The first edition of Practical Financial Modelling was written as something of a handbook: a compilation of everything we felt people working with complicated models needed to know to be able to deliver and function at the top of their field. It was, in many ways, a direct reflection of what I was teaching to financial modellers in the courses we offered at the time at Operis: how to ensure accuracy, avoid errors, and structure work in a clear way. The book expanded on the theories and techniques of best practice we introduced in our short courses and added deeper explanations, examples and more illustrations.

It quickly became clear that the lessons in the book had appeal beyond modelling professionals working on the front lines. In fact, when I released the next edition three years later, the idea was that the increasing use of models by other parts of the financial chain created new questions: managers needed to interpret the models and understand the means by which the analysis was constructed. This second edition, which came out in 2008, served – I hope – as a useful resource during an admittedly difficult period for people working in finance across the world.

Now, seven years on, the book has been updated again. The world of modelling itself is shifting. The enquiries we at Operis now receive have more to do with how models can be understood, interrogated and used to support decisions, beyond a simple assurance of the ‘numbers’ being sound. An ever wider audience is hungry for the tools to properly comprehend models and understand what they actually tell readers in context.

The new edition emphasises the modelling environment which surrounds the modelling process. The ‘process’ remains the old hands on, comprehensive guidance to creating high quality, robust and stable models. The modelling ‘environment’ section (which opens the book) now examines the context in which the modelling takes place. To make it as clear as possible, this has been subdivided into three sections: quality assurance, quality control and (the really important bit!) model review.

In modelling, quality assurance is about understanding the space in which the modelling takes place. It’s the alignment of business concerns and business strategy with the use of models by people at the senior level for decision making. Executives at this level don’t require the same level of hands-on tinkering with the spreadsheet itself, but have a great need to be able to understand the purpose and intended use of the model. This is an essential prerequisite to evaluating the analysis.

The ‘quality control’ section provides the next vital step in being able to trust how models have been built, as far as a senior perspective is concerned. Recognising that a model has been prepared to specific methodologies or standards is important, but unfortunately this is often taken as assurance that the model is entirely fit for purpose. Proper quality control is more than simply ensuring adherence to a set of rules, and is far more about creating an appropriate system that ensures that models are being tested and reviewed.

For most organisations the most enduring quality control method is that of peer review. Regardless of what other checks have been built into the spreadsheet, if a second pair of eyes hasn’t looked at it (whether it be a colleague at the next desk over or an expert level audit by a company like Operis) then model assurance is limited at best; this chapter shows how to make the most of peer review.

The third new chapter then presents the actual techniques of interrogating a model and, although aimed more at the peer reviewer, is for anyone who has that job of checking the spreadsheet. It provides a number of techniques and concepts to both understand the way the model is constructed and to identify the flaws in both logic and analysis that can creep in. We introduce a series of checks that readers can carry out and record which form a key part of the model assurance framework.

In the decade since the first edition deals have become more complex. The massive spreadsheets that have emerged as a result are immensely daunting. It is understandable that many people outside the front line of model building are searching for assurances to build business cases and decisions on these models. If you’re a senior executive, a finance director, a lawyer, or a member of the credit committee, you don’t want to tinker with an expensive, complex spreadsheet. What you do want to do is run robust sensitivities that clearly demonstrate the impact of changing assumptions on the bottom line.

The third edition of Practical Financial Modelling provides the link between understanding the intricacies of hard-core financial modelling at the level of the financial analyst and the assurance required for senior professionals who need to understand and make decisions based on the results of these spreadsheets.

The new edition of Practical Financial Modelling is now available for order.

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Erwan Fournis
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Erwan Fournis
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