Quantitative Models and Construction Methods 1. Model Name: Fund Selection Strategy Based on Trading Motivation Factor and Stock Spread Income Factor - **Model Construction Idea**: This strategy combines the trading motivation factor and the stock spread income factor to select funds with high stock spread income, active trading motivation, and low likelihood of performance manipulation[2][24] - **Model Construction Process**: - The **trading motivation factor** is derived from fund report data, including fund flows, stock buy/sell amounts, and the proportion of top 20 stocks traded[47] - The **stock spread income factor** is calculated from the stock spread income in the fund's profit statement[47] - The strategy adopts a semi-annual rebalancing approach, adjusting positions at the end of March and August each year, and selects funds from active equity funds after deducting transaction costs[24] - **Model Evaluation**: The strategy has shown long-term outperformance against the Wind Active Equity Hybrid Fund Index, with a fee-adjusted annualized excess return of 3.64% since March 2011[24][28] 2. Model Name: Fund Selection Strategy Based on Fund Manager Trading Uniqueness - **Model Construction Idea**: This strategy evaluates the uniqueness of fund managers' trading behaviors by constructing a network based on their holdings and transactions, aiming to identify funds with distinctive trading styles[3][32] - **Model Construction Process**: - A network is built using detailed fund manager holdings and transaction data - A metric is calculated to measure the uniqueness of each fund manager's trading behavior compared to their peers[48] - The strategy adopts a semi-annual rebalancing approach, adjusting positions in early April and September each year, and selects funds from active equity funds, general stock funds, and flexible allocation funds after deducting transaction costs[32] - **Model Evaluation**: The strategy has demonstrated significant outperformance, achieving a fee-adjusted annualized excess return of 5.66% since its inception[32][36] 3. Model Name: Industry-Themed ETF Selection Strategy Based on Filing Information - **Model Construction Idea**: This strategy leverages the forward-looking information from the public disclosure stage of ETF filing materials to construct an industry-themed filing similarity factor (T+1), aiming to capture market investment hotspots[4][39] - **Model Construction Process**: - The T+1 factor is constructed by calculating the similarity between the indices tracked by newly filed ETFs and existing market indices[48] - The strategy adopts a monthly rebalancing approach, selecting ETFs from industry-themed ETFs with a transaction fee rate of 0.1% per side, using the CSI 800 Index as the benchmark[39] - **Model Evaluation**: The strategy has consistently outperformed the CSI 800 Index since December 2018, with a fee-adjusted annualized excess return of 11.33%[39][44] --- Model Backtesting Results 1. Fund Selection Strategy Based on Trading Motivation Factor and Stock Spread Income Factor - **December 2025 Return**: 1.56% (vs. 3.06% for the benchmark)[28] - **Annualized Return**: 10.85% (vs. 7.33% for the benchmark)[28] - **Annualized Volatility**: 21.62% (vs. 19.97% for the benchmark)[28] - **Sharpe Ratio**: 0.50 (vs. 0.37 for the benchmark)[28] - **Maximum Drawdown**: 48.39% (vs. 45.42% for the benchmark)[28] - **Annualized Excess Return**: 3.64%[28] - **IR**: 0.61[28] - **Excess Maximum Drawdown**: 19.22%[28] - **December 2025 Excess Return**: -1.54%[28] 2. Fund Selection Strategy Based on Fund Manager Trading Uniqueness - **December 2025 Return**: 5.36% (vs. 3.06% for the benchmark)[36] - **Annualized Return**: 13.40% (vs. 7.87% for the benchmark)[36] - **Annualized Volatility**: 19.52% (vs. 18.30% for the benchmark)[36] - **Sharpe Ratio**: 0.69 (vs. 0.43 for the benchmark)[36] - **Maximum Drawdown**: 37.26% (vs. 45.42% for the benchmark)[36] - **Annualized Excess Return**: 5.66%[36] - **IR**: 1.09[36] - **Excess Maximum Drawdown**: 10.84%[36] - **December 2025 Excess Return**: 2.27%[36] 3. Industry-Themed ETF Selection Strategy Based on Filing Information - **December 2025 Return**: 5.84% (vs. 3.31% for the benchmark)[43] - **Annualized Return**: 19.22% (vs. 6.90% for the benchmark)[43] - **Annualized Volatility**: 21.05% (vs. 18.85% for the benchmark)[43] - **Sharpe Ratio**: 0.91 (vs. 0.37 for the benchmark)[43] - **Maximum Drawdown**: 34.89% (vs. 42.96% for the benchmark)[44] - **Annualized Excess Return**: 11.33%[44] - **IR**: 0.64[44] - **Excess Maximum Drawdown**: 19.07%[44] - **December 2025 Excess Return**: 2.53%[44]

