Investment Rating - The industry is rated as "Recommended" based on the positive outlook for the digital asset sector and its expected growth due to regulatory advancements [2][32]. Core Insights - The digital asset market is poised for accelerated growth as regulatory frameworks are established, with Hong Kong aiming to become a global innovation hub for digital assets [5][16]. - The release of the "Hong Kong Digital Asset Development Policy Declaration 2.0" emphasizes the LEAP framework, which focuses on legal and regulatory optimization, expanding tokenized product categories, promoting application scenarios, and developing talent and partnerships [16][17]. - The approval of 41 institutions for license upgrades indicates a growing acceptance and integration of virtual asset services within traditional financial frameworks [10][13]. Summary by Sections 1. Hong Kong Digital Asset Regulatory Legislation - Guotai Junan International has become the first Hong Kong-based Chinese broker to offer comprehensive virtual asset trading services after upgrading its license [9]. - A total of 41 institutions have received license upgrades, including 38 brokers, 1 bank, and 1 internet company, indicating a significant shift towards virtual asset services [10][13]. 2. Artificial Intelligence Industry Chain Update - Multiple domestic companies are accelerating their deployment in the AI agent sector, transitioning from technical exploration to practical applications across various industries [20][24]. - Intel plans to outsource marketing positions to Accenture, aiming to enhance operational efficiency and digital capabilities through AI [20]. 3. Market Performance Review - The computer sector saw a 7.71% increase in the fourth week of June 2025, with notable performers including Tianli Technology and ST Guangdao [25].

1. Report Industry Investment Rating No relevant content provided. 2. Core Viewpoints of the Report The report analyzes the primary and secondary market conditions of local government bonds in the week of June 29, 2025, and predicts the issuance plan for the third quarter of 2025. It shows that the issuance volume and net financing of local government bonds in the primary market have increased, the proportion of long - term issuance has risen, and the issuance spread has widened. In the secondary market, the secondary spreads of 3Y, 15Y, and 30Y local government bonds are advantageous, and the turnover rates of local government bonds in Fujian, Sichuan, and Shandong are relatively high. 3. Summary According to the Directory 3.1 Primary Market Issuance Situation - **Net Financing**: This week, local government bonds issued a total of 641.6 billion yuan, with a repayment of 81.3 billion yuan and a net financing of 560.4 billion yuan. The issuance volume and net financing increased [1]. - **Issuance Term**: The issuance proportion of 10Y local government bonds was the highest this week (27%), and the proportion of 10Y and above issuance was 73%, showing an increase compared with last week. The issuance proportion of 7Y local government bonds decreased significantly, with a month - on - month decrease of about 10 percentage points [1][12]. - **Debt - Resolution - Related Local Government Bonds**: This week, special refinancing bonds issued a total of 5.97 billion yuan. In 2025, 33 regions have disclosed plans to issue special bonds to replace hidden debts, with a total of 179.59 billion yuan. Among them, Jiangsu, Sichuan, Shandong, and Yunnan plan to issue 25.11 billion yuan, 11.48 billion yuan, 11.13 billion yuan, and 8.78 billion yuan respectively [15][16]. - **Issuance Spread**: The weighted average issuance spread of local government bonds this week was 11.9bp, which widened compared with last week. The weighted average issuance spread of 30Y local government bonds was the highest, reaching 18.8bp. Except for the 5Y, 10Y, and 20Y local government bonds, the weighted average issuance spreads of other terms narrowed [1][24]. - **Raised Funds Allocation**: As of the end of this week, the main allocation directions of newly - added special bond funds in 2025 were cold - chain logistics, municipal and industrial park infrastructure construction (30%), transportation infrastructure (20%), affordable housing projects (13%), and social undertakings (12%). The proportion of land reserve allocation increased by 10.9% compared with 2024, while the proportion of cold - chain logistics, municipal and industrial park infrastructure construction decreased by 6.9% [2][28]. - **Issuance Plan**: As of the end of this week, 30 regions have disclosed the local government bond issuance plan for the third quarter of 2025, with a total of 2.56 trillion yuan. Among them, the planned issuance in July is 128.1 billion yuan. In addition, the planned issuance of new bonds and refinancing bonds in the third quarter is 161.23 billion yuan and 94.7 billion yuan respectively. Next week, the planned issuance of local government bonds is 6.14 billion yuan, with a repayment of 5.05 billion yuan and a net financing of 1.09 billion yuan, a month - on - month decrease of 54.94 billion yuan [3][30]. 