Download or read book Evaluation of Mechanical Earnings Forecast Models written by and published by GRIN Verlag. This book was released on 2019-06-24 with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt: Seminar paper from the year 2018 in the subject Business economics - Investment and Finance, grade: 2,7, University of Cologne, course: Bachelorseminar Corporate Finance, language: English, abstract: This paper seeks to examine different models to forecast revenue of companies. This is being achieved by examining costs of capital, which are a good representative therefor. The models examined in this paper can be divided into two sections. First, there are mechanical models, second there is one characteristic-based model. The models stand in contrast to analysts’ forecasts. This paper sums up different authors who illustrate, that mechanical models outperform analysts’ forecasts in terms of revenue forecasting. First, the HVZ mode is introduced which is due to outperform analysts’ forecasts. Second, the EP and RI model are introduced, next to a random walk model (RW model) as a benchmark. Objective of this paper is to find out which advantages go along with mechanical models, and whether the quality of forecast could be influenced positively. The topic of revenue forecast is highly relevant for different stakeholders in the financial industry. Based on revenue forecasts investment decisions are met by investors. One advantage of mechanical models therefore, is the greater feasibility due to the greater coverage. Mechanical models rely on firm fundamentals and are hence available for much more companies. Analysts’ forecasts are only available for firms of a certain size upwards. Costs of capital are a topic of focus not only for investment decisions but also for internal application. Apart from the use as a financial ratio it is negatively associated with customer satisfaction. The paper finds out, that the HVZ model outperforms analysts’ forecasts in terms of forecast bias and earnings response coefficient. However, the HVZ model does not outperform analysts’ forecasts in terms of accuracy. The EP and RI model both outperform the HVZ model in terms of all three criteria: forecast bias, earnings response coefficient and accuracy. The characteristic-based model sets up a linear function solely by firm fundamentals, that avoids including unobservable future covariances. Besides, it concludes certain key findings about abnormal earnings volatility and economy-wide risk.
Download or read book The Performance of Mechanical Earnings Forecasts written by Dieter Hess and published by . This book was released on 2019 with total page 46 pages. Available in PDF, EPUB and Kindle. Book excerpt: We analyze three different mechanical models to forecast earnings and compare their forecasts with those of analysts. Moreover, we evaluate implied cost of capital (ICC) estimates that are based on these forecasts. With our analyses we answer three open questions in the literature. 1) Do model forecasts or analysts' forecasts perform better? 2) Are ICCs derived from analysts' forecasts more reliable than ICCs based on model forecasts? And 3) does higher forecast performance also translate into more reliable ICCs? First, we find that analysts' forecasts are even more accurate than the most accurate model forecasts. However, second, we find that model-based ICCs are always more reliable than analyst-based ICCs. Moreover, model-based ICCs are particularly reliable for a sample of firms for which no analysts' forecasts are available. While the lack of reliability of analyst-based ICCs seems to indicate a missing link between forecast performance and ICC reliability, in fact, third, we find that ceteris paribus higher forecast performance translates into more reliable ICCs, that is, within one earnings definition the most accurate forecasts also yield the most reliable ICCs.
Download or read book A Multivariate Analysis of Annual Earnings Forecasts Generated from Quarterly Forecasts of Financial Analysts and Univariate Time Series Models written by William S. Hopwood and published by . This book was released on 1979 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt: The study compares the forecast accuracy of financial analysts, ARIMA models, and various permier models considered in the literature in the predicting of annual earnings per share. Various refinements were made of previously used methodologies. The results of the multivariate analysis indicated that financial analysts provide the most accurate forecasts. In addition, the divergence in accuracy between the various sources of forecasts tend to decrease as the end of the year approaches, while at the same time there is a general increase in accuracy. Also specific results are provided for individual model performance.
