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 predictability of aggregate returns on commodity futures

This paper provides evidence that aggregate returns on commodity futures (without the returns on collateral) are predictable, both in-sample and out-of-sample, by various lagged variables from the stock market, bond market, macroeconomics, and the commodity market. Out of the 32 candidate predictors we consider, we find that investor sentiment is the best in-sample predictor of short-horizon returns, whereas the level and slope of the yield curve have much in-sample predictive power for long-horizon returns. We find that it is possible to forecast aggregate returns on commodity futures out-of-sample through several combination forecasts (the out-of-sample return forecasting R² is up to 1.65% at the monthly frequency).     

Predicting the equity premium with dividend ratios: Reconciling the evidence

This paper evaluates the ability of dividend ratios to predict the equity premium. We conduct an in and out-of-sample comparative study and apply the Goyal and Welch (2003) graphical method to equity premia derived from the UK FTSE All-Share and the S&P 500 indices. Preliminary in-sample univariate regressions reveal that in both markets the equity premium contains an element of predictability. However, the considered out-of-sample models outperform the historical moving average only in the UK context. This is confirmed by the graphical diagnostic which further indicates that dividend ratios are useful predictors of UK excess returns. Our paper provides a possible explanation of why dividend ratios might be more informative in the UK market by linking these findings to the disappearing dividend phenomenon. Finally, Campbell and Shiller (1988) identities are employed to account for the time-varying properties of the dividend ratio and dividend growth processes. It is shown that by instrumenting the models with the identities, forecasting ability can be further improved.     

When does investor sentiment predict stock returns?

We examine the asymmetry in the predictive power of investor sentiment in the cross-section of stock returns across economic expansion and recession states. We test the implication of behavioral theories and evidence that the return predictability of sentiment should be most pronounced in an expansion state when investors' optimism increases. We segregate economic states according to the NBER business cycles and further implement a multivariate Markov-switching model to capture the unobservable dynamics of the changes in the economic regime. The evidence suggests that only in the expansion state does sentiment perform both in-sample and out-of-sample predictive power for the returns of portfolio formed on size, book-to-market equity ratio, dividend yield, earnings-to-price ratio, age, return volatility, asset tangibility, growth opportunities, and 11 widely documented anomalies. In a recession state, however, the predictive power of sentiment is generally insignificant.     

Four centuries of return predictability

We combine annual stock market data for the most important equity markets of the last four centuries: the Netherlands and UK (1629–1812), UK (1813–1870), and US (1871–2015). We show that dividend yields are stationary and consistently forecast returns. The documented predictability holds for annual and multi-annual horizons and works both in- and out-of-sample, providing strong evidence that expected returns in stock markets are time-varying. In part, this variation is related to the business cycle, with expected returns increasing in recessions. We also find that, except for the period after 1945, dividend yields predict dividend growth rates.     

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.     

Predicting the bear stock market: Macroeconomic variables as leading indicators

This paper investigates whether macroeconomic variables can predict recessions in the stock market, i.e., bear markets. Series such as interest rate spreads, inflation rates, money stocks, aggregate output, unemployment rates, federal funds rates, federal government debt, and nominal exchange rates are evaluated. After using parametric and nonparametric approaches to identify recession periods in the stock market, we consider both in-sample and out-of-sample tests of the variables’ predictive ability. Empirical evidence from monthly data on the Standard & Poor’s S&P 500 price index suggests that among the macroeconomic variables we have evaluated, yield curve spreads and inflation rates are the most useful predictors of recessions in the US stock market, according to both in-sample and out-of-sample forecasting performance. Moreover, comparing the bear market prediction to the stock return predictability has shown that it is easier to predict bear markets using macroeconomic variables.     

Forecasting returns: New European evidence

This paper builds on the recent debate on the in-sample and out-of-sample predictability of US aggregate returns using a wide range of predictors by providing new evidence for smaller and less market-oriented European countries.We find evidence that macro and technical predictors can (statistically) improve forecast accuracy and (economically) generate gains to investors; in contrast to the US results, predictability in our sample of European countries exists in recent data. We also find that simple forecast combinations consistently yield substantial benefits both in forecast accuracy and economic gain. For example, the magnitude of the forecasting gains for our European countries is often larger than those found for the US and other G7 countries. We provide initial evidence on the link between country characteristics and out-of-sample forecast performance. Our empirical results indicate that market development is related to the forecast performance of macro variables. There is also some evidence that forecast performance is related to market size and liquidity.     

