Principles and Approaches to Forecasting a Company’s Financial Results and Position

Forecasting a company’s financial results and position is a critical aspect of financial analysis. It involves predicting the future financial performance of a company based on various factors such as historical data, industry trends, and management guidance. The approaches to forecasting can vary significantly depending on the analyst, the company, and the industry.

An analyst at a public research firm often concentrates on short-term forecasts for revenue and earnings per share. Meanwhile, an investor holding a controlling stake in a private company typically develops comprehensive models for a longer-term perspective, spanning multiple years or even decades.

Let’s consider two real-world companies for our discussion – Costco Wholesale Corporation and Amazon.com Inc. (an online marketplace). While these companies operate in different sectors, the principles of financial forecasting apply to both.

Key Elements in Financial Statements Forecasting

When forecasting a company’s financial performance, analysts often focus on four key elements:

• Drivers of Financial Statement Lines: These are factors that influence the lines in a financial statement. For example, for a company like Costco, net sales can be analyzed using drivers like the average number of stores open and the average net sales per store. These drivers can be forecasted individually and then multiplied to get the forecast of net sales. Other key drivers include gross margin and SG&A expenses that can be forecasted as percentages of net sales.
• Individual Financial Statement Lines: Analysts can directly forecast individual financial statement lines. This approach is often used for lines without clear drivers, for less-material items, and for items that the analyst does not have a perspective on. Examples include lines such as amortization expense on the income statement, “other non-current assets” on the balance sheet, and various lines on statements of cash flows for which minimal disclosures are provided.
• Summary Measures: These include metrics like total assets, earnings per share, and free cash flow. Efficiency is a benefit of employing these as forecasting objects. However, it comes with less transparency, making it difficult to audit the forecast. The summary measure must be steady and predictable for this strategy to be effective, or issuer disclosures must be highly limited.
• Ad Hoc Objects: These are things that prior financial statements might not have yet disclosed. Before the issuer records an accrual on its financial accounts, an analyst may occasionally be required to estimate a loss or gain and its timing in order to make an investment decision with regard to the company’s equity or debt instruments. Examples of such situations include announcing a significant court proceeding, a government regulation, or a tax dispute.

Approaches to Financial Forecasting

There are several approaches to financial forecasting, each with its own strengths and limitations. These include:

1. Historical Results Forecast Approach (assumes the past is the precedent)

The Historical Results Forecast Approach is a method used to predict future outcomes based on past results. This approach is considered the easiest and often the default method due to its simplicity and the assumption that past events are likely to recur. However, it’s important to note that past results were produced under specific conditions that may not be the same as current or future conditions.

Applicability of the Historical Results Forecast Approach

• This approach is suitable for companies operating in industries where the analyst does not anticipate any changes in the industry structure.
• It is also applicable to companies that have a low sensitivity to changes in the business cycle.
• Commonly used for forecast objects that are not material or that the analyst does not hold an opinion on.

Limitations of the Historical Results Forecast Approach

• It is less appropriate for companies in cyclical industries. A future period is likely to be at a different point in the business cycle than the current or past period.
• An “over the cycle” average or median may be suitable for a multi-year forecast for a cyclical company but less appropriate for a specific year as it hides the year-to-year volatility.
• This approach is also not suitable for companies that are changing their competitive strategy or undergoing a restructuring, such as making a large acquisition. This makes historical results non-comparable.

2. Historical Base Rates and Convergence Forecast Approach

The Historical Base Rates and Convergence Forecast Approach is a strategic method in financial forecasting. This approach utilizes averages or medians from an industry or peer group as a “base rate” for forecasting, often considering macroeconomic variables such as GDP growth in the calculations.

Key Aspects of the Approach

• Relies on industry or peer group averages or medians as a base for forecasting.
• Requires analyst discretion in object selection and timeframe determination for convergence to the base rate.
• Suitable for established industries with numerous publicly traded counterparts.
• Effective for smaller companies maturing to match the financial profile of larger peers.

Applicability and Examples

• Banks: Useful in forecasting trends for regional banks aligning with industry standards set by giants.
• Automakers: Can be applied to budding electric vehicle manufacturers.
• Restaurants: Beneficial for local restaurant chains aiming to mirror the success patterns of established entities.

