Financial modelling proves to be a powerful tool in business. Although the future is uncertain, the financial modelling process can be used to provide insights and forecast what is likely to happen tomorrow based on decisions being made today.
We’ll cover everything there is to know about why you should use financial modelling, various financial modelling techniques, and software tools that can make it all a breeze.
Financial modelling is a process that summarises a company’s profits and costs in an effort to determine the impact of future events and decisions. It allows for leaders to make decisions now using data that’s available, without having complete information about what is yet to come.
The data involved includes the past, present, and forecasted future. These insights are what empowers executives and stakeholders to make the best business decisions based on what is currently known.
They may also be used to estimate the valuation of a business or conduct competitive analysis against other businesses in the same market.
There are many uses, or objectives, for financial modelling. From financial analysts to company executives, these informative spreadsheets and calculations are heavily impactful.
Some of the main objectives include:
Financial modelling works off of assumptions. Along with good assumptions, a key to accurate modelling is the data you use to carry out the calculations. The model is only as good as your input.
So, it’s always beneficial to utilise an automation software that can pull data from all sources and secure it safely in a centralised location for use.
Financial models are made up of historical data, assumptions, and calculations that work together to predict the future. Financial models are not simply spreadsheets. While Excel is often used to create a financial model, there are crucial aspects that make up a financial model and how it works.
The main characteristics of how a model works is that it:
Financial models are in the world of business all the time. Since the future is unknown and decisions today can greatly affect what’s to come, it’s of great use to have the data available to gauge different scenarios.
Financial models are commonly used to predict future sales growth. With just two main components, namely the prior year’s sales and current year’s sales, a formula can be applied to forecast next year’s sales. Additionally, financial models can be utilised for cost reductions, cash flow, and mergers and acquisitions, to name a few use cases.
The application of different models can provide decision makers with valuable figures, such as the Net Present Value (NPV) of a business or the internal rate of return (IRR).
Many players within businesses will rely on financial models. For example, company executives will utilise such models to make key decisions on whether or not to move forward with a new project. They’ll use the model to estimate potential profits and costs. Then, they can conduct cost/benefit analysis to see if it’s worthwhile and in the company’s best interest to pursue.
Financial analysts use these models to understand and explain how a variable will impact the business. To exemplify, they can analyse how internal or external factors, like regulations or economic policies, may affect the company’s stock price.
Financial modelling may sound difficult and hard to do at the get go, but it doesn’t have to be. Through practice or professional training, you can learn the ropes and become an expert.
Additionally, the aid of technology has made financial modelling easier than ever before. Rather than having to conduct models manually through spreadsheets like Excel, you can utilise financial modelling software to prevent errors and streamline the modelling process.
Data automation software improves the quality and access to your data, all the while making it possible to process data in a fraction of the time it would take to fulfill the same tasks manually.
Financial modelling tools make it simple to visualise models, perform analysis, and even present the findings in an easily understandable manner.
Financial modelling and spreadsheets typically go hand-in-hand.
Whether you are going to utilise the aid of automated software solutions or not, you can benefit from this list of best practices for financial modelling:
Financial modelling can be broken down into a series of steps. When combined, it creates a unified and cohesive picture of what you can expect in your business.
While every analyst or professional may have their own method to create a financial model, the typical steps follow in this order:
Financial models come in different shapes and sizes. The most commonly used financial models are as follows:
The most basic of the financial models, the three statement model is built upon the income statement, balance sheet, and cash flow. You link the three with formulas and can test various assumptions and their effect on these financial statements.
Like the name implies, the merger model is used to analyse the accreditation or dilution of a merger or acquisition. Investment banking and corporate development utilise this model often.
Before a company goes public, it can leverage the IPO model to value the business. It helps to understand how much investors will be willing to pay for the company.
For financial planning and analysis, the budget model can be used to allocate resources on a monthly or quarterly basis.
