Quantitative Models and Construction Methods GAN_GRU Model - **Model Name**: GAN_GRU - **Model Construction Idea**: The GAN_GRU model utilizes Generative Adversarial Networks (GAN) for processing volume-price time series features and then employs the GRU model for time series feature encoding to derive the stock selection factor[2][9]. - **Model Construction Process**: 1. **GRU Model**: - **Volume-Price Features**: Includes 18 volume-price features such as closing price, opening price, trading volume, turnover rate, etc.[10][13][15]. - **Training Data and Input Features**: Uses past 400 days of 18 volume-price features for all stocks, sampling every 5 trading days. The feature sampling shape is 40*18, predicting cumulative returns for the next 20 trading days[14]. - **Training and Validation Set Ratio**: 80% training set, 20% validation set[14]. - **Data Processing**: Extreme value removal and standardization in time series for each feature, cross-sectional standardization at the stock level[14]. - **Model Training Method**: Semi-annual rolling training, training points are June 30 and December 31 each year[14]. - **Stock Screening Method**: Excludes ST and stocks listed for less than half a year[14]. - **Training Sample Screening Method**: Excludes samples with empty labels[14]. - **Hyperparameters**: batch_size is the number of stocks in the cross-section, optimizer Adam, learning rate 1e-4, loss function IC, early stopping rounds 10, maximum training rounds 50[14]. - **Model Structure**: Two GRU layers (GRU(128, 128)) followed by MLP layers (256, 64, 64), with the final output pRet as the stock selection factor[18]. 2. **GAN Model**: - **Generator**: Learns the real distribution of data and generates samples that look like real data. The loss function is: $$L_{G}\,=\,-\mathbb{E}_{z\sim P_{z}(z)}[\log(D(G(z)))]$$ where \( z \) is random noise, \( G(z) \) is the data generated by the generator, and \( D(G(z)) \) is the probability output by the discriminator[20][21][22]. - **Discriminator**: Distinguishes real data from generated data. The loss function is: $$L_{D}=-\mathbb{E}_{x\sim P_{d a t a}(x)}[\log\!D(x)]-\mathbb{E}_{z\sim P_{z}(z)}[\log(1-D(G(z)))]$$ where \( x \) is real data, \( D(x) \) is the probability output by the discriminator for real data, and \( D(G(z)) \) is the probability output by the discriminator for generated data[23][24][25]. - **Training Process**: Alternating training of generator and discriminator until convergence[25][26]. - **Model Structure**: Uses LSTM as the generator to retain the time series nature of the input features and CNN as the discriminator to match the two-dimensional volume-price time series features[29][30][31]. - **Feature Generation**: The generator part of the trained GAN model is used for feature generation, inputting original volume-price time series features and outputting processed features[33][34]. Model Evaluation - **Evaluation**: The GAN_GRU model effectively combines GAN and GRU to process and encode volume-price time series features, showing promising results in stock selection[2][9]. Model Backtest Results - **GAN_GRU Model**: - **IC Mean**: 11.57%[37][38] - **ICIR**: 0.89[38] - **Turnover Rate**: 0.83[38] - **Recent IC**: -0.28%[37][38] - **One-Year IC Mean**: 11.54%[37][38] - **Annualized Return**: 36.60%[38] - **Annualized Volatility**: 24.02%[38] - **IR**: 1.66[38] - **Maximum Drawdown**: 27.29%[38] - **Annualized Excess Return**: 24.89%[38] Quantitative Factors and Construction Methods GAN_GRU Factor - **Factor Name**: GAN_GRU Factor - **Factor Construction Idea**: The GAN_GRU factor is derived from the GAN_GRU model, which processes volume-price time series features using GAN and encodes them using GRU[2][9]. - **Factor Construction Process**: Same as the GAN_GRU model construction process described above[2][9][10][14][18][19][20][21][22][23][24][25][26][29][30][31][33][34]. Factor Backtest Results - **GAN_GRU Factor**: - **IC Mean**: 11.57%[37][38] - **ICIR**: 0.89[38] - **Turnover Rate**: 0.83[38] - **Recent IC**: -0.28%[37][38] - **One-Year IC Mean**: 11.54%[37][38] - **Annualized Return**: 36.60%[38] - **Annualized Volatility**: 24.02%[38] - **IR**: 1.66[38] - **Maximum Drawdown**: 27.29%[38] - **Annualized Excess Return**: 24.89%[38]

