华福证券 2026 年 03 月 03 日 金 融 工 程 3 月行业配置推荐顺周期行业——行业配置策略 月度报告(2026/3) 投资要点： 动态平衡策略 金 融 工 程 定 期 报 告 我们从平衡的角度提出了兼顾胜率和赔率的动态平衡策略。自 2015 年初至 2026 年 2 月 27 日策略年化绝对收益 19.15%，年化相对收 益 12.37%，信息比率为 1.75，相对最大回撤为 10.18%。模型在 2026 年 3 月份推荐行业为有色金属、电力设备及新能源、基础化工、钢铁、 通信、机械。2 月动态平衡策略绝对收益 3.89%，跑赢基准，超额收益 为 1.98%。2026 年以来至 2 月 27 日，动态平衡策略绝对收益 13.83%， 相对偏股混合型基金指数超额收益 5.39%，在主动权益基金中排名 19.60%。 宏观驱动策略 自2016年初至2026年2月27日，综合模型超额年化收益率4.75%， 超额波动率 7.14%，信息比率 0.67，最大回撤 9.51%，IC 均值 4.50%， ICIR16.91%，年化换手 3.12 倍。模型在 2026 年 3 月份推荐的行业包 括石油石化、医 ...

2026 年 03 月 01 日 成交量快速反弹，市场情绪细分指 标出现回升 ——量化择时周报 20260301 相关研究 证券分析师 沈思逸 A0230521070001 shensy@swsresearch.com 邓虎 A0230520070003 denghu@swsresearch.com 联系人 沈思逸 A0230521070001 shensy@swsresearch.com 权 益 量 化 研 究 证 券 研 究 报 告 ⚫ 市场情绪维持稳定：截至 2 月 27 日，市场情绪指标数值为 1.85，较节前的 1.9 小幅下 降，情绪指标周内维持稳定，从情绪角度来看，模型观点偏中性。从具体分项来看，本周 部分情绪指标与上周相比出现回升，市场情绪出现一定回暖迹象，但仍需警惕外部政治风 险冲击。 ⚫ 成交量快速反弹，市场情绪细分指标出现回升：本周价量一致性指标快速回升，表明市场 当前价量匹配程度明显提升，资金关注度与标的涨幅关联性显著增强，短期内价格上涨幅 度与市场关注匹配程度形成较强共振，从价量一致性角度反映市场情绪出现明显修复；科 创 50 相对万得全 A 成交占比周内小幅降低，表明市场风险偏好边 ...

量化择时和拥挤度预警周报（20260220） [Table_Authors] 郑雅斌(分析师) 中盘股或先开启上行趋势 本报告导读： 从技术面来看，高频资金流模型继续显示各大宽基指数信号依旧为负向，但偏向左 侧布局的情绪模型信号转正。结合春节后的日历效应，我们认为，以中证 500 指数 为首的中盘股或先开启上行趋势。 投资要点： | | | | | 021-23219395 | | --- | --- | | | zhengyabin@gtht.com | | 登记编号 | S0880525040105 | | | 曹君豪(分析师) | | | 021-23185657 | | | caojunhao@gtht.com | | 登记编号 | S0880525040094 | [Table_Report] 相关报告 请务必阅读正文之后的免责条款部分 金 融 工 程 周 报 高频选股因子周报（20260209-20260213） 2026.02.16 低频选股因子周报（2026.02.06-2026.02.13） 2026.02.14 绝对收益产品及策略周报（260202-260206） 2026.02.11 大 ...

Core Viewpoint - Market sentiment has cooled, with the sentiment indicator at 2.65 as of February 6, slightly up from 2.6 the previous week, indicating a neutral stance from a sentiment perspective [4][5]. Sentiment Model Viewpoint - The sentiment model indicates a decline in market sentiment, with a rapid decrease in price-volume consistency, suggesting a significant drop in the correlation between price increases and market attention [5][7]. - The sentiment structure indicator is calculated using various sub-indicators, with a scoring method that evaluates the sentiment direction and Bollinger band positions, resulting in a 20-day moving average of the summed scores [2][3]. Market Activity - The price-volume consistency indicator has rapidly declined, reflecting a significant reduction in the degree of price-volume matching, indicating a cooling market sentiment [5][7]. - The total trading volume for the A-share market decreased significantly by 21.43% week-on-week, with an average daily trading volume of 24,066.54 billion yuan, marking a notable drop in market activity [10][14]. Sector Analysis - As of February 6, 2026, the sectors with the highest short-term scores include construction materials and petroleum & petrochemicals, both scoring 93.22, indicating strong short-term performance [28]. - The correlation between sector congestion and weekly price changes is negative at -0.30, suggesting that high congestion sectors like food and beverage are experiencing significant price increases, while low congestion sectors may have more stable valuations [31][32]. Financing and Investment Sentiment - The financing balance ratio has slightly increased and remains above the upper Bollinger band, indicating a high level of leveraged funds and a generally positive risk appetite among investors [21][24]. - The RSI indicator has shown a decline, reflecting a decrease in short-term upward momentum and an increase in selling pressure, indicating a reduction in market participation willingness [23][34]. Overall Market Signals - The current model indicates a preference for large-cap and value styles, with signals suggesting potential strengthening in these areas as indicated by the rapid decline of the 5-day RSI relative to the 20-day RSI [28][35].

