Korean Levered ETFs Force $10 Billion forced Daily Trades in Top US Chip Stocks

韩国杠杆ETF迫使美股核心芯片股出现每日百亿美元被动交易 巴克莱策略师Alexander Altmann警告称，韩国散户对个股杠杆ETF的激增需求迫使基金进行每日机械性再平衡，加剧了全球股市波动，并存在引发更广泛经济收缩的风险。 本次讨论探讨了个股杠杆ETF的激增现象，这一趋势主要由借助AI热潮及SK Hynix、Micron等芯片股进行投资的韩国散户推动。Altmann详细阐述了每日机械性基金再平衡如何造成被迫做空伽马（short gamma）效应，进而加剧盘中市场波动并给银行资产负债表容量带来压力。对话还强调了美国家庭财富的历史性转变——目前股票配置比例（34%）已远超房地产（26%），并介绍了巴克莱用于评估战术性市场择时与实际收益率压力的量化BETTY模型。 受三星财报及 SK 海力士 265 亿美元在美上市等催化剂推动，全球投资者注意力转向韩国半导体与 AI 题材，单日 5% 的基准指数波动已渐成常态。 (0:54) 全球杠杆 ETF 规模一度攀升至 2500 亿至 2700 亿美元高位；与美国主要由资产增值驱动不同，亚太区（以韩国为主）规模从年初的 120-130 亿美元激增至 500-550 亿美元，伴随巨量的一级市场份额创设。 (4:22) 三倍杠杆 ETF 每日收盘前的机械再平衡机制引入了系统性负伽马（short gamma）效应，迫使资金在下跌时机械性抛售、上涨时追买，严重放大了底层资产的价格波动并挤压投行资产负债表。 (6:33) 韩国散户持有本土杠杆 ETF 资产的 93%（美国为 75%）；同时美国家庭股票配置占总财富比重达创纪录的 34%，超出房地产 8 个百分点，令消费与整体经济高度暴露于股市下行风险之中。 (15:52)

Alexander Altmann of Barclays reports that explosive retail adoption of single-stock leveraged ETFs, centered in South Korea, has transformed these instruments into powerful marginal drivers of global equity prices. This rapid expansion compounds systemic fragility through daily mechanical rebalancing flows, dealer balance-sheet constraints, and unprecedented retail equity exposure. Asia's Surge in Leveraged Single-Stock ETFs (4:22) Global assets under management in leveraged ETFs peaked between $250 billion and $270 billion, but Altmann highlights a critical structural divergence between Western and Asian markets. While U.S. AUM grew from roughly $120 billion to more than $200 billion largely due to asset price appreciation, Asia-Pacific AUM jumped from $12–$13 billion at the start of the year to $50–$55 billion, propelled by massive primary share creation. South Korea acts as the core engine of this activity, with retail investors holding 93% of domestic leveraged ETF assets, compared to roughly 75% retail ownership in the United States. Demand has concentrated heavily in semiconductor leaders: South Korea's SK Hynix hosts the largest single-stock leveraged ETF globally, while Micron Technology hosts the largest in the U.S., where the GraniteShares 2x Long MU Daily ETF surged from around $23 to peak above $1,200. Short Gamma Mechanics and Balance-Sheet Strain (6:33) The structural impact of leveraged ETFs stems from their mandatory end-of-day rebalancing. For instance, if a $30 billion triple-levered fund holding $90 billion in synthetic exposure experiences a 10% drop in the underlying asset, its AUM falls to $21 billion. To preserve its 3x leverage mandate, the fund is mechanically compelled to shed over $10 billion of exposure before the close. This non-discretionary rebalancing introduces a powerful short-gamma dynamic that forces funds to sell into declines and buy into rallies, overwhelming traditional volatility-dampening strategies like call-overwriting ETFs. Providing this synthetic leverage requires prime brokerage swap agreements, placing over $400 billion of derivative exposure onto bank balance sheets. Altmann notes that dealer capacity is increasingly scarce and expensive, not only due to leveraged ETF demand, but because equity benchmarks trade near record highs (such as the S&P at 7,500–7,600) and multi-manager hedge fund platforms have expanded to roughly $1 trillion in AUM, competing heavily for prime financing. The AI Wealth Loop and Macro Vulnerability (20:00) Altmann warns that the stock market has effectively merged with the macroeconomy through an 'AI impact squared' dynamic: artificial intelligence narratives propel tech stocks, rising valuations inflate retail portfolios, and the resulting wealth effect stimulates broader consumer spending. In the U.S., equities now account for 34% of household wealth—exceeding dot-com bubble levels—compared to roughly 26% in real estate. The resulting eight-percentage-point divergence represents the widest allocation gap in recorded history. Because household balance sheets are so tightly geared to the stock market, Altmann argues that an unhedged 20% decline in the S&P 500 would erase approximately $16 trillion in wealth—roughly half of U.S. GDP. Such an impairment would hit private consumption directly and likely trigger an immediate recession, creating a vulnerability that standard econometric models fail to capture. Quantitative Timing Warnings and Valuation Strains (27:15) To identify tactical inflection points without subjective sentiment, Barclays tracks the BETTY market-timing model, which aggregates 19 quantitative inputs across real yields, spot-volatility correlations, and systematic positioning. BETTY spent extended periods at overbought warning levels (readings between 6 and 11), a zone where tactical two-month win rates fall below 35% with flat-to-negative average returns, compared to a ~73% hit rate and +1.9% average gain on baseline days. Broader valuation challenges also persist as 10-year real yields hover near 2.3%, placing them in the 95th percentile of post-financial crisis history. While current real yields historically corresponded to S&P 500 price-to-earnings multiples of 14x to 15x (or ~18.5x adjusting for post-COVID corporate profit margins), the market trades at 20.2x to 20.3x forward earnings. Altmann adds that while modern AI tools like Claude and Copilot can rapidly filter asset universes to construct tactical correlation hedges, human judgment remains necessary to navigate liquidity, pairwise correlation, and portfolio sizing constraints. Altmann and the hosts conclude that Asian retail participation in single-stock leveraged products has evolved from a niche trading phenomenon into a systemic determinant of intraday liquidity and price formation. An unresolved question is whether prime brokerage balance sheets and consumption-dependent economies can comfortably absorb a severe semiconductor reversal without mechanical rebalancing accelerating market contagion.

巴克莱股票战术策略主管亚历山大·奥尔特曼（Alexander Altmann）指出，由韩国散户资金大举涌入推动的单只股票杠杆ETF，正在通过每日强制再平衡机制和做空伽马效应放大全球科技股的波动性；与此同时，由于美国家庭财富向股市的配置比例达到历史最高水平，这种高杠杆驱动的资产膨胀正在使实体经济面临前所未有的宏观下行风险。 亚太杠杆ETF激增与散户主导格局 (4:22) 奥尔特曼指出，全球杠杆ETF资产管理规模（AUM）在峰值时达到2500亿至2700亿美元，其增长速度与结构分化引起了机构的高度警惕。与美国杠杆ETF规模由4月初的约1200亿美元增至2000多亿美元（主要受底层资产价格升值驱动）不同，亚太地区（尤其是韩国）的产品规模从年初的120亿至130亿美元暴增至500亿至550亿美元，增幅超三倍，其核心驱动力是极其庞大的一级市场新份额申购与资产价格暴涨的双重叠加。 韩国已成为这一零售投机浪潮的核心阵地。在韩国杠杆ETF中，散户投资者的持有比例高达93%，远超美国约75%的散户持有份额。这种狂热情绪高度集中于半导体核心标的：SK海力士（SK Hynix）催生了全球规模最大的单只股票杠杆ETF，而美光科技（Micron）则承载了美国规模最大的单只股票杠杆ETF（其两倍做多ETF在一年内曾从约23美元飙升至1200美元以上）。 每日强制再平衡与做空伽马效应 (6:33) 杠杆ETF对现货市场的剧烈冲击源于其每日收盘前必须完成的数学再平衡规则。奥尔特曼以一只管理规模为300亿美元的三倍做多ETF为例说明：该基金需通过主经纪商掉期维持900亿美元的名义敞口；若标的资产下跌10%，总敞口降至810亿美元，但基金净资产将骤降至210亿美元。为了将敞口重新调整至符合三倍杠杆要求的630亿美元，该基金必须在收盘前机械性地在现货市场抛售超过100亿美元的敞口。 这种非主观意志的被动调整在市场中引发了显著的“做空伽马”（Short Gamma）动态，迫使杠杆基金在下跌时抛售、在上涨时追买，完全脱离公司基本面。过去，备兑看涨期权（Call-overwriting）等系统性卖出波动率的多头伽马策略能够提供一定的市场缓冲，但随着单只股票杠杆ETF规模呈现几何级暴增，机械再平衡交易已占据压倒性优势，使市场整体伽马暴露转为负值，进而严重加剧了半导体与大盘的单日双向波动。 衍生品扩张与券商资产负债表承压 (8:58) 虽然杠杆ETF本身已获得美国证监会（SEC）等监管机构批准，但其多倍杠杆必须由华尔街投行主经纪商（Prime Brokerage）通过场外掉期协议进行合成提供。多倍杠杆产品在投行资产负债表上堆积了超过4000亿美元的衍生品名义敞口，成为近期股票融资成本攀升的诱因之一。 奥尔特曼阐明，银行资产负债表容量吃紧并非单一因素所致，而是受到多重宏观动向的系统性挤压：一方面，标普500指数处于历史高位区间（约7500至7600点），导致负债表上持有的资产名义价值成比例上升；另一方面，多经理平台型对冲基金（Pod Shops）的管理规模自疫情以来扩张了三倍并达到约1万亿美元，其庞大的多空杠杆交易极大地挤占了交易商有限的负债表配额，加剧了整体融资体系的脆弱性。 AI平方效应与美国家庭财富敞口 (20:00) 策略师最为担忧的并非杠杆ETF本身的交易规模，而是资产价格出现结构性下挫对宏观经济的连带破坏。奥尔特曼警告，当前市场已经形成了“AI平方效应”的复合传导机制：硬件基础设施叙事推动科技股估值和杠杆ETF规模膨胀，暴涨的股票市值转化为财富效应，进而直接拉动零售消费；而股价的攀升又反过来为企业的资本开支提供合理性，呈现出典型的“尾巴摇狗”特征。 与此同时，全社会对股市的风险暴露已达历史极端。当前美国家庭总财富中股票配置比例高达34%，不仅打破历史最高纪录，更显著超过了互联网泡沫时期的水平，而房地产配置比例仅为26%；两者间8个百分点的剪刀差创下历史新高。奥尔特曼强调，目前全球计量经济学模型中几乎没有一个能够承受标普500指数下跌20%的冲击——该跌幅将瞬间抹去约16万亿美元的居民财富（约合美国GDP的一半），并迅速传导至实体经济引发严重衰退。 BETTY择时预警与实际利率估值压力 (27:15) 为了在极端动量行情中摒弃“感觉”与“似乎”等主观噪音，巴克莱建立了纯量化的BETTY择时模型（整合了实际收益率、即期波动率相关性、CTA及杠杆ETF再平衡等19个客观输入项）。奥尔特曼透露，由于动量极度拥挤，BETTY模型近期在超买警戒区间（6至11）停留的时间创下纪录。回测数据显示，一旦触发该预警，市场在未来两个月（42个交易日）内的胜率会从基准日的约73%暴跌至35%以下，平均回报趋于平盘甚至为负，反映出极度不对称的下行风险。 此外，高企的无风险实际回报也对股票估值构成了实质压制。目前美国10年期实际收益率徘徊在2.3%附近（处于后金融危机时期的第95百分位数）。按历史均值对照，这一实际利率水平对应后金融危机时期的标普500市盈率仅为14至15倍；即便考虑到疫情后企业营业利润率中枢上升，合理估值也仅约为18.5倍。然而当前市场远期市盈率高达20.2至20.3倍，表明美股估值存在显著的风险溢价透支。 本期节目总结指出，亚洲散户驱动的高杠杆单只股票ETF已从被动投资工具演变为主导美股核心科技股日内定价的边际力量。尽管监管层对发行更高倍数的杠杆产品保持克制，但衍生品链条对投行资产负债表的巨额占用以及美国家庭财富对股市的空前倾斜，已构成了深刻的结构性隐患；这一正反馈投机机制在面临外部宏观流动性逆转时能否平稳着陆，仍是全球市场面临的核心疑问。