By mixing DCF models together, you can use the sum of the parts model to calculate the Net Asset Value of a company, for example.
Mathematical criteria make up the main two option pricing models, which are Black-Scholes and binomial tree. These models are straightforward given the mathematical formulas.
Like the budget model, the forecasting model is used commonly in financial planning and analysis. Oftentimes, the two will be combined in the same workbook.
The consolidation model withholds information about different business units on different tabs. Then, you can use the consolidation tab to sum up business units. In this sense, it’s similar to the Sum of the Parts model.
The Discounted Cash Flow is based on the 3 statement model and is used to deduce the value of a company using the Net Present Value (NPV) of its future cash flow. Professionals working in equity research and capital markets rely on this type of model.
This advanced type of financial modelling is highly detailed because it uses cash flow waterfalls and circular references. Someone trying to create a LBO model will need to understand debt schedules to do so.
The above models represent an abbreviated list of financial modelling options. Regardless of the type of model you wish to create, you can benefit from using automation solutions to help source, transform, format, and secure the data you need to reap the insights you seek.
There are different stages at which you can implement automation within your financial modelling exercises. Given the nuances involved, an entirely automated process may not be what you actually want, you save time and reduce errors by incorporating automation along the way.
For example, in the first step of financial modelling, you have to pull financial data from three historical years. This can be a time-consuming step if your data exists in various locations and has to be organised. Instead, you can opt to automate the data processing and input step.
Data processing is the steps needed to prepare raw data and transform it into valuable insights. Whether conducted manually or automatically, the data processing cycle typically flows as follows:
By using an automation tool, you will be able to process data more quickly and accurately. You can also rely on the system to locate and pull data on an as-needed basis. This results in:
Financial modelling is data in action. It’s a way to conduct analysis and glean insights from raw data. While every business collects data, not every business knows how to put it to good use.
The power lies within analysis. And, in this brief section, we’ll shed light on how to take advantage of automation to reap the game changing benefits of analysing data properly.
Data insights is, in essence, the value of data analytics. Insights refer to what you learn by analysing data. This form of information is then used to make decisions or design strategies for success. Insights can come in the form of highlighting areas ripe for improvement within your business, or they may help you better understand how to allocate resources for a new project.
Data analytics are mathematical algorithms and statistical models that are used to transform raw data into insights by way of uncovering patterns and trends.
While the first step in achieving optimum data insights resides in collecting quality data, there’s a lot more to the equation than meets the eye. You’ll also need to focus on:
The aforementioned best practices seem doable in theory, but when you get into the practical application, there are many steps that can go awry if you conduct operations manually. Since data moves at a lightning pace, you need to ensure that it’s always up-to-date and of good quality.
Let automation tools do the heavy lifting. Automation tools are able to: collect correct data within context, run continuous models that result in timely analytics for agile decision-making, create tailored reports that can be sent automatically at your desired frequency, and ensure that all sensitive data is securely stored and transferred between parties.
There’s no denying the importance of data when it comes to financial modelling and analytics. In an effort to manage data effectively, data transformation must occur.
The goal of data transformation is to move data from its source to its final setting, or go from source data to target data.
Data can be transformed manually or automatically. Regardless of how your business manages data, the steps will involve:
There are different ways to transform data, and the method that you choose will depend on your end goals and usage for the data. Some of the data transformation options include:
With the goal of either moving data to a new system or making data compatible with existing data sets, many organisations will leverage the aid of automation tools. Software solutions like SolveXia can connect all your data systems, clean your data, and map the raw data to transform it into usable insights.
Consider these best practices when performing data transformation:
All your data needs can be carried out and conveniently managed with automation software. These tools save your business time and money. Automation will transform data and provide you with a host of benefits, like:
For the many uses of financial modelling, there’s an automation tool that can help you access, collect, store, transform, and utilise your data. With the ability to utilise data for insights, you can make optimal business decisions in a timely manner that can help to transform your business for the better.