Group 1 - The market is expected to experience an upward breakout in December, initiating a year-end rally, driven by positive economic policy signals from the upcoming political bureau and central economic work meetings [2][4][14] - The dual focus on the Shanghai 50 and Sci-Tech 50 indices is recommended as a favorable combination for capitalizing on the anticipated market rally [2][14][19] - Key investment opportunities include non-bank financials, resource price increases, service consumption, and technology sectors that emphasize self-sufficiency [2][14][19] Group 2 - The report highlights the importance of the upcoming political bureau and central economic work meetings in shaping economic policy, which is expected to be more proactive, enhancing confidence in economic recovery and corporate profitability [4][15][32] - The liquidity environment is projected to remain stable, with an influx of new capital expected in December, particularly from insurance funds and increased household savings, which may lead to a classic year-end rally [4][18][25] - The focus on cyclical sectors such as coal, basic chemicals, and steel, as well as high-end manufacturing like defense and power equipment, is emphasized due to their potential for performance improvement [5][21][22] Group 3 - The report suggests that the market's style will likely shift towards large-cap stocks, with a recommendation for indices such as CSI 300 and Sci-Tech 50, reflecting a preference for stability and potential growth [20][21] - The analysis indicates that sectors with low inventory levels, optimized production capacity, and improving economic conditions should be prioritized for investment [21][22][23] - The anticipated recovery in consumer spending, particularly in service sectors, is highlighted as a critical driver for economic growth, supported by fiscal policies aimed at boosting consumption [17][32][33]

指数突破可能的三个条件 证券研究报告 ——策略周观点 [Table_ReportDate] 2025 年 11 月 30 日 请阅读最后一页免责声明及信息披露 http://www.cindasc.com 1 策略研究 [策略周报 Table_ReportType] | 李畅 策略分析师 | | --- | | 执业编号：S1500523070001 | | 邮 箱： lichang@cindasc.com | 徐国铨 策略研究助理 邮 箱：xuguoquan@cindasc.com [指数突破 Table_Title] 可能的三个条件 [Table_ReportDate] 2025 年 11 月 30 日 核心结论：11 月以来市场延续宽幅震荡，波动有所加大。我们认为直接 原因在于，市场进入业绩和政策空窗期，依靠预期和估值支撑的行情对 不确定事件或利空因素反应敏感。经济数据走弱和海外市场扰动都可能 加剧市场波动。但背后还有一个重要原因是资金格局的影响。四季度以 来居民资金流入放缓，拉长了牛市中期换手率达到高点后市场缩量震荡 的时间。从指数突破可能的条件来看，年底优先关注 12 月中央经济工 作会议是否能够带 ...

相关研究 债 券 周 评 证 券 研 究 报 告 ⚫ 本周转债市场主要消化上周累积的高估值，正股温和反弹，转债跟涨幅度较弱。但是与前 期估值中枢水平相比，结构发生了一定变化，偏股区估值变化不大，但是低平价偏债区估 值有松动迹象，估值分位数以下行为主，90-100 元平价区间估值回落较为明显。从行业 角度看，本周银行和非银转债表现均较差，平价下行幅度较大，估值也被迫下行，双重压 力下金融转债表现明显偏弱，或是偏债区估值下行的重要拖累因素。当下权益市场波动较 大，上涨趋势相对不明朗，偏债区估值高位情况下保护度不足，可重点关注偏股型转债的 结构性机会。 ⚫ 2. 转债估值 本研究报告仅通过邮件提供给 中庚基金 使用。1 请务必仔细阅读正文之后的各项信息披露与声明 证券分析师 黄伟平 A0230524110002 huangwp@swsresearch.com 王明路 A0230525060003 wangml@swsresearch.com 徐亚 A0230524060002 xuya@swsresearch.com 联系人 徐亚 A0230524060002 xuya@swsresearch.com 2025 年 ...

证券之星消息，11月28日，国企改革（399974）指数报收于1841.64点，涨0.22%，成交863.26亿元，换 手率0.48%。当日该指数成份股中，上涨的有63家，东方电气以5.56%的涨幅领涨，下跌的有33家，招商 蛇口以2.63%的跌幅领跌。 资金流向方面，国企改革（399974）指数成份股当日主力资金净流出合计7.88亿元，游资资金净流入合 计1998.58万元，散户资金净流入合计7.68亿元。成份股资金流向详情见下表： | 代码 | 名称 | 主力净流入(元) | 主力净占比 游资净流入 (元) | | 游资净占比 散户净流入(元) | | 散户净占比 | | --- | --- | --- | --- | --- | --- | --- | --- | | 600036 招商银行 | | 3.25 Z | 15.29% | -3.61 Z | -16.97% | 3588.10万 | 1.69% | | 600118 | 中国卫星 | 1.53 Z | 6.36% | -6095.63万 | -2.54% | -9186.52万 | -3.82% | | 668109 | 紫金矿业 | 1. ...

