Quantitative Factors and Models Summary Quantitative Factors and Construction Process - **Factor Name**: Intraday Skewness Factor **Construction Idea**: This factor captures the skewness of intraday stock returns, reflecting the asymmetry in return distribution[13][16][18] **Construction Process**: The factor is calculated based on the third moment of intraday return distribution, normalized by the cube of standard deviation. The detailed methodology is referenced in the report "Stock Selection Factor Series Research (19) - High-Frequency Factors on Stock Return Distribution Characteristics"[13][16][18] - **Factor Name**: Downside Volatility Proportion Factor **Construction Idea**: This factor measures the proportion of downside volatility in the total realized volatility of a stock[18][19][20] **Construction Process**: The factor is derived by decomposing realized volatility into upside and downside components. The methodology is detailed in the report "Stock Selection Factor Series Research (25) - High-Frequency Factors on Realized Volatility Decomposition"[18][19][20] - **Factor Name**: Post-Open Buying Intention Proportion Factor **Construction Idea**: This factor quantifies the proportion of buying intention in the early trading period after market open[22][23][24] **Construction Process**: The factor is constructed using high-frequency data to identify and aggregate buying signals in the post-open period. The methodology is detailed in the report "Stock Selection Factor Series Research (64) - Low-Frequency Applications of High-Frequency Data Based on Intuitive Logic and Machine Learning"[22][23][24] - **Factor Name**: Post-Open Buying Intensity Factor **Construction Idea**: This factor measures the intensity of buying activity in the early trading period after market open[27][28][29] **Construction Process**: Similar to the proportion factor, this factor aggregates the magnitude of buying signals during the post-open period, normalized by trading volume[27][28][29] - **Factor Name**: Post-Open Large Order Net Buying Proportion Factor **Construction Idea**: This factor captures the proportion of large order net buying in the early trading period after market open[32][34][35] **Construction Process**: The factor is calculated by summing the net buying of large orders during the post-open period and dividing by total trading volume[32][34][35] - **Factor Name**: Post-Open Large Order Net Buying Intensity Factor **Construction Idea**: This factor measures the intensity of large order net buying in the early trading period after market open[37][39][40] **Construction Process**: The factor aggregates the net buying of large orders during the post-open period, normalized by the total number of large orders[37][39][40] - **Factor Name**: Improved Reversal Factor **Construction Idea**: This factor captures the reversal effect in stock returns, adjusted for high-frequency data characteristics[40][43][44] **Construction Process**: The factor is constructed by identifying stocks with extreme short-term returns and measuring their subsequent reversal performance[40][43][44] - **Factor Name**: Deep Learning Factor (Improved GRU(50,2)+NN(10)) **Construction Idea**: This factor leverages a deep learning model combining GRU and neural networks to predict stock returns[63][65][66] **Construction Process**: The model uses 50 GRU units and 10 neural network layers, trained on historical high-frequency data to predict short-term stock returns[63][65][66] - **Factor Name**: Deep Learning Factor (Residual Attention LSTM(48,2)+NN(10)) **Construction Idea**: This factor employs an LSTM model with residual attention mechanisms to enhance prediction accuracy[65][66][68] **Construction Process**: The model uses 48 LSTM units and 10 neural network layers, incorporating residual connections to capture long-term dependencies in high-frequency data[65][66][68] - **Factor Name**: Multi-Granularity Model Factor (5-Day Label) **Construction Idea**: This factor predicts stock returns over a 5-day horizon using a multi-granularity deep learning model[68][69][70] **Construction Process**: The model is trained using bidirectional AGRU (Attention-Gated Recurrent Unit) to capture multi-scale temporal patterns in stock data[68][69][70] - **Factor Name**: Multi-Granularity Model Factor (10-Day Label) **Construction Idea**: Similar to the 5-day label factor, this factor predicts stock returns over a 10-day horizon[69][70][71] **Construction Process**: The model uses the same AGRU architecture as the 5-day label factor but is trained with a 10-day prediction horizon[69][70][71] Factor Backtesting Results - **Intraday Skewness Factor**: - IC: 0.024 (2025), 0.019 (historical) - e^(-RankMAE): 0.327 (2025), 0.324 (historical) - Long-Short Return: 16.90% (2025 YTD), -0.66% (last week) - Long-Only Excess Return: 1.84% (2025 YTD), -0.79% (last week)[9][10][13] - **Downside Volatility Proportion Factor**: - IC: 0.020 (2025), 0.016 (historical) - e^(-RankMAE): 0.325 (2025), 0.323 (historical) - Long-Short Return: 12.93% (2025 YTD), -1.19% (last week) - Long-Only Excess Return: -0.12% (2025 YTD), -1.07% (last week)[9][10][18] - **Post-Open Buying Intention Proportion Factor**: - IC: 0.026 (2025), 0.026 (historical) - e^(-RankMAE): 0.322 (2025), 0.321 (historical) - Long-Short Return: 13.98% (2025 YTD), 0.27% (last week) - Long-Only Excess Return: 7.20% (2025 YTD), 0.28% (last week)[9][10][22] - **Post-Open Buying Intensity Factor**: - IC: 0.029 (2025), 0.030 (historical) - e^(-RankMAE): 0.327 (2025), 0.326 (historical) - Long-Short Return: 18.53% (2025 YTD), 0.05% (last week) - Long-Only Excess Return: 7.09% (2025 YTD), 0.43% (last week)[9][10][27] - **Post-Open Large Order Net Buying Proportion Factor**: - IC: 0.027 (2025), 0.036 (historical) - e^(-RankMAE): 0.319 (2025), 0.322 (historical) - Long-Short Return: 18.25% (2025 YTD), 0.31% (last week) - Long-Only Excess Return: 9.48% (2025 YTD), 0.43% (last week)[9][10][32] - **Post-Open Large Order Net Buying Intensity Factor**: - IC: 0.019 (2025), 0.025 (historical) - e^(-RankMAE): 0.318 (2025), 0.321 (historical) - Long-Short Return: 10.50% (2025 YTD), 0.31% (last week) - Long-Only Excess Return: 7.08% (2025 YTD), 0.24% (last week)[9][10][37] - **Improved Reversal Factor**: - IC: 0.025 (2025), 0.031 (historical) - e^(-RankMAE): 0.331 (2025), 0.330 (historical) - Long-Short Return: 17.44% (2025 YTD), 0.12% (last week) - Long-Only Excess Return: 6.14% (2025 YTD), 0.33% (last week)[9][10][40] - **Deep Learning Factor (Improved GRU(50,2)+NN(10))**: - IC: 0.045 (2025), 0.066 (historical) - e^(-RankMAE): 0.335 (2025), 0.336 (historical) - Long-Short Return: 28.86% (2025 YTD), 1.36% (last week) - Long-Only Excess Return: 2.19% (2025 YTD), 0.06% (last week)[9][10][63] - **Deep Learning Factor (Residual

