City Research Online

Longer-Term Forecasting of Excess Stock Returns—The Five-Year Case

Kyriakou, I. ORCID: 0000-0001-9592-596X, Mousavi, P., Nielsen, J. P. ORCID: 0000-0002-2798-0817 and Scholz, M. (2020). Longer-Term Forecasting of Excess Stock Returns—The Five-Year Case. Mathematics, 8(6), 927.. doi: 10.3390/math8060927

Abstract

Long-term return expectations or predictions play an important role in planning purposes and guidance of long-term investors. Five-year stock returns are less volatile around their geometric mean than returns of higher frequency, such as one-year returns. One would, therefore, expect models using the latter to better reduce the noise and beat the simple historical mean than models based on the former. However, this paper shows that the general tendency is surprisingly the opposite: long-term forecasts over five years have a similar or even better predictive power when compared to the one-year case. We consider a long list of economic predictors and benchmarks relevant for the long-term investor. Our predictive approach consists of adopting and implementing a fully nonparametric smoother with the covariates and the smoothing parameters chosen by cross-validation. We consistently find that long-term forecasting performs well and recommend drawing more attention to it when designing investment strategies for long-term investors. Furthermore, our preferred predictive model did stand the test of Covid-19 providing a relatively optimistic outlook in March 2020 when uncertainty was all around us with lockdown and facing an unknown new pandemic.

Publication Type: Article
Additional Information: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Publisher Keywords: benchmark; cross-validation; prediction; stock returns; long-term forecasts; overlapping returns; autocorrelation
Subjects: H Social Sciences > HF Commerce
H Social Sciences > HG Finance
Departments: Business School > Actuarial Science & Insurance
Date Deposited: 09 Jun 2020 10:21
URI: https://openaccess.city.ac.uk/id/eprint/24325
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