Group 1: Report Industry Investment Ratings - Stock Index: ☆☆☆ [1] - Treasury Bond: ☆☆☆ [1] Group 2: Core Viewpoints of the Report - As of the week ending December 12th, the A - share market showed a structured and volatile trend. The average daily trading volume of the whole market was 1.95 trillion yuan, an increase of nearly 260 billion yuan compared with the previous week. The three major indexes showed different trends, with the Shanghai Composite Index falling 0.34%. There was relatively limited information on short - term incremental policies and economic data, and market structural characteristics emerged [1]. - From the high - frequency macro - fundamental factor scores, for stock index futures, the inflation indicator scored 8 points, the liquidity indicator scored 9 points, the valuation indicator scored 11 points, and the market sentiment indicator scored 9 points. For bond futures, the inflation indicator scored 8 points, the liquidity indicator scored 10 points, and the market sentiment indicator scored 6 points [1]. - In terms of term structure, the weighted annualized basis rates (dividend - adjusted) of the ending positions of IH, IF, IC, and IM were 0.33%, - 2.32%, - 4.16%, and - 9.95% respectively, and the discount of far - month contracts widened again [1]. - The net value of the quantitative CTA strategy for financial derivatives did not change last week. In the long - term, although the social financing data slightly exceeded expectations, the credit data such as M1 and M2 showed sub - seasonal declines and were lower than expected. The credit factor put pressure on stock index futures, with a relatively large decline in IC. In the short - term, the high - frequency real estate and consumption sectors were still weak. The RMB continued to appreciate against the US dollar, and the capital situation remained relatively loose, but the short - term increase was relatively limited. In terms of positions, the risk appetite significantly recovered compared with the previous week. IF and IH remained relatively neutral, while IC and IM had relatively large declines. The overall comprehensive signal was in the neutral range. For bond futures, the capital situation remained loose. After a short - term rise, the positions of bond futures significantly declined. The stock - bond seesaw effect shrank, and the bond market was insensitive to fundamental feedback. The position factor of TF slightly declined, and institutional year - end allocation behavior was relatively cautious. The comprehensive signal was in a neutral and volatile state [1]. Group 3: Summary According to Relevant Catalogs 3.1 Macro - fundamental High - frequency Factor Scores - For economic kinetic energy indicators, including blast furnace开工率, PTA开工率, etc., different indicators showed different weekly changes, numerical values, historical quantiles, and correlations with stock and bond indexes. The scores for stock index futures and bond futures were both 8 points [2]. 3.2 Inflation Indicators - Various inflation - related indicators such as the vegetable basket product wholesale price index, coking coal, etc. had different weekly changes, numerical values, historical quantiles, and correlations with stock and bond indexes. The scores for stock index futures and bond futures were both 8 points [3]. 3.3 Liquidity Indicators - Liquidity - related indicators such as DR007, DR001, etc. had different weekly changes, numerical values, historical quantiles, and correlations with stock and bond indexes. The score for stock index futures was 9 points [4]. 3.4 Index Valuation - Index valuation indicators such as price - to - earnings ratio, price - to - sales ratio, etc. had different weekly changes, numerical values, historical quantiles, and correlations with the stock index. The score for stock index futures was 10 points [5]. 3.5 Market Sentiment: Stock Index - Stock - index - related market sentiment indicators such as margin trading balance, northbound capital inflow, etc. had different weekly changes, numerical values, historical quantiles, and correlations with the stock index. The score for bond futures was 9 points [6]. 3.6 Market Sentiment: Bond - Bond - related market sentiment indicators such as the yield of 10 - year government - developed bonds, the VIX index, etc. had different weekly changes, numerical values, historical quantiles, and correlations with the bond index. The score for bond futures was 6 points [7]. 3.7 Strategy Introduction (Quantitative CTA Strategy) - The product pool includes stock index futures and bond futures. The short - term model focuses on market style, external factors, and capital - related high - frequency financial data. The long - term model focuses on market expectations and macro - economic low - frequency indicators. The position data is synthesized considering institutional long and short positions [15]. - The comprehensive signal strength is weighted by the signals of three independent models (0 - 1). Contracts with the top 2 comprehensive signal strengths greater than or equal to 0.6 are considered for long positions, and those with the bottom 2 less than or equal to 0.4 are considered for short positions. Signals are shielded 7 days before the delivery date. The stop - loss point is set at a daily decline of more than 1%, with equal - weighted allocation of capital. Consecutive two - day same - direction signals are shielded [16][17]. 3.8 Last Week's Situation - The data of IF, IH, IC, IM, T, and TF main contracts from December 8th to 12th, 2025 were all 0 [18]. 3.9 Treasury Bond Futures Cross - variety Arbitrage Strategy - The cross - variety arbitrage strategy is based on the signal resonance of the fundamental three - factor model and the trend regression model. The fundamental factor uses the instantaneous forward - rate function proposed by Nelson and Siegel, which decomposes the interest - rate term structure into level, slope, and curvature. The three - factor model is constructed using PCA, factor rotation, and logistic regression, with signals divided into three types: '1', '0', and '- 1'. The trend regression model is used to filter signals, and trading is carried out when there is resonance. In actual operation, the 10 - 5Y spread is adjusted with a duration - neutral ratio of 1:1.8 [19]. 3.10 TF and T Main Contract Trading Signals - From December 8th to 12th, 2025, the N - S model and trend regression model signals of TF and T main contracts showed different situations [22].