Limitations of the Approach

• Not suitable for new or volatile industries where establishing a base rate is challenging.
• Less applicable to highly cyclical industries due to the potential masking of yearly volatility.
• Not ideal for industry leaders that significantly influence the industry base rate.

3. Management Guidance Forecast Approach

Management guidance includes earnings, revenue, and other targets that public company management may publicly provide for the next quarter, year, or longer. It can be specific or more general and is frequently revised during the year. Guidance is valuable because it provides forward-looking information based on the knowledge and insights of company management. Investors rely heavily on guidance as it forms a significant portion of the information used in quarterly financial analysis.

Characteristics of Guidance

• Guidance typically includes a range, like “2%–4% sales growth,” and involves numerous forecasts and assumptions. These encompass factors like economic growth, cost increases, market share shifts, pricing decisions, and currency exchange fluctuations made by company management.
• Guidance will change if management’s estimates change, and it is not uncommon for companies to suspend guidance altogether in periods of high uncertainty, such as during the COVID-19 pandemic or in recessions.

Investor Focus and Management Expectations

• A key focus of investors is understanding management’s assumptions embedded in guidance and scrutinizing their plausibility.
• While the middle of a guidance range may seem to represent management’s “true” expectations, the upper bound frequently does a better job of doing so. This is because the upper bound is “padded” by pairing it with a pessimistic lower bound in order to make the target easier to overcome and for which management can receive compensation.

Use of Guidance for Forecasts

• Using guidance for forecasts is appropriate when it is provided and when management has demonstrated a track record of reliable estimates. Analysts should analyze past guidance versus actuals.
• Guidance should not be used for companies that are highly sensitive to the business cycle, as management does not have an informational advantage over investors in forecasting macroeconomic variables like GDP or the prices of commodities.
• Investors are skilled at predicting macroeconomic trends, while management excels in predicting company-specific factors. Management’s forecasts are typically more accurate for areas they can control, like operating costs and capital spending.

4. Analyst’s Discretionary Forecast Approach

The Analyst’s Discretionary Forecast Approach is a flexible method in financial forecasting that employs a combination of different techniques instead of relying on a single model or method. This approach comes into play, especially when traditional forecasting methods are found inadequate or non-applicable.

Techniques Included in the Analyst’s Discretionary Forecast Approach

• Surveys: Gathering data through questionnaires to gain insights.
• Quantitative models: Using statistical models to analyze data and trends.
• Probability distributions: Utilizing statistical methods to predict various possible outcomes.
• Analogies to historical precedents that differ from comparable companies or industry averages: Drawing parallels with past events that are not necessarily in line with industry norms.
• Other unobservable inputs: Considering factors that are not directly measurable but influence the forecast.

Applicability of the Analyst’s Discretionary Forecast Approach

This approach is particularly utilized for:

• Companies operating in cyclical industries, where there are recurrent ups and downs.
• Companies that have a limited number of comparables in the market.
• Companies that do not offer management guidance for forecasting.
• Companies witnessing a significant shift, be it in the competitive landscape or regulatory environment.

Examples of the Analyst’s Discretionary Forecast Approach

Here are instances where this approach might be employed:

• Energy Sector: Analysts may use this approach to forecast trends in the energy sector, especially considering the unprecedented shift towards renewable energy and the adoption of new technologies like electric vehicles. Factors such as government emission reduction commitments, proposed legislation, and capital expenditure constraints would be considered.
• Technology Startups: In the case of technology startups that are carving out new niches, analysts might rely on a blend of surveys, expert opinions, and analogies to emerging trends to forecast financial outcomes.

Selecting a Forecast Horizon

Selecting an appropriate forecast horizon is a vital step in the financial forecasting process. It depends on various factors, including the investment strategy, the cyclicality of the industry, company-specific factors, and the preferences or guidelines established by the analyst’s employer. Here, we explore these aspects in detail:

Investment Strategy

• The choice of the forecast time horizon is closely aligned with the investment strategy under consideration. It is crucial to establish a time frame that resonates with the objectives and average holding period stated in the investment strategy.
• Professional investment strategies generally indicate the investment time frame and the average holding period in their objectives. Adhering to these time frames is essential for achieving the expected outcomes.
• For instance, fund managers with a long-term perspective may predominantly concentrate their forecasts on a span of three to five years. In contrast, managers who have a shorter-term focus might prioritize the upcoming one or two quarters.

