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Forecasting stock returns with sum-of-the-parts methodology: international evidence

Author

Listed:
  • Mahtab Athari

    (Concord University)

  • Atsuyuki Naka

    (University of New Orleans)

  • Abdullah Noman

    (University of North Carolina at Pembroke)

Abstract

This study applies and extends the sum-of-the-parts (SOP) method for forecasting stock returns by proposing an adjusted SOP (ASOP) and assessing its performance across 32 developed and emerging markets. It provides a comprehensive evaluation of the SOP methodology’s effectiveness across various markets. It shows ASOP’s improved forecasting accuracy and economic gains for investors, outperforming in 28 countries. This research contributes to the forecasting literature by highlighting ASOP’s effectiveness and practicality with financial and market data. Despite varying performance across markets, the findings suggest ASOP’s potential as a tool for financial analysts and portfolio managers, offering avenues for further research on its applicability in different financial environments.

Suggested Citation

  • Mahtab Athari & Atsuyuki Naka & Abdullah Noman, 2025. "Forecasting stock returns with sum-of-the-parts methodology: international evidence," Journal of Asset Management, Palgrave Macmillan, vol. 26(1), pages 91-114, February.
  • Handle: RePEc:pal:assmgt:v:26:y:2025:i:1:d:10.1057_s41260-024-00380-1
    DOI: 10.1057/s41260-024-00380-1
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    as
    1. Faria, Gonçalo & Verona, Fabio, 2018. "Forecasting stock market returns by summing the frequency-decomposed parts," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 228-242.
    2. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
    3. Ralph S.J. Koijen & Stijn Van Nieuwerburgh, 2011. "Predictability of Returns and Cash Flows," Annual Review of Financial Economics, Annual Reviews, vol. 3(1), pages 467-491, December.
    4. Engsted, Tom & Pedersen, Thomas Q., 2010. "The dividend-price ratio does predict dividend growth: International evidence," Journal of Empirical Finance, Elsevier, vol. 17(4), pages 585-605, September.
    5. Ferreira, Miguel A. & Santa-Clara, Pedro, 2011. "Forecasting stock market returns: The sum of the parts is more than the whole," Journal of Financial Economics, Elsevier, vol. 100(3), pages 514-537, June.
    6. Robert D. Arnott & Clifford S. Asness, 2003. "Surprise! Higher Dividends = Higher Earnings Growth," Financial Analysts Journal, Taylor & Francis Journals, vol. 59(1), pages 70-87, January.
    7. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    8. Anthony Flint & Andrew Tan & Gary Tian, 2010. "Predicting Future Earnings Growth: A Test Of The Dividend Payout Ratio In The Australian Market," The International Journal of Business and Finance Research, The Institute for Business and Finance Research, vol. 4(2), pages 43-58.
    9. Craig Burnside & Martin Eichenbaum & Isaac Kleshchelski & Sergio Rebelo, 2011. "Do Peso Problems Explain the Returns to the Carry Trade?," The Review of Financial Studies, Society for Financial Studies, vol. 24(3), pages 853-891.
    10. 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.
    11. Pesaran, M Hashem & Timmermann, Allan, 1992. "A Simple Nonparametric Test of Predictive Performance," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(4), pages 561-565, October.
    12. John H. Cochrane, 2011. "Presidential Address: Discount Rates," Journal of Finance, American Finance Association, vol. 66(4), pages 1047-1108, August.
    13. Mahtab Athari & Atsuyuki Naka & Abdullah Noman, 2021. "Predicting equity premium with adjusted dividend-price ratio: the USA and international evidence," Review of Accounting and Finance, Emerald Group Publishing Limited, vol. 20(3/4), pages 217-247, September.
    14. Maio, Paulo, 2016. "Cross-sectional return dispersion and the equity premium," Journal of Financial Markets, Elsevier, vol. 29(C), pages 87-109.
    15. Wang, Yudong & Liu, Li & Ma, Feng & Diao, Xundi, 2018. "Momentum of return predictability," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 141-156.
    16. Kandel, Shmuel & Stambaugh, Robert F, 1996. "On the Predictability of Stock Returns: An Asset-Allocation Perspective," Journal of Finance, American Finance Association, vol. 51(2), pages 385-424, June.
    17. 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.
    18. David McMillan & Mark Wohar, 2011. "Sum of the parts stock return forecasting: international evidence," Applied Financial Economics, Taylor & Francis Journals, vol. 21(12), pages 837-845.
    19. Xu, Yexiao, 2004. "Small levels of predictability and large economic gains," Journal of Empirical Finance, Elsevier, vol. 11(2), pages 247-275, March.
    20. McCracken, Michael W., 2007. "Asymptotics for out of sample tests of Granger causality," Journal of Econometrics, Elsevier, vol. 140(2), pages 719-752, October.
    21. Park, Cheolbeom, 2010. "When does the dividend-price ratio predict stock returns?," Journal of Empirical Finance, Elsevier, vol. 17(1), pages 81-101, January.
    22. Ping Zhou & William Ruland, 2006. "Dividend Payout and Future Earnings Growth," Financial Analysts Journal, Taylor & Francis Journals, vol. 62(3), pages 58-69, May.
    23. Narayan, Paresh Kumar & Liu, Ruipeng, 2018. "A new GARCH model with higher moments for stock return predictability," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 56(C), pages 93-103.
    24. Pontiff, Jeffrey & Schall, Lawrence D., 1998. "Book-to-market ratios as predictors of market returns," Journal of Financial Economics, Elsevier, vol. 49(2), pages 141-160, August.
    25. Moosa, Imad & Burns, Kelly, 2014. "The unbeatable random walk in exchange rate forecasting: Reality or myth?," Journal of Macroeconomics, Elsevier, vol. 40(C), pages 69-81.
    26. John H. Cochrane, 2008. "The Dog That Did Not Bark: A Defense of Return Predictability," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1533-1575, July.
    27. 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.
    28. Freeman, Rn & Ohlson, Ja & Penman, Sh, 1982. "Book Rate-Of-Return And Prediction Of Earnings Changes - An Empirical-Investigation," Journal of Accounting Research, Wiley Blackwell, vol. 20(2), pages 639-653.
    29. Dichtl, Hubert & Drobetz, Wolfgang & Neuhierl, Andreas & Wendt, Viktoria-Sophie, 2021. "Data snooping in equity premium prediction," International Journal of Forecasting, Elsevier, vol. 37(1), pages 72-94.
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    More about this item

    Keywords

    Stock return forecasting; Sum of the parts; Out-of-sample performance; Trading strategies; Developed markets; Emerging markets;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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