- The report introduces a convolutional neural network (CNN) model for analyzing price-volume data and predicting future stock prices. The model maps learned features to industry theme indices such as communication, artificial intelligence, and growth momentum indices[4][83][85] - The CNN model constructs standardized graphical representations of price-volume data for individual stocks within specific time windows. These graphical representations are then used for deep learning-based modeling[83][84] - The latest thematic configurations derived from the CNN model include indices such as CSI Communication Equipment Theme Index, ChiNext Artificial Intelligence Index, CSI 5G Communication Theme Index, and ChiNext Growth Momentum Index[4][85][86] - The report evaluates the CNN model as a promising approach for integrating AI into quantitative analysis, particularly for thematic investment strategies[83][86] - Backtesting results and specific performance metrics for the CNN model are not explicitly provided in the report[83][86]

Core Viewpoint - The article discusses the complex and potentially catastrophic financial arrangements within the AI industry, drawing parallels to the financial crisis of 2008, highlighting the risks associated with high levels of debt and interlinked financial structures among major tech companies [5][6][10]. Company Analysis - CoreWeave, a relatively unknown company, has emerged as a significant player in the AI sector, achieving the largest tech IPO since 2021 and doubling its stock price. It has secured major contracts worth $220 billion with OpenAI, $140 billion with Meta, and $60 billion with Nvidia [5][6]. - Despite its impressive contracts, CoreWeave operates at a loss, projecting $5 billion in revenue against $20 billion in expenses for the year. The company has accumulated $14 billion in debt, with over half due within a year, and faces $34 billion in lease obligations by 2028 [6][7]. Financial Structures - The financial model of CoreWeave relies heavily on a few key clients, with Microsoft accounting for 70% of its revenue, and Nvidia and OpenAI contributing an additional 20%. This creates a precarious dependency on a limited customer base [7]. - The AI industry's financialization is driven by the high costs of infrastructure needed for AI systems, with data center spending expected to exceed $400 billion this year and potentially reach $7 trillion by 2030. Creative financing methods are essential to support these investments [8][10]. Interconnectedness and Risks - Major companies like Nvidia, OpenAI, and others are forming intricate financial relationships, often involving equity stakes in exchange for future profits, which obscures the true financial health of these companies [8][9]. - The article warns that if the anticipated AI revolution does not materialize as expected, the financial ties binding these companies could lead to widespread economic repercussions, potentially more severe than the dot-com bubble burst [10][11]. Debt and Financial Instruments - The AI sector is accumulating significant debt, with estimates suggesting it could reach $1.5 trillion by 2028. This high leverage poses risks to the broader financial system if defaults occur [11][14]. - Companies are utilizing complex financial instruments, such as special purpose vehicles (SPVs) and asset-backed securities, to obscure debt levels and manage financing, reminiscent of practices leading up to the 2008 crisis [12][13]. Regulatory Environment - The article highlights concerns over the lack of regulatory oversight for private equity firms involved in AI financing, which could exacerbate risks in the event of a market downturn. The interconnectedness of private credit and traditional financial institutions raises alarms about potential systemic risks [14][15]. - Recent regulatory rollbacks may expose a broader public to the risks associated with AI financing, contrasting with the more reactive approach taken during the 2008 crisis [15][16].

