Quantitative Models and Construction Methods Model Name: mHC-MLP - **Model Construction Idea**: The mHC-MLP model introduces manifold-constrained hyper-connections (mHC) into the traditional MLP framework to address issues such as low signal-to-noise ratio, non-stationarity, and extreme tail behavior in financial data. It achieves this by incorporating multi-stream residual channels, gated fan-in/fan-out mappings, and doubly stochastic manifold projections (via Sinkhorn-Knopp) to enhance numerical stability and extrapolation resistance[1][16][22]. - **Model Construction Process**: 1. **Multi-Stream Residual Channels**: The model expands the single residual channel in traditional ResNet to multiple parallel sub-streams, allowing independent feature representations and dynamic routing between streams[19][20]. 2. **Manifold Constraints**: - Residual mixing matrices are constrained to the Birkhoff polytope (doubly stochastic matrices), ensuring non-negativity, row sums of 1, and column sums of 1. This is achieved using the Sinkhorn-Knopp algorithm during training[22][23][54]. - Fan-in and fan-out mappings are constrained to non-negative values using sigmoid functions, ensuring that output features remain within the convex hull of input features[24]. 3. **Dynamic Routing Mechanism**: The model uses a combination of linear mixing (via residual matrices) and non-linear transformations (via MLP blocks) to balance feature interaction and noise suppression[49][50][51]. 4. **Deep Stacking**: The mHC-MLP extends the network depth to six layers, leveraging the numerical stability provided by manifold constraints to capture higher-order interactions[56][57]. 5. **Initialization and Regularization**: Parameters are initialized with minimal values (e.g., alpha = 0.01) to ensure stable gradient flow during early training stages. Regularization is achieved through manifold constraints rather than traditional dropout or L2 regularization[25][55]. - **Model Evaluation**: The mHC-MLP model demonstrates improved numerical stability, reduced overfitting, and enhanced robustness against noise. However, it may underperform in short-term, high-volatility scenarios due to its conservative nature[2][75][86]. --- Model Backtesting Results mHC-MLP Model - **Cumulative Return**: 49% (compared to 56% for the unconstrained MLP model)[75] - **t-Statistic**: Not explicitly mentioned for mHC-MLP - **IC_IR**: Not explicitly mentioned for mHC-MLP - **Turnover**: Lower than the unconstrained MLP model, indicating better stability[2][75] - **Maximum Drawdown**: Lower than the unconstrained MLP model, reflecting reduced risk exposure[2][75] --- Quantitative Factors and Construction Methods Factor Name: Barra MLP Factor - **Factor Construction Idea**: The Barra MLP factor leverages neural networks to capture non-linear interactions and complex relationships between Barra style factors and residual stock returns, overcoming the limitations of traditional linear factor models[30][31]. - **Factor Construction Process**: 1. **Baseline Risk Model**: A long-term risk model is constructed using the Barra CNE6 framework, incorporating one country factor, 31 industry factors, and 15 style factors (e.g., size, beta, momentum, value)[36][37][38]. 2. **Residual Return Extraction**: Stock returns are decomposed into common factor contributions and residual returns via cross-sectional regression. The residual returns serve as the prediction target for the MLP model[40]. 3. **Rolling Training**: The MLP model is trained using rolling windows of 24, 36, and 72 months to balance bias and variance. Features include the 15 style factors, and the target is the next-period residual return[41]. 4. **Multi-Period Signal Synthesis**: Predictions from the three training windows are standardized (Z-score) and combined using equal weighting or IC-based weighting to generate a composite factor[42][43]. 5. **Orthogonalization**: The composite factor is regressed against the 15 style factors to remove linear correlations, ensuring it provides incremental information[44]. 