Types of Financial Forecasting Model: A Complete Guide

Introduction

Whether a small business wants to launch a new product line or an enterprise plans to go for an initial public offering (IPO), finance teams and CFOs need to assess and analyze their previous financial performances and evaluate the resources required to achieve their objectives. This information is crucial to organize and prepare for future growth opportunities and attract potential investors and shareholders. This is where financial forecasting models and methods come into play.

Financial forecasting models help businesses predict financial outcomes for various aspects of their business operations, like revenues or salaries. It not only helps provide insights into business performance but also helps calculate costs, improve budgets, and allocate resources.

This blog will help you understand everything you need to know about financial forecasting models, starting from financial forecast meaning, the types of models and methods, examples, and more.

What is Financial Forecasting?

Financial forecasting refers to utilizing past financial data and present market trends to make projections for operational efficiency and financial performance in the future. Effective financial forecasting depends on how a business pairs quantitative insight with creative evaluation.

Forecasting enables in anticipating a business’s financial performance by evaluating revenues, profit, cash flows, assets, and liabilities. It includes assumptions and an analysis of the causes behind the changing patterns and trends to identify unforeseen events that can impact a business’s position in the long run.

Why is financial forecasting important?

Financial forecasting is crucial for effective decision-making and identifying potential risks and growth opportunities. These deep insights lead to better budgeting, smarter investment decisions, and increased profitability. Some of the key benefits they provide are:

• Anticipating business performance
  Companies need a deeper understanding of the outcomes that different scenarios can bring to the business. Financial forecasting here helps anticipate financial performance based on trends and changing patterns, enabling companies to better manage and allocate their resources.
• Better contingency management
  Financial forecasting helps manage change more efficiently and make smarter investments during downturns to maximize returns. It allows businesses to use scenario builders and create “what-if scenarios” to identify all possible contingencies and develop strategies to mitigate them quickly and effectively.
• Investor and stakeholder reporting
  Financial forecasting is of immense importance when businesses want to raise capital and attract investors. Forecasting provides a clear and holistic view of the business’s financial position and helps them allocate resources and capital to maximize returns.

Elements of financial forecasting

Financial forecasting usually involves pro forma financial statements. These are primarily business reports that include:

• Income statement or profit and loss statement
  An income statement shows a company’s revenues, expenses, and profit or loss margins over a period of time. It helps forecast future profitability by predicting revenues and operational expenses.
• Balance sheet statement
  A balance sheet gives an overview of a company’s assets, liabilities, and equity at a specific point in time. It assists in projecting financial health and liquidity by analyzing changes in assets and liabilities.
• Cash flow statement
  A cash flow statement monitors the inflows and outflows of cash and cash equivalents for a specific period. It is crucial for predicting future cash flows to ensure the business has sufficient liquidity and operational capability.

Financial Forecasting Models

Financial forecasting models involve studying historical financial data and statistics to help businesses predict financial performance in the long run. These models have varied levels of complexity and help predict outcomes for sales, customer demand, and market trends, enabling informed decision-making.

Here are the four types of financial forecasting models:

1. Top-down financial forecasting

The top-down forecasting model involves analyzing market data and building a business’s revenue projections from there. This model works best when a business wants to evaluate a new opportunity or the initial phase of a new product but doesn’t have any historical data to base its predictions on. It uses the size of a new market as the basis for forecasting and estimates the market share a business will be able to acquire.

Top-down financial forecasting example

For instance, the market for a tech startup is valued at $100 million, and it anticipates capturing 5% of the market share. They decided to run a top-down financial forecast and found out that the projected revenue for the upcoming year would be $5.5 million with a growth rate of 5%.

Pros and cons of top-down forecasting

Pros

• Top-down forecasting is ideal for businesses that wish to streamline their revenues, as a narrowed, product-level forecast does not provide such insights.
• It’s the only viable forecasting model for businesses that are at an early stage and don’t have any extensive financial information.

Cons

• Top-down forecasting is often seen as surface-level forecasting that ignores granular details.
• Top-down forecasting cannot be considered as a robust projection method.

2. Bottom-up financial forecasting

If a business has access to historical data for revenues and expenses, it makes more sense to approach the forecasting bottom-up, unlike the previous method. The bottom-up financial forecasting model uses existing revenue data and cash flow statements to build future scenarios and create detailed forecasts.

