IDEAS home Printed from https://ideas.repec.org/p/mcd/mcddps/2016_04.html
   My bibliography  Save this paper

Stock Prices Predictability at Long-horizons: Two Tales from the Time-Frequency Domain

Author

Listed:
  • Nikolaos Mitianoudis

    (Democritus University of Thrace, Greece)

  • Theologos Dergiades

    (University of Macedonia, Greece)

Abstract

Accepting non-linearities as an endemic feature of financial data, this paper re-examines Cochrane's "new fact in finance" hypothesis (Cochrane, Economic Perspectives -FRB of Chicago 23, 36-58, 1999). By implementing two methods, frequently encountered in digital signal processing analysis, (Undecimated Wavelet Transform and Empirical Mode Decomposition- both methods extract components in the time-frequency domain), we decompose the real stock prices and the real dividends, for the US economy, into signals that correspond to distinctive frequency bands. Armed with the decomposed signals and acting within a non-linear framework, the predictability of stock prices through the use of dividends is assessed at alternative horizons. It is shown that the "new fact in finance" hypothesis is a valid proposition, provided that dividends contribute significantly to predicting stock prices at horizons spanning beyond 32 months. The identified predictability is entirely non-linear in nature.

Suggested Citation

  • Nikolaos Mitianoudis & Theologos Dergiades, 2016. "Stock Prices Predictability at Long-horizons: Two Tales from the Time-Frequency Domain," Discussion Paper Series 2016_04, Department of Economics, University of Macedonia, revised Dec 2016.
    • Handle: RePEc:mcd:mcddps:2016_04
    as

