新加坡太信环球金融集团主席Raymond Tan：对腾讯、阿里等中国科技企业保持长期持有态度

智通财经01-16

智通财经APP获悉，1月16日，对冲基金公司新加坡太信环球金融集团主席Raymond Tan接受了新智基金网专访。Raymond Tan于专访中表示，从投资价值来看，中国科技股目前最大的优势在于估值水平相对偏低。相比之下，美股科技板块在过去几年已经经历了显著的估值扩张，整体定价处于较高区间。而中国科技企业虽然仍在持续发展，但市场估值相对合理，这为全球资金提供了一个重要的分散配置窗口。近几个月部分...

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Tan对腾讯、阿里等成熟的中国科技企业始终保持长期持有态度。他表示，这类公司商业模式清晰、现金流基础稳固、市场地位明确，在复杂环境下具备较强的抗风险能力，适合作为中国科技资产的“核心配置”。相比之下，对于AI领域的新兴科技公司则更加谨慎。他认为，这一领域确实充满想象空间，但新公司面临的变量非常多，包括政策监管、技术迭代速度、商业化落地能力以及市场接受度等；同时，不少企业尚未形成稳定盈利，却已经被赋予较高估值，泡沫和波动风险都不容忽视。因此，他更倾向于观察和筛选，而不是在早期阶段盲目介入。进入2026年，随着德国和中国经济逐步企稳，非美市场重新具备吸引力。随着全球进入多极化与利差收敛阶段，在资产配置上，Raymond Tan认为可以重点关注几类方向：第一，非美资产值得提高比重，尤其是估值相对合理、处于修复阶段的德国和中国市场。第二，黄金等实物资产的重要性明显上升。在通胀不确定、地缘冲突未解、政策可信度下降的环境下，避险需求将持续强化，黄金已不只是“对冲工具”，而是组合中稳定性的重要来源。第三，新兴市场在“本身利率较高、而美国可能转向宽松”的组合下，风险回报正在改善，一旦美元进入结构性走弱阶段，这一板块的弹性会更明显。至于美国资产，Raymond Tan强调称，并不是全面回避，而是要更谨慎。美国核心科技龙头虽整体估值仍处高位，但其技术垄断地位、规模优势与政策支持依然稳固，经历调整后依然具备不可替代的长期配置价值。但投资前提应转向精选标的、节奏控制与仓位管理，而非延续过去“被动拥挤”的配置方式。以下是新智基金网专访的全程实录：PART I 与时代同频的43年投资征程新智基金网：能否和我们分享一下您正式踏进投资圈的时间?Raymond Tan：我的职业起点可以追溯至1978年，那时候日本在亚洲金融市场中居领先地位。我在一家日本期货经纪公司开始了为期一年的买卖交易工作，其后服役于新加坡空军，担任战斗机飞行员。1982年初退伍后，我正式回到金融行业，开展至今的专业金融生涯。如果不算早期铺垫的时间，至今大概有43年实际投资从业经验了，可说是完整经历了亚洲主要金融市场从封闭化到全面开放的转型、也经历了投资方式从传统框架迈向科技化、系统化的深刻演变。新智基金网：您进入投资领域，是受个人学习经历影响，还是家庭背景推动?Raymond Tan：我的职业选择并非源于家庭背景，更多是顺应时代潮流而自然走入金融行业。我进入金融行业是在1980年代，当时美国和英国大力推行新自由主义改革，其核心就是放松边境管制、推动市场与资本自由化；而新加坡早在1975年便启动对外开放进程。随着边境与贸易自由化的推进，新加坡作为全球在亚洲的主要贸易转口中心，贸易量迅速扩大，金融服务需求也随之增长。在政策引导与国际格局变化的共同驱动下，新加坡逐渐确立了亚洲主要金融中心的地位，也为我提供了进入行业的契机。