- The reversal factor performed relatively well, with the GARP portfolio achieving a weekly return of 3.28% from August 1, 2025, to August 8, 2025[1] - The cumulative return of the GARP portfolio in 2025 was 28.2%[1] - The PB-profit combination had a weekly return of 2.86%, with a cumulative return of 20.53% in 2025[5][9] - The small-cap growth portfolio had a weekly return of 4.87%, with a cumulative return of 56.37% in 2025[5][9] - The small-cap value preferred portfolio 1 had a weekly return of 3.67%, with a cumulative return of 48.10% in 2025[5][9] - The small-cap value preferred portfolio 2 had a weekly return of 5.00%, with a cumulative return of 56.61% in 2025[5][9] - The performance of the multi-factor portfolios showed that the aggressive portfolio and the balanced portfolio had weekly returns of 3.37% and 3.19%, respectively[10][11] - The aggressive portfolio and the balanced portfolio had cumulative returns of 61.10% and 49.08% in 2025, respectively[11] - The enhanced CSI 300 portfolio had a weekly return of 1.43%, with a cumulative return of 11.18% in 2025[14][15] - The enhanced CSI 500 portfolio had a weekly return of 2.17%, with a cumulative return of 14.96% in 2025[14][15] - The enhanced CSI 1000 portfolio had a weekly return of 2.01%, with a cumulative return of 22.07% in 2025[14][15] - The performance of the style factors showed that small-cap stocks outperformed large-cap stocks, and high-valuation stocks outperformed low-valuation stocks[5][43] - The performance of the technical factors showed that the reversal factor contributed positive returns, with a weekly long-short return of 0.98%[5][46][48] - The performance of the fundamental factors showed that the SUE factor and the expected net profit adjustment factor contributed positive returns, with weekly long-short returns of 0.51% and 0.34%, respectively[5][50][52]

