The association between qualitative management earnings forecasts and discretionary accounting in the Netherlands

This paper examines whether there is an association between discretionary accounting and the accuracy of long-run forecasts of annual earnings disclosed voluntarily by Dutch companies in the directors’ report. In particular, investigations were made of the consistency in the sign and direction of discretionary accounting techniques and qualitative earnings forecasts. Long-run forecasts are defined, for the purposes of this paper, as forecasts made at least seven months before the year-end. Although not mandatory, qualitative forecasts are released by well over 60% of the listed companies in the Netherlands. Empirical results indicate that there is consistency in the sign and direction of qualitative earnings forecasts and discretionary accounting. After adopting discretionary accounting, the forecast errors are reduced if the company can reach the management earnings forecast (target). In the event that reserves are insufficient to accomplish this goal, managers choose their next best option and take an earnings bath in order to maximize reserves available for future use. By partitioning the sample in various sub-sets it is shown that earnings management and forecast errors occur most in the extreme ranges of financial performance. Overall, the study shows that management engages in discretionary accounting to present results in line with the disclosed qualitative earnings forecasts in their directors’ reports. Whilst discretionary accounting may clearly improve the consistency of companies’ earnings forecasts released via the directors’ reports and the actual earnings, managers’ earnings forecasts are sometimes disclosed in anticipation of planned discretionary accounting actions.     

Public regulatory reform and management earnings forecasts in a low private litigation environment

We examine the impact of continuous disclosure regulatory reform on the likelihood, frequency and qualitative characteristics of management earnings forecasts issued in New Zealand’s low private litigation environment. Using a sample of 720 earnings forecasts issued by 94 firms listed on the New Zealand Exchange before and after the reform (1999–2005), we provide strong evidence of significant changes in forecasting behaviour in the post‐reform period. Specifically, firms were more likely to issue earnings forecasts to pre‐empt earnings announcements and, in contrast to findings in other legal settings, those earnings forecasts exhibited higher frequency and improved qualitative characteristics (better precision and accuracy). An important implication of our findings is that public regulatory reforms may have a greater benefit in a low private litigation environment and thus add to the global debate about the effectiveness of alternative public regulatory reforms of corporate requirements.     

The Role of Supplementary Statements with Management Earnings Forecasts

We investigate managers' decisions to supplement their firms' management earnings forecasts. We classify these supplementary disclosures as qualitative “soft talk” disclosures or verifiable forward‐looking statements. We find that managers provide soft talk disclosures with similar frequency for good and bad news forecasts but are more likely to supplement good news forecasts with verifiable forward‐looking statements. We examine the market response to these forecasts and find that bad news earnings forecasts are always informative but that good news forecasts are informative only when supplemented by verifiable forward‐looking statements, supporting our argument that these statements bolster the credibility of good news forecasts.     

Adaptive learning, forecast-based instrument rules and monetary policy

This paper argues that recently popular forecast-based instrument rules for monetary policy may fail to stabilize economic fluctuations. In a New Keynesian model of output gap and inflation determination in which private agents face multi-period decision problems, but have non-rational expectations and learn over time, if the monetary authority adopts a forecast-based instrument rule and responds to observed private forecasts then this class of policies frequently induce divergent learning dynamics. A central bank that correctly understands private behavior can mitigate such instability by responding to the determinants of private forecasts. This suggests gathering information on the determinants of expectations to be useful.     

Management earnings forecasts and analyst forecasts:Evidence from mandatory disclosure system

Distinct from the literature on the effects that management earnings forecasts(MEFs) properties, such as point, range and qualitative estimations, have on analy...     

Backtesting extreme value theory models of expected shortfall

We use stock market data to analyze the quality of alternative models and procedures for forecasting expected shortfall (ES) at different significance levels. We compute ES forecasts from conditional models applied to the full distribution of returns as well as from models that focus on tail events using extreme value theory (EVT). We also apply the semiparametric filtered historical simulation (FHS) approach to ES forecasting to obtain 10-day ES forecasts. At the 10-day horizon we combine FHS with EVT. The performance of the different models is assessed using six different ES backtests recently proposed in the literature. Our results suggest that conditional EVT-based models produce more accurate 1-day and 10-day ES forecasts than do non-EVT based models. Under either approach, asymmetric probability distributions for return innovations tend to produce better forecasts. Incorporating EVT in parametric or semiparametric approaches also improves ES forecasting performance. These qualitative results are also valid for the recent crisis period, even though all models then underestimate the level of risk. FHS narrows the range of numerical forecasts obtained from alternative models, thereby reducing model risk. Combining EVT and FHS seems to be best approach for obtaining accurate ES forecasts.     

