What is financial forecasting?
Financial forecasting is where you aim to see into the financial future of your business.
It might sound challenging, but predicting a company's future financial trends and forecasts can be surprisingly accurate. We’re not talking tea leaves here, financial forecasting uses sophisticated analysis of current and historical data to build a picture of what the future might look like.
Financial metrics can feed into financial forecasters, making them useful for business development, financial planning, investment research, and risk management.
It’s a way of working out things like projected investment income, assessing the impact of any internal changes, and anything else that could negatively - or positively - impact the company’s future performance.
Many different financial forecasting models and a hefty amount of data are needed for accurate results, so many businesses prefer to use software to simplify the process.
It’s like the ghosts from A Christmas Carol: it involves learning from the past and understanding the present to prepare for the future.
But there’s nothing spooky about financial forecasting: with the right tools and data, it’s a powerful way to ensure your business is running a tight ship.
Why is financial forecasting important?
For any FP&A team or CFO, the benefits of financial forecasting are massive - but the wider company may not immediately understand its inherent value and the vital part it plays in business success.
We’ve got that covered. Here are some ways your business can benefit from financial forecasting models.
Every business wants and needs a budget that’s rooted in reality. When teams come knocking asking for more resource allocation, financial forecasting can help make those decisions.
If there’s a big spend coming up that the company is considering, like an acquisition or partnership, then financial forecasting can give the board an idea of what might happen in the months and years following that decision.
Financial forecasting becomes a necessary and valuable tool in deciding whether it’s financially feasible to go ahead with a plan or reschedule for another time.
On that note, investors might be after some answers if a company changes course on high-stakes decisions like mergers. Financial forecasting is a helpful tool in explaining the wider picture, getting buy-in, and building trust with investors.
Financial forecasting also helps lenders decide whether a business can safely repay debt. A lender can look at the modeling to see if there are any risks with giving the business a loan and make an informed decision.
Helps strategic planning
Got a strategic plan to put together?
Then financial forecasting is your new best friend.
By giving financial insights into current trends, challenges, and growth opportunities, forecasting models can help to predict long-term financial health. It’s perfect for the long-term vision that strategic business planning provides.
Forecasting models provide the business’ bigger picture when it comes to financials. They’re a great way of noticing where resources might be underutilized or overextended earlier to stop a nasty surprise from springing up further down the line.
The more efficiently a company can run, the better revenue and profit - so it makes sense to use financial forecasting models to find any holes.
What are the types of financial forecasting models?
So forecast models are the bees’ knees, right? But with all the data needed and differing quality and types, that doesn’t make them the most straightforward thing to put together. It’s time for a run-down of how these models work.
There are two different approaches to financial forecasting models: qualitative and quantitative. Let’s take a look at the differences so you can make a decision on which might be best for your company.
Quantitative forecasting models
This is the realm of hard data. Quantitative models are best suited for analyzing a company's performance and anything else that relies on financial statements. With numerical data, statistical analysis, and analysis of older data, these models can pick up on trends and patterns and generate forecasts.
A lot of data is needed for quantitative forecasting models - and the more data, the better the results will be. Because the data is objective, there are usually fewer errors and biases. As a result, the quality and breadth of the data matter for optimal results.
Qualitative forecasting models
These methods look at the data that’s hard to quantify: human opinions, market trends, and other subjective factors. They’re also suitable when the financial data is lacking and some estimates need to be pulled in.
Qualitative models don’t need as much data to run, making the quality of the data inputted even more crucial. These models are generally less accurate - it’s hard to predict the future as it is - but they’re more flexible, so they work well for new markets and trends.
The best picture will include a mix of the two model types for a complete financial analysis.
Financial forecasting methods
No business or industry is the same, so different models exist to help companies with a financial modeling system that suits their needs.
The beauty of these models is that different ones can be used for different scenarios—we’ve provided examples below.
Top-down financial forecasting is a qualitative method that looks at the macro-level view of a market which is then broken down into specific sections like product lines, departments, or regional markets to predict revenue.
Think of it like a sculpture: the original piece of marble is gradually chipped away to form a shape. This is like top-down forecasting, where the bigger picture’s data and forecasts are broken down into components where the forecasts are further refined.
Top-down forecasting helps predict sales and revenues in big and complex industries. It’s often complemented by bottom-up forecasting, which is the process in reverse, to further ensure accuracy.
How to calculate
To start with top-down forecasting, you need macroeconomic data like GDP, consumer spending stats, or competitor benchmarking. Initial analysis will get you to the top-level forecasting.
Now determine the market share of your business based on the industry analysis. If relevant, you can determine how it might evolve by looking at competitors, strategic investments, and SWOT analysis.
Now you can estimate the market sales or revenue forecast. This is achieved by multiplying the estimated market size by the market share. You can then break down that figure into whatever components you need, like product lines or distribution channels.
Creating the forecast allows you to project future sales or revenue for each component by looking at growth rates, market trends, and relevant historical data. Combine those totals for the overall top-down financial forecast.
Want some statistical analysis that works out forecasts based on variable changes? Then regression models are the way forward. One popular method is the multiple linear regression model, which looks at the impact of several independent variables on a dependent variable.
It’s a great way to assess the statistical relationship between different components of a business and their impact on revenue. If you have just one independent variable to assess, this is called simple linear regression.
Let’s say a retail company wanted to look at the impact of its marketing spend, store size, and average customer income (the independent variables) on its sales (the dependent variable).
A software program can generate the model by gathering historical data on those four variables. If everything’s good and there’s a statistically significant output, this can be used for forecasting future sales and making data-driven decisions.
