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 Financial Analysts Forecasts and Stock Recommendations written by Sundaresh Ramnath and published by Now Publishers Inc. This book was released on 2008 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: Financial Analysts' Forecasts and Stock Recommendations reviews research related to the role of financial analysts in the allocation of resources in capital markets. The authors provide an organized look at the literature, with particular attention to important questions that remain open for further research. They focus research related to analysts' decision processes and the usefulness of their forecasts and stock recommendations. Some of the major surveys were published in the early 1990's and since then no less than 250 papers related to financial analysts have appeared in the nine major research journals that we used to launch our review of the literature. The research has evolved from descriptions of the statistical properties of analysts' forecasts to investigations of the incentives and decision processes that give rise to those properties. However, in spite of this broader focus, much of analysts' decision processes and the market's mechanism of drawing a useful consensus from the combination of individual analysts' decisions remain hidden in a black box. What do we know about the relevant valuation metrics and the mechanism by which analysts and investors translate forecasts into present equity values? What do we know about the heuristics relied upon by analysts and the market and the appropriateness of their use? Financial Analysts' Forecasts and Stock Recommendations examines these and other questions and concludes by highlighting area for future research.

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 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 Handbook Of Financial Econometrics Mathematics Statistics And Machine Learning In 4 Volumes written by Cheng Few Lee and published by World Scientific. This book was released on 2020-07-30 with total page 5053 pages. Available in PDF, EPUB and Kindle. Book excerpt: This four-volume handbook covers important concepts and tools used in the fields of financial econometrics, mathematics, statistics, and machine learning. Econometric methods have been applied in asset pricing, corporate finance, international finance, options and futures, risk management, and in stress testing for financial institutions. This handbook discusses a variety of econometric methods, including single equation multiple regression, simultaneous equation regression, and panel data analysis, among others. It also covers statistical distributions, such as the binomial and log normal distributions, in light of their applications to portfolio theory and asset management in addition to their use in research regarding options and futures contracts.In both theory and methodology, we need to rely upon mathematics, which includes linear algebra, geometry, differential equations, Stochastic differential equation (Ito calculus), optimization, constrained optimization, and others. These forms of mathematics have been used to derive capital market line, security market line (capital asset pricing model), option pricing model, portfolio analysis, and others.In recent times, an increased importance has been given to computer technology in financial research. Different computer languages and programming techniques are important tools for empirical research in finance. Hence, simulation, machine learning, big data, and financial payments are explored in this handbook.Led by Distinguished Professor Cheng Few Lee from Rutgers University, this multi-volume work integrates theoretical, methodological, and practical issues based on his years of academic and industry experience.

Download or read book New Determinants of Analysts Earnings Forecast Accuracy written by Tanja Klettke and published by Springer Science & Business. This book was released on 2014-04-28 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book An Improved Earnings Forecasting Model written by Richard D. F. Harris and published by . This book was released on 2016 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper, we employ the earnings model developed in Ashton and Wang (2013) to forecast the one- to three-year ahead earnings of individual companies. We find that the model produces forecasts of future earnings that are less biased and more informative than both the consensus analysts' earnings forecasts reported by IBES and the recently developed model-based forecasts of Hou, van Dijk and Zhang (2012).

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 A Contingency Approach to Empirically Comparing Quarterly Earnings Forecasts of Statistical Models to Those of Financial Analysts written by William S. Hopwood and published by . This book was released on 1979 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Essays on Financial Analysts Forecasts written by Marius del Giudice Rodriguez and published by . This book was released on 2006 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation contains three self-contained chapters dealing with specific aspects of financial analysts' earnings forecasts. After recent accounting scandals, much attention has turned to the incentives present in the career of professional financial analysts. The literature points to several reasons why financial analysts behave overoptimistically when providing their predictions. In particular, analysts may wish to maintain good relations with firm management, to please the underwriters and brokerage houses at which they are employed, and to broaden career choice. While the literature has focused more on analysts' strategic behavior in these situations, less attention has been paid to the implications these factors have on financial analysts' loss functions. The loss function dictates the criteria that analysts use in order to build their forecasts. Using a simple compensation scheme in which the sign of prediction errors affect their incomes differently, in the first chapter we examine the implications this has on their loss function. We show that depending on the contract offered, analysts have a strict preference for under-prediction or over-prediction and the size of this asymmetric behavior depends on the parameter that governs the financial analyst's preferences over wealth. This is turn affects the bias in their forecasts. Recent developments in the forecasting literature allow for the estimation of asymmetry parameters after observing data on forecasts. Moreover, they allow for a more general test of rationality once asymmetries are present. We make use of forecast data from financial analysts, provided by I/B/E/S, and present evidence of asymmetries and weak evidence against rationality. In the second chapter we study the evolution over time in the revisions to financial analysts' earnings estimates for the 30 Dow Jones firms over a 20 year period. If analysts' forecasts used information efficiently, earnings revisions should not be predictable. However, we find strong evidence that earnings revisions can in fact be predicted by means of the sign of the last revision or by using publicly available information such as short interest rates and past revisions. We propose a three-state model that accounts for the very different magnitude and persistence of positive, negative and `no change' revisions and find that this model forecasts earnings revisions significantly better than an autoregressive model. We also find that our forecasts of earnings revisions predict the actual earnings figure beyond the information contained in analysts' earnings estimates. Finally, the empirical literature on financial analysts' forecast revisions of corporate earnings has focused on past stock returns as the key determinant. The effects of macroeconomic information on forecast revisions is widely discussed, yet rarely tested in the literature. In the third chapter, we use dynamic factor analysis for large data sets to summarize a large cross-section of macroeconomic variables. The estimated factors are used as predictors of the average analyst's forecast revisions for different sectors of the economy. Our analysis suggests that factors extracted from macroeconomic variables do, indeed, improve on the current model with only past stock returns. In trying to explain what drives financial analysts' forecast revisions, the factors representing the macroeconomic environment must be considered to avoid a potential omitted variable problem. Moreover, the explanatory power and direction of such factors strongly depend on the industry in question.

