r/valueinvesting
为什么“便宜”的内存/半导体股票可能并不便宜——市盈率分析
内存股因AI驱动的峰值盈利导致前瞻市盈率偏低，这是典型的周期陷阱，预示见顶而非价值洼地。
- 内存等大宗商品周期股的前瞻低市盈率是周期晚期的警告，因为盈利处于峰值且风险最高。
- 内存毛利率在峰值时超50%，谷底时为负，用于估值的峰值盈利可能会消失。
- 对周期股依赖滚动或前瞻市盈率是个陷阱，投资者应评估周期位置和正常化的中期盈利。
快速摘要： 内存芯片正经历由人工智能驱动的繁荣，部分股票在明年预期盈利上的估值看起来“便宜”（约10倍市盈率，而AI同行普遍在40倍左右）。但对于大宗商品周期股而言，这种前瞻性的低价往往不是买入良机，而是周期末期的警示信号……
以下是更详细的分析视角：
我们很多人习惯用低市盈率筛选价值股。对大多数企业来说这很合理。但对大宗商品周期股——内存（DRAM/NAND/HBM）、钢铁、航运、太阳能——这一逻辑恰恰相反，而当前内存正是最清晰的案例。
当前格局。 经过2022–2023年的横盘后，内存制造商削减了产能；随后人工智能对HBM的需求爆发式增长。最近一个季度内，DRAM合同价格飙升超过90%，NAND上涨70%以上，行业预计价格将在2026年第三季度至第四季度达到峰值。当一种大宗商品如此火热时，生产商就能大把赚钱——而它们的股价也因此在市盈率上显得“便宜”。
为何市盈率会反转？ 市盈率 = 股价 ÷ 盈利。在周期顶部，盈利巨大 → 市盈率看起来极低（“便宜”），而这恰恰是风险最高的时候；在周期底部，盈利几乎归零 → 市盈率看起来极高（“昂贵”），但此时反而是最安全的阶段。因此，周期股的低市盈率往往更像卖出信号，而非买入信号。（彼得·林奇也说过类似观点；内存是其最纯粹的体现。）
2026年的变数： 这些股票的历史市盈率实际上看起来还算正常（低20倍区间），因为股价随盈利一同上涨。真正显得便宜的是前瞻市盈率——即股价 ÷ 下一年度基于峰值预测的利润——约为10倍。而这正是陷阱所在：它只有在峰值盈利能维持的前提下才真的便宜。
为何峰值盈利并非真实盈利？ 在整个内存周期中，毛利率从顶部的50%以上一路下滑至底部的20%以下甚至为负，营收从峰值到谷底萎缩25%–40%。你在周期顶峰以“便宜”倍数折现的利润，可能在一年或两年内腰斩甚至消失。
替代历史市盈率的更好指标：
- 当前所处周期位置（现货与合约价格对比、库存天数、工厂利用率）
- 正常化后的中期周期盈利，而非峰值季度数据
- 市净率（账面价值远比峰值盈利稳定）
- 成本曲线位置（谁能在崩盘中存活下来）
反向思维——真正的赚钱机会在哪？ 因为市盈率走势颠倒，周期股在顶部附近看起来最好（市盈率低、令人安心），而在底部附近则最差（市盈率高得离谱或为负，负面新闻满天飞），而这时新一轮上行周期其实已近在眼前。因此，投资纪律必须反转：当市场看起来丑陋时才该关注，当屏幕闪烁“便宜”时反而要警惕。机会藏在丑陋之中，而非廉价外表之下。
坦率地说另一面： 这可能是内存行业历史上最“这一次不一样”的情况。DRAM市场已集中为三家玩家（行业自律）；HBM的晶圆用量是普通内存的四倍以上，意味着每比特的人工智能需求都结构性地收紧了商品供应；大量订单签订在多年期固定价格合同上（减少现货波动）；资本开支受到抑制——分析师甚至要到2027年底才开始建模下一次下行周期。
所以真正的问题不是“市盈率是否低”，而是：这些创纪录的利润率是新的底部，还是旧的顶部？ 没人知道。决定这一点的关键信号是：这三家公司能否保持自律，还是会在强势期盲目扩张资本开支？“谨慎”不等于“做空”。
好奇大家怎么看——这是新常态，还只是老剧本的更大声版本？
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.