Core Viewpoint - The article introduces the "Six-Dimensional Investment Capability Analysis" feature in the Shenwan Hongyuan Shen Cai You Dao APP, aimed at helping investors manage risks in margin accounts by evaluating their investment performance across six key dimensions: profitability, risk control, return stability, timing ability, stock selection ability, and industry allocation [2][3]. Group 1: Six-Dimensional Investment Capability Analysis - The Six-Dimensional Radar Chart provides a comprehensive assessment of investment performance across six core dimensions [3]. - The analysis helps investors visualize their strengths and weaknesses in investment capabilities [3]. Group 2: Detailed Dimension Descriptions - **Profitability**: This dimension evaluates the investment return level through account yield, with higher yields indicating stronger profitability [6][9]. - **Risk Control**: Assessed based on the maximum drawdown during the investment period and any record of contract defaults, with lower drawdowns indicating better risk management [11][12]. - **Return Stability**: Calculated using the annualized volatility of the account during the investment period, with lower volatility suggesting more stable returns [15][16]. - **Timing Ability**: Judged by the trading win rate during the investment period, with higher win rates reflecting better timing skills [18]. - **Stock Selection Ability**: Evaluated through the distribution and performance of held stocks, as well as excess return rates, with higher excess returns indicating stronger stock selection [20][21][23]. - **Industry Allocation**: Displays the distribution and performance of holdings across industries, aiding in optimizing industry allocation strategies [24][25]. Group 3: Functionality and Usage - The analysis results are presented in a radar chart format, highlighting areas for improvement and providing objective suggestions for enhancement [27]. - Users can access the Six-Dimensional Investment Capability Analysis by downloading the Shenwan Hongyuan Shen Cai You Dao APP and navigating to the account analysis section [35][36].

Quantitative Models and Construction 1. Model Name: Odds Ratio + Win Rate Strategy - **Model Construction Idea**: This strategy combines the odds ratio and win rate metrics to allocate risk budgets across assets, aiming to optimize returns under historical data constraints [3][46] - **Model Construction Process**: - The odds ratio and win rate metrics are calculated for each asset based on historical data - The risk budgets derived from these two metrics are summed to form a composite score - Asset allocation is determined by the composite score, with higher scores receiving higher allocations - Current allocation recommendation: 11.5% equities, 2.2% gold, 86.3% bonds [3][46] - **Model Evaluation**: The model demonstrates stable performance with low drawdowns, making it suitable for risk-averse investors [3][46] 2. Model Name: Odds Ratio Enhanced Strategy - **Model Construction Idea**: Focuses on maximizing returns by overweighting high-odds assets and underweighting low-odds assets under a volatility constraint [40][41] - **Model Construction Process**: - Odds ratios are calculated for each asset - A fixed volatility constraint is applied to ensure risk control - Asset allocation is adjusted dynamically based on odds ratios - Current allocation recommendation: 15.6% equities, 2.9% gold, 81.5% bonds [40][41] - **Model Evaluation**: The strategy effectively balances risk and return, achieving consistent performance over time [40][41] 3. Model Name: Win Rate Enhanced Strategy - **Model Construction Idea**: Utilizes macroeconomic factors (e.g., monetary policy, credit, growth, inflation, and overseas conditions) to derive win rate scores for asset allocation [43][44] - **Model Construction Process**: - Win rate scores are calculated based on macroeconomic indicators - Asset allocation is determined by the win rate scores, favoring assets with higher scores - Current allocation recommendation: 6.6% equities, 1.7% gold, 91.7% bonds [43][44] - **Model Evaluation**: The strategy is robust in capturing macroeconomic trends, providing a defensive allocation approach [43][44] --- Model Backtesting Results 1. Odds Ratio + Win Rate Strategy - Annualized Return: 7.0% (2011–2025), 7.6% (2014–2025), 7.2% (2019–2025) - Maximum Drawdown: 2.8% (2011–2025), 2.7% (2014–2025), 2.8% (2019–2025) - Sharpe Ratio: 2.86 (2011–2025), 3.26 (2014–2025), 2.85 (2019–2025) [3][46][47] 2. Odds Ratio Enhanced Strategy - Annualized Return: 6.6% (2011–2025), 7.5% (2014–2025), 7.0% (2019–2025) - Maximum Drawdown: 3.0% (2011–2025), 2.4% (2014–2025), 2.4% (2019–2025) - Sharpe Ratio: 2.72 (2011–2025), 3.19 (2014–2025), 3.02 (2019–2025) [40][41][42] 3. Win Rate Enhanced Strategy - Annualized Return: 7.0% (2011–2025), 7.7% (2014–2025), 6.3% (2019–2025) - Maximum Drawdown: 2.8% (2011–2025), 2.3% (2014–2025), 2.3% (2019–2025) - Sharpe Ratio: 2.96 (2011–2025), 3.36 (2014–2025), 2.87 (2019–2025) [43][44][45] --- Quantitative Factors and Construction 1. Factor Name: Value Factor - **Factor Construction Idea**: Measures