Investment Rating - The report maintains an "Outperform" rating for the low-altitude economy industry [1]. Core Insights - The low-altitude logistics scenarios are accelerating, with significant developments such as沃飞长空 receiving the CCAR-135 operational qualification certificate, marking it as the first domestic eVTOL manufacturer qualified for short-distance aerial tours and passenger flights [1][26]. - The low-altitude economy sector has outperformed the market, with the万得低空经济指数 rising by 1.96%, surpassing the Shanghai Composite Index by 0.86 percentage points [6][14]. - National and local policies are increasingly supportive of the low-altitude economy, with initiatives aimed at enhancing drone applications in logistics and tourism [21][22][23]. Summary by Sections Market Review - From May 26 to June 8, the low-altitude economy sector outperformed the market, with the万得低空经济指数 increasing by 1.96% and the国证通用航空指数 rising by 3.12% [6][14]. - The report highlights various local government initiatives aimed at promoting the development of the low-altitude economy, including the establishment of low-altitude flight service platforms in regions like Hubei [25][26]. Policy Dynamics - The National Postal Bureau emphasized the development of drone technology for logistics, aiming to enhance low-altitude economic growth [21]. - The Ministry of Industry and Information Technology, along with other agencies, released a digital transformation plan for the electronic information manufacturing industry, which includes applications for drones and low-altitude logistics [22]. Industry Developments - The report details significant advancements in the industry, such as the launch of the移动机场 2.0 by圣翔航空, which allows for flexible deployment of vertical takeoff and landing airports [34]. - Major contracts and partnerships are being formed, including a 100-unit order from中航租赁 for the御风未来 M1B eVTOL, indicating strong market demand [30][32]. - Joby Aviation's collaboration with a Saudi Arabian company aims to establish a distribution agreement for eVTOLs, with potential deliveries of up to 200 aircraft valued at approximately $1 billion [40].

Investment Rating - The report maintains an "Outperform" rating for the low-altitude economy industry [1] Core Insights - The low-altitude logistics scenarios are accelerating, with significant developments such as沃飞长空 receiving the CCAR-135 operational qualification certificate, marking it as the first domestic eVTOL manufacturer qualified for short-distance aerial tours and passenger flights [1][26] - The low-altitude economy sector has outperformed the market, with the Wande Low Altitude Economy Index rising by 1.96%, surpassing the Shanghai Composite Index by 0.86 percentage points [6][14] - National and local policies are increasingly supportive of the low-altitude economy, with initiatives aimed at enhancing drone development for logistics and delivery services [21][22][23] Summary by Sections Market Review - From May 26 to June 8, the low-altitude economy sector outperformed the market, with the Wande Low Altitude Economy Index increasing by 1.96% and the National General Aviation Index rising by 3.12% [6][14] Policy Dynamics - The National Postal Bureau emphasized the development of drone technology for logistics and delivery, aiming to enhance the low-altitude economy [21] - The Ministry of Industry and Information Technology, along with other agencies, issued a digital transformation plan for the electronic information manufacturing industry, promoting the application of high-precision positioning technologies in consumer drones [22] - Various provinces, including Henan and Guangdong, have released plans to boost the low-altitude economy, focusing on tourism and drone applications [23][24] Industry Developments - The construction of the Hubei low-altitude flight service platform has officially started, aiming for completion by the end of the year [25] - 沃飞长空 has received the CCAR-135 operational qualification certificate, enabling it to conduct short-distance aerial tours [26] - 御风未来 has completed important tests for its M1B eVTOL and secured a 100-unit order from 中航租赁, valued at over 1 billion RMB [30] - 大疆 has acquired land in Shenzhen for its global headquarters in smart aviation, with plans for a significant development project [38] - Joby Aviation has entered a strategic partnership with a Saudi Arabian company to explore opportunities for eVTOL distribution in the Middle East, potentially delivering up to 200 aircraft [39]

Investment Rating - The report maintains a positive outlook on the pharmaceutical industry, highlighting investment opportunities in AI healthcare and innovative drugs [1]. Core Insights - The pharmaceutical sector index increased by 1.13% this week, outperforming the CSI 300 index by 0.25 percentage points, ranking 17th in industry performance [14]. - Year-to-date, the pharmaceutical industry has risen by 7.81%, surpassing the CSI 300 index by 9.36 percentage points, ranking 5th [14]. - The current valuation level (PE-TTM) for the pharmaceutical industry is 28.24 times, with a premium of 86.84% relative to the entire A-share market [19]. - The best-performing sub-sector this week was vaccines, which rose by 2.9%, while the top three sub-sectors year-to-date are chemical preparations, raw materials, and other biological products, with increases of +21.3%, +20.4%, and +16.1% respectively [28]. Summary by Sections Investment Strategy - The report emphasizes the acceleration of AI healthcare development and investment opportunities, with over 830 hospitals in China completing the localization of DeepSeek-R1, marking a new phase in medical intelligence [15]. - Continuous attention is recommended for innovative drugs, especially following the ASCO 2025 conference, where over 70 research abstracts led by Chinese scholars were presented [16]. Market Performance - The pharmaceutical industry has shown strong performance, with a notable increase in the number of stocks with positive returns, totaling 330, while 150 stocks experienced declines this week [29]. - The report lists the top gainers and losers in the pharmaceutical sector, with 易明医药 (Yiming Pharmaceutical) leading with a gain of +33.1% and *ST龙津 (ST Longjin) facing a decline of -36.3% [29]. Recent News and Policies - The report highlights the ongoing trend of AI integration in healthcare, with significant advancements in clinical decision support systems and AI-assisted diagnostics [15]. - It also notes the importance of focusing on drug development that is patient-centered and clinically valuable, aligning with the broader themes of innovation and internationalization in the pharmaceutical industry [16].

