Quantitative Models and Construction Methods 1. Model Name: A-Share Technical Scoring Model - **Model Construction Idea**: The model aims to fully explore technical information to depict market conditions, breaking down the abstract concept of "market state" into five dimensions: price, volume, volatility, trend, and crowding. It generates a comprehensive score ranging from -1 to +1 based on equal-weighted voting of signals from 10 selected indicators across these dimensions[9][14] - **Model Construction Process**: 1. Select 10 effective market observation indicators across the five dimensions[14] 2. Generate long/short timing signals for each indicator individually 3. Aggregate the signals through equal-weighted voting to form a comprehensive score between -1 and +1[9] - **Model Evaluation**: The model provides a straightforward and timely way for investors to observe and understand the market[9] 2. Model Name: Style Timing Model (Small-Cap Crowding) - **Model Construction Idea**: The model uses a crowding-based trend approach to time large-cap and small-cap styles. Crowding is measured by the difference in momentum and trading volume ratios between small-cap and large-cap indices[3][20] - **Model Construction Process**: 1. Calculate the momentum difference between the Wind Micro-Cap Index and the CSI 300 Index across 10/20/30/40/50/60-day windows 2. Compute the trading volume ratio between the two indices over the same windows 3. Derive crowding scores for small-cap and large-cap styles by averaging the highest and lowest quantiles of the above metrics, respectively 4. Combine the momentum and volume scores to obtain the final crowding score. A score above 90% indicates high small-cap crowding, while below 10% indicates high large-cap crowding[25] - **Model Evaluation**: The model effectively captures the dynamics of style crowding and provides actionable insights for timing decisions[20][25] 3. Model Name: Industry Rotation Model (Genetic Programming) - **Model Construction Idea**: The model applies genetic programming to directly extract factors from industry indices' price, volume, and valuation data, without relying on predefined scoring rules. It uses a dual-objective approach to optimize factor monotonicity and top-group performance[28][32][33] - **Model Construction Process**: 1. Use NSGA-II algorithm to optimize two objectives: |IC| (information coefficient) and NDCG@5 (normalized discounted cumulative gain for top 5 groups) 2. Combine weakly collinear factors using a greedy strategy and variance inflation factor to form industry scores 3. Select the top 5 industries with the highest multi-factor scores for equal-weight allocation, rebalancing weekly[32][34] - **Model Evaluation**: The dual-objective genetic programming approach enhances factor diversity and reduces overfitting risks, making it a robust tool for industry rotation[32][34] 4. Model Name: China Domestic All-Weather Enhanced Portfolio - **Model Construction Idea**: The model adopts a macro-factor risk parity framework, emphasizing risk diversification across underlying macro risk sources rather than asset classes. It actively overweights favorable quadrants based on macro momentum[39][42] - **Model Construction Process**: 1. Divide macro risks into four quadrants based on growth and inflation expectations: growth above/below expectations and inflation above/below expectations 2. Construct sub-portfolios within each quadrant using equal-weighted assets, focusing on downside risk 3. Adjust quadrant risk budgets monthly based on macro momentum indicators, which combine buy-side momentum from asset prices and sell-side momentum from economic forecast surprises[42] - **Model Evaluation**: The strategy effectively integrates macroeconomic insights into portfolio construction, achieving enhanced performance through active allocation adjustments[39][42] --- Model Backtesting Results 1. A-Share Technical Scoring Model - Annualized Return: 20.78% - Annualized Volatility: 17.32% - Maximum Drawdown: -23.74% - Sharpe Ratio: 1.20 - Calmar Ratio: 0.88[15] 2. Style Timing Model (Small-Cap Crowding) - Annualized Return: 28.46% - Maximum Drawdown: -32.05% - Sharpe Ratio: 1.19 - Calmar Ratio: 0.89 - YTD Return: 11.85% - Weekly Return: 5.25%[26] 3. Industry Rotation Model (Genetic Programming) - Annualized Return: 32.92% - Annualized Volatility: 17.43% - Maximum Drawdown: -19.63% - Sharpe Ratio: 1.89 - Calmar Ratio: 1.68 - YTD Return: 6.80% - Weekly Return: 3.37%[31] 4. China Domestic All-Weather Enhanced Portfolio - Annualized Return: 11.93% - Annualized Volatility: 6.20% - Maximum Drawdown: -6.30% - Sharpe Ratio: 1.92 - Calmar Ratio: 1.89 - YTD Return: 3.59% - Weekly Return: 1.54%[43] --- Quantitative Factors and Construction Methods 1. Factor Name: Small-Cap Crowding Factor - **Factor Construction Idea**: Measures the crowding level of small-cap style based on momentum and trading volume differences between small-cap and large-cap indices[20][25] - **Factor Construction Process**: 1. Calculate momentum differences and trading volume ratios for multiple time windows 2. Derive crowding scores by averaging the highest and lowest quantiles of these metrics 3. Combine momentum and volume scores to obtain the final crowding score[25] 2. Factor Name: Industry Rotation Factor (Genetic Programming) - **Factor Construction Idea**: Extracts factors from industry indices using genetic programming, optimizing for monotonicity and top-group performance[32][34] - **Factor Construction Process**: 1. Perform cross-sectional regression of standardized daily trading volume against daily price gaps to obtain residuals (Variable A) 2. Identify the trading day with the highest standardized volume in the past 9 days (Variable B) 3. Conduct time-series regression of Variables A and B over the past 50 days to obtain intercepts (Variable C) 4. Compute the covariance of Variable C and standardized monthly opening prices over the past 45 days[38] --- Factor Backtesting Results 1. Small-Cap Crowding Factor - YTD Return: 11.85% - Weekly Return: 5.25%[26] 2. Industry Rotation Factor (Genetic Programming) - Training Set IC: 0.340 - Factor Weight: 18.7% - YTD Return: 6.80% - Weekly Return: 3.37%[31][38]