| 4.83 | | | 所属行业:非银金融+0.63% > | | | 查看所属板块 > | | --- | --- | --- | --- | --- | --- | --- | | | +0.02 +0.42% | | 创投 | | 国企改革 | | | | | | +1.14% | | +1.14% | | | 今 开 | 4.81 | 最 | 高 | 4.84 | 成交量 | 98.4万手 | | 昨 । | 4.81 | 最 | 作 | 4.78 | 成交额 | 4.74亿 | | 门智助 | | 238.32 换手率 | | 1.14% | 总市值 | 487亿 | | 分时 | モ日 | 日K | 園K | 月K | 分钟▼ | 0 | | 5.88 | MA5:4.822 MA10:4.903 MA20:5.026 | | | | | 前复权 | | 4.76 | 2025-09-30 | | 2025-10-24 | | 2025-11-11 | | 天风证券(601162.SH)今日公告称，公司于2025年11月28日收到中国证监会下发的《立案告知书》，因涉嫌信息披露违法违规、违法提供融 ...

股市中一直有句话叫做高抛低吸，那这"高"究竟有多高？"低"究竟有多低呢？我相信这是困扰投资者的一大难题。 事实上，"逢低买入"背后的逻辑正是运用了"均值回归"的市场规律。恰如投资大师安德烈·科斯托拉尼提出的"遛狗理论"所说，股市价值和价格的关系就像是 遛狗时主人和狗的关系。遛狗时人通常缓步向前，而狗有时跑在人前、有时跑在人后，这就像价格有时高于价值、有时低于价值，但迟早会回归价值。 而带入到市场中我们就会发现，在2025年之前市场始终以3000点为锚点，上下波动。因此3000点以下的时候或成了逢低布局的好时机，而如今到了4000点， 我们又该如何布局呢？ 数据来源：Wind 2025年之前上证指数均围绕着3000点波动 用数据说话，高盈利+低估值或有高胜率 "一百个人眼中有一百个哈姆雷特"其实我们光靠猜是没什么意义的，对于行业而言不同行业拥有不同的景气度，当前AI行业40倍的估值大家不嫌贵，而消费 10倍不到的估值却还有人高喊尚未见底。 而如果配合上整体的盈利状况，我们就可以得到以下表格：X轴代表行业营收总收入增长，而Y轴则是PE（TTM）的估值分位数。 行业估值和营收的比较（申万一级） 因此我们只能从数据的 ...

