Core Viewpoints - The report indicates a gradual upward trend in the market, with a focus on mid-year performance guidance. The A-share market continues to show resilience, with the ChiNext index leading gains, while the overall market remains active with daily trading volumes exceeding 2 trillion yuan, a 23.9% increase from the previous period [2][15]. Economic Data - In July, the economy showed steady progress, with new growth drivers emerging. The total social financing increased by 1.13 trillion yuan, with a year-on-year growth of 9.0%. The industrial added value grew by 5.7% year-on-year, while high-tech industries saw a growth of 9.3% [2][3]. Financial Data - The report highlights a significant increase in M1 and M2 money supply, with M1 growing by 5.6% year-on-year and M2 by 8.8%. The M1-M2 gap continues to narrow, indicating an improvement in monetary activity [4][5]. Policy Tracking - Recent policies aimed at boosting consumption include the implementation of fiscal interest subsidies for personal consumption loans and service industry loans, effective from September 1, 2025. These measures are expected to enhance economic circulation and stimulate consumer spending [7]. Market Performance - The A-share market saw a broad-based increase, with 22 out of 31 sectors rising. The communication, electronics, and non-bank financial sectors led the gains, with increases ranging from 6% to 8%. Conversely, sectors such as banking and steel experienced declines [13][15]. Investment Opportunities - The report suggests focusing on sectors with high certainty of growth, including technology growth driven by AI and semiconductors, sectors benefiting from "anti-involution" policies like new energy and traditional cyclical industries, and stable financial sectors [2][15].

Group 1: Historical Bull Markets in A-shares - The classic bull markets in A-shares can be categorized into four types: liquidity-driven bull (2014-2015), fundamental bull driven by post-crisis economic recovery (2008-2009), "Davis Double-Click" bull driven by institutional dividends and profit growth (2005-2007), and a mixed bull market transitioning from leverage to fundamentals (1999-2001) [1][7][8] - The 2014-2015 bull market was characterized by reform expectations without profit support, with industry rotation showing "big finance on stage, technology growth taking over" [1][7] - The 2008-2009 bull market was driven by a "4 trillion" fiscal stimulus and monetary easing, leading to alternating leadership between cyclical and consumer sectors, as well as emerging industries [1][7][8] - The 2005-2007 bull market saw a broad-based rally under the backdrop of stock reform, exchange rate reform, and macroeconomic prosperity, with blue chips leading the rally in the later stages [1][7][8] - The 1999-2001 bull market was initially driven by the tech bubble, followed by a shift to cyclical sectors like energy [1][7][8] Group 2: Future Bull Market in A-shares - The future bull market in A-shares is expected to resemble the new and old kinetic energy conversion seen in Japan from 2012 to 2018, characterized by low inflation and a stable GDP growth [2][3] - The core of the new and old kinetic energy conversion bull market in A-shares is a significant reversal in pricing, with a shift from "new winning over old" to "the last song of the old" [3] - The transition is supported by policies aimed at boosting consumption, fiscal support, monetary easing, and structural transformation, particularly in sectors like AI, innovative pharmaceuticals, military industry, new consumption, and overseas expansion [3] - The current phase in A-shares is identified as "new winning over old," but caution is advised as it may transition to "the last song of the old," where cyclical sectors may lead the market [3]

Core Viewpoint - The A-share market has seen a surge in activity, with nearly 60 listed companies announcing plans to use idle funds for securities investment this year, driven by significant market gains and a strong profit-making effect [1][2]. Group 1: Company Actions - Liou Co. plans to invest up to 3 billion yuan in securities, including new stock subscriptions, stock and bond investments, and entrusted financial management [1]. - He Shun Petroleum announced a plan to invest up to 200 million yuan in idle funds for securities investment [1]. - Other companies like Yiduoli and Delian Group also announced plans to invest 30 million and 60 million yuan, respectively, in securities [1]. Group 2: Market Performance - The Shanghai Composite Index has risen 8.45% this year, encouraging companies to invest idle funds in the securities market [1]. - A total of 57 listed companies have announced plans to use idle funds for securities investment this year, with several companies planning to invest over 1 billion yuan [2]. Group 3: Financial Performance - Among the 57 companies, 52 are expected to be profitable in 2024, while 5 companies, including Liou Co. and Baichang Pharmaceutical, are projected to incur losses [2][3]. - Liou Co. reported a loss in 2024 due to changes in the fair value of previously invested stocks, but it still expects to achieve a profit of 160 million yuan after excluding non-recurring losses [3]. Group 4: Investment Outcomes - Liou Co. previously gained significant profits from its investment in Li Auto, with a peak profit of 4.772 billion yuan in 2020, largely due to stock sales [4]. - Seven Wolves reported substantial non-recurring gains from securities investments, which helped offset declines in its main business revenue [5][6]. - Conversely, Fangda Carbon faced significant losses from its securities investments, with a total loss exceeding 70 million yuan over three years [7][8].

