This study conducts a comprehensive review of the literature published during 1996- 2017 to identify the factors that affect the accuracy of financial analysts' forecasts. We organize our review around three main groups, namely, (a) drivers of analyst forecast accuracy, (b) quality financial reporting, and (c) accounting standards. Among the several factors found, some factors (experience of the analyst, earnings quality, audit quality, IFRS adoption, and annual report readability) have a positive relationship with the accuracy of analysts' forecasts while others (politically connected firms, firms audited by Non-Big 4, and international GAAP differences) have a negative relationship. Our findings contribute to future research by examining the factors affecting analyst forecast accuracy from different perspectives, which will prove to be useful for academicians, regulators, investors, and financial analysts.

Financial analysts provide information in their research reports and thereby help forming expectations of a firm’s future business performance. Thus, it is essential to recognize analysts who provide the most precise forecasts and the accounting literature identifies characteristics that help finding the most accurate analysts. Tanja Klettke detects new relationships and identifies two new determinants of earnings forecast accuracy. These new determinants are an analyst’s “general forecast effort” and the “number of supplementary forecasts”. Within two comprehensive empirical investigations she proves these measures’ power to explain accuracy differences. Tanja Klettke’s research helps investors and researchers to identify more accurate earnings forecasts.

This book provides a detailed analysis of the accuracy of forecasts made by individual analysts across nine different industries. It explores the factors that contribute to forecast accuracy and provides insights into how these factors can be used to improve forecasting performance. The author, Patricia C. O'Brien, is a well-respected economist and her research has significant implications for businesses and investors who rely on forecasts in their decision-making processes. This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work is in the "public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.

Facing limited attention constraints, financial analysts must strategically choose which information to pay attention to and which information to ignore when making earnings forecasts. I rely on rational inattention theory to develop and test hypotheses on factors that determine analyst attention and how analyst attention affects forecast accuracy. I construct a novel measure of attention that varies across stocks followed by the same analyst during the same fiscal period. I find that analyst attention is decreasing in the marginal cost of paying attention and that analysts allocate attention to firms with earnings that are difficult to forecast, suggesting that analysts suffer from a limited attention constraint. Importantly, I find that attentive analysts are more accurate, and the effect of attention is larger for inexperienced analysts and stocks with highly volatile earnings. Finally, I find that attentive analysts are less likely to revise forecasts upwards and are less optimistic even after controlling for firm information environment and analyst ability.

Seminar paper from the year 2001 in the subject Business economics - Investment and Finance, grade: 9, Maastricht University (Economics and Sociology Faculty), language: English, abstract: In all aspects of our daily live, we seek to anticipate or forecast events. Especially organizations and companies are engaged in producing and using a full range of different economic forecasts. The widespread usefulness and application of forecasting systems and statistical and econometric modeling techniques has become solidly entrenched. Being aware of this fact, has led to a fundamental need for better quantitative analysis and business planning. Private and public sectors alike have found it both practical and essential to employ more rigorous analytical framework. Accordingly, more sophisticated forecasting techniques to enhance the level of predictability and confidence are required to foresee future events. The need for such forecasts arises because people are taking positions and enter into commitments about the future. Therefore, a need to form a view about the possible future consequences of these positions or commitments exists. Thus, in economic and business life, forecasts are essential, and errors can be very costly. According to those facts, now the question arises: What factors influence the accuracy if forecasts? In the following paper, three different forecasting methods will be explained and evaluated according to their accuracy. There exist diverse techniques of forecasting; those methods may be either formal or intuitive. Nevertheless, as the future is unknown, all forecasting systems rest ultimately on learning from the past. There exist naïve processes extrapolating the past in a simple way. But those will be prone to error when the world changes. More sophisticated methods seek to foresee change by understanding the source of past changes, and therefore incorporate change in the forecast. The standard output from macro models is a central forecast, that is, a prediction of the most likely path for the variables of interest. But these central forecasts are subject to appreciable uncertainty, and this needs to be taken into account in using them. One way to do so is to associate with the central forecasts an estimate of their possible error.

This study examines the impact of unidentifiable individual differences among financial analysts on the cross section of their earnings forecast accuracy. Various psychological factors, such as decision style and personality traits, are documented to impact individuals' decision making. However, analysts' individual differences in such psychological factors are not captured by identifiable personal attributes employed in finance literature, such as years of experience. In this paper, we employ the concept of analyst fixed effects to control for unidentifiable individual differences. Examining the factors related to forecast accuracy, we document that controlling for these unidentifiable analyst-specific effects improves model fitting, and changes the explanatory power of some of the traditionally used independent variables in the literature. We confirm that the analyst's firm-specific experience, the intensity of following that a firm receives, and the forecast horizon are all significantly and consistently related to forecast accuracy. However, we find that analyst general experience and coverage complexity lose explanatory power when individual differences are controlled for. Furthermore, we document that analyst general experience is not monotonically associated with better accuracy, and that analysts only benefit from increased general experience during the early to middle stages of their career. Finally, we observe that when analysts' individual differences are controlled for, the boldness of a forecast revision is no longer a significant determinant of the improvement of accuracy. This is one of the first studies to highlight the necessity of recognizing individual differences among financial analysts. We argue that this treatment advances the literature of analyst forecast performance, and closely relates financial agents' decision making to psychology theories of decision style and personality.

Excerpt from Forecast Accuracy of Individual Analysts: A Nine-Industry Study Current databases of analysts' forecasts of corporate earnings include predictions from thousands of individuals employed at hundreds of financial service institutions. The purpose of this paper is to analyze whether it is possible to distinguish forecasters with superior ability on the basis of ex Egg; forecast accuracy from panel data. About the Publisher Forgotten Books publishes hundreds of thousands of rare and classic books. Find more at www.forgottenbooks.com This book is a reproduction of an important historical work. Forgotten Books uses state-of-the-art technology to digitally reconstruct the work, preserving the original format whilst repairing imperfections present in the aged copy. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in our edition. We do, however, repair the vast majority of imperfections successfully; any imperfections that remain are intentionally left to preserve the state of such historical works.