Download or read book Misstated Quarterly Earnings Alternative Information and Financial Analyst Earnings Forecast Revisions written by Michael L. Ettredge and published by . This book was released on 1993 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Refining Financial Analysts Forecasts by Predicting Earnings Forecast Errors written by Tatiana Fedyk and published by . This book was released on 2018 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt: Prior research on financial analyst' quarterly earnings forecasts has documented serial correlation in forecast errors. This paper examines 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. Finding that serial correlation in forecast errors is significant and seemingly independent of firm and analyst attributes, I model consensus forecast errors as an autoregressive process. I demonstrate that the model of forecast errors that best fits the data is AR(1), and use the obtained autoregressive coefficients 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.

Download or read book Company Valuation and Information in Analyst Forecasts written by Daniel Kreutzmann and published by Logos Verlag Berlin GmbH. This book was released on 2010 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis focuses on the three primitive value drivers of each company valuation model that is based on fundamental analysis: the discount rate, the expected future payoffs during the explicit forecasting period, and the terminal value at the end of the explicit forecasting period. While the first factor is analyzed theoretically by incorporating the government into the classical valuation framework, this thesis studies the other two factors by investigating forecasts made by professional investors, i.e. financial analysts. In the first part we show that the government's and the shareholders discount rate usually differ and analyze how the government's and shareholders different objectives lead to conflicts in the context of capital budgeting. The empirical part of this thesis shows that macroeconomic information is frequently used by financial analysts when updating their earnings expecations and that target price forecastsmade by financial analysts can be used to predict abnormal returns.

Download or read book An Information Interpretation of Financial Analyst Superiority in Forecasting Earnings written by Lawrence D. Brown and published by . This book was released on 2014 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper develops and tests an information-based model for conditions under which analysts earnings forecasts are likely to be more accurate than forecasts of time-series models. Three information variables are considered, namely the dimensionality of the information set, the precision of the information items, and the correlation amongst the information items. The respective proxy variables for the information variables are firm size, extent of agreement amongst analysts, and the number of lines of business the firm operates in. Evidence is provided that analysts are likely to be more accurate than time series models for larger firms and for firms whereby analysts have more homogeneous earnings forecasts.

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 Financial Analysts Heterogeneous Earnings Expectations and Their Stock Recommendations written by Steven Lustgarten and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In this study we test whether financial analysts' use their earnings forecasts to make stock recommendations. We hypothesize that if analysts use earnings forecasts as a basis for stock recommendations, the likelihood of a buy (sell) recommendation ought to increase (decrease) when the analyst's earnings forecast becomes more optimistic (pessimistic) relative to the market's expectation. The data supports this hypothesis. We also test the extent to which analysts' stock recommendations are based on public and/or on private earnings information. Private information is measured as the difference between the analysts own earnings forecast and the consensus forecasts of other analysts. Public information is measured as the difference between the consensus forecast and the random walk forecast. Our data show that stock recommendations are related to both private and public earnings information, private information is more important. We also find that the relationship between recommendations and forecasts is stronger where earnings are more value relevant. Factors such as higher earnings persistence and growth opportunities, lower market risk and larger firm size make stock recommendations more responsive to earnings forecasts. Stock recommendations are related to forecasted earnings surprises even when the forecast revision is held constant.

Download or read book An Empirical Study of Financial Analysts Earnings Forecast Accuracy written by Andrew Stotz and published by . This book was released on 2017 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the past 12 years, financial analysts across the world have been optimistically wrong with their 12-month earnings forecasts by 25.3%. This study may be the first of its kind to assess analyst earnings forecast accuracy at all listed companies across the globe, covering 70 countries. A review of prior research shows little uniformity in the preparation of the data set, yet differences in how outliers are treated, for example, can create substantially different results. This research lays out six specific steps to prepare the data set before any analysis is done.Three main conclusions come from this research: First, analyst earnings forecasts globally were 25.3% optimistically wrong, meaning on average, analysts started each year forecasting company profits of US$125, but 12 months later that company reported profits of US$100. Second, analysts had a harder time forecasting earnings for companies in emerging markets, where they were 35% optimistically wrong. Third, that analyst optimism mainly occurred when the companies they forecasted experienced very low levels of actual earnings growth, analysts did not make an equal, but opposite error for fast growth companies.