Core Insights - The report highlights the performance of various quantitative investment strategies, with a focus on the "Excellent Fund Performance Enhancement Portfolio," "Expected Selection Portfolio," "Brokerage Golden Stock Performance Enhancement Portfolio," and "Growth Stability Portfolio" [12][13][16][38]. Group 1: Excellent Fund Performance Enhancement Portfolio - The portfolio achieved an absolute return of 2.29% this week, with a relative excess return of 0.48% compared to the mixed equity fund index [21][37]. - Year-to-date, the portfolio has an absolute return of 12.86%, but a relative excess return of -1.63% against the mixed equity fund index [21][37]. - The portfolio ranks in the 44.05 percentile among active equity funds, with a ranking of 1528 out of 3469 [21][37]. Group 2: Expected Selection Portfolio - This portfolio recorded an absolute return of 0.45% this week, with a relative excess return of -1.36% compared to the mixed equity fund index [30][37]. - Year-to-date, it has an absolute return of 24.97% and a relative excess return of 10.48% against the mixed equity fund index [30][37]. - The portfolio ranks in the 13.64 percentile among active equity funds, with a ranking of 473 out of 3469 [30][37]. Group 3: Brokerage Golden Stock Performance Enhancement Portfolio - The portfolio achieved an absolute return of 1.75% this week, with a relative excess return of -0.06% compared to the mixed equity fund index [37][38]. - Year-to-date, it has an absolute return of 16.12% and a relative excess return of 1.64% against the mixed equity fund index [37][38]. - The portfolio ranks in the 32.52 percentile among active equity funds, with a ranking of 1128 out of 3469 [37][38]. Group 4: Growth Stability Portfolio - This portfolio recorded an absolute return of 0.28% this week, with a relative excess return of -1.52% compared to the mixed equity fund index [48][38]. - Year-to-date, it has an absolute return of 29.97% and a relative excess return of 15.49% against the mixed equity fund index [48][38]. - The portfolio ranks in the 8.27 percentile among active equity funds, with a ranking of 287 out of 3469 [48][38]. Group 5: Market Overview - The median stock return this week was 1.72%, with 73% of stocks rising and 27% falling; the median return for active equity funds was 1.81%, with 88% of funds rising and 12% falling [52]. - Year-to-date, the median stock return is 15.73%, with 81% of stocks rising and 19% falling; the median return for active equity funds is 11.54%, with 94% of funds rising and 6% falling [52].

Quantitative Models and Construction Methods - **Model Name**: Maximized Factor Exposure Portfolio (MFE) **Construction Idea**: The MFE portfolio is designed to maximize single-factor exposure while controlling for various real-world constraints such as industry exposure, style exposure, stock weight deviation, and turnover rate. This approach ensures the factor's effectiveness under practical constraints [39][40][41] **Construction Process**: The optimization model is formulated as follows: $\begin{array}{ll}max&f^{T}\ w\\ s.t.&s_{l}\leq X(w-w_{b})\leq s_{h}\\ &h_{l}\leq H(w-w_{b})\leq h_{h}\\ &w_{l}\leq w-w_{b}\leq w_{h}\\ &b_{l}\leq B_{b}w\leq b_{h}\\ &\mathbf{0}\leq w\leq l\\ &\mathbf{1}^{T}\ w=1\end{array}$ - **Objective Function**: Maximize single-factor exposure, where $f$ represents factor values, $f^{T}w$ is the weighted exposure of the portfolio to the factor, and $w$ is the stock weight vector to be solved [39][40] - **Constraints**: - **Style Exposure**: $X$ is the matrix of stock exposures to style factors, $w_b$ is the benchmark weight vector, and $s_l$, $s_h$ are the lower and upper bounds for style factor exposure [40] - **Industry Exposure**: $H$ is the matrix of stock exposures to industries, $h_l$, $h_h$ are the lower and upper bounds for industry exposure [40] - **Stock Weight Deviation**: $w_l$, $w_h$ are the lower and upper bounds for stock weight deviation relative to the benchmark [40] - **Component Weight Control**: $B_b$ is a 0-1 vector indicating whether a stock belongs to the benchmark, $b_l$, $b_h$ are the lower and upper bounds for component weight control [40] - **No Short Selling**: Ensures non-negative weights and limits individual stock weights [40] - **Full Investment**: Ensures the portfolio is fully invested with weights summing to 1 [41] **Evaluation**: This model effectively tests factor validity under real-world