Time series models
Any guesses on what these models are based on? That’s right - time. Or more specifically, data points collected over time. Some examples could include GDP, historical sales revenue, or returns on an asset.
They shine by examining which factors influence sales or revenue during a specific period, such as a company's accounting period. Time series models, such as moving average models, are therefore great for quantifying seasonal patterns in data or identifying any outliers, which can be useful for fraud detection.
It’s like when a tree specialist looks at how many rings a tree has to determine its history and whether weather factors impacted its growth up to that point. Time series models look over time to spot trends and patterns to predict future growth.
If a company wanted to predict its monthly sales revenue using a moving average model for the next year, the first step would be to gather all relevant historical data from the business about previous monthly sales revenues.
Using specialist FP&A software, the company can look at the dependencies, trends, and patterns that arise. If everything checks out, it’s possible to estimate future monthly sales from the model.
A typical model for this scenario would be the autoregressive integrated moving average (ARIMA) model. This is great for short-term forecasting and only needs historical data for its output, but it can’t consider market curveballs.
Need a valuation for an upcoming funding round? Then you need a financial model to determine this. One example is the discounted cash flow (DCF) model, which uses the current cash flow to estimate a business's value.
The DCF model works on the basis that the value of a company is the sum of its future cash flow, discounted by the present figure. This makes it a handy tool for CFOs to assess whether a merger or acquisition is a good idea. It's also suitable for investors looking to put their money into a business raising capital and see what return they’ll get.
We’ll use a straightforward M&A example for the discounted cash flow method. If a company wanted to buy a smaller firm, it would look at its statement of cash slows, balance sheet, historical financial performance, growth prospects, and broader industry trends.
The next step is to determine the appropriate discount rate or rate of return for an investor. But how to determine the discount rate? A weighted average cost of capital (WACC) usually does the trick as it factors in debt and equity financing.
With the discount rate, the company can return the projected future cash flows to the present. If the final figure is lower than expected, that might give a company pause to consider whether that firm is a good buy.
This dynamic financial model links the holy trinity of the cash flow statement, balance sheet, and income statement. By examining how the current financial statements interact, finance pros can assess various factors such as the business’s profitability, solvency, and cash generation.
The three-statement model is great for setting budgets and internal planning to allocate resources more effectively. It’s also good for assessing how well parts of the business are performing financially and whether any tweaks need to be made to improve profitability.
Companies can also use three-model statements to determine whether a planned expansion, like an investment or acquisition, is financially viable.
How to calculate
The process has a few steps with the pro forma financial statements, but the payoff is worth it.
- Starting off with the income statement projections, forecast the future revenue, cost of goods sold, and operating expenses based on historical data. This will produce the estimates needed for future profits and income.
- Now it’s time for the balance sheet forecast. Looking at the projected income statement to work out forecasts on future assets, liabilities, and equity, will give a better picture of the company’s projected financial position.
- The third and final piece of the puzzle is the cash flow statement. Using the projected income statement and balance sheet, you can forecast the future cash flows from any operations and investing activities.
With three separate forecasts required for this model, software programs can make the whole process much smoother and quicker for finance teams.
Got everything together? Then you now have a comprehensive overview of any planned expansion's impact on the business’s finances. This can help a company decide whether sufficient money is coming in, financing is needed, or optimizing the capital structure is better before pressing ahead.
These models can be used for different purposes and with different results. The key to success? Good-quality data can make or break the outputs.
How software can help with financial modeling
An excellent software program for financial modeling will enhance your team’s efficiency, accuracy, and decision-making progress. Want to free them up to focus on the big things? Then software programs are the way to go.
Here are some more benefits of using software for complex financial modeling.
Compare multiple models
Get ready for some advanced analytics. Using software programs makes comparing qualitative and quantitative models a breeze, saving your team time and resources.
Evaluating performance and analyzing different financial scenarios has never been easier. Comparing several financial models at once can lead to spotting risks before they happen, noticing new opportunities, and improving decision-making.
Every organization is different, so you’ll be after tailored solutions to showcase the financial models. Software makes custom calculations, templates, and reports a simple task to suit any business size or industry.
This custom approach can be helpful when a company is scaling, as the models can grow with the company instead of staying static and needing reworking.
Automation and speed
Without software, an FP&A team or specialist would need to assemble an Excel spreadsheet from scratch (as every company’s financial system is unique).
After that, the data must be manually aggregated and inputted into the modeling spreadsheet - a time-consuming practice that can lead to mistakes.
The software eliminates the admin around financial modeling and automates the whole process. Many software programs use machine learning and AI to pull and analyze the data for financial modeling. This saves your team hours of time and reduces busy work.
Seamless data integration
Say goodbye to endless Excel spreadsheets that are constantly outdated. With software, you can easily import, handle and analyze large amounts of data from various sources.
The software then processes the data in real-time, so you’re never left making analyses from incorrect or old data sources. It’s a win-win.
Ever had a situation where someone’s made a change that breaks the whole model? That’s a thing of the past with modern software.
Cloud-based software lets FP&A teams collaborate on one or multiple financial forecasting models with regular updates and version control. This way, everyone’s working with the most up-to-date data and assumptions.
Now you know all about financial forecasting models.
But did you know Cube can help you forecast better?
Cube differs from other FP&A tools because it keeps you in your familiar work environment---Excel.
(Plus Google Sheets for easy cross-department collaboration and reporting.)
This means quick time to value (most FP&A teams onboard within two weeks), power, and flexibility.
Get a free demo by clicking on the image below.