Group 1 - The recent surge in the A-share market indicates the confirmation of a bull market, with the Shanghai Composite Index breaking through previous high points and showing significant trading volume, suggesting a recovery from earlier declines [1][8][6] - The impact of tariffs announced by Trump is viewed as limited, with historical examples indicating that trade wars do not significantly affect economic performance, as seen during the 1930 trade war [1][17][20] - The bull market is expected to generate three wealth effects: stabilizing expectations, supporting consumption, and restoring financing functions, with increased retail participation in the stock market [1][25][39] Group 2 - Historical analysis shows that sectors tend to rotate after new highs, with financials, cyclical resources, and military industries frequently leading the market, while manufacturing and consumer sectors rely more on their own trends [2][43][44] - Potential rotation directions in the current market include non-bank financials and cyclical resource sectors, with expectations for real estate stabilization being crucial for economic recovery [3][7] - The report highlights that the current bull market is characterized by a significant inflow of funds into the stock market, driven by increased retail investor activity and policy support [1][25][39]

Report Industry Investment Rating No relevant content provided. Core Viewpoints - On July 11, 2025, most convertible bond industries rose, and the valuation increased month - on - month. The trading sentiment in the convertible bond market weakened [1]. - The convertible bond price center increased, and the proportion of high - price bonds decreased. The convertible bond valuation increased [2]. - In the stock market, more than half of the underlying stock industry indices rose. In the convertible bond market, 22 industries rose [3]. Summary by Directory 1. Market Main Index Performance - The CSI Convertible Bond Index rose 0.03% month - on - month, the Shanghai Composite Index rose 0.01%, the Shenzhen Component Index rose 0.61%, the ChiNext Index rose 0.80%, the SSE 50 Index fell 0.01%, and the CSI 1000 Index rose 0.85% [1]. - Small - cap growth stocks were relatively dominant. The large - cap growth index rose 0.55%, the large - cap value index fell 0.80%, the mid - cap growth index rose 0.28%, the mid - cap value index fell 0.13%, the small - cap growth index rose 0.68%, and the small - cap value index rose 0.18% [1]. 2. Market Fund Performance - The trading volume in the convertible bond market was 66.069 billion yuan, a 1.25% month - on - month decrease. The total trading volume of the Wind All - A Index was 1736.61 billion yuan, a 14.62% month - on - month increase. The net outflow of the main funds in the Shanghai and Shenzhen stock markets was 14.038 billion yuan [1]. - The yield of the 10 - year treasury bond rose 0.37bp month - on - month to 1.67% [1]. 3. Convertible Bond Valuation - After excluding convertible bonds with a closing price > 150 yuan and a conversion premium rate > 50%, the fitted conversion premium rate of 100 - yuan par value was 25.38%, a 0.08pct month - on - month increase. The overall weighted par value was 94.40 yuan, a 0.52% month - on - month decrease [2][21]. - The conversion premium rates of all types of convertible bonds (divided by stock - bond nature) increased. The conversion premium rate of equity - biased convertible bonds rose 1.23pct, that of debt - biased convertible bonds rose 0.39pct, and that of balanced convertible bonds rose 0.34pct [2]. 4. Industry Performance - In the A - share market, the top three rising industries were non - bank finance (+2.02%), computer (+1.93%), and steel (+1.93%); the top three falling industries were bank (-2.41%), building materials (-0.67%), and coal (-0.60%) [3]. - In the convertible bond market, 22 industries rose. The top three rising industries were non - bank finance (+1.97%), computer (+1.09%), and non - ferrous metals (+1.05%); the top three falling industries were bank (-0.72%), textile and apparel (-0.44%), and media (-0.27%) [3]. - In terms of closing price, the large - cycle sector rose 0.81%, the manufacturing sector rose 0.05%, the technology sector fell 0.22%, the large - consumption sector rose 0.12%, and the large - finance sector rose 0.66% [3]. - In terms of conversion premium rate, the large - cycle sector rose 0.45pct, the manufacturing sector rose 0.35pct, the technology sector fell 0.22pct, the large - consumption sector rose 0.31pct, and the large - finance sector rose 1.2pct [3]. - In terms of conversion value, the large - cycle sector rose 0.18%, the manufacturing sector fell 0.18%, the technology sector rose 0.43%, the large - consumption sector rose 0.22%, and the large - finance sector rose 0.71% [3]. - In terms of pure - bond premium rate, the large - cycle sector rose 0.53pct, the manufacturing sector rose 0.15pct, the technology sector rose 0.59pct, the large - consumption sector rose 0.13pct, and the large - finance sector rose 0.71pct [4]. 5. Industry Rotation - Non - bank finance, computer, and steel led the rise. The daily increase of non - bank finance in the underlying stock market was 2.02%, and 1.97% in the convertible bond market; the daily increase of computer was 1.93% in the underlying stock market and 1.09% in the convertible bond market; the daily increase of steel was 1.93% in the underlying stock market and 0.13% in the convertible bond market [56].

