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The purpose of this paper is to examine the way serial correlation in quarterly earnings forecast errors varies with firm and analyst attributes such as the firm’s industry and the analyst’s experience and brokerage house affiliation. Prior research on financial analysts’ quarterly earnings forecasts has documented serial correlation in forecast errors.

Finding that serial correlation in forecast errors is significant and seemingly independent of firm and analyst attributes, the consensus forecast errors are modeled as an autoregressive process. The model of forecast errors that best fits the data is AR(1), and the obtained autoregressive coefficients are used to predict consensus forecast errors.

Modeling the consensus forecast errors as an autoregressive process, the present study predicts future consensus forecast errors and proposes a series of refinements to the consensus.

These refinements were not presented in prior literature and can be useful to financial analysts and investors.

Given the importance of credit rating agencies’ (CRAs) assessment in affecting international financial markets, it is useful for policymakers and investors to be able to forecast it properly. Therefore, this study aims to forecast sovereign risk perception of the main agencies related to Brazilian bonds through the application of different machine learning (ML) techniques and evaluate their predictive accuracy in order to find out which one is best for this task.

Based on monthly data from January 1996 to November 2018, we perform different forecast analyses using the K-Nearest Neighbors, the Gradient Boosted Random Trees and the Multilayer Perceptron methods.

The results of this study suggest the Multilayer Perceptron technique is the most reliable one. Its predictive accuracy is relatively high if compared to the other two methods. Its forecast errors are the lowest in both the out-of-sample and in-sample forecasts’ exercises. These results hold if we consider the CRAs classification structure as linear or logarithmic. Moreover, its forecast errors are not statistically associated with periods of changes in CRAs’ opinion of any sort.

To the best of the authors’ knowledge, this study is the first to evaluate the performance of ML methods in the task of predicting sovereign credit news, including not only the sovereign ratings but also the outlook and credit watch status. In addition, the authors investigate whether the forecasts errors are statistically associated with periods of changes in sovereign risk perception.

The purpose of this study is to investigate the impacts of interval measured data, rather than traditional point data, on economic variability studies.

The study uses interval measured data to forecast the variability of future stock market changes. The variability (interval) forecasts are then compared with point data‐based confidence interval forecasts.

Using interval measured data in stock market variability forecasting can significantly increase forecasting accuracy, compared with using traditional point data.

An interval forecast for stock prices essentially consists of predicted levels and a predicted variability which can reduce perceived uncertainty or risk embedded in future investments, and therefore, may influence required returns and capital asset prices.

It has been widely documented that investors and analysts underreact to information in past earnings changes, a fundamental performance indicator. The purpose of this paper is to examine whether managers’ voluntary disclosure efficiently incorporates information in past earnings changes, whether analysts recognize and fully anticipate the potential inefficiency in management forecasts and whether managers’ potential forecasting inefficiency entirely results from intentional disclosure strategies or at least partly reflects managers’ unintentional information processing biases.

Archival data were used to empirically test the relation between management earnings forecast errors and past earnings changes.

Results show that managers underreact to past earnings changes when projecting future earnings and analysts recognize, but fail to fully anticipate, the predictable bias associated with past earnings changes in management forecasts. Moreover, analysts appear to underreact more to past earnings changes when management forecasts exhibit greater underestimation of earnings change persistence. Further analyses suggest that the underestimation of earnings change persistence is at least partly attributable to managers’ unintentional information processing bias.

This study contributes to the voluntary disclosure literature by demonstrating the limitation in the informational value of management forecasts. The findings indicate that the effectiveness of voluntary disclosure in mitigating market mispricing is inherently limited by the inefficiency in management forecasts. This study can help market participants to better use management forecasts to form more accurate earnings expectations. Moreover, our evidence suggests a managerial information processing bias with respect to past earnings changes, which may affect managers' operational, investment or financing decisions.

The purpose of this paper is to examine the relationship between Bumiputra (in reference to Malay indigenous race) directors, a proxy for culture and analysts forecast. In addition, the study investigates whether corporate governance affects that relationship.

The sample of this study is based on 664 firm-year observations from 193 firms during the 1999-2009 periods. The authors employ a panel least square regression with both period and industry fixed effects. The authors retrieved of analyst data from the Institutional Broker Estimate System (I/B/E/S) database while the authors hand collected the corporate governance variables. The remaining data were collected from Compustat Global.

The authors find a positive relationship between the proxy of culture, Bumiputra directors and analysts forecast error suggesting that cultural values influences the level of information in the Malaysian capital market.

The research is dependent on the data availability from I/B/E/S database.

The authors extend the work of Haniffa and Cooke (2002) in investigating how cultural values influence the capital market. In addition, this is the first study that investigates culture values and the analysts forecast.

