日前，招商基金首席投资官朱红裕在红动中国2026财富前瞻论坛上发表了《预期差下的2026年权益市场 投资展望》的主题演讲。朱红裕认为，A股市场经过一轮周期性上涨，仍有部分板块与风格较为低估， 中国资产2026年具备全球配置吸引力，并重点关注四大主线机遇——具备全球竞争力的制造业龙头、未 来供需格局趋于改善的行业龙头、估值处于底部且基本面可能有较高变化的行业、长期盈利回报较高且 估值不匹配的行业龙头。 2026权益投资更注重安全边际与确定性 二是未来供需格局趋于改善的行业龙头公司，包括房地产、养殖、化工、轻工等行业龙头。中国大部分 制造业过去几年在全球的地位持续上升，未来在世界占据一定份额后，有望通过提高价格、利润增长， 在全球范围进一步巩固竞争力。 三是估值处于底部且基本面可能有较高变化带来赔率的行业，包括化工等细分行业龙头公司等，类似于 过去几年的煤炭、钢铁、有色等，过去几年从无人问津到成为表现领先的行业。 四是长期盈利回报较高且估值严重不匹配的机场航空服务、保险服务、非白酒的食品等龙头，整体ROE 较高，但股票关注度较低。回顾过去十几年以来股票质地结构变迁，观察不同ROE股票数量占比分布数 据，ROE超过 ...

投资咨询业务资格：证监许可【2012】669号 中信期货研究|⾦融衍⽣品策略⽇报 2025-12-19 股市强势⽅向切换，债市⻓端情绪不稳 ⾦融衍⽣品团队 研究员: 股指期货：强势⽅向再度切换 股指期权：年末⾏为保守，保护看跌应对 国债期货：超⻓端情绪或仍不稳 股指期货方面，强势方向再度切换。周四未能延续周三情绪，主要宽 基弱势为主，其中创业板指深跌2%，量能再度萎缩，同时配置风格呈现保 守化的特征，红利及微盘结构占优。行业方面，机场、煤炭、银行涨幅在 2%之上，高股息、泛消费抗跌，前者作为防御板块配置进行产品降波，后 者博弈元旦春节双节临近，历史上来看消费板块的年末季节性特征明显。 展望后市，目前处于多空因素均难以证伪的阶段，悲观者忌惮日元、AI风 险，乐观者信任政策托底，在缩量博弈氛围中，仍建议谨慎配置，大市值 近期优于小市值。 股指期权方面，年末行为保守，保护看跌应对。昨日标的市场震荡分 化，以上证50为代表的红利股维持定力，其余品种尤其是双创风格下跌幅 度较大，期权市场总成交额70.99亿元以上，相较前一日下降29.54%。情 绪指标方面，持仓量PCR震荡为主，部分品种下行，偏度各品种整体呈上 行趋势 ...

Core Insights - The insurance industry is becoming increasingly cautious about the risks associated with artificial intelligence (AI), leading to significant changes in policy coverage [1][2] - Major insurance companies are seeking to exclude AI-related risks from standard business policies due to concerns over the opaque decision-making processes of AI models [1][2] - Real-world incidents of AI-related claims are prompting insurers to act, highlighting the potential for systemic risks that could arise from AI failures [1][3] Group 1: Insurance Industry Response - Major insurers like AIG, Great American, and WR Berkley are applying to regulators to include exclusion clauses in their policies that specifically address liabilities arising from the use of AI technologies [1][2] - The shift in attitude reflects a growing concern that AI models can lead to numerous interconnected claims, creating unmanageable systemic risks for the insurance sector [2][3] - Insurers are particularly wary of the potential for a single AI model's failure to result in thousands of claims, which could overwhelm their capacity to pay [2] Group 2: Specific Incidents and Examples - Notable cases, such as a Canadian airline's chatbot generating false discounts and Google facing a $110 million lawsuit for erroneous AI search results, underscore the tangible risks associated with AI [1][3] - The engineering firm Arup lost $25 million due to fraud involving a digital clone of an executive, further illustrating the vulnerabilities that insurers are now hesitant to cover [3] Group 3: Limited Coverage Options - Some insurers are exploring limited coverage options, but these often come with strict limitations, such as QBE's policy capping AI-related fines at 2.5% of the total coverage [4] - Chubb has agreed to cover certain AI risks but has explicitly excluded broad AI events that could affect multiple clients simultaneously [4] - Legal experts warn that as AI-driven losses increase, insurers may begin to contest claims in court, potentially requiring a significant systemic event to prompt a change in their approach [4]

