Industry and company analysis - Top down and bottom up forec...

Learning Outcomes

This article explains industry and company analysis for CFA Level 1, including:

• Understanding the role of industry and company analysis in equity valuation and security selection.
• Distinguishing clearly between top-down and bottom-up forecasting approaches and identifying when each is most appropriate.
• Linking macroeconomic, sector, and competitive factors to revenue growth, margin behavior, and earnings sensitivity.
• Translating industry-level forecasts into company-specific projections using assumptions about market size, market share, and pricing power.
• Building company-level bottom-up forecasts that incorporate product mix, geographic exposure, and management guidance while remaining realistic and internally consistent.
• Evaluating the strengths and weaknesses of each approach, including typical biases and sources of forecast error tested on the CFA Level 1 exam.
• Reconciling top-down and bottom-up views to check aggregate plausibility, avoid double counting, and maintain forecast consistency across firms and industries.
• Incorporating the effects of inflation and deflation on sales and costs when projecting revenues and margins.
• Using ratio analysis, sensitivity analysis, and scenario analysis to translate qualitative industry views into quantitative forecasts.
• Applying these concepts to short case vignettes and multiple-choice questions in order to select the most defensible forecasting framework and key input assumptions.

CFA Level 1 Syllabus

For the CFA Level 1 exam, you are required to understand the methods used for industry and company analysis that feed into security valuation, with a focus on the following syllabus points:

• Differentiate between top-down and bottom-up forecasting methods.
• Describe the process of moving from industry forecasts to company-level estimates.
• Identify key external and internal factors affecting company performance (including industry structure, operating gearing, and business risk).
• Explain how to forecast industry and company sales and costs when they are subject to price inflation or deflation.
• Describe how ratio analysis and other techniques (such as sensitivity and scenario analysis) can be used to model and forecast earnings.
• Evaluate the implications of macroeconomic, sector, and company-specific variables on earnings forecasts.

Test Your Knowledge

Attempt these questions before reading this article. If you find some difficult or cannot remember the answers, remember to look more closely at that area during your revision.

1. Which forecasting approach is most consistent with starting from GDP and inflation forecasts, moving to sector growth, and then estimating an individual firm’s revenues?
   1. Bottom-up fundamental analysis
   2. Technical analysis
   3. Top-down forecasting
   4. Relative valuation
2. In which type of industry is forecast uncertainty at the company level most likely to be high?
   1. Regulated utility with stable demand and cost-plus pricing
   2. Local water monopoly with long-term contracts
   3. Cyclical industry with high fixed costs and rapid technological change
   4. Mature industry with many small competitors but low capital intensity
3. When is a bottom-up approach most appropriate for equity analysis?
   1. When macroeconomic forecasts are very reliable and detailed
   2. When a firm’s performance depends mainly on unique projects (e.g., a new patented drug)
   3. When valuing a broad equity index fund
   4. When forecasting aggregate GDP growth for a country
4. An analyst starts with detailed product-line and regional sales projections for a firm, based on management guidance and pipeline data, and only later checks whether the totals look reasonable relative to the industry. This analyst is primarily using:
   1. A pure top-down approach
   2. A pure bottom-up approach
   3. A quantitative factor model
   4. Technical chart-based forecasting

Introduction

Industry and company analysis provide the basis for security valuation. Equity analysts must understand not only a firm’s historical performance but also its likely future revenues, costs, and profits. Forecasting—the process of estimating the future financial performance of firms—can be approached using top-down or bottom-up techniques. Understanding both frameworks, and knowing when to use each, is essential for CFA candidates.

At Level 1, you are expected to recognize how macroeconomic conditions, industry structure, and company-specific characteristics translate into differences in sales growth, margins, and earnings volatility. You should also be able to identify which forecasting approach is being used in a vignette and evaluate whether the assumptions are consistent with the qualitative information given.

Key Term: top-down forecasting
An approach to financial analysis that begins with macroeconomic indicators, moves to sector or industry trends, and then to individual company projections.

Key Term: bottom-up forecasting
A company-centric approach to financial analysis that builds financial projections by aggregating firm-specific data (such as product lines or regions), often without an explicit starting point in macro or industry forecasts.

Key Term: industry analysis
Evaluation of a group of companies producing similar products or services, focusing on competitive structure, growth prospects, profitability, and key risk factors.

Key Term: business risk
The uncertainty of operating income (or earnings before interest and taxes) arising from variability in sales and from the firm’s operating cost structure.

