Dividend Variability and Stock Market Swings

This paper examines the extent to which swings in stock prices can be related to variations in the discounted value of expected future dividends when investors face uncertainty about their future behaviour. I develop an econometric model that accounts for the instability of U.S. dividend growth and discount rates during the past 120 years. Estimates of the model reveal that changing forecasts of future dividend growth account for more than 90% of the predictable variations in dividend-prices. The estimates also imply that instability in the dividend and discount rate processes contribute significantly to the predictability of long-horizon stock returns.     

A refined consumption-wealth ratio and its role on time-varying consumption risk

This paper takes a deeper look at the measurement of the consumption-wealth ratio and analyzes its ability to capture variations in expected future stock returns. I find evidence of stock return predictability by taking a different approach than predictive regressions.     

Are financial returns really predictable out-of-sample?: Evidence from a new bootstrap test

Testing the out-of-sample return predictability is of great interest among academics. A wide range of studies have shown the predictability of stock returns, but fail to test the statistical significance of economic gains from the predictability. In this paper, we develop a new statistical test for the directional accuracy of stock returns. Monte Carlo experiments reveal that our bootstrap-based tests have more correct size and better power than the existing tests. We use the forecast combinations and find that the stock return predictability is statistically significant in terms of reduction of mean squared predictive error relative to the benchmark of historical average forecasts. However, the results from our tests show that the predictability is not economically significant. We conclude that there will be still a long way to go for forecasting stock returns for market participants.     

A new investor sentiment indicator (ISI) based on artificial intelligence: A powerful return predictor in China

This paper utilizes deep learning approach widely documented in artificial intelligence, and proposes an investor-sentiment indicator (ISI) that is consistent with the purpose of forecasting stock market returns. We find that ISI is positively correlated with future stock market returns at a monthly frequency, but negatively associated with subsequent returns over a longer horizon. Moreover, ISI outperforms other well-recognized predictors both in and out of sample, and can predict cross-sectional stock returns sorted by industry. We also show a positive association between monthly ISI and dividend growth rate, which indicates that investors’ expectations about future cash flows may contribute to the return predictability of ISI.     

Expected inflation, expected stock returns, and money illusion: What can we learn from survey expectations?

We show empirically that survey-based measures of expected inflation are significant and strong predictors of future aggregate stock returns in several industrialized countries both in-sample and out-of-sample. Empirically discriminating between competing sources of this return predictability by virtue of a comprehensive set of expectations data, we find that money illusion seems to be the driving force behind our results. Another popular hypothesis – inflation as a proxy for aggregate risk aversion – is not supported by thedata.     

The Timing Ability of Newly Listed NYSE Firms, 1926-1962

We demonstrate that, from 1926 to 1962, the number of new listings on the New York Stock Exchange has predictive ability for future aggregate market returns. The forecasting power of new listings is evident even after controlling for previously documented market predictors, such as the dividend yield. While firms do not appear to time their own performance, tests investigating aggregate market movements around new listing dates are consistent with forecasting ability of the new listing variable. In particular, we use non-parametric regression methods to determine the functional relationship between one-year post-market returns and new listings. We find a decreasing trend in the expected one-year post-market return as a function of the number of new listings each quarter. Subsequent tests show that mean reversion in market returns does not drive the predictive evidence found here.     

Investor sentiment and the prediction of stock returns: a quantile regression approach

We employ quantile regression to provide a detailed picture of the stock return forecasting ability of investor sentiment. We find that investor sentiment predicts aggregate stock returns at lower quantiles. However, the forecasting power is lost at upper quantiles. The results are robust after controlling for a comprehensive set of macroeconomic and financial predictors and for characteristic portfolios. We also show that investor sentiment consists mainly of cash flow news and contains little information about discount rate news. The ability to forecast cash flows increases gradually from the lower quantiles to upper quantiles. Our results do not support that the ability of investor sentiment to predict stock returns comes from a rational forecast of future cash flows.     

Do macroeconomic variables have regime-dependent effects on stock return dynamics? Evidence from the Markov regime switching model

The predictability of stock return dynamics is a topic discussed most frequently in empirical studies; however, no unanimous conclusion has yet been reached due to the ignorance of structural changes in stock price dynamics. This study applies various regime switching GJR-GARCH models to analyze the effects of macroeconomic variables (interest rate, dividend yield, and default premium) on stock return movements (including conditional mean, conditional variance, and transition probabilities) in the U.S. stock market, so as to clearly compare the predictive validity of stable and volatile states, as well as compare the in-sample and out-of-sample portfolio performance of regime switching models. The empirical results show that macro factors can affect the stock return dynamics through two different channels, and that the magnitude of their influences on returns and volatility is not constant. The effects of the three economic variables on returns are not time-invariant, but are closely related to stock market fluctuations, and the strength of predictability in a volatile regime is far greater than that in a stable regime. It is found that interest rate and dividend yield seem to play an important role in predicting conditional variance, and out-of-sample performance is largely eroded when the effects of these two factors on volatility are ignored. In addition, the three macro factors do not play any role in predicting transition probabilities.     

