Group 1 - The A-share market is experiencing a downturn, with the Shanghai Composite Index falling over 0.9% and other indices like the Shenzhen Component and ChiNext Index dropping more than 1% as of June 19 [1] - Despite the overall market decline, sectors such as energy equipment and oil and gas are showing resilience and strength [2] - Analysts from Guotai Junan Securities suggest that after recent market fluctuations, risks are gradually being released, and the market is expected to remain in a range-bound oscillation, advocating for a "barbell strategy" in asset allocation [2] Group 2 - CITIC Securities indicates that the weak dollar trend, supportive capital market policies, and overall improvement in liquidity conditions in the second half of the year may lead to an upward shift in the A-share market's oscillation center [2] - The global fundamental improvement and the implementation of domestic incremental policies, along with the development of emerging industries, are expected to act as key catalysts for market growth [2]

6月18日晚间，有消息称原华金证券首席财富官刘明军在林芝墨脱失联。 流传的寻人启事显示，刘明军报名参加了西藏林芝越野赛事。为提前适应高海拔，6月9日上 午自行赛前练习，随后失联。 寻人启事中提及，刘明军体能好，但是高度近视，如果在眼镜摔坏或者掉了的情况下，几乎 寸步难行。其出发前曾和朋友分享，有人跟他推荐了一个"只有当地人知道的地方"，家属推 测刘明军9日应该也有计划跑到这里。 本期编辑 金珊 李雪琴，被"前老板"实名举报！ 1元甩卖资产！负债473亿，管理层震荡！巨头宣告退出房地产 江铃福特并入长安福特？最新回应 SFC 21君荐读 L 71 据大河报报道，刘明军的哥哥刘勇军在接受媒体采访时，已确认寻人启事属实。 另据红星新闻报道，刘勇军表示，弟弟刘明军今年50岁，在上海从事金融工作，家中还有两 个未成年的儿子。不管结果如何，家人希望能找到他并带他回家。现在除了家人，弟弟的同 事、朋友都在帮忙，"10万元酬金，也是弟弟的同事和朋友在筹集。" 据林芝发布，6月14日喜马拉雅极限越野跑开赛，赛事是海拔超4400米、穿越世界第一大峡谷 的高海拔极限越野跑赛事。 来源 | 21财经客户端（编辑：李益文）综合自大河报 ...

本公司董事会及全体董事保证本公告内容不存在任何虚假记载、误导性陈述或者重大遗漏，并对其内容 的真实性、准确性和完整性承担法律责任。 一、募集资金基本情况 经中国证券监督管理委员会《关于同意天风证券股份有限公司向特定对象发行股票注册的批复》（证监 许可〔2025〕1164号）同意，天风证券股份有限公司（以下简称"公司"）向特定对象发行A股股票 1,476,014,760股，每股发行价格为人民币2.71元，募集资金总额人民币3,999,999,999.60元，扣除不含税 发行费用人民币18,833,000.69元，公司实际募集资金净额为人民币3,981,166,998.91元。截至2025年6月 12日，募集资金已足额划至公司本次发行募集资金专户。2025年6月12日，大信会计师事务所（特殊普 通合伙）出具了《天风证券股份有限公司向特定对象发行A股股票募集资金到位情况验资报告》（大信 验字[2025]-00005号）。 二、《募集资金专户存储三方监管协议》的签订情况和募集资金专户的开立情况 ■ 注：表中募集资金专户存储金额包括部分尚未支付的发行费用。 三、《募集资金专户存储三方监管协议》的主要内容 协议约定的主要 ...

