Fidelity's Chisholm Says Corporate Earnings Expansion Broadens Beyond Big Tech

富达Chisholm称企业盈利扩张已扩展至大型科技股之外 标普493成分股利润加速增长及较低的单位劳动力成本表明，当前股市涨势仍处于早期阶段，而非互联网泡沫。 富达的Denise Chisholm从量化角度论证了股市基本面依然稳健且远未达到投机泡沫的程度，并指出当前企业总资本支出（CapEx）仍低于自由现金流的1倍，相比之下2000年互联网泡沫顶峰时则高达3.5倍至4倍。讨论剖析了半导体板块的抛物线式上涨，解释了像Micron等个股的极高涨幅背后拥有历史性的盈利增长作为支撑，而非单纯的盲目投机。此外，讨论还强调了“标普493”正在形成的周期性复苏，展现了在通胀担忧与美联储利率预期依然可控的情况下，利润率、小盘股盈利和制造业指标正出现向上拐点。 Anthropic 完成 650 亿美元的 H 轮融资，投后估值达到 9,650 亿美元，吸引了红杉资本（Sequoia）、Altimeter 及多家顶级主权财富与对冲基金的联合注资。 (3:34) 高盛将标普 500 指数目标点位上调至 8,000 点，预计今年盈利增速达 24%；富达（Fidelity）指出，当前美股整体资本支出仍低于自由现金流的 1 倍，远未达到互联网泡沫顶峰时期 3.5 至 4 倍的过热水平。 (7:39) 历史量化数据显示，宏观与政治不确定性上升时期，美股反而具有更高的上涨概率，且单一估值指标对未来 12 个月的市场涨跌并无显著的预测能力。 (15:09) 费城半导体指数高出 200 日均线 69%，美光科技跻身万亿美元市值行列；分析指出这一轮激增与底层盈利增速完全一致，反映出当前算力短缺的真实需求而非盲目投机。 (26:25) 美股涨势呈现扩散态势，除去科技七巨头的“标普 493”正走出长达三年的盈利衰退，叠加处于历史低位的单位劳动力成本，有力支撑了全行业创纪录的净利润率。 (37:45)

Fidelity quantitative strategist Denise Chisholm and hosts Downtown Josh Brown and Michael Batnick argue that the equity bull market remains grounded in corporate fundamentals rather than speculative euphoria. They suggest investors may be in the early stages of a multi-year earnings recovery cycle outside mega-cap tech, supported by disciplined capital expenditure and improving market breadth. AI Infrastructure Spending Versus Dot-Com Bubble Excess (7:39) Goldman Sachs raised its S&P 500 price target to 8,000, projecting 24% year-over-year earnings growth for the current year and 13% for 2027, driven by artificial intelligence infrastructure investments. Skeptics frequently compare rising capital spending to the 1990s telecom bust, where fiber optic cable sat unutilized. However, Chisholm points out that during the 2000 dot-com peak, corporate America’s aggregate CapEx stood at 3.5x to 4x free cash flow. In contrast, aggregate corporate CapEx across the S&P 500 today remains under 1x free cash flow. Batnick notes that major tech companies are shifting from capital-light software models into capital-intensive "modern industrials" spending heavily on physical projects. Even so, Chisholm emphasizes that tech hyperscalers maintain high margins and returns on capital, funded directly by cash flow rather than dilutive equity issuance. Brown cites Meta generating $50 billion in annualized revenue from AI-driven Instagram Reels as evidence that current CapEx addresses immediate compute shortages and produces real monetary returns. Semiconductor Momentum Anchored by Earnings Growth (26:25) Extreme momentum in semiconductors—such as the PHLX Semiconductor Index trading 69% above its 200-day moving average and Micron joining the trillion-dollar market cap club after ranking #127 a year earlier—has raised concerns of cyclical overextension. UBS also raised its price target on Micron from $535 to $1,625. However, Chisholm highlights that the semiconductor sector’s 100% gain over the trailing 12 months matches underlying earnings growth rather than multiple expansion. Historical data across all market industries since the 1960s indicates that while 70% to 100% 12-month advances occur only 2% of the time, groups achieving that pace outperform the broader market the following year 65% of the time. Furthermore, Chisholm notes that semiconductor valuations in the bottom quartile historically yield a 70% probability of 12-month outperformance, suggesting the traditional three-year semiconductor cycle may be extending toward a seven-year structural expansion. The S&P 493 Earnings Recovery and Expanding Margins (37:45) Year-to-date market returns contradict the assumption that equities depend entirely on the Magnificent Seven. The Magnificent Seven gained roughly 6.6% year-to-date, trailing the S&P 500 ex-Mag 7's 10.25% gain. Chisholm notes that while equal-weighted index earnings experienced a three-year contraction starting in 2022, they are now four months into an early-stage recovery cycle that historically lasts multiple years. Underlying profitability is supported by unit labor costs adjusted for productivity, which currently sit in the lowest quartile of historical data. Simultaneously, the ISM New Orders index inflected higher for the first time in three years, indicating an end to protracted manufacturing weakness. Forward earnings growth expectations reflect this cyclical broadening, with small-cap financials projected to expand earnings by 66% compared to 7% for large caps. Macroeconomic Sweet Spots in Inflation and Monetary Policy (45:52) Chisholm contends that macro fears regarding inflation and interest rates are often misdiagnosed. Historical data shows that equities perform best when inflation sits between 3% and 4%, functioning as a natural inflation hedge via earnings growth. Equities typically suffer only when inflation accelerates within the top quartile (above 4.5%). Furthermore, Core CPI ex-shelter sits at 2.3%, illustrating that housing supply distortions from locked-in mortgages exaggerate headline inflation. Similarly, historical data indicates a 60% surge in oil over six months results in a Fed rate hike only 25% of the time, because policymakers view energy spikes as a consumer tax that passed through to core inflation only 48% of the time since 1980. Central bank tightening is driven instead by strong capital goods orders. When the Fed hikes rates modestly by 25 to 100 basis points, stock market win rates exceed the baseline 75% rate by confirming underlying economic expansion. Mega-IPO Liquidity Demands and Retail Speculation (55:33) Private capital expansion—exemplified by Anthropic’s $65 billion Series H funding at a $965 billion valuation—previews a massive mega-IPO wave. Expected offerings from SpaceX, OpenAI, and Anthropic could seek approximately $170 billion, matching the $164 billion raised across hundreds of listings during the 1998–2000 tech boom. Brown and Batnick caution that institutional managers may need to sell existing tech holdings to absorb these listings, potentially generating short-term liquidity bottlenecks. Chisholm notes that while high issuance can cause temporary volatility, robust corporate earnings and share repurchases historically absorb new supply. Addressing retail speculation, Chisholm observes that while high-beta tech has rallied off its lows, quality tech has outperformed high-beta tech by roughly 75% on a relative basis since the late 1990s. She views initial rallies in low-quality, speculative shares as typical behavior during early earnings recovery cycles rather than evidence of late-stage mania. While upcoming mega-IPOs could test secondary market liquidity, the hosts and Chisholm conclude that corporate balance sheets, expanding profit margins, and broadening earnings outside mega-cap tech provide durable support. An unresolved question is whether private AI companies can demonstrate audited revenue streams and public market absorptive capacity when their multi-billion-dollar listings debut.

富达投资量化市场策略主管Denise Chisholm与主持人指出，当前美股涨势主要由坚实的盈利与资本支出支撑，而非投机泡沫；随着市场广度向“S&P 493”及小盘股扩散，整体盈利复苏周期可能仍处于早期阶段。 资本支出与盈利支撑牛市而非泡沫 (7:39) 针对高盛将标普500目标点位上调至8000点以及AI资本支出激增引发的泡沫担忧，Denise Chisholm提出量化对比：2000年互联网泡沫顶峰时期，美企总体资本支出达到自由现金流的3.5倍至4倍，而当前资本支出与自由现金流之比仍低于1倍。当前资本支出加速正扩展至所有11个GICS行业，且主要由企业自身充沛的现金流驱动，而非依赖过度发债或股权稀释。高盛预计当年标普500同比盈利增速达24%，2027年为13%。 市场近期的估值收缩实际上体现了投资者的持续戒备心理。虽然科技巨头大举投入基础设施，形态逐渐向“现代工业股”靠拢，但其资本回报率与利润率依然处于高位。Josh Brown以Meta为例说明，其AI推荐算法推动Instagram Reels年化收入达到500亿美元。半导体板块虽大幅攀升且SOX指数偏离200日均线69%，但Chisholm指出过去一年半导体约100%的涨幅与同期底层盈利增速完全吻合，属于业绩驱动而非盲目狂热。 盈利广度向S&P 493与周期制造业扩散 (37:45) 本轮市场上涨正打破仅依赖超大盘科技股的叙事。年初至今，科技七巨头（Mag 7）上涨约6.6%，而剔除这七家公司的标普500其余成分股（S&P 493）上涨了10.25%，市场领头羊呈现分化态势，Alphabet与Micron成为指数前两大贡献者。Chisholm指出，等权重指数在经历自2022年起长达三年的收缩后，目前仅处于“充分就业型复苏”周期的第四个月，未来数年仍有扩张潜力。 在小盘股方面，金融行业预期未来四个季度远期盈利增速达66%，远超大盘股金融板块的7%。Chisholm解释称，ISM新订单指数在经历三年低迷后首次拐头向上，标志着国内制造业迎来周期性复苏。由于中小企业与实际生产能力高度绑定，非大盘科技板块的盈利与利润率正在走出长达三年的低谷，成为市场最被低估的积极催化剂。 通胀 sweet spot 与美联储加息误区 (45:52) 针对投资者对通胀粘性延缓降息的焦虑，Chisholm认为美股的“最佳平衡区间”是通胀处于3%至4%之间，此时股票能凭借盈利增长有效对冲通胀；历史数据显示，仅当通胀进入前四分之一高位（高于4.5%且持续上行）时美股才会承压。此外，当前核心CPI受到住房成本这一供给冲击的扭曲，若剔除住房因素，核心CPI仅为2.3%。 在能源与货币政策方面，历史数据显示油价飙升（如6个月暴涨60%）后美联储在接下来12个月加息的概率仅为25%，因为央行通常将其视为削弱消费能力的“税收效应”，自1980年以来油价传导至核心通胀的概率仅为48%。真正驱动美联储紧缩的是核心资本品订单等强劲需求指标。Chisholm指出，温和加息（25至100个基点）反而伴随着股票历史胜率上升，10年期美债收益率当前水平与健康GDP增速相匹配，类似1990年代5%至6%的常态。 巨额IPO吸筹压力与投机情绪辨析 (55:33) 随着SpaceX、OpenAI及Anthropic等巨型独角兽准备上市，市场关注其潜在的流动性挤压。仅这三家公司就可能从二级市场抽离约1700亿美元资金，甚至单笔交易（含绿鞋期权）募资额便可达860亿美元，或促使机构减持现有科技持仓以腾挪资金。Chisholm指出，短期的流动性瓶颈与特质波动属正常现象，只要底层盈利与企业回购保持强劲，增量股权供应通常能被迅速消化。 针对Roundhill MEME ETF创新高等高贝塔“垃圾股”投机现象，Chisholm通过历史数据辨析：高贝塔科技股在自底部反弹的同时，相对于整体科技板块仍处于自1990年代末以来的长期相对下行通道中，低波动的高质量科技股表现领先高贝塔科技股约75%。这类低质量资产在复苏初期的反弹属于标准周期规律，并不意味着整体市场进入非理性的全面泡沫。 总体而言，当前市场处于由充沛现金流和扎实业绩支撑的良性发展阶段，标普493和小盘股的周期性复苏提供了新的动能。不过，未上市AI巨头庞大的资本需求、巨额IPO对机构流动性的短期抽离，以及高度依赖算力资本支出的回报验证，仍是后续需要密切关注的潜在考验。

## Background 本期节目由 The Compound 频道于 2026 年 6 月 13 日在 YouTube 发布。主持人 Michael Batnick 和 Downtown Josh Brown 邀请富达投资（Fidelity）的量化市场策略总监 Denise Chisholm，共同深入探讨 AI 资本开支是否存在泡沫、美股在普遍怀疑中持续上扬的底层原因、企业盈利增长如何从科技巨头扩散至标普 493 家公司，以及半导体繁荣、通胀走向与潜在的巨额 IPO 浪潮等关键议题。 ## TL;DR 节目核心反驳了美股正处于 2000 年式互联网泡沫的悲观论调，指出当前 AI 与基础设施资本开支是由扎实的经营现金流驱动，整体 CapEx 占自由现金流比例仍处于安全区间。半导体板块的飙升背后拥有超越股价涨幅的实质性复合盈利支撑，而非纯粹的估值泡沫。同时，市场正迎来以标普 493 为代表的盈利广泛复苏早期，单位劳动力成本下降支撑着高利润率。持续弥漫的市场怀疑情绪反而构成了健康的“攀爬担忧之墙”，使得本轮牛市在估值压缩与真实盈利扩张中具备更强的持续韧性。 ## Atmosphere & style 节目开场充斥着美式脱口秀式的幽默闲聊，Josh 对餐厅结账繁琐流程的“暴躁”吐槽与 Batnick 的捧哏迅速拉近了距离。随着 Denise Chisholm 的加入，对谈无缝切换至高密度、严谨扎实的量化宏观推演，充满冷峻的数据反思与反直觉洞见。全片节奏轻快而富有张力，既有对华尔街流行叙事的犀利拆解，又有专业量化策略师从容拆解恐慌的理性笃定，整体氛围专业通透又妙趣横生。 ## Bulletin - [06:07](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=367s) 高盛将标普500目标点位大幅上调至8000点，主要逻辑在于AI基建推动企业EPS在2026年同比猛增24%，而大众对AI商业化和宏观的迟疑反而充当了防止估值过热的“安全阀”。 - [09:24](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=564s) 美股当前资本支出占自由现金流比例依然低于1.0，远低于2000年互联网泡沫峰值时的3.5至4倍，且全市场11个板块的CapEx均在加速，体现出健康的内生增长循环。 - [39:45](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=2385s) 标普493展现强劲盈利扩散势头，年初至今剔除七巨头后的标普成分股上涨10.25%，明显跑赢七巨头的6.6%，长达三年的等权重盈利收缩期正迎来对称性修复周期。 - [28:01](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=1681s) 半导体板块呈现直线上冲并偏离200日均线69%，但过去两年底层盈利复合增速实际跑赢了股价涨幅，且芯片一出厂即满负荷运转，与当年闲置五年的“暗光纤”泡沫有本质差异。 ## Key takeaways - [06:07](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=367s) 高盛将标普500指数目标点位上调至8000点，认为本轮牛市由实体基建盈利驱动而非纯估值泡沫，市场对宏观及商业化回报的担忧有效压制了估值乘数的泡沫化。 - [09:24](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=564s) 本轮AI资本支出热潮由超大规模科技巨头的充沛内部现金流支持，整体美股CapEx与自由现金流之比低于1.0，与2000年泡沫期完全依赖股权融资烧钱截然不同。 - [14:35](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=875s) 宏观不确定性处于历史极端高位时，美股未来上涨及CapEx扩张的概率往往更高；当利空与风险登上主流媒体封面时，资产价格通常早已将其充分折价。 - [17:54](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=1074s) 市场在盈利持续上修的同时对各类地缘及宏观风险保持紧绷警惕，由此导致的估值压缩反而构筑了在非泡沫状态下“攀爬担忧之墙”的健康牛市格局。 - [21:53](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=1313s) 科技板块向重资产资本开支转型，呈现“现代工业股”特征，但因极高的资本回报率和技术天然的通缩平民化属性，其高利润率结构依然得以维系。 - [24:47](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=1487s) Meta旗下Reels年化收入已达500亿美元并超越Netflix，成为AI算法精准分发并实现巨额商业化变现的最直接范本，为高额算力投资提供了坚实注脚。 - [28:01](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=1681s) 费城半导体指数偏离200日均线69%，但这是罕见的盈利复合暴增跑赢股价涨幅所带来的“追赶行情”，历史数据显示单年涨幅超70%的细分行业次年仍有65%概率跑赢大盘。 - [33:06](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=1986s) 当前算力基础设施采购每一秒均处于满负荷计算状态，与2000年电信狂潮中横跨大陆却闲置积灰近五年的“暗光纤”电缆存在本质上的需求真伪区别。 - [37:06](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=2226s) 美国单位劳动力成本处于历史最低25%分位，薪资受控与全要素生产率反弹构成了企业高净利润率的隐形红利，市场对利润率见顶的过度担忧形成了预期差买点。 - [39:45](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=2385s) 标普493在经历整整三年等权重盈利收缩后迎来复苏，年初至今标普剔除七巨头部分涨幅达10.25%，在制造业拐点支撑下该轮扩散修复目前仅展开了约4个月。 - [45:52](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=2752s) 通胀处于3%到4%区间其实是美股历史上的黄金甜点区，且若剔除受供给冲击锁定的住房项，核心CPI实际仅约2.3%，温和通胀并未对股市造成系统性压制。 - [49:11](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=2951s) 原油价格飙升本质上属于对终端消费者的变相征税，削弱非必需品需求，历史数据显示油价半年暴涨60%的情形下未来12个月美联储加息概率仅有25%。 - [55:33](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=3333s) SpaceX、OpenAI与Anthropic潜在的万亿级IPO浪潮预计将募集高达1700亿美元，虽将引发机构投资组合短期的再平衡调仓摩擦，但中长期股价走势仍由底层盈利决定。 - [01:00:56](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=3656s) 高Beta与投机性科技小盘股的反弹属于盈利复苏初期的正常“垃圾股行情”，其相对大盘科技股并未打破长期下行通道，低波动优质科技股依然具备极高跑赢概率。 ## Detailed walkthrough ### [00:00](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=0s) 章鱼智慧与餐厅买单的“早发性暴躁” 节目在一场略带荒诞的闲聊中开场。Josh Brown 提到自己刷到了天体物理学家尼尔·德格拉斯·泰森（Neil deGrasse Tyson）关于章鱼的短视频，泰森称章鱼是地球上最接近外星生物的存在——它们拥有九颗心脏、多个大脑，触手断掉后能重新长出，甚至每条触角都能独立感知、触摸、嗅探和品尝。Josh 表示自己因此再也无法下咽章鱼，虽然他依然吃鱿鱼（calamari），连他自己也说不清为何会有这种心理界限。但每当去地中海餐厅聚餐，看到别人大快朵颐烤章鱼时，他只能一边克制一边忍受内心的道德煎熬。 随即，话锋从章鱼转向了 Josh 对现代餐厅结账流程的强烈不满。他直言不知道这是因为年龄增长还是性格使然，现在的他对用餐完毕后等待账单的那套“繁文缛节”彻底失去了耐心：你必须先向服务员招手示意，服务员说“马上回来”，走去拿账单，放下账单后离开；客人把账单晾在桌上五分钟，放好信用卡后再伸长脖子四处张望找人；服务员收走信用卡后又消失在厨房附近的刷卡机旁，最后拿回小票，客人还得假装核对金额并签字计算小费。Josh 直言自己极度推崇餐饮科技公司 Toast 的掌上刷卡设备（并坦承自己持有 Toast 的股票），他理想中的体验是：“吃下最后一口，扔下叉子就能立刻起身走出餐厅。” Michael Batnick 调侃这是 Josh 展现“温柔一面”、与听众建立情感共鸣的方式，并引用《周六夜现场》（SNL）的小品诊断 Josh 患上了“早发性暴躁综合征”（Early Onset Grumpiness, EOG）。面对 Josh “这种病能治好吗？我为什么不能抱怨？”的抗议，Batnick 笑着回应：“无药可救。” - 章鱼的生理构造特性引发的道德讨论：多大脑、九颗心脏、触手兼具感官功能。 - Josh 对传统餐厅买单“拉锯战”流程的抱怨：招手取单、等待收卡、前台刷卡、签字核对耗时漫长。 - 餐饮 SaaS 解决方案 Toast：缩短结账周期，赋能顾客自主掌控结账节奏。 --- ### [02:47](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=167s) 待客之舞、Anthropic巨额融资与“复仇者联盟” 曾当过多年全职服务员的 Michael Batnick 站在从业者角度为餐厅辩护，指出服务员动作慢的根本原因在于“你并不是唯一的顾客”。面对 Josh 提出的“既然已经是 2026 年了，为什么进店时不能直接刷卡、吃完扔下餐巾就走”的诉求，Batnick 毫不留情地给出了终极解决方案：“那你干脆待在家里点 Uber Eats 好了。” 随后，主持人将话题切入节目录制前刚刚爆出的重磅科技创投新闻——人工智能独角兽 Anthropic 的 H 轮融资。公开披露的投资者名单堪称创投界的“全明星阵容”：本轮融资由 Altimeter Capital、Dragoneer、Greenoaks 和红杉资本（Sequoia）领投，跟投方几乎包揽了华尔街与硅谷的顶尖力量，包括 Capital Group、Coatue、D1 Capital Partners、GIC、Iconiq、Accel、Baillie Gifford、黑石（Blackstone）、博枫（Brookfield）、D.E. Shaw、DST Global、富达（Fidelity Management and Research）、General Catalyst、Insight Partners、Jane Street 以及 Lightspeed。 Batnick 感慨这笔融资规模之大、参与方之多，简直就像《复仇者联盟》的最终决战：“所有人都必须到场。”因为当市场试图在短短六个月的窗口期内消化数万亿美元规模的超大型 IPO 时，必须集结整个资本市场的流动性。作为来自富达的嘉宾，Denise Chisholm 默契地打趣表示，只要是富达投资的项目，就没什么好担心的。 ```mermaid flowchart TD accTitle: Anthropic Mega Funding Round Dynamic accDescr: Diagram illustrating the unprecedented syndicate backing Anthropic's mega round to support upcoming multi-trillion market events. subgraph Leaders ["领投机构 (Lead Investors)"] A["Altimeter / Dragoneer"] B["Greenoaks / Sequoia"] end subgraph Consortium ["巨型资本财团 (Consortium Syndicate)"] C["对冲与公募基金<br/>(Coatue, Fidelity, Capital Group)"] D["主权与另类资产<br/>(GIC, Blackstone, Brookfield)"] E["量化与顶级VC<br/>(Jane Street, General Catalyst)"] end subgraph MarketImpact ["资本市场目标 (Market Endgame)"] F["超大规模估值承载<br/>(Mega Post-Money Valuation)"] G["筹备万亿级超级 IPO 浪潮<br/>(Absorb $2T–$3T IPO Pipeline)"] end Leaders --> F Consortium --> F F --> G ``` - **赞助商要点**：节目间隙插入了富兰克林邓普顿（Franklin Templeton）的市政债券（Munis）推广。今年一季度市政债券资金流入量同比激增 100%，在极具吸引力的税前等效收益率（taxable-equivalent yields）驱动下，这一传统稳健资产受到追捧。富兰克林邓普顿拥有超过 40 年免税收益管理经验，提供丰富的主动型 ETF 与专户策略。 --- ### [06:07](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=367s) 嘉宾介绍与高盛标普8000点预测：AI资本开支与估值“安全阀” 回到正题，Josh 正式向听众介绍了本期重磅返场嘉宾——富达投资（Fidelity Investments）量化研究与投资部门的量化市场策略总监 Denise Chisholm。Denise 自 1999 年加入富达，专注于历史周期分析，通过因子（Factors）、行业板块（Sectors）和主题（Themes）指导分散化投资组合策略，其宏观与量化研究直接赋能富达内部约 150 位基金经理，覆盖超过 450 只共同基金与 ETF。 在被问及标普 500 指数 2026 年底的目标点位时，Denise 遵循一贯的严谨风格，表示量化策略不做具体的点位预测，但认同市场大趋势向上是胜率更高的选择。Josh 随后援引了高盛（Goldman Sachs）最新发布的重磅研报：高盛将标普 500 指数的目标点位从 7600 点大幅上调至 8000 点，主要逻辑在于 AI 繁荣带动的盈利增长将成为股市上行的核心引擎。高盛指出，经历了一季度异常强劲的财报季后，标普 500 成分股的每股收益（EPS）预计今年同比猛增 24%，并将 2027 年的增长预期上调至 13%。 这一看涨逻辑的核心在于市场逐渐意识到：AI 基础设施支出的受益者远不止几家头部科技巨头，而是广泛渗透到了标普 500 的众多产业中。与此同时，高盛提出了一个关键论点：利润增速的边际放缓，叠加大众对 AI 商业化落地及宏观经济的不确定性，恰恰充当了一个“安全阀”（Governor），防止估值倍数出现非理性的无节制扩张。换言之，本轮牛市是由实打实的基建盈利所驱动，而非 1999 年式的纯估值泡沫。Denise 对此深表赞同，并指出底层量化数据明确显示，企业盈利复苏正在从极少数巨头走向更广泛、更分散的行业与个股。 - 高盛将标普 500 指数目标点位由 7600 点上调至 8000 点。 - 标普 500 盈利增速预测：2026 年预计同比增长 24%，2027 年预计增长 13%。 - 估值扩张安全阀（Multiple Governor）：对 AI 长期投资回报率与宏观波动的迟疑，有效压制了估值乘数的泡沫化，使行情维持在盈利驱动的轨道上。 ### [09:24](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=564s) 资本支出周期与自由现金流：泡沫还是健康复苏？ Denise Chisholm 深入分析了资本支出（CapEx）周期的历史度量方式。她指出，2008 年金融危机之后的很长一段时间里，投资者一直在抱怨美国企业不愿投资实体业务，认为当时由股票回购和分红支撑的每股收益增长具有“虚假”成分。而现在市场环境反转，当企业开始加大资本支出时，人们又开始产生恐慌与质疑。然而从宏观经济、股市回报、企业盈利以及就业增长的角度来看，资本支出相对于销售额处于上升通道（即“CapEx 复苏”）实际上是远优于不投资的健康路径。 很多人误以为当前的资本支出热潮仅局限于科技巨头，但数据展现出更广泛的扩散迹象。在标普 11 个全球行业分类标准（GICS）板块中，各行业的资本支出相对销售额都在加速扩张，且整体强度远未达到任何历史泡沫的极端水平。Denise 摆出关键对比数据：在 2000 年互联网泡沫顶峰时，美国企业整体的资本支出高达其自由现金流（Free Cash Flow）的 3.5 至 4 倍；而目前即使把头部超大规模云服务商（Hyperscalers）全算进去，美国企业总体的资本支出占自由现金流的比例依然低于 1。 针对市场普遍担忧的“巨头把所有自由现金流都拿去烧钱买显卡”这一论调，Denise 明确回应“那又怎样？”。在她看来，这仅仅是六七家特定公司在过去几年中的特例行为，属于异质性风险（Idiosyncratic Risk），而非系统性风险（Systemic Risk）。就整个科技板块而言，过去 20 年大部分时间里自由现金流的增长速度都快于资本支出，目前板块总体的资本开支相比自由现金流依然处在甚至略微偏低的健康区间。 - **2000 年泡沫顶峰对比**：当时美股整体资本支出达到自由现金流的 3.5 到 4 倍。 - **当前水平**：即使算上全部科技巨头，美股整体资本开支与自由现金流之比仍保持在 1.0 以下。 - **行业广度**：不仅是科技巨头，GICS 划分的全部 11 个板块目前资本开支占销售比均呈向上加速态势。 ```mermaid flowchart TD accTitle: 资本支出周期的宏观传导逻辑 accDescr: 企业增加CapEx创造就业和经济增长，形成耐用的良性循环 subgraph Corporate ["企业端决策"] A["观察到增长潜力"] --> B["扩大资本支出 (CapEx)"] end subgraph Economy ["实体经济溢出"] B --> C["增加产业链订单与设备采购"] B --> D["创造就业机会与薪资提升"] end subgraph VirtuousCycle ["经济良性循环"] C --> E["驱动 GDP 稳健扩张"] D --> E E --> F["增强经济周期韧性与盈利增长"] F --> A end ``` --- ### [11:38](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=698s) 资本支出的心理偏见与“恨牛市”的永恒批评家 Josh Brown 与 Denise 探讨了为何投资者总倾向于把一波 CapEx 浪潮自动归咎为“泡沫破灭的前兆”。Denise 认为，这纯粹是因为“可得性偏差”（Availability Bias）——距离上一轮席卷全市场的重大资本支出狂潮已经隔了太久，大众记忆中最清晰的样本恰好就是 2000 年崩盘的痛苦结局，因此形成了一见投资大增就联想到泡沫的下意识反应。然而从历史上看，绝大多数资本支出复苏不仅没有引发股市崩盘，反而通过企业投资、创造岗位构筑了美国经济的良性循环（Virtuous Cycle），为经济扩张带来了极强的周期韧性。 Josh Brown 和 Michael Batnick 接着回忆了过去的博文写作经历，指出批评家们自相矛盾的滑稽逻辑： 1. **回购时代**：过去企业通过缩减流通股本（Float Shrink）推高 EPS，不愿将资金投入研发、资本开支或并购，批评家痛骂这是“回购吹起来的脆弱泡沫，一旦回购停止就要崩盘”。 2. **转向投资**：后来企业顺应迫切的产业投资需求停止了盲目回购，市场开始积极奖励那些果断加大资本支出的公司。 3. **如今的批评**：同样的这群批评家立刻改换说辞，反过来指责“企业为什么不搞回购了？凭什么要把大笔现金砸进看不到回报的投资里？” Josh 感慨，市场上永远有一群单纯“讨厌任何牛市”的人，不论市场发生什么变化，他们总能为自己的悲观寻找由头。 --- ### [14:35](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=875s) 股市不是好日子的镜像，而是抵御坏日子的对冲 Denise 从量化分析师（Quant）的视角打破了散户对股市的常识误区。大多数投资者主观上渴望股市只反映“岁月静好”——到处都在健康增长、就业欣欣向荣、广泛繁荣。然而历史数据却证明，股市与其说是“好时代的晴雨表”，不如说是“抵御坏时代的对冲工具”。回顾过去 5 年，世界遭遇了无数突发灾难与系统性危机，标普 500 指数却整体上涨了约 70%。如果投资者一味盯着头条新闻中的各类风险，企图避开所有潜在危机，代价往往是错失美股年均约 8% 的复合回报率，而这正是少数能够切实跑赢通胀的长期资产之一。 Josh Brown 补充道，许多投资者的悲观情绪往往被政治立场和文化厌恶所绑架。当投资者反感现任白宫当局、或看不惯某种社会文化走向时，会产生强烈的认知失调（Cognitive Dissonance）——他们心里无法接受为什么各方面都如此糟糕股市却还在创历史新高，因此断定“市场肯定犯错了”。 Denise 强调量化实证的重要性：逆向指标往往比直觉更准。 - **消费者信心**：当消费者信心跌至历史极低水平时，历史上反而恰恰是买入股票的最佳时机。 - **宏观不确定性（如关税）**：许多投资组合经理常抱怨“环境极度不确定，企业不敢投资招人”。但量化模型将不确定性划分为四分位数后显示，**环境不确定性越处于极端高位，股市未来上涨的概率反而更高，资本支出的概率也更高**。 - **已被充分计入**：当恐惧和问题已经人尽皆知、登上《巴伦周刊》（Barron's）封面时，市场往往早已把不招聘、不投资的利空全额计入价格（Discounted）。Denise 举出历史经典案例：在 1976 至 1985 年期间，美股真正的市场最低点出现在 1978 年——那是在经历两次接连发生的经济衰退以及利率飙升至 15% **之前**就已见底，倘若在 1978 年因为预期到衰退而抛售，就会彻底踏空后来的大牛市。 ### [17:54](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=1074s) 估值压缩与攀爬“担忧之墙”：为何悲观预期反而强化了牛市底气 回顾历史，在市场中试图精确预测宏观风险并等待“完全安全”往往代价惨重。以1982年为例，如果某位投资者坚信要等到风险彻底化解、就业在年底真正企稳复苏才买入，那么他将错失高达40%的累积名义回报。年初时，市场上弥漫着巨大的担忧，包括霍尔木兹海峡面临关闭的潜在黑天鹅事件——当时许多人认为油价会飙升至每桶150美元、标普500指数将直接触及熔断跌停。然而，现实中的市场对此不仅毫无剧烈反应，反而近乎打着哈欠一掠而过。更奇特的是，在企业盈利预测持续上修的同时，股价却出现温和回调，导致估值在半年内出现了罕见的压缩。 ```mermaid flowchart TD accTitle: 市场估值压缩与牛市动能逻辑 accDescr: 展示盈利预期上升与价格调整导致的估值压缩，以及市场持续谨慎情绪如何推动牛市攀爬担忧之墙。 subgraph S1["宏观与盈利态势"] A["黑天鹅担忧未兑现"] --> B["企业盈利预期大幅上调 (29%增长)"] B --> C["股价短期温和回调"] end subgraph S2["估值与情绪分化"] C --> D["估值出现大幅压缩 (PE回落)"] D --> E["股票市场持续保持谨慎与恐慌"] end subgraph S3["后市表现"] E --> F["市场在非泡沫状态下攀爬担忧之墙"] end ``` Denise Chisholm 经常面对来自机构客户的质疑：“过去三年市场涨了这么多，股票估值又这么贵，你怎么还能看多？”她从量化统计的角度给出了强力反驳：无论股市前期涨幅有多大，抑或是估值处于什么分位，从概率学上看都不能直接断定股票会跑输；相反，动量往往会孕育动量（"momentum usually begets momentum"）。想要站在上涨的对立面做空，必须承认自己押注的是一个小概率事件。自2022年以来，美股最显著的特征就是持续的恐惧情绪——即使标普仅下跌2%，看跌/看涨期权比率（Put-Call Ratio）就会瞬间飙升。当股票市场的恐慌程度远高于信用市场时，往往形成一种线性关系，促使大盘继续顽强地“攀爬担忧之墙”（climb the wall of worry）。 - **动量与估值分布**：量化统计表明，股市无论处于哪一个估值四分位数，并不必然导致后续出现负收益或跑输基准。 - **恐慌指标印证**：关税引发震荡时，VIX曾飙升至50；而在此轮回调中，VIX也升至30左右，市场参与者的神经始终处于紧绷状态。 - **非泡沫化表现**：如果市场要追赶高达29%的企业盈利增速并实现估值扩张，股指需要暴涨70%左右；而现实中温和的走势反倒证明整体市场保持了罕见的克制与清醒。 --- ### [21:53](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=1313s) 科技板块的“好球区”与向“现代工业股”的属性转变 今年早些时候，市场出现了一个极具标志性的节点：科技板块的前瞻市盈率与标普500整体指数的前瞻市盈率走向收敛，几乎重合在同一水平线上。这在历史上是极佳的“好球区”（fat pitch）买点——投资者能以大盘整体的平均估值，直接买入这批盈利能力最顶尖的高成长科技领头羊。随后的数据显示，在两者收敛后的短短38个交易日内，科技股相对标普500就打出了超出约18%的惊人超额收益。 面对这种估值压缩与后续轮动，市场也展开了一场深层辩论：这些巨头是否应当经历估值重塑？以往的大型科技公司以轻资产、高软件壁垒、微量资本支出和极高利润率著称；如今，除了苹果资本支出相比去年有所回落、未全面卷入军备竞赛之外，其余巨头正将超过100%甚至120%的自由现金流投向长期的物理基础设施建设。它们在行为上越来越像“现代工业股”（modern industrials），因而传统上高达35倍的PE倍数或许不再理所当然。 Denise 则指出，估值能否维系核心取决于最终的利润率结构。虽然目前科技巨头疯狂推进资本开支，但它们依然奇迹般地保持了极高的回报率和利润率结构。此外，科技行业自带通缩属性——技术往往先是将某项事物变得极度稀缺且昂贵，随后又迅速将其平民化并大幅降维普及。因此，长期的资本开支规模未必会如当前悲观预期那般严重侵蚀自由现金流。 --- ### [24:47](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=1487s) 现金流驱动对比股本驱动：Meta Reels 成为 AI 变现的现实范本 当前的 AI 投资热潮与2000年的互联网泡沫存在本质差别。千禧年的网络泡沫完全由股权融资驱动（equity-funded），充斥着毫无盈利和真实利润支持的盲目烧钱；而今天的巨额基础设施资本开支主要来自于现金流极其充沛的超级现金奶牛（cash flow funded）。尽管未来部分 AI 积压订单的买单方（如 OpenAI 和 Anthropic）可能需要依赖二级市场 IPO 或大规模股权融资来填补预算，但目前最核心的支出方依然是全球资产负债表最强劲的巨擘。 | 周期阶段 / 特征 | 2000年互联网泡沫 | 当前 AI 基础设施浪潮 | |:--- |:--- |:--- | | **主要资金来源** | 股权融资驱动（大量投机性 IPO / 增发） | 现金流驱动（科技巨头的巨额内部经营现金流） | | **利润与现金支撑** | 缺乏实质收入、无利润、高烧钱率 | 拥有极强盈利能力与高资本回报率的护城河 | | **资本运作特征** | 轻资产概念炒作，无实际交付基建 | 建设算力与电力等重型基础设施（现代工业特征） | | **商业化变现速度** | 概念先行，落地变现周期极其漫长 | 快速赋能核心业务（例如 Meta Reels 推荐算法） | 作为 AI 落地变现最直观的例证，Meta 对算力的巨额投入已经直接反映在业绩上。