Quantitative Models and Construction Methods 1. Model Name: Industry Investment Radar - **Model Construction Idea**: The model identifies four states of industry trends (volume increase with price rise, volume increase with price drop, volume decrease with price drop, and volume decrease with price rise) based on the direction of price and trading volume changes. These states are visualized in a polar coordinate system to locate investment opportunities when industries move into specific regions of the radar[7][8][11] - **Model Construction Process**: 1. **State Classification in Cartesian Coordinates**: - Price and trading volume changes are categorized into four states: - Volume increase with price rise (Quadrant 1) - Volume increase with price drop (Quadrant 2) - Volume decrease with price drop (Quadrant 3) - Volume decrease with price rise (Quadrant 4)[11] 2. **Polar Coordinate Transformation**: - **Polar Angle**: Calculated using the arctangent function to represent the ratio of trading volume change to price change $ \theta = \arctan2(\text{Volume Change}, \text{Price Change}) $[14][18] - **Polar Radius**: Calculated using the Mahalanobis distance to measure the distance between the current and historical price-volume data $ \rho = \sqrt{(x-y)^T \cdot \Sigma^{-1} \cdot (x-y)} $ where $x$ is the current price-volume vector, $y$ is the historical price-volume vector, and $\Sigma$ is the covariance matrix[13][14] 3. **State Mapping in Polar Coordinates**: - Quadrants are mapped to specific polar angle ranges: - 0°-90°: Volume increase with price rise - 90°-180°: Volume increase with price drop - 180°-270°: Volume decrease with price drop - 270°-360°: Volume decrease with price rise[17][18] - **Model Evaluation**: The model provides a clear and interpretable framework for identifying industry rotation opportunities, leveraging historical price-volume relationships to predict future performance[8][18] 2. Model Name: Position Parameter Table - **Model Construction Idea**: This model establishes a mapping between historical price-volume states and future returns by dividing the polar coordinate space into regions and calculating the average future returns for each region[29][38] - **Model Construction Process**: 1. **Region Division**: - The polar radius is divided into five equal segments, and the polar angle is divided into 16 equal regions, resulting in 80 distinct regions[29] 2. **Return Mapping**: - For each region, the average future 20-day return is calculated based on historical data[29][38] 3. **Multi-Dimensional Expansion**: - **Dimension 1**: Multiple historical periods are analyzed for their relationship with future 20-day returns[47] - **Dimension 2**: Multiple historical dates are aggregated to identify stable investment regions[45] - **Model Evaluation**: The position parameter table enhances the model's robustness by incorporating multi-period and multi-date data, providing a more comprehensive mapping of historical states to future returns[47][50] --- Model Backtesting Results 1. Industry Investment Radar - **Weekly Rebalancing Portfolio**: - Cumulative Return: 369.06% - Benchmark Return: 80.97% - Excess Return: 288.09%[56] - **Monthly Rebalancing Portfolio**: - Cumulative Return: 388.85% - Benchmark Return: 80.97% - Excess Return: 307.88%[59] - **Semi-Annual Rebalancing Portfolio**: - Cumulative Return: 279.77% - Benchmark Return: 80.97% - Excess Return: 198.80%[60] 2. Position Parameter Table - **Future 20-Day Return Mapping**: - Example