3.2 Secondary Market Situation - **Secondary Spread**: This week, the secondary spreads of 3Y, 15Y, and 30Y local government bonds were advantageous, and the widening amplitudes of the secondary spreads of 3Y and 30Y local government bonds were relatively large. The secondary spreads of 3Y, 15Y, and 30Y local government bonds were relatively high, reaching 17.2bp, 18.5bp, and 18.3bp respectively. From the perspective of the historical quantile in the past three years, the historical quantile of the secondary spread of 30Y local government bonds was relatively high, reaching 77%. Regionally, the secondary spreads of local government bonds over 20Y in various types of regions were relatively high, and the secondary spreads of 10 - 20Y local government bonds in medium - level regions were also relatively high [5][35]. - **Trading Volume**: This week, the trading volume and turnover rate of local government bonds basically remained at the same level as last week. The turnover rates of local government bonds in Fujian, Sichuan, and Shandong were relatively high. The trading volume of local government bonds this week reached 520.6 billion yuan, with a turnover rate of 1.01%. Among them, the trading volumes of local government bonds in Shandong and Jiangsu were large, reaching 5.05 billion yuan and 4.86 billion yuan respectively; the turnover rates in Fujian, Sichuan, Shandong and other places were all higher than 1.6% [5][39].

Domestic Insights - In the last week of June, production data continued to show seasonal weakness, with expected further decline in production growth for June[2] - Summer consumption has become a structural highlight, with a rebound in consumption data and improved travel flow[2] - The real estate market remains weak, with transaction volumes in 30 cities in June showing a larger gap compared to the same period last year[2] - The recent "strong US stocks + weak dollar" pattern is boosting non-US equity risk appetite and liquidity, with expectations for improved domestic equity risk appetite in July[2] Overseas Insights - Trade policy is likely to evolve towards overall easing with localized tightening, as the July tariff exemption period is expected to be extended[2] - The latest version of the OBBB Act is estimated to increase the total deficit by $3.5-4.2 trillion, significantly higher than the House's $2.9 trillion estimate[2] - The Federal Reserve's recent statements show a slight easing in tone, but most officials still oppose a rate cut in July[2] - The US Senate is expected to pass a new budget coordination bill by Q3, with a potential deadline before the X-Date in August-September[2]

Quantitative Models and Construction Methods - **Model Name**: Multi-period interest rate timing strategy **Model Construction Idea**: The model uses multi-period resonance strategies to capture interest rate trends and generate timing signals based on shape recognition algorithms[10][22] **Model Construction Process**: 1. **Signal Generation**: Utilize kernel regression algorithms to identify support and resistance lines of interest rate data. Analyze the breakthrough patterns of interest rate trends across long, medium, and short cycles[10][22] 2. **Portfolio Construction**: - If at least two cycles show downward breakthroughs and the trend is not upward, allocate fully to long-duration bonds - If at least two cycles show downward breakthroughs but the trend is upward, allocate 50% to medium-duration bonds and 50% to long-duration bonds - If at least two cycles show upward breakthroughs and the trend is not downward, allocate fully to short-duration bonds - If at least two cycles show upward breakthroughs but the trend is downward, allocate 50% to medium-duration bonds and 50% to short-duration bonds - In other cases, allocate equally across short, medium, and long durations - Stop-loss mechanism: Adjust holdings to equal-weighted allocation if daily excess returns fall below -0.5%[22] **Model Evaluation**: The strategy demonstrates strong performance with consistent positive returns and high excess return ratios over the long term[22][23] Model Backtesting Results - **Multi-period interest rate timing