Quantitative Models and Construction Methods 1. Model Name: GAN_GRU - **Model Construction Idea**: The GAN_GRU model combines Generative Adversarial Networks (GAN) for processing volume-price time-series features and Gated Recurrent Unit (GRU) for encoding time-series features to create a stock selection factor[2][9] - **Model Construction Process**: 1. **GAN Component**: - **Generator (G)**: Generates realistic data from random noise (e.g., Gaussian distribution). The generator's loss function is: $$ L_{G} = -\mathbb{E}_{z\sim P_{z}(z)}[\log(D(G(z)))] $$ where \( z \) represents random noise, \( G(z) \) is the generated data, and \( D(G(z)) \) is the discriminator's output probability that the generated data is real[20][21] - **Discriminator (D)**: Distinguishes real data from generated data. The discriminator's loss function is: $$ L_{D} = -\mathbb{E}_{x\sim P_{data}(x)}[\log D(x)] - \mathbb{E}_{z\sim P_{z}(z)}[\log(1-D(G(z)))] $$ where \( x \) is real data, \( D(x) \) is the discriminator's output probability for real data, and \( D(G(z)) \) is the output probability for generated data[23][25] - Training alternates between updating the generator and discriminator to improve feature generation and discrimination capabilities[26] 2. **GRU Component**: - Two GRU layers (GRU(128, 128)) are used to encode time-series features, followed by a Multi-Layer Perceptron (MLP) with layers (256, 64, 64) to output predicted returns (\( pRet \))[18] 3. **Feature Input and Processing**: - Input features include 18 volume-price characteristics (e.g., closing price, turnover rate) sampled over the past 40 days to predict cumulative returns for the next 20 days[10][14] - Data preprocessing includes outlier removal, standardization, and cross-sectional normalization[14] - Training is conducted semi-annually with rolling updates[14] 4. **GAN_GRU Integration**: - The GAN generator processes raw volume-price time-series features (Input_Shape=(40,18)) and outputs features encoded by LSTM. These features are then passed to the GRU model for further processing[33][34] - **Model Evaluation**: The GAN_GRU model effectively captures time-series and cross-sectional features, demonstrating strong predictive power for stock selection[2][9] --- Model Backtesting Results 1. GAN_GRU Model - **IC Mean**: 11.57%[37][38] - **ICIR**: 0.89[38] - **Turnover Rate**: 0.83[38] - **Recent IC**: -0.28%[37][38] - **1-Year IC Mean**: 11.54%[37][38] - **Annualized Return**: 36.60%[38] - **Annualized Volatility**: 24.02%[38] - **IR**: 1.66[38] - **Maximum Drawdown**: 27.29%[38] - **Annualized Excess Return**: 24.89%[38] --- Quantitative Factors and Construction Methods 1. Factor Name: GAN_GRU Factor - **Factor Construction Idea**: Derived from the GAN_GRU model, this factor leverages GAN for feature generation and GRU for time-series encoding to predict stock returns[2][9] - **Factor Construction Process**: 1. **Input Features**: 18 volume-price characteristics (e.g., closing price, turnover rate) sampled over the past 40 days[10][14] 2. **GAN Feature Generation**: - LSTM-based generator processes raw time-series features to retain temporal properties[29][33] - CNN-based discriminator identifies realistic features from generated ones[29] 3. **GRU Encoding**: Encodes GAN-generated features using two GRU layers and an MLP to output predicted returns[18][33] 4. **Factor Normalization**: Industry and market capitalization neutralization, followed by standardization[18] - **Factor Evaluation**: The GAN_GRU factor demonstrates robust performance across various industries and time periods, indicating its effectiveness in stock selection[2][9] --- Factor Backtesting Results 1. GAN_GRU Factor - **IC Mean**: 11.57%[37][38] - **ICIR**: 0.89[38] - **Turnover Rate**: 0.83[38] - **Recent IC**: -0.28%[37][38] - **1-Year IC Mean**: 11.54%[37][38] - **Annualized Return**: 36.60%[38] - **Annualized Volatility**: 24.02%[38] - **IR**: 1.66[38] - **Maximum Drawdown**: 27.29%[38] - **Annualized Excess Return**: 24.89%[38] Industry-Specific Performance - **Top 5 Industries by Recent IC (May 2025)**: - Social Services: 30.15% - Defense & Military: 28.07% - Banking: 25.31% - Computers: 24.86% - Real Estate: 12.07%[39] - **Top 5 Industries by 1-Year IC Mean**: - Construction & Decoration: 18.54% - Utilities: 18.14% - Communication: 17.37% - Non-Banking Finance: 16.76% - Defense & Military: 16.53%[39] - **Top 5 Industries by Recent Excess Return (May 2025)**: - Retail: 8.22% - Defense & Military: 7.15% - Social Services: 4.58% - Construction & Decoration: 3.91% - Electronics: 3.64%[42] - **Top 5 Industries by 1-Year Average Monthly Excess Return**: - Oil & Petrochemicals: 5.60% - Building Materials: 5.29% - Home Appliances: 5.06% - Non-Ferrous Metals: 4.57% - Communication: 4.29%[42]