Expected returns and expected dividend growth: time to rethink an established empirical literature

This article examines various state-space and VAR model specifications to investigate the contributions of expected returns and expected dividend growth to movements in the price-dividend ratio. We show that both models involve serious inference problems that need to be dealt with carefully. We propose procedures that offer more reliable inference results, and the corrected inferences indicate that the aggregate data of dividends and returns alone do not provide strong enough evidence to support the notion that the expected returns dominate the stock price variation. However, we show that an alternative measure of cash flows termed the net payout by Larrain and Yogo (2008) appears to lend strong support to the notion that the expected cash flow explains a large fraction of the firm value variation. This finding remains robust in both state-space and VAR decompositions with the corrected inference.     

Dynamic asset allocation with ambiguous return predictability

We study an investor's optimal consumption and portfolio choice problem when he is confronted 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. Unlike in the Bayesian framework, the hedging demand against model uncertainty may cause the investor's stock allocation to decrease sharply given a small doubt of return predictability, even though the expected return according to the VAR model is large. Over much of the parameter space, the robust strategy is very close to the Bayesian strategy with Epstein–Zin preferences and risk aversion chosen to match the same average portfolio holdings. This is true in particular when the IID model is unlikely and the dividend yield is low, as in recent years. However, differences in strategies can be substantial if the IID model is unlikely and the dividend yield is high.     

Structural breaks and GARCH models of stock return volatility: The case of South Africa

This paper investigates the empirical relevance of structural breaks in forecasting stock return volatility using both in-sample and out-of-sample tests applied to daily returns of the Johannesburg Stock Exchange (JSE) All Share Index from 07/02/1995 to 08/25/2010. We find evidence of structural breaks in the unconditional variance of the stock returns series over the period, with high levels of persistence and variability in the parameter estimates of the GARCH(1,1) model across the sub-samples defined by the structural breaks. This indicates that structural breaks are empirically relevant to stock return volatility in South Africa. However, based on the out-of-sample forecasting exercise, we find that even though there structural breaks in the volatility, there are no statistical gains from using competing models that explicitly accounts for structural breaks, relative to a GARCH(1,1) model with expanding window. This could be because of the fact that the two identified structural breaks occurred in our out-of-sample, and recursive estimation of the GARCH(1,1) model is perhaps sufficient to account for the effect of the breaks on the parameter estimates. Finally, we highlight that, given the point of the breaks, perhaps what seems more important in South Africa, is accounting for leverage effects, especially in terms of long-horizon forecasting of stock return volatility.     

SECULAR MEAN REVERSION AND LONG‐RUN PREDICTABILITY OF THE STOCK MARKET

The empirical financial literature reports evidence of mean reversion in stock prices and the absence of out‐of‐sample return predictability over horizons shorter than 10 years. Anecdotal evidence suggests the presence of mean reversion in stock prices and return predictability over horizons longer than 10 years, but thus far, there is no empirical evidence confirming such anecdotal evidence. The goal of this paper is to fill this gap in the literature. Specifically, using 141 years of data, this paper begins by performing formal tests of the random walk hypothesis in the prices of the real S&P Composite Index over increasing time horizons of up to 40 years. Although our results cannot support the conventional wisdom that the stock market is safer for long‐term investors, our findings speak in favor of the mean reversion hypothesis. In particular, we find statistically significant in‐sample evidence that past 15‐17 year returns are able to predict the future 15‐17 year returns. This finding is robust to the choice of data source, deflator, and test statistic. The paper continues by investigating the out‐of‐sample performance of long‐horizon return forecasting based on the mean‐reverting model. These latter tests demonstrate that the forecast accuracy provided by the mean‐reverting model is statistically significantly better than the forecast accuracy provided by the naive historical‐mean model. Moreover, we show that the predictive ability of the mean‐reverting model is economically significant and translates into substantial performance gains.     

Do stock returns hedge inflation at long horizons?

