Investment Rating - The report maintains an "Outperform" rating for the machinery equipment industry [1]. Core Insights - The robotics index outperformed the market, with the Zhongzheng Science and Technology Innovation Robotics Index rising by 1.6%, surpassing the Shanghai Composite Index by 0.4 percentage points and the CSI 300 Index by 0.7 percentage points [5][12]. - The total market capitalization of the machinery equipment industry is approximately 368.19 billion, with a circulating market value of about 341.47 billion [3]. - The industry’s TTM price-to-earnings ratio stands at 31.4, significantly higher than the CSI 300's TTM P/E ratio of 12.5 [3]. Summary by Sections Market Review - The robotics index showed a positive trend, outperforming major indices during the week of June 2 to June 8, 2025 [5][12]. - Notable developments include the announcement of the resignation of Tesla's humanoid robot project leader, Milan Kovac, and the launch of the second-generation tactile sensor by Fulei New Materials [17][19]. Industry Dynamics - Fulei New Materials introduced a second-generation tactile sensor that features significant upgrades over the first generation, including full flexibility and the ability to sense complex surfaces [17][19]. - The Guangdong Province Artificial Intelligence and Robotics Industry Alliance was established to foster collaboration and innovation within the industry [19]. - Keli Sensor made a strategic investment in Yuan Sheng Xian Da Technology, focusing on multi-dimensional tactile sensing solutions [19][20]. Financing Dynamics - Lumos Robotics successfully completed an angel round of financing, with investments aimed at enhancing product development and industry collaboration [32].

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]