固定收益快评 | 证券分析师： | 王艺熹 | 021-60893204 | wangyixi@guosen.com.cn | 执证编码：S0980522100006 | | --- | --- | --- | --- | --- | | 证券分析师： | 吴越 | 021-60375496 | wuyue8@guosen.com.cn | 执证编码：S0980525080001 | | 证券分析师： | 赵婧 | 0755-22940745 | zhaojing@guosen.com.cn | 执证编码：S0980513080004 | 我们定期梳理从公开渠道可获得的最新的可交换私募债（私募 EB）项目情况，对私募可交换债项目做基本 要素跟踪，私募发行条款发行过程可能有更改，请以最终募集说明书为准，发行进度请与相关主承销商咨 询。 证券研究报告 | 2026年01月04日 可交换私募债跟踪 私募 EB 每周跟踪（20251229-20251231） 本周新增项目信息如下：（部分项目因合规原因未予列示） 表1：私募 EB 每周跟踪(2025-12-31） | 债券名称 | 主承销商 | 规模 | 标的股票 | ...

Group 1 - The report emphasizes that the Hong Kong stock market is expected to outperform the market, driven by a dual momentum of a weaker US dollar and improved domestic liquidity in the spring of 2026 [1][2] - The focus for 2026 is on two main themes: AI and PPI, which are anticipated to significantly influence market expectations and corporate profitability [1][2][51] - The report highlights that the AI sector remains a priority for 2026, with expectations for accelerated domestic semiconductor hardware production and increased AI application deployments, particularly in the Hang Seng Technology and Internet sectors [2][59] Group 2 - The report indicates that the recovery in the Hong Kong stock market has already begun, with significant gains observed during the New Year period, suggesting a positive shift in foreign capital inflow [2][73] - It notes that the performance of various sectors in December showed divergence, with materials and industrial sectors benefiting from the recovery, while sectors like innovative pharmaceuticals and consumer goods faced challenges [2][77][84] - The report mentions that the 2025 Hong Kong stock market had a remarkable performance, with the Hang Seng Index returning 27.8%, significantly outperforming A-shares and US stocks, and setting a record for southbound capital inflows [2][84][86] Group 3 - The report discusses the macroeconomic outlook for the US, highlighting a decrease in inflation pressure and a focus on employment data, which will influence future monetary policy decisions [9][12] - It points out that the US real estate market is currently weak, with a significant gap between sellers and buyers, which is expected to impact inflation and economic activity in 2026 [18][22][26] - The report also notes that corporate earnings per share (EPS) for listed companies continue to reach new highs, indicating strong underlying performance despite macroeconomic challenges [40][41] Group 4 - The report identifies that the domestic economic indicators in China have shown some decline, particularly in M1 and social financing, which may affect liquidity and investment sentiment [51][52] - It emphasizes the importance of the upcoming "14th Five-Year Plan" and its implications for technological advancements and industrial growth, particularly in AI and PPI sectors [59][61] - The report suggests that the PPI is expected to rise significantly in the first half of 2026, which will improve profitability for industrial enterprises and influence investment strategies [61][62]

