Quantitative Models and Construction Methods 1. Model Name: GAN_GRU - **Model Construction Idea**: The GAN_GRU model combines Generative Adversarial Networks (GAN) for feature generation and Gated Recurrent Unit (GRU) for time-series feature encoding to construct a stock selection factor[4][13][14] - **Model Construction Process**: 1. **GAN Component**: - The generator (G) learns the real data distribution and generates realistic samples from random noise \( z \) (Gaussian or uniform distribution). The generator's loss function is: $$ L_{G} = -\mathbb{E}_{z\sim P_{z}(z)}[\log(D(G(z)))] $$ where \( D(G(z)) \) represents the discriminator's probability of classifying generated data as real[24][25][26] - The discriminator (D) distinguishes real data from generated data. Its loss function is: $$ L_{D} = -\mathbb{E}_{x\sim P_{data}(x)}[\log D(x)] - \mathbb{E}_{z\sim P_{z}(z)}[\log(1-D(G(z)))] $$ where \( D(x) \) is the probability of real data being classified as real, and \( D(G(z)) \) is the probability of generated data being classified as real[27][29][30] - GAN training alternates between optimizing \( G \) and \( D \) until convergence[30] 2. **GRU Component**: - Two GRU layers (GRU(128, 128)) are used to encode time-series features, followed by a Multi-Layer Perceptron (MLP) with layers (256, 64, 64) to predict returns. The final output \( pRet \) is used as the stock selection factor[22] 3. **Feature Input and Processing**: - Input features include 18 price-volume characteristics (e.g., closing price, turnover, etc.) sampled over the past 400 days, with a shape of \( 40 \times 18 \) (40 days of features)[18][19][37] - Features undergo outlier removal, standardization, and cross-sectional normalization[18] 4. **Training Details**: - Training-validation split: 80%-20% - Semi-annual rolling training (June 30 and December 31 each year) - Hyperparameters: batch size equals the number of stocks, Adam optimizer, learning rate \( 1e-4 \), IC loss function, early stopping (10 rounds), max training rounds (50)[18] 5. **Stock Selection**: - Stocks are filtered to exclude ST stocks and those listed for less than six months[18] - **Model Evaluation**: The GAN_GRU model effectively captures price-volume time-series features and demonstrates strong predictive power for stock returns[4][13][22] --- Model Backtesting Results 1. GAN_GRU Model - **IC Mean**: 0.1119*** (2019-2025)[4][41] - **ICIR (non-annualized)**: 0.89[42] - **Turnover Rate**: 0.83X[42] - **Recent IC**: 0.0331*** (December 2025)[4][41] - **1-Year IC Mean**: 0.0669***[4][41] - **Annualized Return**: 37.40%[42] - **Annualized Volatility**: 23.39%[42] - **IR**: 1.60[42] - **Maximum Drawdown**: 27.29%[42] - **Annualized Excess Return**: 22.42%[4][42] --- Quantitative Factors and Construction Methods 1. Factor Name: GAN_GRU Factor - **Factor Construction Idea**: The GAN_GRU factor is derived from the GAN_GRU model, leveraging GAN for price-volume feature generation and GRU for time-series encoding[4][13][14] - **Factor Construction Process**: - The GAN generator processes raw price-volume time-series features (\( Input\_Shape = 40 \times 18 \)) and outputs transformed features with the same shape (\( Input\_Shape = 40 \times 18 \))[37] - The GRU component encodes these features into a predictive factor for stock selection[22] - The factor undergoes industry and market capitalization neutralization and standardization[22] - **Factor Evaluation**: The GAN_GRU factor demonstrates robust performance across various industries and time periods, with significant IC values and excess returns[4][41] --- Factor Backtesting Results 1. GAN_GRU Factor - **IC Mean**: 0.1119*** (2019-2025)[4][41] - **ICIR (non-annualized)**: 0.89[42] - **Turnover Rate**: 0.83X[42] - **Recent IC**: 0.0331*** (December 2025)[4][41] - **1-Year IC Mean**: 0.0669***[4][41] - **Annualized Return**: 37.40%[42] - **Annualized Volatility**: 23.39%[42] - **IR**: 1.60[42] - **Maximum Drawdown**: 27.29%[42] - **Annualized Excess Return**: 22.42%[4][42] 2. Industry-Specific Performance - **Top 5 Industries by Recent IC (October 2025)**: - Social Services: 0.4243*** - Coal: 0.2643*** - Environmental Protection: 0.2262*** - Retail: 0.1888*** - Steel: 0.1812***[4][41][42] - **Top 5 Industries by 1-Year IC Mean**: - Social Services: 0.1303*** - Steel: 0.1154*** - Non-Bank Financials: 0.1157*** - Retail: 0.1067*** - Building Materials: 0.1017***[4][41][42] 3. Industry-Specific Excess Returns - **Top 5 Industries by December 2025 Excess Returns**: - Banking: 4.30% - Real Estate: 3.51% - Environmental Protection: 2.18% - Retail: 1.76% - Machinery: 1.71%[2][45] - **Top 5 Industries by 1-Year Average Excess Returns**: - Banking: 2.12% - Real Estate: 1.93% - Environmental Protection: 1.50% - Retail: 1.46% - Machinery: 1.23%[2][46]

