IDEAS home Printed from https://ideas.repec.org/a/ers/journl/vxviiy2014i1p3-18.html
   My bibliography  Save this article

Predictive Ability of Three Different Estimates of “Cay†to Excess Stock Returns – A Comparative Study for South Africa and USA

Author

Listed:
  • Noha Emara

Abstract

The results of Lettau and Ludvigson (2001) show that Cay-LL has a significant predictive power both in the in-sample and the out-of-sample forecast of excess return. Our study departs from Lettau and Ludvigson (2001) in adding and comparing other two estimates of cay namely cay-OLS and cay-DLS besides cay-LL for forecasting excess return in both the United States and South Africa. Using quarterly data over the period 1988:1 to 2012:2, the results for the United States suggest that the three alternative measures of cay have positive significant predicting ability for the in-sample and out-of-sample forecasting models. Furthermore, and in line with the results of Lettau and Ludvigson (2001), cay-LL has the least mean squared forecasting errors. For the case of South Africa, lagged excess return and dividend yield beat the three alternative measures of cay in forecasting excess return. The results suggest that for the case of South Africa, the trend deviations of the macroeconomic variables is not a strong predictor of the excess stock returns over a treasury bill rate, and cannot account for a statistical significant variation in future excess returns.

Suggested Citation

  • Noha Emara, 2014. "Predictive Ability of Three Different Estimates of “Cay†to Excess Stock Returns – A Comparative Study for South Africa and USA," European Research Studies Journal, European Research Studies Journal, vol. 0(1), pages 3-18.
  • Handle: RePEc:ers:journl:v:xvii:y:2014:i:1:p:3-18
    as

    Download full text from publisher

    File URL: http://www.ersj.eu/repec/ers/papers/14_1_p1.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Clark, Todd E. & McCracken, Michael W., 2001. "Tests of equal forecast accuracy and encompassing for nested models," Journal of Econometrics, Elsevier, vol. 105(1), pages 85-110, November.
    2. Eleftherios Thalassinos & Diana-Mihaela Pociovalisteanu, 2007. "A Time Series Model for the Romanian Stock Market," European Research Studies Journal, European Research Studies Journal, vol. 0(3-4), pages 57-72.
    3. 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.
    4. 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.
    5. 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.
    6. Brennan, Michael J. & Xia, Yihong, 2005. "tay's as good as cay," Finance Research Letters, Elsevier, vol. 2(1), pages 1-14, March.
    7. 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.
    8. John Y. Campbell & N. Gregory Mankiw, 1989. "Consumption, Income, and Interest Rates: Reinterpreting the Time Series Evidence," NBER Chapters, in: NBER Macroeconomics Annual 1989, Volume 4, pages 185-246, National Bureau of Economic Research, Inc.
    9. repec:fth:harver:1435 is not listed on IDEAS
    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. Sousa, Ricardo M., 2010. "Consumption, (dis)aggregate wealth, and asset returns," Journal of Empirical Finance, Elsevier, vol. 17(4), pages 606-622, September.
    2. Emara, Noha, 2014. "Predictive ability of three different estimates of “cay” to excess stock returns - A comparative study South Africa & U.S. -," MPRA Paper 68684, University Library of Munich, Germany.
    3. Martin Lettau & Sydney Ludvigson, 2001. "Consumption, Aggregate Wealth, and Expected Stock Returns," Journal of Finance, American Finance Association, vol. 56(3), pages 815-849, June.
    4. Rapach, David & Zhou, Guofu, 2013. "Forecasting Stock Returns," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 328-383, Elsevier.
    5. Leland E. Farmer & Lawrence Schmidt & Allan Timmermann, 2023. "Pockets of Predictability," Journal of Finance, American Finance Association, vol. 78(3), pages 1279-1341, June.
    6. Dladla, Pholile & Malikane, Christopher, 2019. "Stock return predictability: Evidence from a structural model," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 412-424.
    7. Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2014. "Forecasting the Equity Risk Premium: The Role of Technical Indicators," Management Science, INFORMS, vol. 60(7), pages 1772-1791, July.
    8. Atsushi Inoue & Lutz Kilian, 2005. "In-Sample or Out-of-Sample Tests of Predictability: Which One Should We Use?," Econometric Reviews, Taylor & Francis Journals, vol. 23(4), pages 371-402.
    9. 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.
    10. Yu-Chin Chen & Kenneth S. Rogoff & Barbara Rossi, 2010. "Can Exchange Rates Forecast Commodity Prices?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 125(3), pages 1145-1194.
    11. Li, Yan & Ng, David T. & Swaminathan, Bhaskaran, 2013. "Predicting market returns using aggregate implied cost of capital," Journal of Financial Economics, Elsevier, vol. 110(2), pages 419-436.
    12. Ekaterini Panopoulou & Sotiria Plastira, 2014. "Fama French factors and US stock return predictability," Journal of Asset Management, Palgrave Macmillan, vol. 15(2), pages 110-128, April.
    13. Amit Goyal & Ivo Welch, 2003. "Predicting the Equity Premium with Dividend Ratios," Management Science, INFORMS, vol. 49(5), pages 639-654, May.
    14. Chen, Long, 2009. "On the reversal of return and dividend growth predictability: A tale of two periods," Journal of Financial Economics, Elsevier, vol. 92(1), pages 128-151, April.
    15. 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.
    16. Boucher, Christophe, 2006. "Stock prices-inflation puzzle and the predictability of stock market returns," Economics Letters, Elsevier, vol. 90(2), pages 205-212, February.
    17. Della Corte, Pasquale & Sarno, Lucio & Valente, Giorgio, 2010. "A century of equity premium predictability and the consumption-wealth ratio: An international perspective," Journal of Empirical Finance, Elsevier, vol. 17(3), pages 313-331, June.
    18. Rapach, David E. & Ringgenberg, Matthew C. & Zhou, Guofu, 2016. "Short interest and aggregate stock returns," Journal of Financial Economics, Elsevier, vol. 121(1), pages 46-65.
    19. Clark, Todd E. & West, Kenneth D., 2006. "Using out-of-sample mean squared prediction errors to test the martingale difference hypothesis," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 155-186.
    20. Dudek, Sławomir, 2008. "Consumer Survey Data and short-term forecasting of households consumption expenditures in Poland," MPRA Paper 19818, University Library of Munich, Germany.

    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:ers:journl:v:xvii:y:2014:i:1:p:3-18. 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: Marios Agiomavritis (email available below). General contact details of provider: https://ersj.eu/ .

    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.