Instability of return prediction models

Paye, Bradley S.; Timmermann, Allan

doi:10.1016/j.jempfin.2005.11.001

Cited by 389 publications

(133 citation statements)

References 74 publications

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“…It is only for the short rate (r s ) that the coe¢ cient is significantly di¤erent from zero during the whole sample period that is analyzed. The overall instability of the regression coe¢ cients over time is in line with the results of the formal test procedures of Pettenuzzo and Timmermann (2005) and Paye and Timmermann (2006).…”

Section: E the Term Spreadsupporting

confidence: 72%

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Predicting Global Stock Returns

Hjalmarsson

2008

SSRN Journal

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I test for stock return predictability in the largest and most comprehensive data set analyzed so far, using four common forecasting variables: the dividend-and earnings-price ratios, the short interest rate, and the term spread. The data contain over 20,000 monthly observations from 40 international markets, including 24 developed and 16 emerging economies. In addition, Idevelop new methods for predictive regressions with panel data. Inference based on the standard …xed e¤ects estimator is shown to su¤er from severe size distortions in the typical stock return regression, and an alternative robust estimator is proposed. The empirical results indicate that the short interest rate and the term spread are fairly robust predictors of stock returns in developed markets. In contrast, no strong or consistent evidence of predictability is found when considering the earnings-and dividend-price ratios as predictors.JEL classi…cation: C22, C23, G12, G15.

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Section: E the Term Spreadsupporting

confidence: 72%

“…Since an international data set of stock returns and forecasting variables provides a panel, the 1 See, for instance, Harvey (1991Harvey ( , 1995, Ferson and Harvey (1993), Campbell (2003), Polk et al (2004), Paye and Timmermann (2006), and Ang and Bekaert (2007).…”

Section: Introductionmentioning

confidence: 99%

Predicting Global Stock Returns

Hjalmarsson

2008

SSRN Journal

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“…In addition, only a small number of variables have been used to predict returns whose performance largely varies through time. Thus structural instability also seems to be an important feature surrounding prediction models (see, for example, Paye & Timmermann, 2006;Rapach & Wohar, 2006). Regarding these findings, recent studies evolve pooling strategies that incorporate information from many predictor variables simultaneously to solve the problems arising from model uncertainty.…”

Section: Econometric Frameworkmentioning

confidence: 99%

Does a lot help a lot? Forecasting stock returns with pooling strategies in a data‐rich environment

Baetje

2017

Journal of Forecasting

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A variety of recent studies provide a skeptical view on the predictability of stock returns. Empirical evidence shows that most prediction models suffer from a loss of information, model uncertainty, and structural instability by relying on lowdimensional information sets. In this study, we evaluate the predictive ability of various lately refined forecasting strategies, which handle these issues by incorporating information from many potential predictor variables simultaneously. We investigate whether forecasting strategies that (i) combine information and (ii) combine individual forecasts are useful to predict US stock returns, that is, the market excess return, size, value, and the momentum premium. Our results show that methods combining information have remarkable in-sample predictive ability. However, the out-of-sample performance suffers from highly volatile forecast errors. Forecast combinations face a better bias-efficiency trade-off, yielding a consistently superior forecast performance for the market excess return and the size premium even after the 1970s.

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“…Lettau and Van Nieuwerburgh (2008) and Paye and Timmermann (2006) note the US dividendprice ratio predictive ability deteriorates during the 1990s; they suggest this could be due to model instability. 19 Our results verify that the US poor dividend-price predictability in recent years is common across a wide range of European countries consistent with Rangvid, Schmeling and Schrimpf (2011).…”

Section: [Insert Table 2 Around Here]mentioning

confidence: 99%

Forecasting returns: New European evidence

Jordan

Vivian

Wohar

2014

Journal of Empirical Finance

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This paper builds on the recent debate on the in-sample and out-of-sample predictability of US aggregate returns using a wide range of predictors by providing new evidence for smaller and less market-oriented European countries.We find evidence that macro and technical predictors can (statistically) improve forecast accuracy and (economically) generate gains to investors; in contrast to the US results, predictability in our sample of European countries exists in recent data. We also find that simple forecast combinations consistently yield substantial benefits both in forecast accuracy and economic gain. For example, the magnitude of the forecasting gains for our European countries is often larger than those found for the US and other G7 countries. We provide initial evidence on the link between country characteristics and out-of-sample forecast performance. Our empirical results indicate market development is related to the forecast performance of macro variables. There is also some evidence that forecast performance is related to market size and liquidity.

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Instability of return prediction models

Cited by 389 publications

References 74 publications

Predicting Global Stock Returns

Predicting Global Stock Returns

Does a lot help a lot? Forecasting stock returns with pooling strategies in a data‐rich environment

Forecasting returns: New European evidence

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