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 ...

Quantitative Models and Construction Methods - **Model Name**: Neutral Constraint Maximum Factor Exposure Portfolio **Construction Idea**: Maximize the exposure of the target factor in the portfolio while maintaining neutrality in industry and style exposures relative to the benchmark index[60][61][63] **Construction Process**: 1. **Objective Function**: Maximize portfolio exposure to the target factor $ \text{Max}\quad w^{\prime}X_{\text{target}} $ 2. **Constraints**: - Industry neutrality: $ (w - w_b)^{\prime}X_{\text{inad}} = 0 $ - Style neutrality: $ (w - w_b)^{\prime}X_{\text{Beta}} = 0 $ - Weight deviation limit: $ |w - w_b| \leq 1\% $ - No short selling: $ w \geq 0 $ - Full allocation: $ w^{\prime}1 = 1 $ - Constituents from benchmark index: $ w^{\prime}B = 1 $ **Evaluation**: The model ensures that the portfolio remains neutral to industry and style biases while maximizing factor exposure[60][61][63] Factor Construction and Definitions - **Factor Name**: Beta Factor **Construction Idea**: Capture the sensitivity of individual stock returns to market returns[14][15] **Construction Process**: - Calculate the regression coefficient of individual stock daily returns against the market index (CSI All Share Index) over the past 252 trading days using a half-life weighting of 63 days **Formula**: $ \text{Beta} = \text{Regression Coefficient} $ **Evaluation**: Reflects market risk sensitivity, useful for identifying high-risk or low-risk stocks[14][15] - **Factor Name**: Book-to-Price (BP) **Construction Idea**: Measure valuation by comparing book equity to market capitalization[14][15] **Construction Process**: - $ \text{BP} = \frac{\text{Shareholders' Equity}}{\text{Market Capitalization}} $ **Evaluation**: Indicates undervaluation or overvaluation of stocks, commonly used in value investing[14][15] - **Factor Name**: Sales Growth (SGRO) **Construction Idea**: Assess growth potential by analyzing historical revenue trends[14][15] **Construction Process**: - Perform regression on annual revenue data from the past five fiscal years - Divide the regression slope by the average revenue to calculate growth rate **Formula**: $ \text{SGRO} = \frac{\text{Regression Slope}}{\text{Average Revenue}} $ **Evaluation**: Useful for identifying companies with strong growth trajectories[14][15] Factor Backtesting Results - **Beta Factor**: Weekly long-short return of 7.50%, monthly return of 8.74%[16] - **Book-to-Price (BP)**: Weekly return of -0.27%, monthly return of 0.39%[21][26][30] - **Sales Growth (SGRO)**: Not explicitly tested in the report[14][15] Portfolio Backtesting Results - **Neutral Constraint Maximum Factor Exposure Portfolio**: - **CSI 300 Enhanced Portfolio**: Weekly excess return of 0.03%, monthly return of 1.91%, annual return of 1.34%[57][58] - **CSI 500 Enhanced Portfolio**: Weekly excess return of -1.29%, monthly return of -1.24%, annual return of -2.54%[57][58] - **CSI 800 Enhanced Portfolio**: Weekly excess return of -0.32%, monthly return of 1.68%, annual return of 1.19%[57][58] - **CSI 1000 Enhanced Portfolio**: Weekly excess return of -0.95%, monthly return of 1.33%, annual return of 13.01%[57][58] - **CSI 300 ESG Enhanced Portfolio**: Weekly excess return of 0.51%, monthly return of 2.44%, annual return of 7.72%[57][58] Factor Performance in Different Stock Pools - **CSI 300 Stock Pool**: - Weekly top-performing factors: Log Market Cap (0.83%), Single Quarter Operating Profit Growth (0.72%), 20-Day Specificity (0.71%)[21][23] - Monthly top-performing factors: Single Quarter EP (3.19%), EP_TTM (2.93%), Single Quarter ROE (2.63%)[24] - **CSI 500 Stock Pool**: - Weekly top-performing factors: 20-Day Specificity (1.39%), 60-Day Volume Ratio (1.13%), 60-Day Reversal (1.00%)[26][28] - Monthly top-performing factors: Single Quarter Revenue Growth (3.31%), Single Quarter Operating Profit Growth (2.73%), Single Quarter ROE Growth (2.72%)[28] - **CSI 800 Stock Pool**: - Weekly top-performing factors: Log Market Cap (1.59%), Single Quarter ROE Growth (1.20%), Single Quarter Operating Profit Growth (1.06%)[30][32] - Monthly top-performing factors: Single Quarter EP (4.36%), Single Quarter ROE Growth (3.90%), Single Quarter ROE (3.90%)[33] - **CSI 1000 Stock Pool**: - Weekly top-performing factors: 60-Day Reversal (1.40%), Single Quarter SP (1.30%), SP_TTM (1.29%)[35][37] - Monthly top-performing factors: Log Market Cap (3.66%), 60-Day Reversal (3.43%), Single Quarter Net Profit Growth (3.24%)[38] - **CSI 300 ESG Stock Pool**: - Weekly top-performing factors: Log Market Cap (1.05%), 20-Day Volume Ratio (0.63%), 20-Day Specificity (0.60%)[40][41] - Monthly top-performing factors: Log Market Cap (4.20%), Single Quarter ROE (2.55%), EP_TTM (2.49%)[42] - **All-Market Stock Pool**: - Weekly top-performing factors: Log Market Cap (24.81% Rank IC), 20-Day Specificity (21.07% Rank IC), 60-Day Reversal (19.50% Rank IC)[44][45] - Monthly top-performing factors: 20-Day Specificity (11.25% Rank IC), 20-Day Three-Factor Model Residual Volatility (10.96% Rank IC), 60-Day Specificity (10.73% Rank IC)[45]