This model gives more accurate projections as the business works with actual figures and reduced assumptions. It starts with the business collecting product information from the ground level and customers and finds its way up to broad-level revenue and expenditure forecasts.

Bottom-up financial forecasting example

Suppose a retail chain wants to estimate sales by gathering projections from all stores. Store A predicts $100,000, and Store B expects $150,000. Summing these gives a total regional forecast of $250,000. Extrapolating this across all regions yields a company-wide sales forecast of $1.48 million.

Pros and cons of bottom-up forecasting

Pros

• This model involves more detailed analysis than all other forecasting models, offering more room for perspectives from various counterparts.

Cons

• In bottom-up financial forecasting, any errors at the micro-level can easily amplify to broad-based forecasting, leading to inaccuracy.
• Bottom-up financial forecasting can also be time-consuming since numerous departments are involved.

3. Delphi financial forecasting

The Delphi model, whose name is derived from the ancient Greek city, allows businesses to frame a forecast based on the opinions of a group of experts. A facilitator initiates collaboration among experts, conducts several rounds of discussions, iterates hypotheses, and applies in-depth analysis to reach a consensus.

In the Delphi forecasting model, a business sends various rounds of questionnaires around its financial data to a panel of experts. With every round, experts prepare a consolidated summary of the previous rounds and adjust their perspectives on forecasts. The goal is to nitpick the common ground and build consensus among experts that can be included in the company’s final projections.

Delphi financial forecasting example

An apparel brand wants to project new shirts’ demand. Experts in marketing predict 9,000 unit sales, finance estimates $500,000 in revenues, and operations projects costs of $200,000. After three rounds of discussion, consensus predicts 8,000 unit sales, $400,000 revenue, and $180,000 costs.

Pros and cons of Delphi forecasting

Pros

• Delphi forecasting is a lot more objective than legacy, in-house financial forecasting.
• Contributions are anonymous, and experts can give unbiased opinions and forecasts.

Cons

• The model doesn’t allow for an open dialogue between management and experts.
• It can consume a lot of time due to the numerous, detailed discussions involved.

4. Statistical financial forecasting

Statistical forecasting involves predicting numbers using various statistical methods and calculations. The term, ‘statistics’ typically covers all historical, quantitative financial data to find out growth rate, profitability, revenues and expenditures, and benchmark forecast numbers.

Statistical financial forecasting example

Suppose a consumer goods company wants to predict the next quarter’s sales based on past sales patterns, seasonality, and economic factors. It uses time series analysis and finds out $1 million per quarter in sales with a 5% seasonal increase, forecasting $1.05 million in revenues for the next quarter.

Pros and cons of statistical forecasting

Pros

• Statistical forecasting relies on a more solid basis than all other forecasting models, providing accurate data.

Cons

• Statistical forecasting for certain data can provide relatively opaque estimates and is not suitable for forecasting qualitative data.

Types of Financial Forecasting Methods

Financial forecasting methods and techniques come with their own set of scope and benefits, depending on the purpose of forecasting and the models used by a business. Here are the four types of forecasting methods:

• Straight Line
• Simple Linear Regression
• Multiple Linear Regression
• Moving Average

1. Straight line

Straight-line forecasting uses historical financial data and basic arithmetic to predict growth and identify future outcomes based on growth trends. This method provides deep insights into short-term business budgeting and methods. However, the straight-line method does not consider changing market conditions, thus failing to give accurate long-term forecasts.

Example: To project its monthly sales growth, a tech startup decides to analyze its past data. If sales were $50,000 in January and increased by $2,000 monthly, December’s sales would be estimated at: $50,000 + ($2000 x 11) = $74,000.

2. Simple linear regression

Simple linear regression helps forecast future values of dependent variables based on previous numbers. It uses a linear relationship between dependent variables and independent ones to frame a trend line. The method is easy to implement, offers low costs, and can identify trends. However, it is not an efficient method to decode complex relationships between variables and is easily influenced by deviations or anomalies.

Example: A boutique fashion brand wants to predict its operational expenses for all months. If March expenses were $50,000, and April’s were $52,000, and so on, applying linear regression will suggest an increase of $2,000 in expenses per month.

