Stock return,dividend growth and consumption growth predictability across markets and time: Implications for stock price movement

This paper links variation in the predictive regressions for stock returns, dividend growth and consumption growth to economic and market factors. The nature of these links can reveal whether movement in asset prices occurs primarily through the discount rate or cash flow channel, while they also help explain the reported mixed results for predictability. Variation is examined through cross-sectional regressions across 15 markets and over time using rolling regressions. The cross-sectional and time-varying parameters are regressed against output growth, interest rates and inflation as well as market variables using fixed effects panel as well as both OLS and logit approaches. The key implication for asset pricing is that although movement occurs through both channels, stock return predictability is more dominated by the discount rate channel and consumption growth predictability more so by the cash flow channel. Intuitively, such a difference may arise as investors and households rebalance their asset holdings and consumption at different speeds. There is also some evidence of money illusion through the inflation variable.     

Predicting dividends in log-linear present value models

In a present value model, high dividend yields imply that either future dividend growth must be low, or future discount rates must be high, or both. While previous studies have largely focused on the predictability of future returns from dividend yields, dividend yields also strongly predict future dividends, and the predictability of dividend growth is much stronger than the predictability of returns at a one-year horizon. Inference from annual regressions over the 1927–2000 sample imputes over 85% of the variation of log dividend yields to variations in dividend growth. Point estimates of the predictability of both dividend growth and discount rates are stronger when the 1990–2000 decade is omitted.     

Regime Shifts in Price‐Dividend Ratios and Expected Stock Returns: A Present‐Value Approach

We incorporate regime shifts in the mean of price‐dividend ratios into the present value model of van Binsbergen and Koijen (2010) who propose a latent variable approach to modeling expected returns and dividend growth rates. We find that accounting for regime shifts results in much lower persistence of expected returns and higher volatility of expected returns, and thus higher in‐sample predictability, when compared to the results from the van Binsbergen and Koijen (2010) model. We also show that the main source of the increase in the mean of price‐dividend ratios in the mid‐1990s is a decrease in the mean of expected returns.     

Dividend Dynamics,Learning, and Expected Stock Index Returns

We present a latent variable model of dividends that predicts, out‐of‐sample, 39.5% to 41.3% of the variation in annual dividend growth rates between 1975 and 2016. Further, when learning about dividend dynamics is incorporated into a long‐run risks model, the model predicts, out‐of‐sample, 25.3% to 27.1% of the variation in annual stock index returns over the same time horizon, with learning contributing approximately half of the predictability in returns. These findings support the view that investors' aversion to long‐run risks and their learning about these risks are important in determining stock index prices and expected returns.     

Predicting dividends in log-linear present value models

In a present value model, high dividend yields imply that either future dividend growth must be low, or future discount rates must be high, or both. While previous studies have largely focused on the predictability of future returns from dividend yields, dividend yields also strongly predict future dividends, and the predictability of dividend growth is much stronger than the predictability of returns at a one-year horizon. Inference from annual regressions over the 1927–2000 sample imputes over 85% of the variation of log dividend yields to variations in dividend growth. Point estimates of the predictability of both dividend growth and discount rates are stronger when the 1990–2000 decade is omitted.     

A STATE‐SPACE MODEL OF SHORT‐ AND LONG‐HORIZON STOCK RETURNS

In this article we propose a new parsimonious state‐space model in which state variables characterize the stochastic movements of stock returns. Using the equally weighted and decile monthly stock returns, we show that (a) a parsimonious state‐space model characterizes the variation in expected returns at any horizon; (b) the extracted expected returns explain a substantial proportion of the variance in realized returns, and the magnitude of this proportion increases significantly with the horizon of returns; (c) the model successfully captures the empirical fact that returns of smaller firms have both stronger positive autocorrelations of short‐horizon returns and stronger negative autocorrelations of long‐horizon returns; and (d) the forecasts of asset returns obtained with the state‐space model subsume the information in other potential predictor variables such as dividend yields. JEL classification: G10, G12.     

Disappearing Dividends: Implications for the Dividend–Price Ratio and Return Predictability

The conventional dividend–price ratio is highly persistent, and the literature reports mixed evidence on its role in predicting stock returns. We argue that the decreasing number of firms with a traditional dividend‐payout policy is responsible for these results, and develop a model in which the long‐run relationship between the dividends and stock price is time varying. An adjusted dividend–price ratio that accounts for the time‐varying long‐run relationship is considerably less persistent. Furthermore, the predictive regression model that employs the adjusted dividend–price ratio as a regressor outperforms the random‐walk model. These results are robust with respect to the firm size.     

