策 略 研 究 策 华福证券 2025 年 12 月 21 日 美国居民部门加杠杆或将深化长期风险——周观 点 投资要点： 近期观点 4、 美国 AI 泡沫一旦破灭，全球美元债务风险有望同步释放。 略 定 期 报 告 5、 关注美元可能阶段性走强所指引的风险信号，随后可能出现 美元美债美股三杀。 6、 中国市场有望在海外风险释放过程中进行风格上的长期大切 换，同时伴随人民币持续大幅升值。 7、 长期看好保险，央国企红利，反内卷行业，中概互联网，军 贸。 风险提示 全球制造业复苏受阻；中美关系改善不及预期；美国地产市场不 健康 团队成员 分析师： 李浩(S0210524050003) lh30530@hfzq.com.cn 分析师： 李刘魁(S0210524050006) llk30550@hfzq.com.cn 相关报告 1、美元体系或将从庞氏融资走向明斯基时刻—— 2025.12.21 2、转型牛深入推进——2026 年资本市场展望— —2025.12.21 3、医疗与消费周报——技术驱动医疗智能化，数 据重塑服务新生态——2025.12.20 证 券 研 究 报 诚信专业发现价值 1 请务必阅读报告末页的 ...

证券研究报告|投资策略报告 产业经济 2025年12月21日 美元体系或将从庞氏融资走向明斯基时刻 证券分析师： 李 浩 执业证书编号：S0210524050003 李刘魁 执业证书编号：S0210524050006 请务必阅读报告末页的重要声明 华福证券 华福证券 核心观点 美元体系的明斯基时刻不仅是金融崩溃，更是资本主义全球化模式的总决算，它正为强调内部均衡的社会主义全球化新范式拉开序幕。 2 华福证券 华福证券 美国实质上正处于"庞氏融资"阶段，并已滑向明斯基时刻边缘：全球收益增长见顶，而地缘政治与高利率驱动的体系维持成本正急剧飙 升且不可逆，导致偿付能力恶化。 AI泡沫与美元庞氏互为表里，若AI叙事破灭，将直接触发美元体系的明斯基时刻：AI泡沫的产生本质上是全球美元债务周期发展到顶部的 表现，讨论AI泡沫何时破灭不如理清美元债务周期的处境。 美元体系正面临结构性拐点，现金流-利息比率出现明显恶化：体系成本因地缘冲突与激进加息大幅上升，而体系收益受逆全球化与增长天 花板压制，导致收益成本倒挂。 本轮危机的结构性特质决定其破坏力或远超2008年，体系容错率已近枯竭： 本轮美元体系成本上升源于维持霸权的刚性 ...

Core Viewpoint - The investment memo by Howard Marks discusses the potential "bubble" in AI investments and emphasizes the need for rational evaluation amidst the current AI technology revolution [1][2]. Group 1: AI Investment Landscape - Oaktree Capital has invested in several data centers, with its parent company Brookfield raising a $10 billion fund for AI infrastructure investments [1]. - Major companies like Oracle, Meta, and Google have issued 30-year bonds for AI investments, with yields only slightly above risk-free rates, raising questions about the wisdom of such long-term debt under technological uncertainty [2][27]. - AI is seen as potentially the greatest transformative technology in history, with significant capital being allocated to it [3][16]. Group 2: Market Behavior and Speculation - The current enthusiasm for AI could lead to a bubble, characterized by excessive optimism and speculative behavior among investors [4][5]. - Historical patterns of bubbles suggest that new technologies often attract irrational exuberance, leading to overvaluation and subsequent losses [7][8]. - The memo highlights the cyclical nature of bubbles, where initial excitement can lead to significant financial losses for investors [5][6]. Group 3: Debt Financing in AI - The use of debt financing in AI infrastructure is increasing, with concerns that this could amplify risks associated with speculative investments [26][28]. - The memo warns that the current phase of speculative financing may lead to unsustainable practices, reminiscent of past financial crises [28][29]. - There is a distinction between healthy and unhealthy debt behaviors in the AI sector, with some companies leveraging debt aggressively without clear revenue prospects [27][28]. Group 4: Uncertainties and Future Outlook - Despite the potential of AI, there is considerable uncertainty regarding its commercialization, the identity of future winners, and the overall market dynamics [18][19]. - The memo raises questions about whether AI will lead to monopolistic markets or remain competitive, impacting profitability for companies involved [19][20]. - Concerns are also expressed about the sustainability of AI-related investments, particularly regarding the lifespan and economic viability of AI infrastructure [30][31].