Core Insights - The report maintains an "Outperform" rating for the agriculture, forestry, animal husbandry, and fishery sector, anticipating a rebound in the meat and dairy cycles, benefiting industries related to dairy and beef cattle [1][4] - The monthly recommended stock portfolio includes leading companies in various segments, such as YouRan Agriculture, Modern Farming, and Muyuan Foods, indicating a focus on industry leaders poised for recovery [1][3] Livestock Sector - The livestock sector is expected to experience a significant reversal, with a focus on beef and dairy cattle. The domestic beef production capacity is anticipated to decrease to levels seen during the 2019 pig cycle, with prices expected to rise until 2028 [14] - The report highlights that the domestic raw milk price has been in decline for nearly four years, leading to production capacity pressures. The "meat and milk ratio" has reached historical highs, which may accelerate the culling of dairy cows [14][36] - Recommended companies in the livestock sector include YouRan Agriculture and Modern Farming, which are expected to benefit from improving raw milk prices and the upward trend in beef prices [14][17] Swine Sector - The swine sector is characterized by a gradual recovery in prices, with the average price of live pigs at 12.67 yuan/kg, reflecting a 13% month-on-month increase [20] - The report emphasizes the importance of leading companies in the swine industry, such as Huazhong Holdings and Muyuan Foods, which are expected to see significant cash flow improvements and higher dividend returns due to their low-cost advantages [15][19] - The overall industry is projected to stabilize, with a focus on valuation recovery for leading firms as the market adjusts to supply and demand dynamics [20][21] Poultry Sector - The poultry sector is witnessing a slight increase in supply, with expectations for demand recovery. The price of broiler chickens has shown a month-on-month increase of 9% [22] - The report notes that the structure of parent stock is changing, which may impact actual supply growth. However, demand is expected to benefit from domestic stimulus policies and macroeconomic improvements [22][28] - Key companies in the poultry sector include Lihua Food and Shengnong Development, which are positioned to maintain good profitability amid these changes [18][22] Pet Industry - The pet industry is identified as a promising consumer segment, with domestic brands rapidly gaining market share. The emotional consumption trend is expected to drive long-term growth in this sector [16][18] - Recommended companies include Guibao Pet, which is focusing on product upgrades and direct sales transformation to capture market opportunities [16][18] Feed and Grain Sector - The feed sector is benefiting from deeper industrialization in livestock farming, with leading companies expected to widen their competitive advantages through technology and service [1][3] - The report indicates that corn prices are at a historical low, with strong support expected from cost structures, while soybean meal prices are also at low valuations, awaiting a cyclical rebound [18][21]

证券研究报告 | 2026年01月04日 2026 年石化化工行业 1 月投资策略 优于大市 推荐炼油炼化、钾肥、磷化工、SAF 投资方向 石化化工行业 2026 年 1 月投资观点： 石化化工是周期性行业，现阶段石化化工行业"内卷式"竞争问题突出， 低质量、同质化的无序竞争导致企业普遍面临增产不增利困境，全行业 营业收入利润率从 2021 年的 8.03%持续降至 2024 年的 4.85%，2025 年 以来部分子行业率先复苏，前三季度行业归母净利润同比增长 10.56%， 行业盈利逐渐企稳复苏。 供给端：化学原料及化学制品制造业投资固定资产累计投资额于 2025 年 6 月开始转负，SW 基础化工行业及多个细分子行业的资本开支连续多 个季度转负，此轮行业扩产周期接近尾声；9 月"石化化工行业稳增长" 政策正式出台，旨在治理企业低价无序竞争、推动落后产能有序退出， 有机硅、己内酰胺、PTA 聚酯等子行业相继响应"反内卷"出台或正在 制定行业指导文件。我们认为，后续将会看到更多化工产品新产能审批 趋严、落后产能（如规模小、能耗高、污染大）将加速出清，石化化工 行业供给过剩问题将得到有效缓解。 需求端：传统需 ...

证券研究报告 | 2026年01月04日 农林牧渔 2026 年 1 月投资策略 优于大市 看好肉奶周期共振反转，奶牛及肉牛相关产业受益 月度重点推荐组合：优然牧业（牧业大周期受益龙头）、现代牧业（国内牧 业龙头企业）、牧原股份（生猪养殖龙头）、德康农牧（创新农户合作模式 的生猪养殖标的）、立华股份（低成本黄鸡与生猪养殖标的）。 各细分板块推荐逻辑：1）肉牛及原奶：牧业大周期反转预计在即，看好国 内肉奶景气共振上行，牧业公司业绩有望迎来高弹性修复。2）生猪：头部 企业现金流快速好转，并有望转型为红利标的，在全行业产能收缩的背景下， 龙头的成本优势有望明显提高，强者恒强。3）宠物：宠物作为新消费优质 赛道，长期景气受益人口趋势，且国内自主品牌正快速崛起，头部宠食标的 中期业绩增长确定性仍较强。4）饲料：畜禽养殖工业化加深，产业分工明 确，饲料龙头凭借技术和服务优势，有望进一步拉大竞争优势。5）禽：供 给波动幅度有限，行情有望随需求复苏，龙头企业凭借单位超额收益优势有 望实现更高现金流分红回报。 农产品价格跟踪：1）生猪：12 月末生猪 12.67 元/公斤，月环比上涨 13%， 7kg 仔猪价格约 231.67 ...