在进入这个行业之前，我只接受过基础经济学教育，几乎没有系统学习过金融或投资相关专业知识，投资知识主要来自实战工作经验。由于当时政府积极推动金融人才建设，专门开设了两年半制的投资分析专业课程，我为了完善自己的专业能力主动报读，可以说，我的投资知识体系完全是围绕和从业需求而建立，实践与理论的结合度非常高。1980 年代，新加坡开始推动金融市场建设，以配合迅速扩张的对外贸易。从早期的大宗商品期货、黄金交易起步，随着全球化进程加速，外汇逐渐成为主要交易品种。为提升市场对投资风险的管理能力，期权工具陆续发展，新加坡也在这一时期成立国际金融交易所，成为区域内重要的期权交易中心。进入 1990 年代后，全球化进一步深化，资本自由流动带来显著冲击，热钱的快速进出使新兴市场更易受到波动影响，各类局部金融危机相继爆发，最终酿成 1997 年亚洲金融风暴，并延伸至随后出现的拉丁美洲危机。而2000 年代新科技推动金融活动快速扩张，引发网络泡沫的破裂；随后在 2008 年爆发全球金融风暴。在这一连串系统性风险事件中，市场对风险管理的需求不断提升，可运用的风险管理工具与对冲策略也逐渐成熟并广泛被采用。金融危机后的量化宽松政策为全球市场注入前所未有的流动性，资产价格泡沫化成为新常态，也重塑了投资市场的结构面貌。其后，疫情时代的额外流动性注入，加上科技管控能力的弱化，更加速了市场性质的改变，使投资者不得不重新审视风险管理框架，并在不断变化的市场环境中寻找新的方法论。在我的职业历程中，我不仅见证了亚洲金融产业的多次迭代，也亲身经历了从新兴市场迈向成熟市场的完整演变过程。早年间，我先后在新加坡、中国香港与中国台湾工作，亲历当地金融市场的逐步开放。从最初的大宗商品期货，到股票、外汇交易的兴起，再到期权等更复杂衍生品工具的应用，我一路见证了市场基础设施与制度体系的不断完善。PART II 投资策略：从配置到量化的动态迭代新智基金网：了解到您目前主要管理对冲基金，能否和我们分享一下您当前的核心投资策略?黄金在您的投资组合中扮演什么角色?Raymond Tan：我们的核心投资理念建立在系统化的全球宏观视野之上，我们并不依赖单一市场方向或单一资产，而是结合长期结构性趋势与短周期市场变化，通过情境驱动(scenario-based)的方式，判断在不同宏观环境下，哪些资产与策略更具优势，并据此进行跨资产配置与多策略布局。多策略架构的目的，是让不同策略在不同市场阶段各自发挥作用，通过组合的互补性来提升整体的稳健度与回报的可持续性，而不是依赖某一个判断长期“押对”。在具体执行层面，相较于传统以长期持有为主的投资方式，对冲基金更强调对市场变化的敏感度与风险管理能力。因此，我们会持续跟踪宏观环境与市场结构的变化，并不断修正与优化投资模型，以确保策略能够适应快速变化的市场条件。对于已经具备长期资产配置的投资者而言，多策略对冲基金更像是一种重要的补充，用来覆盖传统资产较难应对的风险区间，从而增强整体投资组合的稳定性与韧性。近年来，我们对黄金的配置与回报确实显著提升，容易让人误解我们以黄金为主要投资方向。事实上，黄金只是我们多元资产框架中的一个策略工具。近期黄金在组合中的比重有所提高，主要是因为在特定宏观环境下，它的避险属性与流动性特征更符合我们当下的策略需求，而非我们刻意偏好某一单一资产类别。随着市场情境的变化，黄金的配置权重同样会动态调整，这也正是我们多策略、情境驱动投资框架的一部分。新智基金网：我们注意到，您的投资经历似乎可以分为三个阶段：买入并持有、宏观对冲、量化投资。您管理的产品是否真的经历了这样的划分?这背后的转型逻辑是什么?Raymond Tan：你这个总结很精准，但其实不是严格意义上的“划分”，而是随着时代变化的动态调整。