## Background 本期节目由彭博社（Bloomberg）旗下的《Odd Lots》播客于 2026 年 7 月 10 日发布在 RSS 及网页端。节目针对散户席卷股市与人工智能热潮推动芯片需求的宏观背景，聚焦在韩国等亚太市场迎来爆发式增长的单只股票杠杆 ETF，并邀请巴克莱银行全球股票战术策略主管 Alexander Altmann 深度剖析这些金融衍生品带来的巨额名义敞口与系统性市场风险。 ## TL;DR 节目深入拆解了单只股票杠杆 ETF 在韩国散户狂热推动下资产管理规模暴增数倍的现状，以及其每日强制再平衡机制如何向市场持续注入“空头伽马”，从而造成助涨助跌的微观结构失真。嘉宾指出，美国家庭财富中股票资产占比创下 34% 的历史极值，已大幅超越房地产，使得整体经济韧性与科技股行情产生了空前深度的绑定。与此同时，华尔街交易商的资产负债表容量正被居高不下的现货点位、庞大的平台型对冲基金及衍生品敞口严重挤占，巴克莱研发的纯量化择时指标 BETTY 也持续发出动量过度拥挤的极度警报。整场对话揭示了价格反向塑造叙事的“尾巴摇狗”现象，并探讨了在 AI 时代专业投研机构如何从单一基本面钻研转向跨维度的战术策略整合。 ## Atmosphere & style 对谈节奏紧凑而充满智识交锋，主持人的幽默机敏与华尔街顶级策略师严谨犀利的量化思维形成了绝佳共振。Altmann 以不加修饰、数据驱动的直率风格打破了多项市场迷思，用直观生动的算术模型和“番茄沙拉”等趣味隐喻将艰涩的衍生品运作拆解得清晰透彻。现场在调侃散户投机暴富买超跑的轻松戏谑与直面数十万亿美元居民财富暴跌风险的深层忧虑之间自如切换，散发着专业、审慎且扣人心弦的高维金融洞察氛围。 ## Bulletin - `04:15` 全球单只股票杠杆 ETF 规模激增至 2500 亿至 2700 亿美元，其中亚太地区 AUM 数月内自 120 亿美元跃升至超 500 亿美元，韩国散户成为新资金涌入的核心震中。 - `05:25` 杠杆 ETF 为维持敞口每日机械式“追涨杀跌”调仓，向市场注入巨大的空头伽马（Short Gamma），彻底吞噬传统备兑期权策略提供的波动率缓冲垫。 - `17:15` 美国家庭财富中股票资产占比攀升至 34% 的历史极值，高出房地产敞口 8 个百分点；标普 500 指数若发生 20% 回调将瞬间抹去相当于美国半数 GDP 的居民财富。 - `33:00` 巴克莱股票择时模型 BETTY 触发极度预警，10 年期美债真实收益率处于 2.30% 高位，映射出标普 500 动态市盈率存在约 10% 的收缩压力。 ## Key takeaways - `00:00` 全球交易主线出现罕见转变，传统宏观事件反应钝化，以三星与 SK 海力士为代表的韩国半导体与杠杆交易成为驱动全球流动性的核心逻辑。 - `04:15` 美韩杠杆 ETF 扩张动能呈现结构性差异：美国主要由底层正股涨幅驱动，而韩国则伴随极端狂热的散户新增份额创设，散户持有人占比高达 93%。 - `05:25` 杠杆 ETF 每日被动再平衡遵循顺周期规则，标的下跌时尾盘被迫机械抛售，其空头伽马效应导致市场净伽马敞口实质性转负，放大了极端日内波动。 - `10:00` 华尔街股票融资成本飙升的主因是大盘现货点位处于历史高位及 1 万亿美元多策略对冲基金的挤占，杠杆 ETF 衍生敞口进一步加剧了银行资产负债表稀缺。 - `12:30` 单一主经纪商面临集中度信用风控上限，虽然亚洲个别基金曾尝试通过公开期权市场迂回获取杠杆，但主流投行对承接此类敞口保持极度审慎。 - `20:00` 美国家庭财富在股票上的过度暴露创下历史新高，股票与宏观经济之间形成“AI 投资—半导体暴涨—财富效应—托底全美消费”的闭环，股市稳定性即经济稳定性。 - `22:15` 杠杆资金高度集中在科技单票上，挂钩美光与 SK 海力士的做多产品催生了“价格倒推叙事”的确认偏差，动量策略的机械流入进一步扭曲基本面定价。 - `24:50` 巴克莱策略团队坚持“无法量化就没资格讨论”原则，其开发的 BETTY 指标剔除主观情绪，整合 19 项客观数据监控极端动量拥挤与资金流向。 - `27:10` 在政府与企业债供给洪峰压迫实际收益率背景下，BETTY 预警状态下的两月期胜率降至约 35%，市场赔率与风险收益比呈现严重非对称下行特征。 - `33:00` 在 2.30% 真实收益率水平下，即便考虑后疫情时代企业营业利润率走高的常态，标普 500 指数约 20 倍的估值仍较合理估值中枢高出约 10%。 - `40:00` 买方平台型基金对个股的基本面微观拆解已达到极致，促使投行策略研究从单一维度的通才分析转向统筹宏观、因子、衍生品与仓位的跨维度战术输出。 - `44:30` AI 在量化交易实操中具备强大的资产初筛与选材能力，但在流动性约束评估、头寸切分及二次规划调优等“智慧”决策层面仍无法取代人类交易员。 - `47:00` 散户单票杠杆 ETF 已演化为核心龙头股盘中最具影响力的边际交易者，其层层放大的名义敞口正将银行资产负债表与全社会财富推向脆弱的系统性风险边缘。 ## Detailed walkthrough ### `00:00` 宏观退居二线，韩国芯片股与杠杆交易成为全球焦点 节目开场，主持人 Tracy Allaway 与 Joe Weisenthal 指出录音时间为 7 月 9 日。在这一周，传统的宏观事件似乎集体“失灵”并被市场边缘化：无论是伊朗与美国之间再度升级的地缘政治敌对局势，还是最新公布的美联储会议纪要，截至周四上午都未能在市场上掀起太大风浪。如今全球资金真正紧盯的焦点极其罕见地全部转向了韩国。从半导体产业的宏大叙事演变而来的“芯片交易”（Chips Trade），本质上已经变成了“韩国交易”（Korea Trade），甚至演变为眼下全球金融市场中体量最庞大、情绪最狂热的主线逻辑。作为一个在全球体系中不可忽视的重要市场，韩国股市近期每天走出 5% 左右的剧烈波动，而所有交易员和参与者竟然已经对这种巨大的日常振幅感到习以为常。 本周韩国市场迎来了两大标志性重磅事件：一是三星电子（Samsung）最新公布的财报，二是 SK 海力士（SK Hynix）推进在美国上市。然而，韩国股票之所以在全球掀起轩然大波，不仅在于美国市场正在与其高频联动，更在于韩国市场近年来疯狂涌现出一批极具进攻性的金融衍生产品——单只股票杠杆 ETF（Single-stock Levered ETFs）。这类产品正以惊人的方式倒戈影响甚至主导底层正股的走势。Joe 戏谑地指出，看到市场上某些芯片公司股价暴涨了 10 倍，普通投资者依然觉得“涨 40% 根本不够，必须得冲到 86%”，哪怕是在一个本就极高波动的资产上，人们仍急不可耐地要求“再加点杠杆”。这就像在加密货币市场里加 20 倍杠杆炒币一样荒诞而真实。但更深层的现实在于，借用托洛茨基的名言风格来说——“你可能对杠杆不感兴趣，但杠杆却对你极感兴趣”。无论你是否直接买入这些衍生品，当金融衍生产品这条“尾巴”大到开始疯狂“摇晃”底层正股这只“狗”时，身处市场中的每一个参与者都已被卷入杠杆的漩涡之中。 - 宏观脱敏：美伊局势紧张与美联储纪要发布后，美股与全球风险资产反应平淡。 - 市场焦点：韩国芯片产业链（三星财报与 SK 海力士赴美上市）主导全球交投情绪。 - 波动常态化：韩国这一体量巨大的成熟市场，日内出现 5% 上下的震荡已成家常便饭。 - 杠杆狂热：在芯片股年内巨大涨幅的基础上，散户与机构依然大量涌入单只股票杠杆 ETF 寻求翻倍效应。 --- ### `04:15` 亚太与韩国杠杆 ETF 的爆发式增长：“令人惊骇”的名义敞口 为了拆解杠杆 ETF 如何反噬底层资产，节目邀请了巴克莱银行（Barclays）全球股票战术策略主管亚历山大·奥特曼（Alexander Altmann，业内昵称“Altie”）。在近期的一份研报中，Altmann 曾用“令人惊骇”（Terrifying）一词来形容全球单只股票杠杆 ETF 所积聚的名义敞口规模。他在节目中给出了最新的核心量化数据：全球杠杆 ETF 的资产管理规模（AUM）在近期高点已达到约 2500 亿至 2700 亿美元。尽管单看绝对金额这个数字在数万亿美元级的全球股票大盘中似乎不算极度恐怖，但其真正的危险之处在于近几个月来呈现出的“渐近线式、流星般爆发”（Asymptotic and Meteoric）的恐怖增速。 这种扩张在亚太（尤其是韩国）与美国市场之间呈现出截然不同的结构性特征。Altmann 指出，年初时亚太地区的杠杆产品 AUM 仅在 120 亿至 130 亿美元左右，而在短短数月内这一规模就飙升至 500 亿至 550 亿美元，实现了三倍以上的几何级暴增。相比之下，美国市场的杠杆产品 AUM 虽然从 4 月初市场局部低点时的 1200 亿美元攀升至峰值的 2000 亿美元以上，但美国市场的扩张主要来源于底层股票本身惊人的净值涨幅（Price Performance），份额申购（Share Creation）带来的净资金流入并不算突出。而韩国市场则完全不同，韩国不仅承受着正股股价的火箭式蹿升，同时伴随着海量散户狂热申购所推动的巨大份额创设，形成了“资金疯狂涌入 + 资产价格暴涨”的双重共振放大器。 - 全球杠杆 ETF 资产管理总规模：近期阶段性高点达到 2500 亿至 2700 亿美元。 - 亚太地区（APAC）杠杆产品 AUM 激增：从年初的 120 亿–130 亿美元，跃升至 500 亿–550 亿美元，短时间内暴涨超 3 倍。 - 美国杠杆产品 AUM 轨迹：从 4 月初低点的约 1200 亿美元，飙升至峰值的 2000 亿美元上方。 - 美韩两地增长驱动对比：美国市场主要由股价上涨驱动现有份额膨胀；韩国市场则是极端份额创设（新钱入场）与股价上涨并存。 --- ### `05:25` 每日调仓机制解密：如何向市场注入巨大的“空头伽马” 针对杠杆 ETF 的底层运行逻辑，Altmann 用一个极度直观的经典算术模型拆解了其每日强制再平衡机制（Daily Rebalancing），揭示了为何这类产品会从根本上改变股票市场的微观结构。假设某家发行商设立了一只针对单只股票的 3 倍杠杆 ETF，其初始 AUM 为 300 亿美元。为了在账面上维持 3 倍的风险敞口，基金经理必须借道主经纪商（Prime Brokerage），通过总收益互换（Total Return Swap）等场外衍生品工具，在市场上额外建立 600 亿美元的合成敞口，使得总名义敞口达到 900 亿美元。 ```mermaid flowchart TD accTitle: 3倍杠杆ETF单日调仓与做空伽马循环 accDescr: 展示3倍杠杆ETF在正股单日下跌10%后，为了维持目标杠杆倍数而被迫在收盘前机械性抛售底层资产的流程。 subgraph InitialState ["初始阶段 (3倍杠杆)"] A["初始 AUM: 300亿美元"] B["名义总敞口: 900亿美元<br/>(含600亿美元Swap合约)"] A --> B end subgraph MarketShock ["日内冲击: 正股下跌 10%"] C["总敞口损失: 90亿美元<br/>(900亿 * 10%)"] D["新总敞口: 810亿美元<br/>(900亿 - 90亿)"] E["新 AUM 净值: 210亿美元<br/>(300亿 - 90亿)"] B --> C C --> D C --> E end subgraph Rebalance ["尾盘强制机械再平衡"] F["目标总敞口: 630亿美元<br/>(新AUM 210亿 * 3倍)"] G["敞口盈余: 180亿美元<br/>(810亿现存 - 630亿目标)"] H["尾盘必须机械抛售: 180亿美元<br/>(强化跌势，形成空头伽马)"] E --> F D --> G F --> G G --> H end ``` 当该底层股票单日下跌 10% 时，900 亿美元的总敞口瞬间蒸发 90 亿美元，缩水至 810 亿美元。但与此同时，这 90 亿美元的绝对损失全部扣减自基金的自身净值（AUM），导致其 AUM 从 300 亿美元骤降至 210 亿美元。根据招募说明书的要求，基金必须在收盘前恢复 3 倍杠杆匹配，即目标敞口应为 210 亿美元 × 3 = 630 亿美元。然而基金当前账面上仍挂着 810 亿美元的敞口，这意味着为了维持承诺的 3 倍杠杆，基金经理必须在收盘前机械性地向市场砸盘抛售超过 100 亿美元（理论计算值为 180 亿美元）的名义敞口。反之，如果在暴涨的日子里，该机制则会逼迫基金在尾盘机械性地大幅追高买入。 这种“跌时砸盘、涨时追高”的非自主裁量性交易（Non-discretionary flows），在数学本质上等同于给全市场植入了一个巨大的“空头伽马”（Short Gamma）头寸——它不断强化顺周期的顺势动能，剧烈放大股票的日内波动。Altmann 强调，在以前的市场生态中，由于杠杆 ETF 体量较小，市场上大量存在的备兑看涨期权 ETF（Overwriting / Covered Call ETFs）通过持续向市场卖出波动率（Selling Volatility）形成了对冲性的“多头伽马”（Long Gamma）缓冲垫；然而，随着当前单只股票杠杆 ETF 规模爆炸式膨胀，机械再平衡带来的空头伽马已经彻底吞噬了原本的缓冲结构，导致整个市场的净伽马敞口（Net Gamma Profile）急剧转为负值。谈及此类产品的设立门槛时，Altmann 解释称其商业逻辑与其他传统 ETF 无异：发行商拉拢锚定投资人（Anchor Investor）或联合赞助商注入初始种子资金以达到临界规模，再通过投行提供杠杆；这也催生了一个荒谬的当代市场奇观——全球现存的 ETF 衍生产品代码数量，甚至已经远远超过了底层独立上市股票的总数。 - 杠杆再平衡数学模型：AUM 为 300 亿美元的 3 倍产品对应 900 亿美元敞口；跌 10% 导致 AUM 缩水至 210 亿美元，目标敞口降至 630 亿美元，迫使尾盘机械砸盘抛售高达 180 亿美元敞口。 - 顺周期杀伤力：无论涨跌，杠杆 ETF 的每日调仓都遵循“涨买跌卖”，形成天然助涨助跌的空头伽马（Short Gamma）放大器。 - 市场微观结构逆转：过去用于压制波动的备兑期权策略所提供的多头伽马，已被激增的杠杆 ETF 调仓资金完全压制，使市场净伽马显著转负。 - 产品泛滥现状：设立新杠杆 ETF 依赖主经纪商掉期通道与锚定资金；目前全球 ETF 衍生标的的总数量已经实质性超越了单个股票的总体数量。 ### `10:00` 杠杆 ETF 结构、银行资产负债表与融资利率飙升的真相 在美国设立一只杠杆 ETF 在基金架构层面并不复杂，但必须完整通过美国证券交易委员会（SEC）的严格审批。无论市场各方对其高风险属性抱有何种看法，一个无法回避的法律事实是：监管机构确实批准了这些产品的发行与运作。真正的核心门槛在于杠杆的来源。通常，杠杆是由大型投资银行通过主经纪商（Prime Brokerage）业务以合成（synthetic）互换（swap）的形式提供给基金，从而在银行的资产负债表上建立起巨额的名义敞口（notional exposure）。 近期市场上有一种盛行的观点，认为正是这些杠杆 ETF 规模的爆炸式增长推高了整个华尔街的股票融资利率（equity financing costs）。Alexander Altmann 直截了当地对这种说法进行了澄清与“打假”。不可否认，杠杆 ETF 的资产管理规模（AUM）在短时间内迅速突破数千亿美元，加上超过两倍的杠杆，相当于在银行资产负债表上凭空增加了逾 4,000 亿美元的掉期买盘，这确实让交易商的资产负债表感受到明显的紧缩压力。 然而，Altmann 强调，融资利率暴涨的最核心驱动因素根本不是 ETF，而是“大盘点位本身走得太高了”。随着股市行情节节攀升，标的资产的名义现货价格大幅上扬，维持相同头寸所消耗的资产负债表资金成倍增加。与此同时，多策略对冲基金（Multi-Manager Platforms / Pod Shops）自疫情以来的 AUM 几乎翻了三倍，已膨胀至接近 1 万亿美元的体量，其在多头和空头两侧所部署的巨大杠杆，对银行资产负债表造成了极具统治力的挤占。Altmann 总结道：“资产负债表容量是极其稀缺的，而且只要标普 500 指数还在 7,500 到 7,600 点的高位运行，这种稀缺状态就绝不会缓解。” ```mermaid flowchart TD accTitle: 华尔街银行资产负债表稀缺与融资成本上升机制 accDescr: 展示大盘高企、多策略对冲基金扩张以及杠杆ETF爆发三者如何共同消耗银行资产负债表并导致融资利率飙升。 subgraph Drivers ["资产负债表消耗三大推手"] A["股市现货处于历史高位<br/>(S&P 500 位于 7500+ 点)"] B["多策略对冲基金爆发<br/>(规模翻三倍至约 1 万亿美元)"] C["杠杆 ETF 规模激增<br/>(衍生 4000 亿美元以上 Swap 敞口)"] end subgraph Bottleneck ["核心中枢与制约"] D["主经纪商 (Prime Broker) 资产负债表"] E["资产负债表容量严重稀缺<br/>(Balance Sheet Scarcity)"] end subgraph Consequence ["市场影响"] F["全市场股票融资利率大幅飙升<br/>(Equity Financing Rates Squeeze)"] end A -->|"现货单价高企，放大名义价值"| D B -->|"多空双向高杠杆运作"| D C -->|"合成互换占用承载空间"| D D --> E E --> F ``` - 杠杆 ETF 通过投行主经纪商以合成互换（Swap）获取杠杆，相关名义敞口已超 4,000 亿美元。 - 股票融资成本飙升的首要推手是高企的现货点位（Spot Levels），其次是规模达 1 万亿美元的多策略平台型对冲基金，杠杆 ETF 仅是助推因素之一。 - 只要大盘维持在 7,500 点至 7,600 点的历史高位区间，华尔街交易商的资产负债表稀缺性就将长期存在。 --- ### `12:30` 交易商敞口限额与期权借道传闻 随着单只杠杆 ETF 规模以惊人的速度膨胀，市场开始面临现实的授信上限：当某只基金要求两倍杠杆、对应需要约 400 亿美元的名义敞口时，没有任何一家华尔街交易商会愿意向单一交易对手方承担如此集中的信用风险。主持人和市场参与者近期因而传出一种猜测：部分杠杆 ETF 是否因为无法在投行互换渠道获得足够额度，而开始转向公开的期权市场（Options Market）来迂回获取杠杆？ 对此，Altmann 回应称，媒体此前确实对类似现象做过报道，但这主要局限于亚洲的某一两家特定基金，并非美国本地市场的普遍操作。探讨金融机构向哪些对手方提供杠杆，实际上触及了银行风险管理的核心敏感带。Altmann 指出，包括巴克莱在内的大型投行在挑选合作伙伴和对手方时“极其审慎且极度保守”。 从做市商和风控的角度来看，杠杆 ETF 既不是什么闪闪发光的神秘新玩具，也不是什么完全脱离监管的法外之物；银行对待它们的方式与对待任何传统对冲基金或机构客户别无二致。归根结底，投行的本质就是做风险管理，绝不会为了赚取融资费用而盲目突破既定的单一交易对手信用限额。 - 面对数百亿美元的单边杠杆诉求，单一银行由于集中度风控限制，无法无限度提供信用额度。 - 借助期权市场绕道构建杠杆的案例主要发生在个别亚洲基金身上，并非美国本土基金的主流做法。 - 主流投行在杠杆 ETF 的承揽上依然遵循极其保守的传统交易对手尽职调查与风控框架。 --- ### `17:15` 韩国散户“震中”与美国家庭财富的股票历史极值 在探讨杠杆 ETF 时，韩国市场展现出了与美国截然不同的狂热生态。美国产品的增长很大程度上来自底层基础资产本身的上涨，而韩国产品的暴增则几乎完全由汹涌澎湃的“新钱”（净新增认购资金）所驱动。按人均参与度和资金渗透率来看，韩国散户的投机热情足以令美国市场相形见绌。 Altmann 将韩国形容为全球散户大潮的“震中”（Ground Zero）。在韩国，杠杆 ETF 产品的持有人中高达 93% 是散户投资者，而这一比例在美国大约是 75%——虽然 75% 本身已经高得惊人，但在韩国的极端数据面前仍稍显逊色。Altmann 笑着补充道，讨论这一现象时完全不需要进行道德审判，因为事实证明杠杆 ETF 确实让一大批散户大赚特赚，“街上现在多得是靠杠杆 ETF 的超额收益提了迈凯伦超跑的人”。 但剥开这种暴富叙事，真正令 Altmann 深感警惕的，是散户渠道如今对全球股票市场形成的巨大系统性影响。以美国为例，美国家庭财富中股票资产的占比已攀升至 34%，创下人类有现代金融记录以来的最高水平，彻底超越了 2000 年互联网泡沫顶峰时的疯狂。更夸张的是，美国家庭第二大资产类别是房地产，占比约为 26%。股票敞口与房地产敞口之间拉开了整整 8 个百分点的差距，同样创下了历史最大分化纪录。Altmann 感慨道，整个人类社会从未像今天这样，将如此极端的财富重心过度配置和过度暴露在股票市场之中。 - 韩国杠杆 ETF 的散户持有比例高达 93%，美国同类产品的散户占比约为 75%。 - 韩国市场的惊人增长主要由新入场资金（Net Inflows）推动，而不仅是靠资产价格自然上涨。 - 美国家庭总财富中股票资产占比高达 34%，超越 2000 年互联网泡沫顶峰，创历史新高。 - 美国家庭股票资产占比（34%）与第二大资产房地产（26%）之间的差距扩大至 8 个百分点，达到历史最宽水平。 ### `20:00` 股市即经济与“AI 影响力的平方” 真正让专业策略团队夜不能寐的，并不是杠杆 ETF 本身，而是美股市场面临的结构性受损风险。Alexander Altmann 指出，全球几乎没有任何经济学家的计量经济模型中会包含“标普 500 指数下跌 20%”这样的参数单元；然而，一旦标普 500 发生 20% 的结构性回撤，将瞬间抹去约 16 万亿美元的居民财富——这几乎相当于美国 GDP 的一半。这种财富效应的蒸发会立刻演变为对美国消费支出的致命打击，直接将经济推入衰退。正因散户投资者在美股中的敞口规模空前庞大，美国政府也有着极其强烈的动力避免出现任何可能彻底颠覆市场的无序动荡。过去人们常说“经济不等于股市”或“股市不代表经济”，但 Altmann 认为这一逻辑已经逆转：“现在的股市就是经济本身。” 主持人随后将这一论点进一步推演：如果不仅“股市就是经济”，而且“AI 主导了股市”，那么推导出的结论便是“AI 就是经济”。AI 不仅直接贡献了宏观层面的资本支出增长，还通过推高科技股股价创造财富效应，进而托举全美消费，形成了一种“AI 影响力的平方”循环。 ```mermaid flowchart TD accTitle: AI 与股市及宏观经济的循环驱动机制 accDescr: 展示 AI 叙事、股价上涨、财富效应与宏观经济消费之间的正反馈闭环 subgraph TechCore["科技与资本驱动"] AI["AI 资本开支与技术叙事"] TechStocks["芯片与半导体龙头股价飙升"] AI --> TechStocks end subgraph MarketDynamics["资本市场放大器"] LeveredFlows["杠杆 ETF 与动量资金流入"] SP500["标普 500 指数上行 / 估值扩张"] TechStocks --> LeveredFlows LeveredFlows --> SP500 end subgraph MacroEconomy["宏观经济反馈"] WealthEffect["16 万亿美元财富效应"] Consumption["支撑全美居民消费支出"] SP500 --> WealthEffect WealthEffect --> Consumption Consumption -.->|"强化经济韧性并反哺 CapEx"| AI end ``` - 标普 500 指数若发生 20% 的回调，意味着约 16 万亿美元财富蒸发，约占美国名义 GDP 的 50%。 - 散户资金深度绑定美股资产，促使监管机构与政策制定者全力防范资产价格出现无序崩塌。 - 市场形成从“AI 资本支出”到“半导体股价暴涨”，再到“财富效应拉动消费”的宏观闭环。 --- ### `22:15` 价格倒推叙事：美光与海力士杠杆 ETF 的疯狂暴涨 杠杆 ETF 规模的爆炸式扩张高度集中在特定科技板块。数据表明，杠杆 ETF 资产管理规模（AUM）增长的核心几乎全部集中在跟踪纳斯达克、半导体板块或科技单票的产品中。全美资产规模最大的单票杠杆 ETF 挂钩的是美光科技（Micron），而全球范围内规模最大的单票杠杆 ETF 挂钩的则是韩国 SK 海力士（SK Hynix）。这种极端集中导致了“尾巴摇狗”（the tail wagging the dog）的失真现象，即“价格决定叙事”：投资者看到股价持续上涨，便产生确认偏差，认定公司可以追加更多 CapEx 资本开支、AI 叙事依然稳健；但事实上有相当一部分价格动力来自非理性的机械化被动资金流与动量交易。 动量（Momentum）被 Altmann 称为“金融史上最成功的因子策略”，因为标普 500 指数在历史上大约 78% 的时间里都是上涨的，追涨杀跌成为天然具有正期望的策略。这种动量效应在杠杆产品中被放大到了令人咋舌的地步：挂钩美光的两倍做多 ETF（代码 MUU）在短短一年内从 23 美元左右一路飙升至超过 1200 美元的高点。主持人打趣道：“这涨幅够你买好几辆迈凯伦了吧！” 