Investment Rating - The report maintains a "weaker than the market" rating for the U.S. stock market [4] Core Insights - The overall market trend is fading, with continued capital inflow into the semiconductor sector [2] - The S&P 500 index decreased by 1.0% this week, while the Nasdaq fell by 1.5% [1] - Energy sector showed the best performance with a gain of 3.3%, while the automotive sector saw the largest decline at -7.0% [1] Summary by Sections 2.1 Investment Returns - Energy sector recorded a weekly return of 3.3%, while the automotive sector experienced a decline of 7.0% [13] - The capital goods sector increased by 1.1%, and the semiconductor products and equipment sector had a slight gain of 0.2% [13] 2.2 Capital Flows - The estimated net capital inflow for the semiconductor products and equipment sector was $2.061 billion this week [15] - The automotive sector faced significant outflows, with a net capital outflow of $2.562 billion [15] - The capital goods sector saw a net inflow of $394 million [15] 2.3 Earnings Forecast - The earnings per share (EPS) forecast for the semiconductor products and equipment sector was adjusted upward by 0.5% this week [16] - The automotive sector's EPS forecast was increased by 0.7% [16] - Overall, the EPS expectations for all 24 sectors have risen [3] 2.4 Valuation Levels - The report does not provide specific valuation levels in the provided content [18]

证券研究报告 | 2026年01月04日 2026年01月03日 港股市场速览 优于大市 开年整体上涨，风格概念分化 股价表现：整体开年上涨，风格概念分化 本周，恒生指数+2.0%，恒生综指+1.7%。风格方面，大盘（恒生大型股+2.0%） ＞中盘（恒生中型股+0.8%）＞小盘（恒生小型股-0.3%）。 主要概念指数表现分化。上涨的主要有恒生汽车（+4.8%）；下跌的主要有 恒生生物科技（-1.4%）。 国信海外选股策略多数上涨。上涨的主要有自由现金流 30（+2.7%）；下跌 的主要有 ROE 策略进攻型（-2.1%）。 22 个行业上涨，7 个行业下跌，1 个基本持平。上涨的主要有：国防军工 （+8.9%）、石油石化（+5.6%）、电子（+4.4%）、汽车（+4.2%）、传媒（+4.1%）； 下跌的主要有：基础化工（-2.1%）、食品饮料（-2.0%）、农林牧渔（-1.9%）、 医药（-1.3%）、电力及公用事业（-1.1%）。 估值水平：行业分化较大，科技与汽车拉升 本周，恒生指数估值（动态预期 12 个月正数市盈率，后同）+1.4%至 11.7x； 恒生综指估值+2.2%至 11.7x。 主要概念指数 ...

证券研究报告 | 2026年01月03日 港股投资周报 年度收官，港股精选组合本年度上涨 53.23% 港股精选组合绩效回顾 本周，港股精选组合绝对收益-2.49%，相对恒生指数超额收益-1.76%。 本年，港股精选组合绝对收益 53.23%，相对恒生指数超额收益 25.46%。 港股市场创新高热点板块跟踪 我们根据分析师关注度、股价相对强弱、股价路径平稳性、创新高连续性等 角度在过去 20 个交易日创出过 250 日新高的股票池中筛选出平稳创新高股 票。 近期，现代牧业等股票平稳创出新高。 按照板块来看，创新高股票数量最多的是周期板块，其次为消费、大金融、 制造和科技板块，具体个股信息可参照正文。 港股市场一周回顾 宽基指数方面，本周恒生科技指数收益最高，累计收益 0.30%；恒生小型股 指数收益最低，累计收益-1.56%。 行业指数方面，本周能源业行业收益最高，累计收益 2.21%；医疗保健业行 业收益最低，累计收益-3.24%。 概念板块方面，本周卫星导航概念板块收益最高，累计收益 11.42%；富士 康概念板块收益最低，累计收益-6.29%。 南向资金监控 南向资金整体方面，本周港股通累计净流出 38 ...