Fund Manager Profile - Fund manager Qu Shaojie has extensive experience in overseas market investments, managing a total of 1.154 billion CNY across three funds as of Q1 2025[1] - Qu's investment philosophy focuses on long-term holdings of fundamentally strong companies with matching valuations and earnings[1] Fund Performance - The Changcheng Hong Kong Stock Connect Value Selection Multi-Strategy A fund has achieved cumulative returns of 19.39%, 48.59%, and 51.88% over the past three, two, and one years, respectively, ranking in the top 6.8%, 1%, and 1.7% of its peers[2] - Year-to-date return for 2025 is 33.52%, placing it in the top 1.5% of its category[2] Market Resilience - During the market turbulence from October 2022 to June 2023, the fund returned 11.14%, significantly outperforming the peer average of -1.44%[2] - From February 2024 to September 2024, the fund achieved a return of 16.89%, compared to the peer average of 3.19%[2] Portfolio Composition - The fund maintains a high concentration in its top three and five sectors, averaging 73.90% and 85.69% respectively since Qu's tenure began[2] - As of Q1 2025, the fund's stock allocation reached 93.11%, an increase of 4.58% from Q4 2024[2] Stock Selection - Notable heavy positions include Xiaomi Group (9.66%) and Pop Mart (11.70%), reflecting Qu's focus on companies with strong fundamentals and growth potential[4] - The weighted average excess return of the fund's heavy positions is 12.66%, with an excess win rate of 82.61%[4] Risk Management - The fund's maximum drawdown was 20.35% in 2024, with a recovery time of only 51 days[2] - The fund's average turnover rate has decreased to 1.09, indicating a more stable investment approach[2]

Investment Rating - The report assigns a "Buy" rating for the company with a target price of 115.75 CNY over the next six months, based on a current price of 91.45 CNY [1]. Core Insights - The company is a leading domestic cosmetics brand that has surpassed 10 billion CNY in revenue, demonstrating strong brand power and high penetration rates, indicating robust long-term growth potential [6][8]. - The domestic cosmetics market continues to grow, with a projected CAGR of 6.2% from 2022 to 2025, expected to reach 579.1 billion CNY by 2025, highlighting the ongoing trend of domestic brands replacing international ones [6][8]. - The company's makeup brand, 彩棠, has shown impressive growth, with a CAGR of 77.5% from 2021 to 2024, and has consistently ranked among the top ten in Tmall's beauty GMV [6][8]. Summary by Sections 1. Company Overview - The company was established in 2006 and became a publicly traded company in 2017, focusing on the research, production, and sales of cosmetics, including brands like 珀莱雅 and 彩棠 [13][16]. - The company achieved a revenue of 10.78 billion CNY in 2024, marking it as the first domestic beauty brand to surpass the 10 billion CNY threshold [16]. 2. Market Dynamics - The domestic cosmetics market is experiencing a slowdown in growth, with retail sales growth fluctuating significantly, but local brands are gaining market share [38][42]. - The company has shifted its focus to online sales, with online revenue growing from 6.4 billion CNY in 2017 to 102.3 billion CNY in 2024, accounting for over 90% of total revenue [21][56]. 3. Brand Performance - The main brand, 珀莱雅, has seen revenue grow from 2.09 billion CNY in 2018 to 8.58 billion CNY in 2024, while 彩棠 has rapidly increased its revenue from 250 million CNY in 2021 to 1.19 billion CNY in 2024 [24][67]. - The company has successfully implemented a "big product" strategy, enhancing customer loyalty and product lifecycle through continuous upgrades and marketing efforts [74][78]. 4. Financial Projections - The company is expected to maintain a compound annual growth rate (CAGR) of 17.2% in net profit over the next three years, with a projected net profit of 1.55 billion CNY in 2024 [2][6]. - The report estimates a price-to-earnings (PE) ratio of 25 for 2025, supporting the target price of 115.75 CNY [6][8].