Quantitative Models and Construction Methods 1. Model Name: Sentiment Model - **Model Construction Idea**: The sentiment model is designed to measure the strength of market sentiment using factors related to limit-up and limit-down stocks[14] - **Model Construction Process**: The model uses factors such as the proportion of net limit-up stocks, next-day returns of limit-down stocks, proportion of limit-up stocks, proportion of limit-down stocks, and high-frequency board-hitting returns. These factors are aggregated to calculate a sentiment score, with a maximum score of 5. The sentiment score for the current period is 0[14][18] - **Model Evaluation**: The sentiment model indicates that the market sentiment remains low, reflecting weak investor confidence[14][18] 2. Model Name: Moving Average Strength Index - **Model Construction Idea**: This model evaluates the strength of market trends by calculating the moving average strength index based on secondary industry indices[14] - **Model Construction Process**: The moving average strength index is calculated using the performance of secondary industry indices. The current market score is 181, which corresponds to the 62.50th percentile since 2023[14] - **Model Evaluation**: The index suggests that there is still significant room for downward movement in the market[14] 3. Model Name: High-Frequency Capital Flow Model - **Model Construction Idea**: This model uses high-frequency capital flow data to generate buy and sell signals for major broad-based indices[14] - **Model Construction Process**: The model tracks the capital flow trends of indices such as CSI 300, CSI 500, CSI 1000, and CSI 2000. The signals for all indices are currently negative, indicating a bearish outlook[14][18] - **Model Evaluation**: The model shows that all major broad-based indices have turned negative, reflecting weak market conditions[14][18] --- Model Backtesting Results 1. Sentiment Model - Sentiment score: 0 (out of 5)[14][18] 2. Moving Average Strength Index - Current score: 181 (62.50th percentile since 2023)[14] 3. High-Frequency Capital Flow Model - CSI 300: Negative signal - CSI 500: Negative signal - CSI 1000: Negative signal - CSI 2000: Negative signal[14][18] --- Quantitative Factors and Construction Methods 1. Factor Name: Factor Crowding Index - **Factor Construction Idea**: The factor crowding index measures the degree of crowding in specific factors, which can serve as a warning for factor inefficiency[19] - **Factor Construction Process**: The index is calculated using four metrics: valuation spread, pairwise correlation, long-term return reversal, and factor volatility. The composite score is derived from these metrics. For example, the crowding scores for small-cap, low-valuation, high-profitability, and high-growth factors are 0.06, -0.31, -0.01, and 0.28, respectively[19][20] - **Factor Evaluation**: The crowding index provides insights into the potential inefficiency of factors due to excessive capital allocation[19] --- Factor Backtesting Results 1. Factor Crowding Index - Small-cap factor crowding score: 0.06 - Low-valuation factor crowding score: -0.31 - High-profitability factor crowding score: -0.01 - High-growth factor crowding score: 0.28[19][20]

