中信五风格-周期★☆☆ 金融工程组 张婧婕 Z0022617 010-58747784 gtaxinstitute@essence.com.cn 本报告版权属于国投期货有限公司 1 不可作为投资依据，转载请注明出处 基金市场回顾： 操作评级 股债策略收益小幅分化 金融工程周报 权益市场风格 2026年1月5日 周度报告 截至2025/12/31当周，通联全A（沪深京）、中证综合债与南 华商品指数周度涨跌幅分别为-0.31%、-0.20%、-0.71%。 公募基金市场方面，近一周股债策略收益表现分化，短期纯债 策略表现偏强，普通股票策略指数小幅收跌，中性策略产品涨 多跌少；商品方面贵金属ETF净值有所回调，黄金ETF调整幅 度大于白银，有色与能源化工ETF延续上行走势。 中信五风格方面，上周周期风格收涨，其余风格收跌；风格轮 动图显示近期稳定与消费风格相对强弱边际回落，五风格相对 强弱动量均环比走低。公募基金池方面，近一周消费与金融风 格基金平均表现跑赢基准，从基金风格系数走势来看市场对消 费风格偏移度有所回升；本周拥挤度指标相比上周有所升高， 成长风格基金拥挤度提升至历史中高分位区间。 Barra因子：近一周 ...

何荣天表示，目前市场上的量化策略大致分为两类。一类是以多因子模型为代表的大众化的量化策略， 目前规模大、参与者众多，已经形成了明显的"红海"格局。在这一领域，模型和因子高度拥挤，策略显 著趋同，导致边际收益正在下降。另一类则是以专业金融认知为底座，通过独立逻辑寻找市场规律和风 格趋势，虽然小众却更具独特性和穿越周期的能力。这二者的差异使得量化行业逐步形成"工具驱 动"与"认知驱动"这两条不同的发展路径。 守正用奇专注于后者。在何荣天看来，量化投资机构的核心竞争壁垒不在于模型工具，而在于对市场风 格、经济周期、资金行为等多维度的专业理解。 在量化行业竞争加剧的当下，如何在"红海"中保持长期竞争力？广州守正用奇私募基金董事长何荣天给 出的答案是：回到金融本质，以长期有效的专业认知，把握真正可持续的AI量化策略。 相比于市面上同质化程度不断加深的多因子模型，这家成立10年、始终在规模发展上保持克制的量化机 构，选择了一条差异化的投资路径：以风格择时为核心策略，通过"风格估值-动量-有效资金流"的三维 框架捕捉因子贝塔，强调市场内在规律的识别，而非模型堆叠带来的短期超额。 "量化行业未来真正的竞争壁垒不在工具，而在专 ...

Quantitative Models and Construction Methods - **Model Name**: Style Rotation Model **Model Construction Idea**: The model is built from basic style factors such as valuation, market capitalization, volatility, and momentum. It incorporates a style timing and scoring system, leveraging micro-level features and machine learning techniques to optimize style selection and rotation[4][9]. **Model Construction Process**: 1. Start with 80 fundamental micro factors as raw features, categorized based on the proprietary multi-factor system of Dongwu Securities[9]. 2. Construct 640 micro-level features from these factors[4][9]. 3. Replace the absolute proportion division of style factors with commonly used indices as style stock pools, creating new style returns as labels[4][9]. 4. Use a rolling training process with a Random Forest model to avoid overfitting, select optimal features, and generate style recommendations[4][9]. 5. Combine style timing results and scoring outcomes to build a monthly frequency style rotation framework[4][9]. **Model Evaluation**: The model effectively integrates micro-level features and machine learning to enhance style rotation performance, mitigating overfitting risks[4][9]. Model Backtesting Results - **Style Rotation Model**: - Annualized Return: 16.52% - Annualized Volatility: 20.46% - IR: 0.81 - Monthly Win Rate: 57.01% - Maximum Drawdown: 25.68% - Excess Annualized Return (Hedged against Benchmark): 11.04% - Excess Annualized Volatility (Hedged against Benchmark): 11.08% - Excess IR (Hedged against Benchmark): 1.00 - Excess Monthly Win Rate (Hedged against Benchmark): 55.14% - Maximum Drawdown (Hedged against Benchmark): 9.00%[4][10][11]