We investigate the long-horizon relationship between market returns and inflation in the United States. Conventional tests for long horizon predictability may reject the null too frequently when the predictor variable is highly persistent and endogenous and there are overlapping observations. We use a recently developed econometric technique designed to overcome these problems. We find little to no evidence that securities are able to hedge inflation.     

Politics, stock markets, and model uncertainty

The available evidence on the effects of political variables on both returns and volatility of aggregate stock indices is scant and mixed. Applying Bayesian Model Averaging to a panel dataset of 17 parliamentary democracies spanning the post-war period until 1995, we test the robustness of political variables in explaining stock returns and stock return volatility. While we find that the influence of political variables on excess returns is weak, there is evidence of some political variables explaining return volatility.     

An empirical analysis of stock returns around dividend changes

This paper documents significant drift in stock returns following announcements of changes in cash dividends. The magnitude is (i) smaller for increases than for decreases, (ii) inversely related to firm size and positively to dividend yield change, and (iii) concentrated in the first quarter. Beta changes do not explain the drift and it is robust in various subperiods. Next it is shown that dividend increases are positively autocorrelated especially every fourth quarter. The prices keep reacting to future announcements as if the market ignores these autocorrelations. Dividend decreases exhibit weak autocorrelation and the returns are negative for the following three announcements.     

我国上市公司高送转对盈利的信号效应研究

我国上市公司在进行股利分配时,大量采取高送转的方式。本研究通过对578家上交所A股上市公司高送转对盈利的信号效应的分析,揭示了高送转与公司盈利之间的关系。研究结果显示,上市公司进行高送转对公司未来的盈利信号较弱,高送转并不意味着未来盈利的高增长。只有现金红利增加的高送转传递了上市公司未来业绩良好的信号,而现金红利不变和现金红利减少的高送转不是未来盈利良好的信号。     

International Transmission of the 2007–2009 Financial Crisis: Evidence from Japan

We investigate the international transmission of the 2007–2009 financial crisis to Japanese firms by examining both stock returns and changes in operating performance during the crisis. Our results indicate that Japanese firms were affected by the crisis mainly through the trade channel in both stock returns and changes in operating performance. We also find that the liquidity channel played a role in the fall of stock returns in response to the crisis and in the changes in return on assets during the first year of the crisis. We obtain weak evidence for the credit crunch channel and no evidence to support the trade finance channel.     

South African stock return predictability in the context data mining: The role of financial variables and international stock returns

In this paper, we examine the predictive ability, both in-sample and the out-of-sample, for South African stock returns using a number of financial variables, based on monthly data with an in-sample period covering 1990:01 to 1996:12 and the out-of-sample period of 1997:01 to 2010:04. We use the t-statistic corresponding to the slope coefficient in a predictive regression model for in-sample predictions, while for the out-of-sample, the MSE-F and the ENC-NEW tests statistics with good power properties were utilised. To guard against data mining, a bootstrap procedure was employed for calculating the critical values of both the in-sample and out-of-sample test statistics. Furthermore, we use a procedure that combines in-sample general-to-specific model selection with out-of-sample tests of predictive ability to further analyse the predictive power of each financial variable. Our results show that, for the in-sample test statistic, only the stock returns for our major trading partners have predictive power at certain short and long run horizons. For the out-of-sample tests, the Treasury bill rate and the term spread together with the stock returns for our major trading partners show predictive power both at short and long run horizons. When accounting for data mining, the maximal out-of-sample test statistics become insignificant from 6-months onward suggesting that the evidence of the out-of-sample predictability at longer horizons is due to data mining. The general-to-specific model shows that valuation ratios contain very useful information that explains the behaviour of stock returns, despite their inability to predict stock return at any horizon. The model also highlights the role of multiple variables in predicting stock returns at medium- to long run horizons.     

Re-assessing international stock return predictability: evidence from directional accuracy and excess profitability tests

In this article, we assess the informational content of monthly lagged US excess stock returns for sign and mean predictability of stock market returns in 10 industrialized countries using the directional accuracy and excess profitability tests of Pesaran and Timmermann (1992) and Anatolyev and Gerko (2005), respectively. We find only a limited evidence supporting the earlier findings of Rapach et al. (2013) based on the test of Clark and West (2007).     

Firm return volatility and economic gains: The role of oil prices

In this paper we investigate whether the oil price contributes to stock return volatility for 560 firms listed on the NYSE. Using daily data, we find that the oil price is a significant determinant and predictor of firm return variance. We devise trading strategies based on forecasts of firm return variance using the oil prices and historical averages. We find that investors can make substantial gains in returns by using the oil price in forecasting firm return variances.