资产配置 | 动态跟踪 资产配置策略中低波分化，行业策略转向 ——资产配置模型月报 202512 研究结论 报告发布日期 2025 年 12 月 03 日 | 王晶 | 执业证书编号：S0860510120030 | | --- | --- | | | wangjing@orientsec.com.cn | | | 021-63325888*6072 | | 周仕盈 | 执业证书编号：S0860125060012 | | 资产配置不仅仅是风险分散：——主动型 | 2025-11-27 | | --- | --- | | 资产配置新思路 | | | 全天候模型仓位平稳，行业策略推荐科技/ | 2025-11-03 | | 有色/新能源等板块：——资产配置模型月 | | | 报 202511 | | | 关注权益和商品机会：——资产配置月报 | 2025-11-01 | | 202511 | | | 大类资产仓位平稳，行业策略推荐有色/科 | 2025-10-11 | | 技等板块：——资产配置模型月报 202510 | | | 大类资产风险可控，短期关注交易特征： | 2025-09-29 | | ——"2+1 ...

- The multi-asset allocation model is constructed based on macro state recognition, incorporating growth/inflation factors, liquidity, and gold factors to create a dynamic risk budget portfolio [4][33][34] - The growth factor includes PMI, industrial added value, retail sales, fixed asset investment, and export data, while the inflation factor uses CPI and PPI. Liquidity factor is measured by M1 year-on-year growth [34][35] - Equity market characteristics are monitored using ERP (Equity Risk Premium), EP (Earnings Yield), and BP (Book-to-Price ratio) to construct stock-bond cost-effectiveness factors [34][35] - Gold investment factors are constructed using the dollar index, central bank gold purchases, and exchange rates to assess dynamic allocation value [34][35] - The model employs a multi-objective optimization approach, integrating asset momentum into traditional risk parity and risk budget frameworks. ETFs are used for portfolio construction, with dynamic adjustments based on macro signals [37][38] - The industry rotation model incorporates six dimensions: macro, financial, analyst expectations, ETF share changes, public fund/selected fund position momentum, and event momentum [39][41] - The industry rotation model has achieved an annualized return of 28% since 2012, with an annualized excess return of 18.1% over industry equal weight and a monthly excess win rate of 70% [42][43] - The industry rotation ETF strategy employs a five-layer recursive solution method to enhance portfolio performance, achieving an annualized return improvement of over 12% [77][78] - The "Accompanying Style Enhanced FOF" uses a dynamic multi-factor model focusing on Alpha and crowding factors, with quarterly adjustments to optimize fund selection and portfolio construction [46][47] - The "Accompanying Broad-based Enhanced FOF" employs a relative benchmark strategy to control tracking error while maximizing composite factor scores, using a dynamic multi-factor model [53][54] - The "Long-term Capability Factor FOF" combines Brinson model-based decomposition with TM and H-M models for timing and selection capabilities, incorporating style factors for enhanced fund selection [64][66] - The "KF-Alpha+ Trading FOF" uses quarterly data and Kalman filter-based industry estimation to construct Alpha factors, focusing on industry-specific stock selection capabilities [70][73] - The industry rotation ETF portfolio achieved a monthly excess return of 1.5% during the reporting period, with a full-period annualized excess return of 17.79% and an IR of 1.72 [78][79][87]

