Summary of Key Points Core Viewpoint - The public fund industry has reported strong performance in Q2 2025, with both total fund management scale and non-monetary fund management scale reaching historical highs, indicating a positive trend in the market [1][2]. Fund Management Scale - As of the end of Q2 2025, the total public fund management scale reached 34 trillion yuan, while the non-monetary fund management scale was 20 trillion yuan, both marking historical peaks [2][3]. - The total public fund scale increased by 7.04% from Q1 2025 and by 10.76% year-on-year from Q2 2024 [2]. - The non-monetary fund scale grew by 6.85% from the previous quarter, reaching 20.11 trillion yuan [2]. Fund Types and Performance - The largest market scales were seen in money market funds and bond funds, with sizes of 13.93 trillion yuan and 10.77 trillion yuan, reflecting increases of 7.32% and 8.74% respectively [3]. - Equity funds reached a scale of 4.74 trillion yuan, growing by 6.06% quarter-on-quarter, while mixed funds saw minimal growth [3]. - Commodity funds and fund of funds (FOF) experienced significant growth, with increases of 47.79% and 10.28%, respectively [3]. Investment Trends - Public funds increased their allocations to the financial and technology sectors, with increases of 1.82% and 1.71%, while reducing allocations to the consumer sector by 3.9% [5]. - The top three sectors by allocation weight were electronics, pharmaceuticals, and power equipment & new energy, with weights of 18.88%, 11.11%, and 8.8% respectively [5]. - Notably, the automotive sector, which had seen significant investment in the previous quarter, experienced a reduction in holdings [6]. Major Holdings - The top ten holdings of public funds included Tencent Holdings, CATL, and Kweichow Moutai, with Tencent's total market value held by public funds at approximately 59.2 billion yuan [6][7]. - New entrants to the top ten holdings included Xiaomi Group and New Yisheng, while BYD and Wuliangye exited the list [7]. Investor Behavior - Investors showed a preference for money market funds, bond funds, commodity funds, and QDII funds, leading to net subscriptions in these categories, while equity funds and FOFs faced net redemptions [8][9]. - The total fund share exceeded 30 trillion shares by the end of June, with a net subscription of 1.25 trillion shares in the quarter [8]. - Money market funds and bond funds were the main contributors to net subscriptions, with net subscriptions of 887.67 billion shares and 459.25 billion shares, respectively [9]. Redemption Trends - Equity funds experienced net redemptions totaling 140.27 billion shares, with actively managed equity funds leading in redemptions [10]. - FOFs also faced net redemptions of 5.53 billion shares, indicating a shift in investor sentiment away from these products [11].

报 告 摘 要 一、基金仓位监控 普通股票型基金 仓位中位数为91.27%， 偏股混合型基金 仓位中位数为90.08%，与上一季 度相比略有提升，自2020年以来持续围绕在90%仓位震荡。普通股票型基金仓位处在历史 88.71%分位点，偏股混合型基金仓位处在历史93.55%分位点。 普通股票型和偏股混合型基金 港股仓位 均值分别为12.91%和16.85%，均较上一季度进一 步提升。普通股票型配置港股基金数量为233只，偏股混合型配置港股基金数量为1601只， 普通股票型以及偏股混合型基金中配置港股的基金数量占比为58.35%。 二、基金持股集中度监控 基金重仓股占权益配置比重为52.23%，上一期为53.21%，略有降低；另一方面，基金持股 数量在2025年二季度相较于上一季度有所增加，基金经理总体持股数量为2436只，这意味 着，基金经理持仓的股票差异化有所提升。 三、板块配置监控 2025年二季报中披露的主板配置权重为52.44%、创业板配置权重为15.3%、科创板配置权 重为12.31%，港股配置权重为19.94%，其中创业板配置权重较上个季度提升1.78%，主板 较上个季度降低2.64%。 在202 ...