仔细审视造成10月份预算爆表赤字的原因，你会发现还是那些"老面孔"：所有主要类别的支出在10月份 都有所增加，但最令人触目惊心的，依然是美国总利息支出的无情飙升，这一数字在过去12个月里已达 到创纪录的1.24万亿美元，并且正迅速逼近社会保障支出（过去12个月为1.589万亿美元），即将成为政 府最大的支出源头。 10月份的总利息支出达到了创纪录的1044亿美元，创下该月份的历史新高，而在过去12个月利息支出高 达1.24万亿美元的情况下，这意味着每收上来1美元的税款，就有24美分是用来偿还债务利息的。 在2025年初那段短暂的"非理性希望"时期，当时马斯克对政府效率部的执着和削减支出的努力曾让一些 人看到了一丝希望，以为或许有某种（虽然非常痛苦）摆脱这种"明斯基时刻"的出路。 美国政府收入实际上比2024年10月征收的3268亿美元实现了23.7%的稳健增长。这得益于特朗普的关税 政策如今每月带来的稳定贡献，该项在10月份为美国国库增添了310亿美元。 像往常一样，政府支出再次成为了问题的症结所在。10月份的支出总额高达6887亿美元，相当于每天烧 掉超过220亿美元，这一数字比去年同期支出的5842亿美元 ...

Core Insights - The article discusses the evolving understanding of bubbles and debt in the context of the current economic paradigm shift, emphasizing the need for sustainable debt management and market risk pricing [1][2][3] Group 1: Economic Risks and Market Dynamics - The global financial system faces three overlapping risks: debt leverage traps, asset bubble rigidity, and nonlinear shocks from technology finance, particularly AI [2][5] - The current tight funding environment in the U.S. is a result of a combination of policy expectations, fiscal constraints, and asset valuations, which increases volatility in global risk assets [3][4] - The U.S. stock market is at historically high valuations, raising concerns about potential corrections that could impact global innovation and risk asset performance [5][6] Group 2: China's Economic Outlook - Despite global uncertainties, China's market shows relative attractiveness due to improvements in stock market expectations and structural economic transitions [7][8] - The Chinese economy is undergoing a painful but necessary process of clearing out systemic costs, which could create space for new productive forces [7][8] - The Chinese government is focusing on high-quality growth through technological innovation, expanding domestic demand, and enhancing social welfare [8] Group 3: Investment Strategies and Recommendations - Investors are advised to adopt extreme diversification and to be aware of the inherent risks in the current market environment, particularly regarding AI-related assets [19][20] - The article suggests that the next significant market volatility may occur in the U.S. stock market, driven by high valuations and structural weaknesses [20]

Core Viewpoint - The former governor of the People's Bank of China, Zhou Xiaochuan, emphasized that monetary policy is a slow variable and cannot respond quickly to daily fluctuations in vegetable prices, suggesting that rapid responses could lead to unnecessary volatility [1][3]. Group 1: Monetary Policy and AI - Zhou Xiaochuan noted that during his tenure, discussions at the Bank for International Settlements (BIS) concluded that the impact of AI on monetary policy was not yet significant [1][3]. - He highlighted that while AI can influence data collection and analysis related to prices and micro-behavior, monetary policy adjustments are inherently slow and tied to economic cycles [1][3]. Group 2: Financial Stability and Machine Learning - Zhou pointed out that financial instability risks can emerge suddenly, citing the abrupt failures of banks like Silicon Valley Bank and Silvergate Bank as examples [3]. - He proposed that machine learning and deep learning could be crucial in predicting financial instability by analyzing historical financial stability data and changes in the health of financial institutions [3]. - Zhou suggested that the financial system has traditionally relied on structured data, but the analysis of historical events and the emergence of economic bubbles require broader use of AI to process unstructured data and consider social sentiments [3].