6. **Pure Factor Return Calculation**: The orthogonalized factor is incorporated into an enhanced Barra risk model, and its pure factor return is estimated via cross-sectional regression[45]. - **Factor Evaluation**: The Barra MLP factor effectively captures non-linear alpha signals and demonstrates significant cumulative returns and IC_IR values, validating its utility in quantitative strategies[46]. --- Factor Backtesting Results Barra MLP Factor - **Cumulative Return**: Over 15%[46] - **t-Statistic**: 2.8[46] - **IC_IR**: 0.45[46] - **Turnover**: Not explicitly mentioned - **Maximum Drawdown**: Not explicitly mentioned --- Composite Model: mHC-Enhanced Barra MLP Factor - **Model Construction Idea**: The mHC-enhanced Barra MLP factor integrates the mHC architecture into the Barra MLP framework to improve robustness and stability while retaining the ability to capture non-linear interactions[48]. - **Model Construction Process**: The MLP core in the Barra MLP factor is replaced with the mHC-MLP architecture, maintaining the same input features, target variables, and training framework. This modification introduces manifold constraints and dynamic routing to enhance numerical stability and reduce overfitting[48][49][50]. - **Model Evaluation**: While the mHC-enhanced factor demonstrates superior stability and robustness, it may lag in short-term, high-volatility markets due to its conservative design[75][86]. --- Composite Model Backtesting Results mHC-Enhanced Barra MLP Factor - **Cumulative Return**: Not explicitly mentioned - **t-Statistic**: Not explicitly mentioned - **IC_IR**: Not explicitly mentioned - **Turnover**: Lower than the original Barra MLP factor[2][75] - **Maximum Drawdown**: Lower than the original Barra MLP factor[2][75]

Core Viewpoint - The rise of quantitative macro strategies in the private equity industry has become a focal point, with these strategies gaining significant traction compared to traditional subjective macro strategies [2][3]. Group 1: Growth of Quantitative Macro Strategies - Since 2020, hedge funds employing quantitative macro strategies have seen an average annual growth rate of over 15%, significantly outpacing traditional subjective macro strategies [2]. - As of November, the average return for 195 macro strategy products was 25.50%, with subjective macro strategies yielding 26.42% and quantitative macro strategies at 21.42% [2]. - The Sharpe ratio for quantitative macro strategies reached 2.11, compared to 1.57 for subjective macro strategies, indicating better risk-adjusted performance [2]. Group 2: Reasons for Popularity - The global macro environment has become increasingly complex, with challenges such as the COVID-19 pandemic, high inflation, and geopolitical conflicts, making traditional decision-making methods less effective [3]. - Quantitative macro strategies have successfully avoided severe losses by utilizing real-time market liquidity monitoring and stress testing models, prompting a reevaluation of investment methodologies [3]. Group 3: Characteristics of Quantitative Macro Strategies - Quantitative macro strategies utilize systematic, data-driven models to analyze relationships between macroeconomic variables and financial asset prices, enabling automated or semi-automated asset allocation and trading [7]. - Key features include data-driven decision-making, systematic investment processes, multi-dimensional analysis, and a strong focus on risk management [8]. Group 4: Types of Quantitative Macro Strategies - Strategies can be categorized into five types: 1. Fundamental Quantitative Strategies: Based on economic indicators like GDP and inflation [10]. 2. Systematic Trend Following: Identifying momentum factors through price trends [11]. 3. Cross-Asset Relative Value: Arbitraging pricing discrepancies across different markets [12]. 4. Machine Learning Macro Forecasting: Using advanced algorithms to predict economic cycles [13]. 5. Macro Factor Investing: Capturing risk premiums based on growth, inflation, and liquidity factors [10]. Group 5: Differences Between Quantitative and Subjective Macro Strategies - Subjective macro strategies rely on the personal insights and intuition of fund managers, while quantitative macro strategies are based on data, models, and statistical patterns [14]. - Quantitative macro strategies offer greater scalability and consistency in performance, while subjective strategies are more prone to volatility and depend heavily on individual managers [15][16]. Group 6: Future Outlook - The evolution of quantitative macro strategies represents a necessary advancement in macro investment methodologies in the data era, emphasizing the importance of integrating human judgment with machine capabilities [17][18].