strategy**: - **Short-term annualized return**: 7.27%[4][22] - **Short-term maximum drawdown**: 1.56%[4][22] - **Short-term return-to-drawdown ratio**: 6.23[4][22] - **Short-term excess return**: 2.2%[4][23] - **Long-term annualized return**: 6.17%[22] - **Long-term maximum drawdown**: 1.52%[22] - **Long-term return-to-drawdown ratio**: 2.26[22] - **Long-term excess return**: 1.66%[22] - **Excess return-to-drawdown ratio**: 1.18[22] - **Annual absolute return win rate**: 100%[23] - **Annual excess return win rate**: 100%[23] Quantitative Factors and Construction Methods - **Factor Name**: Interest rate structure indicators (level, term, convexity) **Factor Construction Idea**: Transform yield-to-maturity (YTM) data of 1-10 year government bonds into structural indicators to analyze market trends from a mean-reversion perspective[7][9] **Factor Construction Process**: 1. Calculate the level structure indicator as the average YTM across maturities 2. Compute the term structure indicator as the difference between long-term and short-term YTM 3. Derive the convexity structure indicator based on the curvature of the yield curve[7][9] **Factor Evaluation**: The indicators provide insights into the current state of the interest rate market, showing low levels across all three structures[7][9] - **Factor Name**: Multi-period interest rate timing signals **Factor Construction Idea**: Use kernel regression algorithms to identify interest rate trends and generate timing signals based on breakthroughs across long, medium, and short cycles[10] **Factor Construction Process**: 1. Apply kernel regression to identify support and resistance lines for interest rate data 2. Analyze breakthrough patterns across different cycles (monthly for long-term, bi-weekly for medium-term, weekly for short-term)[10] **Factor Evaluation**: The signals are effective in capturing market trends, with the latest signals indicating a neutral-to-bearish stance[10] Factor Backtesting Results - **Interest rate structure indicators**: - **Level structure**: Current reading is 1.51%, positioned at 6%, 4%, and 2% percentiles for 3, 5, and 10-year historical perspectives, respectively[9] - **Term structure**: Current reading is 0.3%, positioned at 13%, 8%, and 10% percentiles for 3, 5, and 10-year historical perspectives, respectively[9] - **Convexity structure**: Current reading is 0.02%, positioned at 18%, 11%, and 11% percentiles for 3, 5, and 10-year historical perspectives, respectively[9] - **Multi-period interest rate timing signals**: - **Long-term signal**: Upward breakthrough[10] - **Medium-term signal**: No signal[10] - **Short-term signal**: Downward breakthrough[10] - **Overall signal**: Neutral-to-bearish[10]

Investment Rating - The report maintains a "Strong Buy" rating for Luzhou Laojiao [1][3]. Core Views - The company is actively embracing new consumption trends in the industry, with a focus on product innovation and channel transformation [1][6]. - The management has a clear understanding of the industry landscape and is strategically planning to adapt to changes, particularly in consumer preferences towards lower-alcohol products [1][6]. - The company aims to improve inventory control and maintain pricing stability for its Guojiao series products, leveraging digital marketing for channel expansion [1][6]. Financial Data and Valuation - The projected EPS for 2025-2027 is 9.47, 10.00, and 10.83, respectively, with a corresponding PE of 12X for 2025 [1][3]. - Total revenue is expected to grow from 30,233 million in 2023 to 35,708 million in 2027, reflecting a compound annual growth rate [2][12]. - The company’s net profit is projected to increase from 13,246 million in 2023 to 15,935 million in 2027, indicating a steady growth trajectory [7][12]. Market Strategy - The company is focusing on penetrating lower-tier markets, aiming to reach four million terminals in the next five years [1][6]. - Digital marketing initiatives are being implemented to enhance direct channel capabilities and meet emerging consumer demands [1][6]. - The company is committed to developing low-alcohol products, with successful innovations like the 28-degree Guojiao 1573 and ongoing research for other low-alcohol variants [1][6]. Financial Ratios - The return on equity (ROE) is projected to be 26.9% for the trailing twelve months, indicating strong profitability [3][13]. - The asset-liability ratio is expected to decrease from 34.4% in 2023 to 22.6% in 2027, reflecting improved financial stability [13]. - The company maintains a high gross margin of approximately 87.4% to 88.3% over the forecast period, showcasing its pricing power [13].

