Despite using Excel for over 20 years - which included building complex valuation models for private equity and intangible asset valuations - I had a suspicion that I was not utilizing the program to its fullest extent.
Mike Rees - who has an impressive list of credentials, including winning the coveted Wilmott Prize awarded to the top graduate in the CQF program - has written a great book that addressed my concerns by going in-depth into the advanced functionality of Excel. For example, I knew very little of the advanced look-up functions, but after plowing through Chapter 1, I now feel relatively fluent in them and realize that I could have saved immense time when calculating federal and state income taxes on projected cash flow streams or in calculating future depreciation across multiple asset classes.
Although very familiar with cash flow modelling, Chapter 3 provided a new take on that topic and gave me some insights I hadn't thought of.
Chapters 4 & 5 were also very helpful in providing exposure to Palisade Corporation's @Risk and @Tree software. As another reviewer noted, a background in probability will make the examples easier to understand, but one can still benefit from a careful reading of the chapters. (As an aside, the Palisade software is easy to download in a trial version, and the sales/support team did a great job following up.) Chapter 6 provides a basic but solid foundation in VBA using the familiar Black-Scholes model. Using this book enabled me to tackle the Staunton/Jackson book on advanced Excel-VBA modeling, which dives much more heavily into VBA programming. In my practice, I have found the combination of this book and the Staunton/Jackson book to be more useful and relevant than the Beninga text, perhaps because of their practitioner orientation.
Thanks to Mike for his hard work in producing such a useful, thought-provoking tome. Anyone in the valuation practitioner space (i.e., the ASA-BV,CBA,ABV crowd) should carefully read this book and integrate its learnings into their own practice.
Michael Rees has produced a text that reaches its goal for advanced users. In addressing Excel functionality as well as practical model design, Rees ensures that the reader is exposed to good logic rules and expanded capabilities that enhance the value of the models the reader will go on to create. In completing the book, the reader will be much better versed in the entirety of the modeling process. It is not enough to know Excel or financial theory alone. Synthesizing the two areas in the context of practicality makes this book invaluable.
Incorporating add-in tools (Palisade Corp's @RISK and PrecisionTree) to extend the model analysis demonstrates how in an uncertain world better tools can make better models generate more information for valuations and decisions. Rees' book demonstrates his many years of experience in the areas of modeling, finance and risk.
Michael Rees succeeds to a large extent in his endeavor of writing a text that addresses the financial modeling process instead of Excel functionality, financial theory, or mathematical models. To his credit, Rees has put together a large number of useful modeling examples in the CD-ROM that is sold with the text. Rees' book assumes that readers have at least an intermediate knowledge of both statistical and financial concepts.
After reviewing select Excel functions and tools relevant to financial modeling, Rees gives his audience of modelers many practical tips about how to design, structure, and build models that are relevant, accurate, and easily understandable. Whoever has experience with models will probably agree with Rees when he writes that the majority of models built are in practice of mediocre quality. Someone other than the author of the model will often experience several challenges in dealing with the model at hand, i.e., too much time spent on understanding the model, complexity of the auditing and validating processes, hard to share with others, over-reliance on the original modeler to maintain or use it, lack of clarity of objectives, and presence of errors and implicit assumptions.
Rees then goes into the modeling of financial statements that is often required in the world of corporate finance for forecasting profit and cash, assessing financing requirements, analyzing credit risk and valuation, etc. This chapter is a little gem. It contains many practical tips. Once again, readers will be reminded that there is not always 100% agreement on the definition of some financial concepts.
Rees then uses Palisade Corporation's add-ins @RISK and PrecisionTree for many modeling examples in the two chapters that he dedicates to risk modeling and real option modeling, respectively. Having some understanding of both statistical and financial concepts is particularly important here to benefit from reading both chapters. Probably, many readers with an advanced knowledge of Excel 2007 will regret that the above-mentioned functionality that Palisade Corporation offers has not yet been systematically integrated into at least Microsoft Office Professional.
Finally, Rees discusses the use of Visual Basic for Applications (VBA) in a range of practical financial modeling situations. Rees points out that many otherwise competent modelers never learn VBA. For this reason, Rees makes the assumption that his audience is not very familiar with VBA. Rees shows how macros, i.e., subroutines and user-defined functions, can be used in a variety of modeling contexts.
In conclusion, Rees has made a valuable contribution to the field of financial modeling. The CD-ROM that is sold with the text plays a key role in achieving this objective.
Rees demonstrates how you can use Excel to perform quite sophisticated modelling. This takes the general ability to define functions and relationships between cells in a spreadsheet and pushes it far beyond simple tabular usages.
Many useful tasks are shown. One example is to perform sensitivity analysis, where you tweak the values or range of values of an input and see the resulting range of output values. This is important, because it lets you get beyond an apparent accuracy in the significant figures of your output. Often, these are just a function of the resolution of the Excel calculator. The book walks thru a sensitivity analysis that lets you see how, with a given model, the output really depends on the input range.
Another important section of the book deals with risk modelling. A stochastic analysis using various important and common probability distributions in your model. This really needs an entire book to itself. But the current discussion is enough for you to start doing nontrivial risk modelling.
Product Details :
Hardcover: 288 pages
Publisher: Wiley; 1 edition (December 3, 2008)
Language: English
ISBN-10: 0470997443
ISBN-13: 978-0470997444
Product Dimensions: 6.9 x 0.9 x 9.8 inches
More Details about Financial Modelling in Practice: A Concise Guide for Intermediate and Advanced Level 1st edition
or
Download Financial Modelling in Practice: A Concise Guide for Intermediate and Advanced Level 1st edition PDF Ebook
No comments:
Post a Comment