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证券研究报告 | 2025年12月01日 主动量化策略周报 CANSLIM 行业轮动策略 12 月配置建议： 关注钢铁、银行、建筑、公用事业、电新等行业 核心观点 金融工程周报 本报告对 CANSLIM 行业轮动策略的样本外表现进行跟踪，从多个维度解析 行业景气度情况并最终给出月度行业配置建议，以供投资者参考。 行业轮动因子表现 上月以来（20251103-20251128），超大单资金净流入金额占比和券商金股 行业变动因子表现较好，SUE、PB 和单季度 ROE 增速因子表现较差； 今年以来（20250102-20251128），公募重仓股动量、SUE 和分析师认可 度和因子表现较好，而成交量调节动量、公募基金持仓行业变动和超大单资 金净流入金额占比因子表现较差。 上月组合绩效回顾 上月以来（20251103-20251128），行业轮动组合收益率-1.09%，同期中 信一级行业等权指数收益率-1.16%，组合超额收益率 0.08%。 今年以来（20250102-20251031），行业轮动组合收益率 20.48%，同期中 信一级行业等权指数收益率 20.20%，组合超额收益率 0.28%。 本月组合推荐情 ...

- The report highlights that in November 2025, the top-performing factors in the broker's gold stock pool were total market capitalization, single-quarter revenue surprise, and SUR, while factors like intraday return, analyst net upgrade magnitude, and analyst net upgrade ratio performed poorly[3][26] - For the year 2025, the best-performing factors were total market capitalization, single-quarter revenue growth, and SUR, whereas EPTTM, expected dividend yield, and BP underperformed[3][26] - The broker's gold stock performance enhancement portfolio achieved an absolute return of -1.06% for the month (20251103-20251128) and an excess return of 1.39% relative to the mixed equity fund index[5][41] - For the year (20250102-20251128), the portfolio achieved an absolute return of 33.65% and an excess return of 4.42% relative to the mixed equity fund index, ranking in the 35.37% percentile among active equity funds[5][41] - The broker's gold stock performance enhancement portfolio has consistently outperformed the mixed equity fund index from 2018 to 2022, ranking in the top 30% of active equity funds each year[12][37][43]

证券研究报告 | 2025年12月01日 主动量化策略周报 CANSLIM 行业轮动策略 12 月配置建议： 关注钢铁、银行、建筑、公用事业、电新等行业 本报告对 CANSLIM 行业轮动策略的样本外表现进行跟踪，从多个维度解析 行业景气度情况并最终给出月度行业配置建议，以供投资者参考。 行业轮动因子表现 上月以来（20251103-20251128），超大单资金净流入金额占比和券商金股 行业变动因子表现较好，SUE、PB 和单季度 ROE 增速因子表现较差； 今年以来（20250102-20251128），公募重仓股动量、SUE 和分析师认可 度和因子表现较好，而成交量调节动量、公募基金持仓行业变动和超大单资 金净流入金额占比因子表现较差。 上月组合绩效回顾 上月以来（20251103-20251128），行业轮动组合收益率-1.09%，同期中 信一级行业等权指数收益率-1.16%，组合超额收益率 0.08%。 今年以来（20250102-20251031），行业轮动组合收益率 20.48%，同期中 信一级行业等权指数收益率 20.20%，组合超额收益率 0.28%。 本月组合推荐情况 我们借鉴 CANSL ...