Core Insights - The emergence of DeepSeek in 2025 is revolutionizing the financial industry by enhancing market prediction models with its dynamic self-correction capabilities and advanced data mining abilities [1][10] - Traditional market prediction models often suffer from fixed weight configurations, leading to distorted judgment results, which DeepSeek aims to address [1][10] Group 1: Impact on Financial Industry - DeepSeek's dynamic self-correction ability optimizes weight based on historical data and current realities, improving prediction accuracy [1] - The model's data mining capabilities allow for the discovery of more relevant data, breaking linear thinking and avoiding "black box" issues [1] - DeepSeek enhances overall strategy intelligence through its powerful reasoning and complex decision-making capabilities [1] Group 2: Educational Initiatives - Guosen Securities has reported a 0.27% increase in annualized returns and a 1.08-fold increase in the Sharpe ratio after integrating DeepSeek into their simulation trading [3] - A masterclass titled "DeepSeek Restructures Strategy Investment Paradigm" has been launched to educate users on utilizing DeepSeek for investment [3][7] - The course, led by Wang Kai, covers various topics including asset allocation optimization, risk parity strategies, and understanding policy semantics [3][11] Group 3: Course Content and Structure - The masterclass is divided into eleven parts, focusing on practical techniques for asset allocation and investment strategies using DeepSeek [3][11] - Key topics include the application of AI in multi-asset frameworks, recreating classic investment portfolios, and understanding market timing and sector rotation [11][12] - The course aims to provide insights into the behavior logic behind key financial institution statements and the implications for investment strategies [11][12]

Group 1 - The core viewpoint is that the digital transformation of financial institutions is entering a critical phase, with a consensus on adopting distributed databases for core systems [1] - OceanBase has achieved the "Hundred Banks Plan," providing database services for over 100 banks, covering more than 190 core systems and over 1,000 key business systems [1] - The upgrade of core systems in financial institutions requires a tripartite synergy of policy guidance, technology drive, and market demand, emphasizing higher requirements for data security, stability, and scalability [1] Group 2 - The essence of digital transformation in financial institutions is to leverage data to reshape traditional business and organizational models, thereby building new competitive advantages [2] - OceanBase has developed best practices for distributed architecture to address challenges faced by clients, such as business scale growth and increasing IT architecture complexity [2] - OceanBase has implemented integrated product practices that solve 80% of user data issues through a unified database approach, including single-machine distributed integration and SQL+AI integration [2]

Group 1 - The core viewpoint of the article emphasizes the introduction of a third set of standards for the ChiNext board to support high-quality, unprofitable innovative enterprises in going public [1] - The establishment of the China Capital Market Society in Shanghai aims to unite various research forces from industry institutions, listed companies, universities, and government departments, creating a high-end think tank for capital market theory research and decision-making consultation [2] - The China Securities Regulatory Commission (CSRC) is accelerating the implementation of key measures for capital market opening by 2025, including the release of an optimized QFII scheme and expanding the number of tradable futures and options for QFII to 100 [3] Group 2 - The announcement of a new Sci-Tech Innovation Growth Tier signifies an important step in the inclusive reform of the capital market, focusing on enhancing the inclusiveness and adaptability of the system [4] - The CSRC aims to deepen reforms of the Sci-Tech Innovation Board and ChiNext Board to create a more attractive and competitive market system and product service matrix [4] - The initiative will better leverage the Sci-Tech Innovation Board as a "testing ground" for further reforms, promoting comprehensive investment and financing reforms and protecting investor rights [4]

炒股就看金麒麟分析师研报，权威，专业，及时，全面，助您挖掘潜力主题机会！ 智通财经6月18日讯（记者 王晨）"盛大的老朋友聚会啊！"面对即将召开的2025年智通财经第四届财富管理论坛，有券商财富管理人士如此赞叹。 是的，2025年智通财经第四届财富管理论坛已进入倒计时2天。 今年，智通财经再度携手华夏基金倾力打造2025年第四届财富管理论坛，以"望海笃行，财富致远"为主题，与行业掌舵者共探新形势下的发展路径。作为 论坛核心环节之一，今日正式解锁圆桌对话嘉宾阵容——由12位券商或财富管理业务掌舵者，围绕两大重磅议题展开思想碰撞。 从扬州到宁波，从苏州到珠海，智通财经与华夏基金连续携手打造财富管理交流平台，每一届都以强大的嘉宾阵容和前瞻议题设置引领行业风向，成为推 动财富管理大发展的重要力量。 今年珠海财富管理论坛，整体参会嘉宾近300位，与会高管近70位，超百位券商财富管理业务部门负责人及核心骨干出席，不仅仅因为强大的主讲与圆桌 嘉宾阵容，财富管理新老朋友的面对面交流，思想碰撞更是每届财富管理论坛的吸引所在。智通财经与华夏基金联手打造的财富管理论坛，已成为行业的 年度重磅盛会。 | | | ◆ 6月19日/星期 ...