我最初进入基金行业时，市场还很不成熟，大家做的都是简单的资产配置，也就是“买入并持有”，那时候也没有太多复杂的工具，只能靠观察长期市场走势做决策。90年代全球化加速、资本流动与金融杠杆扩张，使市场更易受冲击，各类危机频繁出现，风险管理成为核心主题。随着投资框架被重新定义，对冲理念逐渐兴起。作为交易员出身，我对市场波动的敏感度更高，也更早意识到仅靠长期配置已无法应对新的市场环境。因此，我自然而然从单一的配置思维转向对冲与套利策略，以更主动地管理风险、构建更具韧性的投资体系。2008年全球金融危机是资本市场的重要转折点，随着政府大规模干预市场，量化宽松成为常态，全球市场被注入前所未有的流动性，资产价格泡沫化趋势明显。量化宽松时代，资产价格与基本面脱钩日益明显，传统宏观分析难以跟上市场变化。我因而开始引入动量、因子等量化方法，以更客观地理解市场结构与情绪。过去十年，我将投资流程量化系统化，把资讯收集、资料与数据处理转化为可重复的模型与程序，以提高准确性与策略的可验证性。近几年AI发展起来后，量化理念又升级了。我们不再局限于数字与统计的精算，而是开始尝试把宏观知识通过语言模型的方式量化——比如把经济政策、地缘政治等传统上难以结构化的因素，转化为模型可以识别的信号，使策略能够更全面地反映市场真实结构。更有意思的是，2025年又出现了新变化，特朗普重新搅动全球格局，政策节奏与信息发布体系被彻底打乱，过去几个月甚至连最基础的数据都没法及时获取。这种情况下，我们又不得不回到早期的配置思路，但这已不是传统意义上的被动长期持有，而是一种主动性、策略化的“全面核心配置”。比如股票和黄金，在信息受阻、噪音加剧的时期，它们必须像加固城防一样作为基础配置。只要核心逻辑不破裂，整个投资系统就维持稳定，不至于出现结构性错误。回望这40多年，我的投资方法从来不是一套固定公式，而是随着市场环境、政策变化、技术进步持续演化的过程。从宏观分析到对冲理念，从传统量化到 AI 驱动的知识量化，再到特殊时期的主动配置，持续调整与升级正是我们能够穿越周期的根本原因。新智基金网：您刚才提到“多元策略”，能否具体解释一下，在当前的市场环境下，多元策略是如何落地的?它能解决哪些核心问题?Raymond Tan：当前市场呈现出高波动、信息不完整、政策不确定性上升以及结构快速变化等特征。在这种环境下，任何单一策略的有效性周期都在明显缩短，很难持续稳定地产生回报。因此，我们采用多元策略的投资框架，让不同策略在不同市场状态中各自发挥优势，通过结构化的组合方式，降低单一策略在高度不确定环境中失效的风险。需要强调的是，这种分散并非简单地将资金配置到不同资产上，而是针对不同类型的风险来源，设计并配置相应的策略。我们的资金配置覆盖多种策略维度，每一类策略都具备独立的模型、规则与风险控制体系，并最终整合为一个统一的组合框架。该框架能够在市场出现短期冲击时，通过动态调整与风险对冲有效控制回撤，并在必要时迅速切换至核心资产、防御性配置或事件驱动策略，即使个别模型阶段性失效，整体组合仍能保持相对稳定的运行。不同策略之间保持较低的相关性，使其在单一策略承压时能够相互补位，从而增强组合的整体韧性与持续性。在实际执行中，我们会结合环球宏观、套利、动量、事件驱动与风险管理等多类策略，使组合既能够应对短期波动，又能维持长期收益的稳定。在这一多元策略体系中，量化工具是不可或缺的组成部分。随着科技的发展，量化方法已成为识别风险与分散不确定性的重要手段。但我们并不依赖模型进行自动化决策，而是将量化作为辅助工具，用于捕捉短期风险变化、市场情绪与结构性信号。