[Table_Page] 跟踪分析|银行 证券研究报告 [Table_Gr ade] 行业评级 买入 前次评级 买入 报告日期 2024-07-14 [Table_ 相关研究： DocReport] 银行资负跟踪 20240714:税 2024-07-14 期资金面平稳，央行收窄利 率走廊 2024 年 6 月金融数据点评:数 据偏弱，但 M1 增速可能见底 2024-07-14 银行行业:如何解释上半年同 [Table_Title] 银行行业 等待财政，关注相对 ——银行投资观察 20240714 [Table_PicQuote] 相对市场表现 [Table_Summary] 核心观点： ⚫ 板块表现方面：本期（20240708-20240712），Wind 全 A 上涨 1.5%， 银行板块整体（中信一级行业）上涨 3.7%，排在所有行业第 3 位，跑 赢万得全A。国有大行、股份行、城商行、农商行变动幅度分别为 3.35%、 4.77%、2.52%、1.91%。恒生综合指数上涨 2.3%，H 股银行涨幅 2.7%， 跑赢恒生综合指数，跑输 A 股银行。 ⚫ 个股表现方面：A 股银行涨幅前三为招商银行上涨 ...

[Table_Page] 深度分析|传媒 证券研究报告 [Table_Title] 传媒行业 外卖出海研究:中东及香港地区竞争格局与市 [Table_Summary] 核心观点: ⚫ 全球餐饮恢复,线上化率提升促进全球外卖市场发展。根据彭博数据, 2023 年全球餐饮市场规模 3.02 万亿美元,同增 9%,已超过 2019 水 平。疫情带来全球餐饮消费场景变化,线上餐饮消费占比提升。 ⚫ 中东餐饮有宗教性、本土化特点,本土与国际化外卖平台并存。中东地 区阿拉伯餐厅数量领先,连锁、便利餐厅在疫情之后普及率增加。2023 年中东餐饮市场规模为 1196.23 亿美元,沙特餐饮市场 258.72 亿美 元。中东地区在疫情后呈现出加速线上化和去堂食化的趋势,根据 Statista 数据,2023 年海湾六国整体外卖 GMV 为 101 亿美元,预计 2023-2029 年 CAGR 达 5.5%。主要玩家有 Talabat、Jahez、Hunger Station 为代表的本土品牌,以及国际外卖平台 Deliveroo、Uber Eats、 Delivery Hero 借助收并购或者自建进入中东外卖市场的身影。 ⚫ ...

[Table_Page] 投资策略周报|有色金属 证券研究报告 [Table_Title] 金属及金属新材料行业 美国降息概率提升,金价续涨 [Table_Summary] 核心观点: 基本金属:美联储降息预期再提升,预计基本金属价格震荡上行。美 联储降息预期提升,外需仍待进一步改善,内需受高价、淡季影响较平 稳,供给端整体铜矿、阴极铜供给均仍紧张,预计下周基本金属价格震 荡上行。建议关注:洛阳钼业(A+H)、西部矿业、金诚信、中国铝业 (A+H)、云铝股份、天山铝业等。 钢铁:供需转弱,价利承压,关注供给约束。供给端,高炉开工环比下 降,钢厂盈利率大幅下降,铁水供给约束明显。需求端,线螺采购环比 回落。成本端,预计低盈利率继续压制原料采购需求。价利方面,供需 格局短期改善,购销差价预期偏弱,供给约束预期提升,预计短期钢价 震荡为主。建议关注:宝钢股份、华菱钢铁、久立特材、抚顺特钢等。 贵金属:美国通胀超预期回落,美联储 9 月降息概率大幅提升,金价 继续上涨。据 Wind,本周公布的美国 6 月 CPI、核心 CPI 同环比均超 预期回落,美联储主席鲍威尔在 7 月 10 日听证会上表示"美联储无需 等到通 ...

[Table_Page] 投资策略周报|通信 证券研究报告 [Table_Title] 通信行业 工信部发布北斗规模应用试点城市遴选通知,产业链迎政策催化 [Table_Summary] 核心观点: 事件:7 月 12 日,工业和信息化部办公厅发布关于开展工业和信息化 领域北斗规模应用试点城市遴选的通知。《通知》指出,为促进北斗产 业发展,加快北斗应用落地,拓展北斗应用广度和深度,工业和信息化 部拟遴选一批有条件、有基础的城市(含直辖市所辖区县,以下统称城 市),开展北斗规模应用试点工作。 《通知》指出,应围绕大众消费、工业制造和融合创新三个领域,结合 当地北斗产业基础、城市发展特点和建设情况,积极开展试点工作, 加快提升北斗渗透率,促进北斗设备和应用向北斗三代有序升级换代。 大众消费领域,应提升北斗应用感知度和普及率。试点城市以智能手 机、可穿戴设备、平板电脑、共享出行、低空应用无人机等领域为重点, 积极引导企业研制和生产北斗产品,持续提高产品供给能力。深入拓展 北斗在大众消费领域应用深度和广度,培育北斗大众消费应用新模式 新业态。工业制造领域,应加速北斗行业应用赋能。加快在汽车、船舶、 航空器、机器人等重 ...