    Download full text from publisher

    File URL: http://aphrodite.uom.gr/econwp/pdf/dp042016.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jeremy Berkowitz & Lorenzo Giorgianni, 2001. "Long-Horizon Exchange Rate Predictability?," The Review of Economics and Statistics, MIT Press, vol. 83(1), pages 81-91, February.
    2. Theologos Dergiades & Costas Milas & Theodore Panagiotidis, 2015. "Tweets, Google trends, and sovereign spreads in the GIIPS," Oxford Economic Papers, Oxford University Press, vol. 67(2), pages 406-432.
    3. Michis, Antonis A., 2014. "Time scale evaluation of economic forecasts," Economics Letters, Elsevier, vol. 123(3), pages 279-281.
    4. Kilian, Lutz & Taylor, Mark P., 2003. "Why is it so difficult to beat the random walk forecast of exchange rates?," Journal of International Economics, Elsevier, vol. 60(1), pages 85-107, May.
    5. Shiller, Robert J, 1981. "Do Stock Prices Move Too Much to be Justified by Subsequent Changes in Dividends?," American Economic Review, American Economic Association, vol. 71(3), pages 421-436, June.
    6. Summers, Lawrence H, 1986. "Does the Stock Market Rationally Reflect Fundamental Values?," Journal of Finance, American Finance Association, vol. 41(3), pages 591-601, July.
    7. Eugene F. Fama & Kenneth R. French, 2002. "The Equity Premium," Journal of Finance, American Finance Association, vol. 57(2), pages 637-659, April.
    8. 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.
    9. Jyh‐Lin Wu & Yu‐Hau Hu, 2012. "Price–Dividend Ratios and Stock Price Predictability," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 31(5), pages 423-442, August.
    10. John H. Cochrane, 1999. "New facts in finance," Economic Perspectives, Federal Reserve Bank of Chicago, vol. 23(Q III), pages 36-58.
    11. McMillan, David G., 2007. "Bubbles in the dividend-price ratio? Evidence from an asymmetric exponential smooth-transition model," Journal of Banking & Finance, Elsevier, vol. 31(3), pages 787-804, March.
    12. Phillips, P. C. B. & Ouliaris, S., 1988. "Testing for cointegration using principal components methods," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 205-230.
    13. Andrew Ang & Geert Bekaert, 2007. "Stock Return Predictability: Is it There?," The Review of Financial Studies, Society for Financial Studies, vol. 20(3), pages 651-707.
    14. Campbell, John Y & Shiller, Robert J, 1987. "Cointegration and Tests of Present Value Models," Journal of Political Economy, University of Chicago Press, vol. 95(5), pages 1062-1088, October.
    15. Hiemstra, Craig & Jones, Jonathan D, 1994. "Testing for Linear and Nonlinear Granger Causality in the Stock Price-Volume Relation," Journal of Finance, American Finance Association, vol. 49(5), pages 1639-1664, December.
    16. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
    17. Mark E. Wohar & David E. Rapach, 2005. "Valuation ratios and long-horizon stock price predictability," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(3), pages 327-344.
    18. Zacharias Psaradakis & Martin Sola & Fabio Spagnolo, 2004. "On Markov error-correction models, with an application to stock prices and dividends," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 19(1), pages 69-88.
    19. Valkanov, Rossen, 2003. "Long-horizon regressions: theoretical results and applications," Journal of Financial Economics, Elsevier, vol. 68(2), pages 201-232, May.
    20. John Y. Campbell, Robert J. Shiller, 1988. "The Dividend-Price Ratio and Expectations of Future Dividends and Discount Factors," The Review of Financial Studies, Society for Financial Studies, vol. 1(3), pages 195-228.
    21. David M. Cutler & James M. Poterba & Lawrence H. Summers, 1991. "Speculative Dynamics," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(3), pages 529-546.
    22. Kapetanios, George & Shin, Yongcheol & Snell, Andy, 2006. "Testing For Cointegration In Nonlinear Smooth Transition Error Correction Models," Econometric Theory, Cambridge University Press, vol. 22(2), pages 279-303, April.
    23. Walter Torous & Rossen Valkanov & Shu Yan, 2004. "On Predicting Stock Returns with Nearly Integrated Explanatory Variables," The Journal of Business, University of Chicago Press, vol. 77(4), pages 937-966, October.
    24. Sarno,Lucio & Taylor,Mark P., 2003. "The Economics of Exchange Rates," Cambridge Books, Cambridge University Press, number 9780521485845.
    25. Evans, George W, 1991. "Pitfalls in Testing for Explosive Bubbles in Asset Prices," American Economic Review, American Economic Association, vol. 81(4), pages 922-930, September.
    26. Diks, Cees & Panchenko, Valentyn, 2006. "A new statistic and practical guidelines for nonparametric Granger causality testing," Journal of Economic Dynamics and Control, Elsevier, vol. 30(9-10), pages 1647-1669.
    27. Markku Lanne, 2002. "Testing The Predictability Of Stock Returns," The Review of Economics and Statistics, MIT Press, vol. 84(3), pages 407-415, August.
    28. Kilian, Lutz, 1999. "Exchange Rates and Monetary Fundamentals: What Do We Learn from Long-Horizon Regressions?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(5), pages 491-510, Sept.-Oct.
