Mandatory and voluntary information disclosure and the effects on financial analysts: evidence from China

Purpose – This paper aims to make a comparison, different from existing literature solely focusing on voluntary earnings forecasts and ex post earnings surprise, between the effects of mandatory earnings surprise warnings and voluntary information disclosure issued by management teams on financial analysts in terms of the number of followings and the accuracy of earnings forecasts. Design/methodology/approach – This paper uses panel data analysis with fixed effects on data collected from Chinese public firms between 2006 and 2010. It uses an exogenous regulation enforcement to minimise the endogeneity problem. Findings – This paper finds that financial analysts are less likely to follow firms which mandatorily issue earnings surprise warnings ex ante than those voluntarily issue earnings forecasts. Moreover, ex post, they issue less accurate and more dispersed forecasts on former firms. The results support Brown et al.’s (2009) finding in the USA and suggest that the earnings surprise warnings affect information asymmetries. Practical implications – This paper justifies the mandatory earnings surprise warnings policy issued by Chinese Securities Regulatory Commission in 2006. Originality/value – Mandatory earnings surprise is a unique practical regulation for publicly listed firms in China. This paper, for the first time, provides empirical evaluation on the effectiveness of a mandatory information disclosure policy in China. Consistent with existing literature on information disclosure by public firms in other countries, this paper finds that, in China, voluntary information disclosure captures more private information than mandatory information disclosure on corporate earnings ability.

Earnings management in firms seeking to be acquired

Empirical evidence regarding accrual-based earnings management around mergers and acquisitions has been setting-specific as far as target firms are concerned. This might be due to the fact that target firms cannot always anticipate an acquisition proposal, and thus lack the motive and the time necessary to manage their earnings in order to facilitate or impede the deal. In this paper, we provide clear evidence of downward earnings management by a sample of target firms that have both time and motive to engage in such actions. These are firms that publicly announce their intention to be acquired. Publicly ‘seeking a buyer’ represents a rather unusual corporate event, and we find that these firms engage in downward earnings management in the years surrounding the ‘announcement year’. To some extent, this result is explained by overrepresentation of low performance and growth among these firms, and it can be interpreted under alternative explanations. Furthermore, we show that such downward earnings management negatively affects the probability for a ‘seeking buyer’ firm to secure an acquisition within a reasonable amount of time, a possible indication of efficient diligence by prospective buyers having a preference for firms ‘seeking buyer’ with no informationally obscure earnings.

Accrual-based and real earnings management before and after IFRS adoption: the case of Greece

Purpose – The purpose of this study is to examine the use of accrual-based vs real earnings management (EM) by Greek firms, before and after the mandatory adoption of International Financial Reporting Standards (IFRS). The research is motivated by the fact that past studies have indicated the existence of significant levels of EM for Greece in particular before IFRS. Design/methodology/approach – Accrual-based earnings management (AEM) is examined by assessing performance-adjusted discretionary accruals, while real earnings management (REM) is defined in terms of abnormal levels of production costs, discretionary expenses, and cash flows from operations, for a three-year period before and after the adoption of IFRS in 2005. Findings – The authors find evidence on a statistically significant shift from AEM to REM after the adoption of IFRS, indicating the replacement of one form of EM with the other. Research limitations/implications – The validity of the results depends on the ability of the empirical models used to efficiently capture the existence of AEM and REM. Practical implications – IFRS adoption aims to improve accounting quality, especially in countries with high need for such an improvement; however, the tendency to substitute one form of EM with another highlights unintended consequences of IFRS adoption, which do not improve the informational content of financial statements if EM continues under different forms. Originality/value – Under the expectation that IFRS adoption should lead to improvements in accounting quality, this study examines whether IFRS actually led to a reduction of EM practices for a country with exceptionally high levels of EM before IFRS, by accounting for all possible forms of EM.