[Table_Page] 投资策略周报|农林牧渔 证券研究报告 [TAblE_TitlE] 农林牧渔行业 本周猪价小幅上行,水产复苏趋势延续 [TAblE_SummAry] 核心观点: 畜禽养殖:据搜猪网,7月12日,全国瘦肉型生猪出栏均价18.77元/公 斤,环比上周上涨1.40%,同比上涨8.75%。前期产能去化效应显现, 本周猪价继续上行。随着后续供给下降以及需求季节性回暖,3季度猪 价有望继续上行。从生猪公司的中报预告情况来看,2季度业绩基本均 实现扭亏,养殖成本处于改善过程中。展望3季度,养殖成本仍具有下 降潜力,生猪企业有望充分受益猪价上行与成本下行之间"剪刀差"扩 大,整体盈利水平有望超出市场预期。当前行业资产负债率整体处于高 位,预计后续产能恢复速度或较为缓慢。根据Mysteel数据,6月能繁母 猪存栏量环比增长0.37%,环比增幅收窄。当前板块估值再次回落至相 对低位,大型养殖企业重点推荐温氏股份、牧原股份,关注新希望,中 小养殖企业建议关注唐人神、天康生物、华统股份以及川渝生猪龙头企 业。白羽鸡方面,伴随消费旺季逐步到来,据Wind,本周商品代鸡苗和 毛鸡价格开启反弹,分别环比上涨12.8 ...

[Table_Page] 投资策略周报|基础化工 证券研究报告 [Table_Title] 基础化工行业 24Q2 龙头业绩拐点已现,关注美联储降息升温 [Table_Summary] 核心观点: ⚫ 行业基础数据跟踪:据 wind 资讯,7 月 8 日~7 月 12 日,SW 基础化 工板块上涨 1.75%,跑输万得全 A 指数 0.08pct;化工子行业下跌较 多,表现较差的有油气开采、钾肥和氟化工等板块,表现较好的有炭 黑,氨纶,锦纶等板块。 ⚫ 化工品价格下跌较多: 据百川、wind 资讯,在我们跟踪的 336 个产品 中,上涨、持平、下跌的产品数量分别为 67 种、164 种、105 种,占比 分别为 20%、49%和 31%。化工品价格下跌较多。价格涨幅前五: CCMP、醋酸、硫酸、烟煤、120#溶剂油。价格跌幅前五:液氯、硝磺 草酮、聚四氟乙烯 PTFE、异丁醛、工业磷酸一铵。 ⚫ 24Q2 化工行业龙头标的业绩拐点已现。化工行业龙头标的业绩拐点已 现。根据 WIND 资讯,以申万化工一级行业为基础,截至 2024 年 07 月 12 日,化工行业有 131 家 A 股上市公司公布了 2024 ...

[Table_Page] 投资策略周报|建筑材料 证券研究报告 [Table_Title] 建筑材料行业 [Table_Gr ade] 行业评级 持有 前次评级 持有 6 月社融符合预期,淡季水泥价格继续推涨 报告日期 2024-07-14 [Table_Summary] 核心观点: ⚫ 地产政策密集发布,市场预期有所修复,二手房成交环比改善、新房成 交降幅环比收窄,尽管目前地产链建材基本面受地产景气影响仍在左 侧,需等待地产企稳,但优质龙头企业仍表现出较强的经营韧性,如二 手房和存量房翻新需求支撑下零售建材韧性强、水泥和玻璃龙头持续 保持盈利领先优势。建议继续关注政策催化下建材修复机会,同时关注 底部涨价的水泥和部分结构性景气赛道(玻纤、出海、药玻等)。 ⚫ 消费建材:地产仍在寻底,龙头公司经营韧性强。消费建材长期需求稳 定性好(受益存量房需求)、行业集中度持续提升、竞争格局好的优质 细分龙头中长期成长空间仍然很大。2024 年地产仍在寻底,新开工面 积预计回落至中期较低水平,地产政策转变有望先带来销售面积好转; 核心龙头公司盈利领先地产行业实现有韧性的复苏。看好三棵树、兔宝 宝、北新建材、伟星新材、中国联 ...