Core Insights - The integration of AI in the banking sector has significantly transformed customer service experiences, allowing for faster and more accurate problem resolution by bank tellers [1] - AI's influence on monetary policy and financial stability is still under observation, with potential benefits in risk warning but challenges in practical application [4][6] - The application of AI in finance is concentrated in three main areas: backend operations, customer interaction, and financial product offerings, which enhance efficiency and personalization [8] Group 1: AI in Banking - AI has improved the efficiency of bank tellers in resolving customer issues, reducing the time required for service [1] - The shift in customer behavior shows a growing preference for interacting with machines rather than human representatives [5] - AI applications in banking are expected to lead to significant marginal improvements due to the vast amounts of data accumulated over the years [5] Group 2: Risks Associated with AI - The introduction of AI brings new incremental risks, including model stability risk and data governance risk, which are critical for business expansion [8][9] - Industry-level risks include concentration risk, where reliance on a few strong technology providers may increase market concentration, and decision convergence risk, leading to homogenized decision-making across financial institutions [9] Group 3: AI's Impact on Monetary Policy - The impact of AI on monetary policy requires further research, as its influence is not yet clearly defined [4][5] - AI can assist in data collection and processing for monetary policy decisions, but the fundamental nature of monetary policy as a slow variable remains unchanged [5][6] - Historical data and events are essential for predicting financial instability, and AI could play a role in analyzing these factors [6][7]

Core Viewpoint - The discussion at the Bund Summit emphasizes the transformative potential of AI in the financial sector, questioning whether it represents a marginal change or a fundamental shift akin to the steam engine or electricity [3][4]. Group 1: AI's Impact on Financial Systems - AI is seen as a significant marginal change in financial systems, affecting core banking operations, customer behavior, and regulatory frameworks [4][6]. - The historical evolution of banking has transitioned from traditional banking to data processing, with AI applications building on this foundation [6][7]. - AI's role in enhancing operational efficiency and customer service is acknowledged, but its application is still in the early stages and primarily supportive [9][11]. Group 2: Risks Associated with AI in Finance - The risks associated with AI in finance are compared to those from previous technological revolutions, indicating that while the nature of risks may evolve, fundamental risks like credit and market risks remain unchanged [5][10]. - New types of risks include model stability risk and data governance risk, which are critical for individual financial institutions [10]. - Industry-wide risks include concentration risk and decision-making homogeneity, which could lead to systemic issues if not monitored [10][11]. Group 3: Regulatory Considerations - AI's integration into monetary policy and macroprudential regulation is still under observation, with the need for a careful approach due to the slow nature of monetary policy adjustments [8][9]. - The potential for AI to enhance data collection and analysis for regulatory purposes is recognized, but challenges related to transparency and model reliability are highlighted [8][10].