Market Overview - Overall asset performance shows commodities outperforming equities, with the Korean stock market leading gains[4] - MSCI global index increased by 0.6%, but growth momentum has significantly slowed compared to previous weeks[5] - The yield curve for Chinese bonds is steepening, indicating a "bear steepening" trend, while U.S. bonds are experiencing a "bull steepening" trend[6] Commodity and Currency Trends - 10 out of 13 major commodities recorded price increases, with COMEX silver rising by 101.9% year-to-date[7] - The U.S. dollar index fell by 0.5%, with the euro and pound appreciating by 0.4% and 0.8% respectively; the dollar has depreciated by 8.8% since the beginning of the year[7] Consumer and Industrial Insights - Service consumption has improved year-on-year, with Shanghai Disneyland's visitor index up by 75% compared to last year[10] - Real estate transactions in major cities have seen significant declines, with new home sales down by 32.5% year-on-year[30] Financial Sector Developments - As of November 2025, the total net asset value of public funds reached 36 trillion yuan, with equity funds increasing by 1.55%[24] - The performance evaluation of the investment banking sector is shifting towards enhancing investor experience[23] Company-Specific Highlights - Traffic Bank's net profit growth is projected at 2.3% for 2025, with a target price adjustment to 8.58 yuan based on a 0.72x PB valuation[34] - Didi's EBITA is expected to be 46.0 billion yuan in 2025, with a target market value of 234.7 billion yuan[39]

Group 1 - The company is recruiting interns for positions in Shenzhen, Shanghai, and Beijing, requiring in-person internships with a minimum commitment of three days per week for at least three months [1] - The application deadline for submitting resumes is December 31, 2025 [1] - Interns with outstanding performance may have the opportunity for full-time employment after the internship [1] Group 2 - Responsibilities include data processing, analysis, and assisting researchers with quantitative investment projects [2] - Interns will also assist in the development and tracking of financial engineering strategy models [2] - Additional tasks may be assigned by the team [2] Group 3 - Basic requirements include being a master's or doctoral student in STEM fields or financial engineering, with a strong preference for exceptional fourth-year students [3] - Proficiency in programming languages such as Python and familiarity with SQL databases are essential [3] - Candidates should possess strong self-motivation, analytical skills, and effective communication abilities [3] Group 4 - Preferred qualifications include a solid foundation in financial markets, familiarity with key concepts in stocks, bonds, futures, indices, and funds [4] - A strong mathematical background, research project experience, and published academic papers in SCI or EI are advantageous [4] - Familiarity with financial terminals like Wind, Bloomberg, and Tianruan, as well as knowledge of machine learning and deep learning, is a plus [4] Group 5 - Interested candidates should submit their resumes in PDF format to the specified email address, following a specific naming convention for the email subject [5] - Resumes not adhering to the naming format will be treated as spam [5] - Qualified candidates will be contacted for written tests and interviews after the resume collection deadline [5]

