- The report introduces the **Diffusion Index Model** for industry rotation, which has been tracked for four years. The model is based on momentum strategies to capture industry trends. It showed strong performance in 2021 with excess returns exceeding 25% before experiencing a significant drawdown due to cyclical stock adjustments. In 2022, the strategy delivered stable returns with an annual excess return of 6.12%. However, in 2023 and 2024, the model faced challenges, with annual excess returns of -4.58% and -5.82%, respectively. For October 2025, the model suggests allocating to industries such as non-ferrous metals, banking, communication, steel, electronics, and automobiles[26][30] - The **Diffusion Index Model** is constructed by ranking industries based on their diffusion index values, which reflect upward trends. The top six industries as of October 10, 2025, are non-ferrous metals (0.98), banking (0.951), communication (0.909), steel (0.877), electronics (0.823), and automobiles (0.813). The bottom six industries are food and beverage (0.137), consumer services (0.297), real estate (0.407), coal (0.445), transportation (0.457), and construction (0.489)[27][28][29] - The **Diffusion Index Model** achieved an average weekly return of 2.59%, exceeding the equal-weighted return of the CSI First-Level Industry Index by 0.70%. For October, the model's excess return is -0.37%, while the year-to-date excess return is 4.60%[30] - The report also discusses the **GRU Factor Model** for industry rotation, which utilizes minute-level price and volume data processed through a GRU deep learning network. The model has shown strong adaptability in short cycles but struggles in long cycles and extreme market conditions. Since February 2025, the model has focused on growth industries but has faced difficulties in capturing excess returns due to concentrated market themes[32][38] - The **GRU Factor Model** ranks industries based on GRU factor values. As of October 10, 2025, the top six industries are comprehensive (6.64), building materials (5.21), construction (3.55), textile and apparel (3.31), transportation (2.99), and steel (2.88). The bottom six industries are computing (-41.87), food and beverage (-35.34), electronics (-34.87), non-ferrous metals (-28.25), power equipment and new energy (-26.61), and communication (-22.71)[33][36] - The **GRU Factor Model** achieved an average weekly return of 2.88%, exceeding the equal-weighted return of the CSI First-Level Industry Index by 1.01%. For October, the model's excess return is 1.67%, while the year-to-date excess return is -6.55%[36]

- The report does not contain any specific quantitative models or factors for analysis. It primarily focuses on market trends, fund flows, and sector allocations without detailing quantitative methodologies or factor construction. [2][3][4]

Core Viewpoint - The Hong Kong Stock Connect Non-Bank ETF (513750) experienced a stable performance with a 0.00% increase on October 9, 2023, and a trading volume of 1.2 billion yuan, indicating steady investor interest in the fund [1] Group 1: Fund Performance - The latest net asset value of the Hong Kong Stock Connect Non-Bank ETF is 19.838 billion yuan [1] - The fund's share count decreased by 9 million to a total of 11.963 billion shares, while it saw an increase of 86.5 million shares over the past 20 trading days [1] - Since its inception on November 10, 2023, the fund has achieved a return of 65.79%, although it has experienced a decline of 2.00% over the past month [1] Group 2: Management and Benchmark - The fund is managed by GF Fund Management Co., Ltd., with fund managers Luo Guoqing and Cao Shiyu overseeing its operations [1] - The performance benchmark for the fund is the yield of the CSI Hong Kong Stock Connect Non-Bank Financial Theme Index, calculated using the valuation exchange rate [1]

Core Insights - The report presents two industry rotation models: one based on similar expected return differentials and another based on changes in analyst expectations, both aimed at identifying investment opportunities in various sectors [11][22]. Group 1: Similar Expected Return Differential Model - The latest configuration suggests focusing on sectors such as coal, communication, basic chemicals, automotive, real estate, and machinery [21]. - In September 2025, the model achieved a monthly return of +4.56%, outperforming the equal-weighted industry index by +3.66% [21]. - The historical backtest from December 2016 to September 2025 shows that the model has a mean Information Coefficient (IC) of 0.09, indicating strong selection ability [14][15]. Group 2: Analyst Expectation Change Model - The latest configuration highlights sectors including non-bank financials, non-ferrous metals, agriculture, communication, steel, and computers [33]. - In September 2025, the model recorded a monthly return of +1.03%, with an excess return of +0.13% over the equal-weighted industry index [33]. - The historical backtest from December 2016 to September 2025 indicates a mean IC of 0.06, demonstrating significant industry selection capability [23][24]. Group 3: ETF Portfolio Construction - The recommended ETF portfolio for October 2025 includes sectors such as non-bank financials, non-ferrous metals, communication, basic chemicals, and automotive [35]. - Specific ETFs listed include the Huabao CSI All-Share Securities Company ETF and the Southern CSI Non-Ferrous Metals ETF, among others, with significant fund shares [35].

