Quantitative Factors and Construction Methods - **Factor Name**: Large Order Transaction Amount Ratio **Construction Idea**: This factor captures the buying behavior of large funds by analyzing the proportion of large order transaction amounts relative to the total daily transaction amount[7] **Construction Process**: 1. Use tick-by-tick transaction data to identify buy and sell orders based on the sequence numbers of bids and asks 2. Filter transactions by order size to identify large orders 3. Calculate the proportion of large buy order transaction amounts to the total daily transaction amount **Formula**: $ \text{Large Order Transaction Amount Ratio} = \frac{\text{Large Buy Order Transaction Amount}}{\text{Total Daily Transaction Amount}} $ **Evaluation**: This factor effectively reflects the buying behavior of large funds and provides insights into market dynamics[7] - **Factor Name**: Net Active Buy Amount Ratio **Construction Idea**: This factor measures the active buying behavior of investors by analyzing the net active buy amount as a proportion of the total daily transaction amount[7] **Construction Process**: 1. Use tick-by-tick transaction data to classify each transaction as either active buy or active sell based on the buy/sell indicator 2. Calculate the net active buy amount by subtracting the active sell amount from the active buy amount 3. Compute the proportion of the net active buy amount to the total daily transaction amount **Formula**: $ \text{Net Active Buy Amount Ratio} = \frac{\text{Active Buy Amount} - \text{Active Sell Amount}}{\text{Total Daily Transaction Amount}} $ **Evaluation**: This factor provides a clear representation of investors' active buying behavior and is useful for tracking market sentiment[7] --- Factor Backtesting Results - **Large Order Transaction Amount Ratio**: - Top 5 stocks with the highest 5-day average values: 1. *Zaisen Technology (603601.SH)*: 91.4%, time-series percentile: 99.6%[9] 2. *Annie Shares (002235.SZ)*: 91.2%, time-series percentile: 98.4%[9] 3. *Kangxin New Materials (600076.SH)*: 87.9%, time-series percentile: 99.6%[9] 4. *Guangtian Group (002482.SZ)*: 87.6%, time-series percentile: 100.0%[9] 5. *Zhongtai Chemical (002092.SZ)*: 87.5%, time-series percentile: 100.0%[9] - **Net Active Buy Amount Ratio**: - Top 5 stocks with the highest 5-day average values: 1. *Hot Scene Biology (688068.SH)*: 15.9%, time-series percentile: 100.0%[10] 2. *Lanxiao Technology (300487.SZ)*: 14.5%, time-series percentile: 100.0%[10] 3. *Yilian Technology (301631.SZ)*: 14.0%, time-series percentile: 100.0%[10] 4. *Xiamen Bank (601187.SH)*: 14.0%, time-series percentile: 99.2%[10] 5. *Huamao Technology (603306.SH)*: 13.1%, time-series percentile: 99.6%[10] --- Additional Factor Testing Results - **Large Order Transaction Amount Ratio for Broad-Based Indices**: - *Shanghai Composite Index*: 5-day average: 73.0%, percentile: 59.0%[12] - *CSI 300*: 5-day average: 72.0%, percentile: 33.6%[12] - *ChiNext Index*: 5-day average: 71.4%, percentile: 14.8%[12] - **Net Active Buy Amount Ratio for Broad-Based Indices**: - *Shanghai Composite Index*: 5-day average: 0.8%, percentile: 7.8%[12] - *CSI 300*: 5-day average: 2.6%, percentile: 4.9%[12] - *ChiNext Index*: 5-day average: 3.5%, percentile: 2.5%[12] - **Large Order Transaction Amount Ratio for Industries**: - *Non-Bank Financials*: 5-day average: 78.5%, percentile: 95.9%[13] - *Steel*: 5-day average: 78.2%, percentile: 43.9%[13] - *Electric Power and Utilities*: 5-day average: 77.6%, percentile: 13.9%[13] - **Net Active Buy Amount Ratio for Industries**: - *Non-Bank Financials*: 5-day average: 6.3%, percentile: 0.8%[13] - *Electric Power and Utilities*: 5-day average: 1.8%, percentile: 1.6%[13] - *Steel*: 5-day average: 1.4%, percentile: 9.4%[13] - **Large Order Transaction Amount Ratio for ETFs**: - Top ETF: *Guotai Zhongzheng A500 ETF (159338.SZ)*: 91.5%, percentile: 20.1%[15] - **Net Active Buy Amount Ratio for ETFs**: - Top ETF: *Guotai SSE 10-Year Treasury Bond ETF (511260.SH)*: 25.9%, percentile: 87.7%[16]

