Book Image

Hands-On Financial Modeling with Excel for Microsoft 365 - Second Edition

By : Shmuel Oluwa
Book Image

Hands-On Financial Modeling with Excel for Microsoft 365 - Second Edition

By: Shmuel Oluwa

Overview of this book

Financial modeling is a core skill required by anyone who wants to build a career in finance. Hands-On Financial Modeling with Excel for Microsoft 365 explores financial modeling terminologies with the help of Excel. Starting with the key concepts of Excel, such as formulas and functions, this updated second edition will help you to learn all about referencing frameworks and other advanced components for building financial models. As you proceed, you'll explore the advantages of Power Query, learn how to prepare a 3-statement model, inspect your financial projects, build assumptions, and analyze historical data to develop data-driven models and functional growth drivers. Next, you'll learn how to deal with iterations and provide graphical representations of ratios, before covering best practices for effective model testing. Later, you'll discover how to build a model to extract a statement of comprehensive income and financial position, and understand capital budgeting with the help of end-to-end case studies. By the end of this financial modeling Excel book, you'll have examined data from various use cases and have developed the skills you need to build financial models to extract the information required to make informed business decisions.
Table of Contents (19 chapters)
1
Part 1 – Financial Modeling Overview
4
Part 2 – The Use of Excel Features and Functions for Financial Modeling
8
Part 3 – Building an Integrated 3-Statement Financial Model with Valuation by DCF
15
Part 4 – Case Study

Working with lookup functions

Lookup functions are some of the most widely used functions in Excel. Generally, the intention is to fetch a value from one dataset (the source) to another dataset (the target).

Let's first understand what a proper Excel dataset is.

The first row of a dataset is the header row that contains the names of all the fields. As you can see in the illustration in Figure 3.1, the header row of the sales report includes the following fields: Date, Product, Product Code, Salesperson, and so on. Each column in the dataset represents a field, and each row represents a record. Finally, no entire row or entire column in the dataset must be empty, and there must be at least one empty row below the dataset, at least one empty column to the right, one empty row above (unless the dataset begins from row 1), and one empty column to the left (unless the dataset begins from column A) of the dataset.

For example, say you have two datasets: a sales report that...