Market Overview - The three major indices opened slightly higher but then fluctuated downwards, with the Shenzhen Component and ChiNext indices turning negative during the session [1] - As of the midday close, the Shanghai Composite Index was at 3638.40 points, up 0.12%; the Shenzhen Component was at 11163.36 points, down 0.13%; and the ChiNext Index was at 2346.59 points, down 0.52% [1] Sector Performance - Sectors such as semiconductors, medical devices, and logistics showed the highest gains, while small metals, steel, and biopharmaceuticals experienced the largest declines [2] - The semiconductor sector led with a gain of 2.76%, followed by medical devices at 1.69% and logistics at 1.51% [3] - In contrast, the small metals sector fell by 1.28%, steel by 1.22%, and biopharmaceuticals by 1.19% [3] Detailed Sector Analysis - **Top Performing Sectors:** - Semiconductors: +2.76%, total trading volume of 1676.95 million hands, total transaction value of 776.26 billion, net inflow of 62.36 billion, with 144 stocks rising and 17 falling [3] - Medical Devices: +1.69%, total trading volume of 1186.88 million hands, total transaction value of 226.61 billion, net inflow of 4.93 billion, with 101 stocks rising and 29 falling [3] - Logistics: +1.51%, total trading volume of 747.29 million hands, total transaction value of 75.96 billion, net inflow of 10.93 billion, with 37 stocks rising and 12 falling [3] - **Underperforming Sectors:** - Small Metals: -1.28%, total trading volume of 679.55 million hands, total transaction value of 153.61 billion, net outflow of 23.24 billion, with 3 stocks rising and 23 falling [3] - Steel: -1.22%, total trading volume of 2520.18 million hands, total transaction value of 92.64 billion, net outflow of 19.54 billion, with 5 stocks rising and 27 falling [3] - Biopharmaceuticals: -1.19%, total trading volume of 434.29 million hands, total transaction value of 103.89 billion, net outflow of 15.00 billion, with 10 stocks rising and 42 falling [3]

Core Insights - The computer industry experienced the highest net inflow of funds today, amounting to 3.869 billion yuan, with a slight increase in share price of 0.39% [1] - The non-ferrous metals industry faced the largest net outflow of funds, totaling -7.944 billion yuan, with a significant decline in share price of -3.19% [2] Industry Summary - **Computer**: - Trading volume: 8.313 billion shares - Change in trading volume: +20.75% - Turnover rate: 4.66% - Net inflow of funds: 3.869 billion yuan - **Banking**: - Trading volume: 4.792 billion shares - Change in trading volume: +9.96% - Turnover rate: 0.36% - Net inflow of funds: 1.702 billion yuan - **Media**: - Trading volume: 5.739 billion shares - Change in trading volume: -2.15% - Turnover rate: 3.92% - Net inflow of funds: 0.222 billion yuan - **Non-ferrous Metals**: - Trading volume: 5.879 billion shares - Change in trading volume: +4.94% - Turnover rate: 2.68% - Net outflow of funds: -7.944 billion yuan - **Steel**: - Trading volume: 6.411 billion shares - Change in trading volume: -7.86% - Turnover rate: 3.25% - Net outflow of funds: -2.541 billion yuan - **Pharmaceuticals**: - Trading volume: 11.430 billion shares - Change in trading volume: +0.10% - Turnover rate: 4.19% - Net outflow of funds: -2.614 billion yuan - **Automotive**: - Trading volume: 4.712 billion shares - Change in trading volume: -1.55% - Turnover rate: 2.14% - Net outflow of funds: -3.194 billion yuan - **Public Utilities**: - Trading volume: 4.531 billion shares - Change in trading volume: -0.81% - Turnover rate: 1.14% - Net outflow of funds: -3.573 billion yuan - **Food and Beverage**: - Trading volume: 2.057 billion shares - Change in trading volume: -1.94% - Turnover rate: 2.25% - Net outflow of funds: -3.655 billion yuan - **Basic Chemicals**: - Trading volume: 6.845 billion shares - Change in trading volume: -8.46% - Turnover rate: 2.54% - Net outflow of funds: -4.427 billion yuan - **Electronics**: - Trading volume: 8.768 billion shares - Change in trading volume: +18.68% - Turnover rate: 3.17% - Net outflow of funds: -4.741 billion yuan - **Power Equipment**: - Trading volume: 6.999 billion shares - Change in trading volume: +2.42% - Turnover rate: 2.80% - Net outflow of funds: -6.922 billion yuan - **Non-bank Financials**: - Trading volume: 7.107 billion shares - Change in trading volume: +4.79% - Turnover rate: 1.70% - Net outflow of funds: -7.660 billion yuan [1][2]