仅仅推出三年的短视频产品 Reels，其年化营收已经达到惊人的500亿美元，一举超越了 Netflix 的整体收入。Josh Brown 形象地将 Reels 比作“极其让人上瘾的数字化海洛因”：只要在机场航站楼走一圈，十个人里有八个人低头盯着自己的长方形屏幕；如果是小孩子，低头率更是达到十成十。Meta 通过顶尖的 AI 算法精准重塑了用户注意力分配，不仅最大化了使用时长，更实现了广告定向投放效率的几何级跃升，为这轮庞大的资本开支提供了最具说服力的商业回报支点。 ### [26:31](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=1591s) 半导体板块呈现抛物线式暴涨与千亿美元巨头的狂欢 当市场讨论整体环境是否依然“保持理性”时，很多人首先会反驳：“难道你没看半导体板块的走势吗？”迈克尔·巴特尼克（Michael Batnick）调出一张关键图表指出，费城半导体指数（SOX）目前已经高出其 200 日移动平均线整整 69%。纵观过去 25 年的历史数据（至少从 2002 年以来），市场上从未出现过如此极端的大幅偏离，半导体指数几乎呈现出直线上冲的抛物线走势。迈克尔与乔什·布朗（Josh Brown）谈到美光科技（Micron）的惊人爆发，不仅跻身巨无霸行列，更有华尔街大行（如瑞银 UBS）将其目标价从每股 535 美元直接狂飙上调至 1,625 美元，相比此前 751 美元的收盘价足足翻了一倍有余。 乔什直言不讳地指出，无论是存储芯片股的疯涨、韩国股市的联动，还是部分芯片龙头令人瞠目结舌的表现，都正在让大量投资组合经理和市场观察家感到后背发凉。这种氛围迅速勾起了许多资深老兵对 25 年前互联网泡沫时期的记忆——当年思科（Cisco）、朗讯（Lucent）和 JDS Uniphase 的狂欢与当下如出一辙，大家都忍不住怀疑：“历史是不是正在重演？”面对迈克尔开玩笑问及“要不要做空美光”，富达投资（Fidelity）的丹妮丝·奇斯霍姆（Denise Chisholm）表示，抛开单一股票不谈，如果客观拉出整个半导体板块的历史数据，会发现背后的故事远比直觉上的“投机泡沫”更为耐人寻味。 - 费城半导体指数（SOX）高出 200 日均线 69%，创下近 25 年罕见的历史记录。 - 华尔街机构对美光科技等芯片巨头的目标价出现极端上调，引发市场对周期见顶的担忧。 - 投资者的心理阴影主要来自于 2000 年互联网泡沫时代的龙头破裂记忆（如 Cisco、Lucent）。 --- ### [28:01](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=1681s) 盈利增长跑赢股价：数据揭示的“非典型繁荣” 丹妮丝·奇斯霍姆提出，如果抛开个股情绪，以开放的心态审查整个半导体行业的汇总数据，就会发现表面现象与底层逻辑的巨大反差。半导体板块在过去 6 个月内暴涨了 70%，在过去一年中涨幅更是高达 100%。回顾历史，当一个板块在一年内翻倍时，虽然有类似 2000 年泡沫破裂的惨痛案例，但在大约 55% 的情况下，它在接下来的年份里依然能够跑赢大盘。进一步将范围扩大至 1960 年代以来所有 GICS 细分行业，当某个细分子行业单年涨幅达到 70% 至 100% 时（历史上仅发生过大约 2% 的极端情况），次年继续跑赢大盘的概率甚至高达 65%。 这种看似违背常识的动能延续，关键在于投资者并非盲目追高，而是背后存在着极为罕见的“盈利暴增”作为支撑。丹妮丝指出，本轮半导体行情最核心的异常值是盈利能力的复合式爆发：过去两年来，半导体板块的实际盈利增速竟然超过了其股价的涨幅。历史上，半导体行业从来没有在不上演暴跌 100% 又暴涨 100% 的极度波动下，维持长达两年以上超 50% 的持续上行盈利轨迹。大众往往只被暴涨的绝对价格所震慑，却忽略了估值扩张背后的真实利润底座。 ```mermaid flowchart TD accTitle: 半导体板块表现与盈利驱动逻辑 accDescr: 解释半导体板块价格暴涨背后的核心动因在于利润增速超过了股价扩张幅度 subgraph PriceDriver["市场直觉与价格走势"] A["6个月涨幅 +70%<br/>1年涨幅 +100%"] B["SOX 偏离 200日均线 69%"] end subgraph FundamentalReality["基本面支撑与数据规律"] C["盈利增速跑赢股价涨幅<br/>持续2年以上的复合爆发"] D["历史极端高涨行业数据：<br/>次年跑赢大盘胜率达 65%"] end PriceDriver --> FundamentalReality FundamentalReality --> E["结论：并非纯粹的估值泡沫<br/>属于盈利驱动的追赶型行情"] ``` --- ### [29:47](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=1787s) 美光排位的剧烈跃升与周期性估值的认知偏差 普通投资者在盘面上看到的是不可思议的狂暴涨势。《华尔街日报》曾绘制过一张图表，展示各类科技巨头市值跨过关键门槛所需的时间，而美光达成这些里程碑所用的交易日之短令人震惊。迈克尔指出，仅仅在一年前，美光在标普 500 指数中的市值排名还仅为第 127 位，市值不足 1,000 亿美元；十年前它甚至排在第 381 位；而如今，它已一跃冲上全市场第 11 大成分股。这种闪电般的蹿升让场内老手也开始提心吊胆。迈克尔分享身边一位重仓美光的朋友：股票每天以 5% 的幅度狂飙，必须在“还没被暴利冲昏头脑”时设定止盈纪律，因为每天涨 5% 的标的，回调时也能以每周跌 20% 到 30% 的速度洗劫财富。 当朋友辩称“美光的产能已经提前一年被预订一空，这还是传统的周期股吗”时，迈克尔与丹妮丝探讨了周期性行业的核心悖论。对于典型的周期性企业，表面看起来最便宜、市盈率最低的时候，往往恰恰是盈利见顶的危险时刻。但丹妮丝指出，如果观察半导体板块过去十年的历史线性规律，估值越进入历史底部四分位数时，未来 12 个月跑赢市场的概率反而接近 70%。这意味着当前的“估值压缩”更多是利润爆发太快所带来的“追赶行情”（Catch-up Trade）。市场对周期逆转的恐惧情绪其实已经部分被计入到了股价之中，行业底层的运行规律和商业模式可能正在发生深刻演变。 - 美光在标普 500 指数中的排名从 10 年前的第 381 位、1 年前的第 127 位，跃升至全美第 11 位。 - 周期股在估值看似便宜时往往隐含盈利见顶风险，但半导体估值位于底部分位时，历史次年跑赢概率达 70%。 - 半导体产业链过往典型的 3 年库存周期，目前可能已拉长至 7 年甚至更久。 --- ### [33:06](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=1986s) 并非当年的“暗光纤”：当下算力需求与互联网泡沫的本质差异 乔什·布朗回顾了两年前整个华尔街还在把英伟达类比为当年思科的情形，戏称英伟达现在就像是泰勒·斯威夫特（Taylor Swift），而美光成了更新一代年轻人热捧的奥利维亚·罗德里戈（Olivia Rodrigo）。针对目前市场频频将存储芯片与 2000 年电信与光纤泡沫（如 MFNX 等惨遭清算的通讯光缆公司）相提并论的观点，乔什坚决不认同这种历史类比。当年电信泡沫中，资本开支铺设了无数穿越大陆的光纤电缆，结果无人问津、在地下积灰，直到五年后 YouTube 诞生这些宽带才被消化，相关公司早已成批破产。 当前的算力基础设施建设则有着根本区别：美光的存储芯片和英伟达的 GPU 一旦被装进数据中心，每一秒都在被全负荷运行，根本不存在“闲置积灰”的现象。各大科技巨头的资本开支并不是基于“也许三年后有人会用”的虚假假设，而是为了解决“当下眼前的算力严重短缺”。丹妮丝进一步从量化基本面印证了这一区别：在 1990 年代末互联网泡沫膨胀时，科技股中位数的盈利早在 1996 年就已经见顶掉头向下，股价上涨全靠估值泡沫推升；而当下的科技股中位数不仅全部实现扎实盈利，营业利润率（Operating Margin）甚至还在继续走高。现在的基本面健康程度与当年的投机狂潮有着天壤之别。 - 2000 年电信泡沫中过度铺设的“暗光纤”（Dark Fiber）闲置了近 5 年，而当前半导体硬件出厂后立刻投入实际生产。 - 1990 年代科技股中位数的盈利在 1996 年便已触顶，而 2000 年泡沫破裂前基本面与股价脱节长达数年。 - 当前标普科技板块从中位数企业到巨头不仅盈利丰厚，且整体营业利润率依然处于扩张周期中。 ### [35:23](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=2123s) 闭门造车的 AI 巨头：透明度能否化解市场怀疑？ Josh Brown 提出了一个发人深省的思想实验：如果 OpenAI 和 Anthropic 已经是上市公司，拥有公开交易的代码、按季度召开电话会议并向公众披露详尽的财务报表，市场对当下庞大 AI 资本支出（CapEx）的疑虑是否会大幅减轻？目前市场上大量资金流入由这两家未上市公司发起的算力项目，但外界只能偶尔在米尔肯学会全球峰会（Milken Conference）等场合听到高管轻描淡写地透露几句营收传闻，或者在他们需要上电视宣传新产品时才能窥见一点动态。由于缺乏触手可及的数据，投资者难以将巨额的硬件投资与实打实的商业收入清晰挂钩，这直接加剧了资本市场普遍的猜疑与焦虑。 面对这一提问，来自富达（Fidelity）的量化策略总监 Denise Chisholm 给出了意想不到的冷静解答。她认为即便是完全透明的信息披露，也未必能消弭市场固有的怀疑情绪。然而，她反问众人：“我们真的需要让所有人感到心安理得吗？”对于当下的持仓多头而言，持续存在的市场疑虑反而是绝佳的环境——牛市往往在怀疑的墙壁上攀爬，正是因为充斥着悲观主义和不信任，资产价格才没有迅速走向毫无理性的泡沫终局，反而构成了相当健康的投资格局。 ```mermaid flowchart TD accTitle: AI投资与市场情绪传导链 accDescr: 展示未上市AI巨头的巨额CapEx投入如何引发市场疑虑，以及这种怀疑如何反向支撑健康的牛市环境。 subgraph S1["未上市 AI 核心主体"] A["OpenAI / Anthropic"] -->|"缺乏季度财报与代码"| B["营收数据全凭传闻"] A -->|"巨额 AI CapEx 投入"| C["算力硬件与数据中心采购"] end subgraph S2["公开资本市场反馈"] B & C --> D["无法直接追踪 ROI 转化"] D --> E["市场长期弥漫怀疑与焦虑"] E --> F["估值未陷入极端亢奋"] F --> G["为牛市提供健康支撑"] end ``` --- ### [37:06](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=2226s) 净利润率见顶了吗？单位劳动力成本的隐形红利 讨论随即转向华尔街分析师群体中可能存在过度乐观的领域。Michael Batnick 引用了 BC Alpha Research 基于 FactSet 的统计数据，指出标普 500 各大板块的净利润率（Net Profit Margin）目前正处于历史高位，而华尔街分析师竟然普遍预测利润率还将进一步跃升。在估值紧绷的背景下，如果这一预期最终落空，整个牛市基石恐将动摇。 Denise Chisholm 针对这一担忧从量化前瞻指标做出了拆解。她指出，与企业利润率相关性最紧密的宏观指标并非能源或原材料，而是**单位劳动力成本（Unit Labor Costs）**。目前美国的单位劳动力成本正处于历史底部的四分位数（bottom quartile），这在 3% 左右通胀的环境中极为罕见。其背后的根本动力在于：一方面薪资增速保持温和受控，另一方面全要素生产率持续反弹上升。这种组合在历史上与高企的企业利润率呈现高度直接的正相关。 当现场主持人半开玩笑地探讨“企业是否已经将原本用于雇人的薪水转为购买算力和 Token”时，Denise 明确澄清，目前从体量占比来看，机器人与算力替代人工并导致成本转移的规模还微不足道，当下的生产力提升并非完全源于 AI 的替代效应。股票市场之所以在利润率创下新高之际仍出现多倍数压缩（multiple compression），恰恰说明许多资金早已在计入“利润率无法持续”的衰退预期。前瞻性驱动指标明确显示利润率“走高概率大于走低”，而市场主流却选择不信，这再次形成了一个典型的预期差买点。 - **核心前瞻指标**：企业利润率最直接、最紧密的正向驱动力是单位劳动力成本的下降与生产率的提升。 - **历史分位数对比**：当前单位劳动力成本处于历史最低 25% 区间，有力支撑了利润率的高位维系。 - **市场心理矛盾**：股市目前的估值倍数压缩说明资金在提前交易利润恶化的风险，基本面与情绪面形成背离。 --- ### [39:45](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=2385s) 标普 493 觉醒：被忽视的盈利扩散与“Mag 7”掉队 随后，大家探讨起今年牛市中最为关键却被严重低估的趋势——市场广度的扩散（diffusion）。去年市场尚且能看到金融等其他行业的出色表现，而今年公众眼球看似重新被 AI 资本支出牢牢吸引。然而真实的数据图表却揭示了一个截然相反的事实：除去“科技七巨头”（Magnificent 7）的另外 493 家标普公司（S&P 493），其盈利修正与增长正展现出势头迅猛的强劲复苏。 Denise 指出，过去三年间，市值加权盈利增长与等权重盈利增长之间出现了巨大的剪刀差。虽然市值加权数据看起来依然健康，但等权重口径下的盈利增长此前实际上经历了持续整整三年的收缩期。虽然这并非类似传统经济衰退那样断崖式的下滑，但在周期持续时间上却完全等同于一场长达三年的盈利衰退。由于失业率保持低位，这场衰退更像是“充分就业下的企业盈利衰退”，而当下市场正迎来“充分就业下的盈利复苏”。按照历史周期中下行期与修复期的对称性，这轮由标普 493 主导的盈利复苏目前仅仅走过了大约 4 个月，一切才刚刚拉开序幕。 Michael Batnick 补充了极具震撼力的年内表现数据：科技七巨头年初至今仅上涨 6.6%，明显跑输标普 500 大盘；相反，剔除七巨头后的其余股票（X Mag）涨幅却达到了 10.25%。在大盘整体斩获 10% 涨幅的背后，个股贡献度排名中 Alphabet（Google）位居榜首，而以大约 1000 亿美元市值起步的美光科技（Micron）竟然不可思议地跃升至贡献榜第二位，为指数贡献了超过 100 个基点。其后依次是英伟达（Nvidia）、苹果（Apple）、AMD、亚马逊（Amazon）、英特尔（Intel）、博通（Broadcom）、闪迪（SanDisk）和泛林集团（Lam Research）。尽管半导体与硬件股依然占据了核心权重榜单（前十名累计贡献了 2.3%），但整个大盘的整体涨幅已全面向其余 490 多只股票扩散。随着“七巨头”内部表现发生明显分化，几位嘉宾笑称或许很快就得创造新的代名词，甚至调侃未来如果 SpaceX 上市，七巨头就要演变成“Mag 8”了。 - **涨幅分化表现**：年初至今七巨头（Mag 7）涨幅仅 6.6%（跑输指数），而标普剔除七巨头部分（X Mag）涨幅达 10.25%。 - **周期位置判断**：标普 493 结束了长达 3 年的等权重盈利收缩期，当前的盈利全面回暖周期仅展开了 4 个月左右。 - **美光科技异军突起**：年初市值仅约 1000 亿美元的美光成为拉动标普 500 整体涨幅的第二大功臣，贡献度超 100 个基点。 ### [44:00](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=2640s) 小型股与大型股惊人的盈利预期剪刀差 主持人讨论了罗素 2000 指数与标普 500 指数在未来四个季度的预期收益差异。从数据来看，两者之间的盈利增长预期差距极其惊人。在能源和原材料领域，投资者基本能理解背后的周期逻辑，但在金融板块，预期罗素 2000 小型金融股未来将迎来高达 66% 的盈利增长，而大型金融股仅有 7% 的预期增长；在房地产和非必需消费品板块，同样存在巨大的估值与增长差距。 对于这一异常分化，现场讨论了其统计口径问题。罗素 2000 指数中有 20% 甚至更高比例的企业是“未盈利公司”（non-earners），当一家原本亏损的公司从每股收益亏损转为微赚 5 美分时，数学上的盈利增速百分比便会被无限放大。但 Denise Chisholm 指出，即便考虑标普小型股 600 指数（S&P 600，具有盈利门槛筛选），整体趋势方向也是一致的。驱动小型股盈利好转的核心逻辑并非美联储降息预期，而是底层制造业周期复苏的启动。 Denise 解释道，ISM 制造业新订单指数在经历了整整三年的低迷后首次出现拐头向上的拐点。制造业经历长达三年的萎靡期极为罕见，上一次出现类似现象还要追溯到 20 世纪 80 年代。小型企业与美国本土生产能力的关联最为紧密，因此随着制造业从长达三年的隐性衰退中逐步走出并向正常增长回归，小型股的盈利开始迎来从“不盈利”到“恢复盈利”的关键转折。 --- ### [45:52](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=2752s) 通胀预期与“剔除住房的核心 CPI” 话题转向困扰市场的通胀问题。交易市场已经将 2026 年的首次降息预期推迟到 12 月，甚至有被完全移出定价日程、转而讨论加息的风险。然而，Denise 从历史与实证数据出发提出了反直觉的观点：作为股票投资者，市场真的需要通胀快速降温吗？ 历史数据显示，股票市场表现的最佳“黄金区间”（sweet spot）实际上是通胀率处于 3% 到 4% 之间。股票本身具备通胀对冲属性，历史上唯独对股市不利的场景是通胀处于最高的四分之一分位数（Top Quartile）且仍在持续上行——即达到 4.5% 以上的高通胀环境。在中间分位数区间，通胀并非压制股市的决定性变量，盈利增长才是主导力量。只要有盈利增长支撑，股票就能完美发挥抗通胀属性。 此外，Denise 指出当前通胀数据存在严重的结构性失真： - 房贷利率骤升导致原本锁定低息房贷的业主不愿卖房，造成严重的“住房供应冲击”。 - 如果将具有供给冲击性质的住房项（Shelter）剔除，核心 CPI 实际上仅有 2.3% 左右。 - 美联储关注核心 CPI 的深层原因在于，政策制定者不应对不可控的供给冲击“雪上加霜”，如同原油价格暴涨本质上更像是一种“消费税”，美联储绝不应在供给冲击发生时通过加息去抑制无法用货币政策解决的问题。 ```mermaid flowchart TD accTitle: 通胀结构对美联储政策传导图 accDescr: 展示核心通胀拆分、原油冲击特性以及对美联储利率决策逻辑的影响路径 subgraph CoreCPI["核心 CPI 结构拆分"] A["整体核心通胀指标"] --> B["剔除住房核心 CPI: 约 2.3%"] A --> C["住房项 (供给冲击属性)"] end subgraph SupplyFactor["供给驱动变量 (如原油)"] D["原油价格上涨"] --> E["充当居民消费税"] E --> F["压制需求而非引发全面通胀"] end subgraph FedResponse["美联储应对逻辑"] B & F --> G{"加息还是降息？"} G -->|"供给冲击不加息"| H["避免双重紧缩打击实体经济"] G -->|"需求/资本品订单强劲"| I["确认增长周期并温和调节"] end ``` --- ### [49:11](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=2951s) 油价冲击、耐用品订单与“温和加息”之谜 对于市场上广泛担忧的“油价反弹将迫使美联储加息”的言论，Denise 引用实证数据进行了有力反驳。历史数据显示，当对油价在 6 个月内的涨幅分档统计时，原油上涨与未来 12 个月美联储加息概率之间反而呈现负相关。即便油价在半年内暴涨 60%，未来美联储加息的历史概率也仅为 25%。自 20 世纪 80 年代以来，在高达 48% 的案例中油价上涨根本无法顺畅传导至核心通胀，它更类似于直接削弱消费者支出的隐性税收，而非全面价格上涨的助推器。 真正驱动美联储加息的单调正向指标是实体经济增长，特别是核心资本品与耐用品订单数据： - 当前美国耐用品订单和核心资本品订单已经处于历史区间的前四分之一强劲分位数。 - 处在该高增长分位数时，美联储收紧货币政策的概率升至 65% 左右。 Josh Brown 调侃自己在电视上做节目时，嘉宾总是将“加息还是降息”简单二元化为“利好或利空”，却几乎从不深入探讨政策变动的具体幅度。 Denise 揭示了一个违背直觉的数据：当美联储处于每年小幅加息 25 至 100 个基点（即位于中等分位数的温和加息）时，标普 500 指数上涨的概率反而高于基准概率（基准上涨概率约为 75%）。这种“小幅加息而股市不跌反涨”的现象，是因为温和加息证实了经济正处于扩张周期，代表利率紧跟周期而非扼杀周期。当市场焦点落在强劲的内生增长上时，投资者完全能够消化适度的利率抬升。随后 Josh 抛出了下一个疑问：历史上新任美联储主席上任后，究竟有多少人会选择一上来就加息？ ### [52:19](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=3139s) 油价暴涨、通胀传导与美联储的政策悖论 讨论首先切入通胀与能源价格的互动逻辑。Josh Brown 借用欧·亨利的名作《麦琪的礼物》（The Gift of the Magi）打了个生动的比方：夫妻俩各想送对方礼物，妻子剪掉长发卖钱给丈夫买表链，丈夫却卖了金表给妻子买发梳。Josh 认为美联储应对油价飙升的反应就类似这种荒谬的局面——油价飙升对消费者和企业而言无疑是痛苦的，但美联储如果因为担忧油价外溢到其他通胀环节而选择加息，根本无法降低原油本身的供给价格，反倒进一步加重了实体经济的负担，无异于雪上加霜。 Denise Chisholm 对此表示认同，并指出历史数据清晰地表明，美联储其实非常清楚这一点：在历史上，油价飙升幅度越大，美联储反而越不太可能加息，因为央行官员本质上也把油价飙升视作对消费者变相征税。自 1980 年代以来（排除 1970 年代受单位劳动力成本驱动的大通胀期），油价上涨完全传导到其他商品价格的概率不到 50%。这与此前加征关税的情形如出一辙：企业无法单方面决定价格转嫁，最终取决于美国消费者的吸收能力。如果消费者的边际消费倾向在加油站或关税商品（如洗衣机）上被过度挤占，其他领域的消费需求就会萎缩，迫使企业放弃提价甚至降价促销以挽回销量（如汽车、机票等耐用品与服务）。 随后，话题转向 10 年期美债收益率及长端利率走势。Denise 强调，从经济增长的角度来看，当前的长端收益率水平完全正常，毫无异常之处。低利率往往反映的是低增长，而在 1990 年代强劲增长期，10 年期美债收益率长期处于 5% 至 6% 的水平。按历史分位数来衡量，目前的收益率并没有脱离基本面规律。 ```mermaid flowchart TD accTitle: 油价飙升与美联储政策博弈逻辑 accDescr: 展示油价冲击如何通过消费者吸纳能力形成自我调节，而非必须依赖央行加息 subgraph Shock["外部冲击与传导"] A["油价暴涨 / 能源冲击"] --> B["对消费者变相'加税'"] B --> C["非必需品边际消费倾向下降"] end subgraph MarketResponse["企业与市场应对"] C --> D["终端需求数量下滑"] D --> E["企业定价权受挫 / 被迫降价促销"] end subgraph FedDilemma["美联储政策选择"] A -.->|"错误反应：加息"| F["实体经济成本进一步攀升"] A ==>|"历史经验：容忍/不加息"| G["通胀自然吸收率低于50%"] end ``` - 欧·亨利寓言《麦琪的礼物》被用来比喻美联储用加息对抗供应端油价暴涨的荒谬与徒劳。 - 1980 年代至今的数据显示，油价飙升直接外溢并被全面传导的概率低于 50%。 - 10 年期美债收益率与当前 GDP 增长预期匹配良好，对比 1990 年代 5%～6% 的常态水平并无异常。 --- ### [55:33](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=3333s) 巨额 IPO 浪潮：SpaceX、OpenAI 与市场流动性大考 随着 SpaceX（传闻最早于 6 月推进）以及两大前沿 AI 大模型公司 OpenAI、Anthropic 潜在上市计划的逼近，市场正在迎接新一轮超大规模股权融资浪潮。Michael Batnick 拿出一组未作通胀调整的历史数据进行对比：在 1998、1999 和 2000 年的互联网狂潮中，美股三年内分别募集了 340 亿、650 亿和 650 亿美元，累计新增股本发行达 1,640 亿美元；而眼下仅 SpaceX、OpenAI 和 Anthropic 这三家公司合计的潜在募资规模就可能高达 1,700 亿美元。 Josh 提醒大家，当年是数百家初创公司蜂拥上市造就了狂热，而如今仅凭三家超级巨头就可能吞噬如此庞大的资金量。虽然 Denise 和 Michael 都承认，如果按照占美股总市值的百分比来重新换算，这一规模可能仅相当于当年的四分之一左右，但这依然对市场资金池提出了严峻考验。根据节目此前嘉宾测算，单是 SpaceX 一家，基础募资额约 740 亿美元，如果行使绿鞋机制（Greenshoe Option），总发行规模可能膨胀至 860 亿美元左右；若要促成首日挂牌暴涨，二级市场可能需要准备双倍于此的接盘资金。 | 历史时期 / 拟上市标的 | 募资主体 | 募集金额 / 规模 | 市场环境与结构特征 | |:--- |:--- |:--- |:--- | | **1998–2000 年（互联网泡沫期）** | 数百家初创企业集群 | 3 年累计 1,640 亿美元 (34B + 65B + 65B) | 情绪、盈利均处于极端狂热的顶层分位数；未按通胀调整 | | **当下超级独角兽浪潮** | SpaceX、OpenAI、Anthropic | 预估合计约 1,700 亿美元 | 头部极度集中；若按占美股市值比例折算仅约为当年的 25% | | **SpaceX 单笔融资假设** | 单个航天超级巨头 | 基础 740 亿美元；行使绿鞋后达 860 亿美元 | 需二级市场调动巨量资金配置以维持上市溢价 | --- ### [57:54](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=3474s) 调仓阵痛、盈利锚定与投机小盘股异动 面对数以千亿美元计的新股供应，Josh 提出了基金经理层面的现实困境：对于追踪标普 500、纳斯达克或大型成长股指数的机构组合经理而言，SpaceX 属于必须配置的标的。由于投资组合资金总量有限，他们必须卖掉现有的其他科技股持仓来腾挪资金，这可能对整体科技板块带来短期的抽血效应。 Denise 从量化和宏观视角回应指出，这种调仓挑战（Rebalance Challenge）在历史上屡见不鲜，例如罗素价值与成长指数的年度权重重构。短期内，新增股票供给确实会引发局部的、特异性的波动（Idiosyncratic Volatility）；但拉长到一年左右的时间跨度来看，核心驱动力依旧是企业盈利（Earnings）。强劲的基本面盈利最终会吸引资金回流，而如果企业此时恢复强劲的股票回购（Buybacks），也能有效对冲新股发行的稀释效应。 节目尾声探讨了投机资金的扩散现象。Michael 观察到投机狂热似乎再次抬头：Roundhill 推出的 Meme ETF 创下了历史新高。该基金配置了大量尚未盈利的高风险科技与概念股，涵盖航天、量子计算等赛道，包括 Redwire、AST SpaceMobile、Applied Optoelectronics、Iridium、Applied Digital 和 Rocket Lab。 - 机构面临指数基准约束：必须在减持现有科技龙头与配置新兴超级巨头之间权衡。 - 历史经验表明：大规模指数调仓只在极短期造成流动性摩擦，一年的中期周期内企业盈利才是决定股价底色的终极变量。 - Roundhill Meme ETF 创出新高，其重仓标的大多集中在航天与量子计算相关的未盈利投机性科技板块。 ### [01:00:56](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=3656s) 高 Beta 科技股与垃圾股反弹的历史透视 Denise Chisholm 探讨了近期关于“高风险科技股”（risky tech）或垃圾股行情的讨论。市场上流传着大量图表，显示标普科技板块中处于最高四分位数 Beta（最高波动率）的股票正在触及历史新高，其向上的拐点走势与 2000 年互联网泡沫时期极为相似。然而，Denise 指出，如果将这些高 Beta 科技股相对于标普 500 指数重新基准化（rebase），就会发现它们实际上跑输了更广泛的科技板块。从更长期的视角来看，高 Beta 科技股相对于整体科技板块自 1990 年代以来就一直处于下降通道中，目前只不过是反弹回到了历史区间的上轨。 在这种背景下，虽然高 Beta 科技股由于反弹剧烈而显得估值偏高，市场中也确实存在更优质、波动更小的科技资产，但这绝不意味着应当卖出整个科技板块。历史数据显示，即便高风险科技股随后表现不佳，低波动的科技股依然有高达 75% 的概率跑赢大市。结合中位数盈利复苏的周期规律来看，这种在周期初期出现的“垃圾股反弹”（junk rally）完全符合历史规律，投资者无需感到异常，但也切忌盲目追高。 - 高 Beta 科技股相对大盘科技股并未打破长期下行趋势，当前反弹仅触及区间上限。 - 历史数据显示，低波动科技股即便在高风险科技股熄火时，仍有 75% 的概率实现超额收益。 - 周期初期的垃圾股躁动属于中位数盈利复苏阶段的典型特征，属于正常现象。 --- ### [01:02:30](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=3750s) 核心投资启示：三大未被充分定价的宏观逻辑 Josh Brown 对整场对谈的核心结论进行了全面梳理与总结，提炼出三大最关键的投资要点： 首先，市场可能正处于“标普 493”（除七巨头之外的其他标普 500 成分股）盈利增长周期的相对早期。大众普遍因为过去几年股市的持续上涨，先入为主地认为牛市已接近尾声。然而，当前小型股利润率正在扩张，等权重标普 500 指数的盈利增速甚至超越了七巨头，493 只股票的盈利基本面呈现出更扎实的故事。在经历了整整三年的利润承压后，市场很可能正在迎来为期三年的非七巨头盈利与利润率扩张周期，这一潜在的对称修复是当前市场上最被低估的投资线索。 其次是估值倍数的压缩（Multiple Compression）。当前的估值回落并不是因为企业盈利下滑或是股市暴跌导致的被动压缩，而是由于市场对增长的持续性心存疑虑。这种怀疑态度反而构筑了经典的“爬着担忧之墙”（wall of worry）上涨格局，只要盈利坚挺，适度的多重怀疑就构成了极佳的风险收益比。 第三，美联储在下半年大概率不会成为扰动市场的核心负面因素，货币政策的扰动已大为钝化。 ```mermaid flowchart TD accTitle: 核心投资逻辑框架 accDescr: 展示标普493盈利扩张、估值倍数压缩与美联储政策中性共同构成的看多框架 subgraph Earnings ["盈利动力"] A["标普 493 盈利周期早期"] --> B["利润率与等权重指数提速"] B --> C["有望迎来 3 年对称修复周期"] end subgraph Valuation ["估值与情绪"] D["估值倍数温和压缩"] --> E["源于市场普遍怀疑而非业绩暴跌"] E --> F["攀爬'担忧之墙'的健康牛市"] end subgraph Policy ["宏观政策"] G["美联储影响钝化"] --> H["下半年不再构成核心下行风险"] end C --> I["支撑美股持续走牛"] F --> I H --> I ``` --- ### [01:04:19](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=3859s) 波士顿生活漫谈、富达研究与节目尾声 在严肃的投资探讨之余，主持人 Josh Brown 和 Michael Batnick 与 Denise 聊起了波士顿的日常生活。当被问及波士顿的夏天是否宜人时，Denise 直言并不喜欢夏日，秋天才是波士顿最美的季节。Josh 开玩笑提到波士顿就像“去 Dunkin' 甜甜圈排队都能偶遇马特·达蒙”，Denise 则表示波士顿最棒的是海鲜餐厅与海鲜供应商。主持人们回忆起之前拜访富达办公室时入住的海港区（Seaport），赞叹那里全是现代化的漂亮新建筑，而 Denise 则打趣道那里以前不过是一大片停车场。 关于美食，Denise 笑称自己并不是“龙虾卷（lobster roll）爱好者”，Josh 则极力推荐北区（North End）著名的 Mike's Pastry 家的开心果卡诺里卷（pistachio cannoli）。至于波士顿最棒的海鲜晚餐，Denise 幽默且情商极高地回答：“在波士顿，最棒的海鲜晚餐永远是我丈夫做的饭。” 节目的最后，Denise 介绍了获取她研究成果的公开渠道。她每周都会在 LinkedIn 上分享每周图表（Charts of the Week），并提供直接发送到邮箱的通讯订阅。此外，她每月撰写一份白皮书，主持富达的播客节目《Market Insights》，以纯个人音频/视频的形式为不爱阅读文字报告的投资者解读市场，并面向富达客户主办每季度的行业与投资研究网络研讨会（Sector and Investment Research Update）。Josh 和 Michael 对她的到来表示由衷感谢，盛赞她的研究令人惊叹，并号召听众在 LinkedIn 上关注 Denise Chisholm。 ## Notable quotes > [00:00](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=0s) 吃下最后一口，扔下叉子就能立刻起身走出餐厅。 — Josh Brown > [02:47](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=167s) 那你干脆待在家里点 Uber Eats 好了。 — Michael Batnick > [02:47](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=167s) 所有人都必须到场。 — Michael Batnick > [09:24](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=564s) 那又怎样？ — Denise Chisholm > [17:54](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=1074s) momentum usually begets momentum — Denise Chisholm > [26:31](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=1591s) 历史是不是正在重演？ — Josh Brown > [35:23](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=2123s) 我们真的需要让所有人感到心安理得吗？ — Denise Chisholm > [01:04:19](https://www.youtube.com/watch?v=LgmzAXMBbu4&t=3859s) 在波士顿，最棒的海鲜晚餐永远是我丈夫做的饭。 — Denise Chisholm ## Mentioned Josh Brown, Michael Batnick, Denise Chisholm, Neil deGrasse Tyson, Toast, Saturday Night Live (SNL), Uber Eats, Anthropic, Altimeter Capital, Dragoneer, Greenoaks, Sequoia Capital, Capital Group, Coatue, D1 Capital Partners, GIC, Iconiq, Accel, Baillie Gifford, Blackstone, Brookfield, D.E. Shaw, DST Global, Fidelity Investments, General Catalyst, Insight Partners, Jane Street, Lightspeed, Franklin Templeton, Goldman Sachs, Barron's, Meta, Reels, Netflix, Philadelphia Semiconductor Index (SOX), Micron Technology, UBS, Cisco, Lucent, JDS Uniphase, Wall Street Journal, Taylor Swift, Olivia Rodrigo, OpenAI, Milken Institute, BC Alpha Research, FactSet, Alphabet (Google), Apple, Amazon, Broadcom, SanDisk, Lam Research, SpaceX, Russell 2000, Roundhill Meme ETF, Redwire, AST SpaceMobile, Applied Optoelectronics, Iridium, Applied Digital, Rocket Lab, Dunkin', Matt Damon, Mike's Pastry ## Video description On episode 244 of The Compound and Friends, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Michael Batnick⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Downtown Josh Brown⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ are joined by ⁠Fidelity's Denise Chisholm⁠ to discuss: whether AI capex is a bubble, why stocks keep climbing despite persistent skepticism, and how earnings growth is broadening beyond the Mag 7. They discuss semiconductors, margins, inflation, Fed policy, mega-IPOs, and more! This episode is sponsored by Franklin Templeton. Learn more at  ⁠https://www.franklintempleton.com/munis ►00:00 - Cold Open ►06:07 - Intro ►07:39 - S&P 8000? AI Earnings, Price Targets & the Bull Case ►18:03 - Why Valuation Compression Could Be Bullish ►22:55 - Are Big Tech Stocks Becoming “Modern Industrials”? ►26:25 - Semiconductors Are Going Parabolic — Bubble or Earnings Boom? ►33:06 - Is This Like the Dot-Com Bubble, or Still Early? ►39:45 - The S&P 493: The Most Underappreciated Market Story ►45:49 - Inflation, Oil Prices & What the Fed Might Do Next ►55:33 - SpaceX, OpenAI, Anthropic & the Coming IPO Wave ►01:02:30 - The Biggest Takeaways for Investors ►01:04:19 - Boston Favorites, Fidelity Research & Final Thoughts 👕 Check out The Compound shop: https://www.idontshop.com 🎙️Listen to our podcasts: TCAF: https://podcasts.thecompoundnews.com/show/TCAF/ Animal Spirits: https://podcasts.thecompoundnews.com/show/animalspirits/ Ask The Compound: https://podcasts.thecompoundnews.com/show/ATC/ The Compound Newsletter: https://www.thecompoundnews.com/subscribe Linkedin: https://www.linkedin.com/company/the-compound-media/ Instagram: https://instagram.com/thecompoundnews Twitter: https://twitter.com/thecompoundnews Tik Tok: https://www.tiktok.com/@thecompoundnews Investing involves the risk of loss. This podcast is for informational purposes only and should not be or regarded as personalized investment advice or relied upon for investment decisions. Josh Brown and Michael Batnick are employees of Ritholtz Wealth Management and may maintain positions in the securities discussed in this video. All opinions expressed by them are solely their own opinion and do not reflect the opinion of Ritholtz Wealth Management. See our disclosures here: https://ritholtzwealth.com/podcast-youtube-disclosures/ The Compound Media, Incorporated, an affiliate of Ritholtz Wealth Management, receives payment from various entities for advertisements in affiliated podcasts, blogs and emails. Inclusion of such advertisements does not constitute or imply endorsement, sponsorship or recommendation thereof, or any affiliation therewith, by the Content Creator or by Ritholtz Wealth Management or any of its employees. For additional advertisement disclaimers see here https://ritholtzwealth.com/advertising-disclaimers. #thecompound #stockmarket #stocks #Investing #AIStocks #Semiconductors #Magnificent7 [Source](https://www.youtube.com/watch?v=LgmzAXMBbu4)

[0:00] Josh Brown: 吃下最后一口，扔下叉子就能立刻起身走出餐厅。 [2:47] Michael Batnick: 那你干脆待在家里点 Uber Eats 好了。 [2:47] Michael Batnick: 所有人都必须到场。 [9:24] Denise Chisholm: 那又怎样？ [17:54] Denise Chisholm: momentum usually begets momentum [26:31] Josh Brown: 历史是不是正在重演？ [35:23] Denise Chisholm: 我们真的需要让所有人感到心安理得吗？ [64:19] Denise Chisholm: 在波士顿，最棒的海鲜晚餐永远是我丈夫做的饭。

[9:39] Denise Chisholm: At the peak of the bubble in 2000, corporate America in aggregate was spending three and a half to four times their free cash flow at the time. We are still under one in terms of free cash flow. [15:09] Denise Chisholm: Well, what if they don't go wrong in the way that you think that they will? [22:25] Josh Brown: literal heroin [22:25] Josh Brown: the world's most addictive product [33:06] Josh Brown: Nvidia has become Taylor Swift and Micron is Olivia Rodrigo [37:45] Josh Brown: the most underappreciated market story of the year [48:48] Denise Chisholm: Many investors see higher oil prices as the factor that's increasing the odds of a Fed hike. But when you examine the data, oil-driven shocks have historically made the Fed less likely to tighten, not more—functioning more like a tax on the consumer than a catalyst for broad-based price increases. [62:30] Josh Brown: The number one thing for me from this conversation is that we could be relatively early in the earnings growth cycle for the 493... What if we had 3 years of earnings expansion, margin expansion for everything that's not a Mag 7? That might be the most underappreciated aspect of the market.