Regions: - Polar Radius (1/5, 2/5), Polar Angle (4π/8, 5π/8): 5.55% - Polar Radius (0, 1/5), Polar Angle (-5π/8, -4π/8): 4.64% - Polar Radius (1/5, 2/5), Polar Angle (-6π/8, -5π/8): 4.09%[42][44] --- Quantitative Factors and Construction Methods 1. Factor Name: Price-Volume State Factor - **Factor Construction Idea**: This factor captures the relationship between price and trading volume changes to classify industry states and predict future returns[7][8][11] - **Factor Construction Process**: - Derived from the polar coordinate transformation of price and volume data, incorporating both polar radius and polar angle as key metrics[13][14][18] - **Factor Evaluation**: The factor is intuitive and interpretable, effectively linking historical price-volume dynamics to future performance[8][18] 2. Factor Name: Regional Return Factor - **Factor Construction Idea**: This factor quantifies the average future returns of industries based on their historical positions in the polar coordinate system[29][38] - **Factor Construction Process**: - Calculated as the average future 20-day return for each region in the position parameter table[29][38] - **Factor Evaluation**: The factor provides a systematic approach to identifying high-return regions, leveraging historical data to enhance predictive accuracy[45][47] --- Factor Backtesting Results 1. Price-Volume State Factor - **Future 20-Day Return Examples**: - Polar Radius (2/5, 3/5), Polar Angle (5π/8, 6π/8): 3.51% - Polar Radius (0, 1/5), Polar Angle (4π/8, 5π/8): 2.49%[42][44] 2. Regional Return Factor - **Future 20-Day Return Examples**: - Polar Radius (3/5, 4/5), Polar Angle (6π/8, 7π/8): -3.06% - Polar Radius (0, 1/5), Polar Angle (3π/8, 4π/8): -3.95%[42][44]

1. Report Industry Investment Rating No relevant content provided. 2. Core Viewpoints of the Report - Today, the convertible bond market followed the equity market's upward trend, with valuations rising on a month - on - month basis. The trading sentiment in the convertible bond market weakened [1]. - The convertible bond price center increased, and the proportion of high - price bonds rose. The overall weighted average closing price of convertible bonds was 124.13 yuan, up 0.43% from yesterday [2]. - Valuations increased. The fitted conversion premium rate for 100 - yuan par value was 25.30%, up 0.71 pct from yesterday [2]. - In the industry performance, more than half of the underlying stock industry indices rose, with 17 industries rising in the A - share market and 18 in the convertible bond market [3]. 3. Summary by Related Catalogs 3.1 Market Main Index Performance - The CSI Convertible Bond Index rose 0.40% month - on - month, the Shanghai Composite Index rose 0.48%, the Shenzhen Component Index rose 0.47%, the ChiNext Index rose 0.22%, the SSE 50 Index rose 0.62%, and the CSI 1000 Index rose 0.25% [1]. - Small - cap value stocks were relatively dominant. Small - cap value stocks rose 1.06%, while large - cap growth stocks rose 0.14% [1]. | Index Code | Index Name | Closing Price | Daily Change (%) | Weekly Change (%) | Monthly Change (%) | YTD Change (%) | | --- | --- | --- | --- | --- | --- | --- | | 000832.CSI | CSI Convertible Bond | 450.71 | 0.40 | 1.43 | 3.45 | 8.72 | | 889033.WI | Convertible Bond Equal - Weighted | 213.99 | 0.28 | 0.99 | 2.56 | 10.04 | | 8841324.WI | Convertible Bond Index | 1960.13 | 0.34 | 1.94 | 4.41 | 18.75 | | 884257.WI | Convertible Bond Pre - plan | 1713.71 | 0.21 | 1.32 | 5.18 | 17.43 | | 000001.SH | Shanghai Composite Index | 