Report Industry Investment Rating No relevant content provided. Core Viewpoints - Despite the central bank's net withdrawal of 67.17 billion yuan through 7 - day reverse repurchase last week, the money market remained loose. The 1 - year Treasury bond yield dropped by 4.5 BP to 1.41% compared to the end of May. The large - scale purchase of short - term Treasury bonds by state - owned banks amplified the market's expectation of the central bank restarting Treasury bond purchases, which was the main catalyst for the bond market's strength last week [3][84]. - The outright reverse repurchase is carried out once a month in principle. The change in the announcement method has improved policy transparency. Even if it is carried out only once, market liquidity is expected to remain abundant in the short term, and the central bank has various tools to adjust liquidity. There is no need to worry about whether another outright reverse repurchase will be carried out within the month [3][85]. - In terms of institutional behavior, rural commercial banks continued to take profits, while funds became the largest buyers. The cost of adding positions in 7 - 10 - year Treasury bonds for brokers and funds has gradually diverged from that of rural commercial banks [3][86]. - In June, the strategy of giving priority to defense remains unchanged, but left - hand side layout can be gradually carried out. With the approaching peak of inter - bank certificate of deposit (NCD) maturities in June, it is recommended to allocate liquid assets and gradually consider extending the duration [3][87]. Summary by Directory 1. Important Matters - In May, the manufacturing PMI rose to 49.5%, an increase of 0.5 percentage points from April, indicating an improvement in the manufacturing industry's prosperity. The production index was above the critical point, while the new order, raw material inventory, and employment indexes were below it [6]. - On June 5, 2025, the central bank announced a 1 - trillion - yuan outright reverse repurchase operation for June 6, with a term of 3 months. The change in the announcement method improved policy transparency [7]. 2. Money Market 2.1 Open - Market Operations and Fund Rate Trends - From June 3 to 6, 2025, the central bank's open - market operations resulted in a net withdrawal of 67.17 billion yuan. The end of the cross - month effect and the early tender offer of the outright reverse repurchase pushed the fund price closer to the policy rate. The expected maturity of open - market operations from June 10 to 13 will withdraw 93.09 billion yuan [9][10]. - Last week, the money market was generally loose, with overnight fund prices below 1.5%. As of June 6, R001, R007, DR001, and DR007 were 1.448%, 1.551%, 1.412%, and 1.532% respectively, showing a decline compared to May 30 [11]. 2.2 Certificate of Deposit (CD) Rate Trends and Repurchase Transaction Situation - In the primary market, last week, the net financing of inter - bank CDs was - 8.036 billion yuan. City commercial banks were the main issuers. The price - raising behavior of state - owned and joint - stock banks improved, but city and rural commercial banks continued to raise CD issuance prices. The upcoming peak of CD maturities in the next three weeks may increase CD issuance [18][19]. - In the secondary market, due to the early tender offer of the outright reverse repurchase, the yields of inter - bank CDs of all maturities declined last week. For example, the 1 - month, 3 - month, 6 - month, 9 - month, and 1 - year AAA - rated inter - bank CD yields decreased by 0.58 BP, 6.00 BP, 4.75 BP, 4.00 BP, and 4.00 BP respectively [23]. 3. Bond Market - In the primary market, the supply of Treasury bonds accelerated significantly last week, with the issuance of 30 - year and 50 - year special Treasury bonds. As of June 6, the cumulative net financing of various Treasury bonds and local bonds in 2025 was about 3 trillion yuan and 3.73 trillion yuan respectively, significantly higher than the average in the same period from 2021 - 2024. The issuance rhythm of local bonds has gradually slowed down compared to Treasury bonds [27]. - In the secondary market, the interest rate market showed a bull - steepening trend last week. The yields of 1 - year, 3 - year, 5 - year, 7 - year, 10 - year, and 30 - year Treasury bonds decreased by - 6.81 BP, - 7.99 BP, - 4.44 BP, - 4.90 BP, - 3.30 BP, and - 4.00 BP respectively. The spread between the 10 - year and 1 - year Treasury bond yields widened from 20.94 BP to 24.45 BP [27][39]. 4. Institutional Behavior Tracking - The average daily leveraged trading volume in the market in the past month was about 7 trillion yuan, and the market leverage ratio may have increased compared to April. State - owned banks significantly increased their holdings of Treasury bonds with maturities of less than 5 years, rural commercial banks continued to take profits on Treasury bonds with maturities of less than 10 years but with reduced intensity, while the net buying intensity of brokers and insurance companies weakened, and funds significantly increased their holdings [56][65]. - In April 2025, the overall institutional leverage ratio in the inter - bank market decreased slightly but remained at a comparable level. The leverage ratios of commercial banks, securities companies, and other institutions were about 109.26%, 185.03%, and 132.65% respectively [56]. 5. High - Frequency Data Tracking - Last week, the settlement price of rebar futures decreased by 1.25% week - on - week, the wire rod futures price remained flat, the cathode copper futures price increased by 0.65%, the cement price index remained flat, and the Nanhua Glass Index decreased by 1.19%. The CCFI index increased by 3.34% and the BDI index increased by 24.79% week - on - week [81]. - In terms of food prices, the pork wholesale price decreased by 1.82% and the vegetable wholesale price increased by 0.23% week - on - week. The settlement prices of Brent and WTI crude oil futures increased by 12.22% and 2.47% respectively. The central parity rate of the US dollar against the RMB was 7.18 last week [81]. 6. Market Outlook - The money market will remain loose in the short term, and the central bank's attitude of caring for the market will not change. There is no need to worry about whether another outright reverse repurchase will be carried out within the month [3][85]. - In June, the strategy of giving priority to defense remains unchanged. With the approaching peak of NCD maturities, it is recommended to allocate liquid assets and gradually consider extending the duration, such as choosing the active bonds of 10 - year or 30 - year Treasury bonds [3][87].

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]