An Empirical Analysis of Qualitative Management Earnings Forecasts

Despite efforts by the Securities and Exchange Commission (SEC) to encourage corporate disclosure of quantitative management earnings projections, only a small fraction of firms voluntarily do so. Instead of quantitative estimates, a large number of firms choose to disclose qualitative (verbal) assessments of their earnings prospects. This paper is a study of the information characteristics and the usefulness of this alternative form of forecast disclosure to investors. The study examines a sample of qualitative forecast statements from the 1979–1985 period and finds associations between these forecasts and percentage changes in realized earnings per share, the direction of financial analysts' forecast revisions following the disclosure of these forecasts, and abnormal stock returns on the date of their disclosure. These associations are, however, shown to be more significant for negative (bad news) than for positive (good news) forecasts.     

Confirming Management Earnings Forecasts, Earnings Uncertainty, and Stock Returns

In this study we examine the association among confirming management forecasts, stock prices, and analyst expectations. Confirming management forecasts are voluntary disclosures by management that corroborate existing market expectations about future earnings. This study provides evidence that these voluntary disclosures affect stock prices and the dispersion of analyst expectations. Specifically, we find that the market's reaction to confirming forecasts is significantly positive, indicating that benefits accrue to firms that disclose such forecasts. In addition, although we find no significant change in the mean consensus forecasts (a proxy for earnings expectations) around the confirming forecast date, evidence indicates a significant reduction in the mean and median consensus analyst dispersion (a proxy for earnings uncertainty). Finally, we document a positive association between the reduction of dispersion of analysts' forecasts and the magnitude of the stock market response. Overall, the evidence suggests that confirming forecasts reduce uncertainty about future earnings and that investors price this reduction of uncertainty.     

Components of Market Risk and Return

This article proposes a flexible but parsimonious specificationof the joint dynamics of market risk and return to produce forecastsof a time-varying market equity premium. Our parsimonious volatilitymodel allows components to decay at different rates, generatesmean-reverting forecasts, and allows variance targeting. Thesefeatures contribute to realistic equity premium forecasts forthe U.S. market over the 1840–2006 period. For example,the premium forecast was low in the mid-1990s but has recentlyincreased. Although the market's total conditional variancehas a positive effect on returns, the smooth long-run componentof volatility is more important for capturing the dynamics ofthe premium. This result is robust to univariate specificationsthat condition on either levels or logs of past realized volatility(RV), as well as to a new bivariate model of returns and RV.     

Volatility forecasts and at-the-money implied volatility: a multi-component ARCH approach and its relation to market models

This article explores the relationships between several forecasts for the volatility built from multi-scale linear ARCH processes, and linear market models for the forward variance. This shows that the structures of the forecast equations are identical, but with different dependencies on the forecast horizon. The process equations for the forward variance are induced by the process equations for an ARCH model, but postulated in a market model. In the ARCH case, they are different from the usual diffusive type. The conceptual differences between both approaches and their implication for volatility forecasts are analysed. The volatility forecast is compared with the realized volatility (the volatility that will occur between date t and t + ΔT), and the implied volatility (corresponding to an at-the-money option with expiry at t + ΔT). For the ARCH forecasts, the parameters are set a priori. An empirical analysis across multiple time horizons ΔT shows that a forecast provided by an I-GARCH(1) process (one time scale) does not capture correctly the dynamics of the realized volatility. An I-GARCH(2) process (two time scales, similar to GARCH(1,1)) is better, while a long-memory LM-ARCH process (multiple time scales) replicates correctly the dynamics of the implied and realized volatilities and delivers consistently good forecasts for the realized volatility.     

Key performance indicators as supplements to earnings: Incremental informativeness,demand factors,measurement issues,and properties of their forecasts

The documented decline in the information content of earnings numbers has paralleled the emergence of disclosures, mostly voluntary, of industry-specific key performance indicators (KPIs). We find that the incremental information content conveyed by KPI news is significant for many KPIs yet diminished when details about the computation of the KPI are absent or when the computation changes over time. Consistent with analysts responding to investor information demand, we find that analysts are more likely to produce forecasts for a KPI when that KPI has more information content and when earnings are less informative. We also analyze the properties of analysts’ KPI forecasts and find that KPI forecasts are more accurate than mechanical forecasts and their accuracy exceeds that of earnings forecasts. Our study contributes to the literature on the information content of KPIs as well as research on the properties of analysts’ forecasts. We provide evidence on whether and how to regulate voluntary disclosures.

     

Volatility forecasting in the Chinese commodity futures market with intraday data

Given the unique institutional regulations in the Chinese commodity futures market as well as the characteristics of the data it generates, we utilize contracts with three months to delivery, the most liquid contract series, to systematically explore volatility forecasting for aluminum, copper, fuel oil, and sugar at the daily and three intraday sampling frequencies. We adopt popular volatility models in the literature and assess the forecasts obtained via these models against alternative proxies for the true volatility. Our results suggest that the long memory property is an essential feature in the commodity futures volatility dynamics and that the ARFIMA model consistently produces the best forecasts or forecasts not inferior to the best in statistical terms.     