Virgin Atlantic Cargo is one of the largest air freight operators in the world. As part of a wider strategic development initiative, the company has identified forecasting accuracy as of strategic importance to its operational efficiency. This is because accurate forecast enables the company to have the right resources available at the right place and time. The purpose of this paper is to undertake an evaluation of current month-to-date forecasting utilized by Virgin Atlantic Cargo. The study employed demand patterns drawn from historical data on chargeable weight over a seven-year-period covering six of the company's routes.

A case study is carried out, where a comparison between forecasting models is undertaken using error accuracy measures. Data in the form of historical chargeable weight over a seven-year-period covering six of the company's most profitable routes are employed in the study. For propriety and privacy reasons, data provided by the company have been sanitized.

Preliminary analysis of the time series shows that the air cargo chargeable weight could be difficult to forecast due to demand fluctuations which appear extremely sensitive to external market and economic factors.

The study contributes to existing literature on air cargo forecasting and is therefore of interest to scholars examining the problems of overbooking. Overbooking which is employed by air cargo operators to hedge against “no-show” bookings. However, the inability of air cargo operators to accurately predict cargo capacity unlikely to be used implies that operators are unable to establish with an aspect of certainty their revenue streams. The research methodology adopted is also predominantly discursive in that it employs a synthesis of existing forecasting literature and real-life data for accuracy analysis.

Accurate prediction of order demand across omni-channel supply chains improves the management's decision-making ability at strategic, tactical and operational levels. The paper aims to develop a predictive methodology for forecasting near-real-time e-commerce order arrivals in distribution centres, allowing third-party logistics service providers to manage the hour-to-hour fast-changing arrival rates of e-commerce orders better.

The paper proposes a novel machine learning predictive methodology through the integration of the time series data characteristics into the development of an adaptive neuro-fuzzy inference system. A four-stage implementation framework is developed for enabling practitioners to apply the proposed model.

A structured model evaluation framework is constructed for cross-validation of model performance. With the aid of an illustrative case study, forecasting evaluation reveals a high level of accuracy of the proposed machine learning approach in forecasting the arrivals of real e-commerce orders in three different retailers at three-hour intervals.

Results from the case study suggest that real-time prediction of individual retailer's e-order arrival is crucial in order to maximize the value of e-order arrival prediction for daily operational decision-making.

Earlier researchers examined supply chain demand, forecasting problem in a broader scope, particularly in dealing with the bullwhip effect. Prediction of real-time, hourly based order arrivals has been lacking. The paper fills this research gap by presenting a novel data-driven predictive methodology.

This study examines whether rules, particular participants, and executive politics in state tax revenue estimation exert measurable influences on forecast error. Fixed-effects estimation using data from states’ respective fiscal years 1994 to 2003 indicates that all impact state tax revenue forecast accuracy in varying ways, and results suggest that policy can be crafted to effectively mitigate forecast error. Further examination of the quality of participation in tax revenue forecasting as well as the mechanisms of political involvement in this arena is suggested.

This paper seeks to investigate the usefulness of analysts’ earnings forecast revisions in the allocation of funds to different industries and countries. In particular, it asks whether a post analyst revision announcement drift in prices can be exploited to guide an asset allocation strategy based on industry, or country, selection.

The methodolgy is to use monthly consensus I/B/E/S – First Call analysts’ earnings forecasts for companies listed on the main European stock markets over the period January 1987 to December 2001.

It is found that a significant post revision announcement effect for individual companies. However, the abnormal returns evaporate away as the research moves from an individual company level to an industry or country level. The paper provides two kinds of evidence which seem to cast doubt on the analysts’ ability to fully incorporate industry and country specific information into their forecasts: returns are driven more by common components than earnings forecast revisions, and company specific news reflected by the revision signal dominates industry or country news.

Locates the origin of stock price momentum strategies in news about earnings reflected in analysts’ forecasts revisions.

The purpose of this paper is to detect quantitatively the existence of anchoring bias among financial analysts on the Tunisian stock market. Both non-parametric and parametric methods are used.

Two studies have been conducted over the period 2010–2014. A first analysis is non-parametric, based on observations of the sign taking by the surprise of result announcement according to the evolution of earning per share (EPS). A second analysis uses simple and multiple linear regression methods to quantify the anchor bias.

Non-parametric results show that in the majority of cases, the earning per share variations are followed by unexpected earnings surprises of the same direction, which verify the hypothesis of an anchoring bias of financial analysts to the past benefits. Parametric results confirm these first findings by testing different psychological anchors’ variables. Financial analysts are found to remain anchored to the previous benefits and carry out insufficient adjustments following the announcement of the results by the companies. There is also a tendency for an over/under-reaction in changes in forecasts. Analysts’ behavior is asymmetrical depending on the sign of the forecast changes: an over-reaction for positive prediction changes and a negative reaction for negative prediction changes.

The evidence provided in this paper largely validates the assumptions derived from the behavioral theory particularly the lessons learned by Kaestner (2005) and Amir and Ganzach (1998). The authors conclude that financial analysts on the Tunisian stock market suffer from anchoring, optimism, over and under-reaction biases when announcing the earnings.