Assessing the Economic Significance of Return Predictability: A Research Note

The predictability of stock returns is often assessed using classical statistical significance from predictive regressions. Statistical inference, however, can belie the economic importance with which investors regard various predictors. This paper examines the influence that predictors have on an investor's optimal portfolio allocations. The results show that return predictability is sufficient to induce horizon effects in optimal allocations. After incorporating estimation risk, however, little evidence of predictability remains. We also assess the relative importance of three predictor variables. While dividend yield is the most important predictor, optimal allocations are also sensitive to the term spread and the relative bill rate.     

The Predictive Ability of Dividend and Earnings Yields for Long-Term Stock Returns

We use empirical models to examine the predictive ability of dividend and earnings yields for long‐term stock returns. Results show that dividend and earnings yields share a similar predictive power for future stock returns and growth. We find that the predictive power of dividend yields increases with the return horizon, but that yields forecast future returns and growth over a much longer horizon. Finally, dividend and earnings yields exhibit high autocorrelation and strong contemporaneous relations.     

The Gilt-Equity Yield Ratio and the Predictability of UK and US Equity Returns

A number of financial variables have been shown to be effective in explaining the time-series of aggregate equity returns in both the UK and the US. These include, inter alia , the equity dividend yield, the spread between the yields on long and short government bonds, and the lagged equity return. Recently, however, the ratio between the long government bond yield and the equity dividend yield – the gilt-equity yield ratio – has emerged as a variable that has considerable explanatory power for UK equity returns. This paper compares the predictive ability of the gilt-equity yield ratio with these other variables for UK and US equity returns, providing evidence on both in-sample and out-of-sample performance. For UK monthly returns, it is shown that while the dividend yield has substantial in-sample explanatory power, this is not matched by out-of sample forecast accuracy. The gilt-equity yield ratio, in contrast, performs well both in-sample and out-of-sample. Although the predictability of US monthly equity returns is much lower than for the UK, a similar result emerges, with the gilt-equity yield ratio dominating the other variables in terms of both in-sample explanatory power and out-of-sample forecast performance. The gilt-equity yield ratio is also shown to have substantial predictive ability for long horizon returns.     

The Gilt-Equity Yield Ratio and the Predictability of UK and US Equity Returns

A number of financial variables have been shown to be effective in explaining the time-series of aggregate equity returns in both the UK and the US. These include, inter alia , the equity dividend yield, the spread between the yields on long and short government bonds, and the lagged equity return. Recently, however, the ratio between the long government bond yield and the equity dividend yield – the gilt-equity yield ratio – has emerged as a variable that has considerable explanatory power for UK equity returns. This paper compares the predictive ability of the gilt-equity yield ratio with these other variables for UK and US equity returns, providing evidence on both in-sample and out-of-sample performance. For UK monthly returns, it is shown that while the dividend yield has substantial in-sample explanatory power, this is not matched by out-of sample forecast accuracy. The gilt-equity yield ratio, in contrast, performs well both in-sample and out-of-sample. Although the predictability of US monthly equity returns is much lower than for the UK, a similar result emerges, with the gilt-equity yield ratio dominating the other variables in terms of both in-sample explanatory power and out-of-sample forecast performance. The gilt-equity yield ratio is also shown to have substantial predictive ability for long horizon returns.     

Local Trends in Price‐to‐Dividend Ratios—Assessment,Predictive Value,and Determinants

Persistent variations of the log price‐to‐dividend ratio (PD) and their economic determinants have attracted a lively discussion in the literature. We suggest a gradually time‐varying state process to govern the persistence of the PD. The adopted state‐space approach offers favorable model diagnostics and finds particular support in out‐of‐sample stock return prediction. We show that this slowly evolving mean process is jointly shaped by the consumption risk, the demographic structure, and the proportion of firms with traditional dividend payout policy during the past 60 years. In particular, the volatility of consumption growth plays the dominant role.     

International Evidence on the Predictability of Stock Returns

This article examines the predictability of stock returns using international stock market data from eighteen countries. The study finds that the ability of dividend yields to predict stock returns increases as the return horizon lengthens from one month to forty-eight months. These results add to earlier ones, based on U.S. data only, showing that predictability grows with the return horizon. The study also explores why the observed pattern of predictability arises and provides evidence supporting the reasons suggested by Fama and French.     

