Summary of Conference Call Notes Industry or Company Involved - The discussion revolves around various industries and their operational models, particularly focusing on investment strategies and market dynamics. Core Points and Arguments 1. Most models tested across various industries show limited effectiveness, indicating that current strategies may not outperform previous ones [1] 2. The operational efficiency of certain industries is hindered by low activity levels, leading to poor returns and potential misjudgments in trading [2] 3. Instability in institutional models can significantly impact overall results, causing potential losses during market fluctuations [3] 4. Industries face challenges in achieving new highs, which may lead to a reduction in adaptability to changing market conditions [4] 5. The accumulation of industry indices relies on performance growth, making significant collapses rare [5] 6. As indices grow, the relative drawdown decreases, suggesting a stable testing environment for investment strategies [6] 7. Advanced analytical strategies may not cover as many industries but can effectively identify suitable investment opportunities [7] 8. Timing issues in market signals pose challenges, as it is difficult to predict how long it will take for prices to return to previous highs [8] 9. The operational timeframes of various industries lack clear benchmarks, complicating performance assessments [9] 10. Differentiation strategies can effectively navigate uncertain market conditions, especially when historical patterns are not reliable [10] 11. The effectiveness of operational strategies may be lower than those derived from industry-standard configurations, highlighting the importance of volatility management [11] 12. The overall strategy framework may evolve beyond linear combinations, incorporating various technical approaches for enhanced robustness [12] Other Important but Possibly Overlooked Content - The discussion emphasizes the need for continuous adaptation and reassessment of strategies in response to market changes, highlighting the dynamic nature of investment environments.

Quantitative Models and Construction Methods Models Model Name: Industry Allocation Model - **Model Construction Idea**: This model aims to recommend industry sectors based on medium-term trends and specific market conditions[2][3][10] - **Model Construction Process**: - The model identifies sectors that are likely to benefit from current market trends and conditions. - It recommends sectors such as Hong Kong innovative drugs, Hong Kong securities, and photovoltaic sectors due to their potential for reversal and growth. - The model also suggests focusing on technology sectors, including military and communication, as well as A-share banks and gold stocks[2][3][10] - **Model Evaluation**: The model is effective in identifying sectors with potential growth and aligning with current market trends[2][3][10] Model Name: TWO BETA Model - **Model Construction Idea**: This model focuses on recommending technology sectors based on their beta values and market conditions[2][3][10] - **Model Construction Process**: - The model evaluates the beta values of different sectors to identify those with higher potential for growth. - It recommends technology sectors, particularly military and communication, based on their beta values and current market trends[2][3][10] - **Model Evaluation**: The model is useful for identifying high-potential technology sectors based on their beta values[2][3][10] Model Name: Position Management Model - **Model Construction Idea**: This model aims to manage stock positions based on valuation indicators and short-term trends[3][10] - **Model Construction Process**: - The model uses valuation indicators such as PE and PB ratios to determine the stock positions. - It suggests an 80% stock position for absolute return products based on the current valuation levels of the wind All A index[3][10] - **Model Evaluation**: The model provides a balanced approach to managing stock positions based on valuation and market trends[3][10] Model Backtesting Results 1. **Industry Allocation Model**: - **PE Ratio**: 70th percentile[3][10] - **PB Ratio**: 30th percentile[3][10] - **Position Suggestion**: 80%[3][10] 2. **TWO BETA Model**: - **PE Ratio**: 70th percentile[3][10] - **PB Ratio**: 30th percentile[3][10] - **Position Suggestion**: 80%[3][10] 3. **Position Management Model**: - **PE Ratio**: 70th percentile[3][10] - **PB Ratio**: 30th percentile[3][10] - **Position Suggestion**: 80%[3][10] Quantitative Factors and Construction Methods Factor Name: Moving Average Distance - **Factor Construction Idea**: This factor measures the distance between short-term and long-term moving averages to identify market trends[2][9][14] - **Factor Construction Process**: - Calculate the 20-day moving average and the 120-day moving average of the wind All A index. - Compute the distance between the two moving averages. - The formula is: $$ \text{Distance} = \frac{\text{20-day MA} - \text{120-day MA}}{\text{120-day MA}} $$ - If the distance exceeds 3%, the market is considered to be in an upward trend[2][9][14] - **Factor Evaluation**: The factor is effective in identifying market trend shifts from a volatile to an upward trend[2][9][14] Factor Name: Profitability Effect - **Factor Construction Idea**: This factor measures the market's profitability effect to predict the inflow of incremental funds[2][10][14] - **Factor Construction Process**: - Calculate the profitability effect value based on market data. - The current profitability effect value is 3.50%, indicating a positive market trend[2][10][14] - **Factor Evaluation**: The factor is useful for predicting the inflow of incremental funds based on market profitability[2][10][14] Factor Backtesting Results 1. **Moving Average Distance**: - **Distance**: 3.04%[2][9][14] - **Profitability Effect**: 3.50%[2][10][14] 2. **Profitability Effect**: - **Distance**: 3.04%[2][9][14] - **Profitability Effect**: 3.50%[2][10][14]