Industry Cyclicality

• Industry cyclicality is a significant determinant in deciding the forecast time frame. The period chosen should cover a business cycle to facilitate the attainment of anticipated mid-cycle levels of sales and profitability.
• The cyclic nature of the industry necessitates a forecast period that extends sufficiently to capture the fluctuations and trends accurately.

Company-Specific Factors

• Company-specific elements, such as recent acquisitions or restructuring initiatives, play a pivotal role in determining the forecast horizon.
• It is imperative to allocate a time span that allows for the manifestation of the benefits arising from these activities in the financial statements, thus offering a realistic view of the financial prospects.

Analyst’s Employer’s Preferences

• At times, the selection of the forecast horizon may be dictated by the guidelines or preferences set by the analyst’s employer, leaving little room for individual discretion.
• This standardized approach ensures consistency and alignment with the organizational objectives and strategies.

Question #1

A financial analyst is working on a forecast for a company in the energy sector. The company is facing unprecedented changes due to the potential shift to renewable energy and the adoption of technologies like electric vehicles. The analyst needs to consider various factors such as government emission reduction commitments, proposed legislation, and capital expenditure constraints. Which forecasting approach is the analyst most likely to use in this scenario?

1. Quantitative Forecasting Approach.
2. Analyst’s Discretionary Forecast Approach.
3. Historical Forecasting Approach.

The correct answer is B.

The analyst is likely to use the Analyst’s Discretionary Forecast Approach in this scenario. This approach is often used when there are significant changes in the external environment that are likely to impact the company’s future performance. It involves the use of subjective judgment and expertise to make forecasts. In this case, the company is facing unprecedented changes due to the potential shift to renewable energy and the adoption of technologies like electric vehicles.

The analyst needs to consider various factors such as government emission reduction commitments, proposed legislation, and capital expenditure constraints. These factors are complex and interrelated, and their impact on the company’s future performance is uncertain. Therefore, the analyst needs to use his or her judgment and expertise to assess these factors and make a forecast. This approach allows the analyst to incorporate the latest information and changes in the external environment into the forecast.

A is incorrect. The Quantitative Forecasting Approach is based on mathematical models and statistical techniques. It uses historical data to make forecasts. While this approach can be useful in many situations, it may not be appropriate in this case because the company is facing unprecedented changes that are not reflected in the historical data.

C is incorrect. The Historical Forecasting Approach is based on the assumption that the past performance of a company is a good indicator of its future performance. This approach may not be appropriate in this case because the company is facing unprecedented changes that are likely to significantly impact its future performance. The historical data may not provide a reliable basis for forecasting the company’s future performance in the face of these changes.

Question #2

An investment analyst is preparing a forecast for a security that is being considered for a professionally managed investment strategy. The investment objectives of the strategy describe a long-term time frame and an average holding period. In this context, what time period is the analyst most likely to focus their forecasting on?

1. One or two quarters.
2. Three to five years.
3. Indefinite.

The correct answer is B.

Given the long-term investment objectives and average holding period described in the strategy, the analyst is most likely to focus their forecasting on a time period of three to five years. This is because long-term investment strategies typically involve holding securities for several years, and the performance of these securities over this time frame is crucial to the success of the strategy.

The analyst would therefore need to forecast the performance of the security over this period to determine whether it is likely to meet the strategy’s objectives. This would involve analyzing the security’s fundamentals, such as its earnings, cash flows, and financial health, as well as external factors such as economic conditions and industry trends, over a three to five-year horizon.

A is incorrect. A time period of one or two quarters is typically considered short-term in the context of investment forecasting. While short-term forecasts can be useful for certain types of investment strategies, such as trading or tactical asset allocation, they are less relevant for a long-term, buy-and-hold strategy.

C is incorrect. An indefinite time period is not practical for investment forecasting. While it is true that some investment strategies, such as value investing, involve holding securities for an indefinite period until their intrinsic value is realized, this does not mean that forecasts can or should be made over an indefinite time period. Forecasts need to be based on specific assumptions and data, which are typically only available or reliable for a certain time horizon.