Group 1: Current State of Long-term Funds in China - China's long-term funds, including social security and pension funds, have a significantly lower equity investment ratio compared to developed markets, with actual equity investment at only 12.8% against a policy cap of 25% for insurance funds[4] - The investment ratio of pension funds and enterprise annuities in equity assets is around 10%, well below the international average of 30%-50%[4] - The proportion of index-based investments, such as ETFs, in institutional portfolios is less than 15%, compared to 60% in the United States[4] Group 2: Policy Recommendations and Market Potential - The implementation of long-term assessment cycles and relaxation of investment restrictions could significantly increase the equity investment ratio of long-term funds in China[3] - The "Implementation Plan for Promoting Long-term Funds to Enter the Market" aims for public funds to increase their A-share holdings by at least 10% annually over the next three years, potentially adding over 100 billion yuan in long-term funds each year[15] - The report suggests enhancing product innovation and asset allocation systems to attract long-term funds, alongside tax incentives to encourage market entry[8] Group 3: Comparative Analysis with Developed Markets - In the U.S., long-term funds, particularly pension funds, have an equity investment ratio exceeding 80%, with a significant portion allocated to diversified assets like stocks and mutual funds[8] - European pension funds are increasing their equity allocations, focusing on long-term returns through diversified investments and strict regulations[8] - Japan's pension system, led by the Government Pension Investment Fund (GPIF), has become the largest public pension fund globally, emphasizing diversified and international investments[8]

Group 1 - The report indicates a potential small net inflow of northbound funds, estimating a net inflow of 3.3 billion yuan during the week of August 4-8, 2025, compared to a net outflow of 12.1 billion yuan the previous week [11] - The report highlights that the top active stocks in the northbound trading include Ningde Times with a total transaction amount of 9.4 billion yuan, accounting for 19% of the stock's trading volume for the week [11] - The report notes that flexible foreign capital estimated a net inflow of 1.8 billion yuan during the recent week, contrasting with a net outflow of 8.5 billion yuan the week before [11] Group 2 - The report states that a total of 14.8 billion Hong Kong dollars flowed into the Hong Kong stock market during the week of July 30 to August 5, 2025, with stable foreign capital outflow of 29.8 billion Hong Kong dollars and flexible foreign capital outflow of 3.2 billion Hong Kong dollars [18] - It mentions that the main inflows from southbound funds were observed in software and services, as well as retail sectors [23] - The report indicates that stable foreign capital primarily flowed into pharmaceuticals, biotechnology, and life sciences, while flexible foreign capital saw significant inflows into banks and materials [23] Group 3 - The report highlights that foreign capital saw a net outflow from the Japanese stock market amounting to 198 billion yen during the week ending July 28, 2025, compared to a net inflow of 688.9 billion yen the previous week [30] - It also notes that in July, overseas institutional investors withdrew 2.1 billion dollars from the Indian stock market, contrasting with a net inflow of 1.7 billion dollars the previous month [30] - The report states that since 2023, the cumulative net inflow into the Japanese stock market has reached 7.7 trillion yen [30] Group 4 - The report indicates that in the US stock market, global mutual funds saw a net inflow of 6.8 billion dollars in June 2025, reversing a net outflow of 13.3 billion dollars the previous month [37] - It mentions that in Europe, global mutual funds recorded net inflows into the equity markets of the UK, Germany, and France, amounting to 0.2 billion dollars, 19.8 billion dollars, and 17.8 billion dollars respectively [37] - The report highlights that since 2020, the cumulative net inflow into the US equity market has reached 730.9 billion dollars [37]