Group 1: Macro Economic Outlook - The "14th Five-Year Plan" emphasizes three key points: continuity, technological strength, and expanding domestic demand [5][6] - The GDP growth target for 2026 is projected at around 5%, with an average annual growth rate of 4.17% needed over the next decade to meet the 2035 goals [5][6] - The macroeconomic policy is expected to be more proactive, with potential interest rate cuts and an expansion of the broad deficit [9][10] Group 2: Supply and Demand Dynamics - On the supply side, there is a focus on enhancing service supply to stimulate consumption, with a service trade restrictiveness index of 0.225, higher than the OECD average of 0.19 [6] - The demand side anticipates limited recovery in fixed asset investment, with manufacturing investment supported by equipment updates, while real estate investment is expected to narrow its decline [7][8] - CPI is projected to rise by approximately 0.7% in 2026, while PPI could range from -0.7% to 0.5% depending on various scenarios [8] Group 3: Manufacturing and PMI Insights - The manufacturing PMI for November 2025 is reported at 49.2%, indicating a slight recovery but still in the contraction zone [14][15] - The service sector PMI has dropped to 49.5%, reflecting a contraction influenced by seasonal factors and consumer behavior [16] - High-tech manufacturing continues to expand, with a PMI of 50.1%, while the overall manufacturing sector remains under pressure [17] Group 4: Financial Market Perspectives - The bond market is expected to see a slight upward trend in yields due to revised economic expectations [19] - The Hong Kong stock market faced pressure in November 2025, with the Hang Seng Index declining by 0.2% and the Hang Seng Tech Index dropping by 5.2% [21][22] - The CCASS selected 20 portfolio achieved a historical high in excess returns, with a 0.13% return in November compared to a -0.18% return for the Hang Seng Index [27][28]

文/国联民生证券总裁 葛小波 ，国联民生证券战略发展总部副总经理 刘小萌 科创板宣布设立七周年，长三角"硬科技"企业借政策东风加速崛起，未盈 利第五套标准、科创债风险分担、创新券等组合拳显著降低融资成本。证 监会"科创八条" "科创十六条"再拓融资渠道，并购、再融资、跨境需求同 步爆发。证券公司正从"通道"转为"全生命周期合伙人"，须重塑估值体 系、三位一体客户网络与AI、区块链、ETF等金融工程工具，深度嵌入产 业链协同与区域创新生态，与科创企业共生共长，共筑新质生产力竞争壁 垒。 科创板设立以来，已经有数百家企业通过科创板上市，成功地支持了科创企业的发展，当然也存在进一步改善和提高的潜力。近年来，国家出台了一系列 支持科技创新的政策，旨在推动科创企业的发展，提升国家的科技竞争力。中国证监会发布了《关于深化科创板改革 服务科技创新和新质生产力发展的 八条措施》（"科创板八条"）、《资本市场服务科技企业高水平发展的十六项措施》（"科创十六条"）多项深化改革的政策，进一步提升了资本市场对科 技企业，特别是未盈利科技企业的包容性和适应性。上述政策从拓宽融资渠道、降低融资成本、优化市场环境等多方面提升科创企业的融资 ...