Quantitative Models and Construction Methods - **Model Name**: JumpModel **Construction Idea**: JumpModel extends the traditional Hidden Markov Model (HMM) by introducing jump processes to better capture abrupt market state changes, addressing the limitations of smooth state transitions in HMM[13][14][15] **Construction Process**: 1. In HMM, the state transition probability is defined as: $ P(S_{t}|S_{t-1})=P_{i j},\quad S_{t},S_{t-1}\in\{1,2,...,K\} $ Here, $ P_{ij} $ represents the transition probability from state $ i $ to state $ j $[13] 2. JumpModel introduces a jump process to account for abrupt changes: $ P(S_{t}|S_{t-1},J_{t})=(1-\lambda)P_{i j}+\lambda Q_{i j} $ Where $ \lambda $ controls the probability of jump events, and $ Q_{ij} $ represents the transition probability under jump conditions[14][15] 3. Observed variables in JumpModel are modeled with higher variance to capture extreme events: $ Y_{t}|S_{t},J_{t}\sim{\mathcal{N}}{\big(}\mu_{S_{t}}+J_{t},\sigma_{S_{t}}^{2}+\sigma_{J}^{2}{\big)} $ This allows the model to better respond to market shocks and tail risks[16][17][19] **Evaluation**: JumpModel improves responsiveness to market volatility and extreme events, making it more adaptive during rapid market changes compared to HMM[19] - **Model Name**: XGBoost **Construction Idea**: XGBoost leverages ensemble learning to enhance prediction accuracy, particularly in high-dimensional and multi-feature datasets[4][31] **Construction Process**: 1. Features used for training include asset-specific return characteristics (e.g., EMA, Sortino ratio) and macroeconomic indicators (e.g., VIX index, bond yield curve)[32] 2. Preprocessing techniques such as exponential moving averages and log differences are applied to stabilize data and extract key signals[31][32] 3. Default parameters are used to avoid overfitting and ensure generalization across different market environments[34] **Evaluation**: XGBoost demonstrates robust predictive performance, balancing complexity and reliability without requiring extensive parameter tuning[34] - **Model Name**: Mean-Variance Optimization **Construction Idea**: This model dynamically adjusts portfolio weights based on predicted market states to optimize the trade-off between risk and return[5][42] **Construction Process**: 1. Objective function: $ \text{max } \mu - \text{risk} - \alpha \times |w - w_{\text{pre}}|_1 $ Where $ \mu $ represents expected returns, $ \text{risk} $ denotes systematic risk exposure, and $ \alpha |w - w_{\text{pre}}|_1 $ accounts for transaction costs[43] 2. Constraints: $ 0 \leq w \leq w_{\text{max}} $ 3. Covariance matrix is used to model risk transmission and asset interdependencies[43] 4. Rolling window approach is applied for iterative training and validation, ensuring adaptability to market changes[30][43] **Evaluation**: The model effectively balances risk and return, dynamically reallocating weights based on market predictions[45] --- Model Backtesting Results - **JumpModel**: - Annualized return: 6.37%[5] - Information ratio (IR): 0.58[5] - **XGBoost**: - Performance in Shanghai-Shenzhen 300 Index: Successfully avoided major downturns and captured upward trends during backtesting from 2018 to 2025[35][37] - Performance in CSI 500 Index: Higher trading frequency observed due to increased volatility, leading to potential higher transaction costs[39][41] - Performance in long-term bond index: Lower trading frequency due to stable bull market conditions, effectively capturing upward trends[41][44] - **Mean-Variance Optimization**: - Annualized return: 6.37%[49] - Information ratio (IR): 0.58[49] - Sharpe ratio: 1.50 (2022)[55] - Maximum drawdown: 13.9% (2021)[55] - Volatility: 12.9% (2020)[55] --- Quantitative Factors and Construction Methods - **Factor Name**: Jump Intensity Parameter ($ \lambda $) **Construction Idea**: $ \lambda $ determines the sensitivity of JumpModel to market state transitions, balancing responsiveness and stability[20] **Construction Process**: 1. High $ \lambda $ values suppress frequent state transitions, enhancing stability in low-volatility environments[20] 2. Low $ \lambda $ values increase responsiveness to abrupt market changes, suitable for trend reversal scenarios[20] 3. Rolling window cross-validation is used to optimize $ \lambda $ based on Sharpe ratio maximization[30] **Evaluation**: Proper tuning of $ \lambda $ ensures adaptability to varying market conditions, reducing false signals while capturing key transitions[30] --- Factor Backtesting Results - **Jump Intensity Parameter ($ \lambda $)**: - Performance in Shanghai-Shenzhen 300 Index: - $ \lambda = 10 $: Frequent short-term signal generation[22] - $ \lambda = 30 $: Balanced responsiveness and stability[25] - $ \lambda = 50 $: Focused on long-term trends, reduced noise sensitivity[28]