Macroeconomic news, announcements, and stock market jump intensity dynamics

This paper examines the effect of macroeconomic releases on stock market volatility through a Poisson-Gaussian-GARCH process with time-varying jump intensity, which is allowed to respond to such information. The day of the announcement, per se, is found to have little impact on jump intensities. Employment releases are an exception. However, when macroeconomic surprises are considered, inflation shocks show persistent effects while monetary policy and employment shocks reveal only short-lived effects. Also, the jump intensity responds asymmetrically to macroeconomic shocks. Evidence on macroeconomic variables relevance in explaining jump dynamics and improving volatility forecasts on event days is provided.     

Asymmetry,Complementarities, and State Dependence in Federal Reserve Forecasts

Forecasts are a central component of policymaking; the Federal Reserve's forecasts are published in a document called the Greenbook. Previous studies of the Greenbook's inflation forecasts have found them to be rationalizable but asymmetric if considering particular subperiods, for example, before and after the Volcker appointment. In these papers, forecasts are analyzed in isolation, assuming policymakers value them independently. We analyze the Greenbook forecasts in a framework in which the forecast errors for different variables are allowed to interact. We find that allowing the losses to interact makes the unemployment forecasts virtually symmetric, the output forecasts symmetric prior to the Volcker appointment, and the inflation forecasts symmetric after the onset of the Great Moderation.     

Analysts' Cash Flow Forecasts,Audit Effort,and Audit Opinions on Internal Control

We examine the economic impact of analysts’ cash flow forecasts by looking at how external auditors respond to financial analysts’ issuance of cash flow forecasts. Using a differences‐in‐differences approach, we find that financial analysts’ initiation of cash flow forecasts leads to reduced auditor fees and audit report lags. Moreover, after cash flow forecast initiation, firms report fewer Section 404(b) internal control weakness disclosures. These findings suggest that cash flow forecasts constrain earnings manipulation and improve management accounting behavior, thereby reducing inherent and control risk and strengthening firms’ internal control over financial reporting.     

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.     

Index Insurance,Probabilistic Climate Forecasts,and Production

Index insurance and probabilistic seasonal forecasts are becoming available in developing countries to help farmers manage climate risks in production. Although these tools are intimately related, work has not been done to formalize the connections between them. We investigate the relationship between the tools through a model of input choice under uncertainty, forecasts, and insurance. While it is possible for forecasts to undermine insurance, we find that when contracts are appropriately designed, there are important synergies between forecasts, insurance, and effective input use. Used together, these tools overcome barriers preventing the use of imperfect information in production decision making.     

Can credit spreads help predict a yield curve?

In this paper we investigate whether information in credit spreads helps improve the forecasts of government bond yields. To do this, we propose and estimate a joint dynamic Nelson–Siegel (DNS) model of the U.S. Treasury yield curve and the credit spread curve. The model accounts for the possibility of regime changes in yield curve dynamics and incorporates a zero lower bound constraint on yields. We show that our joint model produces more accurate out-of-sample density forecasts of bond yields than does the yield-only DNS model. In addition, we demonstrate that incorporating regime changes and a zero lower bound constraint is essential for forecast improvements.     

Heterogeneous Beliefs, the Term Structure and Time-varying Risk Premia

This paper demonstrates theoretically and empirically that one possible economic explanation of the dynamics of the term structure of interest rates is the time-varying heterogeneous beliefs about future economic conditions. Assuming that each agent forms heterogeneous expectations about both his income shock and others’ beliefs about their income shocks each period, the paper illustrates that heterogeneous beliefs generate time-varying risk premia of the term structure in a closed-form solution. Motivated by this theory, several empirical tests are conducted using the cross-sectional mean and dispersion of belief indices that are extracted as the differences between non-judgemental econometric forecasts based on diffusion indices in Stock and Watson (J Bus Econ Stat, 2002) and professional survey forecasts. It is shown that (a) an increase in the mean belief about inflation steepens the yield curve, (b) the mean and dispersion of interest rate beliefs help explain the mean and the stochastic volatility of the term structure, suggesting that time-varying risk premia may be explained by endogenous uncertainty caused by heterogeneous beliefs in the economy.I am indebted to Mordecai Kurz, Timothy Cogley and Narayana Kocherlakota for constant support and extensive discussions that inspired this work. I would like to thank Randall Moore for providing the Blue Chip financial forecast data. The financial support from the Smith Richardson Foundation to the Stanford Institute for Economic Policy Research is gratefully acknowledged.     

Optimistic bias in management forecasts by Japanese firms to avoid forecasting losses

Listed firms in Japan are effectively compelled to report management forecasts of sales, ordinary income, and net income along with actual earnings and sales each year. Prior studies report that Japanese managers tend to announce optimistic forecasts of earnings. We show that a large part (61.6%) of the overall optimistic bias in management earnings forecasts in Japan can be explained by loss forecast avoiding behavior of a small fraction (5.25%) of firms. Such behavior is caused in part by the view of the main bank and power group that the management forecast of earnings is the manager's earnings target. Our findings suggest that the Japanese stock market recognizes such loss forecast avoidance and accordingly discounts new information in management forecasts.