Overstated Quarterly Earnings and Analysts' Earnings Forecast Revisions*

A primary purpose of accounting is to provide information for decision makers. Accounting misstatements may have a detrimental effect on decision making. The Securities and Exchange Commission (SEC) identifies earnings overstatements as being particularly troublesome to users, as indicated by SEC Accounting and Auditing Enforcement Releases' emphasis on earnings' overstatement errors. This research investigates how security analysts' forecast revisions are affected by accounting earnings overstatement errors, which become known only after the analysts released their revised annual earnings forecasts. The paper investigates the clarifying role that additional information plays in analysts' revisions. The results show that analysts draw significantly different conclusions from earnings containing (unknown) overstatement errors than from accurately reported earnings. In essence, the analysts identify some of the overstatement, at least on average, by making an adjustment that effectively ignores 21 percent of the overstatement error.     

Evidence on the Superiority of Analysts Quarterly Earnings Forecasts for Small Capitalization Firms*

Financial analysts provide information to support investment analysis and decisions for an ever increasing number of firms. As part of their services they also produce earnings forecasts for covered firms. While there has been much research investigating the determinants of financial analyst earnings forecast superiority for large, widely-followed firms, little research has focused on smaller firms. Until recently, these smaller firms have been largely ignored. This study focuses exclusively on small firms and provides evidence of differing behavior for such firms compared to results previously reported for large firms. Errors in quarterly earnings per share forecasts of small firms obtained from a univariate time-series model are also examined. Regression results indicate that time-series model parameters possess information content with respect to forecast accuracy for analyst-covered firms only. These results are obtained after controlling for firm size, model adequacy, and industry, quarter, and year effects. This suggests that analysts are more likely to cover small firms for which they are able to decipher information correlated with that impounded in the “shocks” in the quarterly earnings time series as captured by the time-series model parameters.     

Judgmental Adjustments of Previously Adjusted Forecasts*

Forecasts are important components of information systems. They provide a means for knowledge sharing and thus have significant decision‐making impact. In many organizations, it is quite common for forecast users to receive predictions that have previously been adjusted by providers or other users of forecasts. Current work investigates some of the factors that may influence the size and propensity of further adjustments on already‐adjusted forecasts. Two studies are reported that focus on the potential effects of adjustment framing (Study 1) and the availability of explanations and/or original forecasts alongside the adjusted forecasts (Study 2). Study 1 provides evidence that the interval forecasts that are labeled as “adjusted” are modified less than the so‐called “original/unadjusted” predictions. Study 2 suggests that the provision of original forecasts and the presence of explanations accompanying the adjusted forecasts serve as significant factors shaping the size and propensity of further modifications. Findings of both studies highlight the importance of forecasting format and user perceptions with critical organizational repercussions.     

Combining Forecasts: Multiple Regression versus a Bayesian Approach*

Simple linear combinations of forecasts have consistently been found to be more accurate than individual forecasts. Several recent studies have found that combination forecasts derived by constrained or unconstrained multiple regression are more accurate than a simple average of individual forecasts. This study uses macroeconomic data to compare the accuracy of combination forecasts derived by a Bayesian methodology with the accuracy of composite forecasts derived by multiple regression. Using the forecasts of four macroeconomic variables from five well-known econometric models, the study finds that the Bayesian combination procedure produces more accurate composite forecasts than does the regression combination procedure, based on a version of Theil's U² statistic.     

An Approximate Bayesian Algorithm for Combining Forecasts*

In this paper we propose a consensus forecasting method based on a convex combination of individual forecast densities. The exact Bayesian updating of the convex combination weights is very complex and practically prohibitive. We propose a simple sequential updating alternative method based on function approximation. Several examples illustrate the method.     

An Empirical Estimation of the Investor Loss Function Associated with the Use of Analysts' Forecasts of Earnings

The aggregate investor loss function associated with the use of analysts' forecasts of earnings expresses the relation between loss and prediction error. Prior research suggested that the shape of this loss function should provide guidance in decision rules for investors. This study empirically estimates the investor loss function and finds an asymmetry in the relation between loss and analysts' prediction error. For overestimates of earnings, loss is a quadratic function of error; while for underestimates of earnings, loss is a linear function of error.     