3509.68 | 0.48 | 1.59 | 3.23 | 4.71 | | 399001.SZ | Shenzhen Component Index | 10631.13 | 0.47 | 2.10 | 3.72 | 2.08 | | 399006.SZ | ChiNext Index | 2189.58 | 0.22 | 3.10 | 6.22 | 2.24 | | 000016.SH | SSE 50 Index | 2756.93 | 0.62 | 1.26 | 2.61 | 2.69 | | 000852.SH | CSI 1000 Index | 6406.57 | 0.25 | 1.54 | 3.02 | 7.53 | [7] 3.2 Market Capital Performance - The trading volume in the convertible bond market was 66.907 billion yuan, a 1.57% decrease from the previous day. The total trading volume of the Wind All - A Index was 1.515068 trillion yuan, a 0.81% decrease from the previous day [1]. - The net outflow of main funds from the Shanghai and Shenzhen stock markets was 21.158 billion yuan, and the yield of the 10 - year Treasury bond rose 1.70 bp to 1.66% [1]. 3.3 Convertible Bond Price and Valuation - The overall closing - price weighted average of convertible bonds was 124.13 yuan, up 0.43% from yesterday. The closing price of equity - biased convertible bonds was 164.25 yuan, down 0.91%; that of bond - biased convertible bonds was 115.18 yuan, up 0.46%; and that of balanced convertible bonds was 124.13 yuan, up 0.29% [2]. - The proportion of bonds with a closing price above 130 yuan was 33.19%, up 1.57 pct from yesterday. The largest change in proportion was in the 100 - 110 (including 110) range, with a proportion of 3.19%, down 1.08 pct from yesterday. There were 2 bonds with a closing price below 100 yuan [2]. - The median price was 125.74 yuan, up 0.51% from yesterday. The fitted conversion premium rate for 100 - yuan par value was 25.30%, up 0.71 pct from yesterday. The overall weighted par value was 94.96 yuan, up 0.51% from yesterday [2]. 3.4 Industry Performance - In the A - share market, the top three rising industries were real estate (+3.19%), petroleum and petrochemicals (+1.54%), and steel (+1.44%); the top three falling industries were automobiles (-0.62%), media (-0.54%), and national defense and military industry (-0.41%) [3]. - In the convertible bond market, the top three rising industries were environmental protection (+2.50%), coal (+1.39%), and non - bank finance (+0.95%); the top three falling industries were communications (-0.92%), agriculture, forestry, animal husbandry and fishery (-0.67%), and media (-0.24%) [3]. - In terms of different sectors: - Closing price: The large - cycle sector rose 0.81%, the manufacturing sector rose 0.05%, the technology sector fell 0.22%, the large - consumption sector rose 0.12%, and the large - finance sector rose 0.66% [3]. - Conversion premium rate: The large - cycle sector decreased 1.1 pct, the manufacturing sector increased 0.32 pct, the technology sector increased 0.024 pct, the large - consumption sector decreased 0.13 pct, and the large - finance sector decreased 0.34 pct [3]. - Conversion value: The large - cycle sector rose 1.12%, the manufacturing sector fell 0.41%, the technology sector fell 0.25%, the large - consumption sector rose 0.07%, and the large - finance sector rose 0.96% [3]. - Pure - bond premium rate: The large - cycle sector rose 1.0 pct, the manufacturing sector rose 0.046 pct, the technology sector fell 0.28 pct, the large - consumption sector rose 0.11 pct, and the large - finance sector rose 0.77 pct [4]. 3.5 Industry Rotation - Real estate, petroleum and petrochemicals, and steel led the rise. For example, real estate had a daily increase of 3.19% in the underlying stock market, and its PE (TTM) 3 - year quantile was 97.80%, and PB (LF) 3 - year quantile was 51.03% [53].