Core Insights - Goldman Sachs' investment strategy team has made significant adjustments to the U.S. sector allocation model, recommending a more balanced sector allocation strategy for investors [1] - The updated sector model indicates that an equal-weight sector allocation portfolio has a significantly higher probability of achieving over 5% excess returns compared to an equal-weight S&P 500 index over the next six months [1] Sector Recommendations - The software and services, as well as media and entertainment sectors, continue to hold their previous overweight ratings, while the new materials sector has been included in the core recommendations for the first time [1] - The consumer staples sector has been removed from the priority allocation list [1] - The report emphasizes that the current U.S. stock market exhibits an overly optimistic outlook on the economic prospects, with both downside risks and upside potential present in the actual economic performance [1] Investment Strategy - The strategy report suggests avoiding significant bias towards cyclical or defensive sectors, advocating for a balanced investment portfolio that can withstand market fluctuations [1] - In terms of specific sector selection, software and services (long-term growth expectation of 14%) and media and entertainment (long-term growth expectation of 14%) stand out due to their robust growth prospects, particularly in a moderately growing economy [1] - Defensive sectors such as utilities and real estate are favored due to the expectation of a slight decline in bond yields [1] - Among cyclical sectors, the materials sector is viewed as having a better allocation advantage compared to the energy sector, primarily based on expectations of falling oil prices [1] Adjustments and Market Outlook - The industrial sector has been downgraded due to its overall valuation being at historical highs, with the model indicating the lowest likelihood of achieving significant excess returns over the next six months [2] - Although the consumer staples and healthcare sectors are not explicitly bearish, their allocation priority has been slightly lowered compared to the model's baseline recommendations [2] - The adjustments reflect Goldman Sachs' neutral judgment on the market environment, acknowledging the reasonableness of current market optimism while diversifying allocations to hedge against potential risks [2] - The strategy team highlights that in the context of economic growth uncertainty, sectors that combine growth potential with reasonable valuations will exhibit greater investment resilience, while excessive bets on a single direction may face dual volatility risks [2]

- The industry mainline model uses the Relative Strength Index (RSI) indicator to identify leading industries. The construction process involves calculating the past 20, 40, and 60 trading days' returns for 29 primary industry indices, normalizing the rankings, and averaging them to derive the final RSI value. Industries with RSI > 90% by April are likely to lead the market for the year[11][13][14] - The industry rotation model is based on the "Prosperity-Trend-Crowdedness" framework. It includes two sub-models: the industry prosperity model (high prosperity + strong trend, avoiding high crowdedness) and the industry trend model (strong trend + low crowdedness, avoiding low prosperity). Historical backtesting shows annualized excess returns of 14.4%, IR of 1.56, and a maximum drawdown of -7.4%[16][18][22] - The left-side inventory reversal model focuses on industries with low inventory pressure and potential for restocking. It identifies sectors undergoing a rebound from current or past difficulties. Historical backtesting shows absolute returns of 25.9% in 2024 and excess returns of 14.8% relative to equal-weighted industry benchmarks[28][30][29] - The industry ETF allocation model applies the prosperity-trend-crowdedness framework to ETFs. It achieves annualized excess returns of 15.5% against the CSI 800 benchmark, with an IR of 1.81. The model's excess returns were 6.0% in 2023, 5.3% in 2024, and 7.7% in 2025[22][27][16] - The industry prosperity stock selection model combines industry weights from the prosperity-trend-crowdedness framework with PB-ROE scoring to select high-value stocks within industries. Historical backtesting shows annualized excess returns of 20.0%, IR of 1.72, and a maximum drawdown of -15.4%[23][26][16] - The industry prosperity-trend model achieved excess returns of 3.9% in 2025, while the inventory reversal model showed absolute returns of 1.3% and excess returns of -2.1% relative to equal-weighted industry benchmarks[16][28][30]

Quantitative Models and Construction Methods Model Name: Wind All A Index Timing System - **Model Construction Idea**: The model aims to distinguish the overall market environment by analyzing the distance between long-term and short-term moving averages of the Wind All A Index[1][10][16] - **Model Construction Process**: - Define the long-term moving average (120-day) and short-term moving average (20-day) of the Wind All A Index[1][10] - Calculate the distance between the two moving averages: $$ \text{Distance} = \frac{\text{Short-term MA} - \text{Long-term MA}}{\text{Long-term MA}} $$ where the short-term MA is the 20-day moving average and the long-term MA is the 120-day moving average[1][10] - Monitor the distance value to determine market conditions. If the distance exceeds 3%, it signals a change from a volatile to an upward trend[1][10][16] - **Model Evaluation**: The model is effective in identifying market trends and providing signals for adjusting positions[1][10][16] Model Name: Industry Allocation Model - **Model Construction Idea**: The model recommends industry sectors based on medium-term perspectives and current market trends[2][4][11] - **Model Construction Process**: - Analyze the performance and trends of various industry sectors[2][4][11] - Identify sectors with potential for reversal or growth, such as distressed reversal sectors, innovative drugs in Hong Kong stocks, and photovoltaic sectors benefiting from anti-involution[2][4][11] - Use the TWO BETA model to recommend technology sectors, focusing on military and communication industries[2][4][11] - **Model Evaluation**: The model provides targeted industry recommendations based on current market conditions and trends[2][4][11] Model Name: Position Management Model - **Model Construction Idea**: The model manages stock positions based on valuation indicators and short-term market trends[3][12] - **Model Construction Process**: - Evaluate the overall PE and PB ratios of the Wind All A Index[3][12] - Determine the stock position based on the valuation levels and short-term market trends. For example, with the Wind All A Index at a medium PE level (70th percentile) and a low PB level (30th percentile), the recommended position is 60%[3][12] - **Model Evaluation**: The model helps in managing stock positions effectively by considering valuation levels and market trends[3][12] Model Backtest Results Wind All A Index Timing System - **Distance between Moving Averages**: 2.52%[1][10][16] Industry Allocation Model - **Recommended Sectors**: Distressed reversal sectors, innovative drugs in Hong Kong stocks, photovoltaic sectors, technology sectors (military and communication), A-share banks, and gold stocks[2][4][11] Position Management Model - **Recommended Position**: 60%[3][12]

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]