Quantitative Models and Construction Methods 1. Model Name: GAN_GRU - **Model Construction Idea**: The GAN_GRU model combines Generative Adversarial Networks (GAN) for feature generation and Gated Recurrent Unit (GRU) for time-series feature encoding to construct a stock selection factor[4][13][14] - **Model Construction Process**: 1. **GAN Component**: - The generator (G) learns the real data distribution and generates realistic samples from random noise \( z \) (Gaussian or uniform distribution). The generator's loss function is: $$ L_{G} = -\mathbb{E}_{z\sim P_{z}(z)}[\log(D(G(z)))] $$ where \( D(G(z)) \) represents the discriminator's probability of classifying generated data as real[24][25][26] - The discriminator (D) distinguishes real data from generated data. Its 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 \( D(x) \) is the probability of real data being classified as real, and \( D(G(z)) \) is the probability of generated data being classified as real[27][29][30] - GAN training alternates between optimizing \( G \) and \( D \) until convergence[30] 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 predict returns. The final output \( pRet \) is used as the stock selection factor[22] 3. **Feature Input and Processing**: - Input features include 18 price-volume characteristics (e.g., closing price, turnover, etc.) sampled over the past 400 days, with a shape of \( 40 \times 18 \) (40 days of features)[18][19][37] - Features undergo outlier removal, standardization, and cross-sectional normalization[18] 4. **Training Details**: - Training-validation split: 80%-20% - Semi-annual rolling training (June 30 and December 31 each year) - Hyperparameters: batch size equals the number of stocks, Adam optimizer, learning rate \( 1e-4 \), IC loss function, early stopping (10 rounds), max training rounds (50)[18] 5. **Stock Selection**: - Stocks are filtered to exclude ST stocks and those listed for less than six months[18] - **Model Evaluation**: The GAN_GRU model effectively captures price-volume time-series features and demonstrates strong predictive power for stock returns[4][13][22] --- Model Backtesting Results 1. GAN_GRU Model - **IC Mean**: 0.1119*** (2019-2025)[4][41] - **ICIR (non-annualized)**: 0.89[42] - **Turnover Rate**: 0.83X[42] - **Recent IC**: 0.0331*** (December 2025)[4][41] - **1-Year IC Mean**: 0.0669***[4][41] - **Annualized Return**: 37.40%[42] - **Annualized Volatility**: 23.39%[42] - **IR**: 1.60[42] - **Maximum Drawdown**: 27.29%[42] - **Annualized Excess Return**: 22.42%[4][42] --- Quantitative Factors and Construction Methods 1. Factor Name: GAN_GRU Factor - **Factor Construction Idea**: The GAN_GRU factor is derived from the GAN_GRU model, leveraging GAN for price-volume feature generation and GRU for time-series encoding[4][13][14] - **Factor Construction Process**: - The GAN generator processes raw price-volume time-series features (\( Input\_Shape = 40 \times 18 \)) and outputs transformed features with the same shape (\( Input\_Shape = 40 \times 18 \))[37] - The GRU component encodes these features into a predictive factor for stock selection[22] - The factor undergoes industry and market capitalization neutralization and standardization[22] - **Factor Evaluation**: The GAN_GRU factor demonstrates robust performance across various industries and time periods, with significant IC values and excess returns[4][41] --- Factor Backtesting Results 1. GAN_GRU Factor - **IC Mean**: 0.1119*** (2019-2025)[4][41] - **ICIR (non-annualized)**: 0.89[42] - **Turnover Rate**: 0.83X[42] - **Recent IC**: 0.0331*** (December 2025)[4][41] - **1-Year IC Mean**: 0.0669***[4][41] - **Annualized Return**: 37.40%[42] - **Annualized Volatility**: 23.39%[42] - **IR**: 1.60[42] - **Maximum Drawdown**: 27.29%[42] - **Annualized Excess Return**: 22.42%[4][42] 2. Industry-Specific Performance - **Top 5 Industries by Recent IC (October 2025)**: - Social Services: 0.4243*** - Coal: 0.2643*** - Environmental Protection: 0.2262*** - Retail: 0.1888*** - Steel: 0.1812***[4][41][42] - **Top 5 Industries by 1-Year IC Mean**: - Social Services: 0.1303*** - Steel: 0.1154*** - Non-Bank Financials: 0.1157*** - Retail: 0.1067*** - Building Materials: 0.1017***[4][41][42] 3. Industry-Specific Excess Returns - **Top 5 Industries by December 2025 Excess Returns**: - Banking: 4.30% - Real Estate: 3.51% - Environmental Protection: 2.18% - Retail: 1.76% - Machinery: 1.71%[2][45] - **Top 5 Industries by 1-Year Average Excess Returns**: - Banking: 2.12% - Real Estate: 1.93% - Environmental Protection: 1.50% - Retail: 1.46% - Machinery: 1.23%[2][46]

