Relative P/E Sector Analysis: Why Absolute Multiples Mislead

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title: "Relative P/E Sector Analysis: Why Absolute Multiples Mislead" description: "A 15x P/E stock in tech can be cheap and a 15x P/E utility can be expensive. Here's how to quantify that — and why Tessera's screener leans on sector-relative valuation." publishedAt: "2026-02-18" updatedAt: "2026-02-18" keywords: ["relative p/e", "sector analysis", "stock valuation", "sector rotation", "p/e ratio screening"]

TL;DR

• Absolute P/E filters ("show me everything under 15x") systematically surface entire low-multiple sectors — banks, integrated oil, auto OEMs — rather than actual mispricings.
• Sector-relative P/E measures how far a stock trades from its own peer group's median, isolating idiosyncratic discounts from structural sector economics.
• Tessera ranks candidates by sector-relative P/E discount and then gates them behind a 24-factor quality screen, because a cheap-looking multiple without quality is usually a value trap, not an opportunity.

The problem with absolute P/E

Open any stock screener, set the filter to "P/E less than 15," and look at the output. You will see the same clusters every time: regional banks, integrated energy majors, auto manufacturers, some home builders, a few insurers. Maybe a tobacco name or two. The list looks like a curated bargain bin. It is not.

What you are actually seeing is a list of sectors that structurally trade at low multiples, not a list of stocks that are cheap relative to their own economics. Banks trade at low P/E because earnings are levered, cyclical, and partially opaque. Integrated energy trades low because cash flows are tied to a volatile commodity and terminal-value questions hang over the entire sector. Autos trade low because they are capital-intensive, cyclical, and unionized. The multiple is the market's summary judgment of the sector, not a pricing error.

Running the opposite filter is just as misleading. "P/E greater than 40" surfaces software, semiconductors, and biotech — sectors where recurring revenue, operating leverage, and high returns on invested capital justify a premium multiple. Calling those stocks "expensive" based on the absolute number is the same error in reverse. You are comparing a 40x software company to a 12x bank and pretending the comparison is meaningful. It is not.

The deeper issue: P/E is a shorthand for a discounted cash flow. When growth rates, capital intensity, and risk premiums differ across sectors by an order of magnitude, the multiplier that falls out of the DCF must differ too. Comparing absolute multiples across sectors is equivalent to comparing sprinters and marathoners by average pace — mathematically possible, economically nonsense.

What sector-relative P/E actually measures

Every sector has a natural multiple range driven by the underlying business economics — how much of revenue is recurring, what the return on invested capital looks like through a cycle, how sensitive margins are to input costs, and whether growth is capped by regulation or physical supply. The table below is illustrative (medians drift with rates and sentiment), but the ordering is remarkably stable over decades.

| Sector | Typical median P/E | Why it trades where it does | |---|---|---| | Tech / Software | 25–40x | High recurring revenue, 70%+ gross margins, strong ROIC, operating leverage | | Consumer Staples | 20–25x | Stable demand and pricing power, but low single-digit growth caps the multiple | | Healthcare | 18–28x | Defensive demand, but patent cliffs and regulatory risk compress the upper bound | | Utilities | 12–18x | Rate-regulated returns, capped growth, bond-proxy behavior | | Energy (E&P / integrated) | 8–14x | Commodity-exposed, cyclical, ongoing terminal-value concerns |

Sector-relative P/E asks a narrower and more answerable question: given this stock's peer group, is it trading at a discount to the typical member? A semiconductor name at 17x is not "cheap" in absolute terms — it is well above the S&P 500 average — but if its sector trades around 24x, it is cheap relative to its peers. That gap is where you actually find mispricing, because you have already controlled for the dominant driver of multiple levels (sector economics) and isolated what is left: company-specific sentiment, earnings volatility, cyclical positioning, or misunderstood operational change.

How Tessera computes it

The implementation is mechanical, which is the point — no discretionary judgment, no ad hoc sector-by-sector tuning.

1. Classify. Pull the stock's GICS sector. We use the sector level (11 buckets) rather than the more granular industry group level, because industry medians are noisy with too few constituents to be stable.

2. Build the peer set. Filter to liquid names above the market cap floor — the default screening config uses a $500M minimum, which matches the backtest configuration ("minMarketCap": 500000000). Anything below that level has unstable multiples and insufficient float for the strategy to act on.

3. Compute the sector median P/E. We use a trimmed median (drop the top and bottom 5%) to prevent a handful of mega-caps or negative-earnings names from distorting the reference point.

4. Compute the discount. For each stock in the universe:

   discount = (stock_pe − sector_median_pe) / sector_median_pe

   A negative number means the stock trades below its sector median. Rank most negative first.

