IDEAS home Printed from https://ideas.repec.org/p/grz/wpaper/2019-06.html
   My bibliography  Save this paper

Machine Learning for Forecasting Excess Stock Returns The Five-Year-View

Author

Listed:
  • Ioannis Kyriakou

    (Cass Business School, City, University of London, UK)

  • Parastoo Mousavi

    (Cass Business School, City, University of London, UK)

  • Jens Perch Nielsen

    (Cass Business School, City, University of London, UK)

  • Michael Scholz

    (University of Graz, Austria)

Abstract

In this paper, we apply machine learning to forecast stock returns in excess of different benchmarks, including the short-term interest rate, long-term interest rate, earnings-by-price ratio, and the inflation. In particular, we adopt and implement a fully nonparametric smoother with the covariates and the smoothing parameter chosen by cross-validation. We find that for both one-year and five-year returns, the term spread is, overall, the most powerful predictive variable for excess stock returns. Differently combined covariates can then achieve higher predictability for different forecast horizons. Nevertheless, the set of earnings-by-price and term spread predictors under the inflation benchmark strikes the right balance between the one-year and five-year horizon.

Suggested Citation

  • Ioannis Kyriakou & Parastoo Mousavi & Jens Perch Nielsen & Michael Scholz, 2019. "Machine Learning for Forecasting Excess Stock Returns The Five-Year-View," Graz Economics Papers 2019-06, University of Graz, Department of Economics.
    • Handle: RePEc:grz:wpaper:2019-06
    as

    Download full text from publisher

    File URL: https://unipub.uni-graz.at/obvugrveroeff/download/pdf/9619683?originalFilename=true
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Su, Liangjun & Ullah, Aman, 2006. "More Efficient Estimation In Nonparametric Regression With Nonparametric Autocorrelated Errors," Econometric Theory, Cambridge University Press, vol. 22(1), pages 98-126, February.
    2. Martin Lettau & Stijn Van Nieuwerburgh, 2008. "Reconciling the Return Predictability Evidence," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1607-1652, July.
    3. 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.
    4. Bergmeir, Christoph & Hyndman, Rob J. & Koo, Bonsoo, 2018. "A note on the validity of cross-validation for evaluating autoregressive time series prediction," Computational Statistics & Data Analysis, Elsevier, vol. 120(C), pages 70-83.
    5. Gerrard, Russell & Hiabu, Munir & Kyriakou, Ioannis & Nielsen, Jens Perch, 2019. "Communication and personal selection of pension saver’s financial risk," European Journal of Operational Research, Elsevier, vol. 274(3), pages 1102-1111.
    6. Linton, Oliver B. & Mammen, Enno, 2008. "Nonparametric transformation to white noise," Journal of Econometrics, Elsevier, vol. 142(1), pages 241-264, January.
    7. Juliane Geller & Michael H. Neumann, 2018. "Improved local polynomial estimation in time series regression," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 30(1), pages 1-27, January.
    8. 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.
    9. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    10. Tingting Cheng & Jiti Gao & Oliver Linton, 2019. "Nonparametric Predictive Regressions for Stock Return Prediction," Monash Econometrics and Business Statistics Working Papers 4/19, Monash University, Department of Econometrics and Business Statistics.
    11. Nielsen, Jens Perch & Sperlich, Stefan, 2003. "Prediction of Stock Returns: A New Way to Look at It," ASTIN Bulletin, Cambridge University Press, vol. 33(2), pages 399-417, November.
    12. Scholz, Michael & Sperlich, Stefan & Nielsen, Jens Perch, 2016. "Nonparametric long term prediction of stock returns with generated bond yields," Insurance: Mathematics and Economics, Elsevier, vol. 69(C), pages 82-96.
    13. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    14. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    15. Scholz, Michael & Nielsen, Jens Perch & Sperlich, Stefan, 2015. "Nonparametric prediction of stock returns based on yearly data: The long-term view," Insurance: Mathematics and Economics, Elsevier, vol. 65(C), pages 143-155.
    16. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    17. Masry, Elias, 1996. "Multivariate regression estimation local polynomial fitting for time series," Stochastic Processes and their Applications, Elsevier, vol. 65(1), pages 81-101, December.
    18. 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.
    19. G. Elliott & C. Granger & A. Timmermann (ed.), 2013. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 2, number 2.
    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. Philip Ndikum, 2020. "Machine Learning Algorithms for Financial Asset Price Forecasting," Papers 2004.01504, arXiv.org.