Core Viewpoint - The article discusses the recent changes in China's tariff schedule for 2026, particularly the introduction of new tariff categories for robots, including intelligent bionic robots and cleaning robots, aimed at supporting technological development and the circular economy [3][5]. Group 1: Tariff Adjustments - The 2026 tariff schedule includes new categories for intelligent bionic robots with a most-favored-nation (MFN) rate of 0% and a general rate of 30% [3][5]. - Two new categories for cleaning robots have been added, with MFN rates of 8% and 0%, and general rates of 130% and 30% respectively [3][5]. Group 2: Definition and Features of Intelligent Bionic Robots - Intelligent bionic robots are defined as autonomous or semi-autonomous robots that closely mimic the appearance, structure, or functions of humans or animals [4]. - These robots are equipped with various sensors and technologies, enabling them to perform tasks such as environmental perception, autonomous path planning, and human interaction through natural language processing [4]. Group 3: Industry Impact and Future Outlook - The addition of these tariff categories is expected to help industries and companies better understand trade data and assess overseas market trends [5]. - The tariff adjustments also include a temporary import tax rate below the MFN rate for 935 items, aimed at promoting high-level technological self-reliance and modern industrial system construction [5].

Core Insights - The Chinese government has introduced new tariff categories for intelligent bionic robots and cleaning robots as part of the 2026 tariff adjustment plan, with the most favored nation (MFN) rates set at 0% for intelligent bionic robots and varying rates for cleaning robots [1][3] Group 1: New Tariff Categories - The 2026 tariff adjustment plan includes the addition of intelligent bionic robot tariff categories with an MFN rate of 0% and a general rate of 30%, along with two cleaning robot categories with MFN rates of 8% and 0%, and general rates of 130% and 30% respectively [1][3] - Intelligent bionic robots are defined as autonomous or semi-autonomous robots that closely mimic the appearance, structure, or functions of humans or animals, equipped with advanced technologies for environmental perception and interaction [2] Group 2: Rationale for Changes - The adjustments aim to support technological advancement, circular economy, and the development of the forest economy, with the total number of tariff categories now reaching 8,972 [3] - The introduction of these new tariff categories is expected to help industries and companies accurately grasp trade data and assess overseas market trends [3]

Core Insights - The research team at the Yunnan Astronomical Observatory has discovered a significant number of magnetically active young stars, which are crucial for studying the origins and evolution of stellar magnetic fields and planetary formation mechanisms [2][4]. Group 1: Research Findings - The study utilized spectral survey big data and machine learning techniques to identify over 500 magnetically active young stars, including various types such as T Tauri stars and magnetically active super-saturated stars [4][5]. - The research highlights the differences in magnetic field generation processes between young stars and the Sun, emphasizing the need for more observational samples and near-ultraviolet data to understand these phenomena [4]. Group 2: Methodology - The team employed a simulation of future survey space telescope spectra by lowering spectral resolution, using data from the Guo Shoujing Telescope, and applied a variational autoencoder algorithm for systematic analysis [4]. - Key indicators of stellar magnetic activity, such as hydrogen alpha emission intensity and lithium absorption strength, were analyzed to assess the activity levels and ages of the identified stars [4][5]. Group 3: Future Research Directions - The detailed catalog of newly discovered stars will facilitate further observations, which are expected to provide critical insights into the characteristics and evolutionary patterns of fully convective stellar magnetic fields [5]. - This research aims to deepen the understanding of the role of magnetic fields in stellar early evolution and planetary formation processes [5].

