P/E Ratio for Cyclical Stocks

The P/E ratio for cyclical stocks—those in commodity industries, steel, autos, or industrial goods—is a treacherous metric when earnings are at a cycle peak or trough. A P/E of 5x during a boom looks cheap; a P/E of 20x during a downturn looks expensive. The solution is to use through-the-cycle or normalized earnings, which smooth out the cycle and reveal true relative value.

The Mechanics of Cyclical Earnings Distortion

Suppose a steel company’s earnings swing from $5 per share in a downturn to $10 per share in a boom, on a 10-year cycle. At peak ($10 earnings), the stock trades at $60, yielding a P/E of 6x. At trough ($5 earnings), the stock trades at $40, yielding a P/E of 8x.

A naive investor sees the peak-cycle stock at 6x P/E and thinks it is cheap relative to the 8x trough valuation. In reality, the peak-earnings stock is more likely to compress (earnings will fall), while the trough-earnings stock is likely to expand (earnings will rise). The 6x multiple at peak earnings is actually more expensive on a normalized basis than the 8x multiple at trough earnings.

This illusion tricks countless investors. They buy “cheap” stocks near cycle peaks and sell “expensive” stocks near cycle troughs—the opposite of optimal timing.

Through-the-Cycle Earnings

A through-the-cycle P/E ratio uses normalized earnings, calculated as the average (or median) earnings over a full business cycle, usually 5–10 years. This smooths out the volatility and reveals the true per-share earning power.

Continuing the steel example: if the company’s earnings over the last 10 years averaged $7 per share (ranging from $5 to $10), the through-the-cycle P/E is calculated as stock price ÷ $7. If the stock trades at $60 (peak earnings), the through-the-cycle P/E is $60 ÷ $7 = 8.6x. If the stock trades at $40 (trough earnings), the through-the-cycle P/E is $40 ÷ $7 = 5.7x.

Now the comparison is intuitive: the stock at the peak is more expensive (8.6x) on a normalized basis, and the stock at the trough is cheaper (5.7x). The trough valuation is the better entry point—the opposite of the naive P/E comparison.

How to Compute Normalized Earnings

Normalized earnings can be estimated several ways:

1. Trailing 10-year average: Take the company’s reported earnings per share for the last 10 years and compute the mean (or median). Use the mean if the data is stable; use the median if there are outliers (e.g., a COVID collapse or a one-time windfall).

2. Analyst consensus long-term: Sell-side analysts often publish long-term earnings growth estimates or normalized operating margins. Some research platforms (Yahoo Finance, Seeking Alpha, broker dashboards) publish a “normalized EPS” estimate based on consensus.

3. Regression or smoothing: Apply a trend line or moving average to eliminate cyclical noise. A 5-year rolling average updates annually and is simpler than a full 10-year mean.

4. Industry average or peer comparison: Compare the company’s recent peak and trough to peers in the same industry and construct an estimate from their typical cycle. This is useful if the company is young or if historical data is sparse.

In practice, the most common approach is the trailing 10-year mean, supplemented by analyst consensus if historical data is incomplete or if the cycle has shifted (e.g., structural changes in demand).

Industries Where Cyclicality Is Pronounced

Cyclical distortion is most severe in commodity-linked sectors:

• Oil & gas: Earnings swing with commodity prices; a $40–$50 oil price may make a producer unprofitable, while $80–$100 oil makes it highly profitable.
• Metals & mining: Iron ore, copper, and precious metals prices drive margins and earnings; a decade-long supercycle inflates multiples, then a crash deflates them.
• Automotive: Vehicle sales are tied to credit conditions, employment, and consumer confidence. Boom years yield high earnings; recessions can produce losses.
• Shipping: Freight rates and vessel supply/demand swing sharply. A container ship may earn $20,000/day in a boom and $2,000/day in a glut.
• Construction & building materials: Tied to housing cycles, interest rates, and economic confidence. Earnings can vary 3–4x from peak to trough.
• Chemicals & basic materials: Commodity-linked margins; downstream demand and pricing power are volatile.

Non-cyclical sectors (consumer staples, utilities, healthcare) have more stable earnings, making point-in-time P/E ratios more reliable.

Cyclical Traps for the Unwary

The peak-earnings trap: A company reports record earnings in a boom year. The stock rises sharply. Its P/E looks cheap (6x–8x), and investors pile in, thinking they have found value. Then the cycle turns, earnings collapse 50%, and the stock falls 60%. Investors who bought at the “cheap” peak P/E lose heavily.

The trough-earnings trap (reversed): A company is in a downturn; earnings are depressed. Its P/E looks expensive (15x–20x). Investors avoid it. But the cycle is turning; earnings will recover. Those who buy the expensive trough multiple are rewarded when earnings normalize and the stock re-rates up.

Both traps can be avoided by using normalized earnings and understanding where in the cycle the company sits.

Comparing Cyclical Stocks to Each Other

When comparing two cyclical stocks, use through-the-cycle P/E, EV/EBITDA (enterprise value to earnings before interest, taxes, depreciation, and amortization), or price-to-normalized-earnings. These metrics are far more reliable than spot P/E ratios for relative valuation.

For example, two steel companies both trade at spot P/E of 8x. Company A’s normalized earnings (10-year average) are $2, implying a true P/E of 10x. Company B’s normalized earnings are $1.50, implying a true P/E of 6.7x. Company B is cheaper on a normalized basis, even though both appear the same on spot metrics.

Dividend Yield as an Alternative for Value Investors

For mature cyclical companies with stable dividends, the dividend yield is often a more stable metric than P/E. A company that pays a 4% dividend yield is more likely to sustain that yield through the cycle than to sustain a specific earnings multiple. This is why many value investors use yield as a screen for cyclical sectors.

See also