Key Term: market share
A company’s portion of total industry sales, measured by revenue or by units sold, over a defined period.

Key Term: pricing power
The ability of a firm to raise the prices of its products or services without losing significant sales volume to competitors or substitutes.

Industry analysis frameworks (such as Porter’s five forces) help analysts assess the competitive environment and profit potential. Company analysis adds detail about strategy, product mix, cost structure, management execution, and capital structure. Forecasts then combine these qualitative assessments with quantitative tools such as ratios, common-size statements, and pro forma financial statements.

Key Term: pro forma financial statements
Forecast financial statements (income statement, balance sheet, and cash flow statement) constructed using assumptions about future sales, margins, investment, and financing.

Top-Down Forecasting Approach

The top-down method starts at the broadest level: macroeconomic trends such as GDP growth, inflation, interest rates, exchange rates, and fiscal and monetary policy. Next, the analyst focuses on sectors or industries, factoring in drivers such as regulatory changes, technology adoption, demographics, competitor behavior, and global demand. After industry-level conditions are examined, analysts estimate the potential market share and growth for the target company, adjusting for management quality, strategy, and unique strengths or vulnerabilities.

A disciplined top-down process typically involves:

• Step 1: Macroeconomic outlook

  • Forecast variables such as real GDP, inflation, unemployment, consumer confidence, household wealth, and interest rates.
  • Consider policy stance: expansionary or contractionary fiscal and monetary policy will affect aggregate demand and financing conditions.
  • For internationally exposed firms, incorporate global growth and exchange rate assumptions.

• Step 2: Sector or industry forecasts

  • Link industry demand to macro variables. For example, auto sales may be tied to disposable income, interest rates, and consumer confidence; capital goods to capacity utilization and business investment.
  • Assess industry structure: barriers to entry, intensity of rivalry, bargaining power of buyers and suppliers, and threat of substitutes influence pricing and margins.
  • Decide on assumptions for industry volume growth and average pricing (nominal growth = real volume growth + price inflation).

• Step 3: Company-level projections

  • Estimate the firm’s current and future market share, considering brand strength, distribution, technology, and cost position.
  • Apply pricing power assumptions: can the firm raise prices at, above, or below industry average?
  • Translate these assumptions into revenue, margin, and earnings forecasts and then into pro forma financial statements.

Because the top-down approach is anchored in macro and industry conditions, it promotes consistency across companies. If industry revenues can only plausibly grow at 4–5% per year given GDP and pricing assumptions, it is a warning sign if an analyst’s company forecasts imply 15–20% industry growth without a convincing rationale.

Key Term: forecast consistency
The degree to which individual company forecasts align with sector, industry, and macroeconomic expectations, avoiding contradictions or implausible aggregate outcomes.

Worked Example 1.1

Scenario: An analyst is forecasting sales for a consumer electronics company. She starts with GDP growth projections, then estimates expected industry growth, and only then forecasts individual company sales. What approach is she applying, and what are the advantages?

Answer:
She is applying a top-down forecasting approach. Advantages include:

• Alignment with macro conditions (GDP, inflation, interest rates), reducing the risk that firm forecasts contradict the economic environment.
• A structured path from economy → industry → company, which helps maintain forecast consistency across firms.
• A natural framework for comparing companies within an industry, because each firm’s growth and margins are judged relative to the same industry baseline.

From Industry Forecasts to Company-Level Estimates

A core exam skill is translating an industry view into a specific company forecast. The logic is:

1. Estimate industry size and growth.

   • Example: forecast that industry revenues will be USD 100 billion next year, growing at 6% annually (3% real volume growth + 3% price inflation).

2. Estimate the firm’s market share.

   • Use historical market share, competitive advantages, and planned initiatives (e.g., new products, capacity additions).
   • Check realism: large market share gains often require significant investment or differentiation.

3. Translate into company revenues.

   • Company revenue = (Industry size) × (Company market share)
   • Adjust for geographic mix and currency exposure if needed.

4. Forecast margins and earnings.

   • Use knowledge of cost structure (fixed vs variable costs, economies of scale) to estimate gross and operating margins.
   • Consider industry-specific pressures, such as rising input costs or pricing pressure from powerful buyers.

5. Build pro forma financial statements.

   • Use historical ratios (e.g., fixed asset turnover, receivables days) to forecast investment needs and working capital.