证券研究报告 建议择机入场 2025 年 11 月 23 日│中国内地 量化投资周报 本周观点：建议择机入场 上周，受全球流动性压力、美联储降息预期反复以及 AI 叙事松动多重因素 影响，全球风险偏好下降——VIX 指数攀升至近三个月高位，各类风险资产 均承压，其中比特币、微盘股等对流动性和风偏更敏感的资产领跌。我们的 模型认为 A 股经过上周的调整，整体上消化了过高的估值，观点由防御转 为看平。叠加周五美联储释放了略积极的降息信号，Nowcasting 模型预测 11 月 CPI 或将继续上行至 3.7%-3.8%，但核心 CPI 预计保持平稳，或有利 于市场风偏的恢复。建议择机入场，优选低位防御板块，本周行业轮动模型 加大了对低位消费板块的押注，风格上仍看好红利。 A 股大盘择时模型：上周回调消化了高估值压力，可择机入场 我们以万得全 A 指数作为 A 股大盘代理，从估值、情绪、资金、技术四个 维度对 A 股大盘进行整体方向性判断。今年以来，模型多空择时的扣费后 收益 43.84%，同期 A 股大盘涨跌幅为 20.09%，超额收益为 23.76%；上周 模型超额收益为 10.41%。上周，受全球流动性压力 ...

Group 1 - Market sentiment score has continued to rise, reaching 3 as of November 7, up from 2.7 the previous week, indicating further recovery in market sentiment and a bullish outlook [7][11][19] - The trading volatility between industries has increased rapidly, breaking through the upper Bollinger Band, suggesting accelerated sector switching and a short-term improvement in sentiment [19][22] - The average daily trading volume for the entire A-share market decreased slightly to 20,123.50 billion yuan, with the highest trading day on November 3 at 21,329.04 billion yuan [14][18] Group 2 - The short-term trend scores for industries such as banking, petrochemicals, light manufacturing, electric equipment, and steel have shown significant upward movement, with utilities currently having the highest short-term score of 100 [38][39] - The crowdedness of capital in sectors like electric equipment, steel, and coal has increased, indicating potential volatility risks due to high valuations and sentiment corrections [40][44] - The model indicates a preference for large-cap and value styles, with signals suggesting that these styles may strengthen in the future [49][56]

Quantitative Models and Construction Methods 1. Model Name: Style Rotation Model - **Model Construction Idea**: The model is built from basic style factors such as valuation, market capitalization, volatility, and momentum, gradually constructing a style timing and scoring system[4][9] - **Model Construction Process**: 1. Construct 640 micro features based on 80 basic micro indicators[9] 2. Use common indices as style stock pools to replace the absolute proportion division of style factors, constructing new style returns as labels[4][9] 3. Use a random forest model for style timing and obtain the current score for each style[4][9] 4. Integrate the timing results and scoring results to construct a monthly frequency style rotation model[4][9] - **Model Evaluation**: The model effectively avoids overfitting risks through rolling training of the random forest model and constructs a comprehensive framework from style timing to style scoring and from style scoring to actual investment[9] Model Backtesting Results 1. **Style Rotation Model**: - Annualized Return: 16.18%[10][11] - Volatility: 20.28%[10][11] - Information Ratio (IR): 0.80[10][11] - Win Rate: 59.43%[10][11] - Maximum Drawdown: 25.20%[11] 2. **Market Benchmark (Hedged)**: - Annualized Return: 10.36%[10][11] - Volatility: 10.85%[10][11] - Information Ratio (IR): 0.95[10][11] - Win Rate: 54.72%[10][11] - Maximum Drawdown: 8.53%[11]

