Earnings forecasts and idiosyncratic volatilities

We test the theoretical relation between idiosyncratic return volatilities and the volatilities of cash-flow news based on the expected returns on equity (ROE) for CRSP stocks over the period 1977–2008. Consistent with economic intuition, we find that using analyst forecasts of earnings is superior to using realized earnings to proxy for market expectations about future cash flow news. Our findings are consistent with a market where stock return volatilities are positively and asymmetrically related to changes in the volatilities of expectations for a fundamental driver of cash flow news (ROE). Our findings are robust after correcting for forecast biases, various fundamental variables, newly-listed and mature firms, and periods with and without earnings announcements.     

Analyst versus model-based earnings forecasts: implied cost of capital applications

We find that a composite implied cost of capital (ICC) estimate – based on the earnings forecasts generated by cross-sectional models – is highly correlated with future realised returns in both portfolio- and regression-based tests. By contrast, we find very little evidence for an association with future realised returns for an ICC estimate based on analyst earnings forecasts. We also document the time-varying nature of expected returns and risk premia, and provide up-to-date estimates of an implied Australian market risk premium.     

Accounting Ratios and the Cross‐section of Expected Stock Returns

Under clean‐surplus accounting, the log return on a stock can be decomposed into a linear function of the contemporaneous log return on equity, the contemporaneous log dividend–price ratio (if the stock pays a dividend), and both the contemporaneous and lagged values of the log book‐to‐market equity ratio. This paper studies the implications of this decomposition for the cross‐section of conditional expected stock returns. The empirical analysis reveals that the log accounting ratios capture cross‐sectional variation in both the conditional mean and conditional variance of log stock returns, which is consistent with the decomposition. It also brings fresh insights to the relation between firm size (market equity) and conditional expected stock returns. The evidence indicates that the conditional median return increases with firm size, while the conditional return skewness decreases with firm size. Empirically, the skewness effect outweighs the median effect, leading to the well‐documented inverse relation between size and average returns. The results of out‐of‐sample tests suggest that investors could use the information provided by the observed values of the log accounting ratios to formulate more effective portfolio strategies.     

Earnings dispersion and aggregate stock returns

This paper studies the relation between aggregate stock returns and contemporaneous and future cross-sectional earnings dispersion. We hypothesize that increases in expected earnings dispersion signal increases in uncertainty and increases in unemployment, thereby causing expected returns to rise, which in turn causes prices to decline. We find a positive relation between aggregate stock returns and contemporaneous earnings dispersion because higher earnings dispersion is associated with higher expected returns. Consequently, we also find a negative relation between aggregate stock returns and future (one-year ahead) earnings dispersion, as investors anticipate higher future earnings dispersion and higher expected returns.     

STOCK RETURNS AND VOLATILITY ON CHINA'S STOCK MARKETS

We examine time‐series features of stock returns and volatility, as well as the relation between return and volatility in four of China's stock exchanges. Variance ratio tests reject the hypothesis that stock returns follow a random walk. We find evidence of long memory of returns. Application of GARCH and EGARCH models provides strong evidence of time‐varying volatility and shows volatility is highly persistent and predictable. The results of GARCH‐M do not show any relation between expected returns and expected risk. Daily trading volume used as a proxy for information arrival time has no significant explanatory power for the conditional volatility of daily returns. JEL classification: G15     

There is a risk-return trade-off after all

This paper studies the intertemporal relation between the conditional mean and the conditional variance of the aggregate stock market return. We introduce a new estimator that forecasts monthly variance with past daily squared returns, the mixed data sampling (or MIDAS) approach. Using MIDAS, we find a significantly positive relation between risk and return in the stock market. This finding is robust in subsamples, to asymmetric specifications of the variance process and to controlling for variables associated with the business cycle. We compare the MIDAS results with tests of the intertemporal capital asset pricing model based on alternative conditional variance specifications and explain the conflicting results in the literature. Finally, we offer new insights about the dynamics of conditional variance.     

The pricing of conservative accounting and the measurement of conservatism at the firm-year level

This paper analyzes the relation between equity prices and conditional conservatism and introduces a new measure of conservatism at the firm-year level. We show that the asymmetric properties of conservative accounting, the existence of non-accounting sources of information, and the properties of GAAP related to special items combine to generate a nonlinear relation between unexpected equity returns and earnings news (the shock to expected current and future earnings). Based on this model, we construct a conservatism ratio (CR) defined as the ratio of the current earnings shock to earnings news. CR measures the proportion of the total shock to expected current and future earnings recognized in current year earnings. Ranking firms according to CR, we show empirically that higher CR firms have more leverage, increased volatility of returns, more incidence of losses, more negative accruals, and increased volatility of earnings and accruals, consistent with the literature on conservative accounting.     