Investment Rating - The report assigns an "Overweight" rating for the construction industry [2] Core Insights - The construction industry is currently facing pressure in infrastructure and real estate demand, as indicated by various high-frequency indicators such as cement, rebar, and asphalt [4][5] - The report highlights that the current indicators show low levels of cement production, capacity utilization, and prices, suggesting that infrastructure demand remains under pressure [5][20] - Leading construction companies are seen as having attractive price-to-book (PB) valuations, with potential for valuation improvement driven by state-owned enterprise reforms and market management policies [5] Summary by Sections 1. Construction Industry High-Frequency Indicators - High-frequency indicators in the construction industry, including cement, rebar, asphalt, and machinery, indicate the strength of physical workload [16] - Current indicators show that infrastructure demand is under pressure, with many indicators in low prosperity ranges [16][20] - Recent data indicates that cement inventory is at 65.2%, production is at 1.55 billion tons, and average price is at 271.3 yuan, reflecting weak demand [16][18] 2. Cement Indicators - Cement production and capacity utilization are key indicators of construction prosperity, with current annual production at approximately 68.7% of the peak in 2014 [28] - Cement prices have shown a positive correlation with infrastructure investment over the past three years, reflecting demand-side prosperity [34] - A decline in cement production typically indicates pressure on infrastructure investment, with capacity utilization below 60% signaling low prosperity [41][47] 3. Rebar and Steel Indicators - Rebar prices and production are closely linked to infrastructure investment, with current weekly production at 2.11 million tons indicating low prosperity [7][18] - High line prices are positively correlated with real estate investment growth, maintaining a synchronous relationship [7][19] 4. Machinery Indicators - The sales and operating hours of construction machinery, particularly excavators, reflect the intensity of construction activities [4][7] - Current operating hours for medium-sized excavators are at 64.2 hours, indicating low prosperity [18] 5. Asphalt Indicators - Asphalt production and operating rates are indicators of road transport investment trends, with current operating rates at 33.1%, reflecting low prosperity [8][18] 6. Glass Indicators - Glass inventory and prices are closely related to real estate completion rates, with current inventory at 5.178 million heavy boxes indicating high inventory levels [19][20] - The price of glass is currently at 1268.9 yuan, below the improvement threshold, indicating no recovery in the real estate sector [19][20] 7. Profit Forecasts - The report includes profit forecasts for leading companies in the construction sector, reflecting the overall industry outlook [11]

Investment Rating - The investment rating for the company is "Buy" [1][6]. Core Insights - The report highlights that the competition in the food delivery sector has increased rider costs, but the company's efficiency optimization has led to an improvement in profit margins [2]. - The company is expected to return $3 billion to shareholders through dividends and buybacks from 2025 to 2026, with projected EPS growth of 10%/15%/11% for 2025-2027 [10]. - The target price is set at 444 HKD, based on a PE ratio of 22 times for 2025, which is above the industry average [10]. Financial Summary - Total revenue is projected to grow from 10,978 million HKD in 2023 to 13,347 million HKD in 2027, reflecting a CAGR of approximately 6.3% [4]. - Net profit is expected to increase from 827 million HKD in 2023 to 1,096 million HKD in 2027, with a significant growth of 87.1% in 2023 [4]. - The company’s PE ratio is forecasted to decrease from 20.58 in 2024 to 13.72 in 2027, indicating improving valuation metrics [4]. Operational Performance - In Q2 2025, the company reported revenue of $2.787 billion, a year-on-year increase of 4%, with a core operating profit margin of 10.9% [10]. - Same-store sales increased by 1% year-on-year in Q2 2025, with KFC and Pizza Hut showing positive growth [10]. - The total number of stores reached 16,978, with a net increase of 336 stores in Q2 2025, indicating a 10% year-on-year growth [10]. Profitability Metrics - The restaurant profit margin improved to 16.1% in Q2 2025, up 0.6 percentage points year-on-year, driven by favorable raw material prices and operational efficiencies [10]. - KFC's restaurant profit margin was 16.9%, while Pizza Hut's was 13.3% in Q2 2025, reflecting operational improvements [10].

Domestic Macro Policy - The National Development and Reform Commission (NDRC) plans to accelerate the establishment of new policy financial tools to encourage private enterprises to participate more in major national projects [15][33] - The State Council issued an opinion on gradually implementing free preschool education, which will exempt public kindergarten fees for the last year of preschool starting from the autumn semester of 2025, benefiting approximately 12 million children [15][28] - The Central Committee of the Communist Party and the State Council issued regulations to reduce formalism and lighten the burden on grassroots levels [15][28] Domestic Industry Policy - The People's Bank of China emphasized the continuation of a moderately loose monetary policy and the implementation of key monetary policy measures [16][31] - The Ministry of Industry and Information Technology (MIIT) issued a digital transformation implementation plan for the machinery industry, aiming for significant advancements by 2027 [16][31] - The Ministry of Finance and the State Taxation Administration announced the resumption of VAT on interest income from newly issued government bonds starting August 8 [16][31] Local Policy - Hangzhou's municipal committee emphasized the need to cultivate new growth points in service consumption [5][39] - Shanghai and Jiangsu provinces issued a decision to promote collaborative technological innovation in the Yangtze River Delta [5][39] - Guangdong province introduced loan interest subsidy implementation rules for manufacturing and high-tech enterprises, with a maximum annual subsidy of 20 million yuan per enterprise [5][41] Overseas Dynamics - The U.S. President signed an executive order imposing tariffs ranging from 10% to 41% on countries that have not reached agreements with the U.S., effective from August 7, 2025 [6][44] - OPEC+ agreed to significantly increase oil production in September [6][44] - The Bank of England lowered its key interest rate by 25 basis points to 4% [6][44]

