Multifrequency news and stock returns

Equity prices are driven by shocks with persistence levels ranging from intraday horizons to several decades. To accommodate this diversity, we introduce a parsimonious equilibrium model with regime shifts of heterogeneous durations in fundamentals, and estimate specifications with up to 256 states on daily aggregate returns. The multifrequency equilibrium has higher likelihood than the Campbell and Hentschel [1992. No news is good news: an asymmetric model of changing volatility in stock returns. Journal of Financial Economics 31, 281–318] specification, while producing volatility feedback 10 to 40 times larger. Furthermore, Bayesian learning about volatility generates a novel trade-off between skewness and kurtosis as information quality varies, complementing the uncertainty channel [e.g., Veronesi, 1999. Stock market overreaction to bad news in good times: a rational expectations equilibrium model. Review of Financial Studies 12, 975–1007]. Economies with intermediate information best match daily returns.     

Macro variables and the components of stock returns

We conduct a decomposition for the stock market return by incorporating the information from 124 macro variables. Using factor analysis, we estimate six common factors and run a VAR containing these factors and financial variables such as the market dividend yield and the T-bill rate. Including the macro factors does not have a significant impact in the estimation of the components of aggregate (excess) stock returns—cash-flow, discount-rate, and interest-rate news. Using the macro factors in the computation of cash-flow and discount-rate news does not significantly improve the fit of a two-factor ICAPM for the cross-section of stock returns.     

Excess stock returns and news: evidence from European markets

This paper aims at decomposing the forecast error variance of excess returns in five major European stock markets into the variance of news about future excess returns, dividends and real interest rates. Special emphasis is given on the issue of stationarity and structural breaks in the unconditional mean of dividend yields and their implications for variance decompositions. Empirical results indicate that in some markets the dividend yield is subject to structural breaks in the mean. Evidence from Monte Carlo simulations suggests that this kind of structural breaks cause small-sample bias in variance decompositions of a magnitude comparable to bias introduced by unit roots. Our results constitute a warning about return decompositions that, in particular, use variables in the forecasting equations that may be nonstationary or contain a structural break.     

Macro to micro: Country exposures,firm fundamentals and stock returns

We outline a systematic approach to incorporate macroeconomic information into firm level forecasting from the perspective of an equity investor. Using a global sample of 198,315 firm-years over the 1998–2010 time period, we find that combining firm level exposures to countries (via geographic segment data) with forecasts of country level performance, is able to generate superior forecasts for firm fundamentals. This result is particularly evident for purely domestic firms. We further find that this forecasting benefit is associated with future excess stock returns. These relations are stronger after periods of higher dispersion in expected country level performance.     

The decomposition of US and Euro area stock and bond returns and their sensitivity to economic state variables

This paper decomposes US and Euro area excess stock and bond return innovations into news factors using the Campbell–Schiller methodology. The results indicate that stock return volatility is mostly due to volatility of future excess return news. Inflation news plays a minor role although it is significantly correlated with excess return innovations. For the bond market too, it is future return news—not inflation news—that moves bond returns most. For finite investment horizons, however, asset market movements give a differential importance to the various news components. Results are comparable for the US and the Euro area, but differ in terms of magnitudes. In addition, sensitivities (‘betas’) to a set of state variables are estimated, yielding high interest rate betas and low money growth betas. Generally, inflation, unemployment and leading indicator betas are significant. Asset market exposures to oil and exchange rate changes are more significant for the Euro area than in the US.     

When bad news is good news: Geopolitical risk and the cross-section of emerging market stock returns

Using a news-based gauge of geopolitical risk, we study its role in asset pricing in global emerging markets. We find that changes in risk positively predict future stock returns. The countries with the highest increase in geopolitical uncertainty outperform their counterparts with the lowest change by up to 1% per month. The anomaly is not explained by other established asset pricing effects and remains robust to many considerations. We link the observed phenomenon with investor overreaction to geopolitical news driven by the availability bias.     

