Quantitative Models and Construction Methods - **Model Name**: Multi-cycle timing model for domestic interest rate price-volume trends **Model Construction Idea**: The model uses kernel regression algorithms to capture interest rate trend patterns, identifying support and resistance lines based on the shape of interest rate movements across different investment cycles [10][24] **Model Construction Process**: 1. **Data Input**: Utilize 5-year, 10-year, and 30-year government bond YTM data as the basis for analysis [10][24] 2. **Cycle Classification**: Divide the investment horizon into long-term (monthly frequency), medium-term (bi-weekly frequency), and short-term (weekly frequency) cycles [10][24] 3. **Signal Identification**: Detect upward or downward breakthroughs of support and resistance lines for each cycle [10][24] 4. **Composite Scoring**: Aggregate signals across cycles, assigning scores based on the number of consistent breakthroughs (e.g., 2/3 consistent signals lead to a "buy" or "sell" recommendation) [10][24] **Model Evaluation**: The model effectively captures multi-cycle resonance in interest rate trends, providing actionable timing signals for bond trading strategies [10][24] - **Model Name**: Multi-cycle timing model for U.S. interest rate price-volume trends **Model Construction Idea**: Apply the domestic interest rate price-volume timing model to the U.S. Treasury market [21] **Model Construction Process**: 1. **Data Input**: Use 10-year U.S. Treasury YTM data for analysis [21] 2. **Cycle Classification**: Similar to the domestic model, divide the investment horizon into long-term, medium-term, and short-term cycles [21] 3. **Signal Identification**: Detect upward or downward breakthroughs of support and resistance lines for each cycle [21] 4. **Composite Scoring**: Aggregate signals across cycles, assigning scores based on the number of consistent breakthroughs [21] **Model Evaluation**: The model provides a neutral-to-bullish outlook for U.S. Treasury yields, indicating its adaptability to international markets [21] Model Backtesting Results - **Domestic Multi-cycle Timing Model**: - **5-year YTM**: - Long-term annualized return: 5.5% - Maximum drawdown: 2.88% - Return-to-drawdown ratio: 1.91 - Short-term annualized return (since 2024): 1.86% - Maximum drawdown: 0.59% - Return-to-drawdown ratio: 3.16 - Long-term excess return: 1.07% - Short-term excess return: 0.85% [25][27] - **10-year YTM**: - Long-term annualized return: 6.09% - Maximum drawdown: 2.74% - Return-to-drawdown ratio: 2.22 - Short-term annualized return (since 2024): 2.42% - Maximum drawdown: 0.58% - Return-to-drawdown ratio: 4.19 - Long-term excess return: 1.66% - Short-term excess return: 1.55% [28][32] - **30-year YTM**: - Long-term annualized return: 7.38% - Maximum drawdown: 4.27% - Return-to-drawdown ratio: 1.73 - Short-term annualized return (since 2024): 3.11% - Maximum drawdown: 0.92% - Return-to-drawdown ratio: 3.39 - Long-term excess return: 2.42% - Short-term excess return: 2.87% [33][35] - **U.S. Multi-cycle Timing Model**: - **10-year YTM**: - Current signal: Neutral-to-bullish - Long-term annualized return: Not provided - Maximum drawdown: Not provided - Return-to-drawdown ratio: Not provided [21][23] Quantitative Factors and Construction Methods - **Factor Name**: Interest rate structure indicators (level, term, convexity) **Factor Construction Idea**: Transform YTM data into structural indicators to analyze the interest rate market from a mean-reversion perspective [7] **Factor Construction Process**: 1. **Level Structure**: Calculate the average YTM across maturities (1-10 years) 2. **Term Structure**: Measure the slope between short-term and long-term YTM 3. **Convexity Structure**: Assess the curvature of the yield curve [7] **Factor Evaluation**: The indicators effectively capture the current state of the interest rate market, highlighting deviations from historical averages [7] Factor Backtesting Results - **Interest Rate Structure Indicators**: - **Level Structure**: Current reading: 1.64%, historical 10-year percentile: 7% - **Term Structure**: Current reading: 0.38%, historical 10-year percentile: 16% - **Convexity Structure**: Current reading: -0.09%, historical 10-year percentile: 1% [7]