Worked Example 1.2

Scenario: Suppose an analyst forecasts that global industry sales for a certain product will be 10,000 units next year. The current market share of Company X is 8%. Management plans marketing campaigns and capacity expansion, and the analyst assumes market share will increase to 9%. The average selling price is expected to be USD 500 per unit. What is the analyst’s revenue forecast for Company X, and what sanity check should be applied?

Answer:
Forecasted revenue = Industry volume × Company market share × Price
= 10,000 units × 9% × USD 500
= 900 units × USD 500
= USD 450,000.
A key sanity check is whether the assumed gain in market share (from 8% to 9%) is plausible given the company’s resources and industry conditions. If several competitors are also forecast to gain share, the sum of forecasted shares would exceed 100%, indicating an inconsistency.

Bottom-Up Forecasting Approach

The bottom-up method directly estimates a company’s future performance, typically by starting with internal drivers such as product pipelines, business initiatives, salesforce expansion, capacity changes, or management guidance. Revenues are often built up as:

• Units sold × Average selling price, by product line and region.
• Operating metrics specific to the industry (e.g., same-store sales for retailers, average revenue per user for telecoms, occupancy rate and average daily rate for hotels).

Key Term: operating gearing
The degree to which a company’s operating income responds to changes in sales, driven by the proportion of fixed versus variable operating costs.

Key Term: sensitivity analysis
A technique that examines how forecast results change when a single assumption (such as volume, price, or cost inflation) is varied while other assumptions are held constant.

Key Term: scenario analysis
A technique that evaluates forecast results under internally consistent sets of assumptions (scenarios), such as optimistic, base, and pessimistic macro or industry conditions.

The bottom-up approach is particularly useful when:

• Company outcomes depend heavily on specific projects (e.g., a new plant, a drug approval, a major cost-reduction program).
• Firms within the same industry have very different strategies, product mixes, or geographies.
• Reliable macro or industry forecasts are hard to obtain, or the firm’s performance is only weakly correlated with broad economic conditions.

Analysts using a bottom-up approach often create pro forma financial statements reflecting management’s planned initiatives and then use sensitivity and scenario analysis to understand key risks.

Worked Example 1.3

Scenario: An analyst develops a forecast of revenues by adding up the sales projections for each region and product line based only on company data and management discussions. What approach is this, and what risk does it entail?

Answer:
This is a bottom-up forecasting approach. The main risk is that it may ignore broader industry or macroeconomic factors (such as a recession or rising interest rates), which could make the forecast inconsistent with external trends. It may also be susceptible to optimism bias if management’s guidance is overly positive.

Comparing Top-Down and Bottom-Up Forecasting

Both methods have value, and exam questions often ask you to choose the most appropriate approach given a description.

• Top-down:

  • Strengths:
    • Ensures forecasts are anchored to economic and industry realities.
    • Promotes forecast consistency across companies and sectors.
    • Helpful for asset allocation and sector rotation strategies.
  • Weaknesses:
    • May overlook firm-specific advantages or risks.
    • Can be wrong if macro or industry forecasts are inaccurate.
    • Slow to capture structural changes at the company level.

• Bottom-up:

  • Strengths:
    • Captures company-specific initiatives, such as new products or cost programs.
    • Flexible when firms differ substantially within the same industry.
    • Useful for idiosyncratic businesses whose performance does not track the overall economy closely.
  • Weaknesses:
    • May ignore cyclical downturns, policy shifts, or input cost shocks.
    • Aggregated bottom-up forecasts can imply unrealistic industry or macro outcomes.
    • Often influenced by optimistic management guidance.

In practice, many analysts use a hybrid approach:

• Start top-down to set bounds on overall industry growth and margins.
• Build detailed bottom-up company models within those bounds.
• Compare the two views to identify inconsistencies or mispriced securities.

Worked Example 1.4

Scenario: An analyst forecasts that a country’s real GDP will grow by 2% next year and consumer price inflation will be 3%. The food retail industry historically grows at approximately GDP plus 1% in real terms, and its prices rise in line with inflation. The analyst covers a supermarket chain that has been gaining market share by 0.5 percentage points per year from a base of 10%. Which approach is the analyst likely to use, and what would be a reasonable starting point for the company’s nominal sales growth assumption?

Answer:
The analyst is using a top-down logic: GDP → industry growth → company growth.

