Picture the scene.
A seller arrives with last year’s numbers polished to perfection. Peak earnings. Expansion narrative intact. A buyer sits opposite, cost of capital recalculated twice that morning, risk committee hovering somewhere in the background. Interest rates are higher than they were two years ago. Supply chains are not what they used to be. Regulation moves faster than board cycles. The room is calm. The assumptions are not.
And somewhere in the middle of that table lies a spreadsheet with three letters at the top: DCF.
Discounted Cash Flow remains the intellectual backbone of corporate valuation. Strip away the noise and its logic is elegant. A business is worth the present value of the cash it will generate. Future cash flows discounted by a rate that reflects time and risk. Nothing exotic. No theatrics.
The formula itself is almost disarmingly simple. Cash flow in year one divided by one plus the discount rate to the power of one. Repeat for year two, three, and beyond. Add a terminal value. Apply a weighted average cost of capital that embeds interest rates, equity risk premiums, and business-specific risk. The mathematics is stable.
The environment is not.
The problem in volatile markets is that both sides of the equation begin to move at once. The numerator, projected cash flows, becomes uncertain. The denominator, the discount rate, becomes unstable. When both drift, precision turns into illusion.
Consider the cash flow side first.
In benign cycles, forecasting error is tolerable. Growth trends are visible. Margins move gradually. Capital expenditure follows a rhythm. In volatile conditions, that rhythm disappears. A mid-market manufacturer may project steady mid-single-digit revenue growth based on a decade of performance. Then credit tightens, customers delay orders, input costs spike because a shipping lane closes, and margins compress within a quarter. The five-year forecast that looked prudent in January looks optimistic by October.
Technology businesses face an even more dramatic version of the same challenge. Adoption curves hinge on regulatory clarity, platform economics, or ecosystem integration. A favourable regulatory shift can accelerate growth sharply. A restrictive policy can suppress it just as quickly. Linear projections in such contexts are comforting. They are rarely accurate.
And then there is the terminal value, often responsible for more than half of the total DCF outcome. A modest change in long-term growth or exit multiple assumptions can shift valuation materially. In calm markets this sensitivity is manageable. In unstable ones, it becomes a magnifier of misplaced confidence.
Now look at the discount rate.
The weighted average cost of capital is not a fixed input; it reflects prevailing interest rates, market risk premiums, capital structure and perceived risk. In the low-rate environment of the past decade, assets trading at 12 times EBITDA were not unusual. As rates rise and risk premiums expand, the same asset may attract offers at eight or nine times. The business may be unchanged. The risk pricing is not.
Infrastructure funds, sovereign investors and private equity houses do not share identical costs of capital. An energy asset with long-term contracted cash flows might look compelling to an investor with patient capital and low financing costs. The same asset may appear less attractive to a leveraged buyer facing expensive debt. Valuation, in practice, is not universal. It is buyer-specific.
This is where the conversation shifts from mathematics to strategy.
DCF is rigorous, but it is not omniscient. It produces a number that appears precise, often to the second decimal. That precision can be dangerous. In volatile markets, the likelihood that the exact forecast path will materialise declines significantly. What matters is not whether the base case is correct. It rarely is. What matters is whether the range of outcomes has been properly understood.
Sophisticated valuation practice therefore moves beyond a single-point estimate. Scenario analysis is not cosmetic; it is essential. A base case, a downside case, and an upside case reveal dispersion. Sensitivity testing exposes which assumptions drive the majority of value. In sectors characterised by structural uncertainty, probabilistic approaches such as Monte Carlo simulations provide a distribution rather than a comforting illusion of certainty.
But modelling alone does not resolve valuation tension. Structure does.
Earn-outs, deferred consideration, minority stakes, staged investments and performance-linked tranches are not financial tricks. They are mechanisms for allocating forecasting risk between buyer and seller. When future cash flows are uncertain, insisting on one definitive valuation number is intellectually lazy. Sharing risk through structure is often more elegant and economically rational.
This is where the role of the M&A advisor becomes strategic rather than mechanical.
In volatile markets, advisers are not merely applying models. They are interpreting macro shifts, sector disruption and capital market dynamics, then translating that context into negotiation strategy. They must explain to a seller why a lower multiple is not an insult but a reflection of risk pricing. They must demonstrate to a buyer where strategic value justifies a premium that standalone financial projections might not fully capture.
Comparable company analysis and precedent transactions remain valuable, but they carry their own distortions. Public market multiples can compress in risk-off environments, reflecting sentiment as much as fundamentals. Transaction benchmarks may be scarce when deal volumes slow. Asset-based approaches can establish valuation floors for capital-intensive businesses. Real options frameworks become relevant when embedded growth opportunities are significant and highly uncertain.
No single method dominates. Confidence increases when several frameworks converge within a defensible range.
The deeper truth is this: valuation in volatile markets is less about predicting a precise future and more about disciplined navigation of uncertainty. A DCF model remains logically coherent. It forces structured thinking about cash generation and risk. But it is a tool, not a verdict.
The top-tier adviser understands that valuation is ultimately negotiated reality, grounded in financial logic but shaped by capital markets, strategy and risk appetite. Credibility in such an environment comes from transparency of assumptions, intellectual honesty about uncertainty and rigorous sensitivity analysis.
The spreadsheet may produce a number. The professional produces context.
In periods of stability, DCF can feel definitive. In periods of volatility, it becomes what it always was beneath the surface: a framework for thinking, not a guarantee of accuracy.
Valuing in the fog requires more than calculation. It requires judgment, structure and the discipline to admit that precision and certainty are not the same thing.