1. Report Industry Investment Rating The provided content does not mention the report industry investment rating. 2. Core Viewpoints of the Report - After two years of debt reduction, significant achievements have been made. Local debts are accelerating towards the on - balance - sheet, with fiscal policy taking over from urban investment in 2025. Urban investment focuses on exiting platforms, stabilizing leverage, adjusting structure, and reducing costs to further mitigate risks. After 2028, urban investment bonds are likely to continue to be redeemed at par. Currently, the spread of urban investment bonds is at a low level, and the cost - effectiveness of undifferentiated sinking is not high. It is recommended to select allocation directions based on risk indicators [1]. 3. Summary by Relevant Catalogs 3.1 How Has the Overall Pattern of Local Debt Changed After Two Years of Debt Reduction? - The "front door" is opened wide and the "back door" is blocked, with local debts accelerating towards the on - balance - sheet. Since 2019, the issuance of local government bonds has accelerated, with an annual growth rate of over 15%. The growth rate of urban investment debt has shown a fluctuating downward trend in the past decade, reaching a record low of 3.8% in 2024. By the end of 2024, the proportion of on - balance - sheet government debt had risen to 43.72% [2][15]. - In 2024, the expansion of urban investment slowed down, and in 2025, fiscal policy took over from urban investment. In 2020, the incremental local debt (urban investment + local special bonds) was 10.63 trillion yuan, but the combined increment has not exceeded 10 trillion yuan since then. In 2025, the fiscal deficit increased by 1.6 trillion yuan compared to the previous year. The incremental debt of local governments and urban investment platforms is expected to approach 10 trillion yuan, and the proportion of on - balance - sheet government debt may exceed 45% by the end of the year [3][16]. 3.2 How to View the Urban Investment Risks After 2028? - Risk prevention has become more extensive, evolving from preventing defaults of urban investment bonds to preventing risks of state - owned enterprises. Urban investment is likely to become a state - owned enterprise under the supervision of local state - owned assets supervision and administration commissions, and is unlikely to default on its bonds [20][21]. - From the perspective of assets and liabilities, it is still difficult to completely separate urban investment from local governments. Urban investment still holds a large amount of public - welfare or quasi - public - welfare assets, and the relationship between them remains close [21]. - From the perspective of liquidity, the probability of risk is reduced. After the clearance of hidden debts and the exit from platforms, banks and insurance may open up financing channels for urban investment, and the actual risk may decline [22]. 3.3 What Are the Differences in Urban Investment Financing Among Provinces? 3.3.1 Overall Tightening, with Slight Differences Between Key and Non - key Provinces - The primary issuance review has not been relaxed, and it is difficult for urban investment to increase new financing. Since March 2025, the net financing of urban investment bonds has turned negative. Key provinces have a more significant net outflow, while some non - key provinces such as Shandong and Guangdong still have new increases [23]. 3.3.2 The Proportion of Bank Loans Has Increased, and Some Provinces Are Seeking Increases in Non - standard Financing - As of the end of March 2025, the proportion of bank loans has increased in 18 provinces, with 8 provinces including Ningxia and Hainan having an increase of over 3 percentage points. In non - key provinces, Anhui and Henan have an increase in the proportion of non - standard financing of over 1 percentage point [25]. 3.4 Which Regions Are Facing Increasing Debt Risks? 3.4.1 Macro - level: At the Minsky Moment, the Interest Coverage Ratios of 10 Provinces and Cities Are Less Than 1 - Due to the decline in land sales, although local interest payments have decreased, as of Q1 2025, the government fund revenues of 10 provinces and cities, including Yunnan and Guangxi, have an interest coverage ratio of less than 1 for full - scale debt interest [29][33]. 3.4.2 Micro - level: The Risks in Some Provinces Have Worsened - The debt risks in Henan, Jilin, Anhui, and Hubei have increased compared to before debt reduction. Shandong's overall risk still deserves attention [29]. - In terms of the proportion of risk urban investment platforms, 20 provinces have improved their debt risks, 7 have remained unchanged, and 4 have increased their risks [30]. 3.5 Which Regions Have Achieved Remarkable Results in Debt Reduction? 3.5.1 Debt Reduction Progress - The progress of hidden debt resolution has exceeded half. Jilin, Jiangsu, Shaanxi, Inner Mongolia, and Xinjiang have at least over 10 cities or districts announcing the full clearance of hidden debts [47]. 3.5.2 Stock Bond Scale - As of August 28, 2025, the stock of urban investment bonds was 15.14 trillion yuan, a decrease of 84.321 billion yuan compared to the beginning of the year. Jiangsu, Hunan, Tianjin, and Guizhou have the largest reduction in the stock of urban investment bonds [53]. 3.5.3 Interest Payments - The interest payments of urban investment bonds in some economically strong provinces and provinces receiving more debt reduction support have decreased significantly. Jiangsu, Zhejiang, Tianjin, Hunan, and Shandong have a large decline in interest payments [56]. 3.6 How to View Urban Investment Bonds from the Perspective of Risk Premium? - By constructing a short - term risk indicator (proportion of risk urban investment platforms) and a medium - long - term risk indicator (risk qualification evaluation score), provinces are classified as follows: - Both indicators cross the line (proportion of risk platforms > 20%, risk qualification evaluation score < 40): Guangxi, Tianjin, Gansu, Inner Mongolia, Henan, Jilin, Yunnan, Qinghai, Guizhou. Caution is needed for these regions [59]. - One of the two indicators crosses the line: Shandong, Tibet, Ningxia, Jiangxi, Chongqing, Shaanxi. It is recommended to adopt sinking + duration control when exploring returns in these 6 provinces [59][61]. - Neither indicator crosses the line: Shanghai, Beijing, Shanxi, Hainan, Guangdong, Zhejiang, Fujian, Hebei, Jiangsu, Xinjiang, Anhui, Heilongjiang, Hubei, Hunan, Sichuan, Liaoning. The overall risk in these regions is relatively low, but the spread is generally less than 50bp, with limited room for exploration [61].

