Category: (Book)
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Financial Modelling in Practice: A Concise Guide for Intermediate and Advanced Level is a practical, comprehensive and in-depth guide to financial modelling designed to cover the modelling issues that are relevant to facilitate the construction of robust and readily understandable models.
Based on the authors extensive experience of building models in business and finance, and of training others how to do so this book starts with a review of Excel functions that are generally most relevant for building intermediate and advanced level models (such as Lookup functions, database and statistical functions and so on). It then discusses the principles involved in designing, structuring and building relevant, accurate and readily understandable models (including the use of sensitivity analysis techniques) before covering key application areas, such as the modelling of financial statements, of cash flow valuation, risk analysis, options and real options. Finally, the topic of financial modelling using VBA is treated. Practical examples are used throughout and model examples are included in the attached CD-ROM.
Aimed at intermediate and advanced level modellers in Excel who
wish to extend and consolidate their knowledge, this book is
focused, practical, and application-driven, facilitating
knowledge to build or audit a much wider range of financial
models.
Powerful Excel modeling guideReviewed by T. Terry, 2010-03-08
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.
How to Rev Up Your Financial Modeling Skills SetReviewed by Serge J. Van Steenkiste, 2009-09-08
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.
goes far beyond standard Excel usageReviewed by W Boudville, 2009-04-27
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.