报告认为，印度政府需要持续推进金融结构性改革，进一步完善利率传导机制，并增强汇率灵活性。非 银行金融机构风险也需谨慎防范，审慎监控信贷集中度与金融部门关联性风险。 报告认为，印度政府推进的全面结构性改革提升了潜在经济增长率，特别是2025年9月22日改革后的商 品及服务税（GST）正式实施，大幅简化了印度的税率结构，同步改进了企业注册合规流程，使实际税 率明显下降，有助于刺激国内消费、促进贸易增长、缓解高关税带来的不利影响，并控制整体通胀。 报告指出，加快结构性改革，持续推进财政整顿计划是印度改革的关键。短期来看，实现财政赤字目标 需要严格的财政纪律，在简化商品及服务税的同时，还需密切关注商品与服务税及个人所得税税率下调 的财政影响，并提高关税减免措施的针对性、透明度和时效性。中期来看，有必要增加国内财政收入， 以提高财政政策的缓冲空间，同时采取更具针对性的措施来提升财政支出效率。 报告虽肯定了印度近期的经济表现，但也指出其经济前景仍将面临显著短期风险。一方面，地缘经济割 裂的进一步深化可能导致金融条件收紧、投入成本上升，以及贸易、外国直接投资和经济增长放缓。另 一方面，不可预测的气候变化风险可能对印度农业产 ...

Quantitative Models and Construction Methods 1. Model Name: Three-Strategy Fusion ETF Rotation Strategy - **Model Construction Idea**: The strategy integrates three dimensions: fundamental-driven rotation, quality low-volatility style rotation, and distressed reversal industry discovery. It aims to achieve factor and style complementarity while reducing the risk of single-strategy exposure[35][36] - **Model Construction Process**: 1. **Fundamental Rotation Strategy**: Selects industries based on factors such as exceeding expected prosperity, industry leadership effects, momentum, crowding, and inflation beta[36] 2. **Quality Low-Volatility Style Strategy**: Focuses on individual stock quality, momentum, and low volatility to enhance defensiveness[36] 3. **Distressed Reversal Strategy**: Utilizes PB z-score, long-term analyst expectations, and short-term chip exchange to capture valuation recovery and performance reversal opportunities[36] 4. Combines the three strategies equally to form a composite ETF rotation strategy, achieving multi-dimensional industry screening and reducing single-strategy risks[35][36] - **Model Evaluation**: The strategy effectively balances factor complementarity and style adaptation, providing robust performance across different market conditions[35][36] 2. Model Name: Hotspot Trend ETF Strategy - **Model Construction Idea**: This strategy identifies ETFs with strong upward trends and high market attention, constructing a risk-parity portfolio based on support-resistance factors and turnover ratios[30] - **Model Construction Process**: 1. Select ETFs where both the highest and lowest prices exhibit an upward trend[30] 2. Calculate the relative steepness of the regression coefficients for the highest and lowest prices over the past 20 days to construct support-resistance factors[30] 3. Choose the top 10 ETFs with the highest 5-day turnover ratio/20-day turnover ratio from the long group of the support-resistance factor, indicating increased short-term market attention[30] 4. Construct a risk-parity portfolio using these ETFs[30] - **Model Evaluation**: The strategy demonstrates strong performance, achieving significant excess returns compared to the benchmark[30] 3. Model Name: Capital Flow Resonance Strategy - **Model Construction Idea**: This strategy identifies industries with resonant capital flows by combining financing margin and active large-order capital flow factors, aiming to enhance stability and reduce drawdowns[42][44][45] - **Model Construction Process**: 1. Define the financing margin factor as the market-neutralized financing net buy-in minus securities lending net sell-out, calculated as the two-week change in the 50-day moving average[45] 2. Define the active large-order capital flow factor as the market-neutralized net inflow ranking of industry trading volume over the past year, using the 10-day moving average[45] 3. Exclude extreme industries from the active large-order factor and apply a negative exclusion for the financing margin factor to improve strategy stability[45] 4. Perform weekly rebalancing to select industries with resonant capital flows for long positions[45] - **Model Evaluation**: The strategy achieves stable positive excess returns with reduced drawdowns compared to other capital flow strategies[45] --- Model Backtesting Results 1. Three-Strategy Fusion ETF Rotation Strategy - **2025 YTD Performance**: Portfolio return 25.60%, benchmark return 21.83%, excess return 3.77%, Sharpe ratio 0.24, maximum drawdown -7.18%[39][40] - **Overall Performance (2017-2025)**: Annualized excess return 10.28%, Sharpe ratio 1.09, maximum drawdown -24.55%[40] 2. Hotspot Trend ETF Strategy - **2025 YTD Performance**: Portfolio return 34.49%, benchmark (CSI 300) excess return 19.58%[30] 3. Capital Flow Resonance Strategy - **2018-Present Performance**: Annualized excess return 14.3%, IR 1.4, reduced drawdowns compared to Northbound-Large Order Resonance Strategy[45] - **Last Week Performance**: Absolute return -0.27%, excess return 0.37% (relative to industry equal weight)[45] --- Quantitative Factors and Construction Methods 1. Factor Name: Momentum Factor - **Factor Construction Idea**: Captures the continuation of stock price trends over a specific period[53] - **Factor Construction Process**: 1. Calculate the 1-year momentum as the return over the past 12 months, excluding the most recent month[53] 2. Rank stocks based on momentum and form quintile portfolios[53] - **Factor Evaluation**: Demonstrates strong performance, with the 1-year momentum factor achieving a weekly excess return of 1.13%[53] 2. Factor Name: R&D to Total Assets Ratio - **Factor Construction Idea**: Measures the proportion of R&D investment relative to total assets, reflecting innovation capability[56] - **Factor Construction Process**: 1. Calculate the ratio of total R&D expenses to total assets for each stock[56] 2. Rank stocks based on this ratio and form quintile portfolios[56] - **Factor Evaluation**: Performs well in small-cap indices, with an excess return of 20.25% in the CSI 500 index[56] 3. Factor Name: Single-Quarter ROA YoY Change - **Factor Construction Idea**: Tracks the year-over-year change in return on assets (ROA) for a single quarter, reflecting profitability trends[56] - **Factor Construction Process**: 1. Calculate the year-over-year change in ROA for the most recent quarter, considering preliminary and forecasted data[56] 2. Rank stocks based on this change and form quintile portfolios[56] - **Factor Evaluation**: Excels in large-cap indices, with an excess return of 25.52% in the CSI 300 index[56] --- Factor Backtesting Results 1. Momentum Factor - **Weekly Excess Return**: 1.13%[53] 2. R&D to Total Assets Ratio - **Excess Return in CSI 500**: 20.25%[56] 3. Single-Quarter ROA YoY Change - **Excess Return in CSI 300**: 25.52%[56] - **Excess Return in CSI 500**: 10.16%[56] - **Excess Return in CSI 1000**: 21.98%[56]