Group 1 - The core viewpoint of the report suggests that the asset environment in 2026 may exhibit characteristics of marginal liquidity easing and moderate economic recovery, recommending commodities over stocks and bonds [1] Group 2 - In terms of equities, the report anticipates a 5%-10% increase in the full-year performance of the Wind All A index for 2026, with Hong Kong stocks expected to experience a rebound in earnings and a second round of valuation recovery [1] - The US stock market is projected to maintain fundamental growth momentum under a backdrop of "fiscal + monetary" easing during the mid-term election year [1] Group 3 - For bonds, the 10-year China government bond yield is expected to fluctuate within a range of 1.5%-1.8% throughout the year, with a pattern of initially declining and then rising [1] - The 10-year US Treasury yield is anticipated to remain within a range of 3.9%-4.3% [1] Group 4 - In the commodities sector, the oil supply-demand balance is shifting from surplus to equilibrium, with Brent crude oil projected to fluctuate between $58-$70 per barrel for the year [1] - Gold is expected to maintain strength supported by liquidity easing and geopolitical risks, with a potential to reach $5,000 per ounce, although the rate of increase may slow [1] - Copper is forecasted to have strong support due to supply constraints and electricity demand, with an average price expected to rise to $12,000 per ton [1] Group 5 - Regarding exchange rates, the Chinese yuan is likely entering a period of mild appreciation, with the USD/CNY exchange rate expected to gradually approach 6.8 [1]

Core Viewpoint - The report from CITIC Securities suggests that the macro asset environment in 2026 may exhibit characteristics of marginal liquidity easing and moderate economic recovery, recommending commodities over stocks and bonds [1] Group 1: Equity Market - The report anticipates a 5%-10% increase in the annual performance of the Wind All A index for 2026 [1] - Hong Kong stocks are expected to experience a rebound in earnings and a second round of valuation recovery, leading to a "Davis Double" market scenario [1] - The US stock market is projected to maintain fundamental growth momentum under a backdrop of "fiscal + monetary" easing during the midterm election year [1] Group 2: Bond Market - The 10-year China government bond yield is expected to fluctuate within a range of 1.5%-1.8% for the year, with a pattern of initially declining and then rising [1] - The 10-year US Treasury yield is likely to remain within a range of 3.9%-4.3% [1] Group 3: Commodity Market - The oil supply-demand balance is shifting from surplus to equilibrium, with Brent crude oil projected to fluctuate between $58 and $70 per barrel throughout the year [1] - Gold is expected to maintain strength due to liquidity easing and geopolitical risks, with a potential to reach $5,000 per ounce, although the rate of increase may slow [1] - Copper is anticipated to have strong support driven by supply constraints and electricity demand, with an expected average price increase to $12,000 per ton [1] Group 4: Currency Market - The Chinese yuan is likely entering a period of mild appreciation, with the USD/CNY exchange rate expected to gradually approach 6.8 [1]

