What Are the Different Methods of Financial Forecasting?

Unsure of which financial forecasting method to use? Struggling to understand the difference between quantitative and qualitative forecasts? 

We get it. Forecasting is not the easiest thing to master, especially if you don’t have a financial background.

In this article, we’ve laid out the key financial forecasting methods and techniques in plain English so you can determine which one you should use.

Quantitative Methods

First, we’ll cover quantitative forecasting. It relies on historical and current financial data to predict future outcomes using quantitative data analysis methods.

Quantitative forecasting methods:

• Use financial data and numbers that can be easily measured and analyzed.
• Are based on facts and figures as opposed to guesswork or opinion.
• Use existing financial data to determine what’s happening in the market right now and how it will affect your business.

Although quantitative methods are overall a highly accurate and reliable way to forecast in finance, they are less useful and accurate when it comes to rapidly changing environments or unpredictable market conditions.

Let’s take a look now at some of the most common quantitative forecasting methods and models that businesses use.

1. Time Series Analysis

One of the most common forecasting models in finance is known as time series analysis. This analyzes specific data points collected at chosen intervals of time.

Time series analysis is one of the most widely used methods for understanding seasonal or cyclical patterns, and its purpose is to identify trends, anomalies, sales spikes, and seasonal patterns that provide insights into what might happen in the future.

There are a couple of ways to perform time series data analysis.

Moving averages are used to reveal longer-term trends. It uses a fixed number of recent data points to calculate the average. This data can—and should—be updated as new figures become available.

• Example: A retailer might use weekly data points to track sales over a year. This will reveal general demand trends but will ignore one-off daily spikes. 

Exponential smoothing places more importance on recent data as opposed to older data. This makes it extremely responsive to fluctuations and changes when they occur. 

There are a few variants to use:

• Simple exponential smoothing works best for data that lacks clear trends or seasonality. It’s calculated based on the weighted average of current observations and prior forecasts, without taking time into account.
  • Example: A retailer can predict future sales for a product with stable demand.
• Double exponential smoothing includes adjustments for linear data trends with no seasonal patterns.
  • Example: A growing organization that lacks seasonal data can conduct revenue forecasting.
• Triple exponential smoothing (AKA Holt-Winters method) accounts for both trends and seasonality.
  • Example: A retailer can predict sales volume by accounting for baseline sales and seasonal trends (like the holidays or summer).

2. Econometric Models

Econometric models are a complex forecasting method that rely on mathematical equations and statistics to study how different economic factors are connected. By analyzing past data, these models identify patterns and work out the cause-and-effect relationships that shape the future. 

Given their complexity, specialized software exists to make calculations and analysis more straightforward. For instance, STATA and EViews are two platforms that can perform econometric analysis.

To give you an overview, though, this is how econometric models typically work:

1. Variables such as GDP, interest rates and consumer spending are identified.
2. Cause-and-effect relationships are identified. For instance, “How do unemployment rates affect consumer spending?”
3. Statistical analysis is performed to quantify the relationships.
4. Outcomes are predicted, which estimates future trends.

Econometric financial forecast models are used when you have multiple variables and you want to understand how their dynamics affect the future. 

Here are a couple of scenarios where you would use econometric models:

• A company might want to forecast product demand based on factors like consumer preferences, behaviors and economic status.
• A real estate agency might analyze how house prices react to changes in mortgage rates and population numbers.
• The government may analyze how rising tax rates might affect employment or GDP.

3. Financial Modeling

Financial modeling uses data and calculations to analyze performance based on simulated scenarios, helping assess your business’s financial position. 

For example, financial modeling can be used to work out what might happen to a business if interest rates were to rise.

There are a few ways to carry out financial modeling.

The most commonly used method involves using Excel. Through use of formulas like NPV and IRR, pivot tables and macros, the data can be manipulated to simulate a wide variety of financial scenarios. However, a financial modeling software can also be employed to simplify the process and offer more advanced analysis capabilities.

There are also different key modeling techniques available. We won’t get into the details of them here, but each has a specific use case:

• The 3-statement model is used to forecast and analyze financial performance.
• Discounted cash flow analysis is typically for valuing projects or companies.
• Scenario analysis prepares for uncertainties and unforeseen circumstances.
• The rolling forecast model plans for rapidly changing environments.
• A capital budgeting model is for deciding if major projects or purchases can go ahead.

You can turn to financial modeling whenever you want to understand potential future outcomes in different situations. Of course, we can’t predict the future with absolute certainty, but these models give you a solid foundation to make informed decisions.

Qualitative Methods

Let’s move on to qualitative financial forecasting techniques. Whereas quantitative methods rely on solid data, qualitative data analysis methods are the opposite and use opinions and non-numerical information to make predictions.

Qualitative methods:

• Use expert insights, market research and historical experiences.
• Are based in judgment and subjectivity as opposed to hard facts or numerical data.
• Are useful when there is little to no historical data available (e.g., for new businesses or products).

The beauty of qualitative methods lies in their flexibility and ability to rapidly adapt to changing conditions. However, they are less precise and reliable, and more prone to bias.