constraints, ensuring the factor's predictive power in practical portfolio construction [39][40][41] Quantitative Factors and Construction Methods - **Factor Name**: Specificity **Construction Idea**: Measures the uniqueness of stock returns by evaluating the residuals from a Fama-French three-factor regression [16][19][23] **Construction Process**: - Formula: $1 - R^2$ from the Fama-French three-factor regression, where $R^2$ represents the goodness-of-fit of the regression model [16] **Evaluation**: Demonstrates strong performance in multiple sample spaces, indicating its effectiveness in capturing unique stock characteristics [19][23][25] - **Factor Name**: EPTTM Year Percentile **Construction Idea**: Represents the percentile rank of trailing twelve-month earnings-to-price ratio (EPTTM) over the past year [16][19][23] **Construction Process**: - Formula: Percentile rank of $EPTTM = \frac{\text{Net Income (TTM)}}{\text{Market Cap}}$ over the past year [16] **Evaluation**: Performs well in various sample spaces, particularly in growth-oriented indices [19][23][25] - **Factor Name**: Three-Month Reversal **Construction Idea**: Captures short-term price reversal by measuring the return over the past 60 trading days [16][19][23] **Construction Process**: - Formula: $\text{Return}_{60\text{days}} = \frac{\text{Price}_{t} - \text{Price}_{t-60}}{\text{Price}_{t-60}}$ [16] **Evaluation**: Effective in identifying short-term reversal opportunities, especially in volatile indices [19][23][25] Factor Backtesting Results - **Specificity Factor**: - **Sample Space**: CSI 300 - Weekly Excess Return: 1.18% - Monthly Excess Return: 2.02% - Year-to-Date Excess Return: 4.23% - Historical Annualized Return: 0.51% [19] - **Sample Space**: CSI A500 - Weekly Excess Return: 1.43% - Monthly Excess Return: 2.14% - Year-to-Date Excess Return: 2.71% - Historical Annualized Return: 1.72% [25] - **EPTTM Year Percentile Factor**: - **Sample Space**: CSI 300 - Weekly Excess Return: 0.54% - Monthly Excess Return: 2.01% - Year-to-Date Excess Return: 6.74% - Historical Annualized Return: 3.26% [19] - **Sample Space**: CSI 500 - Weekly Excess Return: 1.01% - Monthly Excess Return: 1.54% - Year-to-Date Excess Return: 1.90% - Historical Annualized Return: 5.24% [21] - **Three-Month Reversal Factor**: - **Sample Space**: CSI 300 - Weekly Excess Return: 0.49% - Monthly Excess Return: 1.35% - Year-to-Date Excess Return: 4.31% - Historical Annualized Return: 1.13% [19] - **Sample Space**: CSI 1000 - Weekly Excess Return: 1.10% - Monthly Excess Return: 2.15% - Year-to-Date Excess Return: 2.59% - Historical Annualized Return: -0.67% [23] Index Enhancement Portfolio Backtesting Results - **CSI 300 Enhanced Portfolio**: - Weekly Excess Return: 0.78% - Year-to-Date Excess Return: 9.31% [5][14] - **CSI 500 Enhanced Portfolio**: - Weekly Excess Return: -0.52% - Year-to-Date Excess Return: 9.90% [5][14] - **CSI 1000 Enhanced Portfolio**: - Weekly Excess Return: 0.07% - Year-to-Date Excess Return: 15.69% [5][14] - **CSI A500 Enhanced Portfolio**: - Weekly Excess Return: 0.26% - Year-to-Date Excess Return: 9.96% [5][14] Public Fund Index Enhancement Product Performance - **CSI 300 Public Fund Products**: - Weekly Excess Return: Max 1.28%, Min -0.98%, Median 0.12% - Monthly Excess Return: Max 4.10%, Min -0.99%, Median 0.61% - Quarterly Excess Return: Max 5.71%, Min -0.90%, Median 1.52% - Year-to-Date Excess Return: Max 9.84%, Min -0.77%, Median 2.87% [31] - **CSI 500 Public Fund Products**: - Weekly Excess Return: Max 1.41%, Min -1.31%, Median 0.04% - Monthly Excess Return: Max 2.56%, Min -0.60%, Median 0.60% - Quarterly Excess Return: Max 5.51%, Min -0.10%, Median 2.60% - Year-to-Date Excess Return: Max 9.88%, Min -0.77%, Median 4.19% [34] - **CSI 1000 Public Fund Products**: - Weekly Excess Return: Max 0.82%, Min -0.47%, Median 0.15% - Monthly Excess Return: Max 3.55%, Min -0.67%, Median 1.07% - Quarterly Excess Return: Max 7.14%, Min -0.58%, Median 3.21% - Year-to-Date Excess Return: Max 15.34%, Min 0.49%, Median 6.75% [36] - **CSI A500 Public Fund Products**: - Weekly Excess Return: Max 1.16%, Min -0.57%, Median -0.04% - Monthly Excess Return: Max 1.89%, Min -1.55%, Median 0.68% - Quarterly Excess Return: Max 3.76%, Min -1.67%, Median 2.20% [38]

