On the Predictability of Japanese Stock Returns Using Dividend Yield

The aim of this paper is to provide a critical and comprehensive reexamination of empirical evidence on the ability of the dividend yield to predict Japanese stock returns. Our empirical results suggest that in general, the predictability is weak. However, (1) if the bubble economy period (1986–1998), during which dividend yields were persistently lower than the historical average, is excluded from the sample, and (2) if positive autocorrelation in monthly aggregate returns is taken into account, there is some evidence that the log dividend yield is indeed useful in forecasting future stock returns. More specifically, the log dividend yield contributes to predicting monthly stock returns in the sample after 1990 and when lagged stock returns are included simultaneously.     

Stock return forecasting: Some new evidence

This paper makes three contributions to the literature on forecasting stock returns. First, unlike the extant literature on oil price and stock returns, we focus on out-of-sample forecasting of returns. We show that the ability of the oil price to forecast stock returns depends not only on the data frequency used but also on the estimator. Second, out-of-sample forecasting of returns is sector-dependent, suggesting that oil price is relatively more important for some sectors than others. Third, we examine the determinants of out-of-sample predictability for each sector using industry characteristics and find strong evidence that return predictability has links to certain industry characteristics, such as book-to-market ratio, dividend yield, size, price earnings ratio, and trading volume.     

Forecasting stock market returns: The sum of the parts is more than the whole

We propose forecasting separately the three components of stock market returns—the dividend–price ratio, earnings growth, and price–earnings ratio growth—the sum-of-the-parts (SOP) method. Our method exploits the different time series persistence of the components and obtains out-of-sample R-squares (compared with the historical mean) of more than 1.3% with monthly data and 13.4% with yearly data. This compares with typically negative R-squares obtained in a similar experiment with predictive regressions. The performance of the SOP method comes mainly from the dividend–price ratio and earnings growth components, and the robustness of the method is due to its low estimation error. An investor who timed the market using our method would have had a Sharpe ratio gain of 0.3.     

Expected returns and expected dividend growth

We investigate a consumption-based present-value relation that is a function of future dividend growth and find that changing forecasts of dividend growth are an important feature of the post-war U.S. stock market, despite the failure of the dividend–price ratio to uncover such variation. In addition, dividend forecasts are found to covary with changing forecasts of excess stock returns over business cycle frequencies. This covariation is important because positively correlated fluctuations in expected dividend growth and expected returns have offsetting effects on the log dividend–price ratio. The market risk premium and expected dividend growth thus vary considerably more than is apparent using the log divided–price ratio alone as a predictive variable.     

Do Decomposed Financial Ratios Predict Stock Returns and Fundamentals Better?

We investigate the prediction of excess returns and fundamentals by financial ratios, which include dividend‐price ratios, earnings‐price ratios, and book‐to‐market ratios, by decomposing financial ratios into a cyclical component and a stochastic trend component. We find both components predict excess returns and fundamentals. Cyclical components predict increases in future stock returns, while stochastic trend components predict declines in future stock returns in long horizons. This helps explain previous findings that financial ratios in the absence of decomposition find weak predictive power in short horizons and some predictive power in long horizons. We also find both components predict fundamentals.     

Output and expected returns

This paper shows for 1929–2003 U.S. data and also for international G-7 data that the ratio of share prices to GDP tracks a large fraction of the variation over time in expected returns on the aggregate stock market, capturing more of that variation than do price–earnings and price–dividend ratios and often also providing additional information about excess returns. The price–output ratio tracks long-term U.S. cumulative stock returns almost as well as the cay-ratio of Lettau and Ludvigson [2001a. Journal of Finance 56, 815–849, 2005. Journal of Financial Economics 76, 583–626], although the cay-ratio tracks variation in U.S. excess returns better. The price–output ratio, however, involves no parameter estimation and is easily constructed for non-U.S. countries.     