3. Multiple linear regression

Multiple linear regression is the most advanced forecasting method of all. It considers all complex relationships between independent and dependent variables and gives more accurate predictions than simple linear regression. However, this method would require more data for accurate projections and outcomes.

For example, the finance team wants to predict the stock prices of the enterprise company using variables like company earnings, market index performance, and interest rates. By building a multiple linear regression, the team can estimate the coefficients for each of these independent variables and quantify their impact on the stock price, make predictions for future stock movements, anticipate market trends and make smarter investment decisions.

4. Moving average

The moving average method assesses financial metrics like revenues, profit margins, revenue growth, and dividend earnings. It uses short-term calculations to build an average value and helps identify the reasons behind the changing patterns. It allows for faster trend identification but can be a slower method to provide forecasts when used for long-term predictions.

Example, a company wants to forecast monthly sales and predict values based on the three-month moving average. It will take the figures for the past three months: $1000, $1200, and $1100 respectively. The forecasted sales for the next month would be the average of these three values:

Forecasted sales = (1000 + 1200 + 1100)/3
Forecasted sales = 1100 units

How To Do Financial Forecasting?

Numerous internal and external outcomes of business operations rely on accurate financial forecasting. Financial forecasting outcomes will impact investor decisions, the amount of borrowing required, working capital that needs to be allocated, and so on. Here are the six steps to perform financial forecasting:

Step 1: Define the purpose

Businesses must know why they are using a financial forecast to make the most of it. They must answer questions like:

• What are they looking for from the financial forecasting results?
• Are they trying to get a better understanding of the company’s budget?
• Is financial forecasting aiming to set benchmarks for sales?

Defining the answers will help businesses set metrics and factors to consider when conducting a financial forecast.

Step 2: Gather historical data and past financial statements

One of the most important exercises in financial forecasting is to compare the past figures with the actuals and the forecast numbers. But for that, a business would need access to accurate financial information and ensure they are included in the forecasting. The historical data can include:

• Revenue
• Liabilities
• Equity
• Comprehensive income
• Fixed costs
• Losses
• Investments
• Expenditures
• Earnings per share

Using AI-led forecasting systems will not only help businesses streamline the data-gathering process but also boost accuracy in forecasting. Features like ERP connectivity and bank connectivity manager provide out-of-the-box integration with all major banks to provide rapid access to bank statements while aggregating the same data over ERP using an API or file-based integration.

Step 3: Set a time frame

Financial forecasting can be done on a weekly, monthly, quarterly, or annual basis. The commonly used forecasting time frame is annual forecasting, but it depends on the nature of the business. Businesses can also adjust their forecasts based on their changing objectives or outcomes. Consequently, financial forecasts for the short-term give more accurate results than long-term forecasts.

One of the best ways to tailor your forecasting to fit the time frame is by using AI-led, auto-ML forecasting. The auto-machine learning system is trained on historical transaction data to create cash forecasts. It selects the best-fit model from hundreds of combinations by category and time frame.

For example, to forecast cash flows from AR in the US for each of the next 14 days, the module selects “WeekOfYearAvg” as the method with the highest prediction accuracy. From Day 15 to Day 90, the module selects “SeasonalAvg” as the best method. These models are automatically reviewed and refreshed daily.

Step 4: Choose the forecasting method and model

There is no one-size-fits-all forecasting method or model. Businesses need to ensure that the forecasting method selected is relevant to their forecasting objectives and business goal, and gives accurate results to improve operational efficiency.

Businesses can use no-code forecast modeling to improve their projections. It allows them to handle data and build models using familiar Excel-like functions and interfaces. These forecast models are also highly scalable and provide connected forecasting units with hierarchical consolidation that combines cash categories and company codes.

For example, the US AR forecast would connect to a US Net Cash forecast as well as a Global AR forecast, and these would each connect to the Global Net Cash forecast. Different users will have access to different levels of consolidated views.

Step 5: Record and monitor the forecast results

Financial forecasting acts like a guide to what a business should be doing to improve its performance. They do not guarantee 100% success for business objectives and goals. Therefore, it’s crucial for them to record and continuously monitor the forecast results, especially whenever there are major internal or external changes in the organization. They should also focus on updating the forecasts to reflect the latest developments.