- The report tracks the performance of various broad-based indices in the A-share market, including their daily, weekly, monthly, and yearly changes. For instance, on November 18, 2025, all tracked indices fell, with the CSI 2000 and CSI 500 experiencing the largest declines of -1.32% and -1.17%, respectively[1][2][10] - The report compares the indices against their moving averages and their positions relative to the highest and lowest points over the past 250 trading days. For example, all tracked indices have fallen below their 5-day and 10-day moving averages, with the CSI 2000 still above its 20-day moving average[13] - The report provides data on the trading volume and turnover rates of the indices. On November 18, 2025, the CSI 2000 had the highest trading volume share at 24.98%, followed by the CSI 300 at 22.28% and the CSI 1000 at 22.17%. The turnover rates for these indices were 4.48, 2.85, and 2.83, respectively[15] - The report analyzes the distribution of daily returns for the indices, noting that the ChiNext Index has the largest negative kurtosis deviation, while the CSI 1000 has the smallest. The CSI 2000 has the smallest negative skewness, while the SSE 50 has the largest[21][23] - The report examines the risk premiums of the indices relative to the 10-year government bond yield. As of November 18, 2025, the SSE 50 and CSI 300 had relatively high 5-year percentile risk premiums of 37.62% and 24.29%, respectively, while the CSI 2000 and CSI 500 had lower values of 15.95% and 13.41%[25][28][29] - The report evaluates the PE-TTM (Price-to-Earnings ratio based on trailing twelve months) of the indices as a measure of valuation. The CSI 1000 and CSI 500 had high 5-year percentile values of 96.86% and 95.45%, respectively, while the CSI 2000 and ChiNext Index had lower values of 82.64% and 55.04%[37][40][41] - The report assesses the stock-bond yield ratio, which compares the inverse of the PE-TTM to the 10-year government bond yield. None of the indices were above their 80% percentile (opportunity value), and none were below their 20% percentile (danger value)[43] - The report tracks the dividend yields of the indices, noting that the ChiNext Index and CSI 1000 had relatively high 5-year historical percentile values of 70.58% and 39.17%, respectively, while the CSI 500 and CSI 2000 had lower values of 16.94% and 13.55%[45][50][51] - The report monitors the net asset value break rates of the indices, indicating the proportion of stocks trading below their net asset value. As of the latest data, the break rates were 22.0% for the SSE 50, 16.0% for the CSI 300, 11.6% for the CSI 500, 7.2% for the CSI 1000, 2.45% for the CSI 2000, 1.0% for the ChiNext Index, and 5.65% for the CSI All Share Index[52]

- The report tracks the performance of enhanced index funds, highlighting the best-performing funds across different indices such as CSI 500, CSI 1000, and CSI 2000. For example, the Ping An CSI 500 Enhanced Index Fund achieved an excess return of 2.03% last week, while the Taiping CSI 1000 Enhanced Index Fund recorded an excess return of 1.84%[5][38][41] - Over the past year, the best-performing enhanced index funds include the E Fund CSI 300 Enhanced Fund with a 12.83% excess return, the Penghua CSI 500 Enhanced Fund with an 18.90% excess return, and the Huaxia CSI 1000 Enhanced Fund with a 28.67% excess return[39][42] - The report also provides detailed performance metrics for various enhanced strategy ETFs, such as the China Merchants CSI 2000 Enhanced Strategy ETF, which achieved a 31.60% excess return over the past year and 22.17% since 2025[27][28][39]

- The report primarily focuses on the performance and valuation analysis of convertible bonds, including market trends, individual bond performance, and valuation metrics[1][2][7] - Convertible bond indices such as Shanghai Convertible Bond Index, Shenzhen Convertible Bond Index, and CSI Convertible Bond Index showed weekly returns of 0.290%, 0.780%, and 0.525%, respectively, compared to equity indices like Shanghai Composite Index (-0.177%) and CSI All Share Index (-0.533%)[7][10] - The convertible bond market's trading volume and value for the week were 204,608.83 million units and 35,675,193.47 million yuan, with week-over-week changes of -0.99% and 1.77%, respectively[7][10] - The median conversion premium rate of the convertible bond market was 26.24%, with an arithmetic average of 40.91%, showing slight week-over-week fluctuations of -0.66% and -0.80%[10][18] - Top-performing convertible bonds for the week included Guocheng Convertible Bond (+31.44%), Dazhong Convertible Bond (+28.89%), Dongshi Convertible Bond (+20.41%), Shouhua Convertible Bond (+11.19%), and Kaisheng Convertible Bond (+9.85%)[19][20] - The worst-performing convertible bonds for the week were Hangyu Convertible Bond (-10.75%), Cehu Convertible Bond (-7.30%), Haomei Convertible Bond (-7.01%), Outong Convertible Bond (-6.87%), and Liugong Convertible Bond 2 (-6.65%)[19][21] - Convertible bond valuation analysis categorized bonds by price ranges (<100, 100-110, 110-120, 120-130, 130-140, >140), with respective median conversion premium rates of 0.00%, 80.98%, 64.74%, 56.00%, 24.90%, and 12.84%[35][36][40]