Long-horizon stock valuation and return forecasts based on demographic projections

Chaoyi Chen, Nikolay Gospodinov, Alex Maynard and Elena Pesavento

Journal of Empirical Finance, 2022, vol. 68, issue C, 190-215

Abstract: We incorporate low-frequency information from demographic variables into a simple predictive model to forecast stock valuations and returns using demographic projections. The demographics appear to be an important determinant of stock valuations, such as the dividend–price ratio. The availability of long-term demographic projections allows us to provide (very) long-horizon forecasts of stock market valuations and returns. We also exploit the strong contemporaneous correlation between returns and valuations to improve return forecasts — something which is not possible in a predictive regression with only lagged predictors. Extensive pseudo out-of-sample forecast comparisons and tests demonstrate the predictive value that an accurate demographic projection can deliver. Although the availability of historical Census Bureau projections is limited, we demonstrate that they could have been employed in real time to improve true long-horizon stock return prediction. We show how the model can be used to adjust predictions under alternative demographic assumptions, incorporating, for example, the demographic impact of COVID-19 or recent changes to immigration policy.

Keywords: Demographics; Stock market valuation; Stock return prediction; Conditional forecasts; Long-horizon forecasts (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:empfin:v:68:y:2022:i:c:p:190-215

DOI: 10.1016/j.jempfin.2022.07.001

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