最终的投资判断仍以宏观环境与政策方向为核心，通过将量化的精确性与宏观判断的方向性相结合，在提升决策质量的同时，避免被短期数据噪音所主导。PRRT III 团队运作：人机结合的决策体系新智基金网：在当前的投资交易中，您的团队是采用个人主导、团队协作，还是引入了自动算法的模式?不同角色在决策中承担什么责任?Raymond Tan：我们是典型的团队分工协作模式，而且不依赖纯自动化。宏观层面的看法非常重要，一个团队必须有自己明确的“View”(观点)，我的核心角色就是在前端提供宏观判断\\尾部风险和收益预期——比如基于当前的地缘政治、经济政策，判断未来市场的大方向，设定合理的收益目标与尾部风险的预期管理。在此基础上，我们有专门的量化团队，他们的核心任务是关注影响短期风险的因素，用更宽泛的方式收集资料、捕捉市场信号。我们确实会用到自动化工具，但不会完全依赖它。比如资料收集可以自动化，但选择什么样的资料、如何进行资料检验和处理，这些都需要人为判断。我们不会直接以数据结果做决策，数据只是用来印证我的宏观观点，确保我的判断和市场实际情况没有太大误差。整个决策流程是有序的：我先提出宏观观点、尾部风险和收益预期，中间团队负责收集资料、分析数据，印证我的观点并提出调整建议，之后把处理好的数据结果反馈给我，我再结合这些数据做最终的决策。这样的模式既能发挥团队的专业优势，又能保证决策的连贯性和准确性。新智基金网：在决策过程中，如何平衡个人宏观判断和数据支撑之间的矛盾?如果个人观点和数据结果不一致，会如何处理?Raymond Tan：我的核心观点是“个人判断不必然正确”，所以我从不排斥数据对我的修正与校准观点。因此，我们建立了系统化的复盘与验证机制，由数据团队对我的观点进行独立检验，将宏观判断和实际数据进行对比之间的差异，并共同探讨偏差的来源——究竟是我忽略了某些因素、数据本身存在偏差，还是市场出现了新的变量。举个例子，我可能基于宏观经济数据判断某一产业将进入上升周期，但数据团队收集到的市场情绪、资金流向等数据却显示相反的趋势。这时候我们不会武断采信任何一方，而是会进一步验证：是经济数据存在滞后性?还是市场情绪被短期事件扭曲?通过这种反复校准，让最终的决策既符合宏观逻辑，又贴合市场实际。我们的组织架构也围绕这一原则运作：科技团队负责数据处理、模型开发和工具构建；宏观研究团队负责宏观框架、经济逻辑与结构性分析；我则负责整合两方的分析结果，在理解数据的基础上做出趋势判断与最终策略决策。这种跨团队、跨方法的合作方式，使我们能够持续修正认知、避免主观偏误，并构建更稳健的投资体系。PART IV 规模与收益：在矛盾中寻找平衡新智基金网：行业内有一个普遍的观点，就是产品收益率会随着规模的扩大而下降。您的产品是否遇到过这样的问题?您是如何应对规模扩张带来的挑战的?Raymond Tan：行业内确实普遍存在一种现象，即产品规模扩大后收益率容易出现下降，但这本质上取决于所采用的投资策略与资产类别。对于以个股精选为主的传统股票基金而言，真正具备足够流动性、同时估值又具吸引力的优质标的数量有限，当资金规模不断放大时，往往会面临明显的“边际递减”，从而压低整体收益率。我们的情况有所不同。首先，在资产配置层面，我们并不局限于单一股票策略，而是覆盖股票指数、货币、大宗商品等多个高流动性市场，这些资产容量充足，更有利于承载规模增长。其次，在策略上，我们采用多策略组合运作，而非依赖单一策略进行集中押注，使资金投向更具选择性与弹性，因此整体收益并不会因规模扩大而自然下滑。从发展历程来看，三十年前我们的客户规模较小，单一策略运作并未构成明显限制；随着客户和资金规模逐步扩大，我们也主动调整了投资模式，引入更为多元的策略体系。