Core Insights - The report indicates that the innovative drug sector is entering a phase of selective stock picking in 2026, following a beta market in 2025. The A-share pharmaceutical industry has risen by 15.9% since the beginning of 2025, underperforming the CSI 300 index by 0.65 percentage points, ranking 17th among industries [2][14]. - The report highlights three key investment directions for 2026: overseas expansion of pharmaceuticals, brain-computer interfaces, and AI in healthcare [2]. Investment Strategy Overview - The innovative drug sector is expected to shift from a broad market rally to a focus on selective stocks in 2026. The average increase for 75 innovative drug sample indices in A-shares reached 54.8%, with Hong Kong's indices doubling [2]. - The report notes that as of December 5, 2025, there were 166 overseas business development (BD) projects, a significant increase from the previous year, with upfront payments reaching $6.3 billion, a growth of over 199% compared to 2024 [2]. Key Investment Directions Overseas Expansion - The report emphasizes the acceleration of Chinese innovative drugs entering international markets, with ADCs and bispecific antibodies being hot topics. The potential for GLP-1R target new drugs remains strong in areas such as long-acting formulations and oral medications [2]. Brain-Computer Interfaces - The report outlines the government's strategic push for brain-computer interfaces as a new economic growth point, with applications in medical rehabilitation for conditions like stroke and spinal cord injuries [2]. AI in Healthcare - The report discusses the establishment of clear short-term and long-term goals for AI in healthcare, covering various applications such as AI health management and clinical decision support systems [2]. Recommended Stocks - The report recommends several companies for investment, including Heng Rui Medicine (600276), BeiGene (688235), Mindray Medical (300760), and others, indicating a diversified approach across the innovative drug and medical device sectors [2].

Quantitative Models and Construction Model Name: DTLC_RL (Decoupled Temporal Contrastive Learning with Reinforcement Learning) - **Model Construction Idea**: The model aims to combine the nonlinear predictive power of deep learning with interpretability by decoupling feature spaces, enhancing representation through contrastive learning, ensuring independence via orthogonal constraints, and dynamically fusing spaces using reinforcement learning[2][11][12] - **Model Construction Process**: - **Feature Space Decoupling**: Three orthogonal latent spaces are constructed to capture market systemic risk (β space), stock-specific signals (α space), and fundamental information (θ space). Each space is equipped with a specialized encoder: TCN for β space, Transformer for α space, and gated residual MLP for θ space[11][12][92] - **Contrastive Learning**: Introduced within each space to enhance robustness by constructing positive and negative sample pairs based on return similarity. The InfoNCE loss function is used to maximize the similarity of positive pairs while minimizing that of negative pairs: $$L_{\mathrm{InfotNCE}}=-E\left[l o g~\frac{e x p\left(f(x)^{\top}f(x^{+})/\tau\right)}{e x p\left(f(x)^{\top}f(x^{+})/\tau\right)+\sum_{i=1}^{N-1}~e x p\left(f(x)^{\top}f(x_{i}^{-})/\tau\right)}\right]$$ where \(f(x)\) is the feature representation, \(x^+\) is the positive sample, \(x^-\) is the negative sample, and \(\tau\) is the temperature parameter[55][56] - **Orthogonal Constraints**: A loss function is added to ensure the outputs of the three spaces are statistically independent, reducing multicollinearity and enhancing interpretability[12][104] - **Reinforcement Learning Fusion**: A PPO-based reinforcement learning mechanism dynamically adjusts the weights of the three spaces based on market conditions. The reward function includes components for return correlation, weight stability, and weight diversification: $$r_{t}=R_{t}^{I C}\big(\widehat{y_{t}},y_{y}\big)+\lambda_{s}R_{t}^{s t a b l e}+\lambda_{d}R_{t}^{d i v}$$ The PPO optimization process includes GAE advantage estimation and a clipped policy loss: $$L^{C L P}=E\left[\operatorname*{min}(r\dot{A},c l i p(r,1-\varepsilon,1+\varepsilon)\dot{A})\right]$$[58][120][121] - **Model Evaluation**: The DTLC_RL model demonstrates strong predictive power and interpretability, with dynamic adaptability to market conditions[2][12][122] Model Name: DTLC_Linear - **Model Construction Idea**: A baseline model for comparison, using a linear layer to fuse the three feature spaces[98][100] - **Model Construction Process**: - The encoded information from the three spaces is concatenated and passed through a linear layer with a Softmax activation to generate fusion weights. The model is trained with a multi-task loss function, including IC maximization, contrastive learning loss, and orthogonal constraints[98][104] - **Model Evaluation**: Provides a benchmark for evaluating the contribution of reinforcement learning in DTLC_RL[98][103] Model Name: DTLC_Equal - **Model Construction Idea**: A simpler baseline model that equally weights the three feature spaces without dynamic adjustments[98] - **Model Construction