Group 1 - The core viewpoint of the article highlights the significant growth of index investment, with the scale of stock ETFs reaching 4.11 trillion yuan by Q3 2025, while the total scale of passive index funds (including ETFs) reached 4.44 trillion yuan [2][6] - The number of passive index funds tracking A-share stock indices has increased to 1,521, with 56 indices having a tracking scale exceeding 10 billion yuan as of November 28, 2025 [5][6] - Major indices with the largest tracking scales include the CSI 300 at 1,181.33 billion yuan, the CSI A500 at 195.35 billion yuan, and the SSE 50 at 188.34 billion yuan [7] Group 2 - The article discusses the impact of index component stock adjustments, which are conducted biannually by index companies, potentially creating trading opportunities due to significant changes in component stocks [6][8] - The methodology for measuring the impact of these adjustments includes calculating the net adjustment scale for individual stocks based on their buy and sell volumes across different indices [9][10] - Stocks expected to see significant net buying include Shenghong Technology, Dongshan Precision, and Guangqi Technology, with projected net buying scales of 4.865 billion yuan, 4.791 billion yuan, and 3.487 billion yuan respectively [10][11] Group 3 - The article identifies stocks with a projected net selling scale exceeding 5 billion yuan, including Yangguang Electric Power, Zhongji Xuchuang, and Hanwha Technology, with expected net selling scales of 5.679 billion yuan, 3.898 billion yuan, and 3.125 billion yuan respectively [12][13] - Stocks with high impact coefficients, indicating significant potential market impact due to adjustments, include Taipai Group, Jiangzhong Pharmaceutical, and Shandong Power, with coefficients of 8.69, 8.44, and 6.99 respectively [11][12]

2025 年 11 月 30 日 总量研究 量能决定短期反弹高度 ——金融工程市场跟踪周报 20251130 本周（2025.11.24-2025.11.28，下同）A 股市场震荡反弹，创业板指领涨主要 宽基指数。量能表现方面，本周主要宽基指数量能逆势收缩，当前量能状态与市 场反弹表现不匹配，后续反弹力度或受量能压制收窄。资金面方面，本周融资增 加额转正，股票型 ETF 资金延续净流出，资金方面仍有分歧。 结合本周市场反弹高度、量能表现以及资金分歧状态，后市反弹力度或减弱，市 场再度进入震荡区间。中长线仍看好"红利+科技"主线，红利或在波动方面占 优。 本周上证综指上涨 1.40%，上证 50 上涨 0.47%，沪深 300 上涨 1.64%，中证 500 上涨 3.14%，中证 1000 上涨 3.77%，创业板指上涨 4.54%，北证 50 指数 上涨 0.75%。 截至 2025 年 11 月 28 日，宽基指数来看，上证指数和上证 50 指数处于估值分 位数"危险"等级，沪深 300、中证 500、中证 1000 和创业板指处于估值分位 数"适中"等级。 中信一级行业分类来看，煤炭、钢铁、建材、轻工制 ...

Group 1: Market Trends and Highs - The report tracks stocks, industries, and sectors reaching new highs, serving as market indicators and highlighting the effectiveness of momentum and trend-following strategies [1][4] - As of November 28, 2025, the distance to the 250-day new highs for major indices are: Shanghai Composite Index at 3.50%, Shenzhen Component Index at 5.40%, CSI 300 at 4.66%, CSI 500 at 6.85%, CSI 1000 at 4.10%, CSI 2000 at 2.78%, ChiNext Index at 8.17%, and STAR Market 50 Index at 13.77% [6][24] Group 2: High-Performing Sectors - Among the CITIC first-level industry indices, the sectors closest to their 250-day new highs include home appliances, textiles and apparel, light industry manufacturing, basic chemicals, and communications [9][24] - Conversely, the sectors furthest from their 250-day new highs include food and beverage, comprehensive finance, non-bank finance, pharmaceuticals, and real estate [9][24] Group 3: New High Stocks Overview - A total of 1,043 stocks reached 250-day new highs in the past 20 trading days, with the highest numbers in the basic chemicals, electric equipment and new energy, and machinery sectors [2][14] - The highest proportion of new high stocks is found in textiles and apparel, electric equipment and new energy, and coal industries, with respective proportions of 39.39%, 36.45%, and 36.11% [14][24] Group 4: Stable New High Stocks - The report identifies 26 stable new high stocks, including Zhongji Xuchuang, Guangku Technology, and Zhongkuang Resources, selected based on analyst attention, relative strength, price path stability, and continuity of new highs [3][20] - The sectors with the most stable new high stocks are cyclical and manufacturing, with 10 and 8 stocks respectively, and the highest number in the cyclical sector is in non-ferrous metals [20][24]

Group 1 - The public fund investment strategy shows robust growth in both scale and number, with active equity funds achieving an average return of 29.69% in 2025, outperforming major indices [2][23][27] - The top three sectors for active equity fund holdings are technology, manufacturing, and cyclical industries, indicating a strong focus on growth-oriented sectors [2][28] - The market outlook for 2026 suggests continued structural opportunities in A-shares, with technology growth remaining a key theme, while Hong Kong stocks are seen as undervalued [2][3] Group 2 - The index fund market has reached a historical high in both scale and number, with total assets amounting to 6.14 trillion yuan, reflecting a significant increase of 32.27% from the previous year [2][37][40] - The ETF segment dominates the index fund market, accounting for 76.10% of total assets, with a notable increase in industry-themed ETFs [2][38][40] - The performance of thematic funds, particularly in technology, has been outstanding, with technology-themed funds achieving an average return of 44.06% in 2025 [2][27][28]