Quantitative Models and Construction Methods - **Model Name**: GAN_GRU **Model Construction Idea**: The GAN_GRU model combines Generative Adversarial Networks (GAN) for processing volume-price sequential features and Gated Recurrent Unit (GRU) for encoding sequential features to construct a stock selection factor [4][13] **Model Construction Process**: 1. **GRU Model**: - The GRU model is based on 18 volume-price features, including closing price, opening price, trading volume, turnover rate, etc. [14][17][19] - Training data includes the past 400 days of volume-price features for all stocks, with feature sampling every 5 trading days. The feature sampling shape is 40x18, using the past 40 days' features to predict the cumulative return over the next 20 trading days [18] - Data processing includes outlier removal and standardization for each feature in the time series and cross-sectional standardization at the stock level [18] - The model structure includes two GRU layers (GRU(128, 128)) followed by an MLP (256, 64, 64). The final output, predicted return (pRet), is used as the stock selection factor [22] - Training is conducted semi-annually, with training points on June 30 and December 31 each year. The training set and validation set are split in an 80:20 ratio [18] - Hyperparameters: batch_size equals the number of cross-sectional stocks, optimizer is Adam, learning rate is 1e-4, loss function is IC, early stopping rounds are 10, and maximum training rounds are 50 [18] 2. **GAN Model**: - The GAN model consists of a generator (G) and a discriminator (D). The generator learns the real data distribution and generates realistic samples, while the discriminator distinguishes between real and generated data [23][24] - Generator loss function: $$L_{G} = -\mathbb{E}_{z\sim P_{z}(z)}[\log(D(G(z)))]$$ where \(z\) represents random noise, \(G(z)\) is the generated data, and \(D(G(z))\) is the discriminator's output probability for the generated data [24][25] - Discriminator loss function: $$L_{D} = -\mathbb{E}_{x\sim P_{data}(x)}[\log D(x)] - \mathbb{E}_{z\sim P_{z}(z)}[\log(1-D(G(z)))]$$ where \(x\) is real data, \(D(x)\) is the discriminator's output probability for real data, and \(D(G(z))\) is the discriminator's output probability for generated data [27][29] - The generator uses an LSTM model to retain the sequential nature of input features, while the discriminator employs a CNN model to process the two-dimensional volume-price sequential features [33][37] **Model Evaluation**: The GAN_GRU model effectively captures volume-price sequential features and demonstrates strong predictive power for stock selection [4][13][22] Model Backtesting Results - **GAN_GRU Model**: - IC Mean: 0.1131*** - ICIR (non-annualized): 0.90 - Turnover Rate: 0.83 - Recent IC: 0.1241*** - One-Year IC Mean: 0.0867*** - Annualized Return: 37.52% - Annualized Volatility: 23.52% - IR: 1.59 - Maximum Drawdown: 27.29% - Annualized Excess Return: 23.14% [4][41][42] Quantitative Factors and Construction Methods - **Factor Name**: GAN_GRU Factor **Factor Construction Idea**: The GAN_GRU factor is derived from the GAN_GRU model, leveraging GAN for volume-price sequential feature processing and GRU for sequential feature encoding [4][13] **Factor Construction Process**: - The factor is constructed using the predicted return (pRet) output from the GAN_GRU model. The factor undergoes industry and market capitalization neutralization, as well as standardization [22] **Factor Evaluation**: The GAN_GRU factor demonstrates robust performance across various industries and time periods, with significant IC values and excess returns [4][13][41] Factor Backtesting Results - **GAN_GRU Factor**: - IC Mean: 0.1131*** - ICIR (non-annualized): 0.90 - Turnover Rate: 0.83 - Recent IC: 0.1241*** - One-Year IC Mean: 0.0867*** - Annualized Return: 37.52% - Annualized Volatility: 23.52% - IR: 1.59 - Maximum Drawdown: 27.29% - Annualized Excess Return: 23.14% [4][41][42] Industry-Specific Performance - **Recent IC Rankings (Top 5 Industries)**: - Social Services: 0.2198*** - Real Estate: 0.2027*** - Steel: 0.1774*** - Non-Bank Financials: 0.1754*** - Coal: 0.1537*** [4][41][42] - **One-Year IC Mean Rankings (Top 5 Industries)**: - Non-Bank Financials: 0.1401*** - Steel: 0.1367*** - Retail: 0.1152*** - Textiles & Apparel: 0.1124*** - Utilities: 0.1092*** [4][41][42] - **Recent Excess Return Rankings (Top 5 Industries)**: - Environmental Protection: 7.24% - Machinery: 4.37% - Real Estate: 4.03% - Textiles & Apparel: 3.89% - Building Materials: 2.91% [4][45][46] - **One-Year Average Excess Return Rankings (Top 5 Industries)**: - Building Materials: 2.15% - Real Estate: 1.97% - Social Services: 1.77% - Textiles & Apparel: 1.71% - Retail: 1.62% [4][45][46]

按申万一级行业分类，创业板成长ETF跟踪的指数权重集中于通信（36.69%）、电力设备（20.11%）、电子（12.66%）、非银金融（10.96%）、计算 机（9.05%）等板块。重仓中际旭创、新易盛、胜宏科技等强势股。 从估值层面来看，创业板成长ETF跟踪的创成长最新市盈率（PE-TTM）仅37.61倍，处于近10年36.77%的分位，即估值低于近10年63.23%以上的时间， 估值适中。建议逢低关注。 早盘A股市场震荡调整，截至上午10：27分纷纷翻红。半导体、卫星板块涨幅居前。 近期表现强势的创业板成长ETF 下跌0.34%，在20日均线附近震荡蓄势。近3个交易日创业板成长ETF累计上涨6.42%，同期创业板指数上涨3.49%，超 额涨幅近3个百分点。 | 2025/11/25-2025/11/27 (3日) | | | | | --- | --- | --- | --- | | 涨跌幅 | | | | | 159967(创业板成长I | 6.42% | 0.035 | 8.99% | | 399006(创业板指) | 3.49% | 102.26 | 5.17% | 从历史表现来看，创业板成长ETF ...