Core Insights - The article highlights the extravagant lifestyle of Vijay Mallya, juxtaposed with the financial troubles faced by his companies, particularly the massive bad debts amounting to $1.2 billion owed to 17 Indian banks [2][7]. Group 1: Business Expansion and Strategy - Vijay Mallya inherited a beer company that held a 40% market share in India and sought to emulate Richard Branson's diverse business empire, leading to aggressive expansion into various sectors including aviation and motorsports [3][4]. - Mallya's successful launch of Kingfisher beer, priced three times higher than regular beer, allowed him to capture 50% of India's premium beer market and expand into over 50 countries [3][4]. - His acquisitions included a $250 million Scottish whiskey brand, a $120 million stake in an Indian airline, and a $200 million investment in a Formula One team, reflecting a strategy focused on high-profile branding rather than solid financial foundations [4][5]. Group 2: Airline Operations and Financial Mismanagement - Kingfisher Airlines was launched with a promise of luxury service, but the pricing strategy led to unsustainable operational losses, with costs exceeding revenues significantly [6]. - The airline's operational model resulted in annual losses exceeding $300 million, as it failed to adhere to basic profitability principles in the aviation industry [6]. - Mallya's reliance on personal credit and celebrity status allowed him to secure $1.2 billion in loans, with 63% of these loans lacking physical collateral, leading to a precarious financial situation [6][7]. Group 3: Collapse and Consequences - By 2012, Kingfisher Airlines reported losses of $900 million, prompting regulatory actions and employee protests due to unpaid wages [7]. - A consortium of 17 banks demanded repayment of the $1.2 billion loan, but Mallya had already transferred funds overseas through complex transactions, leading to investigations [7][8]. - Mallya's departure to London amidst financial turmoil left behind significant debts and unpaid wages, illustrating the consequences of reckless financial practices [8][9]. Group 4: Lessons and Reflections - The narrative serves as a cautionary tale about the dangers of high-leverage expansion and regulatory evasion, aligning with the "Minsky moment" theory, which warns of systemic collapse when debt levels become unsustainable [8]. - Key lessons for entrepreneurs include the necessity of a solid business model based on value creation, the risks associated with high leverage, and the importance of corporate governance over personal charisma [8][9]. - The article concludes that the essence of business lies in balancing ambition with rationality, as exemplified by Mallya's downfall due to a lack of financial discipline [9][10].