the relative attractiveness of value stocks based on odds, trends, and crowding metrics [18][20] - **Factor Construction Process**: - Odds: 0.2 standard deviations (higher indicates cheaper valuation) - Trend: -0.1 standard deviations (moderate level) - Crowding: -1.0 standard deviations (low crowding) - Composite Score: 1.0 (highest among all factors) [18][20] - **Factor Evaluation**: Strong trend and low crowding make it a top-performing factor [18][20] 2. Factor Name: Quality Factor - **Factor Construction Idea**: Focuses on high-quality stocks with favorable odds and low crowding, awaiting trend confirmation [20][21] - **Factor Construction Process**: - Odds: 1.4 standard deviations (high level) - Trend: -0.3 standard deviations (weak level) - Crowding: -0.8 standard deviations (low level) - Composite Score: 0.6 [20][21] - **Factor Evaluation**: Promising long-term potential but requires trend confirmation for stronger performance [20][21] 3. Factor Name: Growth Factor - **Factor Construction Idea**: Targets growth stocks with improving odds and moderate crowding [23][25] - **Factor Construction Process**: - Odds: 0.6 standard deviations (moderate level) - Trend: 0.02 standard deviations (neutral level) - Crowding: -0.1 standard deviations (moderate level) - Composite Score: 0.4 [23][25] - **Factor Evaluation**: Suitable for neutral allocation due to balanced metrics [23][25] 4. Factor Name: Small-Cap Factor - **Factor Construction Idea**: Captures small-cap stocks with strong trends but high crowding and low odds [26][28] - **Factor Construction Process**: - Odds: -0.5 standard deviations (low level) - Trend: 0.9 standard deviations (high level) - Crowding: 0.6 standard deviations (high level) - Composite Score: 0.0 [26][28] - **Factor Evaluation**: High uncertainty due to low odds and high crowding, requiring cautious approach [26][28] --- Factor Backtesting Results 1. Value Factor - Odds: 0.2 standard deviations - Trend: -0.1 standard deviations - Crowding: -1.0 standard deviations - Composite Score: 1.0 [18][20] 2. Quality Factor - Odds: 1.4 standard deviations - Trend: -0.3 standard deviations - Crowding: -0.8 standard deviations - Composite Score: 0.6 [20][21] 3. Growth Factor - Odds: 0.6 standard deviations - Trend: 0.02 standard deviations - Crowding: -0.1 standard deviations - Composite Score: 0.4 [23][25] 4. Small-Cap Factor - Odds: -0.5 standard deviations - Trend: 0.9 standard deviations - Crowding: 0.6 standard deviations - Composite Score: 0.0 [26][28]

Summary of Key Points from the Conference Call Industry Overview - The conference call focuses on the A-share market outlook for July 2025, highlighting the balance between low-valued blue-chip stocks and reasonably valued growth stocks, particularly in the technology sector [1][3][28]. Core Insights and Arguments - **Market Trend**: The A-share market is expected to remain in a fluctuating trend for both the short term and July 2025, primarily due to ongoing fundamental pressures [2][27]. - **Driving Factors**: Recent market gains are attributed to the easing of risk events, improved policy expectations, and inflows from institutional investors [4][12]. - **Geopolitical Risks**: The impact of geopolitical events, such as the Israel-Palestine ceasefire, is viewed as temporary, with ongoing uncertainties related to U.S.-China relations and tariff issues [5][6][25]. - **Economic Indicators**: May economic data shows a decline in export growth and negative profit growth for industrial enterprises, indicating potential underperformance in A-share mid-year reports [13][16]. - **Performance Expectations**: The A-share mid-year performance is anticipated to be weaker than previously expected, with significant pressure on corporate earnings [17][24]. Important but Overlooked Content - **Policy Impact**: The financial support policies for consumption have a limited overall effect on profits but provide some benefits to specific consumption sectors [8][10]. - **Seasonal Trends**: Historical data indicates that July typically exhibits a balanced performance with no clear upward or downward trend, contrary to traditional beliefs [19][20]. - **Liquidity Factors**: The liquidity environment is expected to remain loose, which could positively influence the A-share market despite potential external pressures [26][27]. - **Sector Preferences**: The preferred sectors for investment in July 2025 are expected to be growth and financial sectors, with historical trends supporting this allocation [28][29]. Recommendations for Investment - **Focus Areas**: Suggested sectors for investment include military, non-ferrous metals, electric equipment, new energy, transportation, and large financial sectors, along with technology sub-sectors that are undervalued or have seen limited price increases [35]. - **High Growth Sub-sectors**: Sub-sectors with high expected profit growth include aviation, energy metals, military electronics, and software development [34]. This summary encapsulates the key insights and recommendations from the conference call, providing a comprehensive overview of the A-share market outlook for July 2025.

