DCF Analysis: How to Build a Discounted Cash Flow Model

Building a DCF Model

Projecting Free Cash Flows

Free cash flow (FCF) is the cash a company generates after accounting for capital expenditures needed to maintain its asset base. FCF = Operating Cash Flow - Capital Expenditures. Project FCF for 5-10 years using assumptions about revenue growth, operating margins, tax rates, and reinvestment needs. Start with revenue — the top-line driver of all value. Project revenue growth based on the company's historical trajectory, industry trends, and competitive position. Next, project operating margins — what percentage of revenue becomes operating income. Consider the company's cost structure, pricing power, and economies of scale. Finally, estimate the tax rate and reinvestment rate (capital expenditures and working capital as a percentage of operating income). The quality of your DCF depends entirely on the quality of these projections. Use historical data as a starting point, but adjust for expected changes in the business and industry. Conservative projections are safer than optimistic ones. Read cash flow statements →

Choosing the Discount Rate (WACC)

The discount rate converts future cash flows into present value. The most common discount rate for DCF is the weighted average cost of capital (WACC), which represents the average return expected by all capital providers (both debt and equity). WACC = (E/V x Re) + (D/V x Rd x (1 - Tc)). The cost of equity (Re) is calculated using CAPM: risk-free rate + beta x equity risk premium. The cost of debt (Rd) is the company's borrowing rate. The choice of discount rate has an enormous impact on DCF value. A 1% change in WACC can change the valuation by 15-25%. For high-growth companies with uncertain cash flows, determine the appropriate discount rate by comparing the company's risk profile to the market. The risk-free rate (10-year Treasury yield) provides a baseline. Add an equity risk premium (typically 4-6%) and adjust for the company's beta. A higher discount rate reduces the present value of future cash flows, producing a lower intrinsic value. Learn WACC in detail →

Calculating Terminal Value

Terminal value captures the value of all cash flows beyond the explicit projection period. It often represents 60-80% of total DCF value, making it the most important and most sensitive assumption in the model. There are two common approaches. The perpetuity growth method assumes cash flows grow at a constant rate forever: Terminal Value = Final Year FCF x (1 + g) / (WACC - g). The growth rate (g) should be close to the long-term nominal GDP growth rate (2-3%). The exit multiple method applies a valuation multiple (typically EV/EBITDA) to the final year's EBITDA: Terminal Value = Final Year EBITDA x Exit Multiple. The exit multiple should reflect current market multiples for comparable companies. The perpetuity growth method is more theoretically sound, while the exit multiple method is more market-relative. Using both methods provides a range. Be conservative with terminal value assumptions — a slightly higher growth rate or lower discount rate can dramatically inflate the terminal value and the entire DCF. Understand enterprise value →

Sensitivity Analysis and Scenario Testing

A single DCF output is misleading because the model depends on so many assumptions. Sensitivity analysis shows how the valuation changes as you vary key inputs. Create a table with growth rates on one axis and WACC on the other, and see how the intrinsic value changes across the grid. Scenario analysis builds different narratives: a base case (most likely), an optimistic case (everything goes right), and a pessimistic case (growth disappoints, margins compress). Assign probabilities to each scenario and calculate a probability-weighted intrinsic value. For example, if base case is $100 (60% probability), optimistic is $150 (20%), and pessimistic is $60 (20%), the probability-weighted value is $100 x 0.6 + $150 x 0.2 + $60 x 0.2 = $60 + $30 + $12 = $102. This approach acknowledges uncertainty and prevents anchoring on a single estimate. Always present DCF results as a range, not a point estimate. Learn Monte Carlo simulation →

Common DCF Mistakes

Several common mistakes undermine DCF analysis. Overly optimistic growth projections: projecting high growth far into the future when competition and market saturation will inevitably slow growth. Ignoring competitive advantage period: companies with strong moats can sustain above-average growth and returns longer than commodity businesses. Double-counting cash: including cash on the balance sheet while also projecting it in cash flows. Inconsistent discount rate and cash flow type: using WACC (which applies to the whole firm) with equity cash flows (which belong to shareholders only). Terminal value that dominates the result: if terminal value exceeds 90% of total DCF value, your projection period is too short or your terminal assumptions are too aggressive. Ignoring stock-based compensation: this is a real expense that reduces free cash flow to shareholders. Using nominal cash flows with a real discount rate (or vice versa): always match the inflation treatment. The best defense against these mistakes is transparency — write down every assumption and check each one against reality. Compare DCF with comps →

When DCF Works Best and When It Fails

DCF works best for companies with predictable, stable cash flows — utilities, consumer staples, mature industrial companies, and infrastructure businesses. These companies have operating histories, stable margins, and visible growth drivers that make projection reasonably reliable. DCF is also useful for valuing entire businesses in M&A transactions where the acquirer plans to hold for the long term. DCF fails for companies with highly uncertain or variable cash flows — early-stage biotech, pre-revenue technology, volatile commodity producers, and financial institutions (where cash flow is difficult to define). For these companies, alternative methods like comps, precedent transactions, or asset-based valuation may be more appropriate. DCF also fails when used mechanically without judgment — blindly plugging in numbers and accepting the output without understanding the business. The best analysts use DCF as a framework for thinking about value, not as a truth machine. Learn comps as an alternative →

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