• Real industry volume growth ≈ 2% (GDP) + 1% = 3%.
• Price inflation ≈ 3%.
• Nominal industry growth ≈ 3% (real) + 3% (price) = 6%.
  If the company continues to gain market share, its volume growth might be slightly above the industry, say 3.5% instead of 3%. Keeping prices in line with the industry, a reasonable starting point for nominal sales growth could be around 6.5% (3.5% real + 3% price), subject to further firm-specific analysis.

Drivers and Limitations: Industry Structure and Cost Behavior

Forecast accuracy depends critically on how well the analyst understands both the industry and the firm’s cost structure.

Key factors include:

• Industry cyclicality and growth pattern

  • Durable goods and capital equipment are usually more cyclical than non-durable consumer staples and utilities.
  • Early-stage or rapidly changing industries often have wider forecast ranges.

• Industry structure and competition

  • Porter-type factors—rivalry, entry barriers, buyer power, supplier power, and substitutes—affect both growth and margins.
  • For example, increasing power of large retailers can pressure brewers’ margins, forcing them to offer better terms or raise advertising spending, which lowers return on invested capital.

• Operating gearing and cost structure

  • Firms with high fixed costs (e.g., heavy manufacturing, airlines) have high operating gearing. Small changes in sales can cause large swings in profit.
  • Firms with more variable costs (e.g., outsourcing-heavy models) have more stable profits for a given sales change.

From the curriculum, consider two stylized firms:

• Firm FC with primarily fixed costs has profits that vary widely when sales rise or fall by 25%. Its return on equity might move from 10% to 50% across scenarios.
• Firm RCL with mostly variable costs shows narrower profit swings and more stable return on equity when sales change by the same percentage.

High operating gearing increases business risk, so top-down assumptions about industry volume are especially important in such industries. For firms like Firm FC, an analyst must pay particular attention to downside scenarios in a recession.

• Company differentiation and strategy
  • Strong brands, cost leadership, unique technology, or favorable regulation can allow a firm to earn above-industry margins or grow faster than the industry.
  • Forecasts should explicitly link these qualitative factors to quantitative assumptions about market share and pricing power.

Inflation, Deflation, and Forecasting

Key Term: inflation
A general and sustained increase in the overall price level of goods and services in an economy.

Key Term: deflation
A general and sustained decrease in the overall price level of goods and services in an economy.

Inflation and deflation can significantly affect both sales and costs:

• Industry sales and inflation

  • Nominal revenue growth decomposes into real volume growth plus price inflation.
  • In concentrated industries with strong pricing power (e.g., an oligopoly beer market), firms may be able to pass most input cost increases on to consumers, so prices tend to move roughly with the consumer price index.
  • In highly competitive markets with many substitutes, firms may struggle to raise prices, so inflation squeezes margins rather than boosting revenues.

• Company sales and pricing power

  • A firm with strong pricing power can increase prices at or above the inflation rate without losing much volume.
  • A firm with weak pricing power may have to keep prices flat even when its input costs rise, leading to margin compression.

• Costs and margins

  • Wage inflation, raw material price changes, and energy costs may not move in line with output prices.
  • For high operating gearing firms, cost inflation can have a large impact on profit unless offset by higher prices or efficiency gains.

In both top-down and bottom-up forecasts, it is helpful to:

• Separate real volume assumptions from price assumptions.
• Use explicit inflation assumptions for key cost categories.
• Run scenario analysis for high and low inflation paths to see how sensitive earnings are to inflation shocks.

Using Ratio Analysis and Operating Metrics in Forecasts

The curriculum emphasizes that historical ratio analysis is a starting point for forecasting:

• Profitability ratios (gross margin, operating margin, net margin) help set baseline assumptions for future margins given expected changes in competition and costs.
• Turnover ratios (fixed asset turnover, inventory turnover, receivables days) help forecast capital expenditure and working capital needs as sales grow.
• Capital structure and coverage ratios (debt-to-equity, interest coverage) help assess financial risk and the sustainability of financing.

Industry-specific ratios also support bottom-up forecasts:

• Retail: same-store sales growth, sales per square meter.
• Telecoms or subscription businesses: average revenue per user (ARPU).
• Service companies: revenue per employee, net income per employee.
• Hotels: occupancy rate, average daily rate.

By projecting these operating metrics, analysts can build up revenue forecasts that reflect both expansion (e.g., new stores, more users) and efficiency (e.g., higher sales per store or user).