Report Industry Investment Rating - Not provided in the given content Core Viewpoints - Among the bonds with discounted transactions, "24 Chanrong 08" had a relatively large deviation in bond valuation price. Among the bonds with rising net prices, "22 Vanke 02" had a prominent deviation in valuation price. Among the Tier 2 and perpetual bonds with rising net prices, "25 ABC Tier 2 Capital Bond 04B(BC)" had a relatively large deviation in valuation price; among the senior unsecured bonds with rising net prices, "25 ABC TLAC Non - Capital Bond 02C(BC)" had a prominent deviation in valuation price. Among the bonds with a transaction yield higher than 5%, real - estate bonds ranked high [2]. - The changes in credit bond valuation yields were mainly distributed in the [-5,0) interval. The transaction terms of non - financial credit bonds were mainly distributed within 0.5 years, and the proportion of discounted transactions for bonds with terms between 0.5 and 1 year was the highest; the transaction terms of Tier 2 and perpetual bonds were mainly distributed between 4 and 5 years, and the proportion of discounted transactions for bonds within 1 year was the highest. By industry, the bonds in the light manufacturing industry had the largest average deviation in valuation price [2]. Summary by Relevant Catalogs Discounted Transaction Tracking - Bonds such as "24 Chanrong 08", "24 Chanrong 06", etc. in the non - banking finance industry had relatively large deviations in valuation price, with deviations ranging from - 1.17% to - 0.82%. Bonds like "20 Boshui 02" in the agriculture, forestry, animal husbandry and fishery industry and "25 Qingcheng 09" in the urban investment industry also had certain deviations in valuation price [4]. Tracking of Bonds with Rising Net Prices - Real - estate bonds such as "22 Vanke 02", "22 Vanke 06" etc. had a valuation price deviation of 3.98%. Bonds in the banking industry like "25 ABC Tier 2 Capital Bond 04B(BC)" and "25 ABC TLAC Non - Capital Bond 02C(BC)" also had certain deviations in valuation price [6]. Tracking of Tier 2 and Perpetual Bond Transactions - Bonds such as "25 ABC Tier 2 Capital Bond 04B(BC)", "24 Fudian Bank Tier 2 Capital Bond 01" etc. had different degrees of deviation in valuation yield, with deviations ranging from - 5.35bp to - 0.56bp [8]. Tracking of Senior Unsecured Bond Transactions - Bonds such as "25 ABC TLAC Non - Capital Bond 02C(BC)", "25 CITIC Baixin Bank Small and Micro - enterprise Bond 01" etc. had deviations in valuation yield, with deviations ranging from - 1.46bp to - 0.58bp [10]. Tracking of Bonds with a Transaction Yield Higher than 5% - Real - estate bonds such as "22 Vanke 02", "22 Vanke 06" etc. and non - banking finance bonds like "23 Chanrong 05", "23 AVIC Chanrong MTN001 (Science and Technology Innovation Note)" had a transaction yield higher than 5% [11]. Distribution of Credit Bond Valuation Deviations on the Day - The changes in credit bond valuation yields were mainly distributed in the [-5,0) interval [2]. Distribution of Non - financial Credit Bond Transaction Terms on the Day - The transaction terms of non - financial credit bonds were mainly distributed within 0.5 years, and the proportion of discounted transactions for bonds with terms between 0.5 and 1 year was the highest [2]. Distribution of Tier 2 and Perpetual Bond Transaction Terms on the Day - The transaction terms of Tier 2 and perpetual bonds were mainly distributed between 4 and 5 years, and the proportion of discounted transactions for bonds within 1 year was the highest [2]. Discounted Transaction Proportion and Transaction Scale of Non - financial Credit Bonds by Industry - The bonds in the light manufacturing industry had the largest average deviation in valuation price [2].

