that the implied volatility variable, when included into the training sample, boosts the predictive power of the model significantly.

Suggested Citation

  • Andriyashin, Anton & Härdle, Wolfgang Karl & Timofeev, Roman, 2008. "Recursive portfolio selection with decision trees," SFB 649 Discussion Papers 2008-009, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    • Handle: RePEc:zbw:sfb649:sfb649dp2008-009
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/25251/1/558750052.PDF
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. repec:bla:jfinan:v:43:y:1988:i:3:p:661-76 is not listed on IDEAS
    2. Campbell, John & Shiller, Robert, 1988. "Stock Prices, Earnings, and Expected Dividends," Scholarly Articles 3224293, Harvard University Department of Economics.
    3. Avramov, Doron & Chordia, Tarun, 2006. "Predicting stock returns," Journal of Financial Economics, Elsevier, vol. 82(2), pages 387-415, November.
    4. Fama, Eugene F & French, Kenneth R, 1988. "Permanent and Temporary Components of Stock Prices," Journal of Political Economy, University of Chicago Press, vol. 96(2), pages 246-273, April.
    5. Campbell, John Y., 1987. "Stock returns and the term structure," Journal of Financial Economics, Elsevier, vol. 18(2), pages 373-399, June.
    6. Fama, Eugene F. & French, Kenneth R., 1988. "Dividend yields and expected stock returns," Journal of Financial Economics, Elsevier, vol. 22(1), pages 3-25, October.
    7. Amit Goyal & Ivo Welch, 2003. "Predicting the Equity Premium with Dividend Ratios," Management Science, INFORMS, vol. 49(5), pages 639-654, May.
    Full references (including those not matched with items on IDEAS)

    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. Tim Bollerslev & George Tauchen & Hao Zhou, 2009. "Expected Stock Returns and Variance Risk Premia," The Review of Financial Studies, Society for Financial Studies, vol. 22(11), pages 4463-4492, November.
    2. repec:hum:wpaper:sfb649dp2008-009 is not listed on IDEAS
    3. Phan, Dinh Hoang Bach & Sharma, Susan Sunila & Narayan, Paresh Kumar, 2015. "Stock return forecasting: Some new evidence," International Review of Financial Analysis, Elsevier, vol. 40(C), pages 38-51.
    4. Semenov, Andrei, 2021. "Measuring the stock's factor beta and identifying risk factors under market inefficiency," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 635-649.
    5. John Y. Campbell, 2008. "Viewpoint: Estimating the equity premium," Canadian Journal of Economics, Canadian Economics Association, vol. 41(1), pages 1-21, February.
    6. 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.
    7. repec:grz:wpaper:2012-02 is not listed on IDEAS
    8. Parastoo Mousavi, 2021. "Debt-by-Price Ratio, End-of-Year Economic Growth, and Long-Term Prediction of Stock Returns," Mathematics, MDPI, vol. 9(13), pages 1-18, July.
    9. 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.
    10. Lin, Qi, 2018. "Technical analysis and stock return predictability: An aligned approach," Journal of Financial Markets, Elsevier, vol. 38(C), pages 103-123.
    11. Wayne E. Ferson & Sergei Sarkissian & Timothy T. Simin, 2003. "Spurious Regressions in Financial Economics?," Journal of Finance, American Finance Association, vol. 58(4), pages 1393-1413, August.
    12. Yu, Jialin, 2011. "Disagreement and return predictability of stock portfolios," Journal of Financial Economics, Elsevier, vol. 99(1), pages 162-183, January.
    13. John Y. Campbell & John Cochrane, 1999. "Force of Habit: A Consumption-Based Explanation of Aggregate Stock Market Behavior," Journal of Political Economy, University of Chicago Press, vol. 107(2), pages 205-251, April.
    14. Campbell, John Y, 1991. "A Variance Decomposition for Stock Returns," Economic Journal, Royal Economic Society, vol. 101(405), pages 157-179, March.
    15. Shanken, Jay & Tamayo, Ane, 2012. "Payout yield, risk, and mispricing: A Bayesian analysis," Journal of Financial Economics, Elsevier, vol. 105(1), pages 131-152.
    16. Paye, Bradley S. & Timmermann, Allan, 2006. "Instability of return prediction models," Journal of Empirical Finance, Elsevier, vol. 13(3), pages 274-315, June.
    17. Jurdi, Doureige & Kim, Jae, 2019. "Predicting the U.S. Stock Market Return: Evidence from the Improved Augmented Regression Method," MPRA Paper 94028, University Library of Munich, Germany.
    18. Andrea Buraschi & Andrea Carnelli, 2013. "The economic value of predictability in portfolio management," Journal of Financial Management, Markets and Institutions, Società editrice il Mulino, issue 1, pages 5-22, January.
    19. Chang, Kuang-Liang, 2009. "Do macroeconomic variables have regime-dependent effects on stock return dynamics? Evidence from the Markov regime switching model," Economic Modelling, Elsevier, vol. 26(6), pages 1283-1299, November.
    20. 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.
    21. Fernando Rubio, 2005. "Eficiencia De Mercado, Administracion De Carteras De Fondos Y Behavioural Finance," Finance 0503028, University Library of Munich, Germany, revised 23 Jul 2005.
    22. Hai Lin & Daniel Quill & Henk Berkman, 2016. "Information diffusion and the predictability of New Zealand stock market returns," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 56(3), pages 749-785, September.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

    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:zbw:sfb649:sfb649dp2008-009. 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/sohubde.html .

    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.