Report Information - Report Title: ETF Strategy Index Tracking Weekly Report, Public Fund Weekly Report [1] - Report Date: December 29, 2025 [1] Investment Ratings - Not provided in the report Core Views - The report presents several ETF strategy indices constructed by Huabao Securities, aiming to obtain excess returns relative to the market through different quantitative models and strategies. It also tracks the performance and positions of these indices on a weekly basis [4][5][12] Summary by Directory 1. ETF Strategy Index Tracking - **Overall Performance**: The table shows the performance of various ETF strategy indices last week, including the index name, last week's index return, comparison benchmark, last week's benchmark return, and excess return [13] | Index Name | Last Week's Index Return | Comparison Benchmark | Last Week's Benchmark Return | Excess Return | | --- | --- | --- | --- | --- | | Huabao Research Large - Small Cap Rotation ETF Strategy Index | 1.96% | CSI 800 | 2.50% | -0.54% | | Huabao Research Quantitative Fire - Wheel ETF Strategy Index | 4.18% | CSI 800 | 2.50% | 1.67% | | Huabao Research Quantitative Balance Technique ETF Strategy Index | 1.04% | SSE 300 | 1.95% | -0.91% | | Huabao Research SmartBeta Enhanced ETF Strategy Index | 3.94% | CSI 800 | 2.50% | 1.43% | | Huabao Research Hot - Spot Tracking ETF Strategy Index | 2.03% | CSI All - Share | 2.78% | -0.75% | | Huabao Research Bond ETF Duration Strategy Index | 0.03% | ChinaBond Aggregate Index | 0.04% | -0.01% | 1.1 Huabao Research Large - Small Cap Rotation ETF Strategy Index - **Strategy**: Uses multi - dimensional technical indicator factors and a machine - learning model to predict the return difference between the Shenwan Large - Cap Index and the Shenwan Small - Cap Index. The model outputs signals weekly to predict the strength of the indices in the next week and determines positions accordingly [4][14] - **Performance**: As of December 26, 2025, the excess return since 2024 is 19.71%, the excess return in the past month is - 0.63%, and the excess return in the past week is - 0.54% [4][14] - **Position**: Holds 100% of the SSE 300ETF (fund code: 510300.SH) [18] 1.2 Huabao Research SmartBeta Enhanced ETF Strategy Index - **Strategy**: Uses volume - price indicators to time self - built barra factors, and then maps the timing signals to ETFs based on the exposure of ETFs to 9 barra factors. The selected ETFs cover mainstream broad - based index ETFs and some style and strategy ETFs [4] - **Performance**: As of December 26, 2025, the excess return since 2024 is 22.50%, the excess return in the past month is - 1.98%, and the excess return in the past week is 1.43% [4] - **Position**: Holds 25.05% of the Huaxia Science and Technology Innovation Composite Index ETF (fund code: 589000.SH), 25.04% of the Fuguo Science and Technology Innovation Composite Index ETF (fund code: 589600.SH), 25.02% of the Southern GEM 200ETF (fund code: 159270.SZ), and 24.89% of the Wanjia GEM Composite ETF (fund code: 159541.SZ) [22] 1.3 Huabao Research Quantitative Fire - Wheel ETF Strategy Index - **Strategy**: Starts from a multi - factor perspective, including the grasp of medium - and long - term fundamental dimensions, the tracking of short - term market trends, and the analysis of the behavior of various market participants. It uses valuation and crowding signals to prompt industry risks and multi - dimensionally digs out potential sectors [22] - **Performance**: As of December 26, 2025, the excess return since 2024 is 38.65%, the excess return in the past month is 2.06%, and the excess return in the past week is 1.67% [22][25] - **Position**: Holds 20.97% of the Securities and Insurance ETF (fund code: 512070.SH), 20.60% of the Chemical ETF (fund code: 159870.SZ), 19.54% of the Steel ETF (fund code: 515210.SH), 19.49% of the Oil and Gas ETF (fund code: 159697.SZ), and 19.39% of the New Energy ETF (fund code: 516160.SH) [26] 1.4 Huabao Research Quantitative Balance Technique ETF Strategy Index - **Strategy**: Adopts a multi - factor system including economic fundamentals, liquidity, technical aspects, and investor behavior. It constructs a quantitative timing system to judge the trend of the equity market and establishes a prediction model for the market's large - and small - cap styles to adjust the equity market position distribution [26] - **Performance**: As of December 26, 2025, the excess return since 2024 is - 11.11%, the excess return in the past month is - 1.05%, and the excess return in the past week is - 0.91% [26][27] - **Position**: Holds 9.23% of the Ten - Year Treasury Bond ETF (fund code: 511260.SH), 6.11% of the 500ETF Enhanced (fund code: 159610.SZ), 5.98% of the CSI 1000ETF (fund code: 512100.SH), 32.84% of the 300 Enhanced ETF (fund code: 561300.SH), 22.94% of the Government Financial Bond ETF (fund code: 511520.SH), and 