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. Long-horizon return forecasts give the same strong evidence. These tests exploit the negative correlation of return forecasts and dividend-yield autocorrelation across samples, together with sensible upper bounds on dividend-yield autocorrelation, to deliver more powerful statistics. I reconcile my findings with the literature that finds poor power in long-horizon return forecasts, and with the literature that notes the poor out-of-sample R² of return-forecasting regressions.

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 out-of-sample explanatory power is small, but nonetheless is economically meaningful for mean-variance investors. Even better results can be obtained by imposing the restrictions of steady-state 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 dividend-price ratio, earningsprice ratio, or smoothed earnings-price ratio. Value-oriented 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 long-term Treasury and corporate bonds are correlated with subsequent stock returns [Fama and Schwert

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 1978-1982 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

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 prevailing view in finance is that the evidence for long-horizon 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 2-year horizon estimators and 94 % between the 1- and 5-year 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.

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 dividend-price ratio both in and out of sam-ple, providing statistically and economically significant forecasts. The finite sample likelihood ratio test reliably rejects the hypothesis of constant expected returns.

The demand for durable goods is more cyclical than that for nondurable goods and services. Consequently, the cash flow and stock returns of durable-good producers are exposed to higher systematic risk. Using the NIPA input-output tables, we construct portfolios of durable-good, nondurable-good, and service producers. In the cross-section, a strategy that is long on durables and short on services earns a sizable risk premium. In the time series, a strategy that is long on durables and short on the market portfolio earns a countercyclical risk premium. We develop an equilibrium asset-pricing model that explains these empirical findings.

In perfect and complete financial markets Miller and Modigliani (1961) show that a firm’s value is unaffected by its dividend policy. Taxation, asymmetric information, incomplete contracts, institutional constraints, and transaction costs cause their theorem to fail in practice. We examine the effects of dividend policies on 323 securities that were listed on the London Stock Exchange between 1895 and 1905. The London Stock Exchange operated in an environment of very low taxation and an absence of institutional constraints. This allows us to investigate the worthiness of signalling and agency theories of dividend policy in a setting where many of the imperfections of modern markets do not exist.

Three of the most fundamental changes in the economy since the early 1970s have been (1) the increase in the importance of organizational capital in production, (2) the increase in managerial income inequality, and (3) the increase in payouts to the owners of firms. There is a unified explanation for these changes: The arrival and gradual adoption of information technology since the 1970s has stimulated the accumulation of organizational capital in existing firms. Since owners are better diversified than managers, the optimal division of rents from this organizational capital has the owners bear most of the cash-flow risk. In our model, the IT revolution benefits the owners and the managers in large successful firms, but not the managers in small firms. The resulting increase in managerial compensation inequality and the increase in payouts to owner’s compare favorably to those we establish in the data.

We evaluate predictive regressions that explicitly consider the time-variation 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 out-of-sample 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.