One of the best ways to understand and deep dive into financial forecasting is to compare actual vs. the forecast numbers. And then identify the underlying causes of the changes in patterns and trends. This process is called variance analysis and is a significant element of the financial forecasting process.

Automated forecasting solutions offer a next-gen variance analysis feature that not only helps view historical forecasts and their variance from the actuals but also analyzes historical forecasts and changes in variance over time. Businesses can track forecasts vs. actuals over time for any cash flow category and then drill down to understand the changes in variance over time using the variance grid.

Step 6: Analyze the outcomes and repeat the process

Regular analysis of financial forecasting outcomes is the best way to find out if the forecasts conducted were accurate and effective or not. In addition, continuous financial management helps businesses pave the way for better forecasting for the next time frame, identify and mitigate potential risks, and leverage opportunities for better returns.

Speaking of which, building “what-if scenarios” using scenario builders is one of the best ways to boost forecasts and evaluate the impact of the strategies framed out of the predictions. Businesses can use AI-built scenario builders to easily create and tweak what-if scenarios over base forecasts and compare multiple scenarios with one another.

For example, if a company is planning to build a $100 million factory, it will have to borrow $50 million and use $50 million of its own cash to do this. They will now have to find out if the overall cash will be enough to carry out the operations if they start the project next month (Scenario 1). Or, what will the financial performance look like if the project is delayed for 45 days due to bank approvals (Scenario 2).

How HighRadius Can Help With Financial Forecasting?

Accurate financial forecasting goes beyond gathering numbers and financial data. Manual, conventional ways to process information, errors in projections and loss of time due to dependence on various departments and counterparts, hinder the effectiveness and efficiency of the financial forecasting process. To help businesses escape the chaos, HighRadius treasury management software brings advanced, automated cash forecasting tool.

Our cash flow forecasting tool offers automated, custom-built forecasting models. These models are designed using Excel-like templates and are trained on historical data and heuristic models. In addition, these models offer out-of-the-box integration with all major banks that connect seamlessly with ERPs, gathering all the information required for accurate forecasting.

The cherry on top? The variance analysis and scenario analysis features. Variance Analysis ensures consistent and frictionless tracking of the cash forecasting outcomes while allowing a comprehensive view of all past transactions and analyzing historical changes and variances over time. Scenario Analysis helps analyze the impact of each scenario on various cash flow categories, save the versions of those scenarios as snapshots and access them at any time in the future.

FAQs

1) What is the most widely used method for financial forecasting?

The time series analysis method is commonly used for financial forecasting. It looks at how data changes over time. It leverages historical data to predict future trends, guiding decisions on investments, budgeting, and more, making it particularly useful for short-term forecasts.

2) What is the difference between financial forecasting and financial modeling?

Financial forecasting is a subset of financial modeling. Financial forecasting predicts future financial performance based on historical data and trends. On the other hand, financial modeling encompasses a broader range of tools and techniques to represent a company’s financial operations.

3) Which forecasting method is most reliable?

The reliability of a forecasting method depends on many factors. If a business is a startup with no past data, then a straight-line forecasting method is an ideal choice. Similarly, if a business has many product lines or stores, then multiple linear regression will give accurate forecasts.

4) What are the major reasons why forecasting can fail?

Forecasting can fail to give accurate results for many reasons. Some of them are:

• Over reliance on one forecasting model
• Ignoring market trends and external factors
• Complex forecasting processes
• Irregular forecast updating
• Not considering worst-case scenarios
• Ineffective communication of forecasts

5) What causes poor forecasting?

Some of the reasons that lead to poor forecasting are:

• Inaccurate or insufficient data
• Lack of collaboration among different departments
• Overlooking the impact of external variables
• Relying too much on historical data without considering current changes
• Not regularly reviewing and updating forecasts
• Overcomplicating the forecasting process

6) What is the most important factor in forecasting?

Efficient forecasting stems from the accuracy and reliability of the data used. No matter how sophisticated a forecasting model is, without high-quality data, forecasts may be misleading. Ensuring data integrity through robust collection, validation, and analysis is the key to effective forecasting.