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6 Common Methods Used in Financial Forecasting Models

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Leadership teams regularly agonize over the profitability and growth of their business. New and unprofitable companies—even ones that are established—must be steered towards sustained profitability by competent financial officers and decision-makers who keep their organization well out of harm’s way.

Financial forecasting models are an excellent tool for keeping businesses afloat. These models can help decision-makers turn otherwise disparate data points—like market trends and expert opinions—into critical assessments of their company’s current and future financial health.

Let’s take a closer look at some of the best ways to arrive at more reliable and accurate financial models, as well as the latest tools of the trade.

An Overview of Financial Forecasting

Analysts and business leaders use forecasting to predict a company’s future performance by looking at its past financial performance and current market conditions. 

A business forecast typically includes information derived from a company’s financial statements, the industry as a whole, and includes assumptions about future risks, obstacles, and opportunities. Forecasting models help leaders visualize short- and long-term performance drivers and barriers to business growth and are often used to assess critical metrics tied to a business’s finances—think sales growth, debt obligations, overhead expenses, etc.

Financial forecasting models help identify financial problem areas in any business, what’s causing those problems, and the best possible ways to solve them. Decision-makers also use financial forecasting models to evaluate current business activities, and their long-term viability or value to the company often expressed as a return-on-investment (ROI). Initiatives with low ROI are identified and slashed to make room for better investments.

Most importantly, forecasting helps leadership teams set business goals and create a roadmap they can agree on to achieve those goals.

A Quick Caveat about Financial Forecasting

Crystal balls and palantirs don’t exist, and financial forecasts aren’t foolproof. Even when you account for human bias and triple-check your spreadsheets for errors, these models will never be 100% accurate. Every financial forecasting model relies wholly on past information and assumptions, whether from past data sets or the knowledge and opinion of industry experts. 

Although business leaders often use financial forecasting models as guideposts for meeting business objectives while avoiding risks, financial forecasts aren’t set in stone. They can (and often do) change as market conditions change or the business itself changes internally. 

Financial Forecasting Models Commonly Used by Analysts

By relying on a combination of human expertise and historical data, leadership teams become better equipped to make better-informed, strategic financial decisions for their organization. 

Here, we’ll go over the two major approaches to financial forecasting—qualitative and quantitative forecasting. Each one has its advantages and disadvantages and is suited for different situations. 

Utilizing multiple approaches like these helps turn unbacked assumptions into well-rounded, data-driven predictions about the future performance of your business.

The Qualitative Forecasting Approach

Qualitative forecasting primarily relies on the knowledge of expert and consumer opinions—the kind of information that cannot be endorsed by historical data. It’s instrumental in product development situations where historical information isn’t available or difficult to compile.

Below are some qualitative models that leadership teams rely on to create reliable business forecasts.

1. Expert Opinion

Simply put, this approach asks subject matter experts (in-house or third-party) to form predictions given a specific set of parameters. The leadership team may choose to bring together experts from various disciplines and departments to create a more holistic picture of the company’s future. 

Once their combined input has is gathered, business executives have the freedom to make changes to their forecasting model based on insights they may have garnered.

Because of the expert opinion approach’s straightforward manner, many companies use it, regardless of available resources (or appropriateness). Of course, this comes at a cost: If you rely solely on expert opinions for your forecasts, your accuracy will be limited by smaller sample sizes and the limited knowledge of your personnel.

2. Market Research

This method is often used to assess a market’s need for a particular product or service and relies on data about a company’s current customers and total addressable market (TAM). Market research often includes customer surveys, conversion metrics of existing campaigns, A/B tests, and research into what competitors are doing successfully and unsuccessfully.  

Researchers systematically gather large data sets like these, just as they would for a scientific study or clinical trial, to achieve statistical significance and reduce the impact of human bias inherent to smaller participant pools and unstandardized data collection procedures.

As you can imagine, market research takes a lot of time, money, and effort to deploy and analyze, not to mention that human bias and error can still skew your final decision.

3. Delphi Method

The Delphi method is similar to the expert opinion approach in that it relies on subject matter experts. However, it follows a much more structured and regimented process.