Group 1 - The core view of the report indicates that the equity market is showing signs of upward momentum, with the total A-share index rising by 0.60% and trading volume increasing by 467 billion yuan compared to the previous day [1][2] - The report highlights that the market is attempting to establish new narratives, which historically accompany successful breakthroughs of previous highs at year-end [1][2] - The report suggests that the index is approaching previous highs, with the total A-share index breaking through 6400 points, nearing the highs of October and November [2] Group 2 - The report identifies strong performance in specific sectors, particularly defense, military, and communication industries, which have successfully broken through previous high points, indicating a positive market sentiment towards these sectors [2] - The commercial aerospace sector has led the market with a cumulative increase of 31.12% since November 24, and its trading volume has reached a historical high of 6.05% of total A-share trading volume [3] - The bond market is experiencing a mixed performance, with short-term bonds showing a downward trend while long-term bonds are under pressure due to rising yields influenced by equity market movements [4][5] Group 3 - The report notes that the commodity market has shifted from a broad rally to a more differentiated performance, with precious metals experiencing a decline while industrial metals remain resilient [6] - The report emphasizes that the long-term bullish logic for precious metals remains intact, but short-term volatility may arise due to profit-taking after significant price increases [7] - The report discusses the dynamics in the polysilicon industry, where price increases are being driven by supply-side adjustments, despite ongoing supply-demand imbalances [7]

Group 1: Market Overview - The stock and bond markets have entered a low volatility consolidation phase, with the Wande All A index down by 0.44% and trading volume decreasing to 1.95 trillion yuan, the lowest since August 13 [1][2] - The Hang Seng Index and Hang Seng Technology Index fell by 0.09% and 1.18% respectively, while southbound capital saw a net inflow of 158.22 million HKD, indicating a potential rebound after the market decline [1][2] Group 2: Market Sentiment and Strategy - The market is currently experiencing indecision, with a "triangle" structure forming in the Wande All A daily chart, suggesting a battle between profit-taking and bullish sentiment [2] - If the market continues to oscillate, a diversified allocation strategy is recommended, including some dividend assets to mitigate potential volatility [2] - In the event of a significant market uptrend, increased thematic positions may be warranted, while a substantial downturn would suggest increasing dividend positions to wait for better entry points in technology themes [2] Group 3: Sector Performance - The coal sector has emerged as a leading dividend performer, supported by inventory depletion, with coal stocks decreasing from 78.698 million tons on May 12 to 60.432 million tons by September 29 [3] - The technology sector in Hong Kong is suggested for increased positions, as the Hang Seng Technology Index has retraced to levels seen before significant positive events in early September [3] - The bond market is in a pricing dilemma, with a slight bullish sentiment prevailing, as evidenced by the yield movements of various bonds, particularly the 30-year government bonds showing a yield decline of over 2 basis points [4][5] Group 4: Commodity Market Trends - The commodity market is showing signs of recovery, with precious metals continuing to perform strongly, while industrial metals like aluminum and copper have seen slight increases [8] - The "anti-involution" theme is gaining traction, with related commodities such as polysilicon and coking coal experiencing significant price increases, although the underlying fundamentals remain weak [9] - Despite the recent price highs in precious metals, there has been a notable outflow of capital, indicating profit-taking behavior among investors [8][9]

Group 1 - The article presents a summary of the cumulative performance of the Wind All A index over various time frames, indicating a median performance of 41.24% over T-10 days and 26.91% for the year 2024 [1][1][1] - The win rates for different time frames show a significant increase, with a win rate of 73.3% for T-1 and 66.7% for T+1 [1][1][1] - The performance data from previous years shows fluctuations, with 2023 recording a slight decline of -0.28% over T-10 days, while 2022 had a more substantial drop of -4.25% [1][1][1] Group 2 - The data indicates that the performance of the index has varied significantly over the years, with 2018 showing a positive return of 3.66% over T-10 days, contrasting with the negative returns in 2019 and 2020 [1][1][1] - The cumulative performance trends suggest that the index has experienced both recovery and decline phases, with notable improvements in 2021 and 2024 projections [1][1][1] - The historical data highlights the volatility of the index, with the highest recorded performance in 2014 at 5.11% over T-10 days [1][1][1]

Group 1 - The core idea of the LPPL model is that all systems have a "breaking point," which can be used to predict market bubbles and crashes [7][10][11] - The model is based on the concepts of positive feedback loops and herding behavior, where rising asset prices attract more investors, leading to accelerated price increases [10][11] - The LPPL model uses a polynomial fitting approach to identify periods of accelerated market trends, which can indicate potential market tops [13] Group 2 - The report discusses historical successful predictions of bull and bear market endings using the LPPL model, providing case studies from 2014-2015 and 2006-2007 [21][22][38] - The model's parameters, such as critical time (tc), power-law exponent (α), and angular frequency (ω), are crucial for predicting market behavior and potential breaking points [17][18] - The methodology for applying the LPPL model involves selecting a starting point based on moving averages and continuously updating price data to forecast bubble burst dates [20][24] Group 3 - The report emphasizes the importance of monitoring key indicators like "prediction intervals" and confidence levels to assess the reliability of the LPPL model's forecasts [25][29][36] - The LPPL model's predictions for current market conditions are discussed, indicating that the model has not yet triggered warning thresholds for potential market corrections [39] - The report provides detailed examples of past market behaviors and the corresponding LPPL model predictions, illustrating the model's practical application in real market scenarios [22][24][28]

