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48
A Comprehensive Look at the Empirical Performance of Equity Premium Prediction,” working paper
, 2004
"... Given the historically high equity premium, is it now a good time to invest in the stock market? Economists have suggested a whole range of variables that investors could or should use to predict: dividend price ratios, dividend yields, earningsprice ratios, dividend payout ratios, net issuing rati ..."
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Cited by 125 (4 self)
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Given the historically high equity premium, is it now a good time to invest in the stock market? Economists have suggested a whole range of variables that investors could or should use to predict: dividend price ratios, dividend yields, earningsprice ratios, dividend payout ratios, net issuing ratios, bookmarket ratios, interest rates (in various guises), and consumptionbased macroeconomic ratios (cay). The typical paper reports that the variable predicted well in an insample regression, implying forecasting ability. Our paper explores the outofsample performance of these variables, and finds that not a single one would have helped a realworld investor outpredicting the thenprevailing historical equity premium mean. Most would have outright hurt. Therefore, we find that, for all practical purposes, the equity premium has not been predictable, and any belief about whether the stock market is now too high or too low has to be based on theoretical prior, not on the empirically variables we have explored.
The Dog That Did Not Bark: A Defense of Return Predictability
, 2006
"... If returns are not predictable, dividend growth must be predictable, to generate the observed variation in divided yields. I find that the absence of dividend growth predictability gives stronger evidence than does the presence of return predictability. Longhorizon return forecasts give the same st ..."
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Cited by 73 (6 self)
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If returns are not predictable, dividend growth must be predictable, to generate the observed variation in divided yields. I find that the absence of dividend growth predictability gives stronger evidence than does the presence of return predictability. Longhorizon return forecasts give the same strong evidence. These tests exploit the negative correlation of return forecasts and dividendyield autocorrelation across samples, together with sensible upper bounds on dividendyield autocorrelation, to deliver more powerful statistics. I reconcile my findings with the literature that finds poor power in longhorizon return forecasts, and with the literature that notes the poor outofsample R² of returnforecasting regressions.
Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?
, 2004
"... Goyal and Welch (2006) argue that the historical average excess stock return forecasts future excess stock returns better than regressions of excess returns on predictor variables. In this paper we show that many predictive regressions beat the historical average return, once weak restrictions are i ..."
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Cited by 57 (2 self)
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Goyal and Welch (2006) argue that the historical average excess stock return forecasts future excess stock returns better than regressions of excess returns on predictor variables. In this paper we show that many predictive regressions beat the historical average return, once weak restrictions are imposed on the signs of coefficients and return forecasts. The outofsample explanatory power is small, but nonetheless is economically meaningful for meanvariance investors. Even better results can be obtained by imposing the restrictions of steadystate valuation models, thereby removing the need to estimate the average from a short sample of volatile stock returns. Towards the end of the last century, academic finance economists came to take seriously the view that aggregate stock returns are predictable. During the 1980’s a number of papers studied valuation ratios, such as the dividendprice ratio, earningsprice ratio, or smoothed earningsprice ratio. Valueoriented investors in the tradition of Graham and Dodd (1934) had always asserted that high valuation ratios are an indication of an undervalued stock market and should predict high subsequent returns, but these ideas did not carry much weight in the academic literature until authors such as Rozeff (1984), Fama and French (1988), and Campbell and Shiller (1988a,b) found that valuation ratios are positively correlated with subsequent returns and that the implied predictability of returns is substantial at longer horizons. Around the same time, several papers pointed out that yields on short and longterm Treasury and corporate bonds are correlated with subsequent stock returns [Fama and Schwert
Why is longhorizon equity less risky? A durationbased explanation of the value premium, NBER working paper
, 2005
"... We propose a dynamic riskbased model that captures the value premium. Firms are modeled as longlived assets distinguished by the timing of cash flows. The stochastic discount factor is specified so that shocks to aggregate dividends are priced, but shocks to the discount rate are not. The model im ..."
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Cited by 50 (10 self)
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We propose a dynamic riskbased model that captures the value premium. Firms are modeled as longlived assets distinguished by the timing of cash flows. The stochastic discount factor is specified so that shocks to aggregate dividends are priced, but shocks to the discount rate are not. The model implies that growth firms covary more with the discount rate than do value firms, which covary more with cash flows. When calibrated to explain aggregate stock market behavior, the model accounts for the observed value premium, the high Sharpe ratios on value firms, and the poor performance of the CAPM. THIS PAPER PROPOSES A DYNAMIC RISKBASED MODEL that captures both the high expected returns on value stocks relative to growth stocks, and the failure of the capital asset pricing model to explain these expected returns. The value premium, first noted by Graham and Dodd (1934), is the finding that assets with a high ratio of price to fundamentals (growth stocks) have low expected returns relative to assets with a low ratio of price to fundamentals (value stocks). This
Instability of Return Prediction Models
 JOURNAL OF EMPIRICAL FINANCE
, 2005
"... This study examines evidence of instability in models of ex post predictable components in stock returns related to structural breaks in the coe cients of state variables such as the lagged dividend yield, short interest rate, term spread and default premium. We estimate linear models of excess retu ..."
