Learning Module 7
Company Analysis: Forecasting
Key Outcomes Summary & Practice Problems
What you must be able to do
Curriculum Year: 2026
Explain principles and approaches to forecasting a company's financial results and position.
Explain approaches to forecasting a company's revenues.
Explain approaches to forecasting a company's operating expenses and working capital.
Explain approaches to forecasting a company's capital investments and capital structure.
Describe the use of scenario analysis in forecasting.
1 · Forecast Objects, Principles & Approaches
Analysts build financial statement models to support security valuation. The forecast is a quantitative expression of the analyst's forward‑looking views, justified by analysis.
Four Common Forecast Objects
Object | Description | Pros & Cons |
|---|---|---|
Drivers of financial statement lines | e.g., stores × sales per store; membership × fee | ✅ Explanatory value, accuracy; ❌ More complex |
Individual financial statement lines | e.g., amortisation expense, "other" assets | ✅ Simpler; ❌ Less transparent |
Summary measures | e.g., free cash flow, EPS, total assets | ✅ Efficient; ❌ Opaque, hard to audit |
Ad hoc objects | e.g., pending legal proceedings, regulatory actions | ✅ Captures unique events; ❌ Not regularly disclosed |
Four Forecast Approaches
• Use past values as forecast (e.g., median growth rate)
• Best for mature, non‑cyclical industries
• Weak for cyclical or changing companies
• Use past values as forecast (e.g., median growth rate)
• Best for mature, non‑cyclical industries
• Weak for cyclical or changing companies
• Use company's forward‑looking statements
• Management has firm‑level advantage
• Less reliable for macroeconomic variables
Surveys, quantitative models, analogies
• Best for cyclical, unique, or changing companies
• Requires significant judgment
Selecting a forecast horizon: Determined by investment strategy, industry cyclicality, company‑specific factors, and employer preferences. Long‑term managers: 3–5 years; short‑term: 1–2 quarters. The period should be long enough to reach an expected mid‑cycle level of profitability.
Focus on regularly disclosed objects — otherwise forecasts cannot be confirmed in a timely manner.
Avoid overly complex models — more forecasts, more time to update, often without accuracy improvement.
Non‑recurring items should be considered separately from recurring objects (e.g., exchange rate effects, acquisitions, COVID‑19 related growth).
2 · Forecasting Revenues
Revenue forecasts use top‑down or bottom‑up drivers, and any of the four forecast approaches may be applied.
Growth relative to GDP — forecast nominal GDP, then apply premium/discount based on industry life cycle or cyclicality.
Market growth & market share — forecast market size, then apply expected market share.
Example: Warehouse Club — US retail sales growth × market share (70 bps).
Volumes and average selling prices — units × price.
Product‑line or segment revenues — aggregate segment forecasts.
Capacity‑based — stores × sales per store; same‑store sales growth.
Return‑based — AUM × fee rate (asset managers).
Example: Warehouse — average stores × sales per store; members × fee.
Market Share = Company Revenue / Market Size
If market grows 3.4% and market share gains 2 bps/year, revenue growth = market growth + share gain effect.
Iliso Marketplace case study: Top‑down: forecast US retail sales, then Iliso's GMV market share (0.21% + 2 bps). Bottom‑up: customer accounts × average spend + third‑party merchants × average sales. Both approaches used for cross‑checking.
Separate recurring and non‑recurring revenue — e.g., COVID‑19 e‑commerce growth vs. underlying trend; cryptocurrency‑related GPU sales.
Key risk factors: competition, changes in the business cycle, inflation/deflation, technological developments.
Using both top‑down and bottom‑up objects helps uncover implicit assumptions or errors.
3 · Forecasting Operating Expenses & Working Capital
Operating costs are often forecast using aggregated objects due to limited disclosure. Forecasts should be coherent with revenue forecasts.
Cost of Sales & Gross Margins
Typically forecast as a percentage of sales (or gross margin).
