A cyclical company is one whose earnings demonstrate a repeating pattern of significant increases and decreases. The earnings of cyclical companies, including those in the steel, mining, paper, and chemical industries, fluctuate because the prices of their products change dramatically as demand and/or supply varies. The companies themselves often create too much capacity. Volatile earnings within the cycle introduce additional complexity into the valuation of these cyclical companies. For example, historical performance must be assessed in the context of the cycle. A decline in recent performance does not necessarily indicate a long-term negative trend but rather may signal a shift to a different part of the cycle.
In this chapter excerpt from the eighth edition of our book, Valuation: Measuring and managing the value of companies (Wiley, May 2025),1 we explore the valuation issues particular to cyclical companies. This section of the chapter examines how the share prices of cyclical companies behave and suggests an approach for valuing them.
Cyclical companies’ share price behavior
Suppose you were using the discounted-cash-flow (DCF) approach to value a cyclical company and had perfect foresight about the industry cycle. Would the company’s value and earnings behave similarly? No. A succession of DCF values would exhibit much lower volatility than the earnings or cash flows. DCF reduces future expected cash flows to a single value. As a result, any single year is unimportant. For a cyclical company, the high cash flows cancel out the low cash flows. Only the long-term trend really matters.
Exhibit 1 shows the earnings per share (EPS) and share prices, both indexed, for 15 companies with a four-year cycle. The share prices are more volatile than the DCF approach would predict, which suggests that market prices exhibit the bias of anchoring on current earnings.
What might explain this pattern? We examined equity analysts’ consensus earnings forecasts for cyclical companies, looking for clues to these companies’ volatile stock prices. Consensus earnings forecasts for cyclical companies appeared to ignore cyclicality entirely. The forecasts invariably showed an upward-sloping trend, whether the companies were at the peak or trough of the cycle.
What became apparent was not that the DCF model was inconsistent with the facts, but that the analysts’ projections of earnings and cash flow were to blame (assuming the market followed the analysts’ consensus). This conclusion was based on an analysis of 36 US cyclical companies during the period from 1985 to 1997. We divided them into groups with similar cycles (for example, three, four, or five years from peak to trough) and calculated scaled average earnings and earnings forecasts. We then compared actual earnings with consensus earnings forecasts over the cycle.2
Exhibit 2 plots the actual earnings and consensus earnings forecasts for the set of 15 companies with four-year cycles in primary metals and manufacturing transportation equipment. The consensus forecasts do not predict the earnings cycle at all. In fact, except for the next-year forecasts in the years following the trough, the earnings per share are forecast to follow an upward-sloping path with no future variation.3
One explanation could be that equity analysts have incentives to avoid predicting the earnings cycle, particularly the down part. Academic research has shown that earnings forecasts have a positive bias that is sometimes attributed to the incentives facing equity analysts.4 Pessimistic earnings forecasts may damage relations between an analyst’s employer and a particular company. In addition, companies that are the target of negative commentary might cut off an analyst’s access to management. From this evidence, we could conclude that analysts as a group are unable or unwilling to predict the cycles for these companies. If the market followed analyst forecasts, that behavior could account for the high volatility of cyclical companies’ share prices.
We know it is difficult to predict cycles, particularly their inflection points. So, it is unsurprising that the market does not get them exactly right. However, we would be surprised if the stock market entirely missed the cycle, as the analysis of consensus forecasts suggests. To address this issue, we returned to the question of how the market should behave. Should it be able to predict the cycle and therefore exhibit little share price volatility? That would probably be asking too much. At any point, the company or industry could break out of its cycle and move to one that is higher or lower.
Suppose you are valuing a company that seems to be at a peak in its earnings cycle. You will never have perfect foresight of the market cycle. Based on past cycles, you expect the industry to turn down soon. However, there are signs that the industry is about to break out of the old cycle. A reasonable valuation approach, therefore, would be to build two scenarios and weight their values. Suppose you assumed, with a 50 percent probability, that the cycle will follow the past and that the industry will turn down in the next year or so. The second scenario, also with a 50 percent probability, would be that the industry will break out of the cycle and follow a new long-term trend based on current improved performance. The value of the company would then be the weighted average of these two values.
We found evidence that this is, in fact, the way the market behaves. We valued the four-year cyclical companies three ways:
- with perfect foresight about the upcoming cycle
- with zero foresight, assuming current performance represents a point on a new long-term trend (essentially the consensus earnings forecast)
- with a 50/50 forecast: 50 percent perfect foresight and 50 percent zero foresight
Exhibit 3 summarizes the results, comparing them with actual share prices. As shown, the market does not follow either the perfect-foresight or the zero-foresight path; it follows a blended path, much closer to the 50/50 path. So, the market has neither perfect foresight nor zero foresight. One could argue that this 50/50 valuation is the right place for the market to be.
An approach to valuing cyclical companies
No one can precisely predict the earnings cycle for an industry, and any single forecast of performance must be wrong. Managers and investors can benefit from explicitly following a multiple-scenario probabilistic approach to valuing cyclical companies. The probabilistic approach avoids the traps of a single forecast and allows exploration of a wider range of outcomes and their implications.
Here is a two-scenario approach for valuing cyclical companies in four steps (of course, this approach would also work with more than two scenarios):
Construct and value the normal cycle scenario, using information about past cycles. Pay particular attention to the long-term trend lines of operating profits, cash flow, and return on invested capital (ROIC), because they will have the largest impact on the valuation. Make sure the continuing value is based on a normalized level of profits (that is, a point on the company’s long-term cash flow trend line), not a peak or trough.
Construct and value a new trend line scenario based on the company’s recent performance. Once again, focus primarily on the long-term trend line, because it will have the largest impact on value. Do not worry too much about modeling future cyclicality (although future cyclicality will be important for financial solvency).
Develop the economic rationale for each of the two scenarios, considering factors such as demand growth, companies entering or exiting the industry, and technology changes that will affect the balance of supply and demand.
Assign probabilities to the scenarios and calculate their weighted values. Use the economic rationale and its likelihood to estimate the weights assigned to each scenario.
This approach provides an estimate of the value as well as scenarios that put boundaries on the valuation. Managers can use these boundaries to improve their strategy and respond to signals about which scenario is likely to occur.
Another consideration when valuing cyclical companies in commodity-linked industries is that starting with revenues may not be the best way to model performance. Consider a polyethylene manufacturer, which processes natural gas into polyethylene. The traditional approach to valuation would be to model sales volumes and polyethylene prices to estimate revenues, from which you would subtract the cost of purchasing natural gas (volume times natural-gas prices) and operating costs to estimate operating profits. It may be simpler, however, to model only volumes and the “crack spread”—the difference between polyethylene prices and the cost of natural gas—and then subtract operating costs. What ultimately matters is the crack spread, not the revenues. The crack spread will often be set by the demand–supply balance for polyethylene, not the level of natural-gas prices. For example, during a decline in natural-gas prices, the crack spread might remain constant as producers pass on the reduction in natural-gas prices to customers by lowering polyethylene prices. If volumes were stable, operating profits would be too, despite a decline in revenues.5
At first glance, the share prices of cyclical companies appear too volatile to be consistent with the DCF valuation approach. This chapter excerpt shows, however, that share price volatility can be explained by the uncertainty surrounding the industry cycle. Using scenarios and probabilities, managers and investors can take a systematic DCF approach to valuing and analyzing cyclical companies.
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