[0:00] Good. [0:00] >> We were just talking about your octopus. [0:02] >> What's up, man? [0:02] >> Yeah. [0:03] >> Um [0:05] I saw a reel from Neil deGrasse Tyson on [0:08] these being the closest thing we have to [0:10] aliens. They have like nine hearts, a [0:12] couple of brains. [0:14] >> I did not know that. [0:15] >> off their arm and it grows back. [0:16] >> Well, that part I know. I remember that. [0:17] Yeah, yeah. [0:18] >> Each one of their tentacles can see, [0:20] feel, touch, smell, taste. [0:23] >> I don't eat I don't eat octopus. [0:25] >> Okay, you don't. [0:26] >> But I eat calamari. I eat squid. [0:28] >> Okay. [0:28] >> I don't know why I draw the distinction. [0:30] Um but the I saw a documentary about how [0:32] intelligent octopi are. [0:35] >> Yep. [0:35] >> And I just [0:36] >> Good use of plural there. [0:38] >> So, the problem is we go to like we go [0:39] to Kima, we go to like all these like [0:41] Mediterranean restaurants, and everybody [0:43] gets the grilled octopus. [0:45] You know, I used to like it a lot. [0:47] >> But now you feel bad about [0:48] >> I'm wasting away. [0:49] Um Denise, [0:51] >> [laughter] [0:51] >> here's where I'm at. [0:53] >> Tell me. [0:54] >> I don't know if this is my age or my [0:56] personality. [0:59] I'm now at a stage where I eat out of a [1:02] restaurant [1:02] >> Mhm. [1:03] >> and I have no patience for the bringing [1:06] of the check routine. [1:08] >> Mhm. [1:08] >> Like you have to flag somebody down, [1:11] they come over, I'd like the check, they [1:14] will be right back, they go and get it, [1:15] then they come, they leave it there. [1:18] Then you have to sit with it for 5 [1:19] minutes. [1:19] >> Yep. Please come back. Please come back. [1:21] >> Then you put your credit card in and [1:22] you're looking around for somebody. I'll [1:24] give it to a busboy. I don't give a [1:25] [ __ ] [1:26] >> own Toast? [1:27] >> Hold on. [1:27] >> [laughter] [1:27] >> And then and then they'll come take it [1:30] and then that's a mystery. Where do they [1:32] go? Cuz the thing that they dial it into [1:35] is always by the kitchen. [1:36] >> They're yep. [1:37] >> Fine. [1:37] >> I see it. [1:38] >> Then they come back, then I have to look [1:40] at it and pretend that I'm looking at [1:42] it, and then the and then the signing [1:43] the tip. [1:45] So, when the Toast thing came along, [1:47] okay, I loved it. [1:48] >> Yeah. [1:49] >> And I'm I'm a I own uh shares of Toast [1:52] the stock. But like, can I have a check? [1:54] Yes, here it is, right here. [1:56] >> I have the control. [1:57] >> I just want to press a button. [1:59] Literally, the last forkful, I want to [2:00] drop the fork and walk out of the [2:02] restaurant. [2:03] Now, I don't know if that's me like my [2:05] >> achieve that? Yeah. [2:06] >> This is This is Josh's softer side. This [2:08] is how he connects with the audience. [2:09] >> No, [laughter] it's really bad. My The [2:11] point I was trying to make is I used to [2:14] have a lot more It's not tolerance, [2:17] patience. [2:18] I just want to When I finish something, [2:21] I want to be on to the next thing, and [2:23] the lingering is what [2:25] >> Was it an SNL skit with the doctor and [2:28] the old man that you have EOG? [2:30] >> What is EOG? Am I allowed to say [2:32] >> Early onset grumpiness. [2:34] >> [laughter] [2:34] >> Did you ever [2:35] >> Yeah, I was diagnosed. Yeah. [2:37] >> You have EOG? Can I get rid of it? [2:38] >> No, there's no way you can get [2:39] [laughter] rid of it. Why would I not [2:41] complain? [2:42] >> But isn't it better for the Isn't it [2:44] better for the server, for the [2:46] restaurant owner? [2:47] >> All right, so as a former As a former [2:49] waiter, I was a waiter for years. [2:51] >> Yeah. Full time. [2:52] >> Why does it take so long? [2:53] >> Because you, sir, are not the only [2:55] customer. [2:55] >> But why do we have to do this dance? [2:57] >> What dance? [2:58] >> I just want to It's 2026. I just want to [3:02] put put my fork down, put the napkin on [3:04] the table, and walk out. [3:05] >> can say is usually, like it's 50/50 if [3:07] they bring you a toast. [3:08] >> What if I just give them my credit card [3:10] on the way in? [3:11] >> Just stay home. [3:12] >> Yeah. [3:12] >> [laughter] [3:13] >> All right, problem solved. [3:14] >> out. Uber Eats. [3:15] >> Problem solved. [3:16] >> thought of that. [3:17] I hadn't thought of that. [3:18] >> you in New York frequently? Do you come [3:19] here often? [3:20] >> Not Not that frequently, no. [3:22] >> No. [3:22] >> Well, everyone here is just like me, so [3:24] >> [laughter] [3:25] >> No, I'm not really normally like this. [3:27] >> Yeah, that's okay. [3:27] >> We know you can't do individual stocks, [3:30] we can. So, we'll do the talking. [3:31] >> You do the talking. [3:32] >> Yeah. [3:34] >> So, I know you can't confirm or deny, [3:35] but it's public now. It's public. Uh [3:38] this just happened before we came on [3:39] here, Anthropic series H. [3:43] Where we go? Where Where is this? Series [3:44] H, they're raising $65 billion at [3:48] $965 billion post money. The round is [3:50] being led by Altimeter Capital, [3:52] Dragoneer, Greenoaks, and Sequoia. And [3:54] listen to who's participating. [3:55] Basically, everybody. Um [3:58] uh Capital Group, Coatue, D1 Capital [4:00] Partners, [4:01] >> Travis Kalanick [4:02] >> GIC, Iconic, yeah, and and Thaler, too. [4:04] And Accel, um AMP, AMP, BBC, Baillie [4:09] Gifford, Blackstone, Brookfield, D.E. [4:10] Shaw, DST Global, Fidelity Management [4:13] and Research Company, General Catalyst, [4:15] Insight Partners, Jane Street, [4:17] Lightspeed Partners. I'm like halfway [4:18] done. I'll stop reading, but [4:19] >> Right. [4:19] >> holy [ __ ] [4:20] >> with that amount of money, you need [4:22] everybody. [4:23] >> Basically. [4:23] >> Like it's like the it's like a it's like [4:25] the last Avengers. [4:27] Right? Like everybody's got to show up [4:29] for this. You You're trying to You're [4:32] trying to do $2 million, maybe $3 [4:35] trillion IPOs inside of a 6-month [4:37] window. [4:38] You need all the Avengers. [4:40] >> Mhm. [4:40] >> So, I don't know. [4:41] >> Are we potting? [4:42] >> Yeah. [4:42] >> All right. [4:43] >> We're ready. [4:43] >> Are you nervous about the IPOs? We're [4:45] going to talk about it. [4:46] >> Not the ones that Fidelity's invested [4:48] in. [4:48] >> There we go. [4:49] >> I have it on good authority. [4:51] >> I mean, the cult Look at this. John's on [4:53] John's on the [4:55] >> See you at 2:45. [4:55] >> I'm [5:01] >> [music] [5:07] [music] [5:07] >> Quick trivia question. 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You can [5:53] find this and other information and each [5:54] prospectus or summary prospectus, if [5:56] available, at franklintempleton.com. [5:59] Please read it carefully. All investing [6:00] [music] involves risk, including [6:02] possible loss of principal. Franklin [6:04] Distributors LLC, member FINRA, SIPC. [6:07] >> Business oriented. [6:09] >> Oh my god, ladies and gentlemen, welcome [6:12] to the very finest investing podcast in [6:14] the world. [6:16] I'm your host Downtown Josh Brown. If [6:18] this is your first time joining us, [6:21] man, are you in for [cheering] a treat. [6:22] Or I apologize. [6:24] I'm not sure. [6:26] To my left, my co-host, co-creator of [6:28] the show, [6:30] all around good guy, Mr. Michael [6:31] Batnick. [6:33] >> Hello, hello. [6:34] >> That's it? That's all we got? [6:36] >> Thank you. Thank you. [6:38] >> All right. Far too kind. [6:40] >> [applause] [6:40] >> Our special guest, returning champion, [6:42] she crushed it on the show the first [6:45] time we had we were in Boston. Is that [6:46] right? [6:47] >> That's right. [6:47] >> All right. [6:48] >> [applause] [6:49] >> Denise Chisholm is director of [6:51] quantitative market strategy within the [6:53] quantitative research and investments [6:55] division at Fidelity Investments. [6:58] Denise focuses on historical analysis [7:01] and its application in diversified [7:03] portfolio strategies using factors. [7:06] Oh, they like the factors, Denise. [7:08] >> Oh, good. [7:08] >> Or sectors. [7:10] >> Yeah, that's better. Yeah, I like [7:11] sectors, too. [7:12] >> And themes. Denise joined Fidelity in [7:14] 1999. [7:16] Her research informs the views [music] [7:17] of roughly 150 managers overseeing more [7:20] than 450 [7:22] mutual funds and ETFs. Thank you so [7:24] much. [7:25] >> Yeah, thanks for having me. Thanks for [7:26] having me back. [7:28] >> Question one. [7:30] Where will the S&P 500 finish 2026? [7:33] >> No idea. Don't give us spot estimates. [7:35] So, [7:36] higher or lower is [7:38] >> I think I think that's a good bet. [7:39] >> Yeah. [7:39] >> Uh Goldman Sachs joined the S&P 8000 [7:43] chorus this week. I want to read their [7:46] rationale and [7:48] get your response. [7:50] All of this checks out with me. So, all [7:52] right, earnings growth powered by the AI [7:54] boom will drive further gains in stocks. [7:57] Okay, I think we're all experiencing [7:58] that right now. We would all agree. [8:00] Goldman is going to 8000 from 7600. [8:04] Quote, "Continued earnings growth should [8:07] drive continued equity market upside. [8:09] The increased return forecast reflects [8:11] increased estimates for S&P earnings [8:14] following an exceptionally strong first [8:16] quarter reporting season. I also would [8:18] point out their earnings per share [8:20] forecast would [8:23] equate to 24% year-over-year growth this [8:26] year. And they're bumping up 27 to 13% [8:30] growth. The argument is there's a lot of [8:32] beneficiaries of artificial intelligence [8:34] infrastructure investment in the S&P. [8:37] Maybe that was under appreciated in [8:39] January and we've all woken up to this. [8:41] Last thing, the combination of [8:45] decelerating earnings growth and [8:46] continued uncertainty around both AI and [8:49] the macroeconomic outlook should prevent [8:52] a major increase in valuations. So, [8:55] they're saying not a bubble. The [8:56] governor is there's still this [8:58] uncertainty about AI. So, the [9:00] infrastructure powers the earnings, but [9:03] the multiple stays in check because [9:05] people are a little bit nervous about [9:07] what is it all going to mean. Does that [9:08] sound certainly in line with what what [9:11] you expect? [9:12] >> Yeah, well, I think that there's a lot [9:13] of data that supports what they said and [9:15] then in addition to that, which is to [9:18] say that it is becoming more diffuse. It [9:20] is becoming more broad in terms of the [9:22] recovery. And you can measure it a bunch [9:24] of different ways. And I think the [9:26] interesting part about CapEx cycles is [9:28] that you can measure them historically. [9:29] I mean, post the financial crisis, we a [9:31] lot of investors complained about the [9:33] fact that corporate America wasn't [9:34] spending, right? And that the earnings [9:36] was fake-ish in the sense that it was [9:38] driven by buybacks and dividends, and it [9:40] wasn't real earnings. So, you can [9:42] measure it historically. Now, we're [9:43] starting to to complain, I would say, or [9:46] to be concerned about CapEx. But, CapEx [9:48] is usually the better path, right? So, [9:50] if you say CapEx relative to sales is on [9:52] an upward trajectory, and that's let's [9:54] call that a CapEx recovery, and you [9:56] could call the opposite something that's [9:57] not, you would rather as an investor, [10:00] from a stock market perspective, from an [10:01] earnings perspective, from a GDP [10:02] perspective, and from a job perspective, [10:04] rather have a CapEx recovery. [10:06] That CapEx recovery on the high end is [10:08] definitely driven by CapEx seen in [10:10] technology stocks, but it's getting more [10:12] diffuse. So, all of the sectors now, of [10:15] the 11 GICS sectors, we have the data [10:18] going back in history, you can measure [10:19] it CapEx relative to sales, they're all [10:20] accelerating. And not to such a degree [10:23] that we saw in any kind of bubble. I [10:25] mean, at the bubble time, when you [10:27] measure it, especially relative to free [10:28] cash flow, at the peak of the bubble in [10:30] 2000, corporate America in aggregate was [10:33] spending three and a half to four times [10:35] their free cash flow at the time. We are [10:37] still under one [10:38] in terms of free cash flow, even with [10:40] >> Outside of the hyperscalers. [10:42] >> Even in addition to them, like so, in [10:44] the aggregate. So, yes, outside them, [10:46] we're not really anywhere in that bubble [10:48] territory. [10:48] >> So, when people say they're spending all [10:49] their free cash, you say, "So what?" [10:51] >> So, I say that's six or seven companies [10:53] over the last six or seven years, but [10:56] it's not systemic. So, there's [10:58] idiosyncratic, right? And then there's [11:00] systemic. And again, when you [11:02] idiosyncratically focus on a couple of [11:04] companies, you you could fall prey to [11:06] the fact that, you know, we'll see. Will [11:08] the CapEx estimates come to fruition? [11:10] Will the free cash flow not be there, or [11:12] will the will revenues actually grow, [11:15] you know, in addition to what you think? [11:16] Or maybe they'll figure out other ways [11:18] to generate free cash flow, which we've [11:19] seen in the past. But, you're not seeing [11:21] that for the aggregate of the technology [11:24] index. So, they've grown free cash flow [11:26] above CapEx for the better part of 20 [11:28] years. So, the recent hookup is still [11:32] we're still from an aggregate sector [11:34] perspective under spending in terms of [11:36] CapEx relative to free cash flow. [11:38] >> Why do you think people have so much [11:40] trouble [11:41] accepting a CapEx wave [11:44] and not automatically going to well, it [11:46] surely must be a bubble because [11:48] the numbers are going up from last year. [11:50] Therefore, it's some sort of a mania. [11:53] Like why can't people just accept not [11:55] every wave of CapEx spending [11:57] automatically turns into a pumpkin? [11:59] >> Well, don't you think maybe it's just [12:00] because that's [12:02] what we lived through? Meaning that [12:04] there hasn't been a CapEx cycle in so [12:06] long. [12:06] >> bias. We only remember the last one and [12:09] it turned out badly. [12:10] >> Correct. [12:11] >> Okay. [12:11] >> Correct. [12:12] >> Um have there been examples of CapEx [12:13] bubbles that didn't result in a stock [12:16] market crash? [12:17] >> CapEx bubbles? [12:17] >> Or CapEx waves, sorry, that didn't [12:20] become bubbles and result in a a crash. [12:22] >> Oh, for sure. I'd say like again, back [12:24] to that sort of data, if you say a CapEx [12:25] recovery is when you're spending CapEx [12:27] relative to your sales base and the [12:29] opposite, most CapEx cycles actually [12:31] generate growth rather than create a [12:34] bubble that deters it. So, I mean, if [12:36] you think about it just in the basic uh [12:38] like the virtuous cycle of the US [12:39] economy, when corporate America sees [12:42] growth and spends for growth, they [12:44] create growth by spending, creating [12:46] jobs. So, it's in some ways the it [12:49] reflects the durability of the cycle. [12:51] >> Can we go back to something that you [12:52] said? [12:53] This I think is a really key point. [12:56] Michael and I [12:57] have been around long enough that we [12:59] were probably maybe not doing this show, [13:01] but we're doing blogs or whatever, [13:04] doing this period of time where a lot of [13:06] the earnings per share growth was coming [13:09] as a consequence of float shrink, [13:11] where companies were being rewarded by [13:13] shareholders for not spending on R&D, [13:16] CapEx, M&A, and just like running their [13:19] businesses, buying back stock, and those [13:22] would be the top performers in the [13:24] market or among the top performers, and [13:26] that was like good enough, but you had a [13:29] lot of critics [13:31] railing that it's a it's a bubble [13:33] because it's all being driven by [13:34] buybacks, and once the buybacks stop, [13:37] it'll all come crashing down. And [13:39] actually, what ended up happening was [13:40] not that. The buybacks stopped because [13:43] there became an urgency for investment. [13:45] >> Yeah. [13:46] >> And then all of a sudden, the stock [13:48] market stopped punishing people for [13:50] capex and started rewarding [13:52] and getting more bullish. [13:54] >> Yes. [13:54] >> Um and and buying the stocks of [13:56] companies that were making these [13:58] investments. [13:58] >> Yeah. [13:59] >> And now it's a lot of the same critics, [14:02] and now they've changed their tune. Now [14:04] they're not a [14:05] the buybacks are over, [14:07] but they still hate the market, and now [14:09] the reason they hate the market is cuz [14:10] they're not doing buybacks. They're so [14:11] busy investing. [14:12] >> Yeah. [14:12] >> What are they making all these [14:13] investments for? So, there there is a [14:15] component to the market where they will [14:17] just dislike a rally, and you can come [14:20] up with the reason after it almost [14:21] doesn't matter. Do you you feel that [14:23] there's some of that in the air? [14:25] >> Oh, absolutely. Absolutely. [14:27] I think [14:27] >> Anyone [laughter] that you want to call [14:29] out? [14:29] >> No, I won't [clears throat] call anybody [14:30] out. Yeah. [14:31] >> But you think that's a a real thing? [14:33] >> Well, I think the the most interesting [14:35] part is I think that most investors, and [14:37] I think, you know, for people who study [14:39] history, this is the exception, but most [14:40] investors want the equity to market to [14:43] reflect good times. And good diffuse [14:46] times, meaning that not just a few [14:48] companies are spending, but many [14:49] companies are spending. There is good [14:50] growth. There is good job growth. But [14:52] when you study history, the equity [14:54] market doesn't always reflect good [14:55] times. In fact, if you had to say, if [14:57] you dropped a quant onto it, and I'm [14:58] kind of a quant, and you said, "Is the [15:00] equity market reflective of good times, [15:02] or is it a hedge against bad times?" [15:03] You'd almost say that it's more often [15:05] the hedge [clears throat] against bad [15:06] times. I mean, think of all the things [15:07] that have gone wrong over the last 5 [15:09] years, and the equity market is, you [15:11] know, 70% higher. So, this is the trick, [15:14] I think, of investing, that when you [15:16] study history, like you understand all [15:18] the risks, all the headline, like all [15:19] the problems that can go wrong, and your [15:21] downside risk is, "Well, what if they [15:23] don't go wrong in the way that you think [15:26] that they will?" Then you give up on [15:29] average returns of 8% a year. Right, [15:31] which is one of the few asset classes [15:33] that can actually keep pace with [15:34] inflation. So, I think that's the that's [15:36] the the tug of the the the, you know, [15:38] riddle with equity investing. [15:40] >> I think there are investors also who [15:42] politically [15:44] are just on the other side of the [15:45] administration. [15:47] And that's, you know, perfectly fine. [15:50] They'll have their time in the sun [15:51] someday, too, we assume. Like, it goes [15:53] back and forth. [15:53] >> always goes back and forth. [15:54] >> But there are people that when they [15:56] don't like the direction the country [15:57] seems to be headed in, [15:59] they don't like who's in the White [16:00] House, or they disagree with [16:03] the way like certain things are [16:05] happening in the culture, [16:08] it it's like a cognitive dissonance that [16:10] they almost can't bear. [16:12] They can't process, "Why is the stock [16:14] market higher? Isn't A so bad and B so [16:17] bad and C so bad?" This surely this is [16:21] wrong because I don't feel good right [16:23] now about what I'm seeing. [16:26] >> But the problem is that [16:27] >> think anyone's like fully immune to [16:28] that. Hence, the need for quantitative. [16:31] Okay. [16:31] >> I think that that's what what's helpful, [16:33] right? If you say like consumer [16:34] confidence is at all-time lows, like is [16:36] that a problem? And you go, "Well, wait [16:37] a minute. You'd actually be rather be [16:39] buying equities when consumer confidence [16:40] is low relative to high." [16:42] >> paying attention to that a long time [16:43] ago. [16:43] >> Right? I mean, the same thing with [16:45] uncertainty. Like, we we got this with [16:46] tariffs originally, um, you know, last [16:48] year when people were like, "I don't I [16:50] had a lot of diversified portfolio [16:51] managers come to me and say, 'Denise, [16:52] this is a very uncertain environment, [16:54] and in an uncertain environment, [16:55] corporate America's not going to spend. [16:56] If they don't spend, jobs aren't going [16:57] to grow.'" And you say, "Okay, well, we [16:59] can measure all of those things, and we [17:00] can see if that's true." And the [17:02] interesting part is when you quartile [17:03] out uncertainty, the more uncertain the [17:05] environment, the higher the odds of the [17:07] stock market advancing, and the higher [17:09] the odds that there's capital spending, [17:11] meaning [17:14] >> No, nobody thinks that way intuitively. [17:15] >> But by the [17:16] but it's like the Barron's head like the [17:18] the covers. It's by the time it is so [17:21] visible, it is more likely than not [17:23] already discounted. Corporate America is [17:26] already not spent. Corporate America is [17:27] already not hired. The stock market has [17:29] already worried about all the things [17:30] that you're very visibly worrying about. [17:32] And, I mean, to to put a fine point on [17:34] it, I don't remember if we talked about [17:35] it last time, but remember, I mean, the [17:36] low in the market between like 1976 and [17:39] 1985 was 1978 before either of the [17:42] recessions happened. So, if you said [17:43] like, "Hey, Denise, what's going to [17:45] happen. [17:46] We're going to have two back-to-back [17:47] recessions and rates are going to go to [17:49] 15%." [17:50] >> Nobody buys that. [17:51] >> But, then, if you said like, "I'm going [17:52] to sell in '78. I'm going to get this [17:54] right. I'm going