22.89% of the Short - Term Financing ETF (fund code: 511360.SH) [29] 1.5 Huabao Research Hot - Spot Tracking ETF Strategy Index - **Strategy**: Tracks and mines hot - spot index target products in a timely manner based on strategies such as market sentiment analysis, industry major event tracking, investor sentiment and professional views, policy and regulatory changes, and historical deduction. It constructs an ETF portfolio that can capture market hot spots in a timely manner [29] - **Performance**: As of December 26, 2025, the excess return in the past month is - 1.22%, and the excess return in the past week is - 0.75% [29][32] - **Position**: Holds 38.33% of the Non - Ferrous Metals 50ETF (fund code: 159652.SZ), 23.48% of the Boshi Hong Kong Stock Dividend ETF (fund code: 513690.SH), 19.50% of the Hong Kong Stock Connect Pharmaceutical ETF (fund code: 513200.SH), and 18.69% of the Short - Term Financing ETF (fund code: 511360.SH) [33] 1.6 Huabao Research Bond ETF Duration Strategy Index - **Strategy**: Uses bond market liquidity and volume - price indicators to screen effective timing factors and predicts bond yields through a machine - learning method. When the expected yield is lower than a certain threshold, it reduces the long - duration positions in the bond investment portfolio [6][33] - **Performance**: As of December 26, 2025, the excess return in the past month is 0.18%, and the excess return in the past week is - 0.01% [6][33] - **Position**: Holds 50.01% of the Ten - Year Treasury Bond ETF (fund code: 511260.SH), 25.00% of the Government Financial Bond ETF (fund code: 511520.SH), and 25.00% of the 5 - to 10 - Year Treasury Bond ETF (fund code: 511020.SH) [36]

Summary of Key Points from the Conference Call Industry Overview - The report focuses on global equity strategy, analyzing various regions and sectors based on quantitative frameworks, earnings momentum, and macroeconomic indicators [1][2][3]. Regional Insights - **UK**: Ranked at the top of the regional aggregate scorecard due to cheap valuations and favorable macro conditions, with a score of 1.45 on the MCI scorecard [4][9]. - **Japan**: Ranked second but remains underweight; it has the highest operational leverage and is negatively impacted by tightening monetary conditions [4][9]. - **GEM (Global Emerging Markets)**: Ranked third, supported by stronger economic and earnings momentum, with a tactical overweight focus on Brazil and China [4][9]. - **Europe**: Fourth place, showing deterioration in earnings momentum but remains the cheapest region on the valuation scorecard [4][9]. - **US**: At the bottom of the aggregate scorecard due to extreme valuations, although it ranks top on earnings momentum and risk appetite when excluding valuation [4][9]. Sector Analysis - **Cyclical Sectors**: Overweight on financials, particularly in Europe and Japan, and marginally overweight on technology with a selective approach [5]. - **Defensive Sectors**: Overweight on US healthcare equipment, household products, and flavoring companies due to their cheap valuations [5]. - **Luxury Goods**: Increased allocation to luxury sectors [5]. Crowding and Market Sentiment - The US is identified as the most crowded region historically, while Europe is the least crowded [6][16]. - The most crowded sectors include Autos, Real Estate, and Semiconductors, while Food Producers, Paper, and Beverages are the least crowded [6][16]. Earnings Momentum and Trends - **Earnings vs. Trend**: Software earnings are 10% above trend, Semiconductors are 67% above trend, while Healthcare Equipment is 16% below trend [14]. - **Machine Learning Insights**: Commercial services and software sectors show the most upside potential, while pharmaceuticals and tech hardware are expected to face downside risks [15]. Valuation Insights - **Valuation Scorecard**: Beverages and Household Products are the cheapest sectors, while Semiconductors and Capital Goods are the most expensive [12][48]. - **Overall Sector Rankings**: Food Producers rank at the top, followed by Healthcare Equipment and Beverages [12][45]. Macro and Economic Indicators - The macro scorecard indicates that the near-term scenario involves falling markets, USD, ISM, and Global PMIs, with flat inflation expectations [41][50]. - **Economic Momentum**: Regions are ranked based on Composite PMI new orders and macro surprises, with Europe ex UK showing the highest improvement [30][31]. Recommendations - Analysts recommend a cautious approach towards sectors like Autos and Construction Materials, which are seen as consensus shorts, while Commercial Services and Food Retail are viewed as consensus longs [17]. Conclusion - The report emphasizes a strategic focus on regions and sectors that are undervalued or show strong earnings momentum, while being cautious of crowded sectors and potential macroeconomic headwinds [4][5][6][9].