1. Report Industry Investment Rating No information provided in the report regarding the industry investment rating. 2. Core Viewpoints of the Report - On December 1, the convertible bond market showed a trend of increasing in price with decreasing trading volume, and the valuation increased compared to the previous period. The CSI Convertible Bond Index rose by 0.10%, while the Shanghai Composite Index, Shenzhen Component Index, ChiNext Index, Shanghai 50 Index, and CSI 1000 Index all increased to varying degrees [1]. - The market style favored large - cap growth stocks. Large - cap growth stocks rose by 1.42%, outperforming other styles [1]. - The trading sentiment in the convertible bond market weakened. The trading volume of the convertible bond market was 5.3624 billion yuan, a decrease of 9.67% compared to the previous day, while the total trading volume of the Wind All - A Index was 188.9449 billion yuan, an increase of 18.26% [1]. 3. Summary According to Relevant Catalogs Market Main Index Performance - The CSI Convertible Bond Index closed at 482.09, up 0.10% for the day, down 0.18% for the week, down 0.49% for the month, and up 16.29% since the beginning of 2025. Other convertible bond - related indices also showed different degrees of increase or decrease [7]. - Among the A - share indices, the Shanghai Composite Index closed at 3914.01, up 0.65% for the day; the Shenzhen Component Index closed at 13146.72, up 1.25% for the day; the ChiNext Index closed at 3092.50, up 1.31% for the day [7]. Market Capital Performance - The trading volume of the convertible bond market was 5.3624 billion yuan, a decrease of 9.67% compared to the previous day, while the total trading volume of the Wind All - A Index was 188.9449 billion yuan, an increase of 18.26% [1][8]. - The net outflow of the main funds in the Shanghai and Shenzhen stock markets was 343 million yuan, and the yield of the 10 - year Treasury bond decreased by 0.46bp to 1.84% [1][11][12]. Convertible Bond Price and Valuation - The weighted average closing price of convertible bonds increased to 132.54 yuan, up 0.08% compared to the previous day. The proportion of high - price bonds above 130 yuan increased by 0.51pct to 54.55% [2]. - The fitting conversion premium rate of 100 - yuan par value increased to 31.50%, up 0.04pct compared to the previous day. The overall weighted par value increased by 0.61% to 100.63 yuan [2][17][22]. Industry Performance - In the A - share market, the top three industries in terms of gains were non - ferrous metals (+2.85%), communication (+2.81%), and electronics (+1.58%); the top three industries in terms of losses were agriculture, forestry, animal husbandry and fishery (-0.43%), environmental protection (-0.23%), and real estate (-0.06%) [3]. - In the convertible bond market, the top three industries in terms of losses were environmental protection (-3.58%), steel (-1.37%), and household appliances (-1.01%); the top three industries in terms of gains were communication (+0.85%), electronics (+0.82%), and coal (+0.66%) [3]. ETF Share Changes - The share of Bosera Convertible Bond ETF was 4.237 billion shares, with a net decrease of 27.9 million shares [37]. - The share of Haifutong Convertible Bond ETF was 802 million shares, with a net increase of 10.1 million shares [40].

- The diffusion index model tracks industry rotation based on momentum principles, aiming to capture upward trends in industries. It has been monitored for four years, with notable performance in 2021 and stable returns in 2022. However, it faced challenges in 2023 and 2024 due to market reversals. For December 2025, recommended industries include non-ferrous metals, comprehensive, steel, banking, power equipment & new energy, and electronics[23][24][27] - The GRU factor model utilizes GRU deep learning networks to analyze minute-level volume and price data, focusing on short-cycle performance. It has achieved significant excess returns since 2021 but struggled in 2025 due to concentrated market themes. For the week ending November 28, 2025, industries ranked highest by GRU factors include comprehensive, steel, banking, comprehensive finance, retail, and agriculture[30][31][33] - Diffusion index model weekly rankings show top industries as non-ferrous metals (0.994), comprehensive (0.961), steel (0.939), banking (0.937), power equipment & new energy (0.902), and electronics (0.853). Industries with the lowest rankings include food & beverage (0.343), utilities (0.498), transportation (0.503), real estate (0.548), construction (0.563), and oil & petrochemicals (0.616)[24][25][26] - GRU factor weekly rankings highlight top industries as comprehensive (4.42), steel (3.9), banking (0.5), comprehensive finance (0.43), retail (0.18), and agriculture (-0.33). Industries ranked lowest include communication (-15.26), defense (-9.1), electronics (-8.71), pharmaceuticals (-8.44), computing (-8.11), and real estate (-7.63)[31][32][33] - Diffusion index model achieved an average weekly return of 3.53%, exceeding the equal-weighted return of CICC primary industries by 1.10%. Year-to-date excess return stands at 2.55%[27] - GRU factor model recorded an average weekly return of 1.06%, underperforming the equal-weighted return of CICC primary industries by -1.43%. Year-to-date excess return is -4.45%[33]