目前整体管理规模约在 15 亿美元左右，在现有策略和资产结构下，进一步扩张并未构成实质性压力。此外，我们对客户进行了清晰的分层管理。对于资金体量较大的客户，我们采用独立账户管理模式，根据其风险偏好和目标定制投资策略，避免大规模资金在同一产品中高度集中。这种安排既保障了存量客户的收益稳定性，也为规模的有序扩张留出了空间，实现长期、良性的增长。新智基金网：面对从低通货膨胀到高通货膨胀的转变，以及市场波动性加大的情况，您是如何为客户设定收益目标，确保收益稳定性的?Raymond Tan：我们对收益目标的设定始终遵循一个非常清晰而务实的原则——先定义可实现的稳定回报目标，再反向设计实现路径，而不是被动追逐市场表现。这一理念，实际上可以追溯到90年代亚洲金融风暴之后。当时，许多投资者，特别是以退休人士为主的客户群体，对股票市场的波动产生了深刻的风险厌恶。他们最关心的并非短期高回报，而是可预期、可持续的现金流与资产稳定性。正是在这样的背景下，我们放弃了“必须跑赢市场”的传统思维，转而设定清晰的年度回报目标，通过纪律化执行和严格的风险控制，以更可控的“工程化” 方式实现回报。在执行层面，我们将年度目标拆分为季度执行目标，把全年投资过程视为多个阶段的累积，而非一次性的结果判断。这种做法有助于降低单一时点波动对整体表现的影响，在高通胀和高波动的市场环境中尤为重要，因为它强调的是节奏管理与过程控制，而不是押注于市场的最终表现结果。所有投资决策都运行在结构化的情境框架之中，而非临时起意。我们认为，市场价格的变化往往由不断演变的全球宏观因素所推动，因此通过宏观视角识别潜在的市场低效，并提前围绕关键事件进行布局，例如货币政策取向及利率路径、主要宏观经济数据的趋势变化或政治与政策环境对市场结构与风险偏好的影响。在进入投资之前，潜在的风险、回报区间与结果路径便已被清晰界定。在策略运用上，我们采用多策略组合，不同策略适配不同的市场环境，并通过量化规则强化执行，尽量降低情绪与主观判断对结果的干扰，从而提高整体表现一致性和可重复性。过去几十年，正是这种以稳定性为核心的目标设定与执行体系，帮助我们在不同通胀周期和市场环境中保持相对平稳的表现，也建立了高度稳固的客户关系。许多客户从早期一路相伴，甚至延续到下一代——这本身，就是对稳定收益策略最直接、也最有力的长期验证。PART V 全球视野：中国资产与中美竞争格局新智基金网：您是土生土长的新加坡人，能否和我们分享一下您何时开始关注中国资产投资?背后有哪些重要的契机和经历?Raymond Tan：我和中国市场渊源颇深。早在上世纪90年代中国对外开放初期，我便判断其具备巨大的发展潜力，并率先进入进行地产投资。彼时私募股权(PE)尚未形成成熟概念，更像是早期的探索。作为特区刚起步的深圳，政策与市场规则仍在不断摸索之中，我们也在不完善的制度环境下实践前行，期间难免走过一些弯路。2000年以后，我逐步退出了直接投资，主要原因在于缺乏本地资源支持，外国投资者在中国市场并不具备明显优势。但我并没有完全离开中国市场，期间，我结识了许多中国本土投资人士，并以顾问的身份和他们合作，从宏观层面提供判断与建议，例如把握市场阶段性周期变化，并以相对保守、稳健的视角协助其制定投资决策。这些年，我一直都有关注A股和H股，也持有些中国科技公司的股份。中国市场的吸引力持续上升，尤其是近几年中国在科技领域的加速布局，使我看到了新的结构性机会。新智基金网：当前中美在科技领域的竞争越来越激烈，您如何看待中国科技股的投资价值?