Report Industry Investment Rating - The operation rating for CITIC Five Styles - Finance is ★☆☆, indicating a bullish bias but with limited operability in the market [3][4]. Core Viewpoints - As of the week ending October 24, 2025, the weekly returns of Tonglian All A (Shanghai, Shenzhen, Beijing), ChinaBond Composite Bond, and Nanhua Commodity Index were 3.42%, -0.03%, and 0.94% respectively. In the public - fund market, enhanced index strategies led the gains with a weekly return of 3.89%. Neutral strategies had more gains than losses. Among commodities, precious - metal ETFs pulled back, while soybean - meal and non - ferrous - metal ETFs had a slight rebound, and energy - chemical ETFs stabilized [4]. - All CITIC five styles closed up last Friday, with the growth style leading in returns. The style rotation chart showed that the cyclical and consumer styles weakened compared to the previous period, and the growth style had a significant increase in the indicator momentum. In the public - fund pool, financial and cyclical style funds had better excess performance in the past week. The deviation of products from the consumer style increased marginally, and the overall market congestion indicator continued to rise this week, with the growth and financial styles in a historically high - congestion range [4]. - In the neutral strategy, the stock - index basis showed a marginal recovery trend during the week. The IC contract recovered to around 0.5 times the standard deviation above the three - month average. The average premium rates of the spot - index ETFs corresponding to IC and IM were relatively high, in the top 80% quantile range of the past three months [4]. - Among Barra factors, the medium - and long - term momentum factor had a better return performance this week, with a weekly excess return of 1.70%. The residual volatility and ALPHA factors retreated, and the winning rates of the dividend and leverage factors improved. The cross - section rotation speed of factors continued to increase this week, currently in the top 80% quantile range of the past year [4]. - According to the latest scoring results of the style timing model, the growth and financial styles recovered marginally this week, while the cyclical and stable styles declined. The current signal favors the financial style. The return of the style timing strategy last week was 1.45%, and the excess return compared to the benchmark balanced allocation was - 0.98% [4]. Summary by Related Catalogs Fund Market Review Recent Market Returns - The weekly, monthly, quarterly, and semi - annual returns of Tonglian All A (Shanghai, Shenzhen, Beijing), ChinaBond Composite Bond (net), and Nanhua Commodity are presented in a chart [6]. - The maximum drawdowns of the main public - fund strategy indices in the past three months and their weekly returns are also shown in charts [6]. CITIC Style Index - The net - value trends of CITIC style indices (finance, cycle, consumption, growth, stability) from September 24 to October 23, 2025, are presented in a chart [8][9]. - The relative rotation chart of CITIC style indices shows the relative strength and relative - strength momentum of different styles in different time periods (recent week, last week, recent month, recent three months, recent six months, recent year) [10][11]. - The excess - return performance of fund style indices in different time periods is provided in a table [12]. - The fund - style congestion chart shows the congestion levels of cycle, growth, consumption, and finance styles from September 28 to October 26, 2025 [13]. Barra Factors - The style preference of Barra single factors is within the range of 0 - 1, with a higher value indicating a stronger preference. The excess - return performance of Barra single - factor style strategies and the net - value trends of Barra single - factor style excess since this year are presented in charts [14][16][18].

Group 1: Market Sentiment Model Insights - The market sentiment score has slightly increased to 2.2 as of October 24, compared to 1.9 the previous week, indicating a partial recovery in market sentiment [6][8] - The overall market sentiment is showing increased differentiation, with a decline in price-volume consistency, suggesting reduced capital activity [8][12] - The total trading volume for the entire A-share market has significantly decreased compared to the previous week, with a peak trading volume of 1,991.617 billion RMB on October 24 [14][16] Group 2: Sector Performance Insights - As of October 24, the banking, oil and petrochemical, transportation, public utilities, and construction decoration sectors have shown an upward trend in short-term scores [33] - The coal sector currently has the highest short-term score of 93.22, indicating strong short-term performance [33][34] - The model indicates that the market is currently favoring large-cap and value styles, with strong signals for both [33][44] Group 3: Industry Crowding Insights - Recent high price increases in the electronics and power equipment sectors are accompanied by high capital crowding, suggesting potential volatility risks due to valuation and sentiment corrections [36][41] - The average crowding levels are highest in the power equipment, environmental protection, non-ferrous metals, textile and apparel, and coal sectors [37][40] - Low crowding sectors such as non-bank financials, beauty care, media, computing, and food and beverage have shown lower price increases, indicating potential for excess returns if fundamentals improve [36][40]