to nail the risks. I'm [17:55] going to get the risks right, and I'm [17:57] going to buy back at the end of '82 when [17:59] jobs finally started to recover." You [18:01] lost 40% cumulative nominal returns. [18:04] >> So, we saw a lot of worries in the first [18:06] quarter of the year where the market [18:08] didn't really seem to react to the news [18:10] immediately, and there was a lot of [18:11] people thinking like, [18:13] "Does How is this happen The [18:15] closure of the Strait of Hormuz was [18:17] supposed to be a black swan type of [18:19] event where oil goes up to $150 barrel, [18:23] S&P closes limit down." Like, that's you [18:25] know, it was a black swan type of [18:26] outcome, and the market seemed to be [18:28] sort of yawning past it. [18:30] And, you had this weird dynamic where [18:32] earnings estimates were rising, and [18:35] price was coming down a little bit, and [18:36] so you saw the the valuation compress in [18:39] a big way. So, you went through history, [18:41] and you looked at this Daniel chart one, [18:43] please, of the valuation compress [18:45] compression over a 6-month period. What [18:48] were you saying during this period of [18:51] the multiple coming down with this weird [18:54] dynamic of earnings skyrocketing? [18:56] >> Yeah, so it's an interesting dynamic. [18:57] This is rare that you have this kind of [18:58] multiple compression. But, if you go [19:00] like reverse to when we started the [19:02] year, I had a lot of people, you know, [19:04] asking me just even even from a [19:06] institutional perspective like, [19:08] "How can you be bullish on the market [19:09] when the market is up so much over the [19:11] last 3 years, and stocks are expensive. [19:13] And the problem is again quantitatively [19:15] neither of those things bend your odds [19:18] for underperformance of equities, right? [19:19] So the more the equity market goes up, [19:21] you guys know like momentum usually [19:23] begets momentum, which is not to say [19:24] that it's 100% of the time, but it is to [19:26] say that if you bet on that, if you want [19:28] to take the other side of stocks going [19:29] up, you have to accept the fact that [19:31] it's a lower likelihood, not a higher [19:32] likelihood and you know something that [19:34] the stocks don't know. Uh second same [19:36] thing for, you know, valuations. Like [19:38] there's no quartile difference in terms [19:40] of the odds of an equity market advance. [19:42] Coming into the year, for me what has [19:44] been different since 2022 is the equity [19:46] market is just persistently fearful. You [19:48] can measure it in valuation spreads on a [19:49] rolling measure of the VIX, right? I [19:51] mean during the tariff tantrum, um you [19:53] know, you saw the VIX spike to 50. I [19:55] think during this tantrum we saw it [19:57] spike to 30. Um when you sort of roll it [20:00] out through time, when you look at the [20:02] the fear in the equity market that has [20:03] been persistent relative to the fear in [20:05] the credit market, that's where you [20:06] start to get this linear relationship [20:08] that you potentially want to take the [20:10] other side and the market has more [20:11] likelihood to climb the wall of worry. [20:14] >> I'm so glad you said that because people [20:16] point to the stock market and say, "What [20:17] an all-time high. What do you mean [20:19] there's fear in the market?" Even last [20:20] week or 2 weeks ago when the S&P fell [20:22] 2%. It was nothing. The put-call ratio [20:24] spikes immediately. There is a lot of [20:26] fear and it's really hard to have both [20:29] of those thoughts in your head at the [20:29] same time cuz you feel like, "Oh, this [20:31] is bubble everybody's in." No, and that [20:33] is why it keeps grinding higher. [20:35] >> Correct. I I completely Yeah. [20:36] >> What would be your signal though for [20:38] that unwant Like what what would have to [20:40] happen for you to say [20:42] there's not enough fear in the market, [20:43] multiple expansion again? [20:44] >> Yeah, so the the 15% down sort of [20:46] confirmed that whole thing. [20:49] >> though it is to have 29% earnings growth [20:51] and outrun that with price. [20:52] >> Yeah. [20:53] >> We would have to have like a 70% rally [20:55] in the market in order to get multiple [20:57] expansion. [20:57] >> god we didn't have that happen. That [20:58] would That would have been scary. [20:59] >> Well, that's exactly. So when people say [21:02] like what's what could be the problem, [21:03] something like that where you see very [21:06] little fear in the equity market or a [21:07] bubble like that in terms of that price [21:09] advance or something like that looks [21:11] more like that and good economic times [21:14] top quartile you know GDP growth which [21:16] we're still grinding it out at like two [21:18] two and a half and we'll see where [21:19] earnings growth ends up but you know for [21:22] the most part it's been between 10 and [21:24] 15. [21:24] >> So don't don't you think it's easy to [21:26] make the case that investors as a whole [21:28] are being sober right now even though [21:30] we're about to get to some of the crazy [21:32] [ __ ] that's happening because there is [21:33] obviously crazy [ __ ] happening. But I've [21:35] said this to Josh a million times. In [21:37] today's modern digital markets [21:39] >> Yeah. [21:39] >> in a bull market there will always be [21:41] many maniacal behavior. Like that's just [21:44] there will never not happen again. [21:45] >> Yeah. And side shows. [21:46] >> Yeah, of course. But in the aggregate [21:48] the fact that we are not outpacing [21:50] earnings growth I think the market is [21:51] being pretty sober. [21:52] >> Completely agree. [21:53] >> All right. So [21:55] rewind back to [21:57] um [21:58] I guess earlier in the year. I don't [21:59] know know when we made this chart but [22:01] remember the forward PE of tech next [22:03] chart please Daniel and the forward PE [22:05] of the S&P 500 converged. [22:06] >> Yep. Yeah yeah. [22:07] >> And we were like [22:08] is this good is this bad this seems [22:10] weird. All right. Well that was the fat [22:11] pitch. [22:12] >> Yep. We got it. [22:13] >> Literally the forward PE of tech and the [22:15] whole market converged for a minute. [22:17] >> to buy all of these quote-unquote high [22:19] flyers at the same multiple as the [22:21] overall market. [22:22] >> So we made this last week I forgot to [22:23] put it in the in the deck but even [22:25] better we saved a few Denise. The [22:26] rolling 38 day performance since we made [22:28] this chart or since those lines [22:29] converged. Look at that fat pitch. Look [22:31] at that home run. [22:32] >> I know. [22:32] >> What's was that plus 18%? [22:34] >> So it's a total [22:35] performance but of tech versus the S&P [22:36] 500 that time period. [22:37] >> Yeah I figured. Yeah no there's and we [22:39] have the data going back to the 60s [22:40] right? So I have it going back to the [22:41] 30s. That's I don't really you have [22:43] technology that I was like wow. You have [22:46] to share that with me. There is there's [22:47] a relationship the cheaper technology [22:49] stocks have been relative to the S&P 500 [22:51] >> Tech in the 30s was booms. [22:53] Booms were [laughter] very big. So um [22:56] in in many ways the multiple compression [22:59] is justified because the companies [23:02] themselves are undergoing a [23:04] transformation. [23:05] They're going from capex light [23:07] predominantly software businesses with [23:10] crazy high margins, [23:11] a little bit of R&D, a little bit of [23:13] capex, but nothing crazy to 120% cash [23:17] flow going into long-term infrastructure [23:20] projects. [23:21] Can't say they're capital light anymore. [23:23] >> Right. [23:23] >> Um maybe with the exception of [23:27] uh I mean, even Apple would be a [23:29] stretch. They're the only one not [23:31] engaging in this capex frenzy. Their [23:33] capex actually is down versus last year, [23:36] at least so far. But like [23:38] these stocks should not be trading at 35 [23:41] times earnings anymore. They are [23:43] industrial. They're very modern [23:45] industrials. What do you think about [23:47] that? [23:47] >> It's a great point. [23:48] >> Well, I think it depends on their [23:49] ultimate margin structure, which we [23:51] don't know the answer to yet. I think to [23:53] your point, like [23:53] >> In this transition period while they [23:55] build, [23:56] they look more like heavy industrial [23:59] companies. [23:59] >> With a much higher margin structure and [24:01] return structure. Currently, that's what [24:02] I mean. So, what we know is today, that [24:05] has not been true in the sense that they [24:06] have spent more in terms of capex, and [24:08] yet they have retained this margin [24:10] structure. Now, we will see where it [24:12] ends up. Again, like all estimates don't [24:14] always come to fruition. What they will [24:16] spend, maybe there will be bottlenecks. [24:17] Maybe in fact technology will do what [24:19] technology always does, which is make [24:21] something, you know, very expensive and [24:22] scarce, and then they will make it [24:23] cheaper and abundant. Right? So, maybe [24:25] this capex ends up not being as [24:27] egregious as we think and doesn't weigh [24:29] on free cash flow as much. So, there are [24:31] all kinds of possibilities. I think that [24:34] what was the debate in the bubble was [24:36] that all of the spending was very [24:38] clearly not associated with any margin [24:41] or any earnings. And to date, the [24:43] spending that we're seeing has been [24:45] associated with [24:47] >> So, we made that we've made that point [24:48] before, and we've also made the sort of [24:51] parallel point that the equity-funded [24:55] bubble of 2000 [24:57] is very different than the cash flow [24:59] funded bubble of today. [25:00] >> Yes. [25:00] >> So that though um they that may not stay [25:04] true. [25:05] I think half the backlog for AI CapEx is [25:07] open AI and Anthropic. [25:09] >> Mhm. [25:09] >> And that money is very obviously has to [25:11] come from IPOs or raised assets. So [25:14] that's sort of equity equity issuance [25:17] funded uh spending. [25:18] >> Mhm. [25:19] >> But like the CapEx itself is coming [25:21] directly from the companies that are [25:22] most capitalized to actually do this [25:26] better. [25:26] >> also look at Meta. So they've obviously [25:28] spent a lot of money on AI and Reels, [25:30] which is a 3-year-old product, is now [25:32] doing 50 billion dollars in annualized [25:36] revenue. That's more than Netflix. And [25:37] that is a direct result of the AI spend. [25:40] >> Right. Reels has become the world's most [25:42] addictive product. It's it's literal [25:44] heroin. If you If you walk through an [25:46] airport, [25:47] like and just look to the left, look to [25:49] the right, walk through a terminal, [25:51] you will see eight out of 10 people are [25:53] staring at their rectangle. [25:55] Eight out of 10. [25:56] >> Mhm. [25:56] >> If you If it's kids, it's 10 out of 10. [25:59] >> Mhm. [25:59] >> Um and that's Reels. So a little bit [26:02] TikTok, but a lot of Reels. And they've [26:04] used AI to essentially rewire our [26:07] brains. [26:07] >> Mhm. [26:08] >> We can't stand in an elevator and not [26:10] look down at the phone, right? It's like [26:12] it's captivated. So AI has enabled just [26:15] an incredible step change in how [26:18] addictive uh [26:20] Meta is and how good they are at serving [26:22] ads more more most importantly. [26:24] >> Right. Which is how they generate [26:25] >> So the biggest beneficiary of all this [26:27] spend in the market today and the area [26:30] where if somebody said, "Michael, what [26:31] do you mean the market is sober? Have [26:33] you not looked at semiconductors?" [26:34] >> Yeah. [26:34] >> Okay, fine. Point taken. So chart three, [26:36] Daniel, please. The SOX index is 69% [26:41] above its 200-day moving average, [26:44] >> Yeah. [26:44] >> which is unlike anything going back to [26:47] So I guess we only have data going back [26:49] to 2002 here. [26:49] >> Okay. [26:50] >> Um but we've never seen anything in the [26:52] last 25 years like this. It is going [26:54] straight up. We spoke [26:55] Josh and I were talking about Micron, [26:57] how it just joined the trillion-dollar [26:58] club. It's the 11th stock, and I guess [27:00] Entropic is on its way. So, on Tuesday, [27:03] UBS raised its Chart 4, Daniel, please. [27:06] UBS raised its price target for Micron [27:09] to $1,625 [27:11] from $535, [27:14] more than double its closing price on [27:16] Friday of $751. [27:19] >> Without commenting specifically on [27:20] Micron, [27:21] >> [clears throat] [27:22] >> you would agree this recent development [27:25] of these memory stocks and Korean stock [27:28] market and [27:29] >> All related, right? [27:30] >> some semiconductors that Denise is not [27:32] specifically talking about, you would [27:34] agree this is an area that's causing [27:36] portfolio managers and market watchers [27:39] to be like, [27:40] >> Wait a minute. What's going on? [27:41] >> I'm getting shades of Cisco, Lucent, JDS [27:46] Uniphase. Like, I'm getting I'm getting [27:49] those feelings back from 25 years ago. [27:51] You'd agree there's some element to [27:53] that. [27:53] >> Yeah, so that's where I think the data [27:54] is really interesting. [27:55] >> Should we short Micron? [27:56] >> [laughter] [27:57] >> I'm just teasing you. [27:58] >> I know you're teasing me. I want to [27:59] answer. It doesn't matter anyway. So, [28:01] no, I think that's a really interesting [28:02] point. In terms of like, when you [28:03] aggregate it together, forget Micron or [28:05] any individual stock, and just aggregate [28:07] all the semiconductors together, you go, [28:08] "Okay, well, what's happened over the [28:10] last 6 months, right? They're up 70%. [28:12] What's happened over the last year? [28:13] They're up 100%." And now, let's look at [28:15] the data. Let's just pretend, again, [28:16] back to sort of all the charts, like, [28:18] let's pretend we don't know the answer. [28:19] And let's be open-minded. Now, you look [28:21] at that, and you could say, "Okay, well, [28:23] it's been up 100% before over the last [28:25] year." And two things. One, it's exactly [28:28] in line with the third earnings growth. [28:30] And two, [28:31] >> That's important. [28:32] >> And it gets more important in a second. [28:34] And two, if I bought it every time it [28:36] was up 100%, clearly the bubble was one [28:39] instance that was negative, but more [28:40] often than not, like 55% of the time, it [28:43] goes on to outperform again the next [28:45] year. The interesting part is, let's [28:46] forget semiconductors for a second. [28:47] Let's take all of the industries going [28:50] back to the '60s for anything in any gig [28:52] sector and say, "Well, what if I bought [28:54] them when they were up 70% to 100%?" [28:56] >> Any sector that's up 70% to 100%? [28:58] >> industry, industry. So, one one click [29:00] down. So, that'll be it's only happened [29:02] like 2% of the time, you know, [29:04] historically. But, every sector kind of [29:05] has some participants. 65% of the time, [29:08] they go on to outperform. [29:10] >> That's a lot. [29:11] >> Right? Surprising, too. Which is sort of [29:13] to the point of, "Wait a minute, is [29:15] there something the market is not [29:16] >> But, investors aren't stupid. Like, [29:18] there's a reason why this happened. [29:19] >> not doing this randomly. [29:20] >> Because what the anomaly is has been [29:23] earnings growth. So, over the last 2 [29:25] years, the price performance is less [29:28] than the earnings growth have been for [29:30] semiconductors as an industry over the [29:32] last 2 years. [29:33] >> Right. [29:33] >> That's the anomaly. They've never [29:35] compounded earnings like this without [29:36] being down 100% or, you know, not 100% [29:39] and then up 100%. But, now this up [29:41] trajectory of let's call more than 50% [29:43] has lasted more than 2 years. That's the [29:46] difference than history. [29:47] >> I think the average investor, they see [29:49] the price. [29:49] >> Right. [29:50] >> So, they see like the Wall Street [29:51] Journal had this great chart. The Wall [29:53] Street Journal had this great graphic [29:55] showing the time between reaching $500 [29:59] billion you know, whatever it was a long [30:00] time ago. But, Micron it happened in how [30:03] many trading days? Is that like I don't [30:04] know. I can't see it doesn't say, but [30:06] >> Yeah. [30:06] >> 20. [30:07] >> And it's ridiculous. The speed at which [30:08] this happened. So, we made a chart [30:10] showing that Micron we're showing its [30:12] path in the S&P 500 over time. And a [30:15] year ago, like not a year ago, it was [30:17] the 127th [30:19] largest stock in the S&P. [30:21] It was a it was a less than $100 billion [30:23] market cap just a year ago. And it's now [30:26] number 11. [30:27] >> 10 years ago, it was number 381. [30:29] >> The speed the speed is is an amazing. [30:31] So, Dennis, you have a chart showing the [30:34] relative forward PE of of semis. [30:36] >> Yep. [30:37] >> And they tend to trade at a [30:41] uh I don't know, if But, investors don't [30:43] give them the the premium multiple. [30:45] >> Right. [30:45] >> So, I I had a friend of mine who credit [30:47] to him, he's been in Micron for a while. [30:49] He's made a lot of money. And I was [30:51] saying like, "Hey, listen. [30:53] I'm not like calling the top of Micron [30:55] or anything like that. But, like [30:56] when something goes up 5% a day, [30:59] set some sort of exit plan just while [31:01] you're sober. And maybe he's drunk on [31:03] the on the gains. But, like cuz if and [31:05] when this thing falls, it's just going [31:06] to be disorienting for your brain. Like, [31:07] I'm not making a short-term call. But, [31:09] just have this conversation with [31:10] yourself. [31:10] >> 5% a day. [31:11] >> Right. [31:12] >> It'll fall 20% a week. [31:13] >> Well, it just it just fell 30%. Like, [31:15] after earnings. It just did. So, anyway, [31:17] the point the point is this. He said, [31:19] "Well, it's sold out for a year. It does [31:21] it is it still a cyclical business?" And [31:23] I would say, "Yes, but you know a lot [31:25] more about this than I do." [31:26] >> Well, I would say so the the trick with [31:28] cyclical businesses, if they're cheap, [31:29] right? That could be the peak in [31:30] earnings. Right? So, that's to your [31:32] point. Like, if this is still a cyclical [31:33] business, then valuation doesn't doesn't [31:37] look from a probability perspective the [31:40] way it currently looks for [31:41] semiconductors. Meaning that I have this [31:43] linear relationship. The cheaper the [31:44] stocks are right now over the last 10 [31:46] years, the more likely the stocks are to [31:48] outperform. Right? So, I think some of [31:50] what we're seeing right now is a [31:51] changing business, right? We just talked [31:53] about that in terms of earnings. They've [31:54] never compounded like this. Now, whether [31:56] or not like earnings is a peak, that [31:58] part I don't know. But, what I can say [32:00] is that the valuation compression that [32:02] we just saw, again, that two-year stack [32:04] in terms of the earnings growth, it's [32:06] almost like a catch-up trade. And some [32:08] of this valuation multiple is saying, [32:10] "Look, I understand that there's a lot [32:11] of fear in the market about what this [32:12] might mean." Some of that's in the [32:14] stocks. Right? Which seems absurd to [32:16] your point. Like, back to your [32:18] >> [snorts] [32:18] >> to the point of like [32:19] >> Yeah, there's fear in the stock. It's [32:20] only up It's only up 8x. [32:22] >> Exactly. So, it again, it sort of you [32:24] have to sort of put the the case of yes, [32:26] anything that goes up that much can also [32:28] go down. But, when you think about [32:30] measuring fear quantitatively and [32:32] measuring valuation support, there's you [32:35] know, 70% odds are not 100% odds. But, [32:37] when you see a pattern like that, I [32:39] think you have to be a little bit more [32:40] open-minded that price might be [32:41] understanding something that you don't [32:43] understand. [32:43] >> What's that 70% that you just mentioned? [32:45] >> So, the odds of semiconductor perform [32:47] outperformance over the next 12 months [32:49] based on when your starting point is in [32:50] that bottom quartile of valuation. [32:52] >> you still think they're they're [32:53] cyclical? [32:54] >> Uh yeah, but the nature of the cycle [32:57] itself is probably changed. So, maybe [32:59] the cycle used to be 3 years and now [33:01] it's 7 and we won't know until we get to [33:03] 8 and look back and say, "Oh, that was [33:06] the peak of the cycle." One of the [33:07] things that distinguishes this from the [33:09] 2000 bubble, Micron in particular, [33:12] so I'm old enough to remember 2 years [33:14] ago when they were comparing Nvidia to [33:16] to Cisco. Um [33:18] >> [laughter] [33:18] >> and we don't talk that uh [33:21] Nvidia Nvidia like basically has become [33:24] Taylor Swift and Micron is Olivia [33:26] Rodrigo and like the the young younger [33:29] girls don't care about Taylor Swift [33:31] anymore. Okay. So, um [33:34] so now we're not saying we're not saying [33:37] Nvidia Cisco, maybe we're saying Micron. [33:39] So, people are pointing back to like the [33:41] um the telecom fiber optic boom and how [33:46] like they laid all this fiber and then [33:47] nobody needed it and all those companies [33:49] went bankrupt and then it wasn't till 5 [33:51] years later that YouTube came along and [33:54] all that fiber that they thought was [33:55] worthless and I I was in these stocks [33:58] like MFNX and like I owned all of these [34:01] things. [34:02] Um [34:04] I don't love that comparison or that [34:07] analog because [34:09] Micron's memory is not being installed [34:12] in a data center and then collecting [34:14] cobwebs. Like the stuff that they're [34:16] selling is put into use immediately. [34:19] Now, you could argue whether or not that [34:21] will continue, but that's not a great [34:23] comparison to Cisco and and fiber optic [34:26] cable in in the year 2000. [34:30] This is not CAPEX with the expectation [34:32] that maybe somebody will need it. [34:34] >> Right. [34:34] >> They're literally saying we're short [34:36] compute based on today's demand. Forget [34:38] about what we think demand is going to [34:40] be in 3 years. [34:41] >> Yeah. [34:41] >> Do you think that that point is made [34:44] enough on on the bull side? Do people [34:46] understand it really or [34:48] >> I mean, I think it goes back to the like [34:49] idiosyncratic versus, you know, systemic [34:52] in terms of like how you add it up. So, [34:54] yes, are there individual stocks where [34:56] you could sort of make this comparison, [34:57] but the problem is that in the '90s into [34:59] that bubble, it was everybody, right? [35:01] So, the median technology stock, their [35:03] earnings peaked in 1996. [35:06] >> Whoa. [35:06] >> Right. That was the disconnect. [35:09] >> Yeah. [35:09] >> Right? I mean, this like could we get [35:12] there? Sure. [35:14] But we're a very far cry from there. The [35:16] median tech stock is profitable. [35:18] Obviously, the cap-weighted tech stocks [35:19] are profitable. Operating margins are [35:21] still increasing. Could we look back at [35:23] some point and say this is the peak? But [35:25] again, back to that, okay, let's pretend [35:27] then this is the peak. What if then it's [35:29] 1996? [35:30] >> Mhm. [35:31] >> Okay. Um do you think if Anthropic and [35:34] OpenAI were public companies reporting [35:36] financials on a quarterly basis, holding [35:38] conference calls, and had ticker symbols [35:40] that people could see, [35:42] >> Mhm. [35:42] >> [clears throat] [35:42] >> they the visibility of where the [35:44] spending is going and where the revenue [35:46] is coming from [35:48] would be would would be a better source [35:50] of comfort to people? [35:53] Because I think one of the issues right [35:54] now is a lot of this CapEx is going into [35:56] projects being paid for by Anthropic and [36:01] OpenAI, and they don't have tickers. [36:02] >> The circle door knob. [36:03] >> don't talk to the public unless they [36:05] want to promote something on on TV. [36:07] >> Mhm. [36:07] >> But they they're it's not a uh [36:10] it's not the same as seeing a public [36:12] company spending and then being like, [36:14] okay, I get it though, they're also [36:16] generating revenue. We're getting rumors [36:18] of revenue about these companies. [36:20] They're like casually mentioning their [36:22] revenue [36:23] at uh the Milken conference. Like, we [36:26] don't have [36:27] >> Right. [36:27] >> we don't have the data at our fingertips [36:30] and maybe that's why all the CapEx [36:32] spending it's harder to tie it to [36:35] uh revenue that we can actually see. You [36:37] think that's an issue for investors? Is [36:38] that maybe why there's so much [36:39] suspicion? [36:40] >> Uh yes, but I'm not sure transparency [36:42] will solve the suspicion to your point. [36:44] Like, I think there [36:45] there are skeptics out there. I think [36:47] the skeptics will probably continue and [36:48] in some ways like I don't know, do we [36:50] want comfort? Cuz so far it's been a [36:53] decent investor setup. So, do we want do [36:56] we need people to be more comfortable? [36:58] I'm not I'm not sure the answer. [36:59] >> That's a good question. [37:00] >> Do we need drive Do we need that as a [37:02] driver? [37:03] >> Well, the people who are long right now [37:04] don't need that. They're fine. [37:06] >> One of the areas of the market where [37:08] there might be too much enthusiasm and [37:10] this is hard to say what's priced in or [37:12] not, but it might be it might be the [37:14] analysts. So, this is from BC Alpha [37:18] Research. Um they grabbed this data from [37:20] FactSet. Uh we're looking at net profit [37:22] margin for every sector. [37:25] And they're expected I mean, they're at [37:27] all-time highs and they're expecting [37:28] even more gains. [37:30] >> Yeah. [37:30] >> What happens if this doesn't come to [37:33] fruition? I think you keep saying like [37:35] the diffusion and the breadth of [37:37] companies benefiting from this. Do you [37:39] think that this is more likely than not [37:40] that margins are going to keep going up [37:41] cuz this to me is the this is it. This [37:43] is the this is the whole kit and [37:44] caboodle. [37:45] >> Right. Yeah, so I would say let's look [37:46] at the forward indicators in terms of [37:48] what's correlated to margins and the [37:50] biggest correlation to margins is unit [37:52] labor costs. So, labor especially when [37:55] you sort of compound it for [37:56] productivity, that's you know, corporate [37:58] America's biggest for the most part [38:00] cost, which is not to say that there's [38:02] not other input costs, but that's the [38:03] main I would say that the main [38:05] corollary. And so, when you look at unit [38:07] labor costs over time [38:08] >> Yeah, so uh most sectors, the vast [38:11] majority of sectors and then when you [38:12] aggregate it up for the market overall [38:14] without a doubt. So, I think you have [38:15] this unique combination of unit labor [38:17] costs are in the bottom quartile of [38:18] history, right? So, that is very rare [38:21] relative to like you know you know 3% [38:24] inflation. So it's partly because wage [38:26] pressure has been, you know, uh [38:28] continued, I would say, with, you know, [38:32] in conjunction with productivity [38:33] advancements. So that has the [38:34] correlation to profit margin. So the [38:36] lower unit labor costs have been, direct [38:39] relationship, the higher corporate [38:40] profit margins usually are. [38:42] >> have to put token spend back into the, [38:44] you know, the labor costs? [38:46] >> Why? [38:46] >> Because a lot of the [38:48] >> That's on a cap ex. [38:50] >> human is being spent on the robots. [38:52] >> Already? [38:53] >> machines. [38:53] >> Uh not yet. I would say from a [38:55] proportion perspective, not yet. Maybe [38:57] that will change, but I don't think [38:59] we're there yet. [39:00] >> Oh, so like we're saving money by hiring [39:02] people, but we're just reinvesting that [39:04] money into compute? [39:06] Like that argument? [39:07] >> Yeah. Yeah. And so but I don't think [39:09] that that's where the productivity is [39:10] coming from quite yet. But I do think [39:12] that wage growth has been restrained, [39:13] productivity is on the uptrend, and that [39:15] usually makes corporate America more [39:16] profitable. Again, back to just [39:18] relationships, right? And then you add [39:20] in that fact, too, so you go, "Okay, so [39:21] then these are the expectations, you [39:23] know, they continue, but yet we're [39:26] seeing this, you know, persistent fear [39:27] in the equity market and this multiple [39:29] compression, saying that some people are [39:31] already pricing in the fact that it [39:32] won't continue. Meaning that there's [39:35] already the fear that there are these [39:37] risks. So you end up in the same [39:39] situation where the forward drivers do [39:41] suggest that it's more likely than not [39:43] the profit margins are higher rather [39:45] than lower, and the market doesn't [39:46] believe it. [39:47] >> Let's talk about the diffusion that you [39:49] spoke of. [39:49] >> Yeah. [39:50] >> Um [39:51] I think this is one of the most unsung [39:53] aspects of the bull market this year. [39:56] Last year we talked about it a lot. The [39:58] financials had a great year. [39:59] >> Yeah. [40:00] >> There's a lot of money to be made in [40:01] things other than tech last year. [40:03] >> Yeah. [40:03] >> This year I feel like the focus has gone [40:05] back to AI cap ex being the only game in [40:08] town. Um but here's a chart 11. [40:12] This is the S&P 493. [40:14] >> Mm. Yeah. [40:15] >> Um [clears throat] [40:16] the earnings revisions [40:18] >> for the first quarter [40:19] and [40:21] I I I feel like this is I feel like this [40:24] is trending in the direction that you'd [40:25] want it for the overall market. [40:27] >> Right. [40:27] >> So, it's like more more upside from more [40:30] companies that are not uh Mag 7. [40:33] >> That's correct. I think it's a unique [40:34] setup in the sense that there's been a [40:36] big divergence between cap weighted [40:38] earnings growth and equal weighted [40:40] earnings growth. And while cap weighted [40:41] earnings growth has been, you know, fine [40:44] and good for the last 3 years, equal [40:46] weighted earnings growth has actually [40:47] been contractionary. From a duration [40:49] perspective [40:50] >> Smaller companies have not had the same [40:52] earnings growth. [40:53] >> Correct. And I would say not only the [40:54] same earnings boost, we're actually [40:56] seeing earnings decline. [40:58] >> Yeah. [40:58] >> From not a magnitude perspective like we [41:01] saw in other other recessions, but from [41:03] a duration perspective it was as long as [41:05] those recessions. So, that's why like [41:06] 2022 was kind of recessionary from a [41:09] corporate America profit perspective. It [41:11] wasn't it was a full employment [41:13] recession and this is a full employment [41:14] recovery, but this is just now getting [41:17] back to a situation where equal weighted [41:20] earnings growth can grow and be durable. [41:22] And from a pattern perspective, you see [41:24] the symmetry between the duration of the [41:27] decline and the duration of the support. [41:30] >> was the duration of the decline for? [41:32] >> 3 years? [41:34] >> So, what where are we in this earnings [41:36] recovery for the 493? [41:38] >> Like we're 4 months. [41:39] >> 4 months? [41:40] >> Yeah. Yeah. I mean, I started writing [41:42] about it in the beginning of the year [41:43] that we're just really getting started. [41:46] >> So, the the Mag 7 the Mag 7 are up 6.6% [41:49] year-to-date. [41:50] >> Yep. [41:51] >> Uh [41:51] >> Which is underperforming the market. [41:52] >> X Mag [41:53] >> Right. [41:53] >> X Mag is up 10 and a quarter percent. [41:56] So, I thought this is a this is really [41:58] welcome welcome sight. Daniel, chart [42:00] five, please. [42:02] The top 10 [42:04] um stocks in the S&P 500 in terms of [42:06] what's contributing to the overall gain. [42:09] Google is number one. [42:10] The fact that Micron is number two is so [42:12] insane. It started the year at a hundred [42:16] billion dollars and it's the number two [42:18] contributor. It's added a hundred plus [42:20] basis points to the overall index. It's [42:23] remarkable. After that, you've got [42:24] Nvidia, Apple, AMD, Amazon, Intel, [42:27] Broadcom, Sandisk, and Lam Research. So, [42:30] we're noticing a theme here, okay? [42:31] Obviously. But, they all add up to 2.3%. [42:34] Guess what? The index is up 10%. Pretty [42:37] damn good. [42:38] >> Yeah. [42:39] Well, that's why it's interesting when [42:40] you talk about people saying oh, the [42:41] market's dependent on the Mag 7. The Mag [42:43] 7 underperformed for the better part of [42:45] the last 18 months. [42:46] >> And they themselves are diverging from [42:48] each other. [42:48] >> Correct. [42:49] >> Which I love. [42:49] >> Correct. Which means we'll come we'll [42:51] have to come up with another acronym or [42:52] something. [42:53] >> Well, we're going to have to add SpaceX [42:54] to that to Mag 7. It's going to be Mag [42:56] 8. [42:57] >> Tough acronym. [42:57] >> Okay. [42:58] >> [laughter] [42:58] >> Right. But, it's it's going it's [43:00] definitely going that direction. [43:01] >> Do you want to do this one? [43:02] >> Which one? [43:03] >> Margins outside tech? [43:04] >> All right. So, this is also very [43:06] welcome. [43:08] Margins outside tech. [43:09] >> Is this AI basically the question? [43:11] >> Yeah, so it What What do you think? [43:13] >> Why are Why are margins expanding at [43:14] this rate outside of technology [43:16] companies? Cuz they're the users of AI [43:19] and they're expanding their margins? [43:20] It's too early to bet the store. [43:21] >> think it's too early. I mean, you're [43:22] definitely seeing productivity [43:24] advancements in specifically in [43:25] software, but I don't think it's diffuse [43:28] relative to the rest of corporate [43:30] America. And again, I would go back to [43:32] the If you ask me what's sort of the the [43:35] driver behind that, I would go [43:36] [clears throat] back to the pattern of [43:37] unit labor costs, right? And that's sort [43:39] of the the driver, at least the [43:41] correlator, the tightest correlator that [43:42] I've ever seen with corporate profit [43:43] margins. [43:44] >> So, I want to get your take on on this [43:45] and and sticking with the rest of the [43:47] market. So, small cap earnings, I forget [43:49] what the what the overall estimate is, [43:51] but it's like a it's like 30%. It's like [43:53] a number that you're like wait, what? [43:54] How? So, [43:55] one of our guys, Chartkin Matt, compared [43:58] the small cap earnings, the Russell [44:00] 2000, versus the S&P 500, the expected [44:03] earnings over the next four quarters. [44:04] And the spread amongst the two is [44:08] gigantic. Energy materials, I think we [44:10] we sort of know what the story is there, [44:12] at least in the energy side. But what's [44:13] happening with financials? How are they [44:15] expecting 66% earnings growth for small [44:18] cap financials and just 7% for large [44:21] cap? And similar things with real estate [44:23] and discretionary, the spread is [44:24] gigantic. [44:25] >> Do you feel like it's the calculation in [44:27] terms of the non-earners? [44:28] And how you calculate that from people [44:30] go cuz I mean at the end of the day like [44:32] Russell 2000 what we went from we're [44:35] close to at least 20% or more [44:37] non-earners. [44:38] >> cents and now they're going to earn 5 [44:39] cents. [44:40] >> And how do you calculate [44:41] >> Are they in? Are they out? [44:42] >> I think I think with the 600 [44:44] directionally the same. [44:45] >> Yeah, yeah, directionally the same, but [44:46] I think from a magnitude [44:48] >> So let's just say that we overstated it. [44:49] But what do you think is driving this? [44:51] Do you think it's [44:52] cuz it's not the expectation of a Fed [44:54] cut which we're going to talk about. [44:55] >> No. [44:55] >> Cuz that is going the opposite [44:56] direction. So what do you think [44:58] >> Correct. [44:58] >> What do you think it is? Cuz you're [44:59] right like this the the equivalent of [45:00] small cap earnings have not broken out [45:02] yet. [45:03] >> Correct. No, I mean I I think it's all [45:05] the beginning of a recovery, right? So [45:06] and you're all saying this together. [45:07] It's the manufacturing recovery that [45:09] they're you know in linked linked to. So [45:11] the same thing like when you look at [45:13] ISM, right? ISM new orders inflected [45:16] higher for the first time in 3 years, [45:18] right? It's very rare that the [45:19] manufacturing economy is in I would call [45:21] maybe recession's overstating it, but a [45:24] malaise for 3 years. Last time you saw [45:25] that was like in the '80s, right? And [45:27] you see a big corollary to smaller [45:28] businesses and productive capacity in [45:30] the US. So it is just starting to emerge [45:34] to what normal growth actually looks [45:36] like and that's where I think you get [45:38] from a non-earner perspective to a flip [45:40] to earner perspective. This is the [45:41] beginning. [45:42] Right? This is exactly where it starts. [45:44] So it's all linked to that median [45:46] earnings growth is finally emerging from [45:48] what I would call a recession that's [45:50] lasted the better part of 3 years. [45:52] >> What do we do with inflation? [45:54] >> In What do you mean? Buy or sell it? [45:57] >> It's not trending in the right [45:58] direction. [45:59] It's changing the story of what we [46:02] thought the Trump appointed new Fed [46:04] chair would be able to do, would want to [46:06] do. [46:07] >> Mhm. [46:07] >> They just pushed the first rate cut of [46:09] 2026 into December [46:12] >> Mhm. [46:12] >> uh in terms of the probability based on [46:14] uh trade trading. [46:16] Uh that probably, my guess, I don't [46:19] think we get any kind of relief on the [46:20] inflation numbers next month, the month [46:22] after. I'm guessing they'll push that 26 [46:25] cut [46:26] completely off the table and maybe start [46:28] talking more about a hike. [46:30] Uh [46:31] what do we do with this as investors? [46:32] >> Yeah, so I have a lot of thoughts on [46:34] inflation. So, the interesting part is [46:35] as an investor, right? Do we get any [46:37] relief? [46:38] Do we need relief? [46:40] >> Do we need it? [46:40] >> equity market investor [46:42] >> have to cool off to 2 and 1/2 or can we [46:44] just [46:44] >> The The sweet spot for the equity market [46:46] is between 3 and 4. Right? Inflation. [46:49] Yes. So, I actually have a chart of [46:51] that. I know it's ironic. [46:52] >> Chart 15? [46:53] >> Yeah. I don't think you have it. It's [46:54] back there. Yeah, yeah, yeah. Yeah, [46:55] yeah. [46:56] >> So, I mean, so that's one irony, right? [46:58] Which is what you know, like equities [46:59] act like an inflation hedge, right? The [47:01] The times when you don't want to own [47:03] equities are when it's top quartile [47:04] inflation is top quartile and rising. [47:06] That's 4 and 1/2%. We can debate whether [47:08] or not we're going to get to 4 and 1/2% [47:10] from a core CPI perspective. I don't [47:11] think we will. Uh and we'll talk about [47:13] that next. But, that's the area that you [47:15] don't want to own equities. Anything [47:17] sort of like, you know, in this [47:18] inflationary dynamic, which we're a part [47:20] of, I would call it the middle [47:21] quartiles. It's not the driver, right? [47:24] Earnings growth is the driver. And [47:26] again, if earnings growth is the driver, [47:28] equities can be a valid inflation hedge. [47:30] So, there's an interesting pattern [47:31] there. As it relates to overall [47:33] inflation [47:34] So, this is again, this is I I don't [47:37] want to say controversial, but So, if [47:38] you look at core CPI and you take out [47:41] shelter, if you say, let's call shelter [47:43] a supply shock this time around because [47:45] that's exactly what happened, right? [47:46] Interest rates went up, everybody was [47:47] locked in their mortgages, so nobody [47:48] wanted to sell, so it's acting like a [47:50] supply shock. If you look at core CPI [47:53] ex-shelter, do you know what it was last [47:54] quarter or last month? 2.3%. [47:57] >> Oh. [47:58] >> Right. [47:58] >> The shelter component is what's making [48:00] it look like it's way worse than it is. [48:02] >> Yeah. [48:02] >> Well, people really do care about [48:04] shelter though. [48:04] >> No, but I understand that they should [48:06] care. It's not about what the consumer [48:09] feels. [48:09] >> Yeah. [48:10] >> This is about how the Fed should manage. [48:12] Right, those are different. [48:13] >> Right. [48:13] >> Right. So, the reason why you look at [48:15] core CPI, and we'll get into energy in a [48:17] second, but the reason why you look at [48:19] core CPI is because if you're the [48:20] Federal Reserve, you don't want to [48:21] double down on a supply shock. Meaning [48:23] that if oil acts like a tax hike, the [48:26] last thing you want to do is add rates [48:28] to mitigate something that you can't [48:30] control anyway and make it worse. [48:32] >> Sorry about the uh gasoline prices. [48:35] Here's higher interest rates. [48:36] >> Correct. So, that's why [48:38] >> no sense to me. [48:38] >> That's why they look core CPI. And the [48:40] interesting part is, so when people are [48:42] worried about higher rates this time, [48:43] and you say, "Well, okay, we can observe [48:45] that rates are going higher." But again, [48:47] let's assume we don't know why. So, when [48:49] you look at oil specifically, and you [48:52] chart it out, we have a great chart of [48:53] this. I don't know if you have it up or [48:54] if you have it, but when you sort of [48:56] quartile out the oil like advances that [48:58] you see over time, 10% 20% all the way [49:01] up to 60%, you say, "Is there a pattern [49:03] between what has happened to oil over 6 [49:06] months and what the Federal Reserve will [49:07] do over the next 12 months?" There's a [49:10] negative pattern. [49:11] >> Meaning you get out of reacting to it. [49:13] >> The lower the probability that the Fed [49:15] hike. 25% is not zero, but even when oil [49:18] is up 60, what you see is only a 25% [49:21] chance that the Federal Reserve hikes. [49:23] Why? Because most of the time since the [49:25] 1980s, you don't see a pass-through to [49:27] core inflation. And by most, I mean 48%. [49:30] So, it's not never. [49:31] >> you to yourself. [49:32] >> You said many investors, and Denise, you [49:34] write on LinkedIn weekly. [49:36] >> Yes. [49:36] >> Okay. Many investors see higher oil [49:38] prices as the factor that's increasing [49:40] the odds of a Fed hike. But when you [49:42] examine the data, oil-driven shocks have [49:45] historically made the Fed less likely to [49:47] tighten, not more, functioning more like [49:50] a tax on the consumer [49:51] >> That's great news. [49:52] >> than a catalyst for broad-based price [49:54] increases. And by contrast, what clear [49:57] pattern emerges? [49:58] >> Yeah, growth. Growth is the clear [50:00] pattern. And the funny thing is, like [50:01] when you look at I wrote a lot about the [50:03] manufacturing recovery earlier on the [50:04] year, and durable goods orders are [50:06] actually in the top quartile of their [50:07] range. [50:08] >> Chart 14, Daniel. [50:09] >> Yeah, so that's chart 14. And then you [50:11] do see this pattern, right, between core [50:13] capital goods, right? So the strongest [50:15] core capital goods, that's the quartile [50:16] we're in. You see that relationship, you [50:18] know, that monotonic relationship [50:21] between the higher the growth is in [50:22] terms of durable goods orders, the more [50:24] likely the Fed is to hike. [50:25] >> And which quartile are we in right now? [50:26] >> We're in the top quartile of capital [50:28] goods, yeah. [50:29] >> So you think So you think they're going [50:30] to do it? [50:31] >> I don't know. I mean, it increases your [50:33] odds to 65%. Um I would say that again, [50:36] you got to go with the base case