对于腾讯、阿里这些企业，以及AI领域的新公司，您的投资态度有何不同?Raymond Tan：今年的全球科技竞争格局出现了两个关键的变化，一个是美国在特朗普时期将原本相对隐性的竞争全面公开化，无论是在贸易、科技还是产业政策层面，都被直接上台面；另一个就是中国不再只是被动应对或象征性参与，而是真正、系统性地投入到科技竞争之中。这种格局转变，对全球资本配置逻辑产生了深远影响。从投资价值来看，中国科技股目前最大的优势在于估值水平相对偏低。相比之下，美股科技板块在过去几年已经经历了显著的估值扩张，整体定价处于较高区间。而中国科技企业虽然仍在持续发展，但市场估值相对合理，这为全球资金提供了一个重要的分散配置窗口。事实上，近几个月已经可以观察到部分国际资金重新流入中国资本市场，这本身就是一个值得关注的信号。在具体配置上，我对腾讯、阿里等成熟的中国科技企业始终保持长期持有态度。这类公司商业模式清晰、现金流基础稳固、市场地位明确，在复杂环境下具备较强的抗风险能力，适合作为中国科技资产的“核心配置”。相比之下，对于AI领域的新兴科技公司，我的态度会更加谨慎。这一领域确实充满想象空间，但新公司面临的变量非常多，包括政策监管、技术迭代速度、商业化落地能力以及市场接受度等；同时，不少企业尚未形成稳定盈利，却已经被赋予较高估值，泡沫和波动风险都不容忽视。因此，我更倾向于观察和筛选，而不是在早期阶段盲目介入。总体而言，我认为未来科技竞争的主轴将高度集中在中美两国，虽然目前美国在多个关键领域处于领先地位，但中国的科技产业在政策支持与庞大市场需求的推动下，发展速度非常快，具备长期潜力。因此，在中国科技资产的配置上，我们会坚持以成熟企业为主，同时密切跟踪政策方向与产业节奏，动态调整布局，而不是追逐短期概念。PART VI AI时代：投资逻辑的重构与泡沫判断新智基金网：对比2000年的互联网时代，您认为当前的AI时代是否会出现类似的泡沫破裂?英伟达等头部科技企业是否可能被新的技术或公司替代?Raymond Tan：很多人习惯将当前的AI发展阶段和2000年前后的互联网浪潮相类比，并据此担忧可能重演大规模泡沫破裂的情景。但我并不认同这种简单类比，因为两者所处的时代背景、产业结构与价值实现路径存在本质差异。2000年互联网泡沫的核心问题，并不在于技术方向错误，而在于盈利实现与估值扩张之间的严重错配。当时互联网确实代表长期趋势，但市场过早地将未来数十年的增长预期一次性反映在估值之中。然而现实环境仍处于“旧经济”主导阶段，互联网渗透率有限、应用场景尚未成熟，企业的商业模式和盈利能力远远跟不上资本市场的预期扩张，最终导致估值体系失衡并通过泡沫破裂完成调整。相比之下，当前的AI周期呈现出鲜明的“由上至下”特征。AI 的应用并非率先面向普通消费者，而是率先服务于少数拥有核心资本、算力和决策权的大型企业。这些企业通过AI实现的效率提升和流程重构，已经在现实财务报表中转化为真金白银的利润，而非停留在概念或远期假设层面。这意味着当前 AI 的估值基础远比当年的互联网初期更加扎实，系统性泡沫的风险被先行兑现的盈利有效对冲。更深层的差异在于政策环境的剧变。在2008年金融危机之后，各国政府对资产泡沫和经济周期的调控变得更加主动且成熟。2000年时，政策层面仍普遍允许泡沫通过市场自发性的剧烈崩盘来完成出清；但在今天，AI已上升为大国博弈的核心领域，政府都不会坐视关键技术领域发生失控式崩溃，反而会通过产业政策、财政支持与监管协调，来维持头部企业和核心生态的稳定发展。至于英伟达这些头部科技企业是否可能被迅速替代，我认为这种可能性极低。这类企业已不再只是单一技术供应商，而是在技术、政策协同、资本、产业链、品牌和生态体系等多个层面形成了高度复合的竞争壁垒。通过与国家级机构和大型企业建立长期合作关系，它们在算力、供应链和技术标准层面锁定了未来数年的盈利空间。