Core Insights - The report indicates that 16 industries experienced net inflows of capital, while 15 industries faced net outflows on the trading day [1][2] - The industry with the highest net inflow was non-ferrous metals, with a net inflow of 7.736 billion yuan and a price change of 2.78% [1] - The industry with the largest net outflow was construction decoration, which saw a net outflow of 3.417 billion yuan and a price change of 1.50% [1] Industry Summary - **Non-Ferrous Metals**: - Trading volume: 8.352 billion shares - Change in trading volume: +23.38% - Turnover rate: 3.81% - Price change: +2.78% - Net capital inflow: 7.736 billion yuan [1] - **Financials (Non-Banking)**: - Trading volume: 9.761 billion shares - Change in trading volume: +0.43% - Turnover rate: 2.33% - Price change: +2.06% - Net capital inflow: 5.644 billion yuan [1] - **Construction Decoration**: - Trading volume: 11.253 billion shares - Change in trading volume: +20.61% - Turnover rate: 4.02% - Price change: +1.50% - Net capital outflow: -3.417 billion yuan [2] - **Steel**: - Trading volume: 6.412 billion shares - Change in trading volume: -11.88% - Turnover rate: 3.25% - Price change: +2.68% - Net capital inflow: 1.473 billion yuan [1] - **Electronics**: - Trading volume: 6.581 billion shares - Change in trading volume: +2.97% - Turnover rate: 2.38% - Price change: +1.26% - Net capital inflow: 1.408 billion yuan [1] - **Construction Materials**: - Trading volume: 3.241 billion shares - Change in trading volume: -17.53% - Turnover rate: 4.36% - Price change: +1.61% - Net capital inflow: 0.596 billion yuan [1]

Group 1: ETF Performance and Liquidity - The 300 Dividend Low Volatility ETF has a turnover rate of 2.24% during trading, with a transaction volume of 1.31 billion yuan [3] - As of July 15, the ETF's average daily trading volume over the past week is 1.67 billion yuan, and its latest scale reaches 5.886 billion yuan, marking a one-month high [3] - The ETF has seen continuous net inflows over the past five days, with a maximum single-day net inflow of 143 million yuan, totaling 300 million yuan [3] Group 2: Historical Returns and Rankings - Over the past five years, the net value of the 300 Dividend Low Volatility ETF has increased by 67.26%, ranking 46th out of 995 index equity funds, placing it in the top 4.62% [3] - The ETF's highest monthly return since inception is 13.89%, with the longest consecutive monthly gains being five months and the maximum gain during this period being 14.56% [3] - The ETF has outperformed its benchmark with an annualized return of 8.15% over the last three months as of July 15, 2025 [3] Group 3: Top Holdings and Sector Trends - As of June 30, 2025, the top ten weighted stocks in the ETF include China Shenhua, Gree Electric, Sinopec, and others, collectively accounting for 35.21% of the index [3] - Since the announcement by the National Financial Regulatory Administration regarding adjustments to the regulatory ratio of insurance funds in equity assets, high-dividend assets like bank stocks have gained attractiveness, with over 90% of funds labeled with "dividend" or "high dividend" yielding positive returns this year, averaging a net value increase of 7.2% [5] - The recent notice from the Ministry of Finance encouraging long-term stable investments by insurance funds is expected to create a favorable environment for long-term investments, aligning well with the characteristics of low volatility and high dividend strategies [6]