- The report defines a timing system signal based on the distance between the long-term moving average (120 days) and the short-term moving average (20 days) of the Wind All A Index, which is currently at 1.76%, indicating the market is still in a consolidation pattern[1][3][9] - The industry allocation model recommends mid-term allocation to sectors experiencing a turnaround, such as Hong Kong innovative drugs, new consumption, and Hong Kong finance, with the trend still intact[2][3][10] - The TWO BETA model continues to recommend the technology sector, with a focus on military and communication sectors[2][3][10] - The Wind All A Index's PE ratio is at the 65th percentile, indicating a medium level, while the PB ratio is at the 20th percentile, indicating a relatively low level[2][10] - The position management model suggests a 50% allocation to absolute return products based on the Wind All A Index[2][10] Model Backtest Results - Timing system signal: Moving average distance 1.76%[1][3][9] - Industry allocation model: Mid-term recommendation for turnaround sectors, Hong Kong innovative drugs, new consumption, and Hong Kong finance[2][3][10] - TWO BETA model: Recommendation for technology sector, focus on military and communication[2][3][10] - Wind All A Index PE ratio: 65th percentile[2][10] - Wind All A Index PB ratio: 20th percentile[2][10] - Position management model: 50% allocation to absolute return products[2][10]

Quantitative Models and Construction Methods 1. Model Name: Brinson Attribution Model - **Model Construction Idea**: The model is used to decompose the excess returns of active equity funds into stock selection and sector allocation contributions, providing insights into the sources of fund performance [16][19][22] - **Model Construction Process**: The Brinson model calculates excess returns as follows: $ R_{excess} = \sum_{i=1}^{n} (W_{i,f} - W_{i,b}) \cdot R_{i,b} + \sum_{i=1}^{n} W_{i,f} \cdot (R_{i,f} - R_{i,b}) $ - $ W_{i,f} $: Fund weight in sector $ i $ - $ W_{i,b} $: Benchmark weight in sector $ i $ - $ R_{i,f} $: Fund return in sector $ i $ - $ R_{i,b} $: Benchmark return in sector $ i $ The first term represents the allocation effect, and the second term represents the selection effect [16][19] - **Model Evaluation**: The model highlights that stock selection contributes more significantly to excess returns than sector allocation, with stock selection accounting for 83.17% of the total contribution on average [16][22] --- Model Backtesting Results 1. Brinson Attribution Model - Average stock selection contribution: 5.38% per half-year [22] - Probability of positive stock selection returns: 69.12% [23] - Probability of positive sector allocation returns: 53.66% [23] --- Quantitative Factors and Construction Methods 1. Factor Name: Fund Stability Factor - **Factor Construction Idea**: This factor measures the stability of a fund's sector allocation and its impact on outperforming benchmarks [10][12] - **Factor Construction Process**: Funds are categorized into 16 groups based on static and dynamic sector allocation characteristics: - Static categories: Highly diversified, diversified, concentrated, highly concentrated - Dynamic categories: Highly stable, stable, rotational, highly rotational The average probability of outperforming benchmarks is calculated for each group [10][12] - **Factor Evaluation**: Funds with highly stable and diversified sector allocations have the highest probability of outperforming benchmarks, exceeding 73% on average [12][14] 2. Factor Name: Style Consistency Factor - **Factor Construction Idea**: This factor evaluates the consistency of a fund's style (e.g., large-cap value) and its correlation with performance [27][30] - **Factor Construction Process**: Funds are classified based on their style consistency over time: - Long-term stable allocation - Majority-time allocation - Partial-time allocation - Rare-time allocation The probability of outperforming benchmarks is calculated for each group [27][28] - **Factor Evaluation**: Funds with long-term stable large-cap value styles have the highest probability of outperforming benchmarks, reaching 79.77% [28][30] --- Factor Backtesting Results 1. Fund Stability Factor - Highly diversified-highly stable funds: - Probability of outperforming benchmark: 73.12% - Probability of outperforming benchmark +10%: 57.29% [12] 2. Style Consistency Factor - Long-term stable large-cap value funds: - Probability of outperforming benchmark: 79.77% - Probability of outperforming benchmark +10%: 69.05% [28]