Quantitative Models and Construction Methods 1. Model Name: Heston Model - **Model Construction Idea**: The Heston model is used to calculate the implied volatility of near-month at-the-money options, serving as a market fear index. It reflects market participants' expectations of future volatility [7] - **Model Construction Process**: The Heston model is a stochastic volatility model where the variance of the asset price follows a mean-reverting square-root process. The model is defined by the following equations: $$ dS_t = \mu S_t dt + \sqrt{v_t} S_t dW_t^S $$ $$ dv_t = \kappa (\theta - v_t) dt + \sigma \sqrt{v_t} dW_t^v $$ where: - \( S_t \): Asset price - \( v_t \): Variance of the asset price - \( \mu \): Drift term - \( \kappa \): Speed of mean reversion - \( \theta \): Long-term variance - \( \sigma \): Volatility of variance - \( W_t^S, W_t^v \): Two Wiener processes with correlation \(\rho\) [7] - **Model Evaluation**: The Heston model is widely recognized for its ability to capture the stochastic nature of volatility, making it suitable for modeling market fear indices [7] --- Quantitative Factors and Construction Methods 1. Factor Name: Multi-Industry Timing Factor (Scissors Difference) - **Factor Construction Idea**: This factor is based on the difference in the number of stocks with bullish and bearish signals within an industry. It aims to identify timing opportunities by analyzing the divergence between bullish and bearish signals [14] - **Factor Construction Process**: - Define the number of stocks with bullish signals (\(N_{bullish}\)) and bearish signals (\(N_{bearish}\)) in an industry on a given day - If no bullish or bearish signals are present, set the respective count to 0 - Calculate the scissors difference as: $$ \text{Scissors Difference} = N_{bullish} - N_{bearish} $$ - Normalize the scissors difference to obtain a ratio: $$ \text{Scissors Ratio} = \frac{N_{bullish} - N_{bearish}}{N_{bullish} + N_{bearish}} $$ - Use this ratio to construct an industry timing strategy [14] - **Factor Evaluation**: The backtesting results show that the scissors difference timing model outperforms the respective industry indices in all cases, demonstrating excellent historical performance [14] --- Model Backtesting Results 1. Heston Model - Implied volatility for major indices: - **Shanghai 50**: 13.5% (down 0.91% from last week) - **Shanghai 500**: 15.29% (down 0.11% from last week) - **CSI 1000**: 16.79% (down 1.3% from last week) - **CSI 300**: 13.65% (down 0.83% from last week) [9] --- Factor Backtesting Results 1. Multi-Industry Timing Factor (Scissors Difference) - Backtesting results for selected industries: - **Real Estate**: Strategy annualized return 13.18%, maximum drawdown -34.3%; Index annualized return -1.21%, maximum drawdown -75.09% - **Light Manufacturing**: Strategy annualized return 21.84%, maximum drawdown -37.91%; Index annualized return 2.76%, maximum drawdown -67.79% - **Coal**: Strategy annualized return 28.73%, maximum drawdown -24.76%; Index annualized return -0.1%, maximum drawdown -69.7% - **Pharmaceuticals**: Strategy annualized return 19.22%, maximum drawdown -42.71%; Index annualized return 6.69%, maximum drawdown -55.37% [15][16]

Core Viewpoint - The current A-share market is likely to maintain a strong oscillating trend, similar to the second half of 2014, driven by liquidity and policy easing factors [1][2][3] Group 1: Market Trends - The A-share market in the second half of 2014 and from April to July 2020 was primarily driven by liquidity and policy easing, with a weak economic backdrop but rising stock indices [2] - The current market is expected to continue a structural slow bull trend, with short-term oscillations leaning towards strength [3] - Economic recovery remains weak, with pressures on exports and a potential decline in real estate investment, while corporate earnings are showing signs of recovery [3] Group 2: Sector Performance - In the current environment, sectors such as media, building materials, agriculture, computer, and home appliances are showing superior mid-year profit growth [1] - Growth sectors like media, automotive, pharmaceuticals, power equipment, and new energy, along with blue-chip sectors such as agriculture, non-bank financials, food and beverage, and home appliances, are considered to have high cost-performance ratios [1][3] Group 3: Investment Strategy - Short-term investment strategy suggests a balanced allocation between technology growth and undervalued blue-chip stocks, focusing on sectors with upward policy and industry trends [1][3] - In July and August, the market style is expected to be balanced, with growth potentially outperforming value in August due to economic recovery trends and continued liquidity [4]