在谈到监管界限时，主持人询问美国 SEC 为何批准这些激进产品、为何市面上没有 10 倍甚至 100 倍的杠杆 ETF。Altmann 透露了一个行业花絮：此前在一次美国政府停摆期间，曾有机构尝试申报并上架 5 倍杠杆 ETF（5x levered ETF）。但监管层当时非常明确地划定了杠杆上限红线，强调必须对杠杆规模进行限制，正如开场名言所警示的：“你可能对杠杆不感兴趣，但杠杆却对你非常感兴趣。” - 美光（Micron）是美国规模最大的单票杠杆 ETF 标的；SK 海力士是全球规模最大的单票杠杆 ETF 标的。 - 标普 500 指数历史上涨概率约为 78%，奠定了动量策略长期占优的基础。 - 美光两倍做多 ETF（MUU）在过去一年中价格从 23 美元暴涨至 1200 美元上方。 - 曾有机构在政府停摆缝隙中尝试申报 5 倍杠杆产品，但随即被监管部门明令叫停。 --- ### `24:50` 巴克莱择时指标 BETTY：量化数据与“禁止凭感觉”原则 针对不断积聚的狂热情绪，巴克莱构建了名为 BETTY（Barclays Equity Timing Indicator，巴克莱股票择时指标）的市场择时模型。主持人指出，全市场多项调查显示当前几乎所有人都极度看多，同时又都承认美股估值过高，陷入了“明知泡沫却继续追逐动量”的困境，而 BETTY 也已在破纪录的长时间内处于极度超买、充满投机泡沫的预警区间。 Altmann 详细梳理了 BETTY 的研发背景与核心理念。该指标的雏形诞生于 2018 年，拥有充足的样本外测试数据验证。Altmann 加入巴克莱时，负责股票业务的高管 Scott 和 Ronnie 正致力于重塑巴克莱的股票投研实力，希望将最顶尖、纯量化的专业研究置于客户交流的核心。Altmann 给团队立下了一条绝对军规：“如果一件事情无法量化，你就没有资格谈论它；在策略团队里严禁使用‘感觉’（feels）或‘似乎’（seems）这两个词，谁要是用‘我觉得/看起来’来做推论，就会被立刻开除。” 因此，BETTY 彻底剔除了所有主观情感指标（如 AAII 散户多空情绪指数），而是由 19 个完全基于客观数据的量化输入变量构成。这些输入维度覆盖广泛，涵盖实际收益率（Real yields）、现货与波动率相关性（spot-vol correlation），以及系统化与主观资金流向监控——包括公募基金 Beta、对冲基金敞口、CTA 趋势追踪策略、波动率控制基金（Vol-control funds）仓位和杠杆 ETF 的调仓流量。BETTY 之所以长时间处于极端预警区间，核心原因正是由这些资金流所反映出的“动量极度拥挤”（momentum crowding）。 - 团队原则：“无法量化就没资格讨论”，严禁使用“feels”和“seems”等主观词汇，杜绝主观经验主义。 - BETTY 指标包含 19 个全量化客观输入变量，完全不包含任何主观情绪调查数据。 - 核心监测维度：实际收益率、现货-波动率动态关系、公募与对冲基金 Beta、CTA 仓位、波动率控制基金与杠杆 ETF 流量。 --- ### `27:10` 动量拥挤与资本争夺：BETTY 警报下的非对称风险 除了动量因子极度拥挤之外，实际收益率所反映的“资金争夺战”是当前资产定价的另一大核心矛盾。当前市场正迎来历史罕见的债券供给洪峰：美国联邦政府债务发行量创下纪录，同时投资级企业债（IG bonds）在夏季也迎来了井喷式发行潮。面对如此庞大的固定收益无风险/低风险资产吸纳资本，如果将当前的实际收益率与后金融危机（post-GFC）或后疫情时代的常态相对比，美股当前的估值乘数显然处于不合理的高位。虽然有人拿互联网泡沫时期“实际收益率极高、美股乘数依然暴涨”作为反驳，但 Altmann 强调，当年美国政府可没有像今天这样拿着帽子四处讨钱发债，宏观流动性环境截然不同。 从统计概率来看，BETTY 并不是在预测市场何时会发生灾难性的断崖暴跌，而是在衡量战术层面上不对称的风险收益比（Tactical Asymmetry）。在模型设定中，战术操作的评估周期为 2 个月（即 42 个交易日）： | 市场所处状态 | 统计胜率（42个交易日后录得正收益） | 平均预期回报率 | 风险收益不对称特征 | |:--- |:--- |:--- |:--- | | **历史常态基准**（随机时点买入并持有） | 约 73% | 约 +190 bps（+1.9%） | 胜率较高，收益分布呈健康正向倾斜 | | **BETTY 极度预警状态**（指标读数达 6-7 甚至 10-11） | 降至约 35% 或更低 | 基本为负（Negative） | 下行风险远大于上行空间，赔率极度不利 | BETTY 早在 5 月下旬便首次触发了严重警报。自那以后的数周内，标普 500 指数基本维持原地踏步，而动量板块则出现了一定程度的回调。在 Altmann 看来，这种高位震荡与动量洗盘反而是极为健康的信号——它让市场得以“踩一踩刹车”，出清部分投机泡沫，重置估值结构。考虑到科技龙头扎实的实际盈利表现、AI 驱动的产业趋势，以及美国庞大财政赤字持续注入的流动性，踩刹车后的洗盘反而为未来更可持续的行情构筑了更稳健的底座。 ### `33:00` 真实收益率高企下的美股估值悖论与利润率护城河 抛开短期两到三个月的择时与市场波动不谈，从宏观估值和经典估值倍数的角度来看，当前美股在实际利率高企的背景下显得异常昂贵。当前美国 10 年期真实收益率（Real Yields）徘徊在 2.30% 左右，处于历史分位数的第 95 百分位。在金融危机（GFC）之后的历史区间中，每当真实收益率达到如此极端的水平时，标普 500 指数（S&P 500）的平均市盈率通常低得令人心惊，仅在 14 到 15 倍左右；然而当前市场的实际交易倍数却高达 20.2 至 20.3 倍。 这种估值差异的部分合理性在于企业盈利能力的变化。金融危机后相当长一段时间内，标普 500 成分股的营业利润率远低于当前水平，而更高的利润率理应映射到更高的估值倍数上。如果采用更能反映当前企业利润率格局的“后疫情时代”作为基准，在相同真实收益率水平下，合理的标普 500 估值倍数约为 18.5 倍。这意味着即便将当今更高的利润率全部计入，当前市场估值仍然存在约 10% 的收缩空间。 针对为何使用利润率（Margins）或净资产收益率（ROE）而非“预期盈利增速”作为核心估值支柱，Alexander Altmann 指出，企业的盈利能力直接反映了其护城河与商业模式质量。当一家公司具备极高且持续的利润率与资本回报率时，市场会认定其拥有更宽阔的竞争壁垒和更高的抗风险属性，因而愿意为其赋予更高的估值溢价，这比单纯依赖充满不确定性的未来增长预期更加扎实。 - **10 年期美债真实收益率**：当前约 2.30%，处于历史第 95 百分位的高位。 - **GFC 后可比市盈率**：在同等真实收益率下，标普 500 历史平均估值为 14~15 倍。 - **后疫情时代修正估值**：结合当前高利润率常态，隐含合理估值约为 18.5 倍。 - **当前市场交易估值**：标普 500 动态市盈率约为 20.2~20.3 倍，相对合理中枢高出约 10%。 --- ### `35:15` 从基本面分析到量化因子：衍生品与流动性视角的崛起 主持人调侃道，过去人们常开玩笑说想转世成为股票衍生品策略师，因为在当下的市场中，只要找出一个流动性或资金流向的数据，就能为任何市场异动提供合理解释；相比二十年前市场对个股基本面的执着，如今多策略基金（Multi-strats）、波动率目标基金（Vol-targeting funds）以及 CTA 的机械化资金流正在主导定价。在一段关于“英国口音是否能让策略师显得更有说服力并在播客中吸睛”的轻松玩笑后，Alexander Altmann 分享了自己职业生涯的范式转移。 ```mermaid flowchart TD accTitle: 投资范式与职业转型演变 accDescr: 展现从传统基本面通才分析向因子量化及衍生品资金流分析的转型过程 subgraph Phase1["传统基本面时代 (2004-2011)"] A["JPMorgan 股票销售通才"] --> B["买方单票基本面分析师"] end subgraph Phase2["行业分化与量化觉醒 (2012-2013)"] B --> C["花旗量化因子与战术策略"] C --> D["投资复盘: 发现 Alpha 来源实为 Value 因子"] end subgraph Phase3["现代结构化市场 (当下)"] D --> E["通俗化量化语言对接主观多空经理"] E --> F["流动性、CTA、多策略与衍生品战术策略"] end ``` Alexander Altmann 并非量化出身，他在 2004 年以摩根大通（JPMorgan）的通才销售身份入行，随后在买方担任基本面单票分析师。当他回到卖方花旗银行（Citi）时，行业环境已发生剧变：行业研究日益高度垂直化和精细化，传统的“通才分析师”根本无法与深耕某一狭窄细分赛道的专业研究员抗衡。面对瓶颈，他借助花旗强大的量化业务平台对过往投资组合的 PnL 进行了全面复盘，结果惊讶地发现，自己过去自诩的基本面选股 Alpha，本质上只是踩中了量化定义中的“价值因子（Value Factor）”。 在 2012 至 2013 年间，量化因子对于主流主观投资经理而言依然相当神秘。Altmann 抓住这一契机，将复杂的量化因子与衍生品概念翻译成普通主观多空基金经理听得懂的日常语言，成功实现了向股票衍生品与战术策略师的转型。 - **职业演进路径**：2004 年摩根大通通才销售 $\rightarrow$ 买方单票基本面分析师 $\rightarrow$ 2012 年花旗量化因子策略转型 $\rightarrow$ 巴克莱股票战术策略主管。 - **市场认知变迁**：2012 年前后的 CTA 资金流常被视为市场中不可捉摸的“神秘力量”，如今已成为全市场普及且日常跟踪的基准指标。 - **方法论突破**：通过通俗化量化术语，向传统主观投资人揭示持仓背后的因子暴露与收益驱动机制。 --- ### `38:30` 多策略平台时代的极端深度与投行角色的重塑 随着市场结构演进，买方多策略平台型对冲基金（Platform / Multi-strategy Shops）的崛起彻底颠覆了投研生态。如今多策略平台的各个投资单元（Pods）对单家上市公司的理解深度与颗粒度，相比几十年前传统卖方分析师仅凭一份买入评级报告指点江山的时代，已经提升了数个数量级。买方专业研究员对公司基本面、财务细节和行业微观动态的钻研程度达到了极其疯狂的境地。 在这一背景下，像巴克莱这样处于上升期的顶级投行，其股票研究与机构业务团队的角色也在发生深刻转变。卖方不再仅仅提供泛化的观点输出，而是致力于为极度专业化的顶级机构客户提供高质量的深度服务，其中核心壁垒之一便是提供精准的高管层企业对接服务（Corporate Access），以协助这些超精细化的买方团队进行最高效的基本面与流动性研判。 ### `40:00` 投研体系的范式转移：从单一全能到跨维度通才 Alexander Altmann 深入探讨了券商研究业务与战术策略团队的定位转变。如今买方机构在底层个股上拥有极其细致、堪比法医勘验般的建模能力（forensic modeling），通才型分析师如果只在微观细节上与这些专业机构拼颗粒度，几乎没有任何胜算。因此，巴克莱的策略发生了彻底转向：一方面扩大底层研究覆盖面并强化对上市公司高管的接触通道，通过组织路演直接为客户对接企业管理层；另一方面，战术策略团队不再局限于某一个狭隘的研究切面，而是统筹股票投资的六大关键纵向维度——基本面（Fundamentals）、宏观经济（Economics）、跨资产宏观（Cross-Asset Macro）、包含因子在内的量化（Quant & Factors）、衍生品（Derivatives）以及市场仓位（Positioning）。 Altmann 指出，在当今的人工智能时代，没有任何团队能在所有这些维度上都做到满分 10 分。他的目标是带领战术策略团队在绝大部分领域维持在 7 到 8 分的水平（某些长项达到 9 至 10 分，较弱环节维持在 5 至 6 分）。这种跨领域的综合视野，使他们能够走进任何机构投资者的办公室，指出客户自身尚未察觉到的市场盲点与结构性错配。Joe Weisenthal 对此产生共鸣，并借机类比了当下的财经媒体生态：大众化、泛化报道的传统媒体平台正在式微，而深耕垂直细分领域的垂直专家（niche subject matter experts）正受到前所未有的重视。 - **股票战术策略的六大核心维度**：基本面分析、宏观经济脉络、跨资产联动、量化与因子表现、衍生品市场动态、全市场资金仓位。 - **团队竞争定位**：不追求全知全能的单点极致，而是追求跨维度均值在 7-8 分的综合连接能力，从而提供投资者教育和盲区排查服务。 --- ### `42:15` “知识与智慧”的番茄辩论：量化数据洪流下的宏观视野 媒体与金融行业的这种专业化演变，自然引申到了人工智能在投研中的本质角色。Altmann 强调，在 AI 普及的时代，每个人口袋里都仿佛揣着一支“量化大军”，虽然输出结果的精准度时常存疑，但数据处理量确实实现了数量级的飞跃。然而，无限获取信息并不等于具备洞察力。Altmann 引用了一句经典谚语来剖析其中的差异：“知识是知道番茄在生物学上是一种水果，而智慧则是懂得绝不能把番茄放进水果沙拉里”（"knowledge is knowing that a tomato is a fruit, but wisdom is knowing that it doesn't go in a fruit salad"）。数据模型可以穷尽海量事实，但如何将碎片化的信息拼装成明智的市场判断，依然仰赖投资者的定性智慧。 这个关于番茄的比喻随即引发了一场充满现场趣味的插曲。Joe Weisenthal 幽默地反驳道，他最近在一家网红冻酸奶（Froyo）店看到有人把圣女果当做配料，而且口感相当不错；更何况他在 8 月份自家菜园丰收的番茄甘甜多汁，他坚信某些高品质番茄“完全配得上加入水果沙拉”。Tracy Alloway 和 Altmann 也被逗乐，但话题很快收拢回金融现实：当分析师陷在过分狭隘、极其颗粒化的微观数据中时，往往会“只见树木，不见森林”（miss the forest for the trees），失去对市场整体流动性和宏观风险的把控。 ```mermaid flowchart TD accTitle: 投资研究中的知识与智慧分层 accDescr: 展示了从底层海量数据、中层AI工具能力，到顶层人类投资智慧与宏观视野的转化层级。 subgraph DataLayer ["数据与知识层（AI 赋能）"] A["海量结构化/非结构化数据"] --> B["算力与量化工具（口袋量化团队）"] B --> C["高颗粒度事实认知（知识：番茄是水果）"] end subgraph InsightLayer ["决策与智慧层（人类专长）"] C --> D{"宏观视界与流动性审视"} D -->|剔除噪音| E["战术组合与风险把控（智慧：不把番茄放入水果沙拉）"] D -->|过度颗粒化| F["盲点滋生：只见树木不见森林"] end ``` --- ### `44:30` AI 在对冲篮子构建中的实战边界：选材与调优的距离 Joe Weisenthal 将话题引向具体的量化实操层面：如果需要构建一个新指数或构建投资篮子，当前的模型在严谨的量化工作上到底表现如何？Altmann 结合巴克莱交易台每天面临的真实业务给出了清晰的界限。投行最常见的交易需求之一，是客户持有某种单一标的（如新型经济中的“AI 新型云厂商”/ Neocloud 概念股），希望在不直接做空该标的的前提下，用一组相关股票构成的对冲篮子（hedge basket）来抵消风险。这种业务在数学本质上是一个巨大的“成对相关性模型”（pairwise correlation model），需要结合标的相关性输入到优化器中进行权重分配。 在这一工作流程中，AI 的强项与弱点体现得淋漓尽致。AI 能够出色地完成第一阶段的“选材”工作——搜罗全市场上市的所有同类 Neocloud 公司以及具备高相关性的衍生标的。然而一旦涉及后续的深水区，AI 就束手无策了：它无法自主评估流动性约束、无法进行真正的头寸切分，也无法执行高级二次规划调优。Altmann 借用了量化金融大师伊曼纽尔·德曼（Emmanuel Derman）在自传中的经典隐喻：交易员的工作就像是将批发市场买来的成箱水果清洗、切块并合理配比做成果盘。AI 能够识别哪些是潜在的水果原料，但真正的切片刀工、权重调配以及风控把关，必须由量化系统与交易员完成。 巴克莱在技术前沿领域布局深入，不仅部署了微软的 GitHub Copilot 与 Anthropic 的 Claude 企业授权，还在沙盒中利用外部基础模型与内部自研模型开发私有化工具。实操经验表明，当输入明确、结构化且边界清晰的参数时，AI 处理复杂数据的能力极为强大；但如果抛给它一个抽象、宽泛且飘忽的概念指令，尽管它能生成看似结构完整的宏观论述，其中包含的大量论据却极易出现事实错误，必须经过人工的严格复核与校验。 - **实战对冲需求**：寻找替代股票池，利用成对相关性与资产组合优化算法复制单一名义标的的波动走势。 - **AI 的分工边界**：负责初筛候选资产宇宙（识别原料）；无法独立完成头寸切分、流动性优化与资金加权（制作果盘）。 - **巴克莱技术栈实践**：混合运用 Copilot、Claude 与自研内部模型，在受控沙盒中处理参数清晰的数据任务。 --- ### `47:00` 尾声复盘：杠杆 ETF、家庭资产配置与银行资产负债表的隐性风险 访谈结束后，主持人 Joe Weisenthal 与 Tracy Alloway 对整场对话展开了复盘，梳理出三个最令人警惕的宏观交易线索： 首先，以韩国散户追捧的美股科技单票杠杆 ETF（如 2 倍、3 倍做多产品）为代表的新兴工具，在全球市场中的资金规模爆炸式扩张。这些被动与半被动杠杆产品不再是边缘杂音，而是实质上成为了许多核心龙头标的（尤其是芯片与 AI 产业链）盘中“最重要的边际买家与边际卖家”，其被动再平衡机制放大了标的资产的尾部波动。 其次是监管与系统性承载力的问题。虽然全球主流监管机构极力克制，并未放行 5 倍甚至 100 倍杠杆的超高倍率单票产品，但市场上百种杠杆产品的总体名义敞口叠加后，已经达到了不可忽视的宏观体量。华尔街主经纪商与做市券商为此锁定了巨大的资产负债表容量（balance sheet capacity）。一旦底层重仓资产发生黑天鹅事件或深度流动性枯竭，银行资产负债表与回购质押体系将承受难以预估的冲击。 最后，Joe 提到了本期播客中最令他震撼的宏观数据点：美国家庭资产负债表中的股票敞口与房地产敞口的对比差距已经拉大到惊人的地步。在传统常识中，居民部门财富的核心基石始终是自住房地产；尽管从中位数家庭来看房产依然占据主导，但在全美家庭资产总值（aggregate）层面，股权资产的暴露程度已经远远凌驾于房地产之上。更为严峻的是，这笔庞大的家庭股权财富，正通过杠杆 ETF、期权以及高度拥挤的 AI 交易主题被层层放大，使整个宏观经济的稳定性前所未有地与股市波动绑定在了一起。 - **边际定价权转移**：散户杠杆 ETF 已成为美股流动性最核心的边际驱动力量之一。 - **银行表端隐患**：衍生品对冲要求主经纪商提供天量流动性支持，市场下行期资产负债表弹性面临考验。 - **宏观资产暴露错配**：宏观总资产层面上，家庭股票敞口大幅超越房产敞口，全社会财富与科技股杠杆波动形成了深度捆绑。 ### `50:00` 节目收尾与社群互动：订阅 Odd Lots 及官方渠道 主持人向听众介绍了获取《Odd Lots》更多衍生内容与社群互动的途径。听众可以访问彭博社专属页面获取每日通讯（Newsletter）并收听往期全部剧集。为了让对金融市场、单一股票杠杆 ETF 等议题感兴趣的听众能够持续交流，官方还设立了专属 Discord 社区，支持听众进行全天候讨论。如果听众喜欢探讨个股杠杆 ETF 等市场热点，主播呼吁大家在各大播客平台留下好评。此外，彭博订阅用户可以通过 Apple Podcasts 上的 Bloomberg 专属频道，按照指引享受全程无广告的播客收听体验。 - **官方网站与通讯**：访问 Bloomberg.com/oddlots 订阅每日 Newsletter 并查阅往期节目。 - **互动社群**：通过 discord.gg/oddlots 加入官方 Discord，参与全天候市场话题交流。 - **订阅福利**：彭博订阅会员可在 Apple Podcasts 的彭博专属频道收听全集无广告音频。 ### `50:35` 赞助商资讯：定制推广品、整合医疗服务与透明跨境汇款 节目尾声穿插了多家品牌的赞助信息。首先是定制礼品与宣传品服务商 4imprint，为企业提供从服装、水具到科技产品、手提袋的全品类定制选项，满足不同预算与品牌风格需求，并依托其“360 全方位保障”（360 guarantee）确保订单准时送达。随后是联合健康集团旗下的 Optum，该公司致力于运用数据与技术手段重塑医疗体验，将患者护理、药房系统全面打通，减少处方药等待与预约延误，让医疗流程变得一体化且易于负担。 最后是跨国汇款平台 Wise 的服务推荐。针对传统银行跨境转账普遍存在的隐藏收费与汇率加价问题，Wise 采用用户在 Google 搜索到的实时市场汇率，杜绝不透明加价；大部分汇款可在 20 秒内完成，耗时甚至比听一段播客口播还要短。用户下载 Wise App 即可免费便捷注册，实现高效透明的全球多货币管理。 - **4imprint 定制方案**：涵盖服装、杯具、数码配件与托特包，提供适配各类预算的选项与“4imprint Certain” 360 度服务保证。 - **Optum 医疗科技**：整合患者端与药房数据，提供价格更透明实惠的处方药服务，让医疗护理更连贯高效。 - **Wise 跨境金融**： - 杜绝隐藏费用，直接采用无加价的实时市场基准汇率。 - 极速到账，绝大部分跨境转账能够在 20 秒以内处理完毕。 ## Notable quotes > `00:00` 你可能对杠杆不感兴趣，但杠杆却对你极感兴趣。 — Joe Weisenthal > `04:15` Terrifying — Alexander Altmann > `10:00` 资产负债表容量是极其稀缺的，而且只要标普 500 指数还在 7,500 到 7,600 点的高位运行，这种稀缺状态就绝不会缓解。 — Alexander Altmann > `17:15` 街上现在多得是靠杠杆 ETF 的超额收益提了迈凯伦超跑的人。 — Alexander Altmann > `20:00` 现在的股市就是经济本身。 — Alexander Altmann > `24:50` 如果一件事情无法量化，你就没有资格谈论它；在策略团队里严禁使用‘感觉’（feels）或‘似乎’（seems）这两个词，谁要是用‘我觉得/看起来’来做推论，就会被立刻开除。 — Alexander Altmann > `42:15` knowledge is knowing that a tomato is a fruit, but wisdom is knowing that it doesn't go in a fruit salad — Alexander Altmann > `22:15` 这涨幅够你买好几辆迈凯伦了吧！ — Joe Weisenthal ## Mentioned Tracy Allaway, Joe Weisenthal, Alexander Altmann, Samsung, SK Hynix, Barclays, SEC, Micron, MUU, BETTY, JPMorgan, Citi, Emmanuel Derman, GitHub Copilot, Claude, Anthropic, Microsoft, Odd Lots, 4imprint, Optum, Wise ## Video description <p>Retail participation in the stock market is booming. And of course the biggest story in markets is the AI trade, which has created an incredible amount of demand for chips and memory. These two broad themes have come together in the form of leveraged, single-stock ETFs. And while these products are popular in the US, the scale coming out of Korea is enormous. It's a good week to talk about this intersection, because some of the biggest stories of the week include Samsung's earnings and SK Hynix's new US listing. Barclays's Global Head of Equities Tactical Strategies Alexander Altmann has used the word &ldquo;terrifying&rdquo; to describe the amount of notional exposure coming from these levered ETFs. He explains to us why that is and we talk to him about why, in such a short period of time, the world of levered ETFs has gotten to be so large, with AUM increasing threefold in Asia alone. He also us gets into how he is thinking through risk management and how we as society &mdash; and retail investors in particular &mdash; got to be overexposed on equities and why that keeps him up at night.</p> <p>Read: <a href="https://bloom.bg/4f68KA5">SK Hynix&rsquo;s $26.5 Billion Listing Reopens Asia Route to US Market</a><br><br>Only Bloomberg.com subscribers can get the Odd Lots newsletter in their inbox, plus unlimited access to the site and app. Sign up: <a href="https://www.bloomberg.com/subscriptions/oddlots?in_source=oddlotspodcast" data-link-cookie="1" data-destination="https://www.bloomberg.com/subscriptions/oddlots?in_source=oddlotspodcast">bloomberg.com/subscriptions/oddlots</a></p><p>See <a href="https://omnystudio.com/listener">omnystudio.com/listener</a> for privacy information.</p> [Source](https://podtrac.com/pts/redirect.mp3/tracking.swap.fm/track/UVBrz8bN8aM2Xe47PEPu/traffic.omny.fm/d/clips/e73c998e-6e60-432f-8610-ae210140c5b1/8a94442e-5a74-4fa2-8b8d-ae27003a8d6b/59c7d65a-dfbb-447c-8ed4-b482014569c9/audio.mp3?in_playlist=982f5071-765c-403d-969d-ae27003a8d83)

[0:00] Joe Weisenthal: 你可能对杠杆不感兴趣，但杠杆却对你极感兴趣。 [4:15] Alexander Altmann: Terrifying [10:00] Alexander Altmann: 资产负债表容量是极其稀缺的，而且只要标普 500 指数还在 7,500 到 7,600 点的高位运行，这种稀缺状态就绝不会缓解。 [17:15] Alexander Altmann: 街上现在多得是靠杠杆 ETF 的超额收益提了迈凯伦超跑的人。 [20:00] Alexander Altmann: 现在的股市就是经济本身。 [24:50] Alexander Altmann: 如果一件事情无法量化，你就没有资格谈论它；在策略团队里严禁使用‘感觉’（feels）或‘似乎’（seems）这两个词，谁要是用‘我觉得/看起来’来做推论，就会被立刻开除。 [42:15] Alexander Altmann: knowledge is knowing that a tomato is a fruit, but wisdom is knowing that it doesn't go in a fruit salad [22:15] Joe Weisenthal: 这涨幅够你买好几辆迈凯伦了吧！

[0:54] You may not be interested in leverage, but leverage is interested in you. [24:45] Alexander Altmann: If you can't quantify it, you don't have the right to talk about it. [41:35] Alexander Altmann: Knowledge is knowing that a tomato is a fruit, but wisdom is knowing that it doesn't go in a fruit salad.

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Wise is the smart way to manage the currencies you need around the globe. When you send money abroad using your bank, you could get hit with hidden fees and exchange rate markups. There's a better way. Try Wise. Wise uses the exchange rate you usually find on Google with no unwelcome surprises Plus most transfers happen in under 20 seconds which means your money arrives in less time than you been listening to me It simple and free to sign up when you download the WISE app. Be smart, get wise. T's and C's apply. Bloomberg Audio Studios. Podcasts, radio, news. Hello and welcome to another episode of the All Thoughts Podcast. I'm Tracy Allaway. And I'm Joe Weisenthal. Joe, when I think about the big market events this week. This week. We're recording this July 9th. July 9th. Macro doesn't feel like it matters that much anymore. So we had renewed hostilities between Iran and the U.S. Markets, at least as of Thursday morning, don't seem to care too much. you know we've had some fed minutes they didn't really change things that much the big stories this week unusually for the u.s market have all stemmed from korea yeah no it's true i mean look the chips trade which then becomes the korea trade and all of these things is like the big thing in the entire world And I agree I do not think people are going to focus on much of this other stuff as long as this thing is so crazy And, you know, the Korean market, like we've just come to get used to this major, you know, pretty big market in the grand scheme of things, 5% a day and we've suddenly all become sort of normalized to it. Yeah, this is the big thing. Yeah, so the two big Korea events this week, we had Samsung earnings already. And then today, which means we're recording this podcast either at the best or the worst time, because we don't know how things are going to shake out. But we have a U.S. listing from SK Hynix. And the reason Korean stocks are so interesting right now is it's not just that the U.S. market is kind of like playing off them as well. But it's because you have all these new products in the Korean market, all these things called single stock levered ETFs, which seem to be having an impact on the overall movement of the underlying shares. It's so funny to me. You look at, again, the performance of some of these chip companies, some of them up like 10x. 40% is not enough. We need like 86 Yeah exactly It like you know what I look at this chart You know what The one thing I missing is some leverage here this is always also funny to me when people were like trading crypto on leverage just like what you not getting enough you need like 20 exit but people love it and so yeah but even here's the thing even if you personally don't love leverage leverage has an impact on you totally if you're in that market you may not be interested in leverage but leverage is interested in you exactly one of those guys said all right so we should talk about all these products you know i I can't resist a product tail wagging underlying share price dog. So we really do have the perfect guest. We're going to be speaking with Alex Altman, a.k.a. Altie. He is, of course, the global head of equities, tactical strategies over at Barclays. So Altie, Alex, thanks so much for coming on. Thank you very much for having me. So I believe in one note I read from you, and the reason I wanted to get you on is you specifically use the word terrifying to describe the amount of notional exposure coming from levered ETFs. Can you give us a little bit of context about how big this market actually is now? I think that the, well, look, I'll give you the numbers straight away. And as you highlighted, markets are moving really quickly. So these numbers change a lot every day. And I think at the time when we wrote a [5:00] that it was the local highs of the AUM and it was globally around about 250 270 billion dollars the number itself is actually not enormously terrifying at all i would just say the the asymptotic growth of the AUM over the past few months has been somewhat sensational or just it's just meteoric so just to put some numbers behind that yeah if we look at say the korean market which you guys highlighted at the beginning of the year apac aum so asia pacific aum was somewhere around about 12 or 13 billion that's now around about 50 sorry just wait the aum within these lever of lever products in asia they are they are now around about 50 to 55 billion dollars so talking about a three-fold increase or plus over a relatively short period of time and here in the US if you take the beginning of April yeah beginning of April which was of course the local lows for the market we were talking around about 120 odd billion here in the US and that's now increased I think at its peak it was north of just north of 200 billion so so these numbers can move extremely quickly and what really interesting is is that if you look at the US market it not like you actually seen significant inflows and share creation in that space Most of it, of that AUM growth, has been because of price performance. Oh, interesting. Whereas in Korea, you've actually seen the opposite, not the opposite, but you've seen huge share creation on top of meteoric rises in the share prices. Oh, that's super interesting. But just to be clear, for the Korean products, as the underlying share price goes up, the AUM also tends to go up right so that and then when the underlying goes down it collapses and so that's why you get these big spikes in AUM yes so I think it's worth just like expanding on the mechanics of it just for a second because I think that's really important to debunk and why they've become such a relevant product in the market so if you just take we just actually put some numbers behind this let's say you had one underlier whatever it was and it was an AUM of call it $30 billion, just to make the numbers simple. And let's say it was triple