Investment Rating - The report does not explicitly provide an investment rating for the industry or specific companies [20]. Core Insights - The asset tokenization market in Hong Kong is expected to focus on standardized securities assets, with a gradual expansion of product offerings, particularly in money market funds [2][8]. - The regulatory framework for securities asset tokenization in Hong Kong is clear, distinguishing it from virtual assets, and providing a stable foundation for business implementation [7]. - The core advantages of asset tokenization include automated transaction execution and settlement through smart contracts, which shortens transaction cycles and enhances transparency [9][10]. Summary by Sections Event - On September 26, 2025, Haitong International hosted a seminar on asset tokenization, featuring insights from Bridget Li, CEO of Asseto Fintech, focusing on the issuance process and future industry directions [1][6]. Regulatory Framework - Securities asset tokenization involves mapping real-world securities onto a blockchain to create digital tokens, governed by a distinct regulatory framework in Hong Kong [7]. Market Outlook - The future of Hong Kong's asset tokenization market will likely see an increased focus on standardized financial assets, with money market funds leading the way [2][8]. Core Advantages - Asset tokenization leverages blockchain technology for automatic transaction execution, improving transaction cycles and ownership transparency [9]. Market Misconceptions - There is a misconception that asset tokenization can bypass fundraising restrictions; however, compliant tokenization products are based on traditional assets and do not circumvent existing regulations [10]. Tokenization Process - The asset tokenization process is compliance-driven, encompassing project design, legal due diligence, technology development, and investor compliance management [3][11].

Core Viewpoint - UBS predicts that the Federal Reserve will further cut interest rates in the coming months, which will boost Asian currencies and U.S. stocks, particularly favoring Chinese technology companies [1] Group 1: Federal Reserve and Market Impact - UBS forecasts a total of 75 basis points in rate cuts by the end of Q1 2026, following the first rate cut of the year last week [1] - The firm expects that the U.S. economy will not enter a recession, with U.S. stocks projected to achieve mid-single-digit percentage gains by mid-2026 [1] Group 2: Chinese Market Outlook - UBS maintains an "overweight" rating on Chinese stocks, anticipating further upward movement in the Chinese stock market as household savings flow into the market [1] - The report indicates that the average appreciation of Asian currencies against the U.S. dollar is expected to be 4% over the next 12 months [1] Group 3: Company Performance and Sector Analysis - Companies included in the MSCI China Index are projected to see a 3% year-on-year increase in earnings by Q2 2025, with stable revenue growth [1] - Non-bank financial, technology, and healthcare sectors are expected to perform well, with internet companies showing double-digit growth in quarterly earnings [1] - Chinese companies are optimistic about their operational conditions, emphasizing cost control and pricing strategies [1]

Core Viewpoint - Tongrun Equipment has approved a financial service agreement with Zhengtai Financial Company, which requires shareholder approval, indicating a strategic move to enhance financial management and resource utilization [1] Group 1: Agreement Details - The agreement is set to be effective from 2026 to 2028, with a total credit and maximum deposit balance not exceeding 500 million yuan [1] - Zhengtai Financial Company will provide credit, deposits, and fund settlement services to Tongrun Equipment and its subsidiaries, with pricing based on market principles [1] Group 2: Financial Position - Currently, Tongrun Equipment and its subsidiaries have a deposit balance of 0 yuan and a loan balance of 10.01 million yuan [1] Group 3: Risk Management - The company believes the transaction risks are controllable and has developed a risk disposal plan, which suggests a proactive approach to managing potential financial risks [1]

Group 1 - The military and aerospace industries have long project cycles and require significant upfront funding, making the matching of financial resources, especially credit resources, crucial for support [1] - The company, as a non-bank financial institution, creates specialized loan products to support the development of military entities and provides comprehensive funding solutions by integrating internal and external resources [1][2] - The company prioritizes funding support for military projects that have not yet received state funding, ensuring compliance while facilitating research, production, sales, and project construction [1] Group 2 - The company implements a client manager system to establish stable financial service relationships with member enterprises, tailoring service plans based on annual production and financial changes [2] - The company focuses on addressing member units' needs for resource assurance, cost reduction, efficiency improvement, and risk prevention, providing customized financial solutions [2] - The company enhances overall fund utilization efficiency and risk prevention through a well-structured treasury system and by leveraging differentiated, specialized, and targeted funding support [2] Group 3 - The treasury system aims to improve fund operation efficiency, reduce costs, and prevent risks by monitoring and coordinating financial resources in real-time [3] - The company is incorporating intelligent technology to strengthen risk prevention capabilities, developing a dynamic risk control system that includes monitoring, early warning, and response mechanisms [3] - The company is focused on extracting data value to build a risk matrix model and identify potential risks such as false trade [3] Group 4 - The company's initiatives support the establishment and implementation of a comprehensive regulatory system across all levels of the group [4]