Core Viewpoint - The report anticipates that convertible bonds with positive performance in Q2 2025 will be concentrated in high-end manufacturing sectors such as communication, electronics, military, automotive parts, transportation equipment, industrial control equipment, energy equipment, and electric power equipment, as well as in non-ferrous and basic chemical industries benefiting from price increases [1]. Group 1: Industry Performance Insights - The profit growth in the non-ferrous metal mining industry is expected to reach 41.7% year-on-year, driven by rising prices and increased production and sales of metals like gold, copper, zinc, and silver [2]. - The railway, shipbuilding, aerospace, and other transportation equipment manufacturing sectors are projected to see a profit increase of 56% year-on-year, benefiting from global shipping recovery and significant orders for LNG carriers and container ships [2]. - The computer, communication, and other electronic equipment manufacturing sectors, along with electrical machinery and general equipment manufacturing, are expected to maintain double-digit profit growth due to high demand for AI hardware, smart terminals, and industrial control equipment [2]. - The agricultural and sideline food processing industry is anticipated to experience a profit growth rate of 38.2%, primarily due to the demand for high-value-added products like prepared dishes and health foods [2]. Group 2: Company-Specific Performance - Among the companies that have disclosed their H1 2025 performance forecasts, 272 companies are expected to achieve a non-net profit growth of over 30% in Q2 2025, mainly in the basic chemicals, electric power equipment and new energy, machinery, electronics, and automotive sectors [3]. - In the basic chemicals sector, companies are expected to benefit from price increases in phosphates, pesticides, and refrigerants [3]. - The electric power equipment and new energy sector's high-performing companies are expected to benefit from increased overseas photovoltaic storage orders, domestic ultra-high voltage and smart grid construction, and rising domestic orders for new energy vehicles and military products [3]. - The machinery sector's growth is driven by high demand for industrial mother machines, semiconductor equipment, energy equipment, shipbuilding, and rail transit equipment [3]. - The electronics sector's growth is attributed to increased investment in AI computing power, accelerated domestic substitution of semiconductor equipment and materials, and growth in consumer electronics and smart terminal shipments [3]. - The automotive sector is expected to see high growth due to increased sales of domestic new energy vehicles and accelerated exports of commercial vehicles and automotive parts [3]. Group 3: Performance Forecast Adjustments - A list of 13 convertible bond targets has been identified, which have seen their average net profit forecasts raised by over 5% in the past three months, with more than three forecasting institutions involved, indicating potential marginal improvements in performance [4].

Quantitative Models and Construction Methods 1. Model Name: Galaxy CSI 300 Enhanced Index Fund (007275.OF) - **Model Construction Idea**: The fund aims to track the CSI 300 Index effectively while employing quantitative methods for active portfolio management and risk control to achieve performance exceeding the benchmark index and generate long-term asset appreciation [2][38][60] - **Model Construction Process**: - The fund uses multi-factor stock selection, index replication, and event-driven strategies to enhance returns while optimizing the portfolio and strictly controlling risks [60] - The fund aims to control the absolute value of the daily tracking deviation between the net value growth rate and the performance benchmark within 0.5% and the annual tracking error within 7.75% [38] - **Model Evaluation**: The model demonstrates strong performance in generating excess returns, maintaining low tracking error, and effectively controlling risks [38][42][44] --- Model Backtesting Results 1. Galaxy CSI 300 Enhanced Index Fund - **Annualized Excess Return**: 6.49% since inception [39][42] - **Annual Excess Returns (2020-2025)**: 13.24% (2020), 11.06% (2021), 4.17% (2022), 2.83% (2023), 4.49% (2024), 3.27% (2025 YTD) [43] - **Maximum Drawdown (2020-2025)**: -15.78% (2020), -12.43% (2021), -24.09% (2022), -17.98% (2023), -10.89% (2024), -10.00% (2025 YTD) [44] - **Sharpe Ratio (2020-2025)**: 1.50 (2020), 0.33 (2021), -1.27 (2022), -0.82 (2023), 0.94 (2024), 1.60 (2025 YTD) [44] - **Information Ratio (2020-2025)**: 4.01 (2020), 3.50 (2021), 1.72 (2022), 1.25 (2023), 1.48 (2024), 3.75 (2025 YTD) [44] - **Tracking Error**: Annual tracking error averaged 2.68% from 2020, with a maximum of 3.38%, meeting the target of staying below 7.75% [45] - **2025 YTD Information Ratio**: 3.98, ranking 5th among CSI 300 Enhanced Index Funds [45][47] --- Quantitative Factors and Construction Methods 1. Factor Name: Multi-Factor Stock Selection - **Factor Construction Idea**: The fund employs a multi-factor model to identify stocks with high potential for excess returns based on various quantitative metrics [60] - **Factor Construction Process**: - Factors include valuation, momentum, quality, and risk control metrics - Stocks are selected based on their scores across these factors, aiming to optimize the portfolio for enhanced returns while maintaining alignment with the CSI 300 Index [60] - **Factor Evaluation**: The multi-factor approach has been effective in generating consistent excess returns and controlling risks [60] --- Factor Backtesting Results 1. Multi-Factor Stock Selection - **Excess Returns**: Contributed to the fund's annualized excess return of 6.49% since inception [42][43] - **Risk Control**: Supported low tracking error (average 2.68% annually) and controlled maximum drawdowns [44][45]