Consider input cost shocks — if input costs rise, gross margin may decline even if prices are increased (because equal absolute amount added to both numerator and denominator).
Incorporate hedging strategies (e.g., brewers hedge barley).
Segment mix matters — faster‑growing lower‑margin segments reduce overall margins.
If input costs double and company passes on full increase:
Period 1: Sales 100, COGS 25, Gross margin 75%
Period 2: Sales 125, COGS 50, Gross margin 60%
Absolute gross profit unchanged, but margin declines.
SG&A Expenses
Less directly linked to revenue than COGS; often has a large fixed component.
Break down into variable (selling, distribution) and fixed (general corporate) components.
Segment disclosures often provide operating margin, enabling summary‑based forecasting by segment.
Working Capital Forecasts
Use efficiency ratios (DSO, DOH, DPO) combined with revenue and COGS forecasts.
DSO = Accounts Receivable / (Revenue / 365)
DOH = Inventory / (COGS / 365)
DPO = Accounts Payable / (COGS / 365)
Cash Conversion Cycle = DSO + DOH − DPO
Historical results approach is common; convergence to industry norms may be appropriate for maturing companies.
YY Ltd. example (Exhibit 8): Using historical DSO (15), DOH (73), DPO (126) and forecast revenue/COGS, projected AR, inventory, and AP are calculated. Working capital forecasts are mechanically derived from efficiency ratios and sales/cost projections.
4 · Forecasting Capital Investments & Capital Structure
Capital investments and capital structure forecasts are derived from cash flow and balance sheet projections.
Maintenance CAPEX — sustains current operations; often based on depreciation & amortisation (with inflation adjustment).
Growth CAPEX — expands the business; tied to strategy, expansion plans, and revenue growth.
Forecast object: CAPEX as % of sales, or per new store/unit.
Use leverage ratios as forecast objects: debt to capital, debt to EBITDA, D/E.
Consider management's target capital structure, debt covenants, and credit rating.
Degree of Financial Leverage (DFL) = %Δ Net Income / %Δ Operating Income.
PP&E, net (end) = PP&E, net (beg) + CAPEX − Depreciation
Gross debt = Debt/EBITDA ratio × EBITDA
YY Ltd. example: forecast revenue → EBITDA (margin) → gross debt (target ratio)
Warehouse Club case study: CAPEX at ~2% of sales (historical). Debt/EBITDA ~1.1 (historical). Management guidance on store openings used to forecast growth CAPEX. Conservative capital structure with negative net debt.
5 · Scenario Analysis in Forecasting
Rather than a single point estimate, analysts develop several scenarios based on key risk factors to capture a range of possible outcomes.
Changes in the business cycle
Competition
Inflation or deflation
Technological developments
Identify key risk factors
Develop alternative assumptions (bull, base, bear)
Forecast financial statements under each scenario
Assign probabilities or assess range of outcomes
Microsoft tablet cannibalisation case study (Exhibit 13–21):
• Base case: 30% consumer cannibalisation, 10% non‑consumer → FY2014 EPS impact −$0.25.
• Bull case: 15% consumer, 5% non‑consumer → EPS impact −$0.13.
• Bear case: 40% consumer, 20% non‑consumer → EPS impact −$0.35.
• Sensitivity analysis: altering cannibalisation rates changes EPS by −$0.11 to −$0.38.
• Operating leverage: 70% fixed costs → revenue growth amplifies operating income growth.
Estimated variable cost % = %Δ (Cost of revenue + OpEx) / %Δ Revenue
Microsoft: 5% / 17% = 29% variable, 71% fixed
Fixed costs change at half the rate of sales; variable costs change at same rate.
Sensitivity analysis shows how changes in assumptions affect the forecast (e.g., cannibalisation rate vs. EPS).
Scenario analysis helps investors compare with consensus and implied market valuations.
For cyclical industries or companies undergoing change, scenario analysis is crucial.