Investment Rating - The investment rating for the electric equipment and new energy industry is "Outperform the Market" (maintained) [4][12] Core Viewpoints - The draft amendment to the Price Law aims to clarify standards for identifying unfair pricing behaviors, regulate market pricing order, and alleviate "involution" competition [4][6][7] - The amendment is expected to ease competition in industries such as polysilicon, lithium battery anode and cathode materials, wind power equipment, and energy storage, promoting price stability and improving profitability for related companies [4][7] Summary by Sections Price Law Amendment - The draft amendment includes ten articles focusing on three main areas: 1. Improvement of government pricing content, including the clarification of government-guided pricing mechanisms and the importance of cost monitoring in price setting [6][7] 2. Clarification of standards for identifying unfair pricing behaviors, including low-price dumping, price collusion, and price discrimination [6][7] 3. Establishment of legal responsibilities for pricing violations, including increased penalties for non-compliance with pricing regulations [7] Investment Recommendations - Companies to watch include: - Xinte Energy - GCL-Poly Energy - Wind Power Technology - Sungrow Power Supply - Wanrun New Energy [4][7] Profit Forecasts for Related Companies - Profit forecasts for selected companies indicate varying performance: - Xinte Energy: Expected net profit of -3.9 billion RMB in 2024 - GCL-Poly Energy: Expected net profit of -4.75 billion RMB in 2024 - Wind Power Technology: Expected net profit of 1.86 billion RMB in 2024 - Sungrow Power Supply: Expected net profit of 11.04 billion RMB in 2024 - Wanrun New Energy: Expected net profit of -870 million RMB in 2024 [9]

Investment Rating - The investment rating for the company is "Outperform" [5] Core Insights - The company reported Q2 2025 revenue of $96.4 billion, a year-over-year increase of 14%, with net profit reaching $28.2 billion, up 19% [1][11] - Advertising revenue was strong, totaling $71.3 billion, a 10% increase year-over-year, driven by growth in retail, financial services, and healthcare sectors [1][8] - The cloud business showed robust demand, with revenue of $13.6 billion, reflecting a 32% year-over-year growth, and an operating profit margin (OPM) improvement to 20.7% [2][9] - The company has increased its 2025 capital expenditure (CAPEX) guidance to $85 billion, with Q2 CAPEX at $22.4 billion, a 70% increase year-over-year [2][10] Summary by Sections Advertising Performance - Q2 2025 advertising revenue reached $71.3 billion, a 10% increase year-over-year, with search advertising contributing $54.2 billion, up 12% [1][8] - YouTube advertising revenue was $9.8 billion, a 13% increase, supported by direct response and brand advertising [1][8] - AI tools have significantly enhanced advertising performance, with clients using AI Max and Smart Bidding seeing conversion increases of 14% and 19%, respectively [1][8] Cloud Business - Cloud revenue was $13.6 billion, a 32% increase year-over-year, with OPM rising to 20.7%, up 9.4 percentage points [2][9] - The number of transactions over $2.5 million doubled year-over-year, and new customer acquisition for Google Cloud Platform (GCP) increased by nearly 28% [2][9] - Despite accelerated server deployment, supply-demand tension is expected to persist into 2026 [2][9] Capital Expenditure - The company has raised its 2025 CAPEX forecast to $85 billion, with Q2 CAPEX at $22.4 billion, allocated primarily to servers and data centers [2][10] - The increase in CAPEX reflects strong demand for cloud products and services, with further increases anticipated in 2026 [2][10] Financial Forecasts - Revenue projections for 2025-2027 have been slightly adjusted upward to $393.9 billion, $442.6 billion, and $486.3 billion, respectively, with net profit forecasts also increased [3][4] - The company expects EPS to grow from $8.27 in 2024 to $11.51 in 2027, with a projected PE ratio decreasing from 23 to 17 over the same period [4][24]