Performance Overview - Since 2025, the annualized excess return median for enhanced index funds is 2.82%, with the 75th percentile reaching 8.21%, significantly higher than levels from 2022 to 2024[11] - The annualized excess return median for broad-based enhanced index funds is 3%, an increase of 0.68% compared to 2024[11] - The China Securities A500 Index has shown a remarkable annualized return of 48.97% over the past year, with a total return index close to 52.65%[2] Fund Performance - The China Europe A500 Enhanced Index Fund has achieved a cumulative return of 25.94% since its establishment, outperforming the A500 Index by 7.73%[46] - The fund ranks second among eight similar A500 enhanced funds in terms of performance since inception[46] - The fund's annualized excess return is approximately 11.1%, with a 1-month and 3-month performance ranking first among peers[6] Investment Strategy - The fund employs a "active + quantitative" management model, integrating subjective research with quantitative tools to enhance alpha generation[21] - The investment philosophy is based on GARP (Growth at a Reasonable Price), focusing on identifying quality companies with growth potential within reasonable price ranges[31] - The fund maintains a high index tracking ratio while leveraging active stock selection to contribute diversified alpha, with a correlation of daily excess returns to similar funds generally below 0.4 over the past six months[46] Risk Factors - Key risk factors include macroeconomic downturns, increased stock market volatility, and unexpected tightening of financial regulations[5] - The fund is classified as a high-risk, high-reward equity fund, and past performance does not guarantee future results[5] Fund Composition - As of mid-2025, the fund's total scale is 4.4 billion yuan, with a stock position of 92.73%[55] - The fund is diversified across various sectors, with significant allocations to machinery, agriculture, electronics, and utilities, while underweighting sectors like non-ferrous metals and transportation[55]

Quantitative Models and Construction Methods 1. Model Name: Diffusion Index Industry Rotation Model - **Model Construction Idea**: The model is based on the principle of price momentum, aiming to capture upward trends in industries through a diffusion index[26][27] - **Model Construction Process**: 1. Calculate the diffusion index for each industry based on price momentum 2. Rank industries by their diffusion index values 3. Select top industries for allocation based on their rankings 4. Adjust the portfolio monthly or weekly based on updated diffusion index rankings[26][27] - **Model Evaluation**: The model has shown stable performance in certain years (e.g., 2022 with an annual excess return of 6.12%) but struggled during market reversals or concentrated market themes, such as in 2024 and 2025[26][33] 2. Model Name: GRU Factor Industry Rotation Model - **Model Construction Idea**: This model leverages GRU (Gated Recurrent Unit) deep learning networks to process high-frequency volume and price data, aiming to identify industry rotation opportunities[38] - **Model Construction Process**: 1. Input high-frequency volume and price data into the GRU network 2. Train the GRU model on historical data to identify patterns in industry rotation 3. Generate factor scores for industries based on the GRU model's output 4. Rank industries by their GRU factor scores and allocate to top-ranked industries[38][34] - **Model Evaluation**: The model performs well in short cycles but struggles in long cycles or extreme market conditions. It has shown difficulty in capturing excess returns in concentrated market themes during 2025[33][38] --- Model Backtesting Results 1. Diffusion Index Industry Rotation Model - **Weekly Average Return**: -1.74%[30] - **Excess Return (Weekly)**: -1.41%[30] - **Excess Return (September 2025)**: -1.88%[30] - **Excess Return (2025 YTD)**: 2.76%[25][30] 2. GRU Factor Industry Rotation Model - **Weekly Average Return**: -0.72%[36] - **Excess Return (Weekly)**: -0.38%[36] - **Excess Return (September 2025)**: -0.10%[36] - **Excess Return (2025 YTD)**: -7.78%[33][36] --- Quantitative Factors and Construction Methods 1. Factor Name: Diffusion Index - **Factor Construction Idea**: Measures the breadth of price momentum across industries to identify upward trends[26][27] - **Factor Construction Process**: 1. Calculate the proportion of stocks in an industry with positive price momentum 2. Aggregate these proportions to derive the diffusion index for the industry 3. Rank industries based on their diffusion index values[27][28] - **Factor Evaluation**: Effective in capturing upward trends but vulnerable to reversals and underperformance in counter-trend markets[26][33] 2. Factor Name: GRU Factor - **Factor Construction Idea**: Utilizes GRU deep learning to analyze high-frequency trading data and generate predictive scores for industry rotation[38] - **Factor Construction Process**: 1. Input high-frequency trading data into the GRU network 2. Train the model to recognize patterns in industry rotation 3. Output factor scores for industries based on the model's predictions[38][34] - **Factor Evaluation**: Strong in short-term predictions but less effective in long-term or extreme market conditions[33][38] --- Factor Backtesting Results 1. Diffusion Index - **Top Industries (Weekly)**: Non-ferrous Metals (0.978), Banking (0.968), Communication (0.946), Electronics (0.877), Automotive (0.874), Retail (0.873)[27] - **Bottom Industries (Weekly)**: Food & Beverage (0.354), Real Estate (0.46), Coal (0.487), Transportation (0.543), Construction (0.574), Building Materials (0.618)[27] 2. GRU Factor - **Top Industries (Weekly)**: Non-ferrous Metals (7.4), Petrochemicals (5.38), Coal (4.17), Steel (4.15), Building Materials (3.46), Non-banking Financials (3.08)[34] - **Bottom Industries (Weekly)**: Comprehensive Finance (-19.42), Utilities (-13.41), Electronics (-13.18), Pharmaceuticals (-11.14), Automotive (-10.07), Consumer Services (-10.04)[34]