Domestic Economic Indicators - During the Dragon Boat Festival, domestic travel reached 119 million trips, a year-on-year increase of 5.7%, with total spending of 42.73 billion yuan, up 5.9%[6] - The Caixin Manufacturing PMI for May recorded 48.3, a decrease of 2.1 percentage points from April, marking the first drop below the critical point since October 2022[8] - The Caixin Services PMI for May rose to 51.1, an increase of 0.4 percentage points from April, indicating marginal improvement in the services sector[8] Monetary Policy and Market Trends - The People's Bank of China conducted a 1 trillion yuan reverse repo operation to maintain reasonable liquidity, reflecting a continued "moderately loose" monetary policy stance[13] - M2 growth in Q1 2025 was 7%, while the total social financing stock increased by 8.2%, indicating a stable monetary environment[13] - The average interbank market interest rate, DR007, fell by 14 basis points to 1.6% in May, suggesting overall liquidity is easing[14] International Economic Developments - The U.S. ISM Manufacturing PMI for May was 48.5, indicating a contraction for the third consecutive month, with new orders declining for four months in a row[18] - The Eurozone's May harmonized CPI was 1.9%, falling below the ECB's 2% target for the first time in eight months, prompting a 25 basis point rate cut by the ECB[20][21] - South Korea's new president, Lee Jae-myung, has initiated economic reforms focusing on semiconductor strategy and labor market adjustments, amidst challenges from global economic conditions[22][23] Commodity Market Insights - Brent crude oil prices increased by 4.01% week-on-week, while iron ore and copper prices fell by 0.55% and 0.04%, respectively[26] - Saudi Arabia plans to increase oil production by at least 411,000 barrels per day in August and possibly September, aiming to capture market share amid fluctuating global demand[24]

Group 1 - The report highlights that China's economy is entering a high elasticity period, with structural growth opportunities in high-end manufacturing, urban renewal, and service consumption, supported by domestic investment policies [6][8][29] - Manufacturing investment is expected to maintain an annual growth rate of over 8% due to domestic demand expansion policies, while infrastructure investment growth may exceed 9% [6][8][20] - The report suggests that the real estate market is shifting towards "quality over quantity," which will help stabilize prices amid reduced supply [6][29] Group 2 - The report identifies short, medium, and long-term industry selection strategies, emphasizing sectors such as intelligent manufacturing, beverage and dairy, and chemical pharmaceuticals for short-term focus [6][7] - In the medium term, the report notes that trade conflicts have limited impact on China's industrial development, with technology, services, and education sectors showing strong growth [6][7] - Long-term investment opportunities are seen in high-end manufacturing, pharmaceutical biotechnology, and new discretionary consumption as interest rates decline [6][7] Group 3 - The report indicates that the broad infrastructure investment growth rate is expected to decline slightly in the third quarter, with a projected annual growth rate of over 9% [20][22] - Specific sectors such as electricity, heat, gas, and water supply are experiencing significant investment growth, with fixed asset investment in these areas increasing by 25.5% [20][22] - The report also notes that the approval of central and local projects is accelerating, which will support infrastructure investment in the second half of the year [20][22] Group 4 - The report emphasizes that consumer confidence remains weak, with retail sales growth driven primarily by "trade-in" policies, which are expected to support a modest recovery in consumption [41][43] - The service retail sector is outpacing goods retail, with service retail sales growing by 5.1% compared to 4.7% for goods retail [48] - The report highlights the potential of the "谷子经济" (Guzi Economy), which focuses on emotional value through cultural IP, as a new consumption driver [48]

Domestic Insights - Industrial enterprises' profits increased by 1.4% year-on-year in the first four months, with a slight acceleration of 0.6 percentage points compared to Q1[1] - State-owned enterprises' profits decreased by 1.7% year-on-year, indicating pressure from volume growth but price decline[14] - The manufacturing sector showed strong performance, particularly in equipment manufacturing, which saw a profit increase of 11.2%[13] International Developments - The U.S. tariff policy experienced reversals, with a federal court temporarily halting the implementation of tariffs announced by the Trump administration[16] - The U.S. Treasury Department announced a reduction in short-term debt issuance, reflecting ongoing political negotiations over the debt ceiling[21] - Japan's central bank signaled a cautious approach to monetary policy, with low short-term interest rate hike probabilities amid rising inflation pressures[19] Market Trends - Brent crude oil prices fell by 1.03% week-on-week and decreased by 21.69% year-on-year, indicating a significant decline in energy prices[26] - The price of rebar dropped by 1.81% week-on-week and 14.22% year-on-year, reflecting ongoing challenges in the construction materials market[32] - The average collection period for accounts receivable in state-owned enterprises extended to 70.3 days, indicating increased financial pressure[15]

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].