Investment Rating - The investment rating for the industry is "Outperform the Market" (maintained) [1][4]. Core Viewpoints - The insurance industry is expected to experience short-term premium income growth, reduced interest spread risk, and improved investment return expectations due to multiple catalysts, including a rebound in market risk appetite [2][16]. - The recent adjustment of the predetermined interest rates for life insurance products is anticipated to initiate a new round of "buying before suspension," benefiting the rapid increase in premiums, particularly for dividend insurance [12][16]. - The dynamic adjustment of predetermined interest rates will significantly optimize the pricing rate regulatory mechanism for life insurance products, enhance the efficiency of rate adjustments, and reduce the rigid liability costs for insurance companies [2][16]. Summary by Sections Predetermined Interest Rate Adjustment - The current research value for the predetermined interest rate in the life insurance sector is 1.99%, leading to the first adjustment of the predetermined interest rates this year [2][7]. - The maximum predetermined interest rates for various insurance products have been adjusted: ordinary products to 2.0% (down 50 basis points), dividend insurance to 1.75% (down 25 basis points), and universal insurance to 1.0% (down 50 basis points) [8][11]. Market Conditions and Expectations - The long-end interest rates have recently rebounded, with the 30-year government bond yield increasing from 1.84% to 1.93%, which is expected to narrow the interest spread risk and support the valuation of insurance stocks [15][16]. - The anticipated further reduction in predetermined interest rates in the third quarter is expected to enhance the profitability of insurance companies, particularly in the context of improved equity market performance [15][16]. Competitive Landscape - Dividend insurance is expected to maintain strong competitiveness due to its "guaranteed + floating" yield characteristics, which provide a significant development potential in the current environment of declining returns on wealth management tools [13][16]. - The adjustment in predetermined interest rates is likely to lead to a sustained expansion of premium income, particularly for dividend insurance, as the market reacts to the new pricing dynamics [12][16].

Investment Rating - The report maintains an "Outperform" rating for the food and beverage industry [4][5]. Core Insights - The Chinese restaurant supply chain is undergoing an efficiency revolution driven by the pre-preparation of ingredients and the snackification of dining [1][14]. - The market for the restaurant supply chain is substantial, estimated at approximately 2.4 trillion yuan, with a projected compound annual growth rate (CAGR) of about 9% from 2019 to 2023 [2][50]. - The report highlights the need for a more efficient supply chain system to address the challenges faced by traditional food supply chains, which include high costs, low quality, and lack of standardization [1][23]. Summary by Sections 1. Industry Overview - The restaurant industry in China is expected to reach a market size of 5.57 trillion yuan by 2024, with a year-on-year growth of 5.3% [13]. - The current restaurant market is predominantly composed of small and medium-sized operators, with a chain rate of approximately 20.1% projected for 2024 [1][16]. 2. Direction One: Ingredient Pre-Preparation - Ingredient pre-preparation is seen as a key driver for standardization in the restaurant supply chain, with a market size of around 2.39 trillion yuan in 2023 [2][50]. - The industry is in the early stages of development, with a projected 890,000 production and processing enterprises by April 2025 [2][50]. - Companies like Anjij and Sanquan have established national production capacity barriers, enhancing their competitive edge [2][51]. 3. Direction Two: Snackification of Dining - The trend of snackification reflects a shift in consumer behavior towards more fragmented and personalized food consumption [2][56]. - Emerging channels such as community supermarkets and convenience stores have seen significant growth rates of 10%, 11%, and 76% respectively in 2023 [2]. - The report notes that the demand for ready-to-eat products has increased, with categories like crayfish and marinated snacks leading the way [2]. 4. Comparison with Overseas Markets - The U.S. restaurant supply chain market was valued at approximately $382 billion in 2022, with Sysco achieving revenues of $78.8 billion in 2024 through extensive acquisitions [3][14]. - Japan's frozen food industry has reached maturity, with companies like Kobe Bussan achieving a 12% revenue CAGR through vertical integration [3][14]. 5. Investment Recommendations - The report recommends several companies for investment, including Anjij Food, Qianwei Central Kitchen, Lihigh Food, Weilong Delicious, and Yanjinpuzi [3][4].