Domestic Macro - The National Development and Reform Commission (NDRC) is accelerating the establishment of new policy financial tools to encourage private enterprises to participate in major national projects [6][7] - The State Council issued an opinion on gradually implementing free preschool education, which will exempt public kindergarten fees for the last year of preschool starting from the autumn semester of 2025, benefiting approximately 12 million children [6][7] - The Central Committee of the Communist Party and the State Council issued regulations to reduce formalism and lighten the burden on grassroots levels [6][7] Industry Policy - The People's Bank of China (PBOC) emphasized the continuation of a moderately loose monetary policy for the second half of the year [7][8] - The Ministry of Industry and Information Technology (MIIT) issued a digital transformation implementation plan for the machinery industry [7][8] - Starting from August 8, the interest income from newly issued government bonds and financial bonds will be subject to VAT again [7][8] Local Policy - The Hangzhou Municipal Committee emphasized cultivating new growth points in service consumption [8][9] - The Shanghai Municipal Government issued measures to support enterprises in enhancing basic research, with subsidies up to 10 million yuan [8][9] - The Guangdong Provincial Financial Management Bureau issued guidelines for loan interest subsidies for manufacturing and high-tech enterprises [8][9] Overseas Dynamics - On August 2, U.S. President Trump signed an executive order imposing tariffs ranging from 10% to 41% on countries that have not reached agreements with the U.S., effective from August 7, 2025 [9][25] - OPEC+ agreed to significantly increase oil production in September [9][25] - The Bank of England lowered its key interest rate by 25 basis points to 4% on August 7 [9][25]

Investment Rating - The report rates the industry as "Buy" [4] Core Insights - Founded in 1932, LEGO has become one of the largest toy manufacturers globally, effectively navigating economic cycles due to its resonance across various aspects such as market, users, and operations, which serves as a reference for the development of Blokus [2][3] - In 2024, LEGO is projected to achieve revenue of 74.3 billion Danish Kroner, approximately 83.8 billion RMB, representing a year-on-year growth of 13%, with a net profit of 13.8 billion Danish Kroner, about 15.6 billion RMB, reflecting a 5% increase [6][4] Summary by Sections LEGO: A Global Toy Company - LEGO, established in Denmark in 1932, initially produced wooden toys before transitioning to plastic bricks, becoming a leading toy manufacturer [6][5] - In 2024, LEGO's revenue is expected to reach 74.3 billion Danish Kroner (approximately 83.8 billion RMB), with a year-on-year growth of 12.76% [6][5] Successes and Failures of LEGO - Successes include the choice of the brick segment, which has a long product lifecycle, and the expansion of user demographics, including adult and female consumers [4][5] - Failures include the expiration of patents leading to market share loss and challenges from aggressive expansion strategies [4][5] Exploring Blokus's Development Path - The report draws parallels between LEGO's historical development and the current trajectory of Blokus, which is positioned as a leading player in China's building block toy market, with projected revenue of 2.241 billion RMB in 2024, a year-on-year increase of 156% [4][5] - Blokus's growth is supported by a rich IP portfolio and deep operational strategies, including content-driven marketing and channel expansion [4][5]

Group 1 - The report highlights that domestic equity assets performed well in July 2025, with the risk parity strategy achieving a year-to-date return of 2.65% [2][5][20] - The report provides a summary of various asset allocation strategies, indicating that the domestic asset BL strategy 1 and 2 yielded returns of 2.40% and 2.34% respectively, while the risk parity strategy and macro factor-based strategy returned 2.65% and 2.59% respectively [21][41][42] - The report notes that the domestic equity market saw significant gains, with the CSI 1000 index rising by 4.8% and the Hang Seng Index increasing by 2.78% in July [8][9][10] Group 2 - The report discusses the correlation between different asset classes, indicating that the correlation between the CSI 300 and the total wealth index of government bonds was -38.08%, suggesting a potential for diversification [15][16] - The report outlines the performance of various asset allocation models, with the domestic risk parity strategy showing a maximum drawdown of 0.76% and an annualized volatility of 1.46% [41][42] - The macroeconomic outlook suggests downward risks for growth factors, while inflation expectations may stabilize due to recent policy measures [45][47]