In this financial forecasting method, a group of subject matter experts answers a series of questionnaires. The answers provided in the first questionnaire are used to prepare the questions in the second questionnaire, and so on. This process continues until the facilitators overseeing the process have the information they need to build a financial forecasting model. 

Business leaders use the results of this iterative approach to spot agreements in expert opinions on specific scenarios and to bolster long-term data-based forecasts with specialist consensus.

The Quantitative Forecasting Approach

Compared to qualitative forecasting, the quantitative approach attempts to be as objective as possible. It only looks at large historical and current data sets, such as a company’s sales or revenue growth over several years. This data is modeled repeatedly to answer specific questions and identify discernable trends and patterns.

For example, an equation known as Altman’s Z-score uses statistics, probability, and company data to determine whether the organization will file for bankruptcy within a few years. There are hundreds of algorithms like this one that quantitative analysts will use to build their forecasting models.

Simpler quantitative forecasting models can be built in spreadsheets. But complex scenarios with nuanced questions (that needed answering yesterday) require more sophisticated financial modeling solutions.

Here are some of the most common quantitative forecasting models used by analysts today.

4. Straight Line

Most small business owners use straight-line forecasting when running their numbers. This simple forecasting model is one of the easiest to build and can be used by anyone. It’s “math-light” and relies solely on a company’s historical performance, as well as a few reasonable predictions about future performance.

The straight-line approach is often used when a business expects an increase in future revenues and wants to estimate future growth. If a company has seen a 10% increase in revenue over the past five years, for example, it’s reasonable to use that same growth rate to project future revenues using the straight-line method.

However, it’s important to note that your company’s revenue will be impacted by many other variables that aren’t accounted for using the straight-line method, which doesn’t consider any real risk factors and assumes static and unchanging market conditions.

Even so, many people still find the straight-line method useful for setting internal goals for your company or department.

5. Moving Average

The moving average method looks for patterns in your data sets to estimate the future financial performance of a company.

It breaks down an extensive data set into smaller chunks. It takes each subset’s average to determine your company’s average financial performance over short timeframes—for example, in the next three to five months rather than many years into the future.

By building short-term projections, analysts reduce the level of uncertainty (which only increases over time) and lower the probability of unforeseen risks impacting their forecasts. They also get a better sense of seasonal and cyclical trends that may affect your company’s finances— valuable information that the straight-line method won’t show you.

6. Time Series

Most people have seen a time series chart without realizing it whenever they look at stock market charts. In Google, these charts are standardized to show same-day price movements, calculated at 5-minute intervals.

If the same 1-day chart had shown price movements calculated every hour, instead, it would look very different. Volatile up-and-down price swings that happen over just a few minutes, for example, would be wholly smoothed out on the hourly chart, as if they never happened.

This is why the time series forecasting method can be so useful for financial forecasting. A customizable time series allows analysts to view historical company data over specific time intervals, such as daily, weekly, monthly, quarterly, and annually.

Why is this so important? Let’s say your company made 20% of all sales in a given year due to a single holiday promotion that ran for just one week. Let’s also say that 50% of those holiday sales were made in a single day.

If analysts looked only at higher timeframes, such as quarterly or annual data, the smoothing effect would make it seem like an entire quarter was very profitable, when that wasn’t the case. Outside of that one week, the rest of the quarter could have been below average in sales. They wouldn’t know that unless they looked at shorter time intervals, such as daily, weekly, and monthly time series.

Achieve Consensus with Your Financial Forecasts

Complex forecasting methods aren’t essential for every business decision. But when it comes to significant capital investments, decision-makers need as much accuracy and confidence as they can get.

Synario’s financial forecasting software turns error-prone spreadsheets into error-free economic models. Effortless configuration and customization capabilities ensure that analysts can build and modify dynamic forecasting models in a fraction of the time it would take with spreadsheets (without having to start from scratch each time the board has new questions).

Our financial modeling platform’s patented Multiverse Modeling technology allows you to run several live scenarios at once, so leadership teams can evaluate each one for viability and value right in the boardroom. You can even make changes to your numbers mid-presentation that are immediately reflected in your visualizations.

Discover financial modeling technology that far surpasses Excel’s legacy constraints and achieve consensus with your financial forecasts.