Process**: The outputs of the three spaces are directly averaged to generate predictions[98] - **Model Evaluation**: Serves as a control group to assess the benefits of dynamic weighting in DTLC_RL[98][103] --- Model Backtesting Results DTLC_RL - **IC**: 0.1250[123] - **ICIR**: 4.38[123] - **Top 10% Portfolio Annualized Return**: 34.77%[123] - **Annualized Volatility**: 25.41%[123] - **IR**: 1.37[123] - **Maximum Drawdown**: 40.65%[123] - **Monthly Turnover**: 0.71X[123] DTLC_Linear - **IC**: 0.1239[105] - **ICIR**: 4.25[105] - **Top 10% Portfolio Annualized Return**: 32.95%[105] - **Annualized Volatility**: 24.39%[105] - **IR**: 1.35[105] - **Maximum Drawdown**: 35.94%[105] - **Monthly Turnover**: 0.76X[105] DTLC_Equal - **IC**: 0.1202[105] - **ICIR**: 4.06[105] - **Top 10% Portfolio Annualized Return**: 32.46%[105] - **Annualized Volatility**: 25.29%[105] - **IR**: 1.28[105] - **Maximum Drawdown**: 40.65%[105] - **Monthly Turnover**: 0.71X[105] --- Quantitative Factors and Construction Factor Name: Beta_TCN - **Factor Construction Idea**: Captures market systemic risk by quantifying stock sensitivity to common risk factors like macroeconomic fluctuations and market sentiment[67] - **Factor Construction Process**: - Five market-related features are selected, including beta to market returns, volatility sensitivity, liquidity beta, size exposure, and market sentiment sensitivity[72] - A TCN encoder processes 60-day time-series data, using dilated causal convolutions to capture short- and medium-term trends. The output is a 32-dimensional vector representing systemic risk features[68] - **Factor Evaluation**: Demonstrates moderate stock selection ability and effectively captures market-related information[73] Factor Name: Alpha_Transformer - **Factor Construction Idea**: Extracts stock-specific alpha signals from price-volume time-series data[76] - **Factor Construction Process**: - Thirteen price-volume features are encoded using a multi-scale Transformer model, with separate layers for short-, medium-, and long-term information. Outputs are fused using a gated mechanism and passed through a fully connected layer for return prediction[77][78] - **Factor Evaluation**: Exhibits strong predictive power and stock selection ability, with relatively low correlation to market benchmarks[81][82] Factor Name: Theta-ResMLP - **Factor Construction Idea**: Focuses on fundamental information to assess financial safety margins and risk resistance[88] - **Factor Construction Process**: - Eight core financial indicators, including PE, PB, ROE, and dividend yield, are encoded using a gated residual MLP. The architecture includes input projection, gated residual blocks, and a final output layer[92] - **Factor Evaluation**: Provides stable stock selection performance with lower turnover and drawdown compared to other spaces[95][96] --- Factor Backtesting Results Beta_TCN - **IC**: 0.0969[73] - **ICIR**: 3.73[73] - **Top 10% Portfolio Annualized Return**: 27.73%[73] - **Annualized Volatility**: 27.19%[73] - **IR**: 1.02[73] - **Maximum Drawdown**: 45.80%[73] - **Monthly Turnover**: 0.79X[73] Alpha_Transformer - **IC**: 0.1137[81] - **ICIR**: 4.19[81] - **Top 10% Portfolio Annualized Return**: 32.66%[81] - **Annualized Volatility**: 23.04%[81] - **IR**: 1.42[81] - **Maximum Drawdown**: 27.59%[81] - **Monthly Turnover**: 0.83X[81] Theta-ResMLP - **IC**: 0.0485[95] - **ICIR**: 1.87[95] - **Top 10% Portfolio Annualized Return**: 23.88%[95] - **Annualized Volatility**: 23.96%[95] - **IR**: 0.99[95] - **Maximum Drawdown**: 37.41%[95] - **Monthly Turnover**: 0.41X[95]

Core Insights - The swine industry is entering a "cost competition new pattern," with policy adjustments leading to weak cycles and strong differentiation, resulting in overall micro-profitability in 2025, favoring leading enterprises [4][5] - The beef industry is experiencing a significant supply reduction, creating a large cycle, with domestic beef farming being highly fragmented and facing substantial overcapacity risks due to prolonged losses [4][5] - The edible fungus sector is seeing a rational return of industry capacity, with leading companies solidifying their market positions, particularly in the artificial cultivation of Cordyceps sinensis [4][5] Swine Industry - The breeding sector is characterized by a new cost competition landscape, with the overall industry expected to be micro-profitable in 2025, while leading companies maintain strong profitability [4] - The number of breeding sows is at a reasonable high level, with policies guiding reductions, leading to weaker price fluctuations [4] - Recommended companies include Muyuan Foods (牧原股份), Wens Foodstuffs (温氏股份), and Lihua Agricultural (立华股份) [4] Beef Industry - The beef industry is undergoing deep supply clearance, with significant fragmentation in domestic beef farming, where over 90% of farmers have fewer than 10 cattle [4] - In 2024, beef prices hit a five-year low, with losses exceeding 1,600 yuan per head for eight consecutive