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Cited by 30 (6 self)
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This study examines evidence of instability in models of ex post predictable components in stock returns related to structural breaks in the coe cients of state variables such as the lagged dividend yield, short interest rate, term spread and default premium. We estimate linear models of excess returns for a set of international equity indices and test for stability of the estimated regression parameters. There is evidence of instability for the vast majority of countries. We then attempt to characterize the timing and nature of the breaks. Breaks do not generally appear to be uniform in time: di erent countries experience breaks at di erent times. We do identify a contemporaneous break for the US and UK indices in 1974. There is also some evidence of a break for a cluster of European nations during the 19781982 period. These breaks may relate to the oil price shock of 1974 and the formation of the European Monetary System in 1979. For the majority of intenational indices, the predictable component in stock returns appears to have diminished following the most recent break. We assess the adequecy of the break tests and model selection procedures in a set of
Dynamic Asset Allocation with Ambiguous Return Predictability, working paper
, 2009
"... We study an investor’s optimal consumption and portfolio choice problem when he confronts with two possibly misspecified submodels of stock returns: one with IID returns and the other with predictability. We adopt a generalized recursive ambiguity model to accommodate the investor’s aversion to mode ..."
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Cited by 15 (2 self)
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We study an investor’s optimal consumption and portfolio choice problem when he confronts with two possibly misspecified submodels of stock returns: one with IID returns and the other with predictability. We adopt a generalized recursive ambiguity model to accommodate the investor’s aversion to model uncertainty. The investor deals with specification doubts by slanting his beliefs about submodels of returns pessimistically, causing his investment strategy to be more conservative than the Bayesian strategy. This effect is large for high and low values of the predictive variable. Unlike in the Bayesian framework, the hedging demand against model uncertainty may cause the investor’s stock allocations to first decrease sharply and then increase with his prior probability of the IID model, even when the expected stock return under the IID model is lower than under the predictability model. Adopting suboptimal investment strategies by ignoring model uncertainty can lead to sizable welfare costs.
THE MYTH OF LONGHORIZON PREDICTABILITY
, 2005
"... The prevailing view in finance is that the evidence for longhorizon stock return predictability is significantly stronger than that for short horizons. We show that for persistent regressors, a characteristic of most of the predictive variables used in the literature, the estimators are almost perf ..."
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Cited by 13 (0 self)
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The prevailing view in finance is that the evidence for longhorizon stock return predictability is significantly stronger than that for short horizons. We show that for persistent regressors, a characteristic of most of the predictive variables used in the literature, the estimators are almost perfectly correlated across horizons under the null hypothesis of no predictability. For example, for the persistence levels of dividend yields, the analytical correlation is 99% between the 1 and 2year horizon estimators and 94 % between the 1 and 5year horizons, due to the combined effects of overlapping returns and the persistence of the predictive variable. Common sampling error across equations leads to ordinary least squares coefficient estimates and R 2 s that are roughly proportional to the horizon under the null hypothesis. This is the precise pattern found in the data. The asymptotic theory is corroborated, and the analysis extended by extensive simulation evidence. We perform joint tests across horizons for a variety of explanatory variables, and provide an alternative view of the existing evidence. I.
Filtering Out Expected Dividends and Expected Returns
, 2007
"... This paper suggests a new state space approach to analysis of stock return predictability. Acknowledging that expected returns and expected dividends are unobservable, the Kalman filter technique is used to extract them from the observed history of realized dividends and returns. This approach expli ..."
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Cited by 12 (1 self)
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This paper suggests a new state space approach to analysis of stock return predictability. Acknowledging that expected returns and expected dividends are unobservable, the Kalman filter technique is used to extract them from the observed history of realized dividends and returns. This approach explicitly accounts for the variation in expected dividend growth and allows to make estimates more robust to structural breaks in the means of dividend growth and returns. The constructed predictor outperforms the dividendprice ratio both in and out of sample, providing statistically and economically significant forecasts. The finite sample likelihood ratio test reliably rejects the hypothesis of constant expected returns.
Predictive Regressions with TimeVarying Coefficients ∗
, 2007
"... We evaluate predictive regressions that explicitly consider the timevariation of coefficients in a comprehensive Bayesian framework. This allows for fast and consistent adjustment of regression coefficients to changes in the underlying economic relationships (e.g., changes in the regulatory environ ..."
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Cited by 10 (1 self)
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We evaluate predictive regressions that explicitly consider the timevariation of coefficients in a comprehensive Bayesian framework. This allows for fast and consistent adjustment of regression coefficients to changes in the underlying economic relationships (e.g., changes in the regulatory environment) as we document explicitly for the coefficient of the dividend yield. For monthly returns of the S&P 500 index, we demonstrate statistical and, especially, economic evidence of outofsample predictability. In both cases, the proposed framework outperforms regressions with constant coefficients. One explanation for this improvement is the proposed methodology’s ability to identify periods with high or low prediction uncertainty.
Dividends, total cashflows to shareholders and predictive return regressions, Working Paper
, 2003
"... Abstract—This paper provides new evidence on the predictive power of dividend yields for U.S. aggregate stock returns. Following Miller and Modigliani, we construct a measure of the dividend yield that includes all cash flows to shareholders. We show that this alternative cashflow yield has strong ..."
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Cited by 8 (1 self)
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Abstract—This paper provides new evidence on the predictive power of dividend yields for U.S. aggregate stock returns. Following Miller and Modigliani, we construct a measure of the dividend yield that includes all cash flows to shareholders. We show that this alternative cashflow yield has strong and stable predictive power for returns, and appears robust to a battery of tests that have been proposed in recent critiques of the predictability literature. I.