主动量化策略周报 年度收官，四大主动量化组合本年均战胜主动股基中位数 证券研究报告 | 2026年01月03日 核心观点 金融工程周报 国信金工主动量化策略表现跟踪： 本周，优秀基金业绩增强组合绝对收益-0.14%，相对偏股混合型基金指数超 额收益 0.50%。本年，优秀基金业绩增强组合绝对收益 31.88%，相对偏股 混合型基金指数超额收益-1.31%。今年以来，优秀基金业绩增强组合在主动 股基中排名 47.33%分位点（1642/3469）。 本周，超预期精选组合绝对收益-1.52%，相对偏股混合型基金指数超额收益 -0.89%。本年，超预期精选组合绝对收益 42.21%，相对偏股混合型基金指 数超额收益 9.02%。今年以来，超预期精选组合在主动股基中排名 30.33% 分位点（1052/3469）。 本周，券商金股业绩增强组合绝对收益-0.67%，相对偏股混合型基金指数超 额收益-0.04%。本年，券商金股业绩增强组合绝对收益 40.66%，相对偏股 混合型基金指数超额收益 7.47%。今年以来，券商金股业绩增强组合在主动 股基中排名 32.60%分位点（1131/3469）。 本周，成长稳健组合绝对 ...

Quantitative Models and Construction Methods - **Model Name**: Maximized Factor Exposure Portfolio (MFE) **Model Construction Idea**: The MFE portfolio is designed to test the effectiveness of single factors under real-world constraints, such as industry exposure, style exposure, stock weight limits, and turnover rate. This approach ensures that factors deemed "effective" can genuinely contribute to return prediction in the final portfolio[40][41]. **Model Construction Process**: The MFE portfolio is constructed using the following optimization model: $ \begin{array}{ll} max & f^{T} w \\ s.t. & s_{l} \leq X(w-w_{b}) \leq s_{h} \\ & h_{l} \leq H(w-w_{b}) \leq h_{h} \\ & w_{l} \leq w-w_{b} \leq w_{h} \\ & b_{l} \leq B_{b}w \leq b_{h} \\ & \mathbf{0} \leq w \leq l \\ & \mathbf{1}^{T} w = 1 \end{array} $ - **Objective Function**: Maximize single-factor exposure, where \( f \) represents factor values, and \( f^{T}w \) is the weighted exposure of the portfolio to the factor. \( w \) is the stock weight vector to be optimized. - **Constraints**: 1. **Style Exposure**: \( X \) is the factor exposure matrix for stocks, \( w_b \) is the benchmark weight vector, and \( s_l, s_h \) are the lower and upper bounds for style factor exposure[41]. 2. **Industry Exposure**: \( H \) is the industry exposure matrix, where \( H_{ij} = 1 \) if stock \( i \) belongs to industry \( j \), otherwise \( H_{ij} = 0 \). \( h_l, h_h \) are the lower and upper bounds for industry deviation[41]. 3. **Stock Deviation**: \( w_l, w_h \) are the lower and upper bounds for individual stock deviations from the benchmark[41]. 4. **Constituent Weight**: \( B_b \) is a 0-1 vector indicating whether a stock is a benchmark constituent. \( b_l, b_h \) are the lower and upper bounds for constituent weights[41]. 5. **No Short Selling**: Ensures non-negative weights and limits individual stock weights to \( l \)[41]. 6. **Full Investment**: Ensures the portfolio is fully invested with \( \mathbf{1}^{T}w = 1 \)[42]. - **Implementation**: At the end of each month, MFE portfolios are constructed for each factor under the defined constraints. Historical returns are calculated during the backtest period, accounting for a 0.3% transaction cost on both sides[44]. **Model Evaluation**: The MFE portfolio effectively tests factor performance under realistic constraints, ensuring that selected factors contribute to return prediction in practical applications[40][41]. Quantitative Factors and Construction Methods - **Factor Name**: Standardized Unexpected Earnings (SUE) **Factor Construction Idea**: Measures the deviation