- The report tracks industry prosperity signals using quantitative models and indicators, focusing on coal, livestock, steel, structural materials, and fuel refining industries[9] - For the coal industry, the model uses price factors and capacity factors to estimate monthly revenue and profit growth rates. The formula is based on the monthly price index of thermal coal, which determines the sales price for the following month[10][14] - In the livestock industry, the "slaughter coefficient method" is applied to predict the supply-demand gap for pigs six months ahead. The formula is: $ \text{Slaughter Coefficient} = \frac{\text{Quarterly Pig Slaughter}}{\text{Breeding Sow Inventory (lagged 6 months)}} $ This method effectively identifies pig price upcycles based on historical data[15][16] - For the steel industry, the model incorporates comprehensive steel prices and cost indicators (e.g., iron ore, coke, coal, and scrap steel) to predict monthly profit growth and calculate per-ton profit[18][21] - In the structural materials sector, profitability changes in glass and cement manufacturing are tracked using price and cost indicators. These changes are used to design allocation signals. Additionally, manufacturing PMI and real estate sales data are analyzed to assess potential infrastructure investment expectations[24][26] - For the fuel refining and oil services industry, the model uses changes in fuel prices, crude oil prices, and cracking spreads to estimate profit growth and design allocation signals. The model also considers changes in new drilling activities[27][34][35]

Core Insights - The report suggests focusing on the financial, non-ferrous metals, electronics, and machinery sectors for December [1] - The value-growth rotation strategy has a composite score of 5, indicating a higher score for growth style as of November 30, 2025 [3][6] - The small-cap style has a higher score in the size rotation strategy, with a composite score of 4 [8] Style Rotation Insights - The large-cap stocks are more sensitive to economic prosperity, while growth stocks benefit more from liquidity easing [3][6] - The value-growth rotation strategy yielded a growth index return of -2.85% and a value index return of 0.35% in November 2025 [6] - The size rotation strategy showed a return of -2.46% for the CSI 300 and -2.30% for the CSI 1000 in November 2025 [8] Industry Rotation Insights - The report constructs a four-dimensional engine with macro, fundamental, technical, and crowding indicators for industry index rotation [11] - The top five industries for December based on the industry rotation composite score are banking, electronics, machinery, non-ferrous metals, and non-bank financials [3][23] - The bottom five industries are coal, real estate, construction, oil and petrochemicals, and textiles and apparel [3][23] Macro Indicators - The macroeconomic growth dimension is in the "expansion strengthening/recession alleviation" phase, while the liquidity dimension is in the "easing intensification/tightening slowdown" phase as of November 30, 2025 [13] - The report recommends allocating to the large financial and midstream manufacturing sectors based on these macro indicators [13] Fundamental Indicators - The top five industries based on fundamental indicators are non-bank financials, non-ferrous metals, electronics, telecommunications, and electric equipment and new energy [17] - The bottom five industries based on fundamental indicators are real estate, coal, construction, agriculture, forestry, animal husbandry, and textiles and apparel [17] Technical Indicators - The top five industries based on technical indicators are electronics, banking, telecommunications, non-ferrous metals, and machinery [18] - The bottom five industries based on technical indicators are coal, construction, food and beverage, oil and petrochemicals, and real estate [18] Crowding Indicators - The industries with high crowding indicators include basic chemicals, electric equipment and new energy, agriculture, real estate, and textiles and apparel [22] - The industries with low crowding indicators are machinery, non-bank financials, automobiles, computers, and food and beverage [22]