Core Insights - The report indicates that the A-share market is likely to operate in a "slow bull" manner in 2025, supported by policies aimed at stabilizing the stock market and overlapping trends from the new "Nine National Policies" and a quasi-"4 trillion" investment strategy [9][31]. - The report highlights that the main focus areas for 2025 will be technology, green initiatives, consumption, and infrastructure, as mentioned in the government work report [9][31]. - The A-share market is expected to show slight upward fluctuations in July, with resilience in exports during the 90-day tariff buffer period between China and the U.S. [9][31]. Market Performance - During the period from July 7 to July 11, 2025, all six A-share indices monitored showed an upward trend, with the ChiNext Index rising by 2.36% and the Shanghai Composite Index increasing by 1.09% [2][11]. - The report notes that the Shanghai Composite Index successfully broke through the 3500-point mark but showed signs of weakening momentum, particularly in the banking and insurance sectors, which were the main drivers of the index's rise [3][14][16]. Sector Performance - Among the 31 first-level industries, the real estate and steel sectors had the highest weekly gains of 6.12% and 4.41%, respectively, while coal and banking sectors experienced declines of -1.08% and -1.00% [4][20]. - In the second-level industries, multi-finance and small metals led with weekly gains of 9.30% and 9.07%, while the ground equipment II and gaming II sectors had the highest cumulative gains for 2025 at 56.04% and 35.86% [5][24]. - The report also highlights that the fruit and vegetable processing and exhibition services sectors had the highest weekly gains among the 259 third-level industries, with increases of 13.94% and 13.71%, respectively [6][25]. Macro Data - The report mentions that the June CPI showed a year-on-year growth of 0.1%, marking a return to positive growth after four consecutive months of decline, while the PPI continued to decline, reaching -3.60% [7][27][28]. - The macro short-cycle composite index has been declining for five consecutive months, indicating a potential peak in the short cycle since February 2025 [7][28].

