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.

Discover the role of machine learning and artificial intelligence in business forecasting from some of the brightest minds in the field In Business Forecasting: The Emerging Role of Artificial Intelligence and Machine Learning accomplished authors Michael Gilliland, Len Tashman, and Udo Sglavo deliver relevant and timely insights from some of the most important and influential authors in the field of forecasting. You'll learn about the role played by machine learning and AI in the forecasting process and discover brand-new research, case studies, and thoughtful discussions covering an array of practical topics. The book offers multiple perspectives on issues like monitoring forecast performance, forecasting process, communication and accountability for forecasts, and the use of big data in forecasting. You will find: Discussions on deep learning in forecasting, including current trends and challenges Explorations of neural network-based forecasting strategies A treatment of the future of artificial intelligence in business forecasting Analyses of forecasting methods, including modeling, selection, and monitoring In addition to the Foreword by renowned researchers Spyros Makridakis and Fotios Petropoulos, the book also includes 16 "opinion/editorial" Afterwords by a diverse range of top academics, consultants, vendors, and industry practitioners, each providing their own unique vision of the issues, current state, and future direction of business forecasting. Perfect for financial controllers, chief financial officers, business analysts, forecast analysts, and demand planners, Business Forecasting will also earn a place in the libraries of other executives and managers who seek a one-stop resource to help them critically assess and improve their own organization's forecasting efforts.

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.

Stressing the concrete applications of economic forecasting, Practical Business Forecasting is accessible to a wide-range of readers, requiring only a familiarity with basic statistics. The text focuses on the use of models in forecasting, explaining how to build practical forecasting models that produce optimal results. In a clear and detailed format, the text covers estimating and forecasting with single and multi- equation models, univariate time-series modeling, and determining forecasting accuracy. Additionally, case studies throughout the book illustrate how the models are actually estimated and adjusted to generate accurate forecasts. After reading this text, students and readers should have a clearer idea of the reasoning and choices involved in building models, and a deeper foundation in estimating econometric models used in practical business forecasting.

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.

A simple criterion based on the properties of the forecast error is presented to evaluate the accuracy of forecasts. The efficiency conditions of an optimization problem are used to show that under rational expectations the standard statistical conditions are necessary, but not sufficient to ensure efficiency. This criterion is used to examine the accuracy of the World Economic Outlook projections of growth and inflation for the seven major industrial countries. Time series models are then estimated and the efficiency of the World Economic Outlook projections relative to a benchmark time series model is examined. A number of empirical tests suggest that the year ahead projections of growth and inflation in the World Economic Outlook are unbiased after 1982.

Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.