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Introduction to Financial Forecasting in Investment Analysis
Author
Title
Introduction to Financial Forecasting in Investment Analysis [electronic resource] / by John B. Guerard, Jr.
ISBN
9781461452393
Published
New York, NY : Springer New York : Imprint: Springer, 2013.
Physical Description
XI, 236 p. 20 illus., 10 illus. in color. digital.
Local Notes
Access is available to the Yale community.
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Access restricted by licensing agreement.
Summary
<p>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.</p>
Variant and related titles
Springer ebooks.
Other formats
Printed edition:
Format
Books / Online
Language
English
Added to Catalog
February 05, 2013
Contents
Chapter 1: Why do we forecast?
Chapter 2: Regression Analysis and Forecasting Models
Chapter 3: An Introduction to Time Series Modeling and Forecasting
Chapter 4: Regression Analysis and Multicollinearity: Two Case Studies
Chapter 5: Multiple Time Series Analysis and Causality Testing
Chapter 6: A Case Study of Portfolio Construction using the USER Data and the Barra Aegis System
Chapter 7: More Efficient Portfolios Featuring the USER Data and an Extension to Global Data and Investment Universes
Chapter 8: Forecasting World Stock Returns and Improved Asset Allocation
Chapter 9: Summary and Conclusions.
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