is, [50:37] okay, so something changed in terms of [50:39] the probabilities. Let's be open-minded [50:41] to the probabilities. Then the question [50:43] becomes, okay, let's say they're going [50:44] to hike. Do I care as an equity market [50:46] investor? And that's the unique data [50:49] set, which is you find out really [50:50] quickly [50:51] >> Chart 15, Daniel. [50:52] >> [laughter] [50:53] >> Which is it's not about oh, we're going [50:55] by the magnitude. [50:56] >> important. We so I don't know if you [50:59] know this, I am a television star. [51:01] >> [laughter] [51:02] >> And we spend so much time on TV talking [51:04] in binary terms about hikes versus cuts. [51:08] What's good, what's bad. There's very [51:10] little discussion of magnitude. So let's [51:12] let's talk Let's get Let's get I'll sign [51:14] an autograph after this, but let's get [51:16] it Let's [laughter] get into it. [51:17] >> I think it's interesting. So if there is [51:19] growth, you usually see in these middle [51:21] two quintiles or quartiles of the [51:23] Federal Reserve hiking interest rates. [51:25] So these two quintiles are 0 to 100, [51:28] right? So if you're hiking like 25 basis [51:31] points and maybe 100 basis points over [51:32] the course of four meetings over the [51:34] course of a year, those are your market [51:36] odds, right? And they're higher odds of [51:38] a market advance than baseline, right? [51:40] So baseline 75%, and you would say, if I [51:43] knew everything about what the Fed is [51:45] going to do, whether they're cutting or [51:46] hiking, what's my best probabilities? [51:50] It's the Fed hiking by just a little. [51:51] >> A small hike and a stock and a stock [51:54] market that is not disturbed by it. [51:55] >> Correct, because it's a reflection of [51:57] the now determined to it. Right. [51:59] >> Okay. [51:59] >> And it's more like it's indicative of [52:01] the fact that, you know, rates confirm [52:03] the cycle that rates or the Fed followed [52:07] the cycle rather than potentially create [52:09] it. And if rates are a reflection of [52:11] growth, the market's got no problem with [52:13] it cuz they're focused on growth. [52:14] >> How often do new Fed uh chairpeople come [52:16] in and hike? I know it's not been like a [52:18] million examples, but [52:19] >> I actually don't know. [52:21] >> Well, it ain't going to be this It ain't [52:22] going to be this guy, I can tell you [52:23] right now. Um it reminds me of the O. [52:25] Henry story, The Gift of the Magi, where [52:29] there's a husband and wife No, no. [52:31] There's a There's a husband and wife, [52:32] they each want to buy each other a gift [52:34] and surprise each other. [52:36] >> Oh, I do know the story. Yeah, yeah, [52:37] yeah. Yeah. [52:38] >> And but they don't have money and [52:40] there's a whole thing that goes on. It's [52:41] not important. Um but in the end [52:45] uh he buys her this beautiful hairbrush. [52:48] I don't know if it's like ivory or [52:49] something. She cuts off all her hair for [52:52] the money to get him a gift. [52:53] >> Yes. [52:54] >> So, there's some elements of that where [52:55] it's like, all right, oil price spike [52:57] definitely uncomfortable for the [52:59] consumer, for business, high gasoline, [53:01] very real. [53:03] And then the reaction to that is [53:06] well, we're worried about the reaction [53:08] to that is that is going to make its way [53:10] into other prices in the economy, which [53:13] I understand. [53:14] But then the reaction is going to be [53:16] well, we're going to raise rates. [53:17] >> Right. [53:18] >> Well, first of all, you ain't going to [53:19] change the price of oil by raising [53:20] rates. So, what are you really doing? [53:22] You're just exacerbating the high prices [53:25] that you were worried about in the first [53:26] place. [53:27] >> I think that's correct. [53:28] >> Okay. So, but they I mean, I'm not a [53:30] PhD. Surely they understand this. [53:32] >> Right. I do think that they [53:33] >> If I understand this, they must. [53:34] >> I would say that [53:36] it's clear in the data, right? When you [53:38] see in history the fact that the Federal [53:39] Reserve is less likely to hike the [53:41] bigger the spike in oil, I think that [53:43] they see it exactly the same way, that [53:44] oil is like a tax hike on the consumer. [53:47] Um, and the interesting part, again, [53:49] since the '80s, I mean, this doesn't [53:50] include the '70s and eight like the big [53:52] the big inflation spike, which is [53:54] different because of unit labor costs. [53:55] But, if you say like what are the odds [53:57] that it does get passed through, I would [53:58] say less than 50/50. And I think that we [54:01] saw this movie with tariffs, right? In [54:03] some ways, corporate America doesn't get [54:04] to decide what it can pass through. The [54:06] US consumer decides what it's going to [54:08] absorb. [54:09] And that's, I think, what's very [54:11] different. So, if real incomes aren't [54:13] excessive or, you know, we're not [54:15] getting rained down in terms of 15% GDP [54:17] coming out of COVID or something like [54:18] that, if you have to spend more money at [54:20] the pump or from a tariff perspective, [54:22] more money on washing machines, then [54:24] your marginal propensity to consume goes [54:26] down in other areas, and their marginal [54:28] ability to price goes down with it. So, [54:30] you saw, again, I think, over the last [54:32] 12 months, corporate America like trying [54:34] to increase prices, and then quantity [54:36] demand fell. And so, what did they do? [54:39] They cut prices, right? To increase [54:41] quantity demand. So, I I think [54:43] >> see that? Autos, like airline tickets? [54:46] >> Yes. Yes. [54:47] >> that in some consumer goods. [54:48] >> Yes. [54:49] >> Right. [54:49] >> Yes. [54:49] >> So, forget about Fed Fed funds rates for [54:51] a second, and let's talk about the [54:53] 10-year and the longer end of the curve, [54:55] which people have been nervous about [54:57] over the last couple of weeks. [54:58] Do you think that's appropriately priced [55:01] given how strong demand and growth are, [55:04] and therefore it's not necessarily [55:06] something to be worried about? [55:08] >> say it's not anomalous at all. Like, [55:09] when you look at it Yeah, when you look [55:11] at it relative to growth. I mean, again, [55:13] it goes back to the like would we like [55:14] it lower from an accommodation [55:16] perspective? Mm, not clear in terms of [55:19] the data. Lower rates are often a [55:21] reflection of lower growth. So, no, and [55:24] when you sort of quartile it out, and [55:25] you say, "Okay, where we are relative to [55:27] where GDP is or where it would be," you [55:29] would say there's really nothing to see [55:30] here from a pattern perspective. [55:32] >> was the 10-year in the '90s? It was like [55:33] 5 6%? [55:35] >> way. [55:36] Exactly. [55:36] >> All right, so there are there are the [55:38] IPOs are coming. [55:40] SpaceX is looking like sometime in June. [55:43] Who knows about the the frontier models [55:46] Open AI and and Anthropic. But, let's [55:48] just assume that at some point. [55:50] >> So, [55:52] we have a chart showing the actual money [55:55] raised. And we did not inflation adjust [55:56] this, okay? But, in 1998 and 99 and [56:00] 2000, which was a legitimate IPO boom. [56:02] Obviously, the the numbers were smaller, [56:04] okay? Not inflation adjusted. [56:05] >> You could say mania. [56:05] >> But, it was a legit full-blown mania. [56:07] >> Yeah. [56:08] >> And it's possible [56:10] that at some point over the next year or [56:12] whatever, we're going to have as much [56:14] supply coming to the market from SpaceX, [56:16] Open AI, and Anthropic. [56:18] I mean, this is the this is a big one. [56:20] Do you think there is enough liquidity, [56:22] enough willing buyers? Can the market [56:25] take on this much new equity? [56:27] >> the people listening and not before you [56:28] answer, for the people listening, so [56:30] Michael's chart is like you look at [56:33] 1998, 99, and 2000. You had 34 billion [56:37] worth of IPOs, then 65, then 65. That's [56:40] 164 billion in 3 years of new equity [56:44] issuance. Um [56:47] contrast that with this year. If we were [56:49] to get SpaceX, Open AI, and Anthropic, [56:51] those three deals, we think, would add [56:54] up to something like 170 billion would [56:57] have to be raised. [56:58] >> look way different like I mean, the [56:59] >> And these numbers going up every minute. [57:00] >> Yeah. [laughter] [57:01] >> So, so three companies versus I don't [57:04] know how how many IPOs we think in the [57:06] '98 to 2000 period. [57:08] >> Hundreds. [57:08] >> But, don't we need to rebase it in terms [57:10] of market cap? [57:10] >> yeah, of course, of course. [57:11] >> But, would if we rebased it in terms of [57:12] market cap, wouldn't that look 25% of [57:14] that? [57:15] >> Yeah, probably. [57:16] >> Right. [57:16] >> All right. But, whatever. Let's just [57:17] just it's a lot of money. Delete that [57:19] portion from the show. [57:21] >> No, [laughter] but this is something [57:22] that people are worried about. [57:23] >> Yeah, no, I understand. You guys know [57:25] more than me about like IPOs. And [57:27] clearly, you can look back in history [57:28] and say IPO booms are at least [57:31] correlated with some potential bust in [57:34] the sense that they usually correlate to [57:36] euphoria in the stock market, which is [57:38] usually around boom conditions in the [57:40] economy, boom condition in earnings, [57:42] it's very diffuse. So, they all tend to [57:44] happen together, you know, bullish [57:45] sentiment, you know, exorbitant [57:47] earnings, and by exorbitant I mean like [57:48] top quartile, right? All of these top [57:50] quartile indicators. We're not there in [57:52] any other indicator. [57:54] >> You talk but you talk to portfolio [57:55] managers. [57:56] >> Mhm. [57:57] >> Is anyone talking about what they [58:01] the fact that they might have to sell [58:03] things to participate in some of these [58:07] Like there's a certain amount of money [58:09] you have under management. All right. [58:11] So, now you have a company that you [58:12] already know, if you're being [58:14] benchmarked to the S&P [58:16] or a growth index or the Nasdaq, you [58:18] must own. You You literally must either [58:21] own SpaceX or it crashes and you look [58:23] like a genius or something in the [58:26] middle, but like you have to do [58:27] something. [58:27] >> It's a rebalance challenge. I mean, I [58:29] think that we've seen this over and over [58:30] again with the Russell value and growth [58:32] indices. [58:33] >> Can the market handle 80s [58:35] Uh we had a guest on our show a couple [58:38] days ago. His math, let's assume it's [58:40] the the case. [58:42] He's saying it's like 74 billion [58:45] uh they'll raise, and then if the green [58:48] shoe is exercised, which of course it [58:49] will be, be like 86 billion dollars. [58:52] >> Okay. [58:52] >> That money has to come from somewhere, [58:54] especially if you think they're going to [58:55] engineer a pop. Like there it's going to [58:58] open It's going to open up like not [59:00] down. [59:01] >> Mhm. [59:01] >> You probably need like 2x that in order [59:03] to get a a pop in the stock. [59:06] Somebody has to sell something. That [59:08] money doesn't just come from [59:10] like another planet. So, like is that a [59:12] concern in the near term for investors [59:16] in other tech stocks? Cuz I'm assuming [59:18] that's where the money will come from. [59:19] >> Yeah, I mean, we'll see in the near term [59:21] anything can happen, right? So, in the [59:23] long term, and by long term I mean, [59:24] again, over the course of a year, which [59:26] is generally my time horizon, it will be [59:29] what will matter the most will be [59:31] earnings. [59:32] Right? So, you will get I'm sure you'll [59:34] get more volatility or at least [59:37] idiosyncratic volatility, maybe not at [59:38] the higher highest level of the market [59:40] for a time from an absorption [59:42] perspective, but I think that that might [59:44] be more short-term than you think, which [59:46] is like I said, go back to the rebounds [59:49] that we have. Again, this is a little [59:50] bit of a different issue, but we've seen [59:51] rebalances like that that are sizable [59:53] before in Russell, you know, versus [59:55] growth in 2000 going to sort of the [59:57] mid-cap, and all of that, yes, there are [59:59] absorption issues over the short-term, [60:01] but over the long-term, and again, just [60:03] a one-year time horizon, earnings is [60:05] going to be the driver, and that will [60:07] ultimately be the determinant. [60:08] >> through this we'll get through this [60:10] level of new equity issuance because the [60:12] earnings will bring people back in as [60:14] buyers. [60:15] >> Correct. [60:15] >> This would be a good time for buybacks [60:17] to kick back in. [60:18] >> [laughter] [60:18] >> Yeah, we're going to get all this equity [60:19] issues. [60:20] >> Sticking with like the speculative [60:21] stuff, does does does the speculation [60:24] worry you at all? Is it a total [60:25] sideshow? So, like [60:27] the meme ETF from Roundhill, is it an [60:29] all-time high? It's a relatively newish [60:31] ETF, like they issued it I don't know, [60:32] in the last 12 months, I think. It came [60:34] out in October 25. Okay. Um it's at an [60:37] all-time high in terms of price. [60:39] All-time high. [60:40] >> Oh, [60:40] >> October 25. [60:41] >> it own? [60:41] >> So, the but my point is like the meme [60:44] names are working. [60:45] >> Yeah. Uh it owns not profitable tech. [60:46] >> Yeah, Redwire, AST Space Mobile. So, all [60:48] right, Space, Applied Optoelectronics, I [60:50] don't know what that is, Iridium, so a [60:52] lot of the quantum stuff, Applied [60:53] Digital, Rocket Lab. The the the the the [60:56] junk is working. [60:57] >> Mhm. [60:58] So, yeah, I did an interesting piece on [61:00] this for risky tech, uh and because [61:02] there's a lot of charts making the [61:03] rounds. If you do like towards top [61:04] quartile beta, right, or highest beta [61:06] stocks within S&P technology, they were [61:08] hitting all-time highs, and like the [61:09] inflection was very similar to what we [61:11] saw in 2000. The interesting part about [61:13] that is when you sort of rebase it for [61:14] relative to the S&P 500, they've [61:17] actually lagged broader tech. [61:19] >> Mhm. [61:20] >> And the funny part is and I I had this [61:22] chart. if you look at um if you just [61:24] look at high beta tech versus tech, it's [61:27] been in a downtrend since the '90s. [61:29] >> Yeah. [61:29] >> Like you generally don't want to own [61:31] risk, and it was it sort of oscillated [61:33] right back to the top of the range. So [61:36] you'd say it worked in the way that it's [61:38] worked other cycles, and you would say [61:40] there's nothing anomalous to see here [61:42] relative to the rest of technology. You [61:44] could certainly say that because it's [61:47] rallied so much, it's fairly expensive, [61:49] and there are better portions to own in [61:50] tech that are not high volatile. But you [61:53] also wouldn't say that you would sell [61:54] technology as a sector, meaning that [61:56] even if risky tech underperforms, you [61:59] still see the less volatile tech [62:01] actually outperform to the tune of 75%. [62:04] So it doesn't look when you start to [62:06] normalize things in terms of risky tech [62:08] relative to tech, and you sort of look [62:10] at it relative to other cycles, nothing [62:12] looks particularly anomalous. And then [62:14] if you layer on the fact that, you know, [62:16] sort of let's just get, you know, let's [62:17] think that the median earnings recovery [62:19] that I was talking about is right, [62:21] that's exactly what you should see, a [62:22] junk rally in the beginning that you [62:24] generally speaking don't want to chase. [62:26] >> So I want to you first of all, you're [62:29] amazing. [62:30] >> [laughter] [62:31] >> You know that. [62:34] I want to sum up for the people that um [62:37] are [62:38] listening, watching, but maybe didn't [62:40] absorb every one of these very important [62:42] things. The number one thing [62:45] uh for me from this conversation [62:48] is that we could be relatively early [62:50] in the earnings growth cycle for the [62:52] 493. [62:53] >> Yeah. [62:53] >> Is that right? [62:54] >> That's correct. [62:55] >> So we're all assuming like we're at the [62:56] end of something because stocks have [62:59] been going up for years. It feels like [63:01] uninterrupted. I know it has been [63:02] interrupted. But this concept that all [63:05] of a sudden margins are expanding for [63:07] small caps, the equal weight is seeing [63:09] faster earnings growth than the mag [63:11] seven, the 493 is is a better earnings [63:14] story for this year. [63:15] I don't think the average investor [63:18] is aware of that or understands the [63:20] meaning of that. And you're talking [63:23] about this sort of symmetrical where we [63:25] had 3 years of these companies' earnings [63:27] being under pressure. [63:29] Um and so what if we had 3 years of [63:32] earnings expansion, margin expansion for [63:34] everything that's not a mag seven? [63:36] >> Yep. [63:37] >> That might be the most under under [63:38] appreciated aspect of the market. The [63:40] second Do I have all that right? [63:41] >> Yes. Absolutely. [63:42] >> Okay. The second big takeaway is the [63:44] multiple compression. [63:45] And that's sort of a a favorable setup [63:48] so long as it's not coming as a result [63:50] of earnings falling. [63:51] >> Correct. [63:52] >> And the stock market falling faster. [63:54] That's not what's going on. [63:55] >> Correct. [63:56] >> What's going on is people almost don't [63:57] believe the growth can last. So we're [63:59] seeing, okay, that's not a negative. [64:01] >> more skepticism there is, the more you [64:03] can climb the wall of worry. [64:04] >> Yes. And then the third big point that I [64:05] took from this is [64:07] you don't really see the Fed as being a [64:09] major issue in the second half of this [64:11] year. [64:11] >> Yeah. [64:12] >> Did I miss anything? [64:12] >> No, you summarized very well. [64:14] >> good at this. [64:15] >> Yeah. [64:15] >> Professional podcast host. [64:17] >> [laughter] [64:17] >> You did too. [64:18] >> I know. [64:19] >> Certified. [64:20] >> That's why I'm here. [64:21] >> Jeff, fun? [64:22] >> I did. I always have fun. [64:23] >> your favorite part about living in [64:25] What's your favorite thing about living [64:27] in Boston? [64:28] >> Uh I [64:28] >> Is Boston nice in the summer? [64:30] >> No. [64:30] >> Okay. [64:31] >> Not to me. So, I would say the fall. The [64:34] fall is my favorite. [64:35] >> What do you So, [64:36] besides like you go to Duncan and like [64:38] Matt Damon's standing there, like what [64:39] else do you like about What's like the [64:41] best Boston [64:43] life thing? Uh [64:45] >> The seafood. [64:45] >> We cuz when we went up to see you, [64:47] >> Yeah. [64:48] >> we loved it. Like what's that [64:49] neighborhood that we stayed in? [64:51] >> The Oh, we we stayed Where did we stay? [64:52] By right by your offices? [64:54] >> called? [64:55] >> Uh the [64:56] uh Latter? Oh, you're in the Seaport. [64:58] Oh, that's a fancy area. [65:00] >> It's all like new buildings. Beautiful. [65:02] >> to be parking lots. [65:03] >> Okay. So, the seafood. So, so say more. [65:05] >> I think, yeah. No, the seafood and I I [65:07] would say the seafood um the seafood [65:09] purveyors. I'd say the restaurants. Like [65:11] I think Boston [65:12] >> You don't strike me a lobster roll [65:13] person though. [65:14] >> No, I'm not a lobster roll person. Okay, [65:15] are you? Why don't I strike you as a [65:17] lobster roll person? [65:18] >> Uh [65:18] >> What do lobster roll people look like? [65:19] >> Yeah, we just met. I love I love the the [65:22] the uh [65:23] pistachio cannolis at the North End. [65:25] >> Oh, yeah. No, absolutely. Mike's Pastry. [65:27] Yeah, yeah. [65:28] >> What's the [65:28] >> The Italian food's great. Yeah. [65:30] >> what's the best What's the best seafood [65:32] dinner in Boston? [65:34] >> Oh, I In Boston, no. I have I would have [65:36] to say my husband's uh cooking. I have [65:38] to say. Yeah, yeah. [65:40] >> good to him. [65:40] >> Good answer. [65:42] >> [laughter] [65:42] >> All right. [65:43] Uh we loved having you here. Uh [65:45] >> Thank you so much. [65:46] >> I want to let people know that you are [65:48] um doing research for Fidelity. A lot of [65:50] the stuff that you do makes it out into [65:52] the public eye. Tell people where they [65:54] can find more Denise Chisholm. [65:56] >> Yeah, you can find it on LinkedIn. So I [65:58] do share charts of the week and you can [65:59] subscribe to the newsletter. That way it [66:01] comes right into your email box as [66:03] opposed to sort of filtering it through. [66:04] I also write a monthly white paper. I [66:07] also do my own not like you all do, but [66:09] on podcast uh called Market Insights for [66:12] Fidelity. They let me talk. [66:14] >> Okay. [66:14] >> me talk. But it's just me talking. [66:16] >> situation. [66:17] >> It's [laughter] a hostage situation. [66:19] >> But it's good though. It's It's [66:20] >> video. So just me talking. So for those [66:23] who don't consume my research from a um [66:25] not everybody likes to read. So for [66:27] people who like to listen. Uh and I also [66:29] do a quarterly webcast for clients um [66:31] that is the quarterly uh sector and [66:34] investment research update. [66:35] >> So Fidelity clients can get access to [66:37] that. Okay. Well, we think you're [66:38] magnificent. [66:39] >> Thank you. [66:39] >> Uh you can come on our podcast anytime [66:41] you want. You can do video with us. [66:43] Thank you so much. Guys, follow Denise [66:45] Chisholm on LinkedIn. [66:47] And check with your Fidelity rep for why [66:50] you're not getting her research. It's [66:51] very good. [66:53] All right. Uh we had an awesome time on [66:54] the show this week. We did uh [66:57] we did it all. Uh right? All right. [66:59] Guys, thank you so much for listening. [67:01] Thank you for watching. [67:02] We'll see you soon. [67:05] >> Oh. [67:22] Oh.