Quantitative Models and Construction Methods - **Model Name**: 250-Day New High Distance **Model Construction Idea**: The model tracks the distance of the latest closing price from the highest closing price in the past 250 trading days to identify stocks or indices that are close to their recent highs, which can serve as market trend indicators[11][19] **Model Construction Process**: The formula for calculating the 250-day new high distance is: $ 250 \text{-day new high distance} = 1 - \frac{\text{Closet}}{\text{ts\_max(Close, 250)}} $ - Closet represents the latest closing price - ts_max(Close, 250) represents the maximum closing price in the past 250 trading days If the latest closing price reaches a new high, the distance is 0; if the price falls from the new high, the distance is a positive value indicating the degree of decline[11] **Model Evaluation**: The model effectively captures momentum and trend-following strategies, which have been validated by various studies[11][19] Quantitative Factors and Construction Methods - **Factor Name**: Stable Momentum Factor **Factor Construction Idea**: The factor focuses on stocks with smooth price paths and consistent momentum, as smoother trajectories tend to generate stronger momentum effects[26][28] **Factor Construction Process**: Stocks are filtered based on the following criteria: - **Analyst Attention**: At least 5 buy or overweight ratings in the past 3 months - **Relative Strength**: Top 20% in terms of 250-day price change across the market - **Price Stability**: - **Price Path Smoothness**: Calculated using the ratio of price displacement to price path length - **Momentum Continuity**: Average 250-day new high distance over the past 120 days - **Trend Extension**: Average 250-day new high distance over the past 5 days Stocks meeting these criteria are ranked, and the top 50% are selected[26][28] **Factor Evaluation**: The factor leverages behavioral finance insights, such as the "boiling frog effect," to identify stocks with underappreciated momentum, enhancing its predictive power[26][28] Model Backtesting Results - **250-Day New High Distance Model**: - **Indices**: - Shanghai Composite Index: 0.33% - Shenzhen Component Index: 2.84% - CSI 300: 3.03% - CSI 500: 0.49% - CSI 1000: 0.00% - CSI 2000: 0.00% - ChiNext Index: 8.24% - STAR 50 Index: 6.45%[12][13][16] Factor Backtesting Results - **Stable Momentum Factor**: - **Selected Stocks**: 44 stocks identified as stable momentum stocks, including Borui Pharmaceutical, Shijia Photon, Zhongcai Technology, etc[29][31][33] - **Sector Distribution**: - Manufacturing: 13 stocks - Cyclical: 11 stocks - Others: Remaining stocks distributed across various sectors[29][31][33]

Group 1: High-Tech Manufacturing Index - The Guosen weekly high-tech manufacturing diffusion index A recorded -0.2, while index B was at 50.4, indicating a narrowing decline compared to the previous period[1] - The semiconductor industry shows improved prosperity with rising dynamic random access memory (DRAM) prices[1] - Prices for acrylonitrile and lithium hexafluorophosphate have decreased, reflecting a downturn in the aerospace and new energy sectors[1] Group 2: Price Tracking and Policy Developments - The price of 6-amino penicillanic acid remained stable at 220 RMB/kg, while acrylonitrile decreased by 100 RMB/ton to 8050 RMB/ton[2] - DRAM prices increased by 0.061 USD to 1.5690 USD, whereas wafer prices fell by 0.01 USD to 2.67 USD[2] - A draft amendment to the pricing law was released for public consultation, focusing on government pricing and standards for identifying unfair pricing behavior[2] Group 3: Industry Dynamics and Risks - The establishment of China Fusion Energy Company aims to advance fusion engineering and commercialization in Shanghai[3] - Risks include potential indicator failures due to structural adjustments in high-tech manufacturing and economic policy interventions[4] - Economic growth slowdown poses additional risks to the high-tech manufacturing sector[4]

