r/valueinvesting
Why the "cheap" memory/semiconductor stocks may not be cheap — a P/E analysis
Memory stocks' low forward P/E reflects AI-driven peak earnings, a classic cyclical trap signaling a late-cycle top, not a value bargain.
- Low forward P/E in commodity cyclicals like memory is a late-cycle warning, as earnings are at peak levels and risk is highest.
- Memory gross margins swing wildly from 50%+ at peak to negative at trough, meaning peak earnings used for valuation will likely vanish.
- Relying on trailing or forward P/E for cyclicals is a trap; investors should evaluate cycle position and normalized mid-cycle earnings instead.
TL;DR: Memory chips are in an AI-driven boom, and some of these stocks look "cheap" on next year's earnings (\~10× forward while AI peers sit at 40×). For a commodity cyclical, that forward-cheap is often a late-cycle warning, not a bargain...
Here's a more detailed POV:
A lot of us screen for low P/E as a value habit. For most businesses that's reasonable. For commodity cyclicals — memory (DRAM/NAND/HBM), steel, shipping, solar — it runs backwards, and memory is the cleanest example happening right now.
The setup. After a sideways 2022–23, memory makers cut production; then AI demand for HBM exploded. DRAM contract prices jumped \~90%+ in a single quarter recently, NAND 70%+, and the industry expects a price peak around Q3–Q4 2026. When a commodity is this hot, producers print money — and their stocks start to look "cheap" on P/E.
Why the P/E inverts. P/E = price ÷ earnings. At the cycle top, earnings are huge → P/E looks tiny ("cheap") right when risk is highest. At the bottom, earnings vanish → P/E looks huge ("expensive") right when it's safest. So a low P/E on a cyclical is often closer to a sell signal than a buy. (Peter Lynch also says this; memory is its purest case.)
The 2026 wrinkle: the trailing P/E on these names actually looks fairly normal (low-20s) because the price ran up with the earnings. It's the forward P/E — price ÷ next year's peak-extrapolated profits — that screens cheap (\~10×). And that's the trap: it's only cheap if peak earnings hold.
Why peak earnings aren't real earnings. Across a memory cycle, gross margins swing from 50%+ at the top to the low-20s or negative at the bottom, revenue 25–40% peak-to-trough. The profit you'd be capitalising at a "cheap" multiple near the peak can halve or vanish in a year or two.
What to use instead of trailing P/E:
- Where you are in the cycle (spot vs contract prices, inventory days, fab utilisation)
- Normalised mid-cycle earnings, not the peak quarter
- Price-to-book (book value is far steadier than peak earnings)
- Cost-curve position (who survives the bust)
And the flip side — where the money's actually made: because the P/E runs backwards, a cyclical looks best (a low, reassuring multiple) near the top, and worst (P/E sky-high or negative, grim headlines) near the bottom, where the next upcycle is closest. So the discipline inverts: get interested when it looks ugly, wary when the screen flashes "bargain." The opportunity hides in the ugliness, not the cheapness.
The honest other side: this is the best "this time is different" case memory has ever had. DRAM consolidated to 3 players (discipline); HBM is \~4× more wafer-intensive so every AI bit structurally tightens commodity supply; a lot of it is on multi-year fixed-price contracts (less spot whiplash); and capex is restrained — analysts don't model the next downcycle until late 2027.
So the real question isn't "is the P/E low," it's: are these record margins the new floor, or the old peak? Nobody knows. The tell that decides it: do the three makers stay disciplined, or chase capex into the strength? "Cautious" ≠ "short."
Curious what others here think — new normal, or just a louder version of the same script?
AI slop
Should be instaban tbh. If i wanted to have an investing discussion with chatgpt id go to chatgpt.
Exactly. Need better moderators that can spot these posts before they make it through.
A lot of words to say “can you predict where we are in the cycle”
Even if this is just a supercycle, it's expected to last till 2030, if not further with their 5 year agreements. At the current rate, this means micron will likely have >1 trillion in net revenue form this cycle. They can do anything they want with that and venture into any industry they want. By the time the cycle ends, micron as a company could look very different to what we know it as now, and can easily be rerated as such.
“Expected”
Good framework. Worth adding one layer: the starting point for normalized earnings matters as much as the normalization methodology itself. For memory companies specifically, the depreciation treatment in the as-filed 10-K (XBRL tag DepreciationAndAmortization in the cash flow, or broken out in the PP&E note) often diverges from what analyst models use because some platforms reclassify or smooth D&A in ways that affect the mid-cycle baseline before you even start normalizing. If a company extended its fab useful life in 2022–23 to reduce depreciation at the peak, that flows into every normalized estimate built on consensus data downstream.
The as-filed PP&E note shows the stated useful life assumption by asset category — that's the most direct check on whether the "normalized" number analysts are using started from a clean baseline.
I think memory is definitely cyclical but we are in a super cycle so I don’t know if I would say it is overvalued yet. This is locked in until 2030 for Micron/SK. The question is how bad the fall off is.
I am more curious about the XPUs, GPU, and CPU market. I would think the difference here is they don’t have the same structural limitations (I think - if I am wrong let me know) because they can just scale down without profitability problems which memory can’t.
I think the catch up of open weight models reducing the cost of tokens is actually strongly supportive of staying at peak longer. Cheaper tokens mean the economic rents from AI flow to hardware and hyperscalers rather than the AI labs. Lower token costs also enable the use of AI in more use cases not less. This is obviously horrible for OpenAi and Anthropic, as frontier models might see much more limited use.
But commodity models on commodity hardware is still good for memory makers as long as they consume massive amounts of ram, which they do. It's also still good for data center cpu makers like AMD.