1. Report Industry Investment Rating No relevant content provided in the report. 2. Core Views of the Report - The convertible bond market declined with reduced trading volume today, and the valuation compressed on a month - on - month basis [1]. - The small - cap value style was relatively dominant in the market [1]. - The trading sentiment in the convertible bond market weakened [1]. 3. Summary by Related Catalogs Market Overview - Index performance: The CSI Convertible Bond Index decreased by 0.25% month - on - month, the Shanghai Composite Index decreased by 0.13%, the Shenzhen Component Index decreased by 0.06%, the ChiNext Index increased by 0.16%, the SSE 50 Index decreased by 0.26%, and the CSI 1000 Index decreased by 0.27% [1]. - Market style: Small - cap value was relatively dominant. Large - cap growth decreased by 0.23%, large - cap value decreased by 0.13%, mid - cap growth decreased by 0.60%, mid - cap value decreased by 0.17%, small - cap growth decreased by 0.32%, and small - cap value decreased by 0.01% [1]. - Fund performance: The trading sentiment in the convertible bond market weakened. The trading volume of the convertible bond market was 67.972 billion yuan, a month - on - month decrease of 9.64%; the total trading volume of the Wind All - A Index was 1527.42 billion yuan, a month - on - month increase of 3.58%; the net outflow of the main funds in the Shanghai and Shenzhen stock markets was 28.594 billion yuan, and the yield of the 10 - year Treasury bond increased by 0.02bp to 1.64% on a month - on - month basis [1]. Convertible Bond Price - The central price of convertible bonds decreased, and the proportion of high - price bonds decreased. The weighted average closing price of convertible bonds was 123.61 yuan, a month - on - month decrease of 0.24%. Among them, the closing price of equity - biased convertible bonds was 165.61 yuan, a month - on - month decrease of 1.49%; the closing price of bond - biased convertible bonds was 114.66 yuan, a month - on - month decrease of 0.30%; the closing price of balanced convertible bonds was 123.78 yuan, a month - on - month decrease of 0.38% [2]. - From the distribution of convertible bond closing prices, the proportion of high - price bonds above 130 yuan was 31.62%, a month - on - month decrease of 1.28pct; the range with the largest change in proportion was 100 - 110 (including 110), with a proportion of 4.27%, a month - on - month increase of 0.85pct; there were 2 bonds with a closing price below 100 yuan. The median price was 125.10 yuan, a month - on - month decrease of 0.32% [2]. Convertible Bond Valuation - Valuation compressed. The fitted conversion premium rate of 100 - yuan par value was 24.59%, a month - on - month decrease of 0.87pct; the overall weighted par value was 94.52 yuan, a month - on - month decrease of 0.18% [2]. - The premium rate of equity - biased convertible bonds was 6.43%, a month - on - month increase of 0.04pct; the premium rate of bond - biased convertible bonds was 94.34%, a month - on - month increase of 2.34pct; the premium rate of balanced convertible bonds was 17.97%, a month - on - month increase of 0.30pct [2]. Industry Performance - In the A - share market, the top three industries in terms of increase were media (+1.35%), agriculture, forestry, animal husbandry and fishery (+0.65%), and commerce and retail (+0.48%); the top three industries in terms of decline were non - ferrous metals (-2.26%), basic chemicals (-0.85%), and electronics (-0.82%) [3]. - In the convertible bond market, 22 industries declined. The top three industries in terms of decline were building materials (-1.83%), national defense and military industry (-1.39%), and communication (-0.87%); the top three industries in terms of increase were environmental protection (+5.76%), coal (+0.89%), and media (+0.28%) [3]. - Closing price: The large - cycle increased by 0.72% month - on - month, manufacturing decreased by 0.39%, technology decreased by 0.65%, large - consumption decreased by 0.07%, and large - finance decreased by 0.14% [3]. - Conversion premium rate: The large - cycle decreased by 0.21pct month - on - month, manufacturing decreased by 0.055pct, technology decreased by 0.41pct, large - consumption decreased by 0.19pct, and large - finance decreased by 0.083pct [3]. - Conversion value: The large - cycle increased by 0.94% month - on - month, manufacturing decreased by 0.40%, technology decreased by 0.42%, large - consumption increased by 0.14%, and large - finance increased by 0.72% [3]. - Pure bond premium rate: The large - cycle increased by 0.91pct month - on - month, manufacturing decreased by 0.52pct, technology decreased by 0.97pct, large - consumption decreased by 0.097pct, and large - finance decreased by 0.17pct [4]. Industry Rotation - Media, agriculture, forestry, animal husbandry and fishery, and commerce and retail led the rise. In the stock market, media had a daily increase of 1.35%, agriculture, forestry, animal husbandry and fishery had a daily increase of 0.65%, and commerce and retail had a daily increase of 0.48%. In the convertible bond market, media had a daily increase of 0.28%, environmental protection had a daily increase of 5.76%, and coal had a daily increase of 0.89% [3][57].