Market Performance - The overall performance of the Beijing Stock Exchange (BSE) was weak during the period from May 12 to May 23, 2025, with a closing market value of 780.92 billion yuan on May 23[5] - The BSE 50 Index fell by 3.68% compared to the opening market value on May 12, underperforming the ChiNext by approximately 2.8 percentage points[5] - Among 266 stocks on the BSE, 88 stocks rose, while 170 stocks declined during this period[20] Trading and Valuation Metrics - The total trading volume for the BSE during this period was 353.16 billion yuan, with an average trading amount of 1.33 billion yuan per stock[3] - The turnover rate reached 90.46%, indicating an increase in liquidity compared to the previous period[12] - The median price-to-earnings (PE) ratio for the BSE was 51.76 times, which is at a historical high level[12] New Listings and Upcoming IPOs - Tian Gong Co., Ltd. (920068.BJ) was the only new stock listed during this period, debuting on May 13, 2025, with a first-day increase of 411.9%[28] - The upcoming IPO for Jiao Da Tie Fa (920027.BJ) is set to issue 19.09 million shares at a price of 8.81 yuan per share, with an expected total fundraising of approximately 170 million yuan[30] Sector Highlights - Strong performance was noted in sectors such as shipping, controllable nuclear fusion, and mergers and acquisitions, with stocks like Guo Hang Yuan Yang and Chang Fu Co., Ltd. leading the gains[5] - The top five gainers included Tian Gong Co. (up 422.3%) and Guo Hang Yuan Yang (up 47.2%), while Lin Tai New Materials saw a decline of 32.8%[20][24] Regulatory Updates - The China Securities Regulatory Commission revised the "Major Asset Restructuring Management Measures," which aims to simplify review processes and enhance regulatory inclusiveness, potentially boosting M&A activities on the BSE[35]

Investment Rating - The report does not explicitly provide an investment rating for the industry Core Insights - Historical stock returns are a significant reference for future stock performance, and the report explores effective K-line patterns and their corresponding volume-price states without time and stock constraints [1][16] - The K-line investment framework combines K-line shape recognition with volume-price state recognition to enhance prediction accuracy and investment success rates [2][19] - Effective K-line patterns can assist in constructing investment portfolios and optimizing quantitative investments by providing low-correlated pricing information [5][16] Summary by Sections K-line Pattern Investment Framework - K-line charts represent the trading trajectory formed by the interaction of buyers and sellers, and relying solely on K-line shapes for stock selection is considered one-dimensional [2][19] - The report emphasizes the importance of integrating K-line shapes with volume-price state information to improve predictive accuracy [2][19] K-line Shape Recognition and Volume-Price State Recognition - The report utilizes various K-line characteristics such as Yin & Yang, body ratio, upper shadow ratio, lower shadow ratio, returns, open, high, low, and close prices for shape recognition [3][22] - The most commonly used volume-price states are volume expansion and contraction, along with high and low price levels [3][24] Effective K-line Patterns - Strongly effective positive K-line patterns include the large bullish line and inverted hammer, while effective negative patterns include the hammer and gap-down large bearish line [4] - In multi-K patterns, effective positive shapes include the morning star and three white soldiers, while effective negative shapes include the evening star and three black crows [4] Application of Effective K-line Patterns - Effective K-line patterns can directly aid in portfolio construction and enhance subjective stock selection and position management when combined with macroeconomic and fundamental analysis [5] - K-line patterns can serve as event factors to optimize investment portfolios based on traditional fundamental and volume-price factors [5]

