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Economics - Financial Economics | Introduction to Financial Forecasting in Investment Analysis ( Miscellaneous)

Introduction to Financial Forecasting in Investment Analysis

Guerard, John

2013, XI, 236 p. 20 illus., 10 illus. in color.

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Concise, focused, readable. If you are looking for a text in financial forecasting, its methods and its application to portfolio management this is the book for you. Not only does this book presents the “how to” of the forecasting methodology but it uses it to present, and discuss with worked-out examples, the “how to” of several important aspects of portfolio analysis, optimization and diversification.
A great advantage of this book is its readability, you can go from one end to the other following the theory as it progresses and it moves to the portfolio applications. You can’t get lost in details since the focus is always on the practical side: what you learn in one chapter you use it in the next one, and the reader builds on past chapters to reach into a full-blown portfolio application.
Filled with empirical examples, using different software packages, with many explanations and references on the related literature (both on forecasting and on finance) this is a book that will appeal to students, researchers and practitioners alike. It is highly recommended for your bookshelf.
Dimitrios Thomakos
University of Peloponnese
This book is much more than a text on statistical forecasting techniques. Although it discusses statistical methods, the main meat of the book is concerned with the application of the techniques to forecasting security returns and risk. Thus, the book employs regression analysis to demonstrate how expected returns can be estimated from accounting ratios such as the earnings to price ratio and the book to market ratio. In the case of risk the book focuses mainly on a multifactor model that incorporates a company’s fundamentals.
In a stationary world these statistical predictions of returns should be unbiased, and therefore can be fed directly into a mean-variance model without the problems that bedevil the use of biased subjective forecasts. The final chapters of the book therefore use the risk and return forecasts in combination with a portfolio selection model to indicate the potential gains at the portfolio level from statistical forecasting.
The book is not always for the faint-hearted. Its intellectual underpinning is to be found in the work of Harry Markowitz, but the book brings together the dispersed and voluminous subsequent literature in the area, including a number of contributions from the author. It therefore serves as a comprehensive review of this literature as well as a case study of how statistical methods can be used to forecast risk and return.
Richard Brealey
Emeritus Professor of Finance
London Business School
Quantitative finance has emerged as an inter-disciplinary field that brings together elements of finance, statistics, accounting and optimization. While most practitioners have working knowledge of all of these fields, their core expertise is usually limited to a subset of them. For instance, a portfolio manager with exemplary stock picking skills might have only a preliminary understanding of the assumptions of classical linear regression theory. John’s latest books take a step in the right direction to fill in this gap.
Each chapter of this book addresses an important investment problem. Furthermore, instead of segregating economic, financial and statistical perspectives, the authors bring together all of these perspectives under a common framework to create a stimulating and cohesive narrative. I liked chapters 6 and 7 which deal with the USER and GLER models, raise pertinent questions that arise during application of these models, and provide normative answers to them. The idea of improving risk adjusted returns by accounting for hidden systematic risk exposures is particularly relevant in periods of high volatility as we are currently witnessing. The authors build on some of the earlier work on this topic, and demonstrate its efficacy in both domestic and international markets. Chapters 1-5 provide insightful empirical case studies that highlight advantages as well as pitfalls of applying standard econometric models.
Overall, John has assembled a very good collection of data and applied investment research techniques which should of interest to portfolio managers, risk managers, analysts and consultants.
Anureet Saxena, Ph.D, CFA, CIPM
Investment Analyst
John Guerard has written an informative and interesting book. While the title suggests it is primarily concerned with financial forecasting, and it is, it goes beyond this to present both a primer on portfolio analysis and an intelligent discussion and useful illustration of the role of the forecasting in portfolio allocation. The chapters on forecasting present and illustrate the use of many of the modern forecasting techniques, along with useful financial economic data. The unique aspect of this book is found in the second half, which applies many of the techniques discussed earlier to the portfolio optimization problem.
Not only does the book present the basic models of portfolio optimization from Markowitz through multi-factor models, it also shows how these techniques have and can be used to reach optimum allocation in domestic and world markets. The chapters on portfolio construction and asset allocation alone supply a reason for purchasing this book. Any reader can learn from the theory; while a practitioner can obtain a set of tools that will prove extremely useful.
Martin Gruber, Stern School of Business, New York University

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