Core Insights - The event "Praise for China's Economy - Entrepreneur Night 2025" was officially launched on December 17, highlighting the importance of innovative drug development in the life sciences sector as a key battleground against diseases and for health pursuits [1][3]. Group 1: WuXi AppTec - WuXi AppTec, led by Chairman Li Ge, continues to enhance its integrated end-to-end drug development service platform in 2025, increasing R&D investment [1][3]. - The company integrates multidisciplinary technologies such as chemistry, biology, and pharmacology to provide efficient and high-quality drug development solutions globally [1][3]. - WuXi AppTec is actively exploring the application of artificial intelligence and machine learning in drug discovery, significantly shortening drug screening cycles and improving R&D efficiency [1][3]. Group 2: Jiangsu Hengrui Medicine - Jiangsu Hengrui Medicine, under the leadership of Chairman Sun Piaoyang, remains a benchmark for innovative drug development in China, focusing on oncology, anesthesia, and contrast agents in 2025 [4]. - The company is strengthening its independent innovation capabilities and increasing investments in new drug target discovery and drug design [4]. - Several innovative drug products from Jiangsu Hengrui have entered critical clinical trial phases, with some demonstrating significant efficacy and good safety profiles [4]. Group 3: Kangfang Biopharma - Kangfang Biopharma, led by Chairman Xia Yu, has made significant breakthroughs in the development of bispecific antibody drugs in 2025 [5]. - The company utilizes advanced antibody engineering technology to develop multiple internationally competitive bispecific antibody drugs [5]. - Kangfang Biopharma is actively collaborating with domestic and international research institutions to accelerate the clinical validation process of its drugs [5]. Group 4: Baillie Gifford - Baillie Gifford, under the leadership of Chairman Zhu Yi, showcases strong innovative vitality in drug development in 2025, focusing on oncology treatment [5]. - The company advances multiple innovative drug projects through a combination of independent research and collaborative innovation [5]. - Baillie Gifford has made important progress in drug delivery technology, developing new drug delivery systems that enhance drug targeting and bioavailability [5]. Industry Overview - The Chinese biopharmaceutical industry is facing historic opportunities and challenges amid intense global competition in medical innovation [5]. - Chinese innovative pharmaceutical companies are increasingly establishing their global influence in the biopharmaceutical sector [5].

Group 1 - In 2025, gold is projected to reach approximately $4,300 per ounce, while silver is expected to exceed $60, doubling its value. Platinum and palladium are also anticipated to see significant price increases [2] - The surge in precious metals is driven by central bank gold purchases, geopolitical risks, expectations of Federal Reserve interest rate cuts, and demand from the new energy sector [2] - The year 2026 is expected to continue the upward trend in precious metals, with the performance of the US dollar and the global economy being critical variables [2] Group 2 - The London Stock Exchange Group (LSEG) is collaborating with Tokyo Commodity Exchange to explore the precious metals market under macroeconomic conditions, providing exclusive data on gold, silver, platinum, and palladium [2] - LSEG offers comprehensive solutions for commodity trading, including insights, data management, and seamless execution capabilities to enhance competitive advantages in the market [20][23] - The company emphasizes the importance of timely and accurate data in commodity trading, utilizing structured approaches to leverage fundamental, supply-demand, and alternative data sources [22][31]