Download or read book Mechanical Earnings Forecasts written by Philipp Zacharias-Langhans and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This study aims to compare the performance of earnings forecasts from various cross-sectional models and the consensus of financial analysts for the European setting. My findings indicate that the models of Harris and Wang (2018) and Azevedo et al. (2017) are able to outperform raw consensus analyst forecasts both in terms of bias and accuracy, while analyst forecasts still contain information that is not incorporated in pure model forecasts. Further, I show that the accuracy of earnings forecasts is significantly impacted by the underlying earnings definition, and therefore argue that studies that use varying earnings definitions in their comparison are inconclusive.
Download or read book An Empirical Evaluation of the Relationship Between Errors in Analysts Forecasts of Earnings Per Share and Stock Prices written by Paul A. Janell and published by . This book was released on 1974 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Model Based Earnings Forecasts Vs Financial Analysts Earnings Forecasts written by Richard D. F. Harris and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Existing accounting-based forecasting models of earnings either do not fully consider information that is contained in stock prices or use an ad hoc specification that is not based on rigorous valuation theory. In this paper, we develop an earnings forecasting model built on the theoretical linkages between future earnings and stock prices as well as a number of accounting fundamental variables. We find that our model-based forecasts of earnings are in general less biased and more accurate than both existing model-based forecasts and analysts' consensus forecasts, at both shorter and longer horizons. We also show that the accuracy of both model-based forecasts and financial analysts' forecasts depend on firm-specific characteristics such as firm size and industry membership.
Download or read book A Multivariate Analysis of Earnings Forecasts Generated by Financial Analysts and Univariate Time Series Models written by William S. Hopwood and published by . This book was released on 1978 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt: The study provides evidence on the relative accuracy of forecasts of earnings generated from five sources including statistical models and financial analysts. The statistical models were chosen on the basis of their usage in recent studies in the literature. The results indicate that the five types of forecasts are not significantly different using a multivariate testing procedure.
Download or read book Introduction to Financial Forecasting in Investment Analysis written by John B. Guerard, Jr. and published by Springer Science & Business Media. This book was released on 2013-01-04 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forecasting—the art and science of predicting future outcomes—has become a crucial skill in business and economic analysis. This volume introduces the reader to the tools, methods, and techniques of forecasting, specifically as they apply to financial and investing decisions. With an emphasis on "earnings per share" (eps), the author presents a data-oriented text on financial forecasting, understanding financial data, assessing firm financial strategies (such as share buybacks and R&D spending), creating efficient portfolios, and hedging stock portfolios with financial futures. The opening chapters explain how to understand economic fluctuations and how the stock market leads the general economic trend; introduce the concept of portfolio construction and how movements in the economy influence stock price movements; and introduce the reader to the forecasting process, including exponential smoothing and time series model estimations. Subsequent chapters examine the composite index of leading economic indicators (LEI); review financial statement analysis and mean-variance efficient portfolios; and assess the effectiveness of analysts’ earnings forecasts. Using data from such firms as Intel, General Electric, and Hitachi, Guerard demonstrates how forecasting tools can be applied to understand the business cycle, evaluate market risk, and demonstrate the impact of global stock selection modeling and portfolio construction.
Download or read book Earnings Forecasts written by Chau Thi My Trinh and published by . This book was released on 2015 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Security Analyst Multi year Earnings Forecasts and the Capital Market written by Philip Brown and published by . This book was released on 1985 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Forecasting and Forecast Combining of Quarterly Earnings per Share Via Genetic Programming written by Arturo Rodriguez and published by . This book was released on 2013 with total page 21 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this study we examine different methodologies to estimate earnings. More specifically, we evaluate the viability of Genetic Programming as both a forecasting model estimator and a forecast-combining methodology. When we compare the performance of traditional mechanical forecasting (ARIMA) models and models developed using Genetic Programming we observe that Genetic Programming can be used to create time-series models for quarterly earnings as accurate as the traditional linear models. Genetic Programming can also effectively combine forecasts. However, Genetic Programming's forecast combinations are sometimes unable to improve on Value Line. Moreover, simple averaging of forecasts results in better predictive accuracy than Genetic Programming-combining of forecasts. Hence, as implemented in this study, Genetic Programming is not superior to traditional methodologies in either forecasting or forecast combining of quarterly earnings.