1. Report Industry Investment Rating - No information provided in the given content 2. Core Viewpoints of the Report - In 2026, the "fixed - income asset shortage" and "high equity market sentiment" that drove the convertible bond market in 2025 may continue. The convertible bond market is expected to have a return opportunity of over 10% next year [4]. - The supply - demand structure of the convertible bond market will continue to evolve in 2026, with the term structure becoming "dumbbell - shaped", the "aging" of convertible bonds slowing down slightly, the proportion of funds held in convertible bonds remaining high, and the influence of convertible bond ETFs becoming more prominent [4]. - In 2026, the valuation of convertible bonds is likely to remain high. The market risk appetite is not weak, the convertible bond positions of low - risk - preference investors are at a historical low, and the probability of extreme credit risk events in the short term is limited [4]. - In terms of strategy, attention should be paid to "higher - order" undervaluation. The contradiction between the high demand for undervalued convertible bonds from "fixed - income +" investors with a bond - biased approach and the "weak supply" of traditional low - priced convertible bonds needs to be resolved by constructing more complex undervaluation evaluation criteria [4]. - In the context of the "involution" of clause games, more attention should be paid to the odds. In the high - valuation environment, the valuation of convertible bonds is rapidly compressed before the call - back, and the game space for downward revisions has reached a historical low [4]. 3. Summary According to the Directory 3.1 Convertible Bond "Ecological Niche" Advantage Remains Unchanged, with a Decent Return Space in 2026 - The two factors that drove the convertible bond market in 2025, "fixed - income asset shortage" and "high equity market sentiment", may continue in 2026. As of November 20, 2025, the median parity of the convertible bond market exceeded 100 yuan, and the overall equity nature of convertible bonds was at a historical high. The strong equity market is expected to be the most important support for convertible bonds in 2026 [8]. - The demand for convertible bonds from "fixed - income +" investors, especially bond - biased accounts with stock position limits, is expected to remain high due to the low long - term interest rate environment, which will support the valuation of convertible bonds [8]. - The convertible bond index is expected to have a return space of over 10% in 2026. Based on the delta calculation, if the Shanghai Composite Index reaches 4500 - 5000 points in 2026, the overall return of convertible bonds may be around 8% - 17%, and the actual return space may be higher [8]. 3.2 The "Aging" Speed May Slow Down Slightly, and the Dumbbell Structure Gradually Appears - The contraction speed of the convertible bond market in 2026 is expected to be slightly lower than that in 2025, and the stock size may reach 450 - 500 billion yuan. As of November 21, 2025, the stock size of the convertible bond market was about 550 billion yuan, a decrease of nearly 180 billion yuan from the end of 2024. The net supply of convertible bonds in 2026 may be - 100 billion yuan [13]. - The "aging" of convertible bonds in 2026 may slow down slightly, and the median remaining term of convertible bonds at the end of next year may be about 2.2 years. The main reasons are the redemption of many short - term convertible bonds since 2025 and the recovery of convertible bond supply starting from mid - 2025 [15]. - In 2026, the number of medium - term convertible bonds will significantly decrease, and the term structure will evolve into a "dumbbell - shaped" structure. By the end of 2026, the number of 3 - 4 - year convertible bonds will decline from over 100 at the beginning of the year to about 30. The market may form a structure with medium - and large - sized convertible bonds within 3 years at one end and small - and medium - sized growth technology - related convertible bonds over 4 years at the other end [16]. - In terms of industries, the convertible bonds of non - bank finance, commercial retail, and consumer service industries will all mature by the end of 2026. The non - bank finance industry involves the largest scale and the most targets, with 4 convertible bonds worth 15 billion yuan maturing. The remaining industries' distributions may not change much, and the balance of convertible bonds in the banking, power equipment and new energy, and basic chemical industries significantly leads the others [18]. 