近期，A股市场行情震荡波动，多空双方均有反复纠结的迹象，行业轮动明显加快。 注：数据来源wind，截止2025年10月31日。 这种行情下，对于普通投资者来说，想抓住机会是非常难的。很可能前脚刚上车科技，后脚就开始回调，别的行业又轮动起来了，总是错过一步。 因此，在这种背景下，要在当前的A股市场中获得理想的投资回报，提前确定投资方向成为一个重要的战略问题。 如果你既希望拥有大盘指数的相对稳定，又要在一定程度上兼具中小盘股高成长的红利，那么能够做到二者兼容的中证A500ETF（159338）或许是一个不 错的选择。 中证A500：行业均衡 龙头荟萃 攻守兼备 中证A500指数行业分布全面且均衡，实现100%行业覆盖，且做了行业中性处理，更能代表A股市场。并且汇聚行业龙头，基本包含中证三级行业龙头企 业，覆盖度为94%。 注：数据来源wind，截止2025年10月31日。中证二级行业共35个，中证三级行业共93个，中证A500指数含有中证三级行业91个。行业占比动态变化，仅供 参考。中证三级行业中，将截止2025年10月31日市值排名前两位的公司定义为"行业龙头"。 从成分股来看，中证A500指数成分股包括约50 ...

金融工程 | 证券研究报告 — 周报 2025 年 11 月 30 日 中银量化多策略行业轮动 周报 – 20251127 当前（2025 年 11 月 27 日）中银多策略行业配置系统仓位：非银行金融 （11.5%）、交通运输（9.9%）、通信（8.8%）、基础化工（8.1%）、食 品饮料（7.9%）、有色金属（6.9%）、银行（6.4%）、家电（4.4%）、 纺织服装（4.2%）、综合（4.0%）、钢铁（4.0%）、煤炭（3.9%）、农 林牧渔（3.3%）、国防军工（3.2%）、医药（3.2%）、电力设备及新能 源（3.1%）、机械（1.8%）、电子（1.8%）、石油石化（1.2%）、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》20 ...

被批为"太平间基金经理"的刘彦春 在这条帖子中，刘彦春被提及最多——"一波牛市下来动都不带动的，到现在还亏50%"，从而被贴上"太平间基金经理"的标签。夸张的表达背后，对应的是 景顺长城新兴成长（260108）在2021年高估值阶段后的长周期回撤。 Choice数据显示，截至11月25日，景顺长城新兴成长年内收益刚刚转正，为0.4%；景顺长城绩优成长混合A（007412）、景顺长城内需增长贰号混合A （260109）年内回报也仅在1%左右。若从2021年初高点算起，净值仍远低于当时水平，彼时买入的投资者可能仍承担逾五成的账面亏损。 近日，一条小红书帖子引发共鸣。发帖人回看自己在2021年买入的一批热门主动权益基金，感叹其中一只"动都不带动的"，并给基金经理刘彦春贴上了"太 平间基金经理"的标签。同样被提到的，还有朱少醒、蔡嵩松等当年最受追捧的明星基金经理。 情绪化的表达背后，是一批"山顶买入"的持有人在四年周期中的真实体验：同样是在2021年高位布局，如今这些明星基金和基金经理已经走向截然不同的路 径——有人靠医药、科技反弹修复，有人仍深陷旧核心资产的回撤区间，也有人在监管和更迭中离开公募体系。 如果把时间拉 ...

Core Viewpoint - The equity market's confidence has gradually improved throughout the year, characterized by distinct structural trends in different phases, including AI-led trends, innovative drug sectors, and the recent strength in low-volatility dividend assets [1] Market Trends - The market has experienced significant sector rotation, with notable phases including AI dominance at the beginning of the year, innovative pharmaceuticals after April, and technology growth led by semiconductors and AI in August and September [1] - Following October, low-volatility dividend assets have shown a phase of strength, indicating a shift in investor focus [1] Investment Insights - With the decline in risk-free rates, the cost of capital has decreased, enhancing the attractiveness of equity assets and increasing investor risk appetite [1] - The effectiveness of market pricing is improving, yet small-cap stocks remain under-researched, presenting more opportunities for value discovery [1] - Current market liquidity favors small and micro-cap stocks, providing numerous trading opportunities [1] - The valuation structure indicates that small and micro-cap stocks, primarily assessed by price-to-book (PB) ratios, still offer a good margin of safety compared to large-cap stocks, making them appealing from a defensive standpoint [1]