Quantitative Models and Construction Methods 1. Model Name: Sentiment Model - **Model Construction Idea**: The sentiment model is designed to measure the strength of market sentiment using factors related to limit-up and limit-down stocks[14] - **Model Construction Process**: The model incorporates factors such as the proportion of net limit-up stocks, next-day returns of limit-down stocks, proportion of limit-up stocks, proportion of limit-down stocks, and high-frequency board-hitting returns. These factors are aggregated to generate a sentiment score, with a maximum score of 5. The sentiment score for the current period is 0[14][18] - **Model Evaluation**: The sentiment model indicates weak market sentiment, as reflected by the score of 0[14][18] 2. Model Name: Moving Average Strength Index - **Model Construction Idea**: This model evaluates the strength of market trends by calculating the moving average strength index based on secondary industry indices[14] - **Model Construction Process**: The moving average strength index is calculated using the performance of secondary industry indices. The current market score is 181, which corresponds to the 62.50th percentile since 2023[14] - **Model Evaluation**: The model suggests that the market still has significant downside potential[14] 3. Model Name: High-Frequency Capital Flow Model - **Model Construction Idea**: This model uses high-frequency capital flow trends to generate buy and sell signals for major broad-based indices[14] - **Model Construction Process**: The model tracks high-frequency capital flows and generates signals for indices such as CSI 300, CSI 500, CSI 1000, and CSI 2000. The signals for all indices are currently negative, indicating a bearish outlook[14][18] - **Model Evaluation**: The model shows a bearish signal across all major indices, reflecting weak market conditions[14][18] --- Model Backtesting Results 1. Sentiment Model - Sentiment score: 0 (out of 5)[14][18] 2. Moving Average Strength Index - Current score: 181 (62.50th percentile since 2023)[14] 3. High-Frequency Capital Flow Model - CSI 300: Negative signal - CSI 500: Negative signal - CSI 1000: Negative signal - CSI 2000: Negative signal[14][18] --- Quantitative Factors and Construction Methods 1. Factor Name: Factor Crowding Indicator - **Factor Construction Idea**: The factor crowding indicator measures the degree of crowding in specific factors, which can serve as a warning for factor underperformance[19] - **Factor Construction Process**: The indicator is calculated using four metrics: valuation spread, pairwise correlation, long-term return reversal, and factor volatility. These metrics are aggregated to produce a composite crowding score for each factor. For example: - Small-cap factor crowding score: 0.06 - Low-valuation factor crowding score: -0.31 - High-profitability factor crowding score: -0.01 - High-growth factor crowding score: 0.28[19][20] - **Factor Evaluation**: The crowding scores indicate varying levels of crowding across factors, with low-valuation and high-profitability factors showing negative scores, suggesting potential underperformance[19][20] --- Factor Backtesting Results 1. Factor Crowding Indicator - Small-cap factor crowding score: 0.06 - Low-valuation factor crowding score: -0.31 - High-profitability factor crowding score: -0.01 - High-growth factor crowding score: 0.28[19][20]

Investment Rating - The report does not explicitly provide an investment rating for the industry [43]. Core Insights - The total number of ETFs listed in mainland China is 1,419, with a total scale of 5.46 trillion yuan. Among these, stock ETFs account for the largest share, both in number (1,111) and scale (3.23 trillion yuan) [2][7]. - The A-share market indices showed varied performance, with the Shanghai Composite Index closing at 4,117.95, reflecting a decline of 0.44%. Notably, the petrochemical, communication, and coal sectors experienced significant gains, while military, power equipment, and automotive sectors faced substantial declines [11][12]. - The wide-based ETFs experienced a net outflow of 3890.81 billion yuan this week, with the top three inflows being A500 ETF (+11.23 billion yuan), Double Innovation Leader ETF (+9.27 billion yuan), and Shanghai Index ETF (+5.42 billion yuan). Conversely, the top three outflows were from the CSI 300 ETF by E Fund (-747.27 billion yuan), CSI 300 ETF by Huatai-PB (-742.00 billion yuan), and CSI 300 ETF by Huaxia (-547.13 billion yuan) [30][31]. - The industry crowding degree monitoring indicated that sectors such as non-ferrous metals, oil and petrochemicals, and agriculture are currently crowded, while automotive, home appliances, and pharmaceuticals have lower crowding levels, suggesting potential investment opportunities [35]. Summary by Sections ETF Market Overview - As of January 30, 2026, the total number of ETFs in mainland China is 1,419, with a total scale of 5.46 trillion yuan. Stock ETFs dominate both in quantity (1,111) and scale (3.23 trillion yuan), representing 78.29% and 59.11% of the total respectively [2][7][9]. Domestic and International Equity Market Index Performance - The A-share market indices showed mixed results, with the Shanghai Composite Index down 0.44%. The petrochemical sector saw a rise of 7.95%, while the military sector dropped by 7.69% [11][12][19]. - In the Hong Kong market, the Hang Seng Index rose by 2.38%, while the Hang Seng Technology Index fell by 1.38% [20][21]. Stock ETF Fund Flows - The wide-based ETFs saw a net outflow of 3890.81 billion yuan, with significant inflows into A500 ETF and Double Innovation Leader ETF, while the CSI 300 ETFs faced the largest outflows [30][31][34]. Industry Crowding Degree Monitoring - The monitoring model indicates that sectors like non-ferrous metals and oil and petrochemicals are crowded, while automotive and pharmaceuticals are less crowded, suggesting areas for potential investment focus [35][36]. ETF Product Attention Signals - The report highlights potential arbitrage opportunities in specific ETFs, including a focus on gold and non-ferrous metal ETFs, while cautioning about potential pullback risks [41][42].