相较之下，真正面临淘汰风险的，反而是那些缺乏核心技术、缺乏资本支持、主要依赖概念叙事的小型AI企业。在竞争加剧和资本趋于理性的过程中，这部分企业很可能会率先出清。总体来看，我认为当前 AI 周期更接近一个高度集中、政策支持、盈利先行的结构性升级过程，而非 2000 年那种由预期主导、全面失控的泡沫行情。新智基金网：AI发展对传统的投资估值体系带来了哪些改变?我们应该用什么样的新逻辑来评估AI时代的企业价值?Raymond Tan：AI的崛起正从根本上重塑传统的投资估值体系。过去，我们习惯于通过中产阶级的消费能力、就业率及收入增长来预测企业的未来，这种逻辑本质上是建立在“以个人消费为核心驱动力”的经济模型之上。但在AI时代，这一前提正面临挑战：AI的大规模应用不仅显著降低了企业对人力成本的依赖，使其能通过流程重构和技术替代实现盈利，更由于其对高薪标准化脑力岗位的替代，削弱了传统中产阶级消费市场对经济的支撑作用。换言之，利润创造的重心正在从“需求端扩张”转向“供给端效率提升”。从产业结构来看，头部科技企业之间正在形成一个高度集中、相互强化的“自循环盈利”体系。以英伟达投资Open AI为例，Open AI作为大型 AI 模型开发方在获取大笔资金后，可持续推升算力需求，而这一需求主要通过合作云计算平台甲骨文释放。云服务商在向模型开发方提供 AI 算力服务的同时获得稳定收入，也被迫持续加大对算力基础设施的投入，直接带动对高性能 GPU 与数据中心生态的的投资。这种扩张需求，最终回流至英伟达等上游核心硬件供应商，形成稳定的订单与利润来源。这一体系的核心特征在于，需求并非来自分散的终端消费者，而是由少数具备资本实力、技术能力与规模优势的企业与机构在体系内部非一次性的持续放大。与此同时，AI正在深刻改变中产阶级的就业和收入结构。过去，通过教育和经验积累进入高薪岗位，是中产阶级形成与消费能力扩张的关键基础；但现在，AI正在替代大量标准化、可复制的脑力劳动，使得高薪岗位的数量和门槛同时收缩，中产阶级对经济增长和消费的支撑作用趋于弱化。在这一背景下，继续以消费增长为核心假设的传统估值体系，显然有必要重新思考。进入AI时代，企业价值的评估核心从“关注过去的盈利”转向了对“未来资源的控制力”。我们不再仅仅依赖历史财务数据的简单外推，而是要考察企业是否拥有由核心算法、算力架构及海量数据构成的不可替代的技术壁垒。更重要的是，判断企业的战略方向是否与国家政策导向高度契合，在科技竞争、能源转型等关键赛道中占据核心位置。最后，还需关注企业是否通过在供应链中掌握关键节点，与政府机构或行业头部巨头建立深度的绑定关系，从而锁定长期需求与相对确定的盈利来源。简而言之，未来评估一家企业的价值，本质上是看它对技术、权力和资源掌控的深度与广度。PART VII 市场前瞻：2026年投资方向新智基金网：2025年已经过去，回顾去年，发生了很多重大事件，比如中国市场的底部调整、特朗普的对等关税政策、地缘政治冲突(印巴冲突、以色列冲突等)，黄金价格也突破了4000美元/盎司。对于2026年上半年的投资，您有哪些前瞻判断?哪些资产值得重点关注?核心风险又是什么?Raymond Tan：从宏观的视角来看，全球投资环境正在发生一个重要变化，资金不再“理所当然”地只流向美国。2025 年主导市场的“别无选择(TINA)” 逻辑正在显著弱化，美国资产之所以持续吸金，很大程度上是出于“没有更好的选择”的无奈，而非基本面的持续超越。但进入 2026 年，随着德国和中国经济逐步企稳，非美市场重新具备吸引力。