Investment Rating - The report maintains an "Outperform" rating for the automotive industry as of May 25, 2025 [1] Core Insights - Retail sales of passenger vehicles from May 1-18 reached 932,000 units, a year-on-year increase of 12% and a month-on-month increase of 18%. Cumulatively, 7.804 million units have been sold this year, reflecting an 8% year-on-year growth. The Passenger Car Association expects a stable and positive retail trend for May [6][61] - Xiaomi launched its first SUV, the YU7, equipped with a self-developed intelligent driving system that integrates deep learning and neural network algorithms for precise operation in complex road environments. Dongfeng Motor signed a comprehensive strategic cooperation agreement with Huawei, focusing on smart assisted driving and digital upgrades in automotive R&D and production [6][64] - The report highlights investment opportunities in companies involved in technology R&D, market expansion, and those benefiting from policy subsidies and improved infrastructure in the new energy vehicle sector [6][61] Summary by Sections Passenger Vehicles - From May 1-18, retail sales of passenger vehicles were 932,000 units, up 12% year-on-year and 18% month-on-month. Cumulative sales for the year reached 7.804 million units, an 8% increase year-on-year. The Passenger Car Association anticipates continued positive retail trends for May [6][62] - Key companies to watch include BYD (002594), Geely (0175.HK), Xpeng Motors (9868.HK), SAIC Motor (600104), Changan Automobile (000625), GAC Group (601238), and Leap Motor (9863.HK) [6][62] New Energy Vehicles - Retail sales of new energy passenger vehicles from May 1-18 reached 484,000 units, a 32% year-on-year increase and a 15% month-on-month increase, with a retail penetration rate of 52%. Cumulative sales for the year reached 3.808 million units, a 35% increase year-on-year [6][63] - Key companies to focus on include BYD (002594), Geely (0175.HK), Huayu Automotive (600741), Xinquan Co. (603179), Doli Technology (001311), Chuanhuan Technology (300547), and Wuxi Zhenhua (605319) [6][63] Smart Vehicles - Xiaomi's YU7 SUV features a self-developed intelligent driving system that utilizes deep learning and neural networks for enhanced operational precision. Dongfeng Motor's partnership with Huawei aims to advance smart driving and digital upgrades in automotive production [6][64] - Companies with technological advancements in smart driving algorithms, sensors, and smart cockpit systems are recommended for investment, including BYD (002594), Geely (0175.HK), and SAIC Motor (600104) [6][64][65]

Investment Strategy Transformation - The rapid development of AI, exemplified by DeepSeek, has reshaped investment logic in the tech sector, transitioning from "value stocks" to "growth stocks" in A-shares and Hong Kong stocks[15] - The proportion of holdings in the Sci-Tech Innovation Board has increased from 10.31% in H1 2021 to 41.78% in H2 2023, indicating a significant shift towards "hard tech" investments[18] - The financing balance for the AI index increased by 314.09 billion CNY in Q4 2024, a rise of 48.03%, reflecting a shift in market sentiment towards left-side positioning[23] Performance Comparison - Active tech funds have outperformed passive funds, with an annualized return of 8.98% for active funds from January 2014 to April 2025, compared to 3.85% for passive tech funds[2] - The timing strategy for the CSI TMT index shows a total win rate of 52.36%, with an excess annualized return of 11.39%[2] Sector Diversification - The market is witnessing a diversification of tech sub-sectors, with the concentration of the top three and five tech industries decreasing significantly during recent bullish phases[29] - The investment strategy is evolving from focusing on single sectors to embracing multiple emerging fields, raising the bar for stock-picking capabilities of active tech funds[29] Manager Selection Criteria - Future successful fund managers should focus on reverse investment strategies and industry trends, leveraging a combination of top-down and bottom-up approaches[31] - Managers with a strong technical background or deep academic research in AI are better positioned to navigate the complexities of the evolving tech landscape[31]

