On Predicting Stock Returns with Nearly Integrated Explanatory Variables

Torous, Walter N.; Valkanov, Rossen; Yan, Shu

doi:10.1086/422634

Cited by 300 publications

(184 citation statements)

References 36 publications

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“…Our paper is also related to the recent literature on inference in forecasting regressions with persistent regressors (see e.g., Amihud and Hurwich (2004), Ang and Bekaert (2001), Campbell and Yogo (2002), Lewellen (2004), and Torous, Volkanov, and Yan (2004)). In these papers, asymptotic distributions for OLS regressions are derived under the assumption that the forecasting variable is a close-to unit, yet stationary, root process.…”

Section: Introductionmentioning

confidence: 87%

Reconciling the Return Predictability Evidence In-Sample Forecasts, Out-of-Sample Forecasts, and Parameter Instability*

Lettau

Nieuwerburgh

2005

SSRN Journal

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Evidence of stock return predictability by financial ratios is still controversial, as documented by inconsistent results for in-sample and out-of-sample regressions and by substantial parameter instability. This paper shows that these seemingly incompatible results can be reconciled if the assumption of a fixed steady-state mean of the economy is relaxed. We find strong empirical evidence in support of shifts in the steady-state and propose simple methods to adjust financial ratios for such shifts. The forecasting relationship of adjusted price ratios and future returns is statistically significant, stable over time, and present in out-of-sample tests. We also show that shifts in the steady-state are responsible for the parameter instability and poor out-of-sample performance of unadjusted price ratios that are found in the data. Our conclusions hold for a variety of financial ratios and are robust to changes in the econometric technique used to estimate shifts in the steady-state.

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Section: Introductionmentioning

confidence: 87%

Reconciling the Return Predictability Evidence In-Sample Forecasts, Out-of-Sample Forecasts, and Parameter Instability*

Lettau

Nieuwerburgh

2005

SSRN Journal

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“…[46], [55], [2], [27], and [58] conclude that the statistical evidence of forecastability is weaker once tests are adjusted for high persistence. [1], [2], [16], [42], [57], and [20] derive asymptotic distributions for predictability coefficients under the assumption that the forecasting variable follows a local-to-unit root, yet stationary, process.…”

Section: Motivating Predictive Regressionsmentioning

confidence: 99%

Financial Economics, Return Predictability and Market Efficiency

Nieuwerburgh

Koijen

2009

Complex Systems in Finance and Econometrics

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“…Recent studies estimating market expectations for returns and dividends with different presentvalue models include Menzly, Santos, and Veronesi (2004), Lettau and Ludvigson (2005), Ang and Bekaert (2007), Lettau and Van Niewerburgh (2008), Campbell and Thompson 2 Stambaugh (1999) derives the finite-sample distribution of the predictive regression parameter estimates and finds that the associated t-statistic is biased towards rejection of the null of no predictability. Torous, Valkanov, and Yan (2004) and Campbell and Yogo (2006) study asymptotic testing frameworks in predictive regression settings with nearly integrated predictors.…”

Section: Introductionmentioning

confidence: 99%

“…5 Stambaugh (1999) derives an analytic expression for the bias in univariate predictive regressions. Kothari and Shanken (1997), Amihud and Hurvich (2004), Lewellen (2004) Torous, Valkanov, and Yan (2004), Campbell and Yogo (2006) and Polk, Thompson, and Vuolteenaho (2006) develop methods for studied in present-value models estimated with a latent-variables approach.…”

Section: Introductionmentioning

confidence: 99%

Dividend Growth Predictability and the Price-Dividend Ratio

Piatti

Trojani

2012

SSRN Journal

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Conventional tests of present-value models tend to over-reject the null of no predictability, concluding that price-dividend ratio variations are due to both cash flow and discount rate shocks. We propose a nonparametric Monte Carlo testing method, which does not rely on distributional assumptions to aggregate the information from the time series of price-dividend ratios and dividend growth. We find evidence of return predictability, but no apparent evidence of dividend growth predictability, thus reconciling the diverging conclusions in the literature. Our findings are robust to the specification of the predictive information set and account for the intrinsic probability of detecting predictive relations by chance alone.

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On Predicting Stock Returns with Nearly Integrated Explanatory Variables

Cited by 300 publications

References 36 publications

Reconciling the Return Predictability Evidence In-Sample Forecasts, Out-of-Sample Forecasts, and Parameter Instability*

Reconciling the Return Predictability Evidence In-Sample Forecasts, Out-of-Sample Forecasts, and Parameter Instability*

Financial Economics, Return Predictability and Market Efficiency

Dividend Growth Predictability and the Price-Dividend Ratio

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