Core Viewpoint - The Ministry of Commerce, the People's Bank of China, and the Financial Regulatory Administration have issued a notification to strengthen the collaboration between commerce and finance to boost consumption [1] Group 1: Policy Coordination - The notification emphasizes the need for policy synergy, encouraging local commerce departments to utilize existing funding channels to actively promote consumption activities [1] - It suggests that localities should explore various methods such as financing guarantees, loan interest subsidies, and risk compensation to enhance the collaboration between fiscal, commerce, and financial policies [1] Group 2: Digital Currency and New Consumption Areas - The notification encourages qualified localities to use digital RMB smart contract red envelopes to improve the effectiveness of consumption promotion policies [1] - It highlights the importance of supporting key consumption projects in areas such as health and wellness, cultural tourism, and new consumption fields like digital and green sectors [1] Group 3: Financial Institutions' Role - Banks and non-bank financial institutions are encouraged to leverage their unique strengths and collaborate in consumption promotion activities to enhance the quality and upgrade of consumption [1]

金融工程 | 证券研究报告 — 周报 2025 年 12 月 14 日 中银量化多策略行业轮动 周报 – 20251211 当前（2025 年 12 月 11 日）中银多策略行业配置系统仓位：通信 （9.6%）、银行（9.5%）、交通运输（9.1%）、非银行金融（8.0%）、 食品饮料（7.7%）、电力设备及新能源（7.2%）、钢铁（6.7%）、机械 （6.2%）、基础化工（4.7%）、石油石化（4.7%）、家电（4.4%）、综 合 （3.5% ）、农林牧渔（ 3.5% ）、综合金融（ 3.5% ）、有色金属 （3.5%）、建材（3.4%）、电子（2.4%）、电力及公用事业（1.2%）、 建筑（1.2%）。 相关研究报告 《中银证券量化行业轮动系列（七）：如何把 握市场"未证伪情绪"构建行业动量策略》 20220917 《中银证券量化行业轮动系列（八）："估值泡 沫保护"的高景气行业轮动策略》20221018 《中银证券宏观基本面行业轮动新框架：对传 统自上而下资产配置困境的破局》20230518 《中银证券量化行业轮动系列（九）：长期反 转-中期动量-低拥挤"行业轮动策略》20240914 《中银证券量化行 ...