Sensitivity and scenario analysis are then used to test how sensitive earnings and cash flows are to changes in key ratios or drivers.

Worked Example 1.5

Scenario: A telecom analyst forecasts that a mobile operator will end the year with 5 million subscribers. The expected average revenue per user (ARPU) is USD 15 per month. The analyst assumes no change in subscriber numbers under a downside scenario, but ARPU falls by 10% due to competitive pressure. What are the base-case and downside annual revenue forecasts, and which type of analysis is being performed?

Answer:
Base-case annual revenue:
= Subscribers × ARPU × 12
= 5,000,000 × USD 15 × 12
= USD 900,000,000.
Downside annual revenue (ARPU down 10% to USD 13.50):
= 5,000,000 × USD 13.50 × 12
= USD 810,000,000.
The analyst is performing sensitivity analysis, varying one key input (ARPU) while holding the number of subscribers constant to see the effect on revenue.

Reconciling Top-Down and Bottom-Up Views

For exam purposes, it is important to recognize the need to reconcile different forecasting approaches:

• Aggregate plausibility check

  • If the sum of bottom-up forecasts for all firms in an industry implies far higher growth than the top-down industry forecast, at least some company forecasts are likely too optimistic.
  • The converse—company forecasts implying almost no growth while the industry is forecast to grow strongly—may indicate overly conservative assumptions or missed opportunities.

• Market share realism

  • Aggregated company forecasts may imply that total market share exceeds 100%, which is impossible.
  • Analysts must adjust their assumptions so that all companies’ market shares sum to 100% (or to the relevant coverage share of the industry).

• Avoiding double counting

  • When building forecasts, ensure that the same driver is not counted twice. For example, do not assume higher margins from both operating gearing and separate unexplained margin expansion if both stem from the same sales growth.

Reconciling top-down and bottom-up views helps improve forecast consistency and is a common theme in vignette-based questions.

Exam Warning

A common error is to use bottom-up forecasts in aggregate without checking whether the sum aligns with overall industry or macro projections. For example, if every analyst assumes their covered companies will gain market share, the implied total may exceed 100%. Always check for aggregation errors, unrealistic industry growth, and implausible market share assumptions.

Revision Tip

For CFA exam questions, remember: Top-down = macro to micro; bottom-up = micro to macro. Be able to state at least two strengths and two weaknesses of each, explain how inflation affects sales and costs, and recognize when to use sensitivity and scenario analysis.

Summary

Top-down forecasting starts with the economy and works down through sectors and industries to company projections. It is driven by macro variables (GDP, inflation, interest rates, policy, global growth) and industry structure, and it helps maintain consistency across companies.

Bottom-up forecasting starts at the company level, using detailed information about products, regions, operating metrics, and management plans. It is well suited to firms whose prospects are dominated by specific projects or unique competitive advantages.

Effective industry and company analysis often uses both approaches:

• Top-down to set realistic bounds on overall growth, prices, and margins.
• Bottom-up to capture company-specific initiatives and to build detailed pro forma financial statements.
• Ratio analysis and industry-specific metrics to translate qualitative assessments into quantitative forecasts.
• Sensitivity and scenario analysis to understand how earnings respond to changes in key drivers, including inflation and operating gearing.

CFA Level 1 candidates must be able to distinguish these frameworks, recognize which is being applied in a vignette, and judge whether the set of assumptions is internally consistent and plausible given the macro, industry, and company information provided.

Key Point Checklist

This article has covered the following key knowledge points:

• Define and contrast top-down and bottom-up forecasting frameworks.
• Describe how to move from industry forecasts to company-level estimates using market size, market share, and pricing assumptions.
• Identify how industry structure, competition, and operating gearing influence revenue growth, margins, and earnings volatility.
• Explain how inflation and deflation affect industry and company sales and costs, and how to incorporate these effects into forecasts.
• Understand how ratio analysis and industry-specific operating metrics support revenue and earnings projections.
• Recognize the importance of forecast consistency between macro, industry, and company assumptions.
• Use sensitivity and scenario analysis to test the robustness of bottom-up company forecasts.
• Identify situations in which a hybrid approach, reconciling top-down and bottom-up views, is most appropriate.

Key Terms and Concepts

• top-down forecasting
• bottom-up forecasting
• industry analysis
• business risk
• market share
• pricing power
• pro forma financial statements
• forecast consistency
• operating gearing
• sensitivity analysis
• scenario analysis
• inflation
• deflation