Earnings and Expected Returns

The aggregate dividend payout ratio forecasts excess returns on both stocks and corporate bonds in postwar U.S. data. High dividends forecast high returns. High earnings forecast low returns. The correlation of earnings with business conditions gives them predictive power for returns; they contain information about future returns that is not captured by other variables. Dividends and earnings contribute substantial explanatory power at short horizons. For forecasting long-horizon returns, however, only (scaled) stock prices matter. Forecasts of low long-horizon stock returns in the mid-1990s are caused not by earnings or dividends, but by high stock prices.     

The generation of stock market cycles

This paper demonstrates that the relation between stock market and business cycle dynamics can be conceptualized using a dividend discount model. The interaction of changes in earnings and interest rates throughout the economic cycle are shown to cause changes in the level of stock prices. This implies that monitoring and forecasting these factors can help explain and possibly predict stock price behavior over time.     

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.     

Inflation Illusion and Post-Earnings-Announcement Drift

This paper examines the cross‐sectional implications of the inflation illusion hypothesis for the post‐earnings‐announcement drift. The inflation illusion hypothesis suggests that stock market investors fail to incorporate inflation in forecasting future earnings growth rates, and this causes firms whose earnings growths are positively (negatively) related to inflation to be undervalued (overvalued). We argue and show that the sensitivity of earnings growth to inflation varies monotonically across stocks sorted on standardized unexpected earnings (SUE) and, consistent with the inflation illusion hypothesis, show that lagged inflation predicts future earnings growth, abnormal returns, and earnings announcement returns of SUE‐sorted stocks. Interestingly, controlling for the return predictive ability of inflation weakens the ability of lagged SUE to predict future returns of SUE‐sorted stocks.     

International stock return predictability: Evidence from new statistical tests

We investigate whether stock returns of international markets are predictable from a range of fundamentals including key financial ratios (dividend-price ratio, dividend-yield, earnings-price ratio, dividend-payout ratio), technical indicators (price pressure, change in volume), and short-term interest rates. We adopt two new alternative testing and estimation methods: the improved augmented regression method and wild bootstrapping of predictive model based on a restricted VAR form. Both methods take explicit account of endogeneity of predictors, providing bias-reduced estimation and improved statistical inference in small samples. From monthly data of 16 Asia-Pacific (including U.S.) and 21 European stock markets from 2000 to 2014, we find that the financial ratios show weak predictive ability with small effect sizes and poor out-of-sample forecasting performances. In contrast, the price pressure and interest rate are found to be strong predictors for stock return with large effect sizes and satisfactory out-of-sample forecasting performance.     

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.     

Understanding Stock Price Volatility: The Role of Earnings

In an efficient capital market, asset prices vary when investors change their expectations about cash flows, discount rates, or both. Using dividends to measure cash flows, previous research shows that the aggregate dividend‐price ratio varies due to changes in expected discount rates (returns) rather than expected cash flows. In contrast, using accounting earnings instead of dividends as a measure of cash flows, this paper shows that as much as 70% of the variation in the dividend‐price ratio can be explained by changes in expected earnings. Moreover, the paper documents a significant negative correlation between expected returns and expected earnings, suggesting that variations in a common factor to both may generate significant price volatility. The results are consistent with the dividend‐policy irrelevance hypothesis.     

Rare disaster risk and the expected equity risk premium

Consistent with the predictions of rare disaster models, we find that a proxy for the time‐varying probability of rare disasters helps to explain fluctuations in expectations of the equity risk premium. Our proxy for disaster risk is a recently developed measure of global political instability, and the expected market risk premium is from Value Line analysts' expected stock returns. Consistent with long‐run risk models, uncertainty about expected GDP growth and expected consumption growth is also significantly positively related to the expected market risk premium. We obtain similar results when we use the earnings–price ratio and the dividend–price ratio as proxies for the expected market risk premium.     

Can security analyst forecasts predict gold returns?