months, accelerating the elimination of breeding cows [4] - Recommended companies include Youran Dairy (优然牧业) and Fucheng Co., Ltd. (福成股份) [4] Edible Fungus Sector - The industry is rationally returning to capacity, with leading companies consolidating their market positions [4] - The artificial cultivation of Cordyceps sinensis is entering a performance release period, opening a second growth curve [4] Supply Dynamics in Swine Industry - The supply dynamics of breeding sows are changing in three phases: expansion, stabilization, and reduction, with a notable decrease in sow inventory expected in the latter half of 2025 [15][19] - The feed consumption trends indicate a correlation with sow inventory changes, with feed sales peaking in September 2025 [17] - The profitability of self-breeding operations remains positive despite recent price declines, but losses have begun to emerge as prices drop below 14 yuan per kilogram [20] Cost Trends - The overall trend in breeding costs is declining, supported by lower corn and soybean meal prices, with costs for large-scale and purchased pig farming at 12.40 yuan/kg and 13.31 yuan/kg respectively [34] - The pig-to-grain price ratio has dropped significantly, indicating worsening profitability for farmers [36] Market Opportunities - The "anti-involution" policy is seen as a catalyst for market opportunities, with government efforts to guide production capacity adjustments and improve product quality [58] - The current valuation of the swine breeding sector is at historical lows, with potential for profit recovery as supply reduces and prices stabilize [60] Long-term Outlook - The long-term outlook for the swine industry is shaped by supply-side reforms and capacity reductions, with a strong expectation for capacity restructuring [64] - The policy environment is focused on reducing inefficient production capacity, enhancing the competitive position of leading companies [64]

Investment Rating - The report indicates a positive outlook for the agricultural drone industry, suggesting a strong investment opportunity due to the expected growth and market dynamics [2][4]. Core Insights - Agricultural mechanization is thriving, with agricultural drones becoming a global leader. China is actively promoting agricultural mechanization through subsidies, achieving a comprehensive mechanization rate of over 75% by 2024, ahead of the 14th Five-Year Plan target [4][15]. - The agricultural drone industry is experiencing a technological revolution initiated by China, with significant market share and innovation driving growth. The industry is expected to see a shift towards smart driving technology, reducing operational barriers and enhancing safety [4][24]. - The report highlights the emergence of XAG Technology as a potential leader in the agricultural drone sector, with a strong financial performance and a focus on expanding its product offerings [4][58]. Summary by Sections Industry Overview - The agricultural drone market is characterized by rapid growth, with China's market share being substantial. As of mid-2025, China's agricultural drone ownership exceeded 250,000 units, while the global market surpassed 500,000 units [24][32]. - The market size for the global agricultural drone industry is projected to grow from 16 billion yuan in 2019 to 55 billion yuan by 2024, with a compound annual growth rate (CAGR) of 28.8% [32][30]. Market Trends - Three major trends are identified: 1. Domestic market competition is expected to ease, with prices stabilizing around 30,000 to 40,000 yuan per unit [39]. 2. A transition towards smart driving technology is anticipated, increasing the proportion of end consumers in the market [43]. 3. The overseas market is becoming a new growth area for Chinese agricultural drone companies, with significant expansion into regions like Southeast Asia and Latin America [50][54]. Company Spotlight: XAG Technology - XAG Technology has been a pioneer in the agricultural drone sector, with a focus on innovation and market expansion. The company aims to become the first publicly listed agricultural drone company in Hong Kong, having achieved profitability in 2024 [58][66]. - The company's revenue from agricultural drones is projected to grow from 4.75 billion yuan in 2022 to 9.35 billion yuan by the end of 2024, with expectations to exceed 10 billion yuan in 2025 [72][66]. - XAG's product matrix includes agricultural drones, autonomous vehicles, and smart farm IoT products, with drones accounting for nearly 89% of its revenue in the first half of 2025 [69][70]. Financial Performance - XAG Technology's revenue has shown significant growth, with a turnaround to profitability in 2024, achieving a net profit of 69.42 million yuan. The company continues to expand its market presence both domestically and internationally [66][83]. - The company's gross margin has improved significantly, rising from 17.9% in 2022 to 34.3% in the first half of 2025, reflecting enhanced operational efficiency and cost management [83].