Download or read book On the Value of Generalizing the Box Jenkins Method to More Than More Than One Variable for the Forecasting of Earnings written by William S. Hopwood and published by . This book was released on 1978 with total page 70 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book A Comparative Analysis of Earnings Forecast Models Augmented for Interindustry Dependencies and Phased for Economic Cycles written by Clarence E. Fries and published by . This book was released on 1983 with total page 620 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book An Evaluation of Transfer function and Univariate Time series Earnings Expectation Models written by William S. Hopwood and published by . This book was released on 1980 with total page 46 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book An Examination of the Statistical Significance and Economic Implications of Model Based and Analyst Earnings Forecasts written by Kevin Ow Yong and published by . This book was released on 2014 with total page 54 pages. Available in PDF, EPUB and Kindle. Book excerpt: We address the demand for model-based earnings forecasts by proposing a cross-sectional model which incorporates three salient ideas. First, firm performance converges to expected levels over time; second, amounts from current financial statements are robust predictors of future performance; and third, ordinary least squares (OLS) estimation is unreliable in samples including extreme values. Accordingly, we estimate a cross-sectional earnings forecasting model based on least absolute deviations analysis (LAD), and include profitability drivers derived from financial statements as predictors. In terms of statistical significance, we find that these forecasts are more accurate than forecasts from three extant prediction models and consensus analysts' forecasts. In terms of economic implications, we find that forecasts from our model have greater predictive ability for future abnormal returns than consensus analysts' forecasts. Overall, our results are important because they document the usefulness of a cross-sectional earnings forecasting model for a broad range of diverse firms, including those with little or no analyst coverage.
Download or read book Financial Forecasting Analysis and Modelling written by Michael Samonas and published by John Wiley & Sons. This book was released on 2015-01-14 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: Risk analysis has become critical to modern financial planning Financial Forecasting, Analysis and Modelling provides a complete framework of long-term financial forecasts in a practical and accessible way, helping finance professionals include uncertainty in their planning and budgeting process. With thorough coverage of financial statement simulation models and clear, concise implementation instruction, this book guides readers step-by-step through the entire projection plan development process. Readers learn the tools, techniques, and special considerations that increase accuracy and smooth the workflow, and develop a more robust analysis process that improves financial strategy. The companion website provides a complete operational model that can be customised to develop financial projections or a range of other key financial measures, giving readers an immediately-applicable tool to facilitate effective decision-making. In the aftermath of the recent financial crisis, the need for experienced financial modelling professionals has steadily increased as organisations rush to adjust to economic volatility and uncertainty. This book provides the deeper level of understanding needed to develop stronger financial planning, with techniques tailored to real-life situations. Develop long-term projection plans using Excel Use appropriate models to develop a more proactive strategy Apply risk and uncertainty projections more accurately Master the Excel Scenario Manager, Sensitivity Analysis, Monte Carlo Simulation, and more Risk plays a larger role in financial planning than ever before, and possible outcomes must be measured before decisions are made. Uncertainty has become a critical component in financial planning, and accuracy demands it be used appropriately. With special focus on uncertainty in modelling and planning, Financial Forecasting, Analysis and Modelling is a comprehensive guide to the mechanics of modern finance.
Download or read book Analysis Earnings Forecast An Alternative Data Source for Failure Prediction written by O. D. Moses and published by . This book was released on 1986 with total page 39 pages. Available in PDF, EPUB and Kindle. Book excerpt: This study investigates four properties of earnings forecasts made by financial analysts to determine if systematic differences in these properties exists failing and healthy firms. The four properties are: The level of forecasts, forecast error, forecast bias, and forecast dispersion. Measures reflecting the four properties are used in models to distinguish failing and healthy firms and predict future bankruptcy. Results indicate that measures developed from analysts forecasts of future earnings can be exploited to distinguish failing from healthy firms.