levered. So now they're gonna basically go out and get a load of exposure for another $60 billion. That exposure is typically done through swap agreements, right, so they can actually sort of have this synthetic exposure through prime brokerages and so on and so forth And so now you billion dollars exposure now let say the stock goes down 10 so 90 billion dollars 10 drop so that going to be nine billion dollar drop so you're down to an 81 billion dollar of exposure now but you've lost nine billion dollars of your AUM which was only member 30 billion so the 30 goes down to 21 and then if you multiply that back by three to maintain your triple levered exposure you're going to be at 63 but you've got 81 so you need to reduce down your exposure mechanically by over 10 billion dollars in order to sort of maintain what you've kind of put in your prospectus as you know your triple leverage against that underlier and that's what's creating these mechanical adjustments on a daily basis whether it's down obviously on on down days and then up on up days so effectively you're creating a new short gamma dimension in the market that was relatively small only a couple of years ago and i think really importantly is that there's a lot of dynamics that are moving non-discretionary flows in the market so obviously in the etf world you've got a lot of overwriting etfs that are doing the opposite they effectively selling vol into the market which which is creating a long gamma process And that would typically have netted off against a lot of these levied ETFs But because these levied ETFs have become so large and they rebalancing that now net gamma profile is effectively becoming more negative. Yeah, a bigger role in the market. Yes, exactly. Can you actually just maybe take a step backwards? Can you just walk us through the basic, I don't know if it's like the business or the simple mechanics of setting up a levered ETF. So I have a company I want to, okay, I'm an upstart ETF provider. I want to- No, do the odd lots three times levered ETF. The odd lots three times micron ETF, whatever it is. So it's like, who's providing the leverage? Just talk to us about how this product, you know, it was originally conceived of setting aside market impact and all that stuff for now. Sure. How the product works. The product is very straightforward. It's really no different to any other fund provider in the ETF space. So effectively, if you're creating an ETF and you typically go and get some kind of anchor investor or some kind of co-sponsor who's effectively going to create the initial amount of AUM for you. And then obviously, because you need critical mass. Yeah. There are, as I'm sure you guys have talked about, there's more ETFs in the world than there are single security. [10:00] here in the US. So creating the ETF in the fund is not complicated. It all obviously has to go through SEC regulation. And I think that's an important point to highlight when it comes to all these levered ETFs is whatever your opinion on them may be. The fact is, is that the SEC, the regulator has approved all of these. So in the same way, these funds will then get approved. And from there, of course, the question is, where do you get the leverage? So typically, the leverage will come from a banking partner so through a prime channel that they will effectively seek to get that exposure synthetically through the bank and that in turn obviously will effectively create the balance of of their notional exposure now from a bank's perspective that goes through the prime balance and the interesting thing about the about that is is that some of market participants have been talking about an increase in financing rates that has been driven by these levered ETFs. I think we need to debunk that for a hot second because I know I've gone a little bit off topic, but it is still very much related. No, I actually had a request recently. Someone wanted us to talk about equity financing costs so I glad you brought that up So it is perhaps a contributor You have to acknowledge the fact that if you got north of billion of AUM that has materialized in relatively short order or grown exponentially recently and you're obviously putting over two times leverage on that, you're creating an additional $400 billion plus of stuff that's being bought and put on balance sheets in swap format. It's going to create a degree of tightness within bank balance sheets. That's not in isolation true. The fact of the matter is that bank balance sheets have become tighter because the primary reason is markets have gone up. And as markets have gone up, the price of stuff has gone up. And so that has been, without a doubt, the biggest reason for financing rates rocketing is the fact that spot levels are higher. This has been a contributing factor. But we shouldn't overlook the fact that if you just look at the biggest driver of AUM growth within the hedge fund community, it's the multi-manager platform we're talking about those guys are effectively a trillion dollars of AUM now that's essentially tripled since COVID and obviously those guys are deploying a huge amount of risk on a long and a short side so again that's using balance sheet as well so it's not really so much just the levered ETFs that are going out and asking the banks for balance sheet that causing financing rates to squeeze higher It one of many factors But balance sheet capacity is scarce Balance sheet capacity is scarce and it will remain scarce so long as S&P is trading at 75, 7,600. Well, just on a related note, okay, balance sheet is scarce from the dealers. And it is true that if a levered ETF comes to you and says, I have two times leverage and now I need like $40 billion worth of exposure, you're not going to want to extend that much credit to a single counterparty if you're a dealer. And so one of the things I've heard in the market is that maybe some of the levered ETFs are going out and sourcing leverage, not just through swaps with dealers, but through the options market. Have you heard this? So that was documented, I believe, in the press as well. And I believe that was specific to one particular fund out in Asia, rather than specifically anything that's happened here in the US. So I think that you can open a bit of a can of worms when you start talking about the availability of this leverage and to certain counterparties And I don think that specific necessarily even just to the levered ETF market I think you can really open that up in any counterparty risk when you think about does a bank really want to have So you know a bank will be very careful in Barclays I would say are pretty conservative and extremely diligent about how they choose their counterparties who they want to who they want to partner with And so as a general rule of thumb, you have to always keep that in mind. Just we're in the business of risk management at the end of the day. and so yes this is sort of a i wouldn't even say a new tool it's not a new shiny part of the market but it's it we would treat it exactly the same way as we would any other counterparty whether you're planning a big tech event launching a new campaign or just stocking up on Team Gear, finding the right promotional products makes all the difference. 4imprint offers thousands of options, from on-trend apparel and premium drinkware to tech, totes, and giveaways, so you can find the right fit for any audience, purpose, or budget. You can customize it all, your logo, your message, your look, and many items come with no setup charge to help you save. And if you're really watching the bottom line, you'll find standout [15:00] choices at every price point so you can make a real impact while staying on budget. Plus, you'll get expert help, fast turnaround times, and their 360-degree guarantee. 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Does that have implications in terms of the tail wagging the dog, the spillover effect, this fact that there is just a lot of interest in these products beyond just the products going up? i think that the retail channel in general is one of the most important dynamics for global equities in the world today right could we make the case that korea in particular is kind of ground zero for that retail cohort probably okay you know so for example 93 percent of levered etfs in korea are owned by the retail investor whereas that number is lower in the u it approximately 75 still still high but but but not as high right and some may say well only 75 so why is it not high and also i think we should probably just be very clear like level etfs have made retail an awful lot of money right so you know there are plenty of guys driving around in mclarens yeah because of their returns that they've generated in level etf so you know i think we sort of need to balance out and sort of no judgment here yeah we're just trying to learn how it works but i don't judge anyone for making money there and on balance a lot of money has actually got asked this morning you know have you actually calculated how much money has been made and i said no but on balance a lot of it yeah quite a bit so going back to the read the point on retail take the u.s for example the u.s it's a phenomenon of our lifetimes 34 percent of u.s household wealth is now in equities right it's the highest on record it's higher than dot-com i think what's even more amazing about that stat is if you take the next largest component of household wealth in the u.s is real estate and that is around about 26 