The effect of financial leverage on real and accrual-based earnings management

Past research has documented a substitution effect between real earnings management (RM) and accrual-based earnings management (AM), depending on relative costs. This study contributes to this research by examining whether levels of (and changes in) financial leverage have an impact on this empirically documented trade-off. We hypothesise that in the presence of high leverage, firms that engage in earnings manipulation tactics will exhibit a preference for RM due to a lower possibility—and subsequent costs—of getting caught. We show that leverage levels and increases positively and significantly affect upward RM, with no significant effect on income-increasing AM, while our findings point towards a complementarity effect between unexpected levels of RM and AM for firms with very high leverage levels and changes. This is interpreted as an indication that high leverage could attract heavy outsider scrutiny, making it necessary for firms to use both forms of earnings management in order to achieve earnings targets. Furthermore, we document that equity investors exhibit a significantly stronger penalising reaction to AM vs. RM, indicating that leverage-induced RM is not as easily detectable by market participants as debt-induced AM, despite the fact that the former could imply deviation from optimal business practices.

Volatility forecasting for risk management

Recent research has suggested that forecast evaluation on the basis of standard statistical loss functions could prefer models which are sub-optimal when used in a practical setting. This paper explores a number of statistical models for predicting the daily volatility of several key UK financial time series. The out-of-sample forecasting performance of various linear and GARCH-type models of volatility are compared with forecasts derived from a multivariate approach. The forecasts are evaluated using traditional metrics, such as mean squared error, and also by how adequately they perform in a modern risk management setting. We find that the relative accuracies of the various methods are highly sensitive to the measure used to evaluate them. Such results have implications for any econometric time series forecasts which are subsequently employed in financial decisionmaking.

The European Flood Alert System and the communication, perception, and use of ensemble predictions for operational flood risk management

Although ensemble prediction systems (EPS) are increasingly promoted as the scientific state-of-the-art for operational flood forecasting, the communication, perception, and use of the resulting alerts have received much less attention. Using a variety of qualitative research methods, including direct user feedback at training workshops, participant observation during site visits to 25 forecasting centres across Europe, and in-depth interviews with 69 forecasters, civil protection officials, and policy makers involved in operational flood risk management in 17 European countries, this article discusses the perception, communication, and use of European Flood Alert System (EFAS) alerts in operational flood management. In particular, this article describes how the design of EFAS alerts has evolved in response to user feedback and desires for a hydrographic-like way of visualizing EFAS outputs. It also documents a variety of forecaster perceptions about the value and skill of EFAS forecasts and the best way of using them to inform operational decision making. EFAS flood alerts were generally welcomed by flood forecasters as a sort of ‘pre-alert’ to spur greater internal vigilance. In most cases, however, they did not lead, by themselves, to further preparatory action or to earlier warnings to the public or emergency services. Their hesitancy to act in response to medium-term, probabilistic alerts highlights some wider institutional obstacles to the hopes in the research community that EPS will be readily embraced by operational forecasters and lead to immediate improvements in flood incident management. The EFAS experience offers lessons for other hydrological services seeking to implement EPS operationally for flood forecasting and warning. Copyright © 2012 John Wiley & Sons, Ltd.

Quantile forecasts of daily exchange rate returns from forecasts of realized volatility

Quantile forecasts are central to risk management decisions because of the widespread use of Value-at-Risk. A quantile forecast is the product of two factors: the model used to forecast volatility, and the method of computing quantiles from the volatility forecasts. In this paper we calculate and evaluate quantile forecasts of the daily exchange rate returns of five currencies. The forecasting models that have been used in recent analyses of the predictability of daily realized volatility permit a comparison of the predictive power of different measures of intraday variation and intraday returns in forecasting exchange rate variability. The methods of computing quantile forecasts include making distributional assumptions for future daily returns as well as using the empirical distribution of predicted standardized returns with both rolling and recursive samples. Our main findings are that the Heterogenous Autoregressive model provides more accurate volatility and quantile forecasts for currencies which experience shifts in volatility, such as the Canadian dollar, and that the use of the empirical distribution to calculate quantiles can improve forecasts when there are shifts