- Model Name: Short-term Quantitative Timing Model; Model Construction Idea: The model is based on market sentiment indicators, valuation, macro liquidity, and macro fundamentals to generate timing signals; Model Construction Process: The model uses various indicators such as manufacturing PMI, long-term loan balance growth rate, M1 growth rate, PE and PB valuation percentiles, Beta dispersion, volume sentiment score, volatility, monetary rate, exchange rate expectation, and net financing amount to generate signals. For example, the formula for the volume sentiment score is: $$ \text{Volume Sentiment Score} = \frac{\text{Current Volume} - \text{Mean Volume}}{\text{Standard Deviation of Volume}} $$ where the current volume is the trading volume of the current period, the mean volume is the average trading volume over a specified period, and the standard deviation of volume is the standard deviation of trading volumes over the same period. The model evaluates these indicators to determine the overall market sentiment and generates a timing signal accordingly[9][14][15]; Model Evaluation: The model is highly sensitive to market sentiment indicators, which can lead to frequent signal changes[9] - Model Name: Growth-Value Style Rotation Model; Model Construction Idea: The model uses economic cycle analysis to determine the allocation between growth and value styles; Model Construction Process: The model evaluates the slope of the profit cycle, the level of the interest rate cycle, and the changes in the credit cycle. For example, the formula for the profit cycle slope is: $$ \text{Profit Cycle Slope} = \frac{\text{Current Profit} - \text{Previous Profit}}{\text{Previous Profit}} $$ where the current profit is the profit of the current period, and the previous profit is the profit of the previous period. The model also considers PE and PB valuation differences and turnover and volatility differences between growth and value styles to generate allocation signals[25][26]; Model Evaluation: The model provides significant improvement over the benchmark in terms of annualized returns and Sharpe ratio[25][26] - Model Name: Small-Cap vs. Large-Cap Style Rotation Model; Model Construction Idea: The model uses economic cycle analysis to determine the allocation between small-cap and large-cap styles; Model Construction Process: The model evaluates the slope of the profit cycle, the level of the interest rate cycle, and the changes in the credit cycle. For example, the formula for the interest rate cycle level is: $$ \text{Interest Rate Cycle Level} = \frac{\text{Current Interest Rate} - \text{Mean Interest Rate}}{\text{Standard Deviation of Interest Rate}} $$ where the current interest rate is the interest rate of the current period, the mean interest rate is the average interest rate over a specified period, and the standard deviation of interest rate is the standard deviation of interest rates over the same period. The model also considers PE and PB valuation differences and turnover and volatility differences between small-cap and large-cap styles to generate allocation signals[30][31][32]; Model Evaluation: The model provides significant improvement over the benchmark in terms of annualized returns and Sharpe