1. Report's Industry Investment Rating No relevant information provided in the report. 2. Core Views of the Report - In 2026, with insurance companies' asset - liability management focusing more on "income coverage ratio", the investment side may show a pattern of "holding local government bonds as the bottom - position, a structural differentiation in the demand for treasury bonds, and a rebound in the willingness to allocate secondary and perpetual bonds". Insurance companies' choices of specific bond types may show structural differentiation [2][24]. - The Financial Regulatory总局 issued the "Measures for the Asset - Liability Management of Insurance Companies (Exposure Draft)" in December 2025, which reconstructs regulatory indicators and governance standards, shifting the regulatory logic from "score - oriented" to "threshold management" [4][10]. - In 2026, as the insurance industry accelerates the transformation to floating - income products such as dividend - paying insurance and the regulatory focus shifts to effective duration, the liability duration of insurance companies may decline structurally, and the pressure on asset - liability duration matching at the indicator level of life insurance companies may be further alleviated [4][18]. - Looking forward to the year - end market, abundant liquidity may maintain the short - end advantage, while the long - end needs to focus on whether the pricing is gradually dominated by allocation funds. Investment strategies should consider the balance between odds and win - rates [6][114]. 3. Summary According to the Table of Contents 3.1 ALM Regulatory Update and Insurance Company Behavior Changes - The "Measures for the Asset - Liability Management of Insurance Companies (Exposure Draft)" was issued in December 2025, integrating previous regulatory measures and rules, and reconstructing regulatory indicators. The regulatory logic shifts from "score - oriented" to "threshold management", and "effective duration gap" is established as the core regulatory indicator for life insurance companies [4][10]. - In 2025, the overall asset - liability duration gap of the insurance industry narrowed, but some small and medium - sized insurance companies faced an expansion of the duration gap. In 2026, the liability duration of insurance companies may decline structurally, and the focus of investment may shift to income coverage [14][18]. - In 2026, the investment side of insurance companies may show a pattern where local government bonds are the bottom - position, the demand for treasury bonds is structurally differentiated, and the willingness to allocate secondary and perpetual bonds rebounds [2][24]. 3.2 Important Matters - In December 2025, the central bank conducted a net injection of 200 billion yuan through 6 - month term repurchase agreements, with the 6 - month term repurchase balance reaching 3.7 trillion yuan [29]. - From January to November 2025, the national general public budget revenue was 20.0516 trillion yuan, a year - on - year increase of 0.8%. The expenditure was 24.8538 trillion yuan, a year - on - year increase of 1.4% [31]. - In December 2025, the Federal Reserve cut interest rates by 25 basis points to a target range of 3.5% - 3.75%. The Bank of Japan raised interest rates by 25 basis points to 0.75% [34][37]. 3.3 Money Market - The central bank restarted 14 - day reverse repurchase operations last week, with a net injection of - 1.1 billion yuan. The money market remained relatively loose, with DR001 below 1.3% throughout the week [38][40]. - The issuance scale of inter - bank certificates of deposit (ICDs) last week was 995.79 billion yuan, with a net financing of - 67.06 billion yuan. The issuance cost of state - owned banks increased, while the secondary - market yields of ICDs declined [46][53]. 3.4 Bond Market - In the primary market, the supply scale of interest - rate bonds decreased last week, with an actual issuance of 376.127 billion yuan, a maturity of 347.33 billion yuan, and a net financing of 28.797 billion yuan [56][63]. - In 2025, the net financing scale of national and local government bonds increased significantly. As of December 19, the net financing of national bonds was about 6.23 trillion yuan, and that of local bonds was about 7.11 trillion yuan [58]. - In the secondary market, ultra - long - term bonds fluctuated widely, while short - term interest rates performed well. The yields of various - term treasury and policy - bank bonds changed, and the liquidity premium of active bonds was relatively stable [70][78]. 3.5 Institutional Behavior Tracking - In November 2025, the leverage ratios of banks, securities firms, and other institutions decreased non - seasonally. The leverage ratio of all institutions in the inter - bank market was about 118.04% [87][88]. - Last week, state - owned banks increased their holdings of treasury bonds with maturities of less than 5 years and policy - bank bonds with maturities of 5 - 10 years; rural commercial banks increased their holdings of treasury bonds with maturities of more than 10 years; insurance companies bought long - term treasury and local government bonds; securities firms sold long - term treasury bonds; and funds adjusted their portfolios to shorten the overall duration [87][100]. 3.6 High - Frequency Data Tracking - Last week, the settlement prices of rebar and wire rod futures increased, while the settlement price of cathode copper futures decreased. The cement price index and the Nanhua Glass Index increased. The CCFI index rose, and the BDI index fell [110]. - The wholesale prices of pork increased slightly, while the wholesale prices of vegetables decreased. The settlement prices of Brent crude oil and WTI crude oil futures decreased. The central parity rate of the US dollar against the RMB was 7.06 [110][113]. 3.7 Market Outlook - Looking forward to the year - end market, abundant liquidity may support short - term bonds, while long - term bonds may face short - term fluctuations. As the static yield of ultra - long - term treasury bonds rises, allocation funds may gradually enter the market [6][114]. - Investment strategies suggest gradually building positions in ultra - long - term bonds for odds, but it is recommended to prioritize medium - and short - term treasury and policy - bank bonds and pay attention to trading opportunities of secondary and perpetual bonds with the same maturities. The overall portfolio duration should be controlled in the medium - to - long range of 5 - 7 years [6][114].