5. Filter. Drop anything with a discount worse than −10% (i.e., keep stocks at least 10% below sector median). Everything above that line is, by construction, not offering enough of a valuation edge to bother with.

A worked example. Assume the semiconductor sector median sits around 24x forward earnings. A liquid semi name trades at 17x. The discount is (17 − 24) / 24 = −0.29, or 29% below sector median. That stock clears the −10% threshold with plenty of room and enters the candidate pool for ranking against everything else that also cleared.

Nothing in this step tells you whether to buy the stock. It tells you the stock is cheap relative to peers. The next step — quality screening — determines whether the discount reflects opportunity or decay.

Why this isn't just "value investing"

The obvious objection: "you just described a value screen." Not quite.

Classic value investing ranks by absolute cheapness — low P/E, low P/B, high FCF yield — and assumes mean reversion does the rest. That works in aggregate over very long horizons, but the failure mode is brutal at the single-stock level. Cheap stocks are usually cheap for a reason: the business is in structural decline, accounting is aggressive, the industry is being disrupted, or management is destroying capital. These are the value traps that have been eating value-factor returns for the past decade.

Sector-relative P/E controls for the first-order driver of cheapness (sector), but it does not control for the second-order driver (company quality). A semiconductor name trading at a 29% discount to peers could be mispriced — or it could be the one semi company with inventory problems, customer concentration, or eroding gross margin. Both stories produce the same multiple.

The fix is to combine sector-relative P/E with a quality floor. Tessera grades every stock on 24 factors across four buckets — profitability, financial health, growth, and earnings quality — and collapses the result into a letter-grade Tessera Rating. Stocks that fail the quality screen are removed from the candidate pool before the relative-P/E ranking happens, not after. That order matters: we never rank low-quality names at all, so a 40% sector discount on a deteriorating business cannot sneak into the portfolio.

For the full breakdown of what goes into each factor bucket, see Quality Screening Factors.

Common failure modes (honest caveats)

Sector-relative P/E is not magic. The places it breaks down, in rough order of frequency:

• Sector median distorted by mega-caps. When three names make up 40% of a sector's market cap, their multiples anchor the "median" toward whatever sentiment is attached to those specific companies. We use a trimmed median and a liquidity floor, but in some sectors — consumer discretionary during mega-cap dominance years is a recurring example — the reference point is still wobbly. The signal is weaker in these periods; we scale position sizing accordingly, capping any single position at 7% of NAV.
• Earnings quality matters more than the multiple. A 12x P/E on inflated one-time earnings is not a 12x P/E. Divestiture gains, tax credits, pension accounting changes, and aggressive revenue recognition all pump the denominator temporarily. This is exactly why the earnings quality bucket exists in the 24-factor screen — accruals ratios, cash-flow-to-net-income, and expense capitalization flags catch most of these before they enter the ranking.
• GICS classification is imperfect. Meta is classified as Communication Services, but its economics are clearly tech — ad platform, network effects, high software-like margins. Amazon is Consumer Discretionary; AWS arguably shouldn't be. Where classification is ambiguous, the sector median comparison loses fidelity. There is no clean fix short of building a custom taxonomy, which is its own research project.
• Discount alone doesn't cause reversion. A stock can trade at a 30% sector discount for years. Cheapness is a necessary but not sufficient condition. You also need a catalyst — earnings inflection, management change, capital allocation shift — or the quality floor doing the work of surfacing companies where the market's pessimism is simply wrong. Without one of those, the discount just sits there.

None of these invalidate the approach. They are the reasons the methodology includes a quality gate, a competitive rotation layer that swaps held positions for stronger candidates when the score gap is large enough, and regime-aware risk management on top of the signal itself.

When absolute valuation still matters

Sector-relative analysis is the right tool for ranking stocks within a moment in time. It is the wrong tool for two other questions that still deserve an answer.

The first is stock-versus-itself: how does today's P/E compare to the company's own 10-year distribution? A software name trading at 35x might be cheap versus the sector and simultaneously expensive versus its own history. Both facts can be true. That comparison — absolute multiple against the stock's own long-term normal — is a useful sanity check on whether the sector-relative discount is just sector-wide exuberance that has lifted everyone.

The second is sector-versus-itself: is the entire sector median stretched relative to its long-term norm? Late-stage bubbles compress cross-sectional dispersion and lift every member of a favored sector together. In those moments, the "cheap" member of an expensive sector may still be structurally overpriced. Regime-aware position management helps here — CRISIS and BEAR regimes tighten the quality gate and shrink exposure — but the cleanest defense is checking the sector median itself against its decade-long distribution before leaning into it.

Neither of these replaces sector-relative P/E. They sit on top of it.

Related reading

• Sector Comparison vs Absolute Valuation
• Quality Screening Factors
• Competitive Rotation