Investment Rating - The report maintains a positive outlook on the wind power industry, indicating a long-term growth trend driven by economic factors and increasing demand for renewable energy [6]. Core Insights - Global wind power demand is expected to maintain a long-term boom due to economic drivers and the increasing electrification needs, with projected global new installations of 167GW in 2025, a year-on-year increase of 34%, and 196GW in 2026, a year-on-year increase of 18% [2][13]. - Domestic wind power installations are anticipated to break the five-year planning cycle, with significant contributions from offshore wind, replacement projects, and green electricity connections, leading to continued growth [2][14]. - The overseas wind power market is projected to experience sustained demand growth, with a compound annual growth rate (CAGR) of 14% from 2025 to 2030, particularly in the European offshore wind sector, which is expected to grow at a CAGR of 32% [3][50]. Summary by Sections Economic Drivers of Global Wind Power Demand - The report highlights that the global wind power demand is expected to remain robust due to economic factors and the electrification trend, with specific forecasts for new installations in 2025 and 2026 [2][13]. - Domestic demand is supported by market reforms and initiatives such as "old-for-new" replacements and green electricity connections, with expectations of continued growth in installations [14][19]. Profitability and Investment Recommendations - The report suggests that the profitability of wind turbine manufacturers is set to improve, with a notable increase in the average bidding price for onshore wind turbines in 2025, which is expected to rise by approximately 11% [4][29]. - The report recommends focusing on three main investment lines: turbine manufacturers, offshore cable and foundation suppliers, and component manufacturers benefiting from domestic and international market opportunities [6][51]. Offshore Wind Market Dynamics - The report indicates that the European offshore wind market is poised for significant growth, with a recovery in project bidding expected in 2026 after a period of delays and cancellations [59][67]. - The report emphasizes the importance of policy adjustments in Europe that are likely to enhance project success rates and support continued demand growth in the offshore wind sector [59][61].

摘要 根据 Wind 数据，折价成交个券中，"24 产融 08"债券估值价格偏离幅度较大。净价上涨成交个券中，"25 盱眙 02" 估值价格偏离程度靠前。净价上涨成交二永债中，"25 贵州银行二级资本债 01"估值价格偏离幅度较大；净价上涨成 交商金债中，"25 农行 TLAC 非资本债 02C(BC)"估值价格偏离幅度靠前。成交收益率高于 5%的个券中，金融债排名靠 前。 信用债估值收益变动主要分布在(0,5]区间。非金信用债成交期限主要分布在 0.5 年内，其中 0.5 年内品种折价成交 占比最高；二永债成交期限主要分布在 4 至 5 年，其中 2 年内品种折价成交占比最高。分行业看，农林牧渔行业的债 券平均估值价格偏离最大。 风险提示 统计数据偏差或遗漏，高估值个券出现信用风险 敬请参阅最后一页特别声明 1 固定收益动态(动态) 图表1：折价成交跟踪 | | | | | 大幅折价个券成交跟踪 | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 简 称 | 剩余期限 | 估值价格偏 | 估值 ...