Accruals and the Conditional Equity Premium

Accruals correlate closely with the determinants of the conditional equity premium at both the firm and the aggregate levels. The common component of firm‐level accruals, which cannot be diversified away by aggregation, explains the positive relation between aggregate accruals and future stock market returns. The residual component, which accounts for most variation in firm‐level accruals, is responsible for the negative cross‐sectional relation between firm‐level accruals and future stock returns. Consistent with the risk‐based explanation, aggregate accruals, as a proxy for the conditional equity premium, forecast changes in aggregate economic activity. Moreover, we document a similar comovement of earnings with the conditional equity premium at both the firm and the aggregate levels, which helps explain the negative relation between changes in aggregate earnings and contemporaneous market returns.     

Do corporations manage earnings to meet/exceed analyst forecasts? Evidence from pension plan assumption changes

A significantly larger number of firms increase the expected rate of return on pension plan assets (ERR) to make their reported earnings meet/exceed analyst forecasts than would be expected by chance. In the short run, the stock market reacts positively to these firms’ earnings announcements, suggesting that investors fail to recognize that earnings benchmarks are achieved through ERR manipulation. In the long run, however, firms that employ this earnings management strategy significantly underperform control firms in both stock returns and operating performance.     

Accounting-based downside risk and expected stock returns: Evidence from China

We document that earnings downside risk contains information on firms' future operating performance and is positively associated with expected stock returns in Chinese stock markets, and the return predictability of earning downside risk mainly comes from its accrual downside risk component. The pricing of earnings downside risk is especially evident among firms with more transparent information environment and stronger governance efficacy, such as large firms, non-high-tech firms, old firms, and firms with high analyst coverage. Lastly, we show that aggregated earnings downside risk and its components at the market level are all significantly and positively associated with subsequent stock market returns, which is consistent with the notion that the accounting-based downside risk measures contain information about future macroeconomic conditions.     

Earnings Surprise "Materiality" as Measured by Stock Returns

Ranked earnings surprise portfolios formed from First Call files for 1992–97 are used to assess the annual earnings surprise magnitude for an individual firm sufficient to expect a "significant market reaction." We find that, for an individual firm, the maximum probability of a gain from trading on prior knowledge of any surprise magnitude is .622. The lack of probable trading gains is due to the S–shaped surprise/return relation and the large variance of returns for a given magnitude of surprise. In turn, we find that the S–shape is related empirically to the dispersion of analyst forecasts. Thus, factors underlying dispersion differences are related to the importance or "materiality" of earnings surprise as measured by stock returns and explain at least part of the S–shaped surprise/return relation.     

Predicting firm level stock returns: Implications for asset pricing and economic links

We examine the predictive ability of stock price ratios, stock return dispersion and distribution measures for firm level returns. Analysis typically focusses on market level returns, however, for the underlying asset pricing model to hold, firm-level predictability should be present. Additionally, we examine the economic content of predictability by considering whether the predictive coefficient has the theoretically correct sign and whether it is related to future output growth. While stock returns reflect investor expectations regarding future economic conditions, they are often too noisy to act as predictor. We use the time-varying predictive coefficient as it reflects investor confidence in the predictive relation. Results suggest that a subset of stock price ratios have predictive power for individual firm stock returns, exhibit the correct coefficient sign and has predictive power for output growth. Each of these ratios has a measure of fundamentals divided by the stock price and has a positive relation with stock returns and output growth. This implies that as investors expect future economic conditions to improve and earnings and dividends to rise, so expected stock returns will increase. This supports the cash flow channel as the avenue through which stock return predictability arises.     

Comparing the Post–Earnings Announcement Drift for Surprises Calculated from Analyst and Time Series Forecasts

Post–earnings announcement drift is the tendency for a stock's cumulative abnormal returns to drift in the direction of an earnings surprise for several weeks following an earnings announcement. We show that the drift is significantly larger when defining the earnings surprise using analysts' forecasts and actual earnings from I/B/E/S than when using a time series model based on Compustat earnings data. Neither Compustat's policy of restating earnings nor the inclusion of “special items” in reported earnings contribute significantly to the disparity in drift magnitudes. Rather, our results suggest that this disparity is attributable to differences between analyst forecasts and those of time‐series models—or at least to factors correlated with these differences. Further, we document that analyst forecasts lead to return patterns around future earnings announcements that differ from those observed when using time‐series models, suggesting that the two types of surprises may capture somewhat different forms of mispricing.     