Group 1: Polling Data - After the first debate, Trump's polling advantage has widened significantly, with his support in key swing states showing a clear lead over Biden[6] - In the latest polls, Trump's support in seven swing states is notably higher, with five states showing substantial leads over Biden[20] - Biden's job approval rating remains low, with a disapproval rate of 60% as of June 28, 2024[19] Group 2: Election Dynamics - The first debate has negatively impacted Biden's campaign, with his winning probability dropping from 45.0% on June 26 to 30.0% by June 29[33] - The upcoming election is likely to be a rematch between Trump and Biden, following their overwhelming victories in their respective party primaries[10] - Key issues for voters in swing states include the economy and inflation, with many expressing dissatisfaction with Biden's handling of these matters[49] Group 3: Economic Policies - Trump's economic policies during his term included significant tax cuts, with the corporate tax rate reduced from 35% to 21%[68] - Biden's proposed tax increases aim to raise the corporate tax rate from 21% to 28% and restore the top personal income tax rate from 37% to 39.6%[81] - Both candidates advocate for tariffs on Chinese imports, with Trump suggesting a baseline tariff of 10% on all imports[106]

Quantitative Models and Construction Methods Model Name: Large Order Ratio Factor - **Construction Idea**: The factor is based on Level 2 tick-by-tick order data, focusing on the ratio of large orders to total orders[2][10][24] - **Construction Process**: - Aggregate tick-by-tick order data to reflect the original order size[24] - Define large orders as those with transaction volumes greater than the mean plus N standard deviations[27] - Construct large buy order ratio factors (BigBuy_1p0, BigBuy_1p5, BigBuy_2p0) and large sell order ratio factors (BigSell_1p0, BigSell_1p5, BigSell_2p0) using different standard deviation thresholds[27] - Formula: $ \text{BigBuy}_1p0 = \frac{\text{Large Buy Orders}}{\text{Total Orders}} $[27] - **Evaluation**: The factor shows significant performance in predicting stock returns, especially in short-term trading windows[28][30] - **Test Results**: - 5-day window: RankIC mean 5.4%, win rate 64%, annualized return 33.15%, max drawdown 13.43%, Sharpe ratio 1.83[31] - 20-day window: RankIC mean 7.9%, win rate 70%, annualized return 28.08%, max drawdown 9.18%, Sharpe ratio 1.80[33] Model Name: Time-Dimension Decoupled Large Order Ratio Factor - **Construction Idea**: Decouple large order ratio factors based on different time periods within the trading day[10][38] - **Construction Process**: - Define large orders within specific time windows (e.g., first 15 minutes, first 30 minutes, last 15 minutes, last 30 minutes)[38] - Construct factors such as BigBuy_1p0_09301000 for large buy orders in the first 30 minutes[38] - Formula: $ \text{BigBuy}_1p0_09301000 = \frac{\text{Large Buy Orders (09:30-10:00)}}{\text{Total Orders}} $[38] - **Evaluation**: Time-dimension decoupled factors provide more stable returns and lower drawdowns compared to non-decoupled factors[40][42] - **Test Results**: - 5-day window: RankIC mean 3.4%, win rate 63%, annualized return 29.78%, max drawdown 13.02%, Sharpe ratio 1.63[44] - 20-day window: RankIC mean 4.5%, win rate 70%, annualized return 26.63%, max drawdown 8.37%, Sharpe ratio 1.73[48] Model Name: Order-Dimension Decoupled Large Order Ratio Factor - **Construction Idea**: Decouple large order ratio factors based on the buy and sell attributes of orders[10][56] - **Construction Process**: - Define factors based on combinations of large and small buy and sell orders (e.g., BigBuy_BigSell_1p0, BigBuy_SmallSell_1p0)[56] - Formula: $ \text{BigBuy_BigSell}_1p0 = \frac{\text{Large Buy Orders + Large Sell Orders}}{\text{Total Orders}} $[56] - **Evaluation**: Order-dimension decoupled factors show improved performance in multi-directional trading strategies[57][60] - **Test Results**: - 5-day window: RankIC mean 6.7%, win rate 72%, annualized return 31.01%, max drawdown 15.44%, Sharpe ratio 1.67[60] - 20-day window: RankIC mean 9.8%, win rate 82%, annualized return 28.57%, max drawdown 11.53%, Sharpe ratio 1.80[62] Model Name: Multi-Dimension Decoupled Large Order Ratio Factor - **Construction Idea**: Combine time and order dimension decoupling to construct more refined large order ratio factors[10][68] - **Construction Process**: - Define factors based on both time and order attributes (e.g., BigBuy_BigSell_1p0_09301000, BigBuy_BigSell_1p0_14301457)[68] - Formula: $ \text{BigBuy_BigSell}_1p0_09301000 = \frac{\text{Large Buy Orders + Large Sell Orders (09:30-10:00)}}{\text{Total Orders}} $[68] - **Evaluation**: Multi-dimension decoupled factors provide the most stable and high-performing returns across different market conditions[69][71] - **Test Results**: - 5-day window: RankIC mean 3.3%, win rate 66%, annualized return 29.02%, max drawdown 14.70%, Sharpe ratio 1.56[72] - 20-day window: RankIC mean 4.9%, win rate 75%, annualized return 26.56%, max drawdown 9.82%, Sharpe ratio 1.68[76] Model Backtest Results Large Order Ratio Factor - **5-day window**: RankIC mean 5.4%, win rate 64%, annualized return 33.15%, max drawdown 13.43%, Sharpe ratio 1.83[31] - **20-day window**: RankIC mean 7.9%, win rate 70%, annualized return 28.08%, max drawdown 9.18%, Sharpe ratio 1.80[33] Time-Dimension Decoupled Large Order Ratio Factor - **5-day window**: RankIC mean 3.4%, win rate 63%, annualized return 29.78%, max drawdown 13.02%, Sharpe ratio 1.63[44] - **20-day window**: RankIC mean 4.5%, win rate 70%, annualized return 26.63%, max drawdown 8.37%, Sharpe ratio 1.73[48] Order-Dimension Decoupled Large Order Ratio Factor - **5-day window**: RankIC mean 6.7%, win rate 72%, annualized return 31.01%, max drawdown 15.44%, Sharpe ratio 1.67[60] - **20-day window**: RankIC mean 9.8%, win rate 82%, annualized return 28.57%, max drawdown 11.53%, Sharpe ratio 1.80[62] Multi-Dimension Decoupled Large Order Ratio Factor - **5-day window**: RankIC mean 3.3%, win rate 66%, annualized return 29.02%, max drawdown 14.70%, Sharpe ratio 1.56[72] - **20-day window**: RankIC mean 4.9%, win rate 75%, annualized return 26.56%, max drawdown 9.82%, Sharpe ratio 1.68[76] Selected Large Order Factor Portfolio Performance All Market - **Annualized Return**: 36.61%[89] - **Max Drawdown**: 17.52%[89] - **Sharpe Ratio**: 2.03[89] - **Excess Annualized Return**: 33.07%[89] CSI 300 - **Annualized Return**: 12.24%[97] - **Max Drawdown**: 14.51%[97] - **Sharpe Ratio**: 0.75[97] - **Excess Annualized Return**: 13.40%[97] CSI 500 - **Annualized Return**: 22.55%[103] - **Max Drawdown**: 9.08%[103] - **Sharpe Ratio**: 1.12[103] - **Excess Annualized Return**: 18.67%[103] CSI 800 - **Annualized Return**: 18.54%[114] - **Max Drawdown**: 7.22%[114] - **Sharpe Ratio**: 1.14[114] - **Excess Annualized Return**: 18.95%[114] CSI 1000 - **Annualized Return**: 24.61%[123] - **Max Drawdown**: 10.43%[123] - **Sharpe Ratio**: 1.36[123] - **Excess Annualized Return**: 17.39%[123] ChiNext - **Annualized Return**: 36.20%[131] - **Max Drawdown**: 25.15%[131] - **Sharpe Ratio**: 1.59[131] - **Excess Annualized Return**: 25.07%[131]

Xml [Table_Page] 投资策略周报|国防军工 证券研究报告 [Table_Title] 国防军工行业 中航电测重组过会,看好板块中长期景气度 [Table_Summary] 核心观点: 中航电测百亿重组过会,中国核电拟定增募资不超 140 亿元,看好板 块中长期确定性和景气度。7 月 11 日,中航电测发布公告向航空工业 集团发行股份购买其持有的成都飞机工业(集团)有限责任公司 100% 股权并购事项过会,交易金额 174 亿元。据北京商报,上述并购系注 册制以来交易金额最高、规模最大的深市重组项目。7 月 11 日,中国 核电公告拟向控股股东中核集团和战略投资者社保基金会发行 A 股股 票,其中中核集团拟认购 20 亿元,社保基金会拟认购 120 亿元。募 集资金总额不超 140 亿元,将用于辽宁徐大堡核电站、福建漳州核电 站和江苏田湾核电站等项目。 新质生产力方向,船厂生产经营数据同比改善明显,船舶景气度持续 向上,中国船舶、中国动力 24H1 业绩同比改善明显。航空出海、大 飞机、低空经济等进展持续更新。7 月 12 日,据"中国船舶报"公众 号,2024 年上半年,上海三大央企船厂江南造船 ...