同时，全球利率环境也在发生变化：欧洲和不少新兴市场的降息周期已经接近尾声或完成，而美国反而可能因为经济放缓而进入更明显的宽松阶段，全球货币政策正出现明显利差收窄，这会削弱美元的优势，也增加资金重新分散配置的动力。另外，日本利率政策逐步正常化迈向加息，也意味着过去多年盛行的“借日元、买高风险资产”的套利交易，存在反转风险，一旦集中平仓，容易对高估值风险资产形成放大式波动。随着全球进入多极化与利差收敛阶段，在资产配置上，我认为可以重点关注几类方向。第一，非美资产值得提高比重，尤其是估值相对合理、处于修复阶段的德国和中国市场。第二，黄金等实物资产的重要性明显上升。在通胀不确定、地缘冲突未解、政策可信度下降的环境下，避险需求将持续强化，黄金已不只是“对冲工具”，而是组合中稳定性的重要来源。第三，新兴市场在“本身利率较高、而美国可能转向宽松”的组合下，风险回报正在改善，一旦美元进入结构性走弱阶段，这一板块的弹性会更明显。至于美国资产，并不是全面回避，而是要更谨慎。美国核心科技龙头虽整体估值仍处高位，但其技术垄断地位、规模优势与政策支持依然稳固，经历调整后依然具备不可替代的长期配置价值。但投资前提应转向精选标的、节奏控制与仓位管理，而非延续过去“被动拥挤”的配置方式。还有一个需要注意的趋势，就是财富分化越来越严重。在霸权主义当道的背景下，有钱有能力的人会掌握更多资源，弱势群体的安全网会逐渐消失，这会影响整个社会的消费结构和经济增长动力。反映在投资上，就是要聚焦“有权力、有资源、有技术垄断能力”的企业，远离那些依赖传统消费驱动的资产。在风险方面，短期最需要关注的仍然是通胀是否再度回升，一旦通胀反弹，央行可能被迫收紧政策，市场流动性会受到冲击。更深层的风险来自政策和市场结构本身。美国长期依赖财政刺激和政策托底的模式，正在受到高债务和政治周期的限制，市场对“政府一定会救市”的信心并不再像过去那样牢固。如果美元利差优势消退过快，可能引发资金从美债和高估值资产中快速撤出；再加上日元套利交易集中平仓，容易造成市场的放大波动。最后，由于过去一段时间经济数据本身存在滞后和失真，一旦 2026 年上半年真实的增长和通胀情况逐步显现，市场可能需要在较短时间内完成重新定价，波动性自然会上升。","kind":"news","is_publish_news":true,"is_publish_highlight":false,"is_publish_live":false,"is_publish_wemedia":null,"editions":null,"column":"","sentiment":"1","news_tag":"shareholding","news_rank":0,"isVideo":false,"video":null,"symbols":[],"gpt_button":0,"need_auth":false,"need_login_tip":false,"code":"91000000","status":"200"},"commentList":[],"isCommentEnd":true,"newsSizeData":{"likeSize":0,"commentSize":0,"repostSize":0,"favoriteSize":0,"likeStatus":false,"favoriteStatus":false},"APP":{"userAgent":"Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; 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