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113
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
On the importance of measuring payout yield: Implications for empirical asset pricing
 Journal of Finance
, 2006
"... We investigate the empirical implications of using various measures of payout yield rather than dividend yield for asset pricing models. We find statistically and economically significant predictability in the time series when payout (dividends plus repurchases) and net payout (dividends plus repurc ..."
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Cited by 52 (6 self)
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We investigate the empirical implications of using various measures of payout yield rather than dividend yield for asset pricing models. We find statistically and economically significant predictability in the time series when payout (dividends plus repurchases) and net payout (dividends plus repurchases minus issuances) yields are used instead of the dividend yield. Similarly, we find that payout (net payout) yields contains information about the cross section of expected stock returns exceeding that of dividend yields, and that the high minus low payout yield portfolio is a priced factor. WHILE THE IRRELEVANCE THEOREM of Miller and Modigliani (1961) implies that there is no reason to suspect that dividends play a role in determining equity price levels or equity returns, the theorem is silent on the usefulness of dividends in explaining these variables. It is then, perhaps, not surprising that there is a considerable literature exploiting the properties of dividends and dividend yields to better understand the fundamentals of asset pricing both in the time series and in the cross section. Motivation for the former comes from variations of the Gordon growth model in which dividend yields can be written as the return minus the dividend’s growth rate (see, e.g., Fama and French (1988)), from consumptionbased asset pricing models in which the firm’s dividends covary with aggregate consumption (e.g., Lucas (1978) and Shiller (1981)), and so forth. Additional motivation comes from crosssectional heterogeneity in tax, agency, and asymmetric information considerations (e.g.,
The empirical riskreturn relation: a factor analysis approach
, 2007
"... Existing empirical literature on the riskreturn relation uses a relatively small amount of conditioning information to model the conditional mean and conditional volatility of excess stock market returns. We use dynamic factor analysis for large datasets to summarize a large amount of economic info ..."
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Cited by 36 (6 self)
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Existing empirical literature on the riskreturn relation uses a relatively small amount of conditioning information to model the conditional mean and conditional volatility of excess stock market returns. We use dynamic factor analysis for large datasets to summarize a large amount of economic information by few estimated factors, and find that three new factors termed “volatility,” “risk premium,” and “real” factors contain important information about onequarterahead excess returns and volatility not contained in commonly used predictor variables. Our specifications predict 1620 % of the onequarterahead variation in excess stock market returns, and exhibit stable and statistically significant outofsample forecasting power. We also find a positive conditional riskreturn correlation.
Financial Markets and the Real Economy
, 2006
"... I survey work on the intersection between macroeconomics and finance. The challenge is to find the right measure of “bad times,” rises in the marginal value of wealth, so that we can understand high average returns or low prices as compensation for assets’ tendency to pay off poorly in “bad times.” ..."
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Cited by 19 (1 self)
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I survey work on the intersection between macroeconomics and finance. The challenge is to find the right measure of “bad times,” rises in the marginal value of wealth, so that we can understand high average returns or low prices as compensation for assets’ tendency to pay off poorly in “bad times.” I survey the literature, covering the timeseries and crosssectional facts, the equity premium, consumptionbased models, general equilibrium models, and labor income/idiosyncratic risk approaches.
Detecting and Predicting Forecast Breakdowns
, 2008
"... We propose a theoretical framework for assessing whether a forecast model estimated over one period can provide good forecasts over a subsequent period. We formalize this idea by defining a forecast breakdown as a situation in which the outofsample performance of the model, judged by some loss fun ..."
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Cited by 17 (1 self)
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We propose a theoretical framework for assessing whether a forecast model estimated over one period can provide good forecasts over a subsequent period. We formalize this idea by defining a forecast breakdown as a situation in which the outofsample performance of the model, judged by some loss function, is significantly worse than its insample performance. Our framework, which is valid under general conditions, can be used not only to detect past forecast breakdowns but also to predict future ones. We show that main causes of forecast breakdowns are instabilities in the data generating process and relate the properties of our forecast breakdown test to those of structural break tests. The empirical application finds evidence of a forecast breakdown in the Phillips’ curve forecasts of U.S. inflation, and links it to inflation volatility and to changes in the monetary policy reaction function of the Fed.
Predictable returns and asset allocation: Should a skeptical investor time the market
 Journal of Econometrics
, 2009
"... are grateful for financial support from the Aronson+Johnson+Ortiz fellowship through the Rodney L. White Center for Financial Research. This manuscript does not reflect the views of the Board of Governors of the Federal Reserve System. Predictable returns and asset allocation: Should a skeptical inv ..."
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Cited by 16 (0 self)
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are grateful for financial support from the Aronson+Johnson+Ortiz fellowship through the Rodney L. White Center for Financial Research. This manuscript does not reflect the views of the Board of Governors of the Federal Reserve System. Predictable returns and asset allocation: Should a skeptical investor time the market? We investigate optimal portfolio choice for an investor who is skeptical about the degree to which excess returns are predictable. Skepticism is modeled as an informative prior over the R 2 of the predictive regression. We find that the evidence is sufficient to convince even an investor with a highly skeptical prior to vary his portfolio on the basis of the dividendprice ratio and the yield spread. The resulting weights are less volatile and deliver superior outofsample performance as compared to the weights implied by an entirely modelbased Are excess returns predictable, and if so, what does this mean for investors? In classic studies of rational valuation (e.g. Samuelson (1965, 1973), Shiller (1981)), risk premia are constant over time and thus excess returns are unpredictable. 1
Presidential Address: Discount Rates
 Journal of Finance
, 2011
"... Discountrate variation is the central organizing question of current assetpricing research. I survey facts, theories, and applications. Previously, we thought returns were unpredictable, with variation in pricedividend ratios due to variation in expected cashflows. Now it seems all pricedividend ..."
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Cited by 15 (0 self)
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Discountrate variation is the central organizing question of current assetpricing research. I survey facts, theories, and applications. Previously, we thought returns were unpredictable, with variation in pricedividend ratios due to variation in expected cashflows. Now it seems all pricedividend variation corresponds to discountrate variation. We also thought that the crosssection of expected returns came from the CAPM. Now we have a zoo of new factors. I categorize discountrate theories based on central ingredients and data sources. Incorporating discountrate variation affects finance applications, including portfolio theory, accounting, cost of capital, capital structure, compensation, and macroeconomics. ASSET PRICES SHOULD EQUAL expected discounted cashflows. Forty years ago, Eugene Fama (1970) argued that the expected part, “testing market efficiency,” provided the framework for organizing assetpricing research in that era. I argue that the “discounted ” part better organizes our research today. I start with facts: how discount rates vary over time and across assets. I turn
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.