Report Industry Investment Rating No relevant content provided. Core Viewpoints of the Report - A new round of deposit rate cuts has been implemented, leading to capital inflows into bond - related ETFs. The central bank's rate cuts signal a new cycle of monetary easing, driving funds towards bond ETFs such as the Southern Shanghai Stock Exchange Benchmark - Market - Making Corporate Bond ETF [2][15]. - The "12th Fortune Forum" Quantitative Investment Sub - forum discussed investment opportunities in the era of ETFs. It emphasized that the core of Chinese ETF innovation lies in optimizing constituent stocks through quantitative strategies, and the intelligent upgrade of ETFs is reshaping the investment ecosystem with large - model technology [3][16]. Summary by Relevant Catalogs 1 ETF and Index Product Focus - A new round of deposit rate cuts by major banks and a 10 - basis - point cut in LPR have pushed China's monetary policy into a new easing cycle. Bond - related ETFs like the Shanghai Stock Exchange Corporate Bond ETF (511070) have seen capital inflows exceeding 1 billion yuan each [2][15]. - The "12th Fortune Forum" Quantitative Investment Sub - forum explored investment opportunities in the ETF era. It pointed out that Chinese ETF innovation focuses on constituent stock optimization via quantitative strategies, and large - model - empowered ETFs are becoming mainstream. The forum also highlighted opportunities in Hong Kong's structural scarce assets and the advantages of the Smart Beta strategy [3][16]. 2 Last Week's Market Performance Review 2.1 Main Asset Class Index Performance - Domestic equity broad - based indices all declined last week. The Shanghai 50, CSI 300, ChiNext, CSI 500, and STAR 50 fell by 0.18%, 0.18%, 0.88%, 1.1%, and 1.47% respectively. Bond - related indices had mixed performance, with some rising and some falling. Overseas equity indices also showed mixed results, and commodity - related indices had both increases and decreases [4][17]. 2.2 Shenwan Primary Industry Performance - Last week, Shenwan primary industries had mixed performance. The pharmaceutical and biological, comprehensive, and non - ferrous metals sectors led the gains, rising 1.78%, 1.41%, and 1.26% respectively. The computer, machinery, and communication sectors led the losses, falling 3.02%, 2.48%, and 2.31% respectively [19]. 3 Valuation Situation - Last week, the valuation quantiles of major equity broad - based indices all declined. The ChiNext, CSI 300, Wind All - A, Shanghai 50, CSI 800, CSI 1000, and CSI 500 decreased by 0pp, 0.27pp, 0.74pp, 0.91pp, 1.23pp, 1.69pp, and 1.85pp respectively. Most Shenwan primary industries' valuation quantiles also declined [5][23]. 4 ETF Scale Changes and Trading Volume 4.1 ETF Scale Changes - Last week, the scale of non - monetary ETFs decreased by 15.324 billion yuan, with a net inflow of - 7.153 billion yuan. By type, stock - type ETFs' scale decreased by 26.758 billion yuan, while commodity - type ETFs' scale increased by 5.432 billion yuan, and bond - type ETFs' scale increased by 10.846 billion yuan. Among equity broad - based ETFs, the Hang Seng Index theme ETF had the largest scale increase, and the CSI A500 theme ETF had the largest decrease [6][7][32]. 4.2 ETF Trading Volume - Compared with the previous week, the daily average trading volume of some ETFs increased significantly. For example, the GF China Hong Kong Innovative Drug ETF in the cross - border non - Hong Kong stock ETF category had the largest increase in daily average trading volume [45]. 5 ETF Performance - Last week, the best - performing cross - border non - Hong Kong stock ETF was the GF China Hong Kong Innovative Drug ETF (+7.99%), the best - performing cross - border Hong Kong stock ETF was the Invesco Great Wall CSI Hong Kong Stock Connect Innovative Drug ETF (+8.24%), the best - performing stock - type ETF was the Huaxia CSI Shanghai - Hong Kong - Shenzhen Gold Industry Stock ETF (+6.63%), the best - performing commodity - type ETF was the Huaan Gold ETF (+3.86%), and the best - performing bond - type ETF was the Penghang ChinaBond 30 - Year Treasury Bond ETF (+0.31%) [8][48]. 6 ETF Margin Trading and Short - Selling - Last week, the total margin - buying amount was 49.249 billion yuan, a decrease of 15.255 billion yuan from the previous week. The total margin - selling volume was 233 million shares, a decrease of 12 million shares from the previous week [51]. 7 Current ETF Market Scale - As of last Friday (May 23, 2025), there were 1,164 listed ETFs in the market, with a total scale of 4.094487 trillion yuan. Among stock - type ETFs, scale - index ETFs had the largest scale, followed by theme - index ETFs [53]. 8 ETF Listing and Issuance - Last week, 3 ETFs were listed for trading. 18 new ETFs were established, mostly passive index funds, except for two passive index bond funds [9][62].