ratio[30][31][32] - Model Name: Four-Style Rotation Model; Model Construction Idea: The model combines the conclusions of the growth-value and small-cap vs. large-cap rotation models to determine the allocation among four styles: small-cap growth, small-cap value, large-cap growth, and large-cap value; Model Construction Process: The model uses the signals generated by the growth-value and small-cap vs. large-cap rotation models to allocate the portfolio among the four styles. For example, if the growth-value model suggests overweighting value and the small-cap vs. large-cap model suggests overweighting large-cap, the allocation would be adjusted accordingly[33][34]; Model Evaluation: The model provides significant improvement over the benchmark in terms of annualized returns and Sharpe ratio[33][34] Model Backtest Results - Short-term Quantitative Timing Model: Annualized Return 16.24%, Annualized Volatility 14.70%, Maximum Drawdown 27.70%, Sharpe Ratio 0.9613, IR 0.5862, Monthly Win Rate 68.21%, Quarterly Win Rate 68.63%, Annual Win Rate 85.71%[16][19][22] - Growth-Value Style Rotation Model: Annualized Return 11.51%, Annualized Volatility 20.85%, Maximum Drawdown 43.07%, Sharpe Ratio 0.5316, IR 0.2672, Monthly Win Rate 58.00%, Quarterly Win Rate 60.00%, Annual Win Rate 85.71%[27][29] - Small-Cap vs. Large-Cap Style Rotation Model: Annualized Return 11.92%, Annualized Volatility 22.75%, Maximum Drawdown 50.65%, Sharpe Ratio 0.5283, IR 0.2386, Monthly Win Rate 60.67%, Quarterly Win Rate 56.00%, Annual Win Rate 85.71%[32] - Four-Style Rotation Model: Annualized Return 13.03%, Annualized Volatility 21.60%, Maximum Drawdown 47.91%, Sharpe Ratio 0.5834, IR 0.2719, Monthly Win Rate 59.33%, Quarterly Win Rate 62.00%, Annual Win Rate 85.71%[34][35]

Group 1 - The report introduces a quantitative model solution for addressing the value-growth style switching issue, combining investment expectations based on odds and win rates [1][8] - The recent performance of the growth style portfolio was 5.49%, while the value style portfolio returned 3.33% [1][8] Group 2 - The estimated odds for the growth style is 1.10, and for the value style, it is 1.09, indicating a negative correlation between relative valuation levels and expected odds [2][14] - The current win rate for the growth style is 68.88%, while the value style has a win rate of 31.12%, based on seven indicators [3][16] Group 3 - The latest investment expectation for the growth style is calculated to be 0.44, while the value style has an investment expectation of -0.35, leading to a recommendation for the growth style [4][18] - Since 2013, the annualized return of the style rotation model based on investment expectations is 26.96%, with a Sharpe ratio of 0.99 [4][19]

证券研究报告 | 基金研究（公募） 2025 年 6 月 29 日 权益市场收涨，中游制造、TMT 基金表现领先 基金市场一周观察（20250623-20250627） 本周权益市场整体收涨，小盘成长风格占优；行业方面，本周综合金融表现领 先；计算机、综合、国防军工等也表现较好；债市整体下行，可转债市场上 行。全市场主动权益基金平均回报 2.74%；短债基金收益均值为 0%，中长债 基金收益均值为-0.03%；含权债基平均正收益；可转债基金平均正收益。 xuyanhong@cmschina.com.cn 高艺 S1090524020001 gaoyi2@cmschina.com.cn 李巧宾 S1090524070011 liqiaobin@cmschina.com.cn 徐肖雅 研究助理 xuxiaoya@cmschina.com.cn 江帆 研究助理 jiangfan3@cmschina.com.cn 敬请阅读末页的重要说明 ❑ 市场概况：本周权益市场整体收涨，小盘成长风格占优；行业方面，本周综 合金融表现领先；计算机、综合、国防军工等也表现较好。 ❑ 主动权益：样本内全市场基金平均回报 2.74%，收益 ...