Core Insights - The rapid development of the AI industry is accelerating iterations across various sectors, presenting significant industrial opportunities [3] - The scale of the AI industry is projected to be at least 100 times larger than the previous generation, indicating substantial growth potential [5] Group 1: Trends in AI Development - The first major trend is the transition from discriminative AI to generative AI, now evolving towards agent-based AI, with task lengths doubling and accuracy exceeding 50% in the past seven months [7] - The second trend indicates a slowdown in the scaling law during the pre-training phase, with more focus shifting to post-training stages like reasoning and agent applications, while reasoning costs have decreased by 10 times [7] - The third trend highlights the rapid advancement of physical and biological intelligence, particularly in the intelligent driving sector, with expectations for 10% of vehicles to have L4 capabilities by 2030 [7] Group 2: AI Risks and Industry Structure - The emergence of agent-based AI has significantly increased AI risks, necessitating greater attention from global enterprises and governments [8] - The fifth trend reveals a new industrial structure characterized by foundational large models, vertical models, and edge models, with expectations for 8-10 foundational large models globally by 2026, including 3-4 from China and the same from the U.S. [8] - The future is anticipated to favor open-source models, with a projected ratio of 4:1 between open-source and closed-source models [8]

Core Insights - The rapid development of the AI industry is accelerating iterations across various sectors, presenting significant industrial opportunities [1] Group 1: Trends in AI Industry - The first major trend is the transition from discriminative AI to generative AI, now evolving towards agent-based AI, with task length doubling and accuracy exceeding 50% in the past seven months [3] - The second trend indicates a slowdown in the scaling law during the pre-training phase, shifting focus to post-training stages like inference and agent applications, with inference costs decreasing by 10 times while computational complexity for agents has increased by 10 times [3] - The third trend highlights the rapid development of physical and biological intelligence, particularly in the smart driving sector, predicting that by 2030, 10% of vehicles will possess Level 4 autonomous driving capabilities [3] Group 2: Future Projections and Risks - The fourth trend points to a significant rise in AI risks, with the emergence of agents increasing risks at least twofold, necessitating greater attention from global enterprises and governments [4] - The fifth trend reveals a new industrial landscape for AI, characterized by a combination of foundational large models, vertical models, and edge models, with expectations that by 2026, there will be approximately 8-10 foundational large models globally, including 3-4 from China and 3-4 from the U.S. [4] - The future is expected to favor open-source models, with a projected ratio of 4:1 between open-source and closed-source models [4]

Group 1 - The core viewpoint of the article revolves around the evolving role of AI from a mere tool to a collaborative partner, emphasizing its increasing integration into daily life and various industries [2][3][5] - AI is transitioning from being a novelty to a companion, evolving from efficiency tools to life interfaces, with a focus on user engagement metrics shifting from Daily Active Users (DAU) to Daily Presence Duration (DPU) [5][6] - The emergence of "physical agents" and the application of AI in sectors like agriculture, healthcare, and manufacturing are highlighted, showcasing AI's potential to replace traditional processes and enhance productivity [5][6] Group 2 - Jeffrey Hinton discusses the rapid advancements in AI capabilities, particularly in reasoning, and suggests that human abilities are not inherently irreplaceable by machines [7][9][10] - Hinton posits that AI could potentially exhibit emotions, drawing parallels between human emotional responses and AI's cognitive behaviors, indicating a blurring line between human and machine capabilities [10] - Kevin Kelly emphasizes the diversity of AI applications, advocating for a decentralized approach where specialized AIs can operate independently, thus fostering innovation and reducing data monopolies [11][13][14] Group 3 - Demis Hassabis expresses optimism about AI's potential to solve significant global challenges, such as disease and climate change, while also highlighting the need for responsible development and international cooperation to manage risks [16][18][19] - Hassabis warns of the unknown risks associated with AI, advocating for thorough research to quantify these risks and ensure that powerful technologies are aligned with human values [20]