Report Industry Investment Rating No information provided in the content. Core Viewpoints of the Report - The market fluctuated upward, and the term spread widened. The market was mainly driven by deposit rate cuts in the first half - week and shifted to focus on the money market and fiscal supply in the second half - week. The market showed a pattern of "short - term strength and long - term weakness" [3][97]. - There was an asymmetric cut in deposit and lending rates, and CD price hikes reappeared. The non - symmetric cut aimed to relieve banks' interest margin pressure, but it might exacerbate financial disintermediation. State - owned and joint - stock banks' CD issuance accounted for about 58% last week, and their CD price hikes were more obvious [3][97]. - Trading desks increased their positions in 7 - 10Y Treasury bonds during the market adjustment to lower the cost of adding positions. The central cost of adding positions for major trading desks has moved up to around 1.67%, which may be an important position for both bulls and bears in the short term [3][98]. - After the deposit rate cut, more attention should be paid to market liquidity. Policy implementation may need to wait for economic data verification or fiscal supply pressure, and in the short term, policy may not be the main market driver. "Deposit migration" may coincide with the peak of government bond supply, and the maturity of inter - bank CDs in June is as high as 4.16 trillion yuan, so factors such as the stability of large commercial banks' liabilities and the issuance rhythm of government bonds need to be focused on [3][98]. - In the current state of unclear direction, a "high - probability" portfolio may be more suitable for the liability side of asset management products. It is recommended to allocate coupon - bearing assets, adopt a "coupon + carry trade" strategy, use a neutral - duration barbell portfolio, and select 2 - year AA - / AA - rated credit bonds and 10 - year local bonds as the underlying coupon - bearing assets. The spread between the active and sub - active 10 - year CDB bonds may indicate an upcoming bond - swapping market [3][99]. Summary by Relevant Catalogs 1. Important Matters - On May 20, the 1 - year and 5 - year LPR were cut by 10BP, and state - owned banks simultaneously lowered deposit rates. The 1 - and 2 - year deposit rates were cut by 15BP, and the 3 - and 5 - year fixed - deposit rates were cut by 25BP. This aimed to relieve banks' interest margin pressure but may exacerbate financial disintermediation and affect the stability of commercial banks' liabilities [6]. - On May 22, the central bank conducted a 5000 - billion - yuan MLF operation, with a net investment of 3750 billion yuan in May, achieving three consecutive months of over - renewal [8]. - From January to April 2025, China's total retail sales of consumer goods increased by 4.7% year - on - year. Catering performed slightly better than commodity retail. Some consumer categories such as home appliances and cultural office supplies grew strongly, while clothing, textiles, and automobiles were relatively weak. Consumption was policy - driven, and the "618" promotion and subsequent policy support are expected to continue to boost consumption [13]. 2. Money Market 2.1 Open Market Operations and Money Rate Trends - Last week, the central bank's open market operations turned to net investment, with 5000 billion yuan of MLF invested, achieving three consecutive months of over - renewal. The non - bank money price generally increased, but DR007 remained below 1.6%. From May 19 to 23, the central bank's net investment was 12000 billion yuan, and it is expected to withdraw 9460 billion yuan from May 26 to 30 [17][18]. 2.2 CD Rate Trends and Repo Transaction Volume - In the primary market, the net financing of inter - bank CDs was negative last week. The issuance scale was 7143.30 billion yuan, and the maturity scale was 7383.40 billion yuan, with a net financing of - 240.10 billion yuan. State - owned banks were the largest issuers due to "deposit migration". The issuance rates of inter - bank CDs increased compared with the previous week [26][29][31]. - In the secondary market, the yields of inter - bank CDs across all tenors increased. The 1Y - 3M spread is currently at the 32.14% quantile level [35]. 