[Table_Title] 每日复盘 分行业看，30 个中信一级行业普遍上涨；表现相对靠前的是：通信 （2.78%），建材（2.11%），电力设备及新能源（2.10%）；表现相对靠后的 是：电力及公用事业（-0.25%），银行（-0.23%），交通运输（0.16%）。概念 板块方面，多数概念板块上涨，英伟达、BC 电池、PCB 等大幅上涨；肝素、 昨日触板、退税商店等板块走低。 资金筹码方面，主力资金 7 月 8 日净流入 114.50 亿元。其中超大单净流 入 151.39 亿元，大单净流出 36.89 亿元，中单资金净流出 148.69 亿元，小 单持续净流入 34.29 亿元。 7 月 8 日，上证 50、沪深 300、中证 500 以及中证 1000 等 ETF 大部分成 交额较上一交易日增加。华夏上证 50ETF、华泰柏瑞沪深 300ETF、嘉实沪深 300ETF、易方达沪深 300ETF、南方中证 500ETF、南方中证 1000ETF 和华夏中 证 1000ETF 成交额分别为 13.02 亿元、24.74 亿元、4.19 亿元、7.76 亿元、 10.07 亿元、10.52 亿元和 2.63 ...

Core Viewpoint - The recent discussions on "anti-involution" are driven by policy guidance and market expectations, with a focus on promoting product quality and orderly competition while addressing low-price chaos in various industries [2] Group 1: Impacted Industries - The industries most affected by the current "anti-involution" include upstream raw materials related to real estate and infrastructure (such as coal, steel, and cement), equipment manufacturing overlapping with new productive forces (including automotive, electrical machinery, and electronic device manufacturing), and certain downstream consumer goods sectors (such as pharmaceuticals and food manufacturing) [3] - Emerging industries may experience a greater impact from "anti-involution," as recent government reports emphasize the need to cultivate new and future industries while addressing homogeneous competition in sectors like new energy vehicles and photovoltaics [4] Group 2: Policy Implementation and Observations - The consensus is that the approach to "anti-involution" will be moderate, considering the significant presence of private enterprises in affected industries, with many sectors having a high proportion of private companies [6] - Employment concerns are also crucial, as the new industries most affected by "involution" employ a substantial number of workers, making abrupt capacity reductions potentially harmful to job stability [6] - The market is currently in a wait-and-see mode regarding the form and intensity of "anti-involution" policies, with future market movements dependent on clearer policy signals [7] Group 3: Need for Comprehensive Policy Support - High-intensity capacity reduction may require comprehensive policy support, balancing social stability and the specifics of capacity overhang, including timelines for exit and risk mitigation strategies [8] - Observations should not only focus on supply-side changes but also on demand-side updates, as changes in supply structure are necessary but not sufficient for industry recovery [8]