亮点4 双赛道竞技,全方面展现实力 面田门宏源ETF买岛尺亮 以赛促学, 坑特ETF投资 报名开始 比赛开始 报名结束 比赛结束 6月12日 6月18日 8月29日 9月5日 赛事背景 . . 2024年资本市场迎来政策红利,ETF凭借分散风险、交 易便捷等优势,成为投资者重要选择。 好看日书记后八分如本公中学学校思论坛中 男毛会龙 但此仍随,中力公标近分污于夕系英立公可止以后4月 三届ETF实盘大赛,打造专业实战平台,助力投资者提升 配置能力。新手可学习成长,老手能切磋技艺,共同把握 市场机遇,力争共享投资红利。 主办方 5大赛 亮点1 老牌券商底蕴,专业ETF服务 申万宏源证券作为国内历史悠久的综合性券商,凭借深厚的市 场积淀和专业的投研能力,为投资者提供全市场ETF交易支 持。本次大赛支持沪深市场所有场内ETF品种(货币ETF除 外），助力投资者把握不同市场机会。 亮点2 站式ETF投教专区 为了帮助ETF投资者迅速补充知识"电量",本次大赛专设【大 咖论市】、【大赛专区投教视频】、【ETF理财课堂】等模 块,一站式帮助投资者了解投教知识。 亮点3 互动有礼,边学边玩 大赛设丰富的互动活动,轻松参与即有 ...

Core Viewpoint - The economic recovery in May shows a positive trend, with industrial growth driven by government policies, but a potential decline in the second quarter is anticipated [1] Economic Performance - In May, the industrial added value for large-scale enterprises increased by 5.8% year-on-year in real terms [1] - The overall economic performance is expected to exhibit fluctuations due to rising uncertainties in both internal and external environments [1] Market Outlook - The second half of the year may present a dual bull market for stocks and bonds, supported by a potential easing of US-China trade relations and risk mitigation from "quasi-stabilization" funds [1] - A structural market trend is anticipated in A-shares, characterized by alternating low volatility dividends and technological growth [1] Fixed Income - The 10-year government bond yield is expected to decline to around 1.5% amid a low probability of large-scale domestic demand stimulus within the year [1]