- The **Artificial Intelligence Global Asset Allocation Model** applies machine learning to asset allocation problems, utilizing factor investment principles to score and rank assets, ultimately constructing a monthly quantitative equal-weighted strategy for global asset allocation[38][39][41] - The model's suggested weights for January include: government bond index (68.27%), SHFE gold (28.55%), Nasdaq (1.02%), ICE Brent oil (1.24%), and CSI 500 (0.92%)[38][41] - Historical performance from January 2021 to December 2025 shows annualized return of 6.78%, Sharpe ratio of 1.04, maximum drawdown of 6.66%, and excess annualized return of -0.38% compared to the benchmark[39][42] - Year-to-date return for the strategy is 7.18%, while the benchmark return is 18.14%[40][42] - The **Dynamic Macro Event Factor-Based Stock-Bond Rotation Strategy** incorporates macro timing modules and risk budgeting frameworks to generate stock-bond allocation weights for three risk profiles: aggressive, balanced, and conservative[43][44][45] - January stock weights are: aggressive (55.00%), balanced (14.60%), and conservative (0.00%)[43][45] - December macro signals include 60% strength for both economic growth and monetary liquidity dimensions[43][45] - Historical performance from January 2005 to December 2025 shows annualized returns of 20.03% (aggressive), 10.84% (balanced), and 5.88% (conservative), outperforming the benchmark's 8.97%[44][49] - Year-to-date returns are 15.77% (aggressive), 4.23% (balanced), and 0.70% (conservative), compared to the benchmark's 15.95%[44][49] - The **Dividend Style Timing Strategy** leverages 10 indicators from economic growth and monetary liquidity dimensions to construct a timing strategy for dividend indices, showing enhanced stability compared to the CSI Dividend Total Return Index[50][51][53] - January recommended allocation for CSI Dividend is 0%, as most signals did not indicate a bullish outlook[50][54] - Historical performance includes annualized return of 16.18%, maximum drawdown of -21.22%, and Sharpe ratio of 0.93, outperforming the CSI Dividend Total Return Index's annualized return of 11.28% and Sharpe ratio of 0.57[50][53]

Market and Industry Overview - In the past month, major domestic market indices have generally risen, with the CSI 500, National Index 2000, CSI 1000, Shanghai-Shenzhen 300, and SSE 50 increasing by 6.17%, 3.99%, 3.56%, 2.28%, and 2.07% respectively [1][12] - Among the 19 industries in the CITIC first-level industry classification, the defense and military industry, non-ferrous metals, telecommunications, and comprehensive finance saw significant gains, with the defense and military industry leading at a monthly increase of 21.24% [1][12] - Conversely, the pharmaceutical, food and beverage, and real estate industries lagged behind, with monthly declines of -4.09%, -4.34%, and -4.47% respectively [1][12] Industry Rotation Strategy Performance - In December, the performance of factors was notable, with profitability, quality, valuation momentum, and analyst expectations all achieving positive IC values, particularly the profitability factor with an IC of 55.67% [2][21] - All factors contributed positively to long-short returns, with the analyst expectations factor yielding a long-short return of 6.16%, while profitability, quality, and valuation momentum provided returns of 3.75%, 3.47%, and 3.88% respectively [2][21] - For the year 2025, quality, valuation momentum, analyst expectations, and research activity factors all showed positive IC averages of 7.27%, 1.37%, 2.44%, and 7.34% respectively [2][22] Current Industry Recommendations - The January recommendations from the enhanced industry rotation strategy include real estate, non-ferrous metals, defense and military, basic chemicals, and electronics, with significant changes from the previous month [4][49] - The defense and military industry received joint recommendations from both the enhanced strategy and the research-selected strategy, indicating a higher level of attention [5][49] - The research-selected strategy for January includes computer, transportation, coal, steel, and defense and military industries, with increased research activity noted in the computer and transportation sectors [4][51]