Neural Network Earnings per Share Forecasting Models: A Comparative Analysis of Alternative Methods

In this paper, we present a comparative analysis of the forecasting accuracy of univariate and multivariate linear models that incorporate fundamental accounting variables (i.e., inventory, accounts receivable, and so on) with the forecast accuracy of neural network models. Unique to this study is the focus of our comparison on the multivariate models to examine whether the neural network models incorporating the fundamental accounting variables can generate more accurate forecasts of future earnings than the models assuming a linear combination of these same variables. We investigate four types of models: univariate‐linear, multivariate‐linear, univariate‐neural network, and multivariate‐neural network using a sample of 283 firms spanning 41 industries. This study shows that the application of the neural network approach incorporating fundamental accounting variables results in forecasts that are more accurate than linear forecasting models. The results also reveal limitations of the forecasting capacity of investors in the security market when compared to neural network models.     

Development of a Prediction Model for Hospital Closure Using Financial Accounting Data*

Numerous research studies have examined the use of financial accounting data in the prediction of corporate bankruptcy. Partly due to a lack of available data, however, little work has been done in developing a closure prediction model specifically for hospitals. Using cost reports from the Health Care Financing Administration and a sample of 71 closed hospitals and a matched sample of 71 open hospitals, the current study examines the relationship between 21 financial accounting ratios and hospital closure. Univariate logit results indicate that hospital closure is significantly related to 17 of the 21 ratios one year prior to closure. Results are also presented using a multivariate model, and for the relationships two years prior to closure. The current study provides information helpful to users in identifying financial variables which may be important indicators of hospital closure.     

预测信息披露与盈余管理

本文从信息披露的角度出发,通过一个委托-代理模型分析预测信息的披露与盈余管理的关系。我们的分析认为,由于我国上市公司管理层披露的预测信息在一定程度上减少了管理层与投资者间的信息不对称程度,且管理层故意错误地披露预测信息需要承担额外的成本。因此,在一定的条件下,管理层披露预测信息将有利于减少管理层盈余管理行为。相关的实证研究数据也证实了我们的结论。     

What to Learn from Near Misses: An Inductive Learning Approach to Credit Risk Assessment

This paper presents a new dimension of inductive learning for credit risk analysis based on the specific impact of Type I and Type II credit errors on the accuracy of the learning process. A Dynamic Updating Process is proposed to refine the credit granting decision over time and therefore improve the accuracy of the learning process. The new dimension is tested on credit files of small Belgian businesses. Results indicate an improvement of the learning process in terms of predictive accuracy, stability, and conceptual validity of the final decision tree.     

REPORTING OF MANAGEMENT FORECASTS: AN EIGENVECTOR MODEL FOR ELICITATION AND REVIEW OF FORECASTS

Increasingly, business firms will be disclosing management forecasts in financial reports to investors. Single estimates based on a consensus view (e.g., Delphi estimates) may be used. It seems logical, however, to consider methods that seek formally to derive internally consistent subjective views regarding the sensitivity of assumptions and interacting events on forecasts. The major purpose of this paper is to extend micro-level cross-impact analysis to a macro-level eigenvalue analysis approach that elicits views of the effects of assumptions and interaction events on entire scenarios. Both methods are illustrated and contrasted. They are complementary rather than alternative methods. In particular, each method offers some potential in the development of operational guidelines for elicitation and reporting of management forecasts. In addition, the eigenvalue analysis offers several key advantages that make it potentially useful in preparing entire scenario forecasts.     

