Approaches to Forecasting a Company’s Revenues

Forecasting a company’s revenues is a critical aspect of financial analysis. It involves estimating future revenues using various approaches and considering different risk factors that might influence a company’s financial standing. In this lesson, we delve into different forecasting objects and approaches, highlighting their real-world applications and importance in modern financial analysis. Let’s begin by understanding the types of forecast objects that are commonly used.

Forecast objects for revenues, instrumental in company analysis for predicting future revenues, can be classified as either top-down or bottom-up drivers. These drivers aid in formulating an accurate picture of a company’s potential future revenue streams. Below, we will explore each of these drivers with real-world examples.

Top-Down Forecast Objects

1. Growth relative to GDP growth Utilizing this approach involves comparing the growth rate of a company with the nominal GDP growth rate. For instance, if a telecommunications company has historically grown at 1.5 times the rate of GDP growth, analysts may use this ratio to forecast future revenues. Further, the company’s position in its life cycle or sensitivity to business cycles is considered to derive premiums or discounts in percentage points.
2. Market growth and market share In this approach, the focus is on predicting the growth rate of the company’s product market and evaluating the changes in market share over time. For instance, if a smartphone manufacturing company holds a 20% market share and the market is expected to grow by 10%, the company’s revenue forecast might be adjusted accordingly. Regression analysis might be employed to estimate the relationship if a predictable relationship between product market revenue and GDP exists.

Bottom-up Forecast Objects

1. Volumes and average selling prices: This method entails preparing individual forecasts for the volumes and prices of the company’s products and then multiplying them to get a revenue forecast. For example, a car manufacturer may estimate revenues by forecasting the number of cars to be sold and the average price per car.
2. Product-line or segment revenues: Here, forecasts are made for individual products, business lines, geographical areas, or reporting segments and then aggregated to form a total revenue forecast. For instance, a multinational corporation might forecast revenues separately for different regions and then aggregate them.
3. Capacity-based measures: This approach, commonly used in retail, bases forecasts on parameters like the number of stores and sales per store or the growth in sales from new-store openings. For instance, a retail chain might estimate future revenues based on projected sales per store and the planned number of new store openings.
4. Return- or yield-based measures: Forecasts in this category are based on account balances and revenue yields on them. For example, for a bank, net interest income can be calculated using the formula:

   $$\text{Net Interest Income} = (\text{Loans} \times \text{Average Interest Rate}) – (\text{Deposits and Liabilities} \times \text{Their Average Interest Rate}) $$

Using a combination of both top-down and bottom-up objects can assist in uncovering implicit assumptions or errors that might arise from employing a single approach.

Top-Down Drivers

$$\begin{array}{l|l}
\textbf{Forecast Method} & \textbf{Examples} \\
\hline
\text{Historical Results} & – \text{Utilizing past GDP growth rates to} \\
& \text{anticipate potential market size for the} \\
& \text{real estate industry.} \\
& – \text{Assessing previous years’ consumer} \\
& \text{behavior patterns during holiday seasons to} \\
& \text{project market share for retail businesses.} \\
& – \text{Analyzing past economic conditions to} \\
& \text{predict market trends for investment sectors.} \\
\hline
\text{Historical Base Rates and Convergence} & – \text{Leveraging industry average profit margins} \\
& \text{from the past decade to estimate future} \\
& \text{market share for manufacturing firms.} \\
& – \text{Utilizing historical data on market penetration} \\
& \text{rates in similar markets to predict potential} \\
& \text{market size for a new product launch.} \\
& – \text{Predicting market trends for the automotive} \\
& \text{industry based on peer group average growth} \\
& \text{rates over the past 5 years.} \\
\hline
\text{Management Guidance} & – \text{Using management’s projections on the} \\
& \text{expansion into new markets to gauge potential} \\
& \text{market size growth for a telecommunications company.} \\
& – \text{Incorporating management’s guidance on} \\
& \text{product innovations to estimate market share} \\
& \text{expansion for a tech company.} \\
& – \text{Considering company’s forecasted trends in} \\
& \text{consumer preferences to adapt market strategies} \\
& \text{for a food and beverage company.} \\
\hline
\text{Analyst’s Discretionary Forecast} & – \text{Conducting expert surveys to gauge} \\
& \text{anticipated market trends in the renewable} \\
& \text{energy sector.} \\
& – \text{Developing customized quantitative models to} \\
& \text{forecast market size for emerging technologies.} \\
& – \text{Synthesizing data from different sources to} \\
& \text{project market share dynamics for e-commerce platforms.} \\
\end{array}
$$