1. Report Industry Investment Rating No relevant content provided. 2. Core Viewpoints of the Report - Credit - related data shows that among discounted - traded bonds, "23 Chanjin 09" has a large deviation in bond valuation price; among bonds with rising clean prices, "22 Vanke 06" ranks high in valuation price deviation; among secondary and perpetual bonds with falling clean prices, "22 Industrial Bank Secondary 01" has a small valuation price deviation; among commercial financial bonds with falling clean prices, "25 Weihai Bank Small - and - Micro - enterprise Bond" has a small valuation price deviation; among bonds with a trading yield higher than 5%, real - estate bonds rank high [2]. - Credit bond valuation yield changes are mainly distributed in the (0, 5] range. Non - financial credit bond trading terms are mainly distributed between 2 and 3 years, with the 3 - to 4 - year variety having the highest proportion of discounted trades; secondary and perpetual bond trading terms are mainly distributed between 4 and 5 years. By industry, bonds in the agriculture, forestry, animal husbandry, and fishery industry have the largest average valuation price deviation [2]. 3. Summary According to Relevant Catalogs 3.1 Discounted - Traded Bond Tracking - Bonds such as "23 Chanjin 09", "24 Chanjin 08", etc. in the non - bank financial industry have relatively large valuation price deviations, with "23 Chanjin 09" having a deviation of - 1.05% and a remaining term of 2.69 years, and a trading volume of 16 million yuan [4]. - Bonds in other industries like "20 Boshui 02" in agriculture, forestry, animal husbandry, and fishery, and "Shaanxi Coal KY13" in the coal industry also have certain valuation price deviations [4]. 3.2 Tracking of Bonds with Rising Clean Prices - "22 Vanke 06", "22 Vanke 04", and "22 Vanke 02" in the real - estate industry have large positive deviations in valuation prices, with "22 Vanke 06" having a deviation of 4.15% and a trading volume of 297 million yuan [5]. - Many urban investment bonds also show positive deviations in valuation prices, such as "20 Zunhe 02" and "25 Raochuang 04" [5]. 3.3 Secondary and Perpetual Bond Trading Tracking - Secondary and perpetual bonds of various banks, including state - owned banks, joint - stock banks, and city commercial banks, have a valuation price deviation of - 0.01%. For example, "22 Industrial Bank Secondary 01" has a trading volume of 112,946 million yuan [6]. 3.4 Commercial Financial Bond Trading Tracking - "25 Weihai Bank Small - and - Micro - enterprise Bond" and "25 Weihai Bank Green Bond" have a valuation price deviation of 0.00%, with trading volumes of 32,901 million yuan and 11,966 million yuan respectively [7]. - Some commercial financial bonds have a valuation price deviation of - 0.01%, such as "23 Beijing Rural Commercial Small - and - Micro - enterprise Bond" and "25 CITIC Baixin Bank Small - and - Micro - enterprise Bond 01" [7]. 3.5 Tracking of Bonds with a Trading Yield Higher than 5% - Real - estate bonds like "22 Vanke 06", "22 Vanke 04", etc., and non - bank financial bonds such as "23 Chanjin 05", "23 Chanjin 13" are among the bonds with a trading yield higher than 5% [8]. 3.6 Distribution of Credit Bond Trading Valuation Deviations on the Day - Credit bond valuation yield changes are mainly distributed in the (0, 5] range [2]. 3.7 Distribution of Non - financial Credit Bond Trading Terms on the Day - Non - financial credit bond trading terms are mainly distributed between 2 and 3 years, and the 3 - to 4 - year variety has the highest proportion of discounted trades [2]. 3.8 Distribution of Secondary and Perpetual Bond Trading Terms on the Day - Secondary and perpetual bond trading terms are mainly distributed between 4 and 5 years [2]. 3.9 Discounted - Trade Ratio and Trading Volume of Non - financial Credit Bonds in Each Industry - Bonds in the agriculture, forestry, animal husbandry, and fishery industry have the largest average valuation price deviation, while different industries have different trading volumes [2][18].