Macro news and bond yield spreads in the euro area

This paper analyses the effects of newspaper coverage of macro news on the spread between the yield on the 10-year German Bund and on sovereign bonds in eight countries belonging to the euro area (Belgium, France, Greece, Ireland, Italy, the Netherlands, Portugal and Spain) using daily data for the period 1999–2014. The econometric analysis is based on the estimation of a VAR-GARCH model. The results can be summarized as follows. Negative news have significant positive effects on yield spreads in all GIIPS (Greece, Ireland, Italy, Portugal and Spain) countries but Italy before September 2008; markets respond more to negative news, and their reaction has increased during the recent financial crisis. News volatility has a significant impact on yield spread volatility, the effects being more pronounced in the case of negative news and bigger in the most recent crisis period, especially in the GIIPS countries. Further, the conditional correlations between yield spreads and negative news increase in absolute value during the financial crisis (especially in the GIIPS countries), indicating a higher sensitivity of the former to the latter.     

International comovement of stock market returns: A wavelet analysis

The assessment of the comovement among international stock markets is of key interest, for example, for the international portfolio diversification literature. In this paper, we re-examine such comovement by resorting to a novel approach, wavelet analysis. Wavelet analysis allows one to measure the comovement in the time–frequency space. In this way, one can characterize how international stock returns relate in the time and frequency domains simultaneously, which allows one to provide a richer analysis of the comovement. We focus on Germany, Japan, UK and US and the analysis is done at both the aggregate and sectoral levels.     

The distribution of earnings news over time and seasonalities in aggregate stock returns

Over the past 55 years returns on stock market indexes have on average been higher during the first half-month of calendar quarters 2 through 4 than at other times. Coincidentally, aggregate corporate earnings news arriving at the market during these half-month periods tends to be good, whereas earnings reports arriving later are more likely to convey bad news. In addition firms tend to publish bad-news earnings reports on Mondays, coincident with negative Monday effects in stock returns. The coincidence of earnings news arrival and market seasonalities leads to conjectures about informational reasons for observed seasonalities.     

Trading volume and stock returns: A meta-analysis

I examine 468 estimates on the relationship between trading volume and stock returns reported in 44 studies. I study publication bias together with Bayesian and frequentist model averaging to explain the heterogeneity in the estimates. The results yield three key conclusions. First, publication bias distorts the findings of the primary studies. Second, the predictability of stock returns varies with different markets and stock types. Third, different data characteristics, structural variations and methodologies used drive the heterogeneity in the results of the primary articles. In particular, one should be cautious when using monthly data or VAR models.     

Forecasting stock returns: A predictor-constrained approach

We develop a novel method to impose constraints on univariate predictive regressions of stock returns. Unlike previous approaches in the literature, we implement our constraints directly on the predictor, setting it to zero whenever its value falls within the variable’s past 24-month high and low. Empirically, we find that relative to standard unconstrained predictive regressions, our approach leads to significantly larger forecast gains. We also show how a simple equal-weighted combination of our constrained forecasts leads to further improvements in forecast accuracy, generating forecasts that are more accurate than those obtained using current constrained methods. Further analysis confirms that these findings are robust to the presence of model instabilities and structural breaks.     

Predicting stock returns in the presence of COVID-19 pandemic: The role of health news

This study derives its motivation from the current global pandemic, COVID-19, to evaluate the relevance of health-news trends in the predictability of stock returns. We demonstrate this by using data covering top-20 worst-hit countries, distinctly in terms of reported cases and deaths. The results reveal that the model that incorporates health-news index outperforms the benchmark historical average model, indicating the significance of health news searches as a good predictor of stock returns since the emergence of the pandemic. We also find that accounting for “asymmetry” effect, adjusting for macroeconomic factors and incorporating financial news improve the forecast performance of the health news-based model. These results are consistently robust to data sample (both for the in-sample and out-of-sample forecast periods), outliers and heterogeneity.     

A monthly effect in stock returns

The mean return for stocks is positive only for days immediately before and during the first half of calendar months, and indistinguishable from zero for days during the last half of the month. This ‘monthly effect’ is independent of other known calendar anomalies such as the January effect documented by others and appears to be caused by a shift in the mean of the distribution of returns from days in the first half of the month relative to days in the last half.     