1. Market Research

As a business owner, you’re likely already familiar with market research and its process. Analyzing information about a target audience is essential for many reasons, including helping you predict the future of your company. 

For instance, customer preferences and behaviors can tell you how you might shape your marketing strategies or improve your products.

There are different ways and tools to conduct market research. The most common ones include:

• Surveys
• Focus groups
• Interviews
• Market analysis
• Product testing
• Social media observation
• Online tools like Google Analytics

What exactly does all of this have to do with financial forecasting?

Market research helps protect your business’s financial health, gives you a competitive edge, and ensures market relevance. Besides, it enables more targeted marketing and reduces risk and costly mistakes in strategy or product launches.

2. Delphi Method

This method relies on expert opinions from a group of experienced individuals. Multiple rounds of questionnaires are used to generate outcomes on a specific topic or scenario.

Importantly, to prevent bias or influence, the experts participate anonymously. Each member of the group is unaware of who the other participants are.

Here’s how it works:

• First, the problem or topic is defined and a panel of experts (with relevant knowledge or experience) is chosen.
• The questionnaires are designed and conducted. 
• After each round, feedback is gathered.
• The questions/feedback process is repeated until a consensus is achieved.
• The final conclusions are then analyzed and compiled into a report.

You can use this method when you don’t have historical data available or when you need to rely heavily on expert knowledge. 

For instance, the Delphi method is useful for predicting how technology might advance or how market trends might evolve for new and emerging industries.

Be aware that this method is time-consuming. The process involves multiple rounds of questions, and you’re relying on multiple people to get answers back to you in a timely fashion (if you’ve been in business for a while, you’ll know that doesn’t always happen).

3. Scenario Planning

Our last qualitative method is known as scenario planning. This tool is invaluable when you need to prepare for uncertain futures.

The method works by developing multiple scenarios based on differing assumptions and variables. Instead of focusing on a single outcome, scenario planning explores a range of possibilities and allows you to plan for each.

This is how it works:

• First, the variables that are most likely to influence the future are identified.
• A set of plausible futures is created: best case, worst case and most likely case.
• The impacts of each case are analyzed to see how it would affect the organization.
• Finally, the organization can create contingency plans to adapt to any outcome.

Scenario planning is useful in many instances, but it is most useful when trying to understand the level of upcoming risk. Additionally, it proves essential when determining where and how to allocate resources depending on future conditions.

A couple of use cases might look like this:

• A manufacturing company might model how economic downturns or technological breakthroughs could impact its operations.
• A logistics company could look at how to best plan for supply chain disruptions caused by geopolitical events or natural disasters.

Hybrid Methods

Now, let’s throw in a curveball. You don’t have to rely on a singular method to perform financial forecasting.

Hybrid methods combine quantitative and qualitative methods to give you the best of both worlds.

1. Combining Quantitative and Qualitative Approaches

By combining the strengths of both model types, you gain a more comprehensive view of the future, leading to better decisions and enhanced accuracy.

Moreover, this approach reduces reliance on a singular source of information and offers more flexibility, allowing you to adapt your methods based on the amount of historical data available.

Let’s look at a couple of hybrid financial forecasting examples:

• A clothing retailer wants to understand future demand for a new clothing line.
  • It uses past sales data (quantitative) and questionnaires on trends (qualitative).
  • The outcome results in a prediction of which styles are likely to sell and subsequently reduces inventory risks.

• A tech company is looking to launch a product in a country with limited historical data.
  • It applies global sales trends (quantitative) and surveys local experts about consumer preferences (qualitative).
  • The company adjusts its pricing and marketing strategy to fit the local market.

2. Machine Learning (ML) and AI in Forecasting

Less than 20 years ago, most forecasting had to be completed manually using Excel. Now, ML and AI have made accurate forecasting easier than ever, thanks to their ability to analyze vast amounts of data in seconds.

Not only can these technologies leverage complex data, but they can also spot subtle patterns that may otherwise be missed.

Additionally, these tools can update and adapt forecasts in real-time making them effective for ongoing analysis and dynamic scenarios.

For instance:

• An AI algorithm can analyze stock data, news articles and social media opinions to make better stock market predictions.
• AI can analyze historical retail data, seasonality and customer behavior to forecast retail sales.
• Banks use ML models to predict the likelihood of loan defaults by analyzing things like credit history, income and wider economic conditions.

Technology’s capability in handling colossal datasets means we can now gain insights on a scale never seen before and in lightning-fast time.

And we should not forget that AI is just taking its first steps. Who knows how much better and faster forecasting will become a few years down the line?

Final Thoughts

Hopefully this article has given you a good oversight of the various financial forecasting models and why you might use them. Both quantitative and qualitative methods can help you plan for future success.

However, we understand that financial forecasting can be challenging for many business owners. If you feel like you might need some help, let Finvisor assist you. 

Our financial experts, including CFO and advisory services, can perform all types of financial analysis to help your business thrive and grow.

For more information, get in touch with our friendly team.