Economic Outlook - 2026 is crucial for the "14th Five-Year Plan," aiming for a strong economic start with significant long-term implications[2] - The focus will be on investment-driven growth, particularly in infrastructure, healthcare, and urban renewal[2][4] Fiscal Policy - Central and state-owned enterprises will lead infrastructure investments, with a focus on addressing local fiscal challenges[2][7] - The shift from "three guarantees" (people's livelihood, wages, and operations) to "five guarantees" (including debt repayment) highlights the need for sustainable fiscal policies[2][11] Budget and Spending - The general public budget expenditure for 2026 is expected to increase by over 1 trillion yuan compared to 2025, with a deficit expansion contributing 220 billion yuan[2][14][16] - The 2026 deficit rate is projected to remain similar to 2025, with efforts to enhance tax collection and streamline fiscal policies to boost revenue[2][14] Investment Strategy - Investment recovery is critical, with a focus on major projects that can stimulate demand and stabilize the economy[5][6] - The government plans to allocate 220 billion yuan for early-stage construction projects, emphasizing urban infrastructure and public services[6][7] Risks and Challenges - There is a risk of misinterpretation of policies, which could hinder investment recovery[3][18] - Local governments face significant debt pressures, potentially limiting their ability to drive investment growth[18]

Macroeconomic Liquidity - The US dollar index has rebounded, and the degree of inversion in the China-US interest rate differential has deepened. The nominal and real interest rates of 10Y US Treasury bonds have both increased, indicating a rise in inflation expectations [2][13] - Offshore dollar liquidity has marginally loosened, while the domestic interbank funding situation remains balanced, with a narrowing of the yield spread between 10Y and 1Y bonds [2][18] Market Trading Activity, Volatility, and Liquidity - Overall market trading activity has continued to rise, with trading heat in sectors such as military, textiles, light industry, retail, and consumer services all above the 80th percentile [3][24] - The volatility of major indices has also increased, with sectors like communication, electric power, electronics, and chemicals remaining above the 80th percentile historically [3][31] - Market liquidity indicators have declined, although the liquidity indicators for the oil and petrochemical sector remain above the 80th historical percentile [3][36] Institutional Research - The sectors with the highest research activity include electronics, pharmaceuticals, machinery, electric power, and computers, while research interest in retail, oil and petrochemicals, automobiles, and home appliances has also increased [4][42] Analyst Forecasts - Analysts have adjusted the net profit forecasts for the entire A-share market for 2025/2026, with increases in sectors such as real estate, transportation, chemicals, electric power, and machinery [4][21] - The proportion of stocks in the entire A-share market with upward revisions to their 2025/2026 net profit forecasts has increased, while the proportion of stocks with downward revisions has decreased [4][18] - The net profit forecasts for the ChiNext Index for 2025/2026 have been downgraded, while those for the Shanghai 50 and CSI 300 have been adjusted up and down, respectively [4][23] Northbound Trading Activity - Northbound trading activity has rebounded significantly, with a notable net purchase of A-shares, particularly in sectors such as non-ferrous metals, military, and automobiles [5][31] - The ratio of total buy and sell amounts in the top 10 active stocks has increased in sectors like non-ferrous metals, military, and automobiles, while it has decreased in electronics, communication, and electric power [5][32] Margin Financing Activity - Margin financing activity has slightly decreased but remains at a relatively high level since November 2025, with net purchases primarily in military, electric power, and media sectors [6][35] - The proportion of financing purchases in electric power, public utilities, home appliances, and food and beverage sectors has increased [6][38] Active Equity Fund Positions - The positions of actively managed equity funds have continued to rise, with significant increases in sectors such as communication, electric power, and electronics, while reductions were seen in military and consumer services [7][45] - The correlation of actively managed equity funds with large/mid-cap growth and mid/small-cap value has increased, while the correlation with small-cap growth and large-cap value has decreased [7][48] - New equity fund establishment sizes have decreased, with actively managed funds seeing a decline while passive funds have seen an increase [7][50]

- The report discusses a rotation model for micro-cap stocks, which uses the relative net value of micro-cap stocks to the "Mao Index" as a key indicator. If the relative net value is above its 243-day moving average, it suggests investing in micro-cap stocks; otherwise, it suggests investing in the Mao Index. Additionally, the 20-day closing price slope of both indices is used to determine potential style shifts. When the slopes diverge and one is positive, the model recommends investing in the index with a positive slope[17][24][29] - A timing risk control indicator is constructed based on the 10-year government bond yield (threshold: 0.3) and the volatility crowding degree of micro-cap stocks (threshold: 0.55). If either indicator reaches its threshold, a closing signal is triggered[17][24][29] - The report evaluates eight major stock selection factors across different stock pools (All A-shares, CSI 300, CSI 500, and CSI 1000). Among these, technical factors (IC mean: 9.30%) and volatility factors (IC mean: 6.86%) performed well, while consensus expectation factors (IC mean: -3.20%) and market capitalization factors (IC mean: -2.71%) showed weaker performance[44][45][46] - For convertible bonds, the report constructs quantitative bond selection factors, including equity-related factors (e.g., equity growth and financial quality) and valuation factors (e.g., parity and floor premium rates). The IC mean and long-short portfolio net value of these factors are tracked regularly[53][54][57]