登录新浪财经APP 搜索【信披】查看更多考评等级 根据万家国证港股通科技交易型开放式指数证券投资基金发起式联接基金（基金简称：万家国证港股通 科技ETF发起式联接；基金代码：A类：026107，C类：026108，以下简称"本基金"）招募说明书、基 金份额发售公告的规定，本基金自2025年11月19日至2025年11月26日通过万家基金管理有限公司（以下 简称"本公司"）指定的销售机构公开发售。 依照本公司与华宝证券股份有限公司（下文简称为"华宝证券"）签署的销售代理协议，自2025年11月19 日起，本公司将增加华宝证券代理本基金的基金发售业务，投资者可在华宝证券办理本基金的认购业 务。待基金成立后，投资者也可在华宝证券办理申购、赎回、转换、定投等其他业务。华宝证券可支持 办理认购的份额类别以及具体业务办理流程、规则等以各机构的规定为准，相关业务办理状况亦请遵循 其各自规定执行。 投资者可通过以下途径咨询详情： 1、华宝证券股份有限公司 客服电话：400-820-9898 网址：www.cnhbstock.com 万家基金管理有限公司 2025年11月19日 万家基金管理有限公司关于旗下部分基金新增国信证券 ...

近期，沪指时隔十年重回4000点，这轮市场上涨的根本动力主要来自于资本市场信心的修复。过去较长 时间，中国优质资产的估值相比于海外始终处于较低位，"反内卷"政策落地、"九三阅兵"、党的二十届 四中全会等系列利好事件落地，都极大提振资本市场信心，居民资金开始加速入市，市场估值中枢得到 显著修复。数据显示，年内行情中，有色金属板块表现最为亮眼。不过，该板块前期的强劲涨势主要得 益于行业整体β行情的集中释放。随着市场进一步演化，如何在β动能之外，精准捕捉行业内细分领域 的超额机会，成为投资者面临的核心问题。 截至11月3日（下同），沪指年内涨幅接近19%，连续6个月收涨。在这一轮行情中，有色金属行业成为 最大赢家，申万31个行业中，有色金属指数以73.77%的涨幅领跑市场。 然而，超越行业整体表现的机会，往往藏在细分领域。相比投资者所熟悉的有色金属指数，该行业的细 分指数中证工业有色金属主题指数表现更为突出，年内涨幅达74.57%。跟踪该指数的唯一场内ETF万家 中证工业有色金属主题ETF（交易代码：560860，简称：工业有色ETF）年内涨幅达78.08%，最新规模 已超55亿元，较去年末暴增近15倍，成为年内最 ...

Core Viewpoint - The small-cap stocks, represented by the CSI 2000 and National CSI 2000 indices, have shown a significant recovery after a decline of over 6% since reaching their peak on August 27, with various ETFs tracking these indices experiencing notable gains in recent trading sessions [1][3]. Group 1: Market Performance - The CSI 2000 index and National CSI 2000 index saw a cumulative decline of over 6% since their peak on August 27, but have since continued to rise [1]. - As of September 4, several ETFs tracking the CSI 2000 index reported daily gains ranging from 1.76% to 2.01%, with year-to-date performance showing increases between 28.94% and 53.11% [3][4][8]. - The Wind Micro-cap Index has surged by 68.72% year-to-date, while the CSI 2000 and CSI 1000 indices have increased by 32.47% and 24.47%, respectively [6]. Group 2: Fund Performance and Flows - The largest ETF tracking the CSI 2000 index is the Huatai-PB CSI 2000 ETF, with a latest scale of 2.329 billion [7][10]. - The year-to-date performance of various ETFs shows that the China Merchants CSI 2000 Enhanced ETF leads with a gain of 53.11%, followed by the Silver Hua CSI 2000 Enhanced ETF at 45.21% [8][10]. - In terms of net fund flows, the Huatai-PB CSI 2000 ETF experienced a net outflow of 1.467 billion, while the China Merchants CSI 2000 Enhanced ETF saw a net inflow of 585 million [8]. Group 3: Investment Trends - The current market environment favors small-cap stocks due to a focus on marginal changes in industry structure during periods of rapid technological iteration and policy encouragement for innovation [6]. - Historical patterns suggest that micro-cap stocks may face a weakening trend, although structural opportunities may still exist [7]. - The investment landscape is characterized by institutional investors holding significant pricing power, which typically benefits large-cap stocks, while individual investors tend to favor small-cap stocks [6].