The effect of macroeconomic news on stock returns: New evidence from newspaper coverage

Previous literature has produced weak evidence to support the hypothesis that real economic news affects stock returns. This is, in part, attributed to the difficulty of measuring how investors interpret macroeconomic announcements in different economic environments. In this paper, we choose a different approach of measuring macroeconomic news to better estimate its effect on stock returns. Since newspaper stories provide an interpretation of the statistical releases, we choose newspaper stories as our measure of news. Our findings indicate that news about GDP and unemployment does affect stock returns.     

Explanatory factors of the inflation news impact on stock returns by sector: The Spanish case

We study the short run response of daily stock prices on the Spanish market to the announcements of inflation news at an industrial level, deepening the potential explanatory factors of this response (risk-free interest rate, risk premium and growth expectations). We observe a positive and significant response of the stock returns in case of “bad news” (total inflation rate higher than expected one) in recession, and also in case of negative inflation surprises (“good news”) in non-economic recession. This behaviour is consistent with the evolution of the company dividend growth expectations, since we observe that the relationship between this theoretical component of the stock price and the unexpected inflation, to a large extent, seems to explain the observed behaviour.     

Speculative trading and stock returns: A stochastic dominance analysis of the Chinese A-share market

The pricing of A-shares in China has long puzzled financial economists. This paper applies recent tests of stochastic dominance (SD) to examine whether differences in the return distributions of A- and B-shares in China are consistent with market efficiency. As SD is nonparametric, market efficiency can be examined without the joint test problem arising from misspecifications in the asset pricing benchmark. Our results show A-shares have second-order dominated B-shares from 1996 to 2005. This dominance was most significant during the market segmentation period, but has continued, albeit to a lesser extent even after the B-share market was opened to local investors in 2001. Our results are robust to using residual returns from an international asset pricing model instead of raw returns. We conclude that the superior performance of A-shares cannot be attributed to risk. The results are more likely due to a return bias caused by intense speculation among retail individuals under limited arbitrage.     

A Bayesian analysis of a variance decomposition for stock returns

We apply Bayesian methods to study a common vector autoregression (VAR)-based approach for decomposing the variance of excess stock returns into components reflecting news about future excess stock returns, future real interest rates, and future dividends. We develop a new prior elicitation strategy, which involves expressing beliefs about the components of the variance decomposition. Previous Bayesian work elicited priors from the difficult-to-interpret parameters of the VAR. With a commonly used data set, we find that the posterior standard deviations for the variance decomposition based on these previously used priors, including “non-informative” limiting cases, are much larger than classical standard errors based on asymptotic approximations. Therefore, the non-informative researcher remains relatively uninformed about the variance decomposition after observing the data. We show the large posterior standard deviations arise because the “non-informative” prior is implicitly very informative in a highly undesirable way. However, reasonably informative priors using our elicitation method allow for much more precise inference about components of the variance decomposition.     

Predicting stock returns: A risk measurement perspective

This paper proposes a new and efficient model selection strategy to obtain significant stock returns predictability from a risk measurement perspective. The risk interval is defined as the distance between the current actual return and the returns' historical average. The model selection strategy involves switching stock return forecasting models according to different risk intervals from the mean reversion and extreme value theory. This new strategy generates encouraging results in the empirical analysis. A mean-variance investor can realize sizeable economic gains by allocating assets through this new approach relative to competing forecasting models. Furthermore, the strategy performs robustly under alternative settings from both statistical and economic perspectives.     

Co-movement of Asia-Pacific with European and US stock market returns: A cross-time-frequency analysis

We investigate the co-movement of 13 Asia-Pacific stock market returns with that of European and US stock market returns using the wavelet coherence method. Our results show consistent co-movement between most of the Asia-Pacific stock markets and that of Europe and the US in the long run. We also uncover evidence of a wide variation in co-movement across the time scale of the financial crises. The co-movement dynamics of the Asia-Pacific markets with that of Europe and the US are different during the two financial crises. The difference in the co-movement dynamics could be the result of the different natures of the financial crises or a change in regime.     

An analysis of inflation and stock returns for the UK

Whether common stocks provide a hedge against inflation has been long debated. This paper focuses on this question by investigating the relationship between inflation and stock returns in the short term and medium term and under different inflationary regimes using the UK data. Empirical evidence suggests that the UK stock market fails to hedge against inflation in the short term. However, in the medium term there is mixed results. Results from different inflationary regimes show that the relationship between inflation and returns varies in different regimes.