Core Insights - The report emphasizes a dynamic balance strategy that has achieved an annualized absolute return of 18.85% and a relative return of 12.26% from January 2015 to January 30, 2026, with a maximum drawdown of 10.18% [3] - Recommended industries for February 2026 include non-ferrous metals, basic chemicals, electric equipment and new energy, communication, light manufacturing, and steel [3][25] - The macro-driven strategy has generated an annualized excess return of 4.77% since January 2016, with a maximum drawdown of 9.51% [4][45] - The multi-strategy approach has yielded an annualized relative return of 6.32% since May 2011, with a maximum drawdown of 13.24% [5][66] - The extreme style high beta strategy has achieved an annualized relative return of 9.93% since July 2013, but has underperformed in 2026 with a relative excess return of -4.02% [5][80] Industry Performance Summary - In January 2026, the A-share market saw the CSI 300 index rise by 1.65%, while the CSI 500 index increased by 12.12% [16] - The top-performing sectors in January were non-ferrous metals, media, oil and petrochemicals, building materials, and electronics [16] - The dynamic balance strategy outperformed its benchmark in January with an absolute return of 9.18% and an excess return of 4.05% [22][55] - The macro-driven strategy achieved an absolute return of 6.76% in January, with an excess return of 1.20% [4][48] - The multi-strategy approach recorded an absolute return of 4.65% in January, but underperformed its benchmark with an excess return of -0.42% [5][69] Recommended Industries - The dynamic balance strategy recommends non-ferrous metals, basic chemicals, electric equipment and new energy, communication, light manufacturing, and steel for February 2026 [3][25] - The macro-driven strategy suggests food and beverage, defense and military, pharmaceuticals, non-ferrous metals, communication, and basic chemicals for February 2026 [4][24] - The multi-strategy approach recommends real estate, construction, banking, communication, textiles and apparel, pharmaceuticals, basic chemicals, and non-ferrous metals for February 2026 [5][56] - The extreme style high beta strategy recommends transportation, electric utilities, basic chemicals, machinery, banking, and oil and petrochemicals for February 2026 [5][74]

Core Viewpoint - The overall market sentiment indicators are stable, with rapid fund switching observed, indicating a bullish sentiment in the market [4][5]. Group 1: Market Sentiment Indicators - The market sentiment structure indicators include various metrics such as industry trading volatility, trading congestion, price-volume consistency, and others, which collectively inform the sentiment direction [2][3]. - As of January 30, the market sentiment indicator value is 2.6, a slight increase from 2.35 the previous week, suggesting a stable sentiment with a bullish bias [4]. - The sentiment structure indicator has fluctuated around the zero axis within the range of [-6, 6] over the past five years, with significant volatility observed in 2023 [3]. Group 2: Sub-indicator Analysis - The industry trading volatility has shown a slight recovery, indicating increased frequency of fund switching between different sectors, while the industry trend indicator has rapidly declined, suggesting growing divergence in short-term industry outlooks [5][18]. - The price-volume consistency indicator remains high, reflecting a strong correlation between market attention and stock price movements, indicating active market sentiment [7]. - The financing balance ratio has slightly increased, indicating that leveraged funds are maintaining a high level of sentiment, with overall investor risk appetite remaining positive [19]. Group 3: Sector Performance and Trends - The short-term score for the food and beverage sector has risen significantly, while growth and small-cap styles are currently favored [26]. - The highest short-term scores are observed in the oil and petrochemical, construction materials, and non-ferrous metals sectors, indicating strong performance in these areas [26][27]. - The average congestion levels are highest in sectors like non-ferrous metals and oil and petrochemicals, while the lowest are in transportation and real estate, suggesting varying levels of market focus and potential risks [32][34].

Group 1 - The market sentiment indicator as of January 30 is at 2.6, a slight increase from 2.35 the previous week, indicating overall stability in sentiment with a bullish model perspective [2][9]. - The price-volume consistency indicator remains high, suggesting a strong correlation between market attention and stock price movements, reflecting an active market sentiment [13][16]. - The trading volume of the entire A-share market increased by 9.44% week-on-week, with an average daily trading volume of 30,632.46 billion yuan, indicating a slight recovery in market activity [19]. Group 2 - The short-term score rankings show that the oil and petrochemical, construction materials, non-ferrous metals, light industry manufacturing, and communication sectors are leading, with both oil and petrochemical and construction materials scoring 98.31, the highest among sectors [43][44]. - The industry crowding indicator shows a positive correlation with weekly price changes, with high crowding sectors like oil and petrochemical leading in gains, while low crowding sectors like commercial retail and environmental protection lag behind [46][50]. - The model indicates a preference for small-cap and growth styles, with the 5-day RSI showing a rapid decline relative to the 20-day RSI, suggesting potential weakening of signals in the near term [43][53].