1. Report Industry Investment Rating - There is no information about the report industry investment rating in the provided content. 2. Core View - The core view includes basic economic data such as the cumulative year - on - year growth rate of fixed - asset investment at 2.80, the year - on - year growth rate of total retail sales of consumer goods in the current month at 4.80, the year - on - year growth rate of exports in the current month at 5.90, and M2 at 8.30 [4]. 3. Summary by Related Content Government Bond Net Financing - Government bond net financing was 208.6 billion yuan in the 29th week (7/14 - 7/20) and 303.6 billion yuan in the 30th week (7/21 - 7/27). As of the 29th week, the cumulative amount was 8.4 trillion yuan, exceeding the same period last year by 4.5 trillion yuan, mainly due to the misalignment of special bonds for replacing implicit debts and the rapid issuance of treasury bonds [1][7]. - The sum of treasury bond net financing and new local bond issuance was 247.2 billion yuan in the 29th week and 239.4 billion yuan in the 30th week. As of the 29th week, the cumulative general deficit was 6.7 trillion yuan, with a progress of 56.6% [1][7]. Treasury Bond Net Financing - Treasury bond net financing was 5.82 billion yuan in the 29th week and 1.07 billion yuan in the 30th week. As of the 29th week, the cumulative amount was 3.8 trillion yuan, with a progress of 57.5%, exceeding the same period in the past five years. The total annual treasury bond net financing is 6.66 trillion yuan. In 2025, the central deficit is 4.86 trillion yuan, with 1.8 trillion yuan of special treasury bonds arranged: 1.3 trillion yuan is ultra - long - term special treasury bonds (300 billion yuan for consumer goods trade - in), and 500 billion yuan is for supplementing the capital of state - owned large - scale banks [1][8]. Local Bond Net Financing - Local bond net financing was 150.5 billion yuan in the 29th week and 292.9 billion yuan in the 30th week. As of the 29th week, the cumulative amount was 4.6 trillion yuan, exceeding the same period last year by 2.8 trillion yuan [1][10]. New General Bond - New general bond issuance was 2.76 billion yuan in the 29th week and 2.33 billion yuan in the 30th week. In 2025, the local deficit is 80 billion yuan. As of the 29th week, the cumulative issuance was 49.41 billion yuan, with a progress of 61.8%, exceeding the same period last year [2][11][13]. New Special Bond - New special bond issuance was 161.4 billion yuan in the 29th week and 205.4 billion yuan in the 30th week. In 2025, the planned issuance of new special bonds is 4.4 trillion yuan. As of the 29th week, the cumulative issuance was 2.4 trillion yuan, with a progress of 54.3%, exceeding the same period last year. Special new special bonds of 71.7 billion yuan have been issued, of which 25.22 billion yuan has been issued since July, accounting for 41% of new special bonds. The 80 - billion - yuan quota may be issued by the end of the third quarter. Land reserve special bonds of 25.58 billion yuan have been issued. As of July 6, the cumulative number of disclosed projects for acquiring idle land was 4,343 parcels, with a capital scale of 489.7 billion yuan [2][15]. Special Refinancing Bond - Special refinancing bond issuance was 0 billion yuan in the 29th week and 1.19 billion yuan in the 30th week. As of the 29th week, the cumulative issuance was 1.8 trillion yuan, with a progress of 91% [2][32]. Urban Investment Bond - Urban investment bond net financing was - 1.24 billion yuan in the 29th week and is expected to be - 3.9 billion yuan in the 30th week. As of this week, the balance of urban investment bonds is about 10.3 trillion yuan [2][34].

Investment Rating - The report maintains an "Outperform the Market" rating for the company [5][3][23] Core Views - The company is experiencing short-term pressure on performance due to the timing of major project confirmations, but there is clear growth momentum ahead [8][3] - The company has completed revenue recognition for the UMC Singapore project, and the VSMC project is transitioning smoothly [10][3] - The semiconductor industry is shifting supply chains to Southeast Asia due to geopolitical factors, with Singapore emerging as a preferred location for semiconductor companies [3][23] Financial Performance Summary - In H1 2025, the company reported revenue of 1.68 billion yuan, a year-on-year decrease of 41%, and a net profit of 161 million yuan, down 32% [8][2] - The company's gross margin improved to 16.9%, up 5.9 percentage points year-on-year, while the net margin increased to 9.56%, up 1.2 percentage points [2][12] - Operating cash flow for H1 2025 was a net inflow of 880 million yuan, with a cash collection ratio of 145% [18][22] Future Earnings Forecast - The company is expected to achieve net profits of 477 million yuan, 816 million yuan, and 713 million yuan for the years 2025, 2026, and 2027, respectively, with corresponding earnings per share of 2.24 yuan, 3.83 yuan, and 3.34 yuan [3][4][23] - The revenue forecast for 2025 is 4.57 billion yuan, reflecting a decrease of 15.1% compared to 2024 [4][3] Dividend Policy - The company plans to distribute a mid-term dividend totaling 213 million yuan, with a payout ratio of 133%, reflecting a strong commitment to shareholder returns [22][3]