Quantitative Models and Construction Methods - **Model Name**: Timing System Model **Model Construction Idea**: This model uses the distance between the short-term moving average (20-day) and the long-term moving average (120-day) of the Wind All A Index to determine the overall market environment and identify market trends [1][9][12] **Model Construction Process**: 1. Calculate the 20-day moving average (short-term) and the 120-day moving average (long-term) of the Wind All A Index 2. Compute the difference between the two moving averages: $ \text{Difference} = \text{20-day MA} - \text{120-day MA} $ 3. Evaluate the absolute value of the difference. If the absolute value is less than 3%, the market is considered to be in a consolidation phase [1][9][12] **Model Evaluation**: The model effectively captures the market's consolidation phase and provides a clear signal for timing decisions [1][9][12] - **Model Name**: Industry Allocation Model **Model Construction Idea**: This model identifies industries with medium-term growth potential and recommends allocation based on sectoral trends and macroeconomic factors [2][3][10] **Model Construction Process**: 1. Analyze macroeconomic factors and market sentiment 2. Identify sectors with potential for recovery or growth, such as "distressed reversal" sectors 3. Recommend specific industries, such as innovative pharmaceuticals, automobiles, and new consumption in the Hong Kong market, as well as technology sectors like consumer electronics [2][3][10] **Model Evaluation**: The model provides actionable insights for medium-term industry allocation, focusing on sectors with growth potential [2][3][10] - **Model Name**: TWO BETA Model **Model Construction Idea**: This model focuses on identifying high-growth sectors, particularly in technology, and recommends allocation based on their performance trends [2][3][10] **Model Construction Process**: 1. Analyze the performance of high-beta sectors, such as technology and consumer electronics 2. Monitor the upward trend of specific industries, such as banking and gold stocks, to identify allocation opportunities [2][3][10] **Model Evaluation**: The model is effective in identifying high-growth sectors and provides a focused approach to sectoral allocation [2][3][10] - **Model Name**: Position Management Model **Model Construction Idea**: This model determines the recommended equity allocation based on valuation indicators and short-term market trends [2][10][12] **Model Construction Process**: 1. Evaluate the PE and PB valuation levels of the Wind All A Index 2. Assess the relative position of these indicators within their historical ranges 3. Combine valuation analysis with short-term market trend signals to recommend an equity allocation level (e.g., 50% for absolute return products) [2][10][12] **Model Evaluation**: The model provides a balanced approach to equity allocation, considering both valuation and market trends [2][10][12] Model Backtesting Results - **Timing System Model**: The moving average difference is 0.68%, with the absolute value remaining below 3%, indicating a consolidation phase [1][9][12] - **Position Management Model**: - PE valuation level: 60th percentile, indicating a medium level - PB valuation level: 20th percentile, indicating a relatively low level - Recommended equity allocation: 50% [2][10][12]

Quantitative Models and Construction Methods 1. Model Name: Industry Allocation Model - **Model Construction Idea**: This model aims to identify and recommend industries with potential for medium-term outperformance based on specific market conditions and sectoral dynamics [2][3][9] - **Model Construction Process**: The model evaluates industries based on their recovery potential ("distressed reversal sectors") and ongoing trends. It recommends sectors such as Hong Kong-listed innovative pharmaceuticals, automobiles, and new consumption industries. Additionally, it highlights sectors like technology (e.g., consumer electronics) and those with upward momentum, such as banking and gold stocks [2][3][9] - **Model Evaluation**: The model is effective in identifying sectors with medium-term growth potential and aligns with current market trends [2][3][9] 2. Model Name: TWO BETA Model - **Model Construction Idea**: This model focuses on identifying sectors with high beta characteristics, particularly in the technology domain, to capture growth opportunities [2][3][9] - **Model Construction Process**: The TWO BETA model emphasizes technology-related sectors, such as consumer electronics, as key areas of focus. It also considers sectors with strong upward momentum, like banking and gold stocks [2][3][9] - **Model Evaluation**: The model is suitable for identifying high-growth sectors in a market environment characterized by volatility and selective sectoral strength [2][3][9] 3. Model Name: Timing System Signal - **Model Construction Idea**: This model uses moving average distances to assess market conditions and provide timing signals for market entry or exit [2][3][8] - **Model Construction Process**: - The model calculates the distance