Stock and bond joint pricing,consumption surplus,and inflation news

In this paper, Bekaert et al.’s (2010) model is modified by allowing consumption growth to depend on dividend yield rather than dividend growth. With a simplified inflation dynamic, the general equilibrium model is characterized by a system of linear and affine stochastic equations. From these equations, a closed-form solution jointly pricing equity and bonds is derived. The generalized method of moments is used to demonstrate that our model’s calibrated moments broadly match the first and second moments of stocks, bonds, and other macroeconomic variables in the US. Our estimated equity premium is 6.0%, which closely matches its actual value of 5.6%. The predicted risk aversion is countercyclical. Moreover, an out-of-sample test indicates the significant improvement of predictive power on the price–dividend ratio over Campbell and Cochrane’s (1999) model. Our model can further capture the dramatic increase in the price–dividend ratio after the 1990s.     

In-sample vs. out-of-sample tests of stock return predictability in the context of data mining

We undertake an extensive analysis of in-sample and out-of-sample tests of stock return predictability in an effort to better understand the nature of the empirical evidence on return predictability. We find that a number of financial variables appearing in the literature display both in-sample and out-of-sample predictive ability with respect to stock returns in annual data covering most of the twentieth century. In contrast to the extant literature, we demonstrate that there is little discrepancy between in-sample and out-of-sample test results once we employ out-of-sample tests with good power. While conventional wisdom holds that out-of-sample tests help guard against data mining, Inoue and Kilian [Inoue, A., Kilian, L., 2004. In-sample or out-of-sample tests of predictability: which one should we use? Econometric Reviews 23, 371–402.] recently argue that in-sample and out-of-sample tests are equally susceptible to data mining biases. Using a bootstrap procedure that explicitly accounts for data mining, we still find that certain financial variables display significant in-sample and out-of-sample predictive ability with respect to stock returns.     

Non-linear dynamics in international stock market returns

Recent empirical finance research has reported non-linear dynamics within asset returns. However, much of this extant research has focussed upon asset markets within the US and UK. This paper examines whether such dynamics are also present in a series of six international equity index returns. Using empirical models which are consistent that the theoretical behavioural finance noise trader motivation of non-linearity, whereby market dynamics differ between small and large returns, our results suggest these models improve the in-sample fit and out-of-sample forecast over linear alternatives. Further, the point of regime transition differs between positive and negative returns indicating that noise traders are more likely to engage in trend-chasing behaviour in up markets and anchoring behaviour in down markets. Finally, the forecast gain in the Asia-Pacific markets is greater than in the European markets suggestive that limits to arbitrage are greater perhaps as fundamental traders knowledge of market dynamics and noise trader behaviour is still evolving.     

Adjusted dividend-price ratios and stock return predictability: Evidence from China

This paper examines the predictive ability of dividend-price ratios on stock returns in the Chinese A-share market. The results show that in both the in-sample and out-of-sample settings, stock returns are positively predicted by the raw dividend-price ratio and multiple adjusted dividend-price ratios over the 2002–2018 period, especially for the pre-2008 period. However, this predictive power disappeared after the China Securities Regulatory Commission (CSRC) released the unique Semi-Mandatory Dividend Rule (the Rule) in 2008, and this sudden decrease in predictive ability has shown no signs of reversing. Tests using the event-study methodology show a reduction in the positive relation between dividend-price ratios and stock returns in the short term, indicating that the Rule has an adverse impact on the signaling effect of dividend announcements. We further demonstrate that the interference of the Rule with the information conveyed by the dividends explains the disappearance of the predictive power of dividend-price ratios. For firms with insufficient cash flow, dividends provide a negative signal of the firms' value after the enactment of the Rule instead of a positive signal of strong future cash flows. Consequently, the Rule has a negative impact on the efficiency of the cash-flow channel for predicting returns and buries the predictive power of dividend-price ratios.     