Bottom-Up Drivers

$$\begin{array}{l|l}
\textbf{Forecast Method} & \textbf{Examples} \\
\hline
\text{Historical Results} &
\begin{array}{l}
– \text{Analyzing historical sales data per store to} \\
\text{forecast future sales for a retail chain,} \\
\text{considering seasonal variations and promotions.} \\
– \text{Using past membership growth rates to forecast} \\
\text{future membership numbers for a gym, factoring} \\
\text{in new location openings and marketing campaigns.} \\
– \text{Evaluating historical data on store expansions to} \\
\text{anticipate future growth for a supermarket chain,} \\
\text{considering market saturation and consumer trends.}
\end{array} \\
\hline
\text{Historical Base Rates and Convergence} &
\begin{array}{l}
– \text{Utilizing industry average sales per store data to} \\
\text{project company sales, considering economic} \\
\text{factors and consumer preferences.} \\
– \text{Estimating future membership pricing trends based} \\
\text{on industry averages, considering competitive pricing} \\
\text{strategies and market demand.} \\
– \text{Leveraging industry average data on store expansions} \\
\text{to project company growth, considering regional} \\
\text{market dynamics and consumer behavior.}
\end{array} \\
\hline
\text{Management Guidance} &
\begin{array}{l}
– \text{Considering management’s guidance on store expansion} \\
\text{plans to predict future growth for a retail business,} \\
\text{taking into account market competition and consumer demand.} \\
– \text{Integrating company’s projections on sales per store} \\
\text{to develop a comprehensive sales forecast, considering} \\
\text{product innovations and marketing strategies.} \\
– \text{Incorporating management’s strategies on membership} \\
\text{pricing to anticipate future revenue streams,} \\
\text{considering market trends and consumer preferences.}
\end{array} \\
\hline
\text{Analyst’s Discretionary Forecast} &
\begin{array}{l}
– \text{Applying advanced statistical models to forecast} \\
\text{sales per store for a new retail brand, considering} \\
\text{market dynamics and consumer insights.} \\
– \text{Using specialized surveys and market research to} \\
\text{predict membership growth for a subscription-based} \\
\text{service, considering industry trends and consumer behavior.} \\
– \text{Developing complex predictive models to anticipate} \\
\text{the growth trajectory of a startup, considering various} \\
\text{market and company-specific factors.}
\end{array} \\
\end{array}
$$

Separating Recurring and Non-recurring Revenue or Revenue Growth

In forecasting, it is essential to separate non-recurring items and effects from recurring ones, as they have different drivers. This separation helps in avoiding inflation or deflation of the forecast object’s size. These items can be classified into disclosed and non-disclosed non-recurring items.

Disclosed non-recurring items are disclosed by the company’s management, including the effects of changes in exchange rates, extra selling days, acquisitions/divestitures, and other “one-time” revenues or gains. These are separated to focus the forecast on “underlying” revenue or growth. Analysts might also incorporate proprietary exchange rate forecasts in revenue projections.

Non-disclosed, non-recurring items are not quantified by management, requiring analyst judgment to estimate. For instance, during the COVID-19 pandemic, a surge was seen in e-commerce sales. However, many e-commerce companies saw a decline in revenue in 2022, indicating that some of the growth was non-recurring.

Forecasting revenues is a vital aspect of financial analysis, involving various approaches such as using historical results, base rates and convergence, management guidance, and discretionary forecasts by the analyst. While forecasting, analysts must consider several risk factors, which might vary across companies. The common risk factors to consider include competition, changes in business cycles, inflation or deflation, and technological developments.

Due to the considerable range of possible results, analysts frequently create multiple forecasts for a company’s financial statements, known as scenarios. These scenarios are developed using varying perspectives on critical risk factors.

Question

When conducting revenue forecasts, analysts must take into account various risk factors. These factors can differ from one company to another, but some are common to all businesses. Which of the following is least likely considered a common risk factor in revenue forecasting?

1. Competition.
2. Technological developments.
3. Company’s brand image.

The correct answer is C.

While a company’s brand image can certainly impact its revenue, it is not typically considered a common risk factor in revenue forecasting. Revenue forecasting is a financial projection that is based on the sales that a company expects to generate in the future. It is influenced by a variety of factors, including market conditions, competition, and technological developments. These factors can directly impact a company’s ability to generate sales and, therefore, its revenue. However, a company’s brand image is more related to its reputation and customer perception, which can indirectly impact revenue but is not a direct risk factor. It is more subjective and difficult to quantify, making it less suitable for inclusion in a revenue forecast. While a strong brand image can certainly contribute to higher sales, it is not a risk factor in the same way that competition or technological developments are.

A is incorrect. Competition is indeed a common risk factor in revenue forecasting. The presence of competitors can impact a company’s market share and pricing power, both of which can directly impact its revenue. Therefore, analysts must take into account the competitive landscape when conducting revenue forecasts.

B is incorrect. Technological developments are also a common risk factor in revenue forecasting. Technological advancements can disrupt industries and change the way business is conducted, potentially impacting a company’s revenue. For example, a company that fails to adapt to new technologies may lose market share to competitors that do, resulting in lower revenue. Therefore, analysts must consider the potential impact of technological developments when conducting revenue forecasts.