1. Report Industry Investment Rating No information provided in the given content. 2. Core Viewpoints of the Report - The bond market rebounded for several days, and the net inflow of bond ETFs increased significantly. The bond ETF market is expected to resonate with the recovery trend of underlying bond assets at the end of the year and benefit from the year - end market [3][6]. - The capital flow is highly concentrated in science - innovation bond ETFs, which are the main contributor to the net inflow [3][7]. 3. Summary According to Relevant Catalogs 3.1 1.1 各类债券 ETF 资金净流入情况 - The net inflows of interest - rate bond ETFs, credit bond ETFs, and convertible bond ETFs last week were +2.937 billion yuan, +14.569 billion yuan, and - 1.730 billion yuan respectively, with a total net inflow of 15.776 billion yuan in the bond ETF market. The year - to - date net inflow reached 462.608 billion yuan. As of December 19, 2025, the bond ETF fund size was 74.3012 billion yuan, up 2.88% from the previous week's close and 313.31% from the beginning of the year. The net inflow scale increased by 264.3% compared with the previous week, mainly driven by credit bond ETFs [3][6]. - The science - innovation bond ETFs had the largest net inflow of 15.965 billion yuan last week, followed by local government bond ETFs (+2.483 billion yuan) and benchmark market - making credit bond ETFs (+1.066 billion yuan). The short - term financing ETF had the largest net outflow of 2.795 billion yuan, followed by convertible bond ETFs (-1.730 billion yuan) [7]. 3.2 1.2 各类债券 ETF 份额走势 - As of the close on December 19, 2025, the share changes of treasury bond ETFs, policy - financial bond ETFs, local government bond ETFs, credit bond ETFs, and convertible bond ETFs compared with December 12, 2025, were 0.46%, 0.49%, 15.94%, 1.99%, and - 2.68% respectively, and the total change in bond - type ETFs was 0.33%. The announcement of the central government allocating 500 billion yuan from the local debt balance limit to supplement local finances on December 15 might be the direct catalyst for the increase in local government bond ETF shares [20]. 3.3 1.3 主要债券 ETF 份额及净值走势 - The shares of major bond ETFs decreased, with the convertible bond ETF having the largest net outflow of shares. As of the close on December 19, 2025, the share changes of 30 - year treasury bond ETF, policy - financial bond ETF, 5 - year local government bond ETF, urban investment bond ETF, and convertible bond ETF compared with December 12, 2025, were - 14.02 million shares, - 2.52 million shares, no change, no change, and - 92.90 million shares respectively [22]. - The net values of major bond ETFs were boosted. As of the close on December 19, 2025, compared with December 12, 2025, the net value changes of 30 - year treasury bond ETF, policy - financial bond ETF, 5 - year local government bond ETF, urban investment bond ETF, and convertible bond ETF were 0.43%, 0.17%, 0.05%, 0.03%, and 0.44% respectively [26]. 3.4 1.4 基准做市信用债 ETF 份额及净值走势 - The share performance of the 8 existing benchmark market - making credit bond ETFs was divergent. As of the close on December 19, 2025, compared with December 9, 2025, the share changes of these ETFs were - 4.50 million shares, 1.00 million shares, no change, no change, 10.00 million shares, 15.00 million shares, - 6.00 million shares, and - 3.80 million shares respectively [29]. - The net values of all 8 ETFs increased. As of the close on December 19, 2025, compared with December 12, 2025, the net value changes of these ETFs were 0.05%, 0.04%, 0.04%, 0.04%, 0.05%, 0.07%, 0.07%, and 0.06% respectively [30]. 3.5 1.5 科创债 ETF 份额及净值走势 - The tracking indexes of science - innovation bond ETFs with leading share increases were mostly AAA science - innovation bonds. The net share inflow of the 24 existing science - innovation bond ETFs last week was 369.32 million shares, a 15.10% increase from the previous week. The top three products in terms of share inflow were Science - Innovation Bond ETF Harvest, Science - Innovation Bond ETF Huaxia, and Science - Innovation Bond ETF ICBC [35]. - The net values of science - innovation bond ETFs rebounded slightly. As of the close on December 19, 2025, the average net values of the first - batch and second - batch science - innovation bond ETFs increased by 0.04% and 0.05% respectively compared with the previous week's close [37]. 3.6 1.6 单只债券 ETF 市场表现情况 - Benefiting from the strong bond market, the net values of bond ETF products generally increased. The 30 - year treasury bond ETF, convertible bond ETF, and 30 - year treasury bond ETF Bosera had the leading increases of 0.46%, 0.44%, and 0.41% respectively. The net value and share trends of the convertible bond ETF were divergent, possibly due to capital cashing in floating profits [40]. - In terms of premium/discount rates, the benchmark treasury bond ETF, Science - Innovation Bond ETF Taikang, and Science - Innovation Bond ETF Dacheng had the leading premium rates of 0.04%, 0.02%, and 0.02% respectively. In terms of scale changes, Science - Innovation Bond ETF Harvest (+5.346 billion yuan), Science - Innovation Bond ETF Huaxia (+3.666 billion yuan), and 0 - 4 Local Government Bond ETF (+2.440 billion yuan) had the top three net inflows, while the short - term financing ETF, 30 - year treasury bond ETF, and convertible bond ETF had the top net outflows of - 2.795 billion yuan, - 1.607 billion yuan, and - 1.247 billion yuan respectively [40].