Equity premium prediction and the state of the economy

We detect cyclical variation in the predictive information of economic fundamentals, which can be used to substantially improve and simplify out-of-sample equity premium prediction. Economic fundamentals based on stock-specific information (notably the dividend yield) deliver better predictions in expansions. Economic fundamentals based on aggregate information (notably the short rate) deliver better predictions in recessions. Accordingly, a simple forecast combination of one predictor that generates cyclical forecasts and one predictor that generates countercyclical forecasts can deliver statistically significant and economically valuable equity premium predictions in both expansions and recessions. A prominent two-predictor forecast combination that performs well is the dividend yield and the short rate. Strategies designed for ex-ante timing of the business cycle can provide additional economic gains in equity premium prediction.     

The Predictability of Equity REIT Returns: Time Variation and Economic Significance

This article presents evidence on predictability of excess returns for equity REITs relative to the aggregate stock market, small-capitalization stocks, and T-bills using best-fit models from prior time periods. We find that excess equity REIT returns are far less predictable out-of-sample than in-sample. This inability to forecast out-of-sample is particularly true in the 1990s. Nevertheless, in the absence of transaction costs, active-trading strategies based on out-of-sample predictions modestly outperform REIT buy-and-hold strategies. However, when transaction costs are introduced, profits from these active-trading strategies largely disappear.     

Do the Investment and Return‐on‐Equity Factors Proxy for Economic Risks?

We study the information content of two new return factors, the investment factor (IA) and the return‐on‐equity factor (ROE), as proposed by Chen, Novy‐Marx, and Zhang in 2011. First, IA is a strong predictor for future gross domestic product (GDP) growth despite the presence of other financial and economic variables. IA subsumes the pricing power of the GDP factor for the cross section of asset returns. Second, ROE is closely related to innovations in dividend yield and term spread. When modeled together with innovations in state variables that forecast future investment opportunities, IA and ROE lose their explanatory power.     

Research and Development Activity and Expected Returns in the United Kingdom

Fama and French (1992) show that size and book-to-price dominate CAPM betaand other variables such as the price-earnings ratio and dividend yield in explainingthe cross-section of US stock returns. Comparable evidence for the UK points to abook-to-price effect, but not a size effect (Chan and Chui, 1996; Strong and Xu, 1997).In this paper, our first contribution is to show that a measure of research and development (RD) helps explain cross-sectional variation in UK stock returns. Our cross-sectional results on the association between stock returns and RD are consistent with recent US evidence reported by Lev and Sougiannis (1996, 1999) and Chan, Lakonishok and Sougiannis (2001). Fama and French (1993, 1995, 1996) also show that a three-factor model captures a high proportion of the time series variation in portfolio returns, again for the US. Our second contribution is to show, for the UK, that a modification to the three-factor model to take account of RD activity can significantly enhance the explanatory power of the three-factor model. We show that, as a practical matter, estimated risk premia based on the modified three-factor model can differ considerably from risk premia estimated using the CAPM or the three-factor model. In particular, risk premia for industries in whichfew firms undertake RD activities tend to be over-estimated.     

Portfolio performance of linear SDF models: an out-of-sample assessment

We evaluate linear stochastic discount factor models using an ex-post portfolio metric: the realized out-of-sample Sharpe ratio of mean–variance portfolios backed by alternative linear factor models. Using a sample of monthly US portfolio returns spanning the period 1968–2016, we find evidence that multifactor linear models have better empirical properties than the CAPM, not only when the cross-section of expected returns is evaluated in-sample, but also when they are used to inform one-month ahead portfolio selection. When we compare portfolios associated to multifactor models with mean–variance decisions implied by the single-factor CAPM, we document statistically significant differences in Sharpe ratios of up to 10 percent. Linear multifactor models that provide the best in-sample fit also yield the highest realized Sharpe ratios.     

tay's as good as cay

The empirical evidence that the consumption–wealth ratio, cay, has strong in-sample predictive power for future stock returns has been interpreted as evidence that consumers take account of future investment opportunities in planning their consumption expenditures. In this paper we show that the predictive power of cay arises mainly from a “look-ahead bias” introduced by estimating the parameters of the cointegrating regression between consumption, assets, and labor income in-sample. When a similar regression is run, replacing the log of consumption with an inanimate variable, calendar time, the resulting residual, which we label tay, is shown to be able to forecast stock returns as well as, or better than, cay. In addition, both cay and tay lose their out-of-sample forecasting power when they are re-estimated every period with only available data.     

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.     

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.