between the short-term moving average (20-day) and the long-term moving average (120-day) of the Wind All A Index - Current data: - 20-day moving average: 5063 - 120-day moving average: 5079 - Distance: -0.32% (short-term moving average below long-term moving average) - The absolute value of the distance is less than 3%, indicating a market in a consolidation phase [2][3][8] - **Model Evaluation**: The model provides a clear and quantitative framework for assessing market trends and timing decisions [2][3][8] 4. Model Name: Position Management Model - **Model Construction Idea**: This model determines optimal portfolio allocation based on valuation metrics and market trends [3][9] - **Model Construction Process**: - The model evaluates the Wind All A Index's valuation levels: - PE ratio: 60th percentile (moderate level) - PB ratio: 10th percentile (low level) - Based on these metrics and short-term market trends, the model recommends a portfolio allocation of 50% for absolute return products [3][9] - **Model Evaluation**: The model effectively balances valuation considerations with market dynamics to guide portfolio allocation [3][9] --- Backtesting Results of Models 1. Industry Allocation Model - Recommended sectors: Hong Kong-listed innovative pharmaceuticals, automobiles, new consumption, technology (e.g., consumer electronics), banking, and gold stocks [2][3][9] 2. TWO BETA Model - Focus sectors: Technology (e.g., consumer electronics), banking, and gold stocks [2][3][9] 3. Timing System Signal - Moving average distance: -0.32% (absolute value < 3%, indicating a consolidation phase) [2][3][8] 4. Position Management Model - Recommended portfolio allocation: 50% for absolute return products [3][9]

- The report defines a market timing system using the distance between the long-term moving average (120 days) and the short-term moving average (20 days) of the Wind All A Index to distinguish the overall market environment[2][8][13] - The distance between the 20-day moving average and the 120-day moving average has narrowed from -2.80% to -1.33%, indicating the market is in a volatile state[2][8][13] - The industry allocation model recommends sectors such as Hang Seng Medical, Hong Kong automotive, and new consumption industries from a mid-term perspective[2][3][9] - The TWO BETA model continues to recommend the technology sector, focusing on information innovation and communication[2][3][9] - The Wind All A Index's overall PE is around the 60th percentile, indicating a medium level, while the PB is around the 10th percentile, indicating a relatively low level[3][9] - The position management model suggests an absolute return product with Wind All A as the main stock allocation should have a 50% position[3][9] - The market is expected to continue to decline in trading volume, with a potential rebound when the volume shrinks to around 900 billion[2][3][9] Model Backtest Results - The distance between the 20-day and 120-day moving averages is -1.33%[2][8][13] - The Wind All A Index's PE is at the 60th percentile[3][9] - The Wind All A Index's PB is at the 10th percentile[3][9] - The recommended position for absolute return products is 50%[3][9]

Quantitative Models and Construction Methods - **Model Name**: Industry Allocation Model **Model Construction Idea**: This model aims to recommend industry sectors based on medium-term perspectives, focusing on sectors with potential for recovery or growth trends[2][3][10] **Model Construction Process**: The model identifies sectors with recovery potential ("困境反转型板块") and growth opportunities. It recommends sectors such as healthcare (恒生医疗), export-related consumer sectors (e.g., light industry and home appliances), and technology sectors (信创, communication, solid-state batteries). Additionally, it highlights sectors with ongoing upward trends, such as banking and gold[2][3][10] **Model Evaluation**: The model provides actionable insights for medium-term industry allocation, emphasizing sectors with recovery potential and growth trends[2][3][10] - **Model Name**: TWO BETA Model **Model Construction Idea**: This model focuses on identifying technology-related sectors with growth potential[2][3][10] **Model Construction Process**: The TWO BETA model recommends technology sectors, including 信创, communication, and solid-state batteries, based on their growth potential and market trends[2][3][10] **Model Evaluation**: The model effectively identifies technology sectors with strong growth potential, aligning with market trends[2][3][10] - **Model Name**: Timing System Model **Model Construction Idea**: This model evaluates market conditions by analyzing the distance between short-term and long-term moving averages to determine market trends[2][9][14] **Model Construction Process**: 1. Define the short-term moving average (20-day) and