of actual quarterly net profit from expected profit, standardized by the standard deviation of expected profit. It captures earnings surprises[17]. **Factor Construction Process**: $ SUE = \frac{\text{Actual Quarterly Net Profit} - \text{Expected Quarterly Net Profit}}{\text{Standard Deviation of Expected Quarterly Net Profit}} $ **Factor Evaluation**: SUE is a widely used factor for capturing earnings surprises and has shown effectiveness in predicting stock returns[17]. - **Factor Name**: DELTAROE **Factor Construction Idea**: Measures the change in return on equity (ROE) compared to the same quarter of the previous year, reflecting profitability improvement[17]. **Factor Construction Process**: $ DELTAROE = \text{Quarterly ROE} - \text{ROE of the Same Quarter Last Year} $ **Factor Evaluation**: DELTAROE is effective in identifying companies with improving profitability, which can lead to positive stock performance[17]. - **Factor Name**: Non-Liquidity Shock **Factor Construction Idea**: Measures the average absolute daily return over the past 20 trading days, divided by the average trading volume, capturing liquidity risk[17]. **Factor Construction Process**: $ \text{Non-Liquidity Shock} = \frac{\text{Average Absolute Daily Return (20 Days)}}{\text{Average Trading Volume (20 Days)}} $ **Factor Evaluation**: This factor is useful for identifying stocks with higher liquidity risks, which may impact their returns[17]. Factor Backtest Results - **Standardized Unexpected Earnings (SUE)**: - **CSI 300 Universe**: Weekly return: 0.43%, monthly return: 2.55%, YTD return: 12.65%, historical annualized return: 4.22%[19]. - **CSI 500 Universe**: Weekly return: 0.07%, monthly return: 1.02%, YTD return: 7.47%, historical annualized return: 5.50%[21]. - **CSI 1000 Universe**: Weekly return: -0.36%, monthly return: 1.55%, YTD return: 20.90%, historical annualized return: 6.47%[23]. - **CSI A500 Universe**: Weekly return: -0.07%, monthly return: 1.17%, YTD return: 11.28%, historical annualized return: 4.55%[25]. - **DELTAROE**: - **CSI 300 Universe**: Weekly return: 0.33%, monthly return: 2.78%, YTD return: 18.51%, historical annualized return: 4.52%[19]. - **CSI 500 Universe**: Weekly return: -0.58%, monthly return: -0.75%, YTD return: 8.13%, historical annualized return: 7.56%[21]. - **CSI 1000 Universe**: Weekly return: -0.56%, monthly return: 1.36%, YTD return: 12.58%, historical annualized return: 8.77%[23]. - **CSI A500 Universe**: Weekly return: 0.01%, monthly return: 2.94%, YTD return: 20.42%, historical annualized return: 4.48%[25]. - **Non-Liquidity Shock**: - **CSI 300 Universe**: Weekly return: -0.06%, monthly return: -0.29%, YTD return: -1.78%, historical annualized return: 0.40%[19]. - **CSI 500 Universe**: Weekly return: -0.35%, monthly return: 0.79%, YTD return: -2.82%, historical annualized return: 0.18%[21]. - **CSI 1000 Universe**: Weekly return: 0.47%, monthly return: -1.66%, YTD return: 5.34%, historical annualized return: 2.23%[23]. - **CSI A500 Universe**: Weekly return: 0.13%, monthly return: -0.34%, YTD return: -3.95%, historical annualized return: 1.50%[25].