This paper examines whether security analyst earnings forecasts for firms primarily operating in the gold market can be utilised to predict returns on the price of gold. We first demonstrate that analysts are at least in part basing their earnings forecasts for gold firms on the return expectations of the gold commodity market. We show this by providing evidence that analyst coverage impounds not only market-wide and industry information, but also gold price information for these firms — as measured via its impact on stock return synchronicity. We then examine if the difference between forecast and observed earnings for these firms has predictive value for changes in the price of gold whilst controlling for a number of macroeconomic factors. We find that this difference does hold predictive power, but also has some limitations. However, there is potential for it to be used as an additional variable within gold forecasting frameworks.     

Stock dividends in China: signalling or liquidity explanations?

We test alternative hypotheses on a sample of Chinese stock dividends. The inverse Mills ratio, a signal about future performance, is positively related to announcement returns but does not predict higher future performance. Analysts do not revise their earnings forecasts after the announcement date. Our results are more consistent with liquidity‐based theories. We find that managers choose higher stock dividend ratios if share prices deviate more from the industry‐wide average. Increases in proportional spreads, depth, and the number of trades and decreases in average trade size, and price impact suggest greater participation of liquidity and small investors following stock dividends.     

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.     

Stock price synchronicity and analyst coverage in emerging markets

This paper examines the relation between the stock price synchronicity and analyst activity in emerging markets. Contrary to the conventional wisdom that security analysts specialize in the production of firm-specific information, we find that securities which are covered by more analysts incorporate greater (lesser) market-wide (firm-specific) information. Using the R² statistics of the market model as a measure of synchronicity of stock price movement, we find that greater analyst coverage increases stock price synchronicity. Furthermore, after controlling for the influence of firm size on the lead–lag relation, we find that the returns of high analyst-following portfolio lead returns of low analyst-following portfolio more than vice versa. We also find that the aggregate change in the earnings forecasts in a high analyst-following portfolio affects the aggregate returns of the portfolio itself as well as those of the low analyst-following portfolio, whereas the aggregate change in the earnings forecasts of the low analyst-following portfolio have no predictive ability. Finally, when the forecast dispersion is high, the effect of analyst coverage on stock price synchronicity is reduced.     

Dividends, Asymmetric Information, and Agency Conflicts: Evidence from a Comparison of the Dividend Policies of Japanese and U.S. Firms

We compare dividend policies of U.S. and Japanese firms, partitioning the Japanese data into keiretsu, independent, and hybrid firms. We examine the correlation between dividend changes and stock returns, and the reluctance to change dividends. Results are consistent with the joint hypotheses that Japanese firms, particularly keiretsu-member firms, face less information asymmetry and fewer agency conflicts than U.S. firms, and that information asymmetries and/or agency conflicts affect dividend policy. Japanese firms experience smaller stock price reactions to dividend omissions and initiations, they are less reluctant to omit and cut dividends, and their dividends are more responsive to earnings changes.     

Consumption, Aggregate Wealth, and Expected Stock Returns

This paper studies the role of fluctuations in the aggregate consumption–wealth ratio for predicting stock returns. Using U.S. quarterly stock market data, we find that these fluctuations in the consumption–wealth ratio are strong predictors of both real stock returns and excess returns over a Treasury bill rate. We also find that this variable is a better forecaster of future returns at short and intermediate horizons than is the dividend yield, the dividend payout ratio, and several other popular forecasting variables. Why should the consumption–wealth ratio forecast asset returns? We show that a wide class of optimal models of consumer behavior imply that the log consumption–aggregate wealth (human capital plus asset holdings) ratio summarizes expected returns on aggregate wealth, or the market portfolio. Although this ratio is not observable, we provide assumptions under which its important predictive components for future asset returns may be expressed in terms of observable variables, namely in terms of consumption, asset holdings and labor income. The framework implies that these variables are cointegrated, and that deviations from this shared trend summarize agents' expectations of future returns on the market portfolio.