long-term moving average (120-day) for the Wind All A Index 2. Calculate the difference between the two moving averages: $ \text{Difference} = \text{20-day MA} - \text{120-day MA} $ - Latest values: 20-day MA = 4946, 120-day MA = 5088 - Difference = -2.80% (previous week: -3.63%) 3. Monitor the absolute value of the difference; when it falls below 3%, the market is considered to be in a consolidation phase[2][9][14] **Model Evaluation**: The model provides a clear signal for market consolidation, aiding in timing decisions[2][9][14] - **Model Name**: Position Management Model **Model Construction Idea**: This model determines the recommended equity allocation based on valuation levels and short-term market trends[3][10] **Model Construction Process**: 1. Assess valuation levels of the Wind All A Index: - PE ratio: 50th percentile (medium level) - PB ratio: 10th percentile (low level) 2. Combine valuation levels with short-term market trends to recommend a 60% equity allocation for absolute return products[3][10] **Model Evaluation**: The model provides a systematic approach to position management, balancing valuation and market trends[3][10] Backtesting Results of Models - **Industry Allocation Model**: No specific numerical backtesting results provided[2][3][10] - **TWO BETA Model**: No specific numerical backtesting results provided[2][3][10] - **Timing System Model**: - Latest moving average difference: -2.80% - Previous week difference: -3.63% - Absolute difference < 3%, indicating a consolidation phase[2][9][14] - **Position Management Model**: - Recommended equity allocation: 60%[3][10]

Core Viewpoint - The article discusses the impact of the recent "reciprocal tariffs" announced by Trump on the global market, particularly focusing on the Chinese market and its recovery trends following the initial shock [1][3]. Market Performance Summary - Following the announcement of tariffs on April 2, the Hong Kong stock market experienced significant volatility, with a notable drop on April 7 that erased all gains for the year. However, by May 2, the Hang Seng Tech Index rebounded by 19.1%, while MSCI China, Hang Seng Index, and Hang Seng China Enterprises Index saw rebounds of 13.6%, 13.5%, and 13.3% respectively. The Shanghai Composite Index and CSI 300 had smaller rebounds of 5.9% and 5.0% [1]. - Sector performance from April 8 to May 2 showed that Information Technology (+29.0%), Healthcare (+19.2%), and Consumer Discretionary (+14.3%) led the gains, while sectors like Banking (+4.9%), Utilities (+5.6%), and Energy (+5.9%) lagged behind [1]. Industry Analysis Framework - The article proposes an industry analysis framework based on demand sources, categorizing industries into three main types: 1. Industries primarily dependent on the U.S. market, which face significant challenges in finding alternative demand. 2. Industries with demand from markets outside the U.S., which are less directly affected by U.S. tariffs. 3. Industries with domestic demand, which are influenced by domestic policy support [4][6]. Impact of Tariffs on Different Industries - Industries with primary demand from the U.S. are categorized based on their ability to find alternative markets and their bargaining power. Sectors like Media, Software Services, and Textiles have shown resilience due to higher profit margins and U.S. import dependency, while smaller firms in shipping and medical supplies face greater challenges [6][10]. - Industries with demand from other markets, particularly those with established market shares and competitive advantages, are expected to perform better. Sectors such as Technology Hardware and Home Appliances have shown potential for growth in non-U.S. markets [11][14]. - Domestic demand-driven industries, particularly in consumption and infrastructure, are closely tied to government policy support. The article highlights the importance of fiscal measures to mitigate external shocks [18][20]. Historical Context and Future Outlook - The article draws parallels with the 2018-2019 trade tensions, noting that the current market dynamics reflect similar patterns of initial decline followed by recovery phases. The sectors that are less dependent on U.S. demand have shown more resilience, while those heavily reliant on U.S. markets have faced significant declines [21][25]. - The potential impact of tariffs on GDP and corporate profits is discussed, with estimates suggesting that a significant drop in exports to the U.S. could lead to a decline in GDP growth and a downward adjustment in profit forecasts for Hong Kong stocks [34][35]. - The article concludes with a projection of market indices under different scenarios, emphasizing the need for policy support to counterbalance the negative effects of tariffs and the importance of sector-specific strategies for investors [37].