Combining Neural Networks and Statistical Predictions to Solve the Classification Problem in Discriminant Analysis*

A number of recent studies have compared the performance of neural networks (NNs) to a variety of statistical techniques for the classification problem in discriminant analysis. The empirical results of these comparative studies indicate that while NNs often outperform the more traditional statistical approaches to classification, this is not always the case. Thus, decision makers interested in solving classification problems are left in a quandary as to what tool to use on a particular data set. We present a new approach to solving classification problems by combining the predictions of a well-known statistical tool with those of an NN to create composite predictions that are more accurate than either of the individual techniques used in isolation.     

Structure of Uncertainty and Decision Making: An Experimental Investigation*

Decisions in the real world usually involve imprecise information or uncertainty about the precesses by which outcomes may be determined. This research reports the results of a laboratory experiment which examined whether the structure of uncertainty, namely, both the center and the range of the probability distribution describing the uncertainty, is an important determinant of choice. Specifically, it examines how the uncertainty of audit by the Internal Revenue Service of income tax returns affects taxpayers' decisions about intentional noncompliance. The context is relevant as almost nothing is known about how taxpayers assess detection risks using the probability information they have. The study focuses on intentional noncompliance. The factors affecting it are distinct and separate from those affecting unintentional noncompliance. Other factors that affect intentional tax noncompliance, such as risk, tax rates, and penalty rates, were controlled in the experiment. It was hypothesized that the lower the mean and the lesser the range (ambiguity) of the perceived audit probability, the greater the international noncompliance. As hypothesized, the analysis indicates that both the mean and the range of the perceived audit probability rate affect intentional noncompliance, though the effect of ambiguity is greater at a relatively higher level of mean. This result suggests that the strength of the information describing an uncertain event is captured better by both the mean and the range of the uncertainty than either of those components singly.     

“COMPANY FORECAST ACCURACY FOR EXPONENTIAL SMOOTHING MODELS OF EARNINGS-PER-SHARE DATA FOR FINANCIAL DECISION MAKING”: A COMMENT

The practice of abandoning all but the most accurate among a set of alternative forecasting methods is shown to result in the loss of potentially useful information. The particular case of two forecasts is considered in detail. It is demonstrated practically that the inclusion of even a relatively poor forecasting method can enhance a superior one significantly.     

投资者法律保护与公司上市后长期业绩表现--基于法和金融理论的实证分析

公司上市后长期业绩不佳是世界范围内的一个"异象",主流的理论解释集中在信息不对称和利益冲突两个问题上,可见投资者保护的增强有利于缓解这种"异象".为此,本文基于法和金融理论,以1991-200O年在上海证券交易所上市的公司为样本,实证检验了我国中小投资者法律保护发展对公司上市后长期业绩的影响.研究结果表明,随着我国中小投资者法律保护的发展,公司上市后长期业绩表现不佳的"异象"有所缓解.     

Improving Revenue Management Decision Making for Airlines by Evaluating Analyst‐Adjusted Passenger Demand Forecasts*

To maximize revenue, airline revenue management analysts (RMAs) attempt to protect the right number of seats for late‐booking, high‐revenue‐generating passengers from low‐valued leisure passengers. Simulation results in the past showed that a major airline can generate approximately $500 million per year through efficient RM operations. Accurate passenger demand forecasts are required, because reduced forecast error significantly improves revenue. RMAs often adjust the system forecasts to improve revenue opportunity. Analysis of system forecast performance and analyst adjustment is complex, because one must account for all unseen demands throughout the life of a flight. This article proposes a method to account for unseen demand and evaluate forecast performance (adjusted or unadjusted) through a forecast monitoring system. Initial results from one major airline's origin‐destination market data justify the value of RMA forecasting adjustments.     

Judgmental Forecasting: Cognitive Reflection and Decision Speed

This research analyzes how individual differences affect performance in judgmental time‐series forecasting. Decision makers with the ability to balance intuitive judgment with cognitive deliberation, as measured by the cognitive reflection test, tend to have lower forecast errors. This relationship holds when controlling for intelligence. Furthermore, forecast errors increase for very fast or very slow decisions. We provide evidence that forecast performance can be improved by manipulating decision speed.