Market Sentiment - The market sentiment remains cautious, with investors not fully trusting the current rally due to past experiences of losses [1][3] - The banking sector is perceived to be suppressing the index, despite its low weight, impacting overall market performance [1] Index Performance - The market is expected to reach 3500 points soon, with the Hong Kong stock market showing positive trends [3] - The real resistance level for the market is identified at 3700 points, which needs to be surpassed for a significant bullish sentiment [3] - The white liquor sector is highlighted as a potential driver for index growth, with expectations of a 300-point increase if it performs well [3] Sector Analysis - The current market dynamics are characterized by sector rotation, with the securities sector being a key player in the ongoing rally [5] - White liquor, insurance, and banking are identified as the main sectors capable of lifting the Shanghai Composite Index [5] - The cyclical sectors like steel, cement, and coal are noted to have diminished influence compared to previous decades [5] Trading Strategy - Investors are advised to maintain their trading plans and not to overthink market movements, focusing instead on holding positions for potential gains [7] - The expectation is for a rapid upward movement in the market, suggesting that current levels should not be viewed as a peak [7]

A股、港股都涨了，白酒还有拉升空间，上证指数不会止步3500点。踏空的人很多，卖飞的人也很多， 大家都在许愿希望市场跌，作为对手盘，我们许愿市场涨。 大家都没有对错，只是立场。轻仓的盼跌，重仓的盼涨，大家怎么可能没有分歧，都是从个人的利益角 度罢了，谁也别笑话谁。 今日，上证指数随时剑指3500点了，一个长期困扰A股压力位，只有突破了才会有新的行情。 大盘指数没有问题。 当前，指数的节奏很简单，只需要快速拉升就行了。3500点需要直线拉升，而且大幅远离才叫站稳了， 不是说拉升到3600点就叫结束了。 如果想站稳3500点，上证指数必须突破4000点。就像站稳3000点需要往上拉升到3600点以上。这是震荡 向上的结构性问题，市场肯定会有急跌回调，所以要拉升回踩的空间。 慢牛就是进三退二，不会单边上涨，行业轮动拉升，指数震荡上行。大家都认为会跌的情况下，市场又 晃晃悠悠的到3500点了。 个人对指数很乐观，对股票没有想法。大家如果是股民可能会被误导，不要因为我看好指数就让您做出 持有股票的决定了，你的股票是100%会跟着指数上涨吗？ A股要迎来新一轮变盘了 一旦突破3500点，如果白酒、证券、地产共振反弹的情况 ...