Group 1: Gold and Jewelry - In H1 2025, gold prices surged, leading to a decline in gold jewelry consumption while investment gold consumption increased, continuing the trend from 2024 [13][17] - The report anticipates that in H2 2025, gold prices may fluctuate at high levels due to geopolitical conflicts and economic downturns, with central banks continuing to purchase gold [23] - Recommended companies include Laopuhuang, Chow Tai Fook, Chao Hong Ji, and Cai Bai Co., which are expected to benefit from the ongoing trends in gold consumption [23][24][26][30] Group 2: Cosmetics - The cosmetics market showed weak performance in H1 2025, with a cumulative year-on-year growth of 4.1% from January to May, lagging behind overall retail growth [32][35] - Long-term trends in the cosmetics industry remain focused on increasing penetration rates and domestic brand substitution, with a recommendation to focus on brands like Mao Ge Ping and Shangmei Co. for their strong performance and growth potential [35][36][42] - Mao Ge Ping is highlighted for its high-end positioning and significant growth in both online and offline channels, while Shangmei Co. has shown impressive performance during promotional events [36][42] Group 3: Personal Care - The personal care sector, particularly in sanitary napkins and oral care, is expected to maintain stable demand, with domestic brands leading the market [49][51] - The oral care segment is experiencing a shift towards higher-value products driven by consumer demand for efficacy, with domestic brands like Deng Kang Oral Care gaining market share [53][54] - Key companies to watch include Baiya Co. and Deng Kang Oral Care, which are well-positioned to capitalize on these trends [49][53] Group 4: Apparel and Footwear - The apparel retail sector showed moderate growth in H1 2025, with a year-on-year increase of 3.3% in retail sales from January to May [8][14] - Outdoor brands are performing exceptionally well, with high-end outdoor brands like Amer Sports and Anta showing significant revenue growth [8][15] - Recommended companies include Anta Sports for its strong outdoor brand growth and Mercury Home Textiles for its effective marketing strategies [15][16] Group 5: Textile Manufacturing - The textile manufacturing sector is witnessing a shift in export share towards Southeast Asia, with a notable decline in imports from China to the U.S. [8][18] - The report indicates that U.S. apparel imports from Southeast Asia are increasing, while imports from China are decreasing, suggesting a strategic shift in manufacturing locations [18][19] - Companies with diversified production capabilities across regions are recommended for investment consideration [18][19] Group 6: Home Furnishings - The home furnishings market is experiencing growth driven by government policies encouraging upgrades, with furniture retail sales in May 2025 showing a year-on-year increase of 25.6% [8][20] - Key players in the home furnishings sector include Gujia Home and Oppein Home, which are expected to benefit from the ongoing market trends [20][21]

Core Insights - In May, industrial enterprises' profits turned negative year-on-year, with a total profit of 27,204.3 billion yuan, reflecting a decline of 1.1% compared to the previous year, and a significant drop of 9.1% in May alone [16][29] - The report highlights a mixed performance across various sectors, with TMT (Technology, Media, and Telecommunications) showing resilience while resource sectors and essential consumption face challenges [29] Industry Overview - The industrial profit margin weakened due to factors such as export slowdown, insufficient effective demand, and price pressures, leading to a negative profit growth in May [3][29] - The TMT sector experienced a year-on-year profit growth of 11.9%, driven by strong demand for smart consumer devices, with some industries like intelligent consumer equipment manufacturing seeing a profit increase of 101.5% [28][29] - In the resource sector, profits in the mining industry saw a year-on-year decline of 29.0%, while manufacturing and electricity sectors also reported reduced profit growth [20][29] Information Technology Sector - The Philadelphia Semiconductor Index and Taiwan Semiconductor Industry Index both increased, indicating a positive trend in the semiconductor market [31] - DDR4 DRAM prices rose by 4.42% for 8GB modules and 5.88% for 16GB modules, reflecting a recovery in memory prices [34] - North American PCB shipments turned positive year-on-year, although order growth has slowed [31][34] Midstream Manufacturing - The cumulative installed capacity of solar power generation in China increased year-on-year, indicating growth in the renewable energy sector [4][30] - Prices for silicon wafers in the photovoltaic industry declined, while production of packaging equipment and metal forming machine tools saw a slowdown in growth [4][30] Consumer Demand - The report noted an increase in pork prices and a rise in profits for pig farming, while prices for chicken chicks decreased [4][30] - The film industry showed positive trends with box office revenues increasing year-on-year, reflecting a recovery in consumer spending [4][30] Resource Sector Tracking - Industrial metal prices generally increased, with a decline in inventories, while coal prices remained stable [4][30] - The cement price index showed a downward trend, indicating challenges in the construction materials market [4][30]