    29. Kanas, Angelos, 2005. "Nonlinearity in the stock price-dividend relation," Journal of International Money and Finance, Elsevier, vol. 24(4), pages 583-606, June.
    30. Wolf, Michael, 2000. "Stock Returns and Dividend Yields Revisited: A New Way to Look at an Old Problem," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(1), pages 18-30, January.
    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. Maynard, Alex & Ren, Dongmeng, 2019. "The finite sample power of long-horizon predictive tests in models with financial bubbles," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 418-430.
    2. McMillan, David G., 2009. "Revisiting dividend yield dynamics and returns predictability: Evidence from a time-varying ESTR model," The Quarterly Review of Economics and Finance, Elsevier, vol. 49(3), pages 870-883, August.
    3. McMillan, David G., 2007. "Bubbles in the dividend-price ratio? Evidence from an asymmetric exponential smooth-transition model," Journal of Banking & Finance, Elsevier, vol. 31(3), pages 787-804, March.
    4. David G. McMillan, 2010. "Level‐shifts and non‐linearity in US financial ratios," Review of Accounting and Finance, Emerald Group Publishing Limited, vol. 9(2), pages 189-207, May.
    5. McMillan, David G., 2009. "Are share prices still too high?," Research in International Business and Finance, Elsevier, vol. 23(3), pages 223-232, September.
    6. David G. McMillan & Mark E. Wohar, 2010. "Stock return predictability and dividend-price ratio: a nonlinear approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 15(4), pages 351-365.
    7. David G. McMillan, 2010. "Present Value Model, Bubbles and Returns Predictability: Sector‐Level Evidence," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 37(5‐6), pages 668-686, June.
    8. Vicente Esteve & Manuel Navarro-Ibáñez & María A. Prats, 2013. "The present value model of US stock prices revisited: long-run evidence with structural breaks, 1871-2010," Working Papers 04/13, Instituto Universitario de Análisis Económico y Social.
    9. Vicente Esteve & Manuel Navarro-Ibáñez & María A. Prats, 2013. "The present value model of U.S. stock prices revisited: long-run evidence with structural breaks, 1871-2010," Working Papers 1305, Department of Applied Economics II, Universidad de Valencia.
    10. David G. McMillan, 2010. "Present Value Model, Bubbles and Returns Predictability: Sector-Level Evidence," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 37(5-6), pages 668-686.
    11. McMillan, David G., 2013. "Consumption and stock prices: Evidence from a small international panel," Journal of Macroeconomics, Elsevier, vol. 36(C), pages 76-88.
    12. Kaihua Deng & Chang-Jin Kim, 2015. "Predicting Stock Returns — The Information Content Of Predictors Across Horizons," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 10(02), pages 1-27, December.
    13. Bekiros, Stelios D., 2014. "Exchange rates and fundamentals: Co-movement, long-run relationships and short-run dynamics," Journal of Banking & Finance, Elsevier, vol. 39(C), pages 117-134.
    14. Theologos Dergiades & Panos K. Pouliasis, 2023. "Should stock returns predictability be ‘hooked on’ long‐horizon regressions?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 718-732, January.
    15. Chen, Shyh-Wei & Xie, Zixiong, 2017. "Asymmetric adjustment and smooth breaks in dividend yields: Evidence from international stock markets," International Review of Economics & Finance, Elsevier, vol. 48(C), pages 339-354.
    16. Michael Scholz & Jens Perch Nielsen & Stefan Sperlich, 2012. "Nonparametric prediction of stock returns guided by prior knowledge," Graz Economics Papers 2012-02, University of Graz, Department of Economics.
    17. David G. McMillan, 2009. "Are Uk Share Prices Too High? Fundamental Value Or New Era," Bulletin of Economic Research, Wiley Blackwell, vol. 61(1), pages 1-20, January.
    18. Maynard, Alex & Shimotsu, Katsumi, 2009. "Covariance-Based Orthogonality Tests For Regressors With Unknown Persistence," Econometric Theory, Cambridge University Press, vol. 25(1), pages 63-116, February.
    19. Stéphane Goutte & David Guerreiro & Bilel Sanhaji & Sophie Saglio & Julien Chevallier, 2019. "International Financial Markets," Post-Print halshs-02183053, HAL.
    20. Xie, Zixiong & Chen, Shyh-Wei, 2015. "Are there periodically collapsing bubbles in the REIT markets? New evidence from the US," Research in International Business and Finance, Elsevier, vol. 33(C), pages 17-31.

    More about this item

    Keywords

    Stock prices and dividends; Time-frequency decomposition.;

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C29 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Other

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:mcd:mcddps:2016_04. 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: Theodore Panagiotidis or Anastasia Litina (email available below). General contact details of provider: http://www.uom.gr/index.php?tmima=3 .

    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.