percent so the eight percentage point difference between those two sort of assets so to speak is also the widest on record we as a society have never been this over-indexed or overexposed to [20:00] to equities and so i think that what you've seen within say the levered etf space is really just another small part of that broader ecosystem that has contributed to this enormous wealth creation and one of the things that we think about a lot as a team people ask us what keeps you awake at night it's not levered etfs that keep you awake at night what keeps you awake at night is that you have a structural impairment to equities yeah that that effectively no economist on the planet has a cell in their econometric model that says 20 impairment to the S&P that basically destroys let's just call it around about 16 trillion dollars of wealth so let's just say that's half of US GDP right and that is an instant impairment to US consumption it's your recession straight out the gate I don't want to get all sort of bearish or anything but I think it's just important to highlight within the broader context that that retail investors have a huge amount of exposure and therefore in turn the u.s government has a huge amount of incentive to try and not do anything too disorderly that could that could completely derail i mean my my my big stat is is that people used to say the economy is not the stock market or the stock market is not the economy this has been said many times on the podcast yeah i just think the stock market is the economy now not in the traditional sense of like oh the stock market is going up therefore the economy is doing great but yeah a 20 impairment to the stock market is kind of i think it will trigger a meaningful downturn in u.s consumption the thing that worries me now is it's not just the stock market is the economy it's like ai is the stock market and therefore ai is the economy as well like we know it's been driving not just macro growth but if it's also driving stocks and we're getting a wealth effect and it's also driving consumption, then I don't know, it's like an AI impact squared. Right. And look, again, just to talk about facts, the bulk of Levit ETF AUM has grown within that cohort as well. The largest AUMs are all within either NASDAQ rated products or semis, conductors or single names, most of which are, I mean, the largest single name Levit ETF in the US is Micron. So that kind of tells you the largest single name and levered ETF in the world is Hynix. So these dynamics do play an important role, and especially when you start thinking about the tail wagging the dog, and price dictates narrative, right? So people see prices going up, that gives them a confirmation bias about, oh that means maybe we can spend more money on CapEx or that means that the AI story is alive and well And that not necessarily not true But how much of that is getting distorted by these sort of these non flows that are pushing prices up or indeed down Or just general momentum, right? Like momentum attracting more flows. Correct. I mean, let's face it. Momentum is the most successful factor strategy in history, right? You know, S&P goes up 78% of the time. Therefore, by definition, investors generally want to be long momentum. They want to be long stuff that goes up and they want to be short the stuff that goes down if they do indeed short. The MUU, the triple levered, I think it's triple levered, whatever it is, that was trading at 23 a year ago. And it recently had a peak of over 1,200. So that'll get you a lot of McLarens, won't it? Yeah. I mean, again, this is the thing. And for as a team, our main management. Oh, sorry. It's 2X. It's a 2X ETF. It's a 2X ETF. That's OK. But actually, can I ask you a question about this? So you mentioned that all of these products, like they go through the regulatory process and, you know, we have the SEC. I don't know what Korea's equivalent is, but I assume they have some sort of similar body. What is like the SEC's bar? Is it just like 100 times celebrity? Yeah Like what is like what No seriously Or like you know 10x Like what is the point Is it just like okay this checks a certain set of boxes Because clearly it not about like I doubt it should be you know it like oh is this product good or bad Right That not really the question Does this product meet a criteria or doesn What is the criteria And why don we have 10x ETF So there's actually an anecdote. There was a time, if I'm not mistaken, relatively recently during the government shutdown where there was an attempt to try and list and shelf some. I don't think it was 10x, but there was, I believe, some 5x levered ETFs. And effectively, the regulator, I believe at that point, just kind of made it abundantly clear that there are limitations to leverage just from a. to your beginning point you know you may not be interested in leverage leverage is probably interested in you yeah and again i'm not privy to those conversations in any way shape or form but i do know and understand that there are limitations to what a regulator would deem acceptable okay from a leverage perspective quite reasonably as sure just going back to the momentum trade more broadly so barclays you have this index product i can't remember the exact name but It's like the market timing product. Betty. Betty. Betty. Barclays Equity Timing. [25:00] indicator. Yeah. And that has been like in, I don't know if you use the specific term overbought, but like it's basically been in sort of bubblish speculative territory for some time now, like a record amount of time, right? Yep. That's right. Yeah. Okay. What should investors do with this information? Sure. Because I feel like all the surveys right now show everyone's super bullish. Everyone thinks stocks are overvalued. And it's like, so what? If the trade is momentum, then you just keep going yeah so so betty yeah we love betty so this index was created shortly after i joined barclays but it was the first iteration was created back in 2018 so there's there's a reasonable amount of out of sample data around this framework it's got 19 inputs none of those inputs we have a mantra on my team i tell everyone this in my team every week which is if you can't quantify it you don't have the right to talk about it and using the word feels and seems are banned If you use the word feels and seems, you're fired. I think we'd be in trouble, Joe. Yeah, but it's a good rule. But the idea is it's a very simple rule, which is we just want to try and really deep root the team in quantifiable data. Right You know and just to take a step back when Scott and Ronnie who run the equity business when they sort of effectively were hired into Barclays to kind of really revamp as an ascendant equity business one of their main priorities was like we need content at the center of the mindshare in order to sort of like really partner and hold hands with our clients and so that's when they brought me in and because we wanted to focus on quantifiable content as opposed to it so that's the backstory as to why betty was created because we wanted to have a market timing model that removed feels and seams and and and so it's got 19 inputs it's got everything ranging from real yields to spot vol correlation to looking at discretionary flows like what are mutual fund beaters what are hedge fund beaters what are cta's doing what are vol control doing what level etf do what so everything from the discretionary and non-discretionary vertical from the vol vertical from the from positioning we just the only thing we didn't include was sentiment indicators we weren't interested in feels and seems so we there's no ai bull bear index or anything like that and so your question was it's been in this record sort of warning territory now yes it has been in record warning territory and effectively that's been driven by primarily momentum crowding now as you know at the time of recording we had a big momentum pullback over the past couple of weeks so that actually sort of been a relatively healthy and reselling that real yields problematic if you competing for capital at the same time as between the equity market obviously record year for issuance possibly this year. Government, going to be record year of issuance. IG, record year of issuance. Yeah, this is the other big market story is like a blockbuster summer for IG bond issuance. Absolutely. So if you think about what real yields effectively represent, that dynamic of that competition of capital versus equities if you just generally look at where real yields are today in say a post gfc environment or a post-covid environment and benchmark it against where equity multiples are equity multiples shouldn't be this high right so we can get into a bigger debate about oh dot com real yields were very high and an equity multiple skyrocketed i'm like yeah but the government wasn't asking you cap in hand for tons of money so that didn't end great yes but but But hell of a ride it was in the build-up to that. So, but, so Betty effectively is taking all of these inputs. And what is it telling you right now? It's just telling you that the forward return profile of the S&P from an asymmetry perspective on a tactical two-month time horizon is just not great right now. We looking at if you were to pick a random day in time and say all right I going to buy the S and hold it for two months 42 trading days The average return would be around about 190 bps not bad right And your hit rate of making money is about 73 So betty when it