    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. Ioannis Kyriakou & Parastoo Mousavi & Jens Perch Nielsen & Michael Scholz, 2020. "Longer-Term Forecasting of Excess Stock Returns—The Five-Year Case," Mathematics, MDPI, vol. 8(6), pages 1-20, June.
    2. Ioannis Kyriakou & Parastoo Mousavi & Jens Perch Nielsen & Michael Scholz, 2021. "Short-Term Exuberance and Long-Term Stability: A Simultaneous Optimization of Stock Return Predictions for Short and Long Horizons," Mathematics, MDPI, vol. 9(6), pages 1-19, March.
    3. Ioannis Kyriakou & Parastoo Mousavi & Jens Perch Nielsen & Michael Scholz, 2020. "Short-Term Exuberance and long-term stability: A simultaneous optimization of stock return predictions for short and long horizons," Graz Economics Papers 2020-20, University of Graz, Department of Economics.
    4. Enno Mammen & Jens Perch Nielsen & Michael Scholz & Stefan Sperlich, 2019. "Conditional Variance Forecasts for Long-Term Stock Returns," Risks, MDPI, vol. 7(4), pages 1-22, November.
    5. Zhao, Albert Bo & Cheng, Tingting, 2022. "Stock return prediction: Stacking a variety of models," Journal of Empirical Finance, Elsevier, vol. 67(C), pages 288-317.
    6. Ioannis Kyriakou & Parastoo Mousavi & Jens Perch Nielsen & Michael Scholz, 2021. "Forecasting benchmarks of long-term stock returns via machine learning," Annals of Operations Research, Springer, vol. 297(1), pages 221-240, February.
    7. Borup, Daniel & Christensen, Bent Jesper & Mühlbach, Nicolaj Søndergaard & Nielsen, Mikkel Slot, 2023. "Targeting predictors in random forest regression," International Journal of Forecasting, Elsevier, vol. 39(2), pages 841-868.
    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. 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.
    10. Baetje, Fabian & Menkhoff, Lukas, 2016. "Equity premium prediction: Are economic and technical indicators unstable?," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1193-1207.
    11. Massimo Guidolin & Alexei G. Orlov, 2022. "Can Investors Benefit from Hedge Fund Strategies? Utility-Based, Out-of-Sample Evidence," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 12(03), pages 1-61, September.
    12. Nima Nonejad, 2021. "Bayesian model averaging and the conditional volatility process: an application to predicting aggregate equity returns by conditioning on economic variables," Quantitative Finance, Taylor & Francis Journals, vol. 21(8), pages 1387-1411, August.
    13. Colak, Gonul & Fu, Mengchuan & Hasan, Iftekhar, 2022. "On modeling IPO failure risk," Economic Modelling, Elsevier, vol. 109(C).
    14. Cheng, T. & Gao, J. & Linton, O., 2019. "Nonparametric Predictive Regressions for Stock Return Prediction," Cambridge Working Papers in Economics 1932, Faculty of Economics, University of Cambridge.
    15. James T. E. Chapman & Ajit Desai, 2023. "Macroeconomic Predictions Using Payments Data and Machine Learning," Forecasting, MDPI, vol. 5(4), pages 1-32, November.
    16. Gerrard, Russell & Hiabu, Munir & Nielsen, Jens Perch & Vodička, Peter, 2020. "Long-term real dynamic investment planning," Insurance: Mathematics and Economics, Elsevier, vol. 92(C), pages 90-103.
    17. Byron Botha & Rulof Burger & Kevin Kotzé & Neil Rankin & Daan Steenkamp, 2023. "Big data forecasting of South African inflation," Empirical Economics, Springer, vol. 65(1), pages 149-188, July.
    18. Giulia Dal Pra & Massimo Guidolin & Manuela Pedio & Fabiola Vasile, 2016. "Do Regimes in Excess Stock Return Predictability Create Economic Value? An Out-of-Sample Portfolio Analysis," BAFFI CAREFIN Working Papers 1637, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    19. Lof, Matthijs & Nyberg, Henri, 2024. "Discount rates and cash flows: A local projection approach," Journal of Banking & Finance, Elsevier, vol. 162(C).
    20. Hui Chen & Winston Wei Dou & Leonid Kogan, 2024. "Measuring “Dark Matter” in Asset Pricing Models," Journal of Finance, American Finance Association, vol. 79(2), pages 843-902, April.

    More about this item

    Keywords

    Benchmark; Cross-validation; Prediction; Stock returns; Long-term forecasts; Overlapping returns; Autocorrelation;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies

    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:grz:wpaper:2019-06. 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: Stefan Borsky (email available below). General contact details of provider: https://edirc.repec.org/data/vgrazat.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.