- **A-share multi-dimensional timing model**: The model evaluates the overall directional judgment of the A-share market using four dimensions: valuation, sentiment, funds, and technical indicators. Each dimension provides daily signals with values of 0, ±1, representing neutral, bullish, or bearish views. Valuation and sentiment dimensions adopt a mean-reversion logic, while funds and technical dimensions use trend-following logic. The final market view is determined by the sum of the scores across all dimensions [9][15][16] - **Style timing model for dividend style**: The model uses three indicators to time the dividend style relative to the CSI Dividend Index and CSI All Share Index. The indicators include relative momentum, 10Y-1Y term spread, and interbank pledged repo transaction volume. Each indicator provides daily signals with values of 0, ±1, representing neutral, bullish, or bearish views. The final view is based on the sum of the scores across all dimensions. When the model favors the dividend style, it fully allocates to the CSI Dividend Index; otherwise, it allocates to the CSI All Share Index [17][21] - **Style timing model for large-cap and small-cap styles**: The model uses momentum difference and turnover ratio difference between the CSI 300 Index and Wind Micro Cap Index to calculate the crowding scores for large-cap and small-cap styles. The model operates in two crowding zones: high crowding and low crowding. In high crowding zones, it uses a small-parameter dual moving average model to address potential style reversals. In low crowding zones, it uses a large-parameter dual moving average model to capture medium- to long-term trends [22][24][26] - **Sector rotation model**: The genetic programming-based sector rotation model selects the top five sectors with the highest multi-factor composite scores from 32 CITIC industry indices for equal-weight allocation. The model updates its factor library quarterly and rebalances weekly. The factors are derived using NSGA-II algorithm, which evaluates factor monotonicity and performance of long positions using |IC| and NDCG@5 metrics. The model combines multiple factors with weak collinearity into sector scores using greedy strategy and variance inflation factor [29][32][33][36] - **China domestic all-weather enhanced portfolio**: The portfolio is constructed using a macro factor risk parity framework, which emphasizes risk diversification across underlying macro risk sources rather than asset classes. The strategy involves three steps: macro quadrant classification and asset selection, quadrant portfolio construction and risk measurement, and risk budgeting to determine quadrant weights. The active allocation is based on macro expectation momentum indicators, which consider buy-side expectation momentum and sell-side expectation deviation momentum [38][41] --- Model Backtesting Results - **A-share multi-dimensional timing model**: Annualized return 24.97%, maximum drawdown -28.46%, Sharpe ratio 1.16, Calmar ratio 0.88, YTD return 37.73%, weekly return 0.00% [14] - **Dividend style timing model**: Annualized return 15.71%, maximum drawdown -25.52%, Sharpe ratio 0.85, Calmar ratio 0.62, YTD return 19.53%, weekly return -3.43% [20] - **Large-cap vs. small-cap style timing model**: Annualized return 26.01%, maximum drawdown -30.86%, Sharpe ratio 1.08, Calmar ratio 0.84, YTD return 64.58%, weekly return -2.22% [27] - **Sector rotation model**: Annualized return 33.33%, annualized volatility 17.89%, Sharpe ratio 1.86, maximum drawdown -19.63%, Calmar ratio 1.70, weekly return 0.14%, YTD return 39.41% [32] - **China domestic all-weather enhanced portfolio**: Annualized return 11.66%, annualized volatility 6.18%, Sharpe ratio 1.89, maximum drawdown -6.30%, Calmar ratio 1.85, weekly return 0.38%, YTD return 10.74% [42]

- The style rotation model is constructed based on the Dongwu quantitative multi-factor system, starting from micro-level stock factors. It selects 80 underlying factors as original features, including valuation, market capitalization, volatility, and momentum, and further constructs 640 micro features. The model replaces the absolute proportion division of style factors with common indices as style stock pools, creating new style returns as labels. A random forest model is trained in a rolling manner to avoid overfitting risks, optimizing features and obtaining style recommendations. The framework integrates style timing, scoring, and actual investment[9][4] - The performance of the style rotation model during the backtesting period (2017/01/01-2025/09/30) shows an annualized return of 16.41%, annualized volatility of 20.43%, IR of 0.80, monthly win rate of 58.49%, and a maximum drawdown of 25.54%. When hedging against the market benchmark, the annualized return is 10.54%, annualized volatility is 10.85%, IR is 0.97, monthly win rate is 55.66%, and the maximum drawdown is 8.79%[10][11] - The style rotation model's latest timing directions for October 2025 are value, large market capitalization, momentum, and low volatility[2][19] - The latest holdings of the style rotation model for October 2025 include indices such as CSI Central Enterprise Dividend (ETF code: 561580.SH), CSI Bank (ETF code: 512700.SH), CSI Film and Television (ETF code: 159855.SZ), CS Battery (ETF code: 159796.SZ), and CSI All Real Estate (ETF code: 512200.SH)[3][19]