is in this kind of territory, so six, seven, it was high as sort of 10 or 11, it was telling you that you only had around about a 35% chance or even lower of making money in the S&P over that same time horizon, your average return was basically negative. So it's not so much necessarily that it's telling you, oh my gosh the market's going to crater it's really just an illustration of a bunch of quantitative inputs that are telling you actually the asymmetry is not great and that's exactly what we've seen the model first flashed as a warning signal late may s&p's kind of gone enough done nothing since then and so maybe in anticipation of some big correction in the equity market isn't forthcoming and that's okay i still take a lot of validation in the model that is kind of telling you that to just sort of pump the brakes a bit, let some of this froth in the equity market come out and actually get a reset, which then actually sets us up better. I mean, ultimately earnings are still good, right? We're still driving AI in the economy and we can get into a debate about whether that's sustainable or not. And then we've also got to work off the assumption we're still running a massive fiscal deficit. And whilst that environment is happening, it's quite hard [30:00] to create any kind of fundamental economic downturn. and premium drinkware to tech, totes, and giveaways so you can find the right fit for any audience, purpose, or budget. You can customize it all. Your logo, your message, your look. And many items come with no setup charge to help you save. And if you're really watching the bottom line, you'll find standout choices at every price point so you can make a real impact while staying on budget. Plus, you'll get expert help, fast turnaround times, and their 360-degree guarantee. so you can be 4imprint certain your order will arrive on time and look exactly right. Whatever your goal, 4imprint makes it easy to find your perfect promo match. 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And most transfers happen in under 20 seconds, which means your money arrives in less time than you've been listening to me. Join millions, saving billions. Be smart. Get Wise. Visit wise.com or download the Wise app today. T's and C's apply. So setting aside market timing and what's going to happen in the next two or three months, etc., just on the sort of like bigger question, the combination of market valuations, however you want to measure them, all the classical ratios that people like, versus the increase in real yields like how historically expensive is this market right now All right So if we take the post GFC environment the challenge is always going to be that S margins were much lower for a long period after the GFC than where they are today OK. And we have to acknowledge that high margins should map into higher multiples. so but we'll do the full comparison so post gfc because there's not that many periods where real yields are this high real yields today on a 10-year basis are running about the 95th percentile and they're around about i think 230 give or take so with that being said if you take what the average smp multiple when real yields were this high or higher it's a scary low number the smp multiple would be around about 14 to 15 times and we're currently trading on around about 20.2 20.3. If you take the post-COVID environment, which I think is probably a better representation of what S&P operating margins look like today, we're still low. It's around about 18 and a half times. So you're still talking about basically a 10% multiple contraction versus where the- Let's ask you a very simple question. Why are margins a more important factor here than expected growth or expected [35:00] earnings growth? I mean, like in my mind, I would pay higher multiples because I think that the earnings are going to have a faster growth rate than they used to have. That seems intuitive to me. Why is margins the lever here that we're looking at? It's a good question. Well, it's not exclusively just margins. You can sort of measure it as ROE as well. There's different ways you can do it, but effectively, businesses are becoming more profitable. If it's a more profitable business, it's going to be working off the assumption to a degree, it may not necessarily be accurate, but that they're effectively that has a bigger moat it's more okay it's a more all right high quality business and therefore you'll put that on a higher premium so to speak can i ask a slightly personal question which is i used to joke that i wanted to be reincarnated as an equity derivative strategist because then i could just find a number to like justify anything that's happening in markets but does it feel like people taking more seriously now versus say 20 years ago when people we're still talking about like fundamentals and stocks. Now you have this environment where flows are super important. You have all these mechanical things happening. You have the multi-strats, as you pointed out, vol targeting CTAs all of this stuff is getting bigger Does it feel like equity derivatives are more important now Oh I thought you were going to say where it my British accent that makes me sound more Well that too People take me more seriously because I I will say we get higher listenership on episodes when our guests have a British accent. It's crazy because everyone knows how much Americans love a British accent. But what's annoying is the Brits know it too and exploit that. You know what you're doing. It's the reason why I stayed here for 10 years. married in America, you know, living the American dream. Sorry, you know what you're doing. I'll get I'll get back to the the actual question at hand. So is there increased validation from having a more quantifiable world that we operate in? Yes. But but on a person, if you're asking a personal question, I don't think that's a personal question. If I was to give a personal answer, my career wasn't always like that. I started my career as a fundamental single stock analyst and I actually started my career actually as a generalist sales on at JP Morgan in 2004 but I then became a general generalist analyst so to speak on the buy side and I did that within the adventure world for several years and then when I went to the back to the sell side I had to pivot I had to change my investment approach because of the fact that the industry was becoming increasingly specialized And as a generalist I was struggling to make an impact on clients that were becoming much more sophisticated in the weeds on their particular sectors and areas of granularity that I just couldn't compete with. And so at the time I was working at Citi and Citi had a very good quant business and i was like what's all this quantum factor stuff what is all that and i genuinely didn't know and this is 2012 to be clear 2013 and so i took it on a personal crusade to understand the post-mortem of my portfolio management experience prior understanding why was my pnl doing certain things which i didn't understand at the time and as it turned out i was just a value investor but the concept of being a value investor other than sort of the ben graham a warren buffett model was a bit as in value from a factor perspective now it's ubiquitous sort of 14 years ago but back then it was really novel that you were going to customers discretionary customers generalists or just long short guys who weren't quant guys and explaining to them this quant phenomenon in layman parlance basically and that was the start of a pivot into a much more sort of quantity derivative knowledge experiment and it just ballooned from there yeah this is kind of what i getting at right like it has become more important in the market i remember speaking of 2012 do you remember joe the headlines where like people would talk about cta flows or something like that and it would be like a mysterious force in markets now everyone knows now that's what it's become yeah and it's interesting to think too like if you if i just think in my mind about one of these platform shops multi-strategy whatever the understanding of the comp of you know the companies that they that pod invests in like they must on the buy side must be orders of magnitude greater than it was in the sort of heyday of like the sell side analysts who would like oh we're going to issue a buy rating on ge vernova or whatever like that. So those investors know that company insanely well. Oh, totally. And look, to put some anecdote around that, Barclays as a bank, as an equity business, as a research house, again, ascendant equity business, we've been working really hard over the past three years to hold our clients' hands and make them aware that we are a really prime and tier one equity franchise. A lot of that is about corporate access. [40:00] right so actually having more research coverage and having more access to more companies which in turn we can roadshow and actually give our customers access to those companies because it is that important to them to your point exactly joe like they have so much granularity and detail in a forensic modeling now that if you're a generalist it's very very hard to compete on on such a granular level so yes i pivoted away completely and focused on the different mantra was if you think about the verticals of equity investment the way i see it is that you've got fundamentals which is obviously what we talked about you've got economics you've got cross assets macro you've got quant which i would include factors