- Model Name: Diffusion Index Model; Model Construction Idea: The model is based on the principle of price momentum; Model Construction Process: The model tracks the weekly changes in the diffusion index of various industries, ranking them based on their diffusion index values. The formula used is $ \text{Diffusion Index} = \frac{\text{Number of Stocks with Positive Momentum}}{\text{Total Number of Stocks}} $; Model Evaluation: The model captures industry trends effectively but may face challenges during market reversals[5][27][28] - Model Name: GRU Factor Model; Model Construction Idea: The model utilizes GRU (Gated Recurrent Unit) deep learning networks to analyze minute-level price and volume data; Model Construction Process: The model ranks industries based on their GRU factor values, which are derived from the GRU network's analysis of trading information. The formula used is $ \text{GRU Factor} = \text{GRU Network Output} $; Model Evaluation: The model performs well in short cycles but may struggle in long cycles or extreme market conditions[6][13][33] - Diffusion Index Model, IR value 2.05%, weekly average return 0.24%, monthly excess return -1.00%, annual excess return 2.05%[25][30] - GRU Factor Model, IR value -4.52%, weekly average return 1.32%, monthly excess return 0.77%, annual excess return -4.52%[32][37] - Factor Name: GRU Industry Factor; Factor Construction Idea: The factor is derived from GRU deep learning networks analyzing minute-level trading data; Factor Construction Process: The factor values are calculated based on the GRU network's output, ranking industries accordingly. The formula used is $ \text{GRU Factor} = \text{GRU Network Output} $; Factor Evaluation: The factor captures short-term trading information effectively but may face challenges in long-term or extreme market conditions[6][13][33] - GRU Industry Factor, IR value -4.52%, weekly average return 1.32%, monthly excess return 0.77%, annual excess return -4.52%[32][37]

Group 1 - The report highlights that the domestic A-share market has seen a significant improvement in liquidity risk, with a total transaction volume of 1.44 trillion yuan this week, driven by continuous buying from state-owned funds [5] - The report indicates a positive outlook for the military industry, particularly in the context of the marine economy, which is expected to catalyze growth in the sector [5] - The report notes that the semiconductor and domestic consumption sectors present potential investment opportunities due to improved risk appetite and capital inflows [5] Group 2 - The report details that the "Xinxuan ETF Absolute Return Strategy" has achieved an annualized return of 14.23% over the past three years, with a maximum drawdown of only 8.6% [10] - The "All-Weather Multi-Asset Risk Parity Strategy" has yielded a return of 20.85% since the beginning of 2024, with a maximum drawdown of 3.62% [14] - The "China-US Core Asset Portfolio" has delivered an annualized return of 34.05% since early 2015, outperforming various indices [20] Group 3 - The report states that 17 new index funds were filed this week, including 3 ETFs and 5 linked funds, indicating a growing interest in index-based investment products [34] - A total of 20 new public funds were established this week, raising a total of 5.328 billion yuan, with 11 new index funds accounting for 3.226 billion yuan of that total [35] - The report mentions that 6 new funds are set to be listed next week, including the "E Fund National Value 100 ETF" and the "Industrial Bank China Hong Kong Stock Connect Automotive Industry Theme ETF" [38] Group 4 - The report indicates that A-share ETFs experienced a net outflow of 136.4 billion yuan, while bond ETFs saw a net inflow of 122.9 billion yuan [43] - The report highlights that the Hong Kong ETF market has seen a net inflow of 88 billion yuan, reflecting a positive sentiment towards cross-border investments [48] - The report notes that commodity ETFs, particularly gold ETFs, have seen an increase in investment, with a net inflow of 23.18 billion yuan [52]