3. Bond Market Primary Market - The supply of Treasury bonds accelerated significantly, and 30 - year and 50 - year special Treasury bonds were issued last week. As of May 23, the cumulative net financing of various Treasury bonds in 2025 was about 2.87 trillion yuan, and that of local bonds was about 3.54 trillion yuan, significantly higher than the average from 2021 - 2024. The issuance of local bonds has gradually slowed down compared with Treasury bonds [39]. - The cumulative net financing of long - term Treasury bonds and long - term local bonds was 11937.60 billion yuan and 32676.99 billion yuan respectively as of May 23. The net financing of long - term local government bonds increased significantly in Q1 and slowed down in Q2, while the issuance of ultra - long - term special Treasury bonds boosted the net financing of long - term Treasury bonds in Q2 [42]. - Last week, 89 interest - rate bonds were issued, with a total issuance of 9683.22 billion yuan, a maturity of 2153.47 billion yuan, and a net financing of 7529.74 billion yuan. As of last week, 1.60 trillion yuan of special refinancing bonds had been issued, mainly with long - and ultra - long - term tenors [46][50]. Secondary Market - Interest rates fluctuated upward. The yields of 1 - year, 3 - year, 5 - year, 7 - year, 10 - year, and 30 - year Treasury bonds changed by - 0.27BP, - 1.14BP, - 1.20BP, - 1.98BP, 4.15BP, and 1.10BP respectively. The 10Y - 1Y Treasury bond yield spread widened to 27.27BP [53]. - The trading activity of 10 - year Treasury and CDB active bonds decreased. The average daily trading volume of the 10 - year Treasury active bond (250004) decreased by about 19.95%, and its average turnover rate decreased by about 2.97 percentage points. The average daily trading volume of the 10 - year CDB active bond (250205) decreased by about 7.26%, and its average turnover rate decreased by about 6.41 percentage points [56]. - The spread between the 10 - year Treasury active and sub - active bonds widened, while the spread between the 10 - year CDB active and sub - active bonds narrowed slightly, indicating a possible upcoming CDB bond - swapping market [58]. - The term spread widened, but the 10 - year - 1 - year Treasury bond term spread remained at a historical low. The long - and ultra - long - term local - Treasury bond spread generally narrowed, and the 10 - year local - Treasury bond spread was more likely to be compressed from the supply perspective [59][65]. 4. Institutional Behavior Tracking - The market leverage level rebounded significantly. The institutional leverage ratio in April decreased slightly but remained at a comparable level. Last week, rural commercial banks' purchases of Treasury bonds were concentrated in the 5 - 10 - year tenor, with a significantly weakened buying force. Insurance companies changed from buying to selling 10 - year - plus Treasury bonds, and state - owned and joint - stock banks were the main sellers of long - term Treasury bonds [66][79]. - The central cost of adding positions for major trading desks has moved up to around 1.67%. The average duration of all pure - bond funds increased from 2.49 years to 2.61 years, and that of high - performing pure - bond funds increased from 4.37 years to 4.55 years [80][88]. - Considering capital occupation and tax costs, commercial banks and insurance companies can obtain relatively higher returns by investing in local bonds [90]. 5. High - Frequency Data Tracking - Last week, the settlement price of rebar futures increased by 0.03% week - on - week, wire rod futures increased by 2.16% week - on - week, cathode copper futures decreased by 1.47% week - on - week, the cement price index decreased by 1.78% week - on - week, and the Nanhua Glass Index decreased by 4.40% week - on - week. The CCFI index increased by 0.23% week - on - week, and the BDI index increased by 5.84% week - on - week [94]. - The pork wholesale price increased by 0.24% week - on - week, and the vegetable wholesale price remained flat. The settlement price of Brent crude oil futures increased by 10.20% week - on - week, and that of WTI crude oil futures decreased by 3.09% week - on - week. The central parity rate of the US dollar against the RMB was 7.19 [94].