On the distribution and conditional heteroscedasticity in Taiwan stock prices

This paper studies the distribution and conditional heteroscedasticity in stock returns on the Taiwan stock market. Apart from the normal distribution, in order to explain the leptokurtosis and skewness observed in the stock return distribution, we also examine the Student-t, the Poisson–normal, and the mixed-normal distributions, which are essentially a mixture of normal distributions, as conditional distributions in the stock return process. We also use the ARMA (1,1) model to adjust the serial correlation, and adopt the GJR–generalized autoregressive conditional heteroscedasticity (GARCH (1,1)) model to account for the conditional heterscedasticity in the return process. The empirical results show that the mixed–normal–GARCH model is the most probable specification for Taiwan stock returns. The results also show that skewness seems to be diversifiable through portfolio. Thus the normal–GARCH or the Student-t–GARCH model which involves symmetric conditional distribution may be a reasonable model to describe the stock portfolio return process¹.     

Long‐Term Evidence on the Effect of Aggregate Earnings on Prices

We examine the time‐series properties and determinants of the relation between aggregate earnings information and stock prices (aggregate earnings response coefficient or AERC) employing return decompositions with data since 1871. We confirm that AERC is negative even though firms respond positively to individual firm earnings information, but we also find that AERC is time varying. Furthermore, we show that AERC components based on expected earnings, cash flows, and discount rates are also time varying and differ in relative importance.     

News Arrival, Jump Dynamics, and Volatility Components for Individual Stock Returns

This paper models components of the return distribution, which are assumed to be directed by a latent news process. The conditional variance of returns is a combination of jumps and smoothly changing components. A heterogeneous Poisson process with a time‐varying conditional intensity parameter governs the likelihood of jumps. Unlike typical jump models with stochastic volatility, previous realizations of both jump and normal innovations can feed back asymmetrically into expected volatility. This model improves forecasts of volatility, particularly after large changes in stock returns. We provide empirical evidence of the impact and feedback effects of jump versus normal return innovations, leverage effects, and the time‐series dynamics of jump clustering.     

The Conditional Beta and the Cross-Section of Expected Returns

We examine the cross-sectional relation between conditional betas and expected stock returns for a sample period of July 1963 to December 2004. Our portfolio-level analyses and the firm-level cross-sectional regressions indicate a positive, significant relation between conditional betas and the cross-section of expected returns. The average return difference between high- and low-beta portfolios ranges between 0.89% and 1.01% per month, depending on the time-varying specification of conditional beta. After controlling for size, book-to-market, liquidity, and momentum, the positive relation between market beta and expected returns remains economically and statistically significant.     

Beyond accounting and back: An empirical examination of the relative relevance of earnings and “other” information

Ohlson (1995) models firm value as a function of book value, earnings, and analysts' earnings forecasts which capture “other” information not yet reflected in the financial statements. Within this framework, stock returns reflect information from earnings and forecasts, each of which is different in terms of reliability and timeliness. For the period 1984–2012, this paper examines time trends and the influence of aggregate market conditions on the relative relevance of earnings and forecasts. In this context, relative relevance is defined as the incremental explanatory power of earnings or forecasts, relative to their combined explanatory power with respect to the cross-section of stock returns. This inquiry is motivated by anecdotal evidence and recent research, which suggests that aggregate market conditions influence the usefulness of accounting information for investors. The findings show that while the relative relevance of earnings has remained stable, the relative relevance of forecasts has increased over time. I also find that the relative relevance of earnings is higher in bad years, i.e. years with low market returns or elevated market uncertainty. Overall, the results reported in this study suggest that despite the increase in the relevance of timely “other” information, investors tend to rely more on reliable accounting information during bad years.     

Time Variations and Covariations in the Expectation and Volatility of Stock Market Returns

This article investigates empirically the comovements of the conditional mean and volatility of stock returns. It extends the results in the literature by demonstrating the role of the commercial paper—Treasury yield spread in predicting time variation in volatility. The conditional mean and volatility exhibit an asymmetric relation, which contrasts with the contemporaneous relation that has been tested previously. The volatility leads the expected return, and this time series relation is documented using offset correlations, short-horizon contemporaneous correlations, and a vector autoregression. These results bring into question the value of modeling expected returns as a constant function of conditional volatility.     

Time-varying risk The case of the American computer industry

A bivariate GARCH-in-mean model for individual stock returns and the market portfolio is designed to model volatility and to test the conditional Capital Asset Pricing Model versus the conditional Residual Risk Model. We find that a univariate model of volatility for individual stock returns is misspecified. A joint modelling of the market return and the individual stock return shows that a major force driving the conditional variances of individual stocks is the history contained in the market return variance. We find that a conditional residual risk model, where the variance of the individual stock return is used to explain expected returns, is preferred to a conditional CAPM. We propose a partial ordering of securities according to their market risk using first and second order dominance criteria.