- Model Name: Diffusion Index Model; Model Construction Idea: The model is based on the principle of price momentum; Model Construction Process: The model tracks the weekly changes in the diffusion index of various industries, ranking them based on their diffusion index values. The formula used is $ \text{Diffusion Index} = \frac{\text{Number of Stocks with Positive Momentum}}{\text{Total Number of Stocks}} $; Model Evaluation: The model captures industry trends effectively but may face challenges during market reversals[5][27][28] - Model Name: GRU Factor Model; Model Construction Idea: The model utilizes GRU (Gated Recurrent Unit) deep learning networks to analyze minute-level price and volume data; Model Construction Process: The model ranks industries based on their GRU factor values, which are derived from the GRU network's analysis of trading information. The formula used is $ \text{GRU Factor} = \text{GRU Network Output} $; Model Evaluation: The model performs well in short cycles but may struggle in long cycles or extreme market conditions[6][13][33] - Diffusion Index Model, IR value 2.05%, weekly average return 0.24%, monthly excess return -1.00%, annual excess return 2.05%[25][30] - GRU Factor Model, IR value -4.52%, weekly average return 1.32%, monthly excess return 0.77%, annual excess return -4.52%[32][37] - Factor Name: GRU Industry Factor; Factor Construction Idea: The factor is derived from GRU deep learning networks analyzing minute-level trading data; Factor Construction Process: The factor values are calculated based on the GRU network's output, ranking industries accordingly. The formula used is $ \text{GRU Factor} = \text{GRU Network Output} $; Factor Evaluation: The factor captures short-term trading information effectively but may face challenges in long-term or extreme market conditions[6][13][33] - GRU Industry Factor, IR value -4.52%, weekly average return 1.32%, monthly excess return 0.77%, annual excess return -4.52%[32][37]

Group 1: Economic Overview - The US economy is showing signs of slowing down, with the Atlanta Fed's GDPNOW model predicting a 2.6% annualized GDP growth for Q2, down 0.3 percentage points from previous estimates [2] - Employment data indicates a divergence from economic trends, with initial jobless claims decreasing to 233,000, below seasonal levels, and the unemployment rate unexpectedly dropping to 4.1% [2] - Long-term inflation expectations have slightly increased, with the 5-year breakeven inflation rate rising by 0.1 percentage points to 2.4% [3] Group 2: US Market Reactions - The US stock market rose by 1.7% due to stronger-than-expected employment data, alleviating concerns about economic slowdown from trade policy uncertainties [3] - The likelihood of interest rate cuts has diminished, with expectations returning to two cuts of 50 basis points, and the probability of a July cut dropping to zero [3] - The bond market is expected to maintain high volatility, with strategies suggesting a focus on short to medium-term US bonds [4] Group 3: China Economic Insights - China's economic growth is projected at approximately 5.2% for Q2, with June manufacturing PMI at 49.7, indicating a slight contraction [6] - Real estate investment is expected to decline significantly, with cumulative growth projected to drop to -11.2% due to seasonal factors and high base effects from last year [6] - External demand for Chinese exports may recover, aided by the easing of trade restrictions with the US and a rebound in US import demand [7] Group 4: Domestic Market Dynamics - The domestic market is influenced by the central government's focus on "anti-involution" policies, which are expected to lead to significant policy announcements in the second half of the year [8] - The A-share market saw the Shanghai Composite Index rise by 1.4%, driven by strong performance in banking and sectors benefiting from supply-side reforms [10] - The bond market experienced slight gains, with a balanced outlook expected in the short term, while potential policy adjustments could increase market volatility [9] Group 5: Currency and Commodity Outlook - The US dollar is expected to weaken in the medium term due to concerns over US debt sustainability and rising uncertainties from tariff suspensions [4] - The Chinese yuan is anticipated to maintain a neutral trend, influenced by both positive and negative factors in the trade environment [4] - Gold prices may experience short-term fluctuations but are expected to have strong medium-term support due to ongoing central bank purchases [4]