Core Insights - The report focuses on cross-asset timing strategies for interest rates, systematically backtesting various assets (including stock indices, commodities, and bonds) to identify performance under different market conditions [1]. Group 1: Cross-Asset Rotation Effects - The "stock-bond seesaw" effect arises from shifts in risk appetite, where strong economic expectations lead to capital flowing into equity markets, putting pressure on bond prices and raising yields [2][14]. - The relationship between commodities and bonds is closely tied to inflation expectations, with rising commodity prices typically leading to higher inflation and interest rates, which suppress bond valuations [2][14]. Group 2: Timing Strategies in Commodity and Equity Markets - In equity markets, strategies focused on volatility structures yield higher excess returns compared to trend-based moving average strategies, particularly in high-volatility environments [3]. - For commodities, timing strategies exhibit high odds and low win rates, aligning with the trend-driven nature of commodity trading. Multi-signal strategies outperform in various market conditions due to their adaptability [3][51]. Group 3: Cross-Asset Timing Strategies - The report employs a "cross-validation signal triggering method" for each asset, enhancing the robustness of cross-asset timing strategies. The "look at stocks, trade bonds" and "look at commodities, trade bonds" approaches aim to mitigate drawdowns while maintaining excess returns [4][86]. Group 4: Future Optimization Outlook - A dynamic weighting mechanism is proposed to adjust the importance of different market signals based on macroeconomic conditions, enhancing the adaptability of strategies over time [5]. - The report suggests exploring pair trading strategies in the foreign exchange market to provide additional support for cross-asset trading logic [5].

Core Viewpoint - The article discusses the performance of the Chinese stock market in July, highlighting that A-shares outperformed Hong Kong stocks due to various factors, including government policies and market dynamics [4][5]. Group 1: Market Performance - In July, major Chinese stock indices such as the Wind All A, CSI 300, and Hang Seng Index saw increases of +4.75%, +3.54%, and +2.91% respectively, indicating a stronger performance of A-shares compared to Hong Kong stocks [4]. - The article notes that small-cap stocks in A-shares showed stronger performance than large-cap stocks during this period [4]. Group 2: Factors Influencing A-share Performance - The central government's "anti-involution" supply-side reform measures announced on July 1 are believed to have positively impacted investor sentiment, particularly in cyclical industries that are expected to recover [5]. - The high market activity and significant gains in individual stocks have improved risk appetite among investors, leading to increased market participation [5]. Group 3: Bull Market Dynamics - The article explores how bull markets form, emphasizing that economic improvement is not a prerequisite for a bull market; rather, market valuations and investor sentiment play crucial roles [8][14]. - Historical data shows that the Producer Price Index (PPI) can reflect macroeconomic conditions, and past bull markets have occurred even during periods of negative PPI growth [9]. Group 4: Investor Behavior in Bull Markets - The article identifies common reasons why many investors fail to profit during bull markets, including selling stocks during market lows out of fear and missing out on subsequent gains [15][16]. - It highlights the psychological barriers and decision-making challenges investors face, such as fear of missing out and the difficulty in identifying the right stocks to buy [17][18]. Group 5: Current Market Strategy - The article suggests that the current market may be characterized as a structural bull market, with potential for cyclical recovery in certain sectors due to government policies [21]. - It advises investors to avoid perfectionism in their investment strategies and to focus on achieving reasonable returns rather than waiting for the perfect entry point [22].

Market Performance - The Shanghai Composite Index closed at 3415.45 points, up 1.00% [1] - The Shenzhen Component Index closed at 10193.85 points, up 1.45% [1] - The ChiNext Index closed at 2056.82 points, up 1.94% [1] - The CSI 300 Index closed at 3899.85 points, up 1.09% [1] - The STAR 50 Index closed at 976.04 points, up 1.51% [1] - The CSI 500 Index closed at 5750.42 points, up 1.34% [1] - The CSI 1000 Index closed at 6173.70 points, up 1.57% [1] Year-to-Date Performance - The Shanghai Composite Index has increased by 1.90% year-to-date [2] - The Shenzhen Component Index has decreased by 2.12% year-to-date [2] - The ChiNext Index has decreased by 3.96% year-to-date [2] - The CSI 300 Index has decreased by 0.89% year-to-date [2] - The CSI 500 Index has increased by 0.43% year-to-date [2] - The CSI 1000 Index has increased by 3.63% year-to-date [2] - The CSI 2000 Index has increased by 11.69% year-to-date [2] - The North Asia ESO has increased by 36.00% year-to-date [2] - The Wande All A Index has increased by 3.54% year-to-date [2] - The Wande Micro-Stock Index has increased by 31.94% year-to-date [2]