Quantitative Models and Construction Methods - **Model Name**: A-Share Industry Rotation Model **Model Construction Idea**: The model uses six factors—capital, valuation, sentiment, momentum, overbought/oversold, and profitability—to build a scoring system for industry evaluation[17] **Model Construction Process**: - **Capital Factor**: Based on industry net inflow rate of major funds - **Valuation Factor**: Uses the valuation percentile of the industry over the past year - **Sentiment Factor**: Derived from the proportion of rising constituent stocks - **Momentum Factor**: Based on MACD indicator - **Overbought/Oversold Factor**: Uses RSI indicator - **Profitability Factor**: Based on the consensus forecast EPS percentile of the industry over the past year[17] **Model Evaluation**: The model provides a comprehensive scoring system to assess industry rotation trends[17] - **Model Name**: Consensus Stock Selection Model **Model Construction Idea**: The model identifies high-growth industries and selects stocks with high similarity between high-frequency capital flow trends and stock price trends[20] **Model Construction Process**: - Filters high-growth industries at the Shenwan secondary industry level based on the past 30-day performance - Calculates momentum, valuation, and frequency of price increases for stocks within these industries - Uses high-frequency minute-level capital flow data to compute changes in inflow/outflow for each stock - Selects stocks with the highest similarity between capital flow trends and price trends within the top-performing secondary industries[20] **Model Evaluation**: The model effectively identifies stocks with strong capital flow and price trend alignment[20] --- Model Backtesting Results - **A-Share Industry Rotation Model**: - **Top Scoring Industries**: Comprehensive (+10), Non-ferrous Metals (+10), Electronics (+7)[18][19] - **Low Scoring Industries**: Banking (-15), Petrochemicals (-9), Transportation (-8)[19] - **Consensus Stock Selection Model**: - **Selected Industries**: Communication Equipment, Ground Armament II, Components[21] - **Selected Stocks**: - Communication Equipment: New Yisheng, Move Communication, Feiling Kesi, Hengtong Optoelectronics, Meixin Technology - Ground Armament II: Great Wall Military Industry, Optical Shares, Inner Mongolia First Machine, Sweet Qin Equipment, Ganfa Technology - Components: Jingwang Electronics, Deep South Circuit, Fangbang Shares, Zhongjing Electronics, Shenghong Technology[21] --- Quantitative Factors and Construction Methods - **Factor Name**: Capital Factor **Construction Idea**: Measures industry net inflow rate of major funds[17] **Construction Process**: Aggregates daily net inflow data for transactions exceeding 10,000 shares or 200,000 yuan[12] **Evaluation**: Reflects the strength of capital flow within industries[17] - **Factor Name**: Valuation Factor **Construction Idea**: Uses industry valuation percentile over the past year[17] **Construction Process**: Calculates the relative valuation position of the industry within a one-year window[17] **Evaluation**: Indicates whether an industry is undervalued or overvalued[17] - **Factor Name**: Sentiment Factor **Construction Idea**: Based on the proportion of rising constituent stocks[17] **Construction Process**: Computes the percentage of stocks within the industry that have increased in price[17] **Evaluation**: Captures market sentiment towards the industry[17] - **Factor Name**: Momentum Factor **Construction Idea**: Uses MACD indicator to measure price trends[17] **Construction Process**: Applies MACD calculations to industry-level data[17] **Evaluation**: Identifies industries with strong upward or downward trends[17] - **Factor Name**: Overbought/Oversold Factor **Construction Idea**: Uses RSI indicator to assess market conditions[17] **Construction Process**: Calculates RSI values for industries to determine overbought or oversold conditions[17] **Evaluation**: Helps identify potential reversals in industry trends[17] - **Factor Name**: Profitability Factor **Construction Idea**: Based on consensus forecast EPS percentile over the past year[17] **Construction Process**: Aggregates EPS forecasts and calculates relative percentile rankings[17] **Evaluation**: Reflects the earnings potential of industries[17] --- Factor Backtesting Results - **Capital Factor**: Comprehensive (++), Non-ferrous Metals (++), Electronics (++), Banking (---), Petrochemicals (---), Transportation (---)[19] - **Valuation Factor**: Comprehensive (+++), Non-ferrous Metals (++), Electronics (+), Banking (-), Petrochemicals (---), Transportation (---)[19] - **Sentiment Factor**: Comprehensive (-), Non-ferrous Metals (+++), Electronics (+++), Banking (--), Petrochemicals (---), Transportation (---)[19] - **Momentum Factor**: Comprehensive (+++), Non-ferrous Metals (+++), Electronics (+), Banking (--), Petrochemicals (---), Transportation (---)[19] - **Overbought/Oversold Factor**: Comprehensive (+++), Non-ferrous Metals (+++), Electronics (+), Banking (--), Petrochemicals (---), Transportation (---)[19] - **Profitability Factor**: Comprehensive (+++), Non-ferrous Metals (+++), Electronics (+++), Banking (---), Petrochemicals (---), Transportation (---)[19]