Market Overview - In November, the A-share market experienced a general decline, with the STAR Market 50 index dropping the most by 7.1%, while the Shanghai 50 index fell the least by 1.3%. Other major indices such as CSI 300, ChiNext, and CSI 1000 saw declines of -2.7%, -4.5%, and -3.4% respectively. The performance across industries showed significant divergence, with sectors like comprehensive services, banking, and media leading in gains [1][8][10] - The Hong Kong stock market also showed a volatile trend in November, influenced by fluctuations in the Federal Reserve's interest rate expectations and increasing concerns over the AI bubble. As of November 26, 2025, the Hang Seng Hong Kong 35 index rose by 1.1%, while the Hang Seng Index and Hang Seng China Enterprises Index saw minimal changes of 0.1% and -0.1%, respectively. The Hang Seng Technology Index dropped by 4.9% [1][10][11] A-share Insights - The market is believed to still be in a bull phase, but may enter a period of wide fluctuations in the short term. Compared to previous bull markets, there remains considerable room for index growth, but the emphasis on a "slow bull" policy may prioritize the duration of the bull market over its magnitude. Short-term catalysts appear weak, leading to a potential focus on defensive and consumer sectors, while TMT and advanced manufacturing sectors are recommended for mid-term attention [2][13][14][16][19] - In the context of market fluctuations, defensive sectors such as banking, utilities, and coal, along with consumer sectors like food and beverage, are highlighted as potential areas for investment. Historical trends suggest that previously lagging sectors may perform better during periods of market turbulence [16][17] Hong Kong Market Insights - The outlook for the Hong Kong market remains positive, with expectations of continued upward movement due to strong overall profitability and relatively low valuations. The "dumbbell" strategy is recommended, focusing on technology growth and high dividend stocks. Key areas of interest include domestic policies supporting self-sufficiency in chips and high-end manufacturing, as well as independent internet technology companies [3][21][24] - The report emphasizes the importance of high dividend, low volatility strategies, particularly in sectors such as telecommunications, utilities, and banking, which can provide stable returns [21][24] Stock Recommendations - For December 2025, the A-share stock selection includes: Sunlord Electronics, Zhongji Xuchuang, Huayou Cobalt, Sinopec, PetroChina, Zhengguang Co., Haier Smart Home, Hengli Hydraulic, Hangcha Group, and Goldwind Technology [26][27] - The recommended stocks for the Hong Kong market include: Tencent Holdings, China Mobile, China Tower, CNOOC Services, Huiju Technology, Sinopec Engineering, and AIA Group [30][31]

Core Insights - The report provides a summary of recommended stocks across various industries for December 2025, highlighting investment logic and potential growth opportunities for each company [2]. Industry Summaries Construction - Shenghui Integrated (603163.SH) is a Taiwanese cleanroom engineering service provider and a core engineering supplier for Google's TPU, poised to benefit from TSMC's expansion in the U.S. with potential orders from TSMC Arizona and multiple North American data centers [2]. Banking - China Merchants Bank (600036.SH) is expected to attract investors due to its stable operations and a projected dividend yield of 4.62% for the 2024 annual report, with increased demand for low-volatility stocks as market fluctuations rise [2]. Electronics - Aojie Technology (688220.SH) is positioned to benefit from the AI trend, with its unique 2-5G full-standard cellular communication capabilities and strong ASIC customization experience, which are expected to drive growth in wearable technology and other applications [2]. Power Equipment and New Energy - Delijia (603092.SH) maintains a leading market share in wind power main gearboxes, with a projected global market size of $11.563 billion by 2030 and a compound annual growth rate of 5.10% from 2024 to 2030, indicating stable growth prospects [2]. Basic Chemicals - Yaqi International (000893) is set to increase its potash fertilizer production capacity significantly, benefiting from a rising global potash market [2]. Agriculture, Forestry, Animal Husbandry, and Fishery - Youran Dairy (9858.HK), a leading global dairy farming company, is expected to benefit from rising milk prices and beef prices, leading to improved performance [2]. Internet - Alibaba (9988.HK) is experiencing accelerated growth in cloud revenue, with a 34% year-on-year increase in FY26Q2, and is expected to continue improving profitability through enhanced user engagement and AI integration [2]. Pharmaceuticals - Yifeng Pharmacy (603939.SH) is anticipated to see profit improvements due to ongoing optimization of its store structure and a clear plan for non-pharmaceutical profit growth [2]. Home Appliances - Midea Group (000333.SZ) is focusing on dual-driven strategies in domestic and international markets, with strong cash flow and a favorable dividend yield, despite facing some pressure in Q4 [2]. Non-Bank Financials - Ping An Insurance (601318.SH) is increasing investments in high-quality long-term assets, with potential for valuation improvement as market conditions shift [2].