Investment Rating - The report recommends a "Buy" rating for the commercial aerospace sector, particularly highlighting the potential of private rocket companies and their impact on satellite networking in China [4][7]. Core Insights - The global satellite frequency and orbit resources are limited, with the US leading in the number of satellites in orbit. China is advancing two large-scale internet satellite constellation projects, aiming for a total of approximately 28,000 satellites [4][43]. - In 2024, a total of 263 space launch missions were executed globally, marking an 18% increase from 2023. The US and China accounted for 86% of these launches, with the US conducting 158 missions and China 68 missions [19][24]. - The commercial aerospace market in China is characterized by a dual development model of "national teams and private teams," with private rocket companies expected to enhance launch capacity and reduce costs significantly [4][45]. Summary by Sections Global Launch Activity - In 2024, 263 space launches were conducted globally, with 258 successful missions. The US accounted for 60% of the launches, while China completed 68 launches, representing 26% of the total [19][24][26]. - SpaceX dominated the commercial launch market, executing 117 missions, which is 67% of the global commercial launch count [24][25]. Satellite Constellation Plans - The US has established significant satellite constellation plans, including SpaceX's Starlink with approximately 42,000 satellites and Amazon's Project Kuiper with over 3,200 satellites. China is also advancing its GW constellation and G60 constellation projects [34][36]. Cost Reduction and Technological Advancements - The cost of satellite launches in China has decreased from approximately 115,000 RMB per kilogram in 2020 to about 75,000 RMB per kilogram in 2024, with expectations of further reductions as reusable rocket technology matures [49][51]. - The successful first flight of several private rockets, such as the Zhuque-3, indicates a growing capacity in the commercial launch sector, which is crucial for meeting the demands of satellite constellation deployments [4][51]. Policy Support and Market Dynamics - Recent government policies have elevated commercial aerospace to a national strategic priority, encouraging private investment and innovation in the sector [48][49]. - The report emphasizes the importance of ongoing policy support to address key challenges in the commercial aerospace industry, particularly in enhancing launch capabilities and fostering technological advancements [4][58].

1. Report Industry Investment Rating - Not provided in the document 2. Core Viewpoints of the Report - Last week, the traditional "stock-bond seesaw" effect failed again, with both the stock and bond markets rising and falling together. Long-term interest rates fluctuated sharply between the "reality of loose money" and the "frustration of strong expectations," and the oversold of ultra-long-duration assets reflected the crowding of market funds and the fragility of market sentiment [3][91]. - In the last four trading weeks of the year, the fact that the "sales new rules" have not fully "landed" remains the main market concern, but the approaching important meetings have restored the "loose money" expectation. The focus of market gaming may still be the emotional fluctuations caused by marginal policy changes [3][92]. - The report maintains the judgment of a recovery market in December but expects the downward space of interest rates to be relatively limited. It is recommended to adopt a left-side layout configuration rhythm, prioritize switching positions to medium - and short - term treasury bonds and policy financial bonds, and pay attention to trading opportunities of secondary perpetual bonds of the same term. As the meeting window approaches, gradually increase the offensive nature of the portfolio, control the overall duration center of the portfolio within the medium - to long - term range of 5 - 7 years, and avoid high - congestion assets [3][92][93]. 3. Summary According to the Directory 3.1 Important Matters - On December 5, 2025, the central bank will conduct a 1000 - billion - yuan 3 - month (91 - day) fixed - quantity, interest - rate - tendered, multi - price - winning bidder - selected买断式逆回购 operation. The net investment of the central bank in treasury bonds in November was 5 billion yuan, far lower than the market's relatively optimistic expectation of 100 billion yuan. On December 5, 2025, six major banks stopped selling 5 - year large - denomination certificate of deposit products [6][9]. 3.2 Money Market 3.2.1 Open Market Operations and Fund Interest Rate Trends - From December 1 to 5, 2025, the central bank's 7 - day reverse repurchase operation had a net investment of - 84.8 billion yuan. It is expected that the basic currency will have a maturity withdrawal of 66.38 billion yuan from December 8 to 12, 2025. At the beginning of the month, the fund market was generally loose, and DR001 fell below 1.3% for the first time this year [14][15]. 3.2.2 Certificate of Deposit Interest Rate Trends and Repurchase Transaction Situations - In the primary market, the issuance scale of inter - bank certificates of deposit last week was 495.91 billion yuan, a decrease of 63.54 billion yuan from the previous week. The net financing scale was 47.1 billion yuan, an increase of 289.69 billion yuan from the previous week. The issuance interest rates of inter - bank certificates of deposit generally increased last week. In the secondary market, the yields of inter - bank certificates of deposit generally increased last week [25][31][34]. 