证券研究报告 | 2025年12月13日 港股市场创新高热点板块跟踪 我们根据分析师关注度、股价相对强弱、股价路径平稳性、创新高连续性等 角度在过去 20 个交易日创出过 250 日新高的股票池中筛选出平稳创新高股 票。 近期，绿源集团控股等股票平稳创出新高。 按照板块来看，创新高股票数量最多的是周期板块，其次为消费、科技、制 造、大金融和医药板块，具体个股信息可参照正文。 港股市场一周回顾 本年，港股精选组合绝对收益 59.33%，相对恒生指数超额收益 29.83%。 港股精选组合绩效回顾 本周，港股精选组合绝对收益-1.94%，相对恒生指数超额收益-1.53%。 港股投资周报 能源板块领跌，港股精选组合年内上涨 59.33% 概念板块方面，本周电力设备概念板块收益最高，累计收益 9.19%；婴童概 念板块收益最低，累计收益-7.14%。 南向资金监控 南向资金整体方面，本周港股通累计净流出 34 亿港元，近一个月以来港股 通累计净流入 757 亿港元，今年以来港股通累计净流入 13898 亿港元，总 体来看近期南向资金呈现出整体流入的走势。 本周港股通资金中，小米集团-W、招商银行和零跑汽车流入金额最多， ...

2025 年 12 月 12 日 行业日报 看好/维持 非银行金融 非银行金融 内需为本，改革为楫：2025 年中央经济工作会议精神与对金融行 业影响解读 ◼ 走势比较 (30%) (20%) (10%) 0% 10% 20% 24/12/12 25/2/22 25/5/5 25/7/16 25/9/26 25/12/7 非银行金融 沪深300 ◼ 子行业评级 从"总量扩张"转向"质效提升"，注重跨周期调节。会议提出五个 "必须"，包括"必须充分挖掘经济潜能"、"必须坚持政策支持和改革创 新并举"等，凸显政策思路从短期逆周期调节向中长期结构性改革深化。 与 2024 年会议相比，本次会议更注重供需平衡的修复，而非单纯的需求 刺激，政策工具更强调"存量与增量并重"。例如：财政政策从"加大强 度"转为"保持必要的财政赤字、债务总规模和支出总量"，重心转向优 化支出结构、化解地方财政压力，而非进一步扩大赤字；货币政策明确将 "促进经济稳定增长、物价合理回升"作为核心目标，提出"灵活高效运 用降准降息等多种政策工具"，但更注重传导机制畅通，避免"大水漫灌"； 这一导向对金融行业意味着政策环境趋于稳定，金融机构需更聚焦 ...

证券研究报告|专题报告 金融工程 2025年12月12日 新老经济的平衡 ----2026年A股市场策略展望 证券分析师： 李杨 执业证书编号： S0210524100005 请务必阅读报告末页的重要声明 华福证券 华福证券 投资要点 2 华福证券 华福证券 ➢ 2025年市场表现回顾。2025年，在政策托底下，经济环境逐步企稳。从资产端看，PMI持续位于荣枯线以下，呈现"弱企稳"特点。 PPI同比降幅收窄，CPI整体向上修复，经济结构性复苏催生小盘科技股引领的"快牛"行情。实际经济缓坡向下名义经济向下幅度更 大。负债端实现由"空股多债"向"多股空债"的转变，股债性价比扭转，资金交易逻辑变化。 "固收+"基金规模映射广义资管资金交易 倾向，2024年12月以来固收+基金规模稳步增长，权益持仓向电子、有色、新能源等高弹性板块增配。市场风格加速变迁，从一季 度小盘成长占优过渡到三季度大盘成长估值拔升，高波资产优势凸显，但后续风格切换。公募基金市场维持存量博弈，缺乏增量 ，主动权益基金偏配科技板块。超额储蓄见顶下行，开始流入权益市场。整体上，2025年经济企稳，但负债端状态向增量转换。 ➢ 新经济与传统经济的平衡。 ...

海信视像公告称，为优化财务管理，公司拟与海信财务公司续签《金融服务协议》，在2026年度开展金 融业务，构成关联交易。各类业务预计额度为：存款最高40.00亿元，信贷业务最高10.00亿元，融资类 业务利息0.20亿元，结售汇1.00亿元，代理类业务0.10亿元。协议有效期1年，存贷款利率不低于/高于 同期主要商业银行。该交易已通过董事会审议，尚需股东会批准。 ...

Core Viewpoint - The increasing correlation between non-bank financial assets and banking and insurance assets poses risks that are harder to penetrate and spread quickly, as highlighted by the Deputy Director of the National Financial Regulatory Administration, Xiao Yuanqi, at the Asia Insurance Forum 2025 [1] Regulatory Environment - Stricter regulations on capital requirements for solvency and leverage limits on large risk exposures are essential to prevent insurance companies from blindly increasing risk appetite for short-term high returns, thereby enhancing the stability of insurance assets [1] Market Trends - Since the 2008 global financial crisis, the rapid development of non-bank financial intermediaries, alongside a relaxed financing environment, has led to a significant increase in asset multiples, with global private credit exceeding $2 trillion [1] - Insurance companies, as key providers of funds, have expanded investment channels and improved asset-liability structures, resulting in higher yields [1] Credit Risk Concerns - The complexity and low transparency of these asset structures, often lacking ratings or having low ratings, increase the credit risk faced by insurance companies, as borrowers typically have high leverage and a greater probability of defaulting [1]