3.3 The Proportion of Funds May Further Increase, Pay Attention to Convertible Bond ETFs - As of the end of October 2025, the proportion of convertible bonds held by funds is estimated to reach 47%, the highest level since the data was released. The increase in the proportion of convertible bonds held by public funds is mainly due to the decrease in the scale of convertible bonds held by insurance and annuity funds. By October 2025, the scale of convertible bonds held by insurance may be less than 50 billion yuan, a decrease of nearly 30% from August 2025, and the scale of convertible bonds held by annuities may be close to 130 billion yuan, a record low [20]. - In 2026, the proportion of convertible bonds held by funds is expected to remain high and may even reach a new high. Retail investors have a long - term trend of reducing their holdings of convertible bonds. Insurance and annuity funds may participate in the convertible bond market through FOFs in 2026 due to the low net supply of large - scale and high - rating convertible bonds and the high overall valuation of convertible bonds [22]. - The scale of convertible bond ETFs may continue to expand, and attention should be paid to the potential impact of the high proportion of ETFs on the convertible bond market. As of the end of October 2025, the market value of convertible bonds held by convertible bond ETFs reached 67.84 billion yuan, accounting for nearly 10% of the convertible bond market. The high valuation of newly issued convertible bonds may be related to convertible bond ETFs [27]. 3.4 Valuation is Supported, and There is Room for More Optimism - In 2025, the valuation of convertible bonds increased significantly, and the implied volatility returned to the central level of 2023. As of November 20, 2025, the premium rate per 100 yuan of convertible bonds continued to break through historical highs, and the median implied volatility of convertible bonds exceeded 40% [30]. - In 2026, the valuation of convertible bonds is likely to remain high. The current market risk appetite is not weak, the convertible bond positions of low - risk - preference investors are at a historical low, and the probability of extreme credit risk events in the short term is limited [30]. - The high point of convertible bond valuation may be around 35% - 40%, and considering the possible decline in long - term interest rates and the increase in the bond floor of convertible bonds in 2026, the high point of valuation may be even higher [33]. 3.5 In the High - Valuation Environment, It is Recommended to Focus on "Higher - Order" Undervaluation - In 2025, convertible bond investors clearly preferred undervalued targets. As of November 21, 2025, the return of the low - price strategy was 21.1%, with an excess return of 4.6% compared to the CSI Convertible Bond Index. The relatively "abnormal" excess return may be mainly due to institutional behavior [37]. - In 2026, it may be more difficult for the pure low - price strategy to obtain excess returns. The market is facing the contradiction between the high demand for undervalued convertible bonds from "fixed - income +" investors with a bond - biased approach and the "weak supply" of traditional low - priced convertible bonds. It is recommended to focus on "higher - order" undervaluation [41]. - The convexity strategy may be a good entry point for "higher - order undervaluation". Since 2025, the series of convexity strategies have achieved excellent results, with a Calmar ratio of over 3 and a return of over 20% [42]. 3.6 The "Involution" of Clause Games, and More Attention Should Be Paid to the Odds - In the fourth quarter of 2025, the experience of convertible bond clause games was not good. In the high - level volatile environment of the equity market, listed companies became more cautious in considering convertible bond clauses. As of November 21, 2025, only 1 convertible bond proposed a downward revision in that month, and the ratio of downward - revision announcements to possible downward - revision announcements was 0.04:1, both the lowest levels since March 2023. The ratio of call - back announcements to non - call - back announcements in November was 1.57:1, the highest level in 2025 [45]. - In the high - valuation environment, the convertible bond call - back game has become "involution". The difference in the average conversion premium rate of convertible bonds with a call - back progress of 80% - 100% and those with a progress of 0 - 20% has rapidly expanded since September 2025, and the conversion premium rate of convertible bonds with a high call - back progress has fallen to a historical low [47]. - The game space for convertible bond downward revisions is narrowing. Under the dual influence of high valuation and institutional preference for undervalued convertible bonds, the average difference between the prices of all convertible bonds eligible for downward revision and the expected price after a full downward revision has narrowed to a relatively low level since 2021 [48]. - The report also lists the convertible bonds whose cooling - off periods for downward revisions and call - backs will end in 2026 [52][53][54].

