Financial modeling is a technical skill that falls under the umbrella of fintech – financial technology. It is a subcategory of mathematical modeling that focuses specifically on financial data and is used for financial and business decision making.
In the context of financial modeling, a “model” is a mathematical representation of a real-world situation. Nearly any situation can be represented by a model – sales of a product, students enrolling in a course, bacteria growing in a petri dish, etc. Models help to explain situations, understand the relationship between variables, and make predictions about the future.
Financial models are designed to take in inputs in the form of business data and variables and produce outputs in the form of metrics or forecasts. For example, a budget model may take historical spending and revenue data as an input and produce spending limits for the next quarter as an output. Other input variables that could help improve the model might include which quarter is coming up (Q1, Q2, etc.), how many new hires will join the company, or the current national interest rate.
Financial models produce outputs by plugging the inputs into specified formulas. Oftentimes, complex models contain many different formulas that reference one another.
Most often, financial models are built in spreadsheet platforms like Microsoft Excel.
Analysts write formulas in the cells of the spreadsheet and, when data is entered into another section of the spreadsheet, the formulas take the data as an input and work to produce the desired outputs.
Financial modeling is used to perform financial analysis with robust data sets. Bankers, analysts, traders, business owners, economists and accountants all use this concept in their work.
Examples of situations that could be represented by a financial model include:
A useful application of financial modeling is predictive forecasting. In predictive modeling, analysts enter historical data and the model produces a forecast about the future. As an example, a business analyst might use historical data about the past year’s sales to predict the coming year’s churn rate. Predictive models produce outputs that will not be absolutely accurate but that can help businesses make decisions and have a reasonable expectation of what will happen.
In contrast, descriptive models simply produce information about the present or past. A business analyst might use a descriptive model to calculate how a company’s profits have changed over the past ten years or to calculate how much of a product is currently in stock.
1. Brush up on your Excel skills
Excel is a necessity when you’re building financial models. You’ll need to know more than the bare bones of copying, pasting, entering, and deleting data too. Make sure to learn the syntax for entering formulas, techniques for quickly manipulating data, and shortcuts.
Some helpful formulas to learn are =IPMT, =RATE, =SLOPE, =XIRR, =MIRR, and =AVERAGE.
2. Develop a firm understanding of finance and business analytics
Data skills will take you far in the world of analytics but it’s tough to perform analysis or build models for a business that you don’t understand. Before setting out to build financial models, learn the ins and outs of finance and understand the business models that you’ll be working with.
Understand how the business makes a profit and what costs it faces. It’s also useful to understand factors that cause fluctuations and how the business has grown over time.
In terms of finance basics, read up on capital markets and investing, interest rates, loans and amortization, present value and future value, credit, accounting, and cash flows. Investopedia is a great one-stop shop for these kinds of skills.
3. Understand how metrics relate to one another
In order to properly write formulas for your financial models you need to know how all of the variables relate mathematically to one another. One of the first relationship’s to learn is that Profit = Total Revenue – Total Cost. Do you know which variables are used to calculate revenue for your business? How about cost? Which metrics are effected by interest rates and how?
4. Get comfortable working with data
To build a good financial model you will probably need a lot of data. You’ll need to know how to collect this data, clean it, manipulate it so that it is compatible with your model, and display it in a way that makes sense to you and others.
Data analytics bootcamps can provide an excellent foundation in data. We have a comprehensive guide to choosing a data analytics bootcamp that you can check out if you want to level-up your data skills.
5. Learn about standard financial models
Sometimes you don’t have to invent the wheel! There are a number of financial models that are used frequently in business and finance. Analysts often use these as a starting point and can expand upon them to meet any specific needs of their organization.
Common financial models to learn about include:
6. Try using pre-built templates
For the common model types we mentioned above, you can usually find a template to work off of so you don’t need to build the whole thing from scratch. The Corporate Finance Institute has a library of templates that you can browse. Try downloading one and playing around with it. Even if you don’t yet have real data to analyze you can see what happens when you add values as inputs.
7. Take a class or bootcamp in fintech
Financial models are an important unit in the course plans for most fintech bootcamps. In more advanced bootcamps you’ll even learn how coding languages like Python can be used to create polished user-friendly models and perform advanced analysis.