What is a Forecast?
Please note that this article is intended for educational purposes only and should not be deemed to be or used as legal, employment, or health & safety advice. For guidance or advice specific to your business, consult with a qualified professional.
A financial forecast is a prediction about a company’s future performance. The prediction can be based on data from a variety of sources. For example, it can include data from the company’s financial statements, general data from the sector and broader macroeconomic indicators.
An example of forecasting
There are numerous approaches businesses can take to financial forecasting. All of them, however, ultimately boil down to qualitative forecasting methods and quantitative forecasting methods.
Qualitative forecasting methods leverage human judgement. The judgement doesn’t necessarily have to be informed. In fact, sometimes it’s important that it’s not informed. For example, market researchers usually want to speak to average members of the public rather than subject-matter experts.
By contrast, there are times when it’s highly desirable to get informed opinions from subject-matter experts. These would typically be when businesses need to take important decisions quickly and/or with limited data.
Qualitative forecasting can also be used in combination with quantitative forecasting. A typical example of this would be a management team analysing a report created using quantitative forecasting techniques.
Quantitative financial forecasting takes the exact opposite approach. It aims to remove the human element from forecasting and focuses purely on analysing the data.
To a certain extent, quantitative forecasting has been made easier thanks to the development of digital technology. That said, computers are only as good as their programming and their data.
In the modern world, the act of collecting data can have significant practical, financial and legal implications. For example, if companies are using personally identifiable data, they need to think about GDPR (and data security).
The framework of forecasting
Whatever financial forecasting model is chosen, the process used to implement it typically follows much the same lines.
Set your question
It is impossible to overstate the importance of asking the question you really want to be answered. For example, sales and revenue are linked but not the same. If you want to know about sales, you need to phrase your question so that it reflects that, and vice versa.
Determine your assumptions
Assumptions can be dangerous, but they are also useful and can frequently be necessary. For example, if you are undertaking financial forecasting to predict your budget for next year, then you are assuming your company will still be in business next year.
Choose a suitable forecasting model
Choosing the right forecasting model can also be a challenge. Often, companies need to balance thoroughness with budget constraints.
Identify the relevant variables
Relevant variables are variables that point, in some way, towards the answer to the question you posed. For example, if you were trying to estimate revenue, then sales data would usually be a relevant variable.
Gathering data often requires financial outlay. This means that, again, companies often need to strike a balance between thoroughness and budget.
Collect the necessary data
Once you’ve identified what data you need, your next step is to collect it. In some cases, this can take a lot of planning. For example, if you want to run a focus group, you need to find participants and get them together in the real world or online.
Analyse the collected data
This is the point when you create your forecast. It may also be the time when you put your forecast to real-world use. For example, if you’ve created a financial forecast, you may use it to predict your cash from and to set at least an estimated budget.
Validate the process (if possible)
If possible, you should review the forecast later in the light of what actually happened. If it was inaccurate, you might be able to improve it. If it was accurate, you might be able to use the lessons learned for other forecasts, financial and otherwise.
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