Predicting international stock returns with conditional price-to-fundamental ratios

Lawrenz, Jochen; Zorn, Josef

doi:10.1016/j.jempfin.2017.06.003

Cited by 25 publications

(10 citation statements)

References 82 publications

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“…Several researchers argue this may be due to the presence of breaks (see, for example, Paye and Timmermann, 2006;Lettau and van Nieuwerburgh, 2008) or that periods of predictability are only short-lived (Timmermann, 2008). However, our view is more aligned with the recent work of Algaba and Boudt (2017) and Lawrenz and Zorn (2017) who argue, in different ways, that adjustments are required to the ldp ratio to reveal the short horizon predictive power. Extending the view that ldp by itself is not the best variable to predict future stock returns, we argue that the presence of a cyclical component within the ratio will provide improved forecast power.…”

Section: Accepted Manuscriptsupporting

confidence: 57%

“…As with some of the above cited work, this generalised ratio is based on the view that the relation between prices and dividends is time-varying, which they introduce through rolling and recursive regressions. Lawrenz and Zorn (2017) use a conditional price ratio to predict stock returns. Here, the price ratio (the paper uses the price relative to dividends, earnings and cash flow), is conditioned on both the series historical average as well as the global average.…”

Section: Review Of the Stock Return Predictabilitymentioning

confidence: 99%

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Stock return predictability: Using the cyclical component of the price ratio

McMillan

2019

Research in International Business and Finance

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We examine whether the cyclical component of the log dividend-price and price-earnings ratios contain forecast power for stock returns. While the levels of these series contain slow moving information for predicting long horizon returns, they typically provide poor short horizon forecasts. Using three approaches to extract the ratios cyclical component, we conduct several in-and out-of-sample tests. In-sample estimation using the cyclical component leads to economically sensible values, as well as an improved fit compared to the ratio level results. Out-of-sample evidence reveals forecast improvement over a historical

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Section: Accepted Manuscriptsupporting

confidence: 57%

Section: Review Of the Stock Return Predictabilitymentioning

confidence: 99%

Stock return predictability: Using the cyclical component of the price ratio

McMillan

2019

Research in International Business and Finance

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“…Cross-sectional regressions are used, for instance, by Bali and Cakici (2010), Fisher et al (2017), and Stocker (2016). Sometimes this approach is supplemented with different types of panel regressions, as in Hjalmarsson (2010), Lawrenz and Zorn (2017), and Bali and Cakici (2010). Wisniewski and Jackson (2018) apply pooled ordinary least squares and two-way fixed-effects regressions.…”

Section: Methodological Choicesmentioning

confidence: 99%

The Cross Section of Country Equity Returns: A Review of Empirical Literature

Zaremba

2019

JRFM

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The last three decades brought mounting evidence regarding the cross-sectional predictability of country equity returns. The studies not only documented country-level counterparts of well-established stock-level anomalies, such as size, value, or momentum, but also demonstrated some unique return-predicting signals such as fund flows or political regimes. Nonetheless, the different studies vary remarkably in terms of their dataset and methods employed. This study aims to provide a comprehensive review of the current literature on the cross-section of country equity returns. We focus on three particular aspects of the asset pricing literature. First, we study the choice of dataset and sample preparation methods. Second, we survey different aspects of the methodological approaches. Last but not least, we review the country-level equity anomalies discovered so far. The discussed cross-sectional return patterns not only provide new insights into international asset pricing but can also be potentially translated into effective country allocation strategies.

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“…In addition, to the extent that the long-horizon forecasts behave similarly among assets, the researcher can pool the asset return regressions to effectively increase the sample size. Indeed, although the studies of Hjalmarsson (2010) and Lawrenz and Zorn (2017) were not focused on long horizons, both documented similar patterns across assets for using valuation ratios to predict stock returns. They documented stronger statistical evidence when pooling the regression equations to estimate the coefficient.…”

Section: Practical Suggestionsmentioning

confidence: 99%