The finite sample power of long-horizon predictive tests in models with financial bubbles

Alex Maynard and Dongmeng Ren

International Review of Financial Analysis, 2019, vol. 63, issue C, 418-430

Abstract: Using finite sample simulation methods, we assess the power of long-horizon predictive tests and compare them to their short-run counterparts, when the true underlying model contains financial asset bubbles. Our results indicate that long-run predictive tests using valuation predictors – specifically the dividend price ratio – do pick up the in-sample return predictability inherent in the asset bubbles. However, after size-adjustment, the long-run predictive framework has little advantage over its short-run counterpart when the predictor is highly persistent, but can provide non-trivial, yet still modest, power improvements when the predictor is moderately persistent. Finally, we provide a brief intuitive explanation for why a model with temporary collapsing bubbles may yield in-sample predictive power without implying the existence of profitable out-of-sample trading strategies.

Keywords: Asset bubbles; Predictive regression; Long-horizon regression; Stock return predictability (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:63:y:2019:i:c:p:418-430

DOI: 10.1016/j.irfa.2016.10.006

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