Stock Return Predictability: Evaluation based on interval forecasts

Amélie Charles (acharles@audencia.com), Olivier Darné and Jae Kim (j.kim@latrobe.edu.au.tel)
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Amélie Charles: Audencia Business School
Jae Kim: La Trobe University [Melbourne]

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Abstract: This paper evaluates the predictability of monthly stock return using out-of-sample interval forecasts. Past studies exclusively use point forecasts, which are of limited value since they carry no information about intrinsic predictive uncertainty. We compare the empirical performance of alternative interval forecasts for stock return generated from a naïve model, univariate autoregressive model, and multivariate model (predictive regression and VAR), using U.S. data from 1926. It is found that neither univariate nor multivariate interval forecasts outperform naïve forecasts. This strongly suggests that the U.S. stock market has been informationally efficient in the weak-form as well as in the semi-strong form.

Keywords: Autoregressive Model; Bootstrapping; Financial Ratios; Forecasting; Interval Score; Market Efficiency (search for similar items in EconPapers)
Date: 2022-04
New Economics Papers: this item is included in nep-fmk and nep-for
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Published in Bulletin of Economic Research, 2022, 74 (2), pp.363-385. ⟨10.1111/boer.12298⟩

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Journal Article: Stock return predictability: Evaluation based on interval forecasts (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03656310

DOI: 10.1111/boer.12298

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