Quantitative Models and Construction Methods 1. Model Name: Wantushi AI Model - **Model Construction Idea**: The Wantushi AI model evaluates indices based on their potential upward probability and selects ETFs with favorable characteristics for investment[6] - **Model Construction Process**: 1. The model assigns a score to indices based on their upward probability. Indices with scores above 0.8 are selected[6] 2. For each selected index, the corresponding ETFs are identified[6] 3. Among these ETFs, those with a 30-day average daily trading volume exceeding 30 million RMB are retained[6] 4. Finally, the ETF with the lowest IOPV premium rate for each index is chosen[6] - **Model Evaluation**: The model systematically filters ETFs based on liquidity and valuation metrics, ensuring a focus on high-quality investment options[6] --- Backtesting Results of Models 1. Wantushi AI Model - **Index Code: 932000 (CSI)** - Upward Probability: 93.52% - Selected ETF: 159552 (CSI 2000 Enhanced ETF)[7] - **Index Code: 399006 (SZ)** - Upward Probability: 93.37% - Selected ETF: 159977 (Tianhong ChiNext ETF)[7] - **Index Code: 399673 (SZ)** - Upward Probability: 92.49% - Selected ETF: 159682 (ChiNext 50 ETF)[7] - **Index Code: 399850 (SZ)** - Upward Probability: 90.15% - Selected ETF: 159350 (Shenzhen 50 ETF by Fuguo)[7] - **Index Code: 000852 (SH)** - Upward Probability: 85.86% - Selected ETF: 159680 (1000 Enhanced ETF)[7] - **Index Code: 399303 (SZ)** - Upward Probability: 85.82% - Selected ETF: 159628 (Guozheng 2000 ETF)[7] - **Index Code: 399293 (SZ)** - Upward Probability: 85.03% - Selected ETF: 159814 (ChiNext Large Cap ETF)[7] - **Index Code: 399330 (SZ)** - Upward Probability: 84.81% - Selected ETF: 159901 (Shenzhen 100 ETF)[7] - **Index Code: 399296 (SZ)** - Upward Probability: 84.46% - Selected ETF: 159967 (ChiNext Growth ETF)[7] - **Index Code: 000905 (SH)** - Upward Probability: 83.23% - Selected ETF: 510580 (CSI 500 ETF by E Fund)[7] - **Index Code: 000906 (SH)** - Upward Probability: 81.05% - Selected ETF: 515800 (800 ETF)[7]

Group 1 - The strategy for low to medium risk asset allocation includes risk parity and risk budgeting models, where risk parity allocates equal risk weights across different assets, while risk budgeting allows investors to set asset risk weights based on their risk preferences [1] - The correlation between major asset classes such as equities (A-shares, Hong Kong stocks, US stocks), bonds, and commodities (precious metals, energy, chemicals) is relatively low, making it suitable to construct portfolios using corresponding ETFs [1] - The long-term correlation between bonds and equities or commodities ranges from 0 to -30%, indicating a "stock-bond seesaw" effect due to the counter-cyclical nature of interest rates affecting bond yields, while equities and commodities reflect the health or expectations of the real economy [1] Group 2 - A simple construction method for the model involves selecting broad-based indices for equities such as CSI 300 ETF, CSI 500 ETF, ChiNext ETF, and National 2000 ETF, while the bond portion can include government bond ETFs, policy financial bond ETFs, and local government bond ETFs [2] - For the commodity portion, gold ETFs and commodity futures ETFs can be included, with advanced construction methods allowing for a core-satellite approach or sector rotation strategy for equities [2]

深圳证券交易所 SHENZHEN SHENZHEN STOCK EXCHANGE 深交所ETF投资问答(39) kes i m g T t x 154 B d UN - 编者按 - 」 风险平价／风险预算模型 ▪ 风险预算放型 放松对风险均衡配 置的严格要求,投 资者可以根据自身 风险偏好去设定各 资产风险权重。 风险平分旗型 属于风险预算模型 的一个特例,是对 投资组合中不同资 产分配相同的风险 权重的一种资产配 置理念。 选择大类资产 E3 商品 les ... 风险预算模型 风险模型 2% 2% 2 000 资产组合构建 举列 大类资产之间的相关性较低，适合选 ....... 择相应的ETF资产构建组合。 例如,债券和权益类、商品类ETF的长 期相关性在0至-30%之间，"股债晓晓 板"模式长期存在,短期也会有"股债 双杀"的局面。 近年来我国指数型基金迅速发展,交易型开 放式指数基金(ETF) 备受关注。为帮助广 大投资者系统全面认识ETF，了解相关投资 方法,特摘编由深圳证券交易所基金管理部 编著的《深交所ETF投资问答》（中国财政 经济出版社2024年版）形成图文解读。本 篇是第39期，继续为大家 ...