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New Determinants of Analysts’ Earnings Forecast Accuracy

New Determinants of Analysts’ Earnings Forecast Accuracy
Author: Tanja Klettke
Publisher: Springer Science & Business
Total Pages: 120
Release: 2014-04-28
Genre: Business & Economics
ISBN: 3658056347

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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.


Handbook Of Financial Econometrics, Mathematics, Statistics, And Machine Learning (In 4 Volumes)

Handbook Of Financial Econometrics, Mathematics, Statistics, And Machine Learning (In 4 Volumes)
Author: Cheng Few Lee
Publisher: World Scientific
Total Pages: 5053
Release: 2020-07-30
Genre: Business & Economics
ISBN: 9811202400

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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.


Financial Analysts' Forecasts and Stock Recommendations

Financial Analysts' Forecasts and Stock Recommendations
Author: Sundaresh Ramnath
Publisher: Now Publishers Inc
Total Pages: 125
Release: 2008
Genre: Business & Economics
ISBN: 1601981627

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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.


The Timing of Analysts' Earnings Forecasts

The Timing of Analysts' Earnings Forecasts
Author: Ilan Guttman
Publisher:
Total Pages: 48
Release: 2009
Genre:
ISBN:

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Existing literature assumes that the order and timing of analysts' earnings forecasts are determined exogenously. However, analysts choose when to issue their forecasts. This study develops a model that endogenizes the timing decision of analysts and analyzes their equilibrium timing strategies. In the model, analysts face a trade-ocurren; between the timeliness and the precision of their forecasts. The model introduces a timing game with two analysts, derives and analyzes its unique pure strategies equilibrium, and provides new empirical predictions about the precision and timing of analysts' forecasts. The equilibrium has one of two patterns: either the times of the analysts' forecasts cluster, or there is a separation in the times of the forecasts. The less informed and less similar the analysts are, the more likely it is that they forecast at different points in time. All else equal, analysts with a higher precision of initial private information tend to forecast earlier, and analysts with a higher learning ability tend to forecast later.


Analysts' Use of Earnings Forecasts in Predicting Stock Returns

Analysts' Use of Earnings Forecasts in Predicting Stock Returns
Author: Sati P. Bandyopadhyay
Publisher:
Total Pages: 17
Release: 2014
Genre:
ISBN:

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Little attention has been paid to a principal decision context in which analysts' earnings forecasts are prepared, namely, as an input to their recommendations. We use two data sets, Value Line, USA, and Research Evaluation Service, Canada, and examine the importance of analysts' earnings forecasts for their stock price forecasts via three hypotheses: (1) analysts' earnings forecasts are important for their stock price forecasts; (2) analysts' long-term earnings forecasts are more important than their short-term earnings forecasts for their predictions of stock prices over a particular stock price forecast horizon; (3) the importance of analysts' earnings forecasts for their stock price forecasts rises as the joint earnings and stock price forecast horizon increases. We show that: (1) when the earnings forecast horizon is the next fiscal year, forecasted earnings explain only 30% of the variation in forecasted price; (2) the importance of forecasted earnings for forecasted price rises as the earnings forecast horizon increases; (3) in the long run, (i.e. three to five years hence), forecasted earnings explain about 60% of the variation in forecasted price. Decision usefulness is an ex ante concept, but tests regarding the usefulness of earnings for stock price generally have used actual (not expectational) data. Our evidence suggests that earnings expectations are decision useful, where the decision context is sell-side analysts' stock price forecasts. Our results are potentially important to users of sell-side analyst research reports. When a stock recommendation is accompanied only by short-run earnings forecasts, investors need to closely examine estimates of non-earnings variables to assess the quality of stock recommendations. In contrast, when stock recommendations are accompanied by both short-run and long-run earnings forecasts, investors need to examine estimates of non-earnings information variables less closely.


Introduction to Financial Forecasting in Investment Analysis

Introduction to Financial Forecasting in Investment Analysis
Author: John B. Guerard, Jr.
Publisher: Springer Science & Business Media
Total Pages: 245
Release: 2013-01-04
Genre: Business & Economics
ISBN: 1461452392

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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.


An Empirical Study of Financial Analysts Earnings Forecast Accuracy

An Empirical Study of Financial Analysts Earnings Forecast Accuracy
Author: Andrew Stotz
Publisher:
Total Pages: 122
Release: 2017
Genre:
ISBN:

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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.