Quantitative Models and Construction Methods 1. Model Name: Diffusion Index Model - **Model Construction Idea**: The model is based on the principle of price momentum, aiming to capture upward trends in industries and sectors[22][23] - **Model Construction Process**: The diffusion index is calculated for each industry based on its price momentum. The model ranks industries by their diffusion index values and selects the top-performing industries for portfolio allocation. The model has been tracking out-of-sample performance since 2021, with adjustments made monthly or weekly based on updated diffusion index rankings[22][23] - **Model Evaluation**: The model has shown strong performance in capturing industry trends during momentum-driven markets but struggles during market reversals[22][36] 2. Model Name: GRU Factor Model - **Model Construction Idea**: This model leverages minute-level price and volume data processed through a GRU (Gated Recurrent Unit) deep learning network to generate industry factors for rotation strategies[37] - **Model Construction Process**: The GRU model uses historical price and volume data as input to train a deep learning network. The network identifies patterns and generates factors that are used to rank industries. The top-ranked industries are selected for portfolio allocation. The model is updated weekly to reflect changes in the rankings[30][31][37] - **Model Evaluation**: The GRU model performs well in short-term trading environments but has shown limited effectiveness in long-term scenarios. It is also sensitive to extreme market conditions[37] --- Backtesting Results of Models 1. Diffusion Index Model - **Weekly Average Return**: -5.50% - **Excess Return over Equal-Weighted CSI First-Level Industry Index**: -0.42% - **November-to-Date Excess Return**: -1.13% - **Year-to-Date Excess Return**: 1.22%[26][22][23] 2. GRU Factor Model - **Weekly Average Return**: -4.71% - **Excess Return over Equal-Weighted CSI First-Level Industry Index**: 0.35% - **November-to-Date Excess Return**: 2.92% - **Year-to-Date Excess Return**: -2.74%[35][30][31] --- Quantitative Factors and Construction Methods 1. Factor Name: Diffusion Index - **Factor Construction Idea**: The diffusion index measures the momentum of industries by analyzing price trends and ranks industries based on their momentum[22][23] - **Factor Construction Process**: The diffusion index is calculated for each industry using price momentum data. Industries are ranked based on their diffusion index values, and the top-ranked industries are selected for portfolio allocation. The index is updated weekly or monthly to reflect changes in industry momentum[22][23] - **Factor Evaluation**: The factor effectively captures upward trends in industries but may underperform during market reversals[22][36] 2. Factor Name: GRU Industry Factor - **Factor Construction Idea**: The GRU industry factor is derived from minute-level price and volume data processed through a GRU deep learning network to identify patterns and rank industries[37] - **Factor Construction Process**: The GRU model processes historical price and volume data through a deep learning network. The network generates factors that are used to rank industries. The top-ranked industries are selected for portfolio allocation, with updates made weekly[30][31][37] - **Factor Evaluation**: The factor is effective in short-term trading environments but less so in long-term scenarios. It is also sensitive to extreme market conditions[37] --- Backtesting Results of Factors 1. Diffusion Index Factor - **Weekly Average Return**: -5.50% - **Excess Return over Equal-Weighted CSI First-Level Industry Index**: -0.42% - **November-to-Date Excess Return**: -1.13% - **Year-to-Date Excess Return**: 1.22%[26][22][23] 2. GRU Industry Factor - **Weekly Average Return**: -4.71% - **Excess Return over Equal-Weighted CSI First-Level Industry Index**: 0.35% - **November-to-Date Excess Return**: 2.92% - **Year-to-Date Excess Return**: -2.74%[35][30][31]