Report Summary 1. Report Industry Investment Rating No industry investment rating information is provided in the report. 2. Core View of the Report The report focuses on the performance of the quantitative fund market, summarizing the performance of major indices and quantitative funds in the past week, the overall performance and distribution of different types of public - offering quantitative funds, and the top - performing quantitative funds this week. It shows that A - shares rose overall this week, while the excess returns of quantitative funds declined. Active quantitative funds rose, while the excess returns of index - enhanced funds fell, with only the CSI 300 index - enhanced funds recording positive excess returns [1][2][4]. 3. Summary by Relevant Catalog 3.1 Near - Week Performance of Major Indices and Quantitative Funds - A - shares rose overall from June 23rd to June 27th, 2025, with the CSI 300, CSI 500, and CSI 1000 having weekly returns of 1.95%, 3.98%, and 4.62% respectively [3][8]. - Active quantitative funds rose 2.83%, while the excess returns of index - enhanced funds declined. Only the CSI 300 index - enhanced funds had a positive average excess return of 0.07%, while the CSI 500 and CSI 1000 index - enhanced funds had average excess returns of - 0.35% and - 0.20% respectively. Market - neutral funds fell slightly by 0.10% [4][11]. 3.2 Performance of Different Types of Public - Offering Quantitative Funds - **CSI 300 Index - Enhanced Funds**: In the past week, the return was 2.02%, the excess return was 0.07%, the maximum drawdown was - 0.75%, and the excess maximum drawdown was - 0.27% [15]. - **CSI 500 Index - Enhanced Funds**: The past - week return was 3.63%, the excess return was - 0.35%, the maximum drawdown was - 0.31%, and the excess maximum drawdown was - 0.48% [15]. - **CSI 1000 Index - Enhanced Funds**: The past - week return was 4.42%, the excess return was - 0.20%, the maximum drawdown was - 0.41%, and the excess maximum drawdown was - 0.44% [16]. - **Other Index - Enhanced Funds**: The past - week return was 3.30%, the excess return was - 0.05%, the maximum drawdown was - 0.60%, and the excess maximum drawdown was - 0.39% [16]. - **Active Quantitative Funds**: The past - week return was 2.83%, the maximum drawdown was - 0.49%, and the return dispersion was 1.51% [17]. - **Market - Neutral Funds**: The past - week return was - 0.10%, the maximum drawdown was - 0.26%, and the return dispersion was 0.53% [17]. 3.3 Performance Distribution of Different Types of Public - Offering Quantitative Funds The report presents the performance trends of different types of public - offering quantitative funds in the past six months, as well as the performance distribution in the past week and the past year. Index - enhanced funds show their excess return performance [18]. 3.4 Top - Performing Public - Offering Quantitative Funds - **CSI 300 Index - Enhanced Funds**: Funds such as Anxin Quantitative Selection CSI 300 Index - Enhanced had good performance, with a past - week excess return of 0.88% [32]. - **CSI 500 Index - Enhanced Funds**: Funds like Suxin CSI 500 Index - Enhanced had a past - week excess return of 0.63% [33]. - **CSI 1000 Index - Enhanced Funds**: ICBC CSI 1000 Enhanced Strategy ETF had a past - week excess return of 1.24% [34]. - **Other Index - Enhanced Funds**: China Merchants Shanghai Composite Index - Enhanced had a past - week excess return of 1.10% [35]. - **Active Quantitative Funds**: Jinxin Quantitative Selection had a past - week return of 8.11% [36]. - **Market - Neutral Funds**: China Post Absolute Return Strategy had a past - week return of 1.50% [37].

证券研究报告 | 基金研究（公募） 2025 年 06 月 28 日 金融科技、港股证券 ETF 领涨，资金持续大幅流入债券 ETF ETF 基金周度跟踪（0623-0627） ❑ 风险提示：图表中列示的数据结果仅为对市场及个基历史表现的客观描述， 并不预示其未来表现，亦不构成投资收益的保证或投资建议。 徐燕红 S1090524120003 xuyanhong@cmschina.com.cn 汪思杰 研究助理 wangsijie@cmschina.com.cn 敬请阅读末页的重要说明 基金研究（公募） 本报告重点聚焦 ETF 基金市场表现，总结过去一周 ETF 基金市场、不同热门 细分类型 ETF 基金、创新主题及细分行业 ETF 基金的业绩表现和资金流动， 供投资者参考。 ❑ 市场表现： 本周（6 月 23 日-6 月 27 日）主投 A 股的 ETF 全部上涨。其中，国防军工 ETF 涨幅最大，规模以上基金平均上涨 6.39%；消费 ETF 涨幅相对较小， 规模以上基金平均上涨 0.89%。此外，商品 ETF 出现下跌，规模以上基金 平均下跌 1.54%。 ❑ 资金流动： 资金继续大幅流入债券 ETF，全 ...