Quantitative Models and Construction Methods 1. Model Name: Industry Investment Radar - **Model Construction Idea**: The model identifies four states of industry trends (volume increase with price rise, volume increase with price drop, volume decrease with price drop, and volume decrease with price rise) based on the direction of price and trading volume changes. These states are visualized in a polar coordinate system to locate investment opportunities when industries move into specific regions of the radar[7][8][11] - **Model Construction Process**: 1. **State Classification in Cartesian Coordinates**: - Price and trading volume changes are categorized into four states: - Volume increase with price rise (Quadrant 1) - Volume increase with price drop (Quadrant 2) - Volume decrease with price drop (Quadrant 3) - Volume decrease with price rise (Quadrant 4)[11] 2. **Polar Coordinate Transformation**: - **Polar Angle**: Calculated using the arctangent function to represent the ratio of trading volume change to price change $ \theta = \arctan2(\text{Volume Change}, \text{Price Change}) $[14][18] - **Polar Radius**: Calculated using the Mahalanobis distance to measure the distance between the current and historical price-volume data $ \rho = \sqrt{(x-y)^T \cdot \Sigma^{-1} \cdot (x-y)} $ where $x$ is the current price-volume vector, $y$ is the historical price-volume vector, and $\Sigma$ is the covariance matrix[13][14] 3. **State Mapping in Polar Coordinates**: - Quadrants are mapped to specific polar angle ranges: - 0°-90°: Volume increase with price rise - 90°-180°: Volume increase with price drop - 180°-270°: Volume decrease with price drop - 270°-360°: Volume decrease with price rise[17][18] - **Model Evaluation**: The model provides a clear and interpretable framework for identifying industry rotation opportunities, leveraging historical price-volume relationships to predict future performance[8][18] 2. Model Name: Position Parameter Table - **Model Construction Idea**: This model establishes a mapping between historical price-volume states and future returns by dividing the polar coordinate space into regions and calculating the average future returns for each region[29][38] - **Model Construction Process**: 1. **Region Division**: - The polar radius is divided into five equal segments, and the polar angle is divided into 16 equal regions, resulting in 80 distinct regions[29] 2. **Return Mapping**: - For each region, the average future 20-day return is calculated based on historical data[29][38] 3. **Multi-Dimensional Expansion**: - **Dimension 1**: Multiple historical periods are analyzed for their relationship with future 20-day returns[47] - **Dimension 2**: Multiple historical dates are aggregated to identify stable investment regions[45] - **Model Evaluation**: The position parameter table enhances the model's robustness by incorporating multi-period and multi-date data, providing a more comprehensive mapping of historical states to future returns[47][50] --- Model Backtesting Results 1. Industry Investment Radar - **Weekly Rebalancing Portfolio**: - Cumulative Return: 369.06% - Benchmark Return: 80.97% - Excess Return: 288.09%[56] - **Monthly Rebalancing Portfolio**: - Cumulative Return: 388.85% - Benchmark Return: 80.97% - Excess Return: 307.88%[59] - **Semi-Annual Rebalancing Portfolio**: - Cumulative Return: 279.77% - Benchmark Return: 80.97% - Excess Return: 198.80%[60] 2. Position Parameter Table - **Future 20-Day Return Mapping**: - Example Regions: - Polar Radius (1/5, 2/5), Polar Angle (4π/8, 5π/8): 5.55% - Polar Radius (0, 1/5), Polar Angle (-5π/8, -4π/8): 4.64% - Polar Radius (1/5, 2/5), Polar Angle (-6π/8, -5π/8): 4.09%[42][44] --- Quantitative Factors and Construction Methods 1. Factor Name: Price-Volume State Factor - **Factor Construction Idea**: This factor captures the relationship between price and trading volume changes to classify industry states and predict future returns[7][8][11] - **Factor Construction Process**: - Derived from the polar coordinate transformation of price and volume data, incorporating both polar radius and polar angle as key metrics[13][14][18] - **Factor Evaluation**: The factor is intuitive and interpretable, effectively linking historical price-volume dynamics to future performance[8][18] 2. Factor Name: Regional Return Factor - **Factor Construction Idea**: This factor quantifies the average future returns of industries based on their historical positions in the polar coordinate system[29][38] - **Factor Construction Process**: - Calculated as the average future 20-day return for each region in the position parameter table[29][38] - **Factor Evaluation**: The factor provides a systematic approach to identifying high-return regions, leveraging historical data to enhance predictive accuracy[45][47] --- Factor Backtesting Results 1. Price-Volume State Factor - **Future 20-Day Return Examples**: - Polar Radius (2/5, 3/5), Polar Angle (5π/8, 6π/8): 3.51% - Polar Radius (0, 1/5), Polar Angle (4π/8, 5π/8): 2.49%[42][44] 2. Regional Return Factor - **Future 20-Day Return Examples**: - Polar Radius (3/5, 4/5), Polar Angle (6π/8, 7π/8): -3.06% - Polar Radius (0, 1/5), Polar Angle (3π/8, 4π/8): -3.95%[42][44]

Market Overview - The total scale of equity ETFs in the market reached 37,631.20 billion yuan, with an increase of 208.04 billion yuan in total scale over the past week, and a net outflow of 132.28 billion yuan [1]. Fund Inflow and Outflow Direction - In terms of major categories, industry and thematic ETFs saw a net inflow of 116.39 billion yuan, while broad-based and strategic ETFs experienced a net outflow of 338.22 billion yuan [2]. - Within the broad-based and strategic ETFs, the top three sectors for net inflow were: Sci-Tech Innovation 50, Strategy-Dividend, and Shenzhen 100. The top three sectors for net outflow were: CSI 300, CSI A500, and CSI 1000 [3]. - For industry and thematic ETFs, the top five sectors for net inflow were: Securities, Semiconductor Chips, Military Industry, Photovoltaics, and Innovative Drugs. The top five sectors for net outflow were: Entertainment Media, State-Owned Enterprises, Telecommunications, Biotechnology, and Steel [3]. Financial Sector Insights - The financial sector continued to attract capital inflow, with the leading securities ETF (159993) accumulating 3.22 billion yuan over the week, surpassing a total scale of 20 billion yuan. There are expectations for mergers and acquisitions in the securities sector, driven by the backdrop of a "Financial Power" strategy [3]. - The approval of virtual asset trading service licenses for Chinese securities firms' Hong Kong subsidiaries opens a new chapter for financial innovation, potentially enhancing trading sentiment within the securities sector [3].