3.3 Bond Market - In the primary market, the supply scale of interest - rate bonds decreased last week, with an actual issuance of 430.717 billion yuan and a net financing of 128.844 billion yuan. As of December 5, 2025, the cumulative net financing scale of various treasury bonds in 2025 was about 6.23 trillion yuan, and that of various local bonds was about 7.11 trillion yuan, showing a significant increase compared with the average values from 2021 to 2024. As of last week, the issuance scale of special refinancing bonds in 2025 had reached 2.29 trillion yuan, mainly with long - term and ultra - long - term maturities [38][44][48]. - In the secondary market, at the beginning of the month, the short - term interest rates were stable, while the ultra - long - term interest rates continued to be affected by market noise and increased significantly. The yields of 1 - year, 3 - year, 5 - year, 7 - year, 10 - year, and 30 - year treasury bonds changed by - 0.01BP, - 1.46BP, 1.39BP, 0.17BP, 0.68BP, and 7.20BP respectively. The 10Y - 1Y treasury bond yield spread increased from 43.95BP to 44.64BP. The yields of the same - term CDB bonds also changed, and the 10Y - 1Y CDB bond yield spread increased from 34.94BP to 37.66BP. The implied tax rate of 10 - year CDB bonds increased slightly [51]. 3.4 Institutional Behavior Tracking - Last week, the leveraged trading scale was generally stable due to the relatively loose fund market. In the cash bond market, state - owned banks significantly increased their holdings of treasury bonds within 5 years and local bonds within 10 years; rural commercial banks mainly increased their holdings of 5 - 10 - year policy financial bonds and treasury bonds over 5 years; insurance companies continued to prefer local bonds over 10 years; securities firms and funds were the main sellers last week [68][73]. - In October 2025, the leverage ratio of all institutions in the inter - bank market was about 118.77%, an increase of about 0.06 percentage points from September. The leverage ratios of commercial banks, securities companies, and other institutions in the inter - bank market in October 2025 were about 110.31%, 191.29%, and 132.17% respectively [68]. 3.5 High - Frequency Data Tracking - Last week, the settlement price of rebar futures increased by 2.47% week - on - week, the settlement price of wire rod futures remained flat, the settlement price of cathode copper futures increased by 5.02% week - on - week, the cement price index decreased by 0.40% week - on - week, and the South China Glass Index decreased by 4.70% week - on - week. The CCFI index decreased by 0.62% week - on - week, and the BDI index increased by 9.92% week - on - week. In terms of food prices, the wholesale price of pork decreased by 0.84% week - on - week, and the wholesale price of vegetables increased by 3.31% week - on - week. The settlement prices of Brent crude oil futures and WTI crude oil futures increased by 0.09% and 1.91% respectively week - on - week. The central parity rate of the US dollar against the RMB was 7.07 last week [88]. 3.6 Market Outlook - The report maintains the judgment of a recovery market in December but expects the downward space of interest rates to be relatively limited. It is recommended to adopt a left - side layout configuration rhythm, prioritize switching positions to medium - and short - term treasury bonds and policy financial bonds, and pay attention to trading opportunities of secondary perpetual bonds of the same term. As the meeting window approaches, gradually increase the offensive nature of the portfolio, control the overall duration center of the portfolio within the medium - to long - term range of 5 - 7 years, and avoid high - congestion assets [3][92][93].

Economic Growth Projections - The economic growth target for 2026 is set at around 5%, with an expected actual growth rate of approximately 4.9%[3] - Nominal GDP growth is projected to rise to about 4.2%[3] - Manufacturing investment growth is anticipated to reach around 5.2%, driven by high-end and intelligent upgrades[3] Investment and Infrastructure - Broad infrastructure investment growth is expected to be around 6%, supported by major projects under the "14th Five-Year Plan"[3] - Real estate investment decline is projected to narrow to approximately -10% due to improved supply-demand dynamics[3] Consumption and Prices - Consumer spending is expected to increase, with retail sales growth projected at around 5%[3] - CPI is forecasted to recover moderately to 0.5%, while PPI is expected to remain between -1% and 0%[3] Policy and Fiscal Measures - The budget deficit ratio may exceed 4%, with new special bond limits around 4.5 trillion yuan[3] - Monetary policy is expected to remain "moderately loose," with potential small rate cuts of about 25 basis points and interest rate reductions of approximately 10 basis points[3] Global Economic Context - The U.S. job market is cooling, and inflation pressures are manageable, but uncertainties remain regarding future interest rate paths[3] - Emerging markets may see marginal economic slowdown in 2026, with internal performance continuing to diverge[3] Asset Allocation Strategies - Overweight positions are recommended in U.S. equities and gold, benefiting from liquidity easing and fiscal expansion[3] - Underweight positions in oil are suggested due to high inventory levels and weak demand[3] Risks - Risks include lower-than-expected domestic economic growth, geopolitical tensions, and potential overseas recession exceeding expectations[3]