Predictive Regressions: A Present‐Value Approach

We propose a latent variables approach within a present‐value model to estimate the expected returns and expected dividend growth rates of the aggregate stock market. This approach aggregates information contained in the history of price‐dividend ratios and dividend growth rates to predict future returns and dividend growth rates. We find that returns and dividend growth rates are predictable with $R^2$ values ranging from 8.2% to 8.9% for returns and 13.9% to 31.6% for dividend growth rates. Both expected returns and expected dividend growth rates have a persistent component, but expected returns are more persistent than expected dividend growth rates.     

Value versus Growth: Time‐Varying Expected Stock Returns

Is the value premium predictable? We study time variations of the expected value premium using a two‐state Markov switching model. We find that when conditional volatilities are high, the expected excess returns of value stocks are more sensitive to aggregate economic conditions than the expected excess returns of growth stocks. As a result, the expected value premium is time varying. It spikes upward in the high volatility state, only to decline more gradually in the subsequent periods. However, out‐of‐sample predictability of the value premium is close to nonexistent.     

On the reversal of return and dividend growth predictability: A tale of two periods

A disconcerting, albeit generally accepted, finding is that aggregate stock returns are predictable by dividend yield but dividend growth is unpredictable. I show that part of this lack of dividend growth predictability stems from how dividend growth is constructed. I then show a dramatic reversal of predictability in the 134 years during 1872–2005: stock returns are largely unpredictable in the first seven decades, but become predictable in the postwar period; dividend growth is strongly predictable in the prewar years but this predictability disappears in the postwar years. New evidence on the predictability of long-run returns and dividend growth is also shown.     

When does the dividend–price ratio predict stock returns?

If the dividend–price ratio becomes I(1) while stock returns are I(0), the unbalanced predictive regression makes the predictability test more likely to indicate that the dividend–price ratio has no predictive power. This might explain why the dividend–price ratio evidences strong predictive power during one period, while it exhibits weak or no predictive power at other times. Using international data, this paper demonstrates that the dividend–price ratio generally has predictive power for stock returns when both are I(0). However, this paper also shows that the dividend–price ratio loses its predictive power when it becomes I(1). The results are shown to be robust across countries.     

MODELS OF STOCK MARKET PREDICTABILITY

I briefly review the success of past studies purporting to explain equity valuations and predict future equity returns. The Campbell‐Shiller mean reversion models are contrasted with an expanded version of the so‐called Federal Reserve model. At least from 1970 to 2003, Federal Reserve–type models did somewhat better at predicting long‐horizon returns than did a mean reversion model based on dividend yields and price‐earnings multiples. However, timing investment strategies based on any of these prediction models do no better than a buy‐and‐hold strategy. Although some predictability of returns exists, there is no evidence of any systematic inefficiency that would enable investors to earn excess returns.     

Dividend growth and return predictability: A long-run re-examination of conventional wisdom

We re-examine dividend growth and return predictability evidence using 165 years of data from the Brussels Stock Exchange. The conventional wisdom holds that time-varying dividend yield is predominately explained by changes in expected returns and that expected dividend growth is only weakly forecastable. However, we find robust dividend growth predictability evidence in every time period. A lack of dividend smoothing is the most important reason for the disconnect with previous evidence. Furthermore, we find return predictability in the post-World War II period when we adjust the dividend yields for changing index composition, business cycle variation and structural breaks. This is explained by a simultaneous increase in equity duration, induced by an increasing importance of growth stocks.     

Sticky prices or economically-linked economies: The case of forecasting the Chinese stock market

We explore whether economic links via trade affect aggregate Chinese stock market returns. We find that market return indices from countries that China net imports from can forecast the Chinese aggregate market return at the weekly time horizon. The stock returns of countries that China net exports to have no consistently significant OOS predictability.The economic intuition for our results follows from the fact that China has positioned itself as a low-cost provider competing on price. As a low-cost provider China has a more difficult time passing cost increases through to export customers because of sticky prices. However, import costs, e.g., raw materials, are subject to both consumption and speculative demand and thus vary. We can conclude that costs will drive short term economic gains for the overall Chinese economy. One interpretation of our results is that supply shocks are absorbed within 2 weeks.