you've got derivatives you've got positioning right and then you've probably got some other stuff as well but my view is the way that i i run the tactical strategies team is that we don't need to be a 10 out of 10 in all of these i'd love to be but it's just i don't think it's possible in a world of ai we can obviously augment some of our game in certain verticals but the aim of of our game is if we can be like a seven or an eight in all of these and some of them we you know will be less and some of them will be more some of them will be nines or tens and others will be like fives or sixes It basically means that what we can do is we can go into any customer any client any investor and say hey there something about the market that I sure I can help you on that you not as aware of And that's where I think that the evolution of that kind of education, investor education has come to now rather than just focusing on just one vertical. Yeah, I don't mean to get all media navel-gazy here, but I think we are seeing a similar story in media where the niche subject matter experts are becoming much more important, much more popular versus the sort of generalized news platforms. You don't have to comment on that, by the way. No, but it does parlay back into the world of finance and the world of AI, because in the world of AI, everyone can have an army of quants in their pockets now of questionable accuracy. But the point is you can crunch orders of magnitude more data. So where's the real value? I'm a big believer that the value I see it kind of think about it as knowledge and wisdom. Right. Everyone can now have access, infinite knowledge, but it doesn't necessarily mean that they've got wisdom. I mean the old saying is that knowledge is knowing that a tomato is a fruit but wisdom is knowing that it doesn go in a fruit salad You know this came up on another episode And by the way Tracy I just want to say I meant to send you this I saw someone with one of these viral Froyo places that are like they put little cherry tomatoes Actually, that could be good. Yeah, that's what I'm saying. Tomato is a fruit. Not to get wildly off topic, but my tomatoes so far this year are freaking amazing. And come later this month, in August, you're going to have like... No, this is my take, actually. Boxes full. I love the saying, knowledge, wisdom, tomatoes, et cetera. But I actually think some tomatoes actually do make the cut to make it into a fruit sale. This is my only point. But I understand the point. Yeah. And so I think it's the same when it comes to sort of data and the world of finance. Yeah, you can be incredibly granular and specialized, but sometimes you're going to miss the forest for the trees. Can I just ask, not to turn this into just another AI conversation, but you must actually have some insight on the question. on some of these things, let's say you want to construct an index or some new thing. How good are the models right now at really reliably being able to do quantitative work? I know you can get a lot of the way there and even someone like me can hack together or something but to the standards that have like how much do they still like No this isn nowhere near something that I would like then be ready to turn into a chart and send to a client Yeah, I think that's a great question because we're doing that all the time. So, you know, we'll have a customer come in and let's say they've got a particular security, a single name item. They want to hedge that item and they want to hedge it with a basket. right that's that's one of our most common kind of business problems that we face off with and so we need to build a universe of securities that effectively replicates that particular instrument without using that instrument of course and as a consequence of that we're effectively running a what is essentially a giant pairwise correlation model and then we've got to optimize the inputs depending on what individual correlations against that underlying instrument are it's relatively straightforward if you've got a really cool optimizer if you ask ai to do it what it's okay at is selecting what instruments you should so let's say you're like i need all right this is a household consumer product as you know what let's do it in ai let's make it more new economy so we've got all right we've got a particular ai vertical let's say it's a neo cloud all right we need to hedge that neo cloud with something else all right so [45:00] AI is quite good at selecting, let's go and get all the other neoclouds that are listed, and then let's find a whole bunch of other companies that most of which you'll be aware of, maybe some you're not aware of, that actually have a reasonably high correlation to that instrument that you're trying to hedge. That's kind of where it ends. It's not very good at telling you, all right, what about the liquidity considerations? But it can roughly sort of determine the first step, the ingredients, right? Like you're making Emmanuel Derman in his book talked about like the job of the trade is to make a it's weird we're going back to the fruit salad question but you have a bunch of wholesale fruit and then you slice it up in a certain way it could it can identify the ingredients and then it's your job to figure out the optimal like you can't do the proper slicing and allocation exactly so it's it's yeah it's pretty good at it's pretty good identifying what my universe is meant to look like because i get the sometimes i see these notes and i have never tried this but like from i don't know i somehow i get i got on like jp morgan's like trading desk thing and they're like oh by that we think it's time to go long this basket of you know energy related companies and short this basket of like whatever it is and i've been curious i never tried it like could i get a model to like back out what these ingredients are if i gave the model a line and a theme Yeah So again I would get this real credit to Barclays just in terms of we been pretty leading edge in experimenting and adopting as much AI as we can And so we have Copilot. We've got Claude licenses. We're working with other partners to sort of build in-house stuff, either using external models or our own internal models. But the point being is that we've had plenty of time to operate in the sandbox now. And I'd say where AI is without a doubt most powerful is when you're giving it very identifiable data parameters. Right. So whether it's structured or unstructured data and say, hey, I need to try and figure out whatever my parameters are. That's the data where it's still less good is if you're basically giving it unstructured parameters to say, just can you think about an ethereal topic and sort of come back to me with an answer and it will come back with a pretty comprehensive answer but most of the time it needs to be fact and or annotated or just tested it to scrutinize joe you successfully turned this into another ai conversation well how can you talk about quantitative identity you know how can you talk about this Yeah we were doing pretty good for 40 minutes So all right Alex thank you so much for coming on AllBots Really appreciate it. Truly the perfect guest. Thank you so much for having me. Thank you. so joe that was a fantastic conversation a couple things stand out so first of all it does seem like with the growth of all these products the explosion especially in asia that we we have products who are sort of like more important marginal buyers and sellers yeah in the market at a minimum. The other thing I was thinking about is like, okay, a regulator isn't necessarily going to approve a five times levered product or a hundred times levered product. But is there a point at which like the aggregate number of all these products actually becomes more of a concern? Totally. I mean, there's two things. One, you have to take seriously like where we are in terms of balance sheet capacity and how much balance sheet is being allocated to this leverage and what happens to bank balance sheets in the event of some major downturn And then I thought just the idea of like I had not realized how big that gap has grown between household equity exposure and household real estate exposure. I still feel like that's probably like a pretty, at least to me, like a pretty eye-opening stand because I know there's a lot of equity exposure and it's become important and no one needs to like argue with me. It's like the stock market is the economy. But in my mind, I'm like, oh, it's still like real estate is the core of like people's holdings. And not only is it not the core, at least in the aggregate, I'm sure the median household is still way more exposed to their house than the stock market. But in overall, it's pretty staggering. And then you start wondering geared to the stock market, geared to the stock market. How much of the stock market is now geared towards leverage and then how much of it is also geared towards A.I. It's yeah, it's a lot. No, it's a lot. And he really spelled it out well. Yeah, we have to have him back on. Definitely. Shall we leave it there for now? Yeah, let's leave it there. This has been another episode of the All Thoughts Podcast. I'm Tracy Alloway. You can follow me at Tracy Alloway. And I'm Joe Weisenthal. You can follow me at The Stalwart. Follow our producers, Carmen Rodriguez at CarmenArmin, Dashiell Bennett at Dashbot, Kale Brooks at Kale Brooks, and Kevin Lozano at Kevin Lloyd Lozano. 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