- The report introduces the "Information Distribution Uniformity (UID) factor" as a stock selection factor, which is constructed using minute-level data of individual stocks to calculate daily high-frequency volatility[1][6] - The UID factor significantly outperforms traditional volatility factors in stock selection, even after removing the interference of commonly used market styles and industries, with an annualized ICIR of -3.17[1] - The performance of the UID factor from January 2014 to September 2025 includes an annualized return of 26.48%, annualized volatility of 9.88%, an information ratio (IR) of 2.68, a monthly win rate of 78.72%, and a maximum monthly drawdown of 6.05%[1][7][12] - In September 2025, the 10-group long portfolio of the UID factor in the entire A-share market had a return of 1.84%, the 10-group short portfolio had a return of 0.04%, and the 10-group long-short hedged portfolio had a return of 1.80%[1][11] - The construction process of the UID factor involves using minute-level price data to calculate the high-frequency volatility of individual stocks and then constructing the UID factor based on the uniformity of information distribution[1][6] - The report highlights that the UID factor, despite being a single factor, shows good stock selection ability and can be a valuable addition to the factor library[1][6]

Quantitative Models and Construction Methods - **Model Name**: Multi-cycle timing model for domestic interest rate price-volume trends **Model Construction Idea**: The model uses kernel regression algorithms to capture interest rate trend patterns, identifying support and resistance lines based on different investment cycles. It provides composite timing signals by analyzing the shape of interest rate movements across long, medium, and short cycles[10][24][29] **Model Construction Process**: 1. **Data Input**: Use 5-year, 10-year, and 30-year government bond YTM data as the basis for analysis[10][24][29] 2. **Cycle Definition**: Define long, medium, and short cycles with average switching frequencies of monthly, bi-weekly, and weekly, respectively[10][24][29] 3. **Signal Generation**: - If at least two cycles show downward breakthroughs of the support line and the interest rate trend is not upward, allocate fully to long-duration bonds - If at least two cycles show downward breakthroughs of the support line but the interest rate trend is upward, allocate 50% to medium-duration bonds and 50% to long-duration bonds - If at least two cycles show upward breakthroughs of the resistance line and the interest rate trend is not downward, allocate fully to short-duration bonds - If at least two cycles show upward breakthroughs of the resistance line but the interest rate trend is downward, allocate 50% to medium-duration bonds and 50% to short-duration bonds - In other cases, allocate equally across short, medium, and long durations[24][29][29] **Model Evaluation**: The model demonstrates strong adaptability across different market environments and provides consistent timing signals based on multi-cycle resonance[10][24][29] - **Model Name**: Multi-cycle timing model for U.S. interest rate price-volume trends **Model Construction Idea**: The domestic price-volume timing model is applied to the U.S. interest rate market, analyzing long, medium, and short cycles to generate composite timing signals[21][23][24] **Model Construction Process**: 1. **Data Input**: Use 10-year U.S. Treasury YTM data for analysis[21][23][24] 2. **Cycle Definition**: Define long, medium, and short cycles with average switching frequencies of monthly, bi-weekly, and weekly, respectively[21][23][24] 3. **Signal Generation**: Similar to the domestic model, signals are generated based on the number of cycles showing breakthroughs of support or resistance lines and the direction of interest rate trends[21][23][24] **Model Evaluation**: The model effectively captures U.S. interest rate trends and provides reliable timing signals for investment decisions[21][23][24] Model Backtesting Results - **Domestic Multi-cycle Timing Model** - **5-year YTM**: - Long-term annualized return: 5.5% - Maximum drawdown: 2.88% - Return-to-drawdown ratio: 1.91 - Short-term annualized return (since 2024): 1.86% - Maximum drawdown: 0.59% - Return-to-drawdown ratio: 3.15 - Long-term excess return: 1.07% - Excess return-to-drawdown ratio: 0.62 - Short-term excess return: 0.86% - Excess return-to-drawdown ratio: 2.18[25][27][37] - **10-year YTM**: - Long-term annualized return: 6.09% - Maximum drawdown: 2.74% - Return-to-drawdown ratio: 2.23 - Short-term annualized return (since 2024): 2.35% - Maximum drawdown: 0.58% - Return-to-drawdown ratio: 4.07 - Long-term excess return: 1.66% - Excess return-to-drawdown ratio: 1.16 - Short-term excess return: 1.56% - Excess return-to-drawdown ratio: 3.46[28][32][37] - **30-year YTM**: - Long-term annualized return: 7.38% - Maximum drawdown: 4.27% - Return-to-drawdown ratio: 1.73 - Short-term annualized return (since 2024): 2.98% - Maximum drawdown: 0.92% - Return-to-drawdown ratio: 3.26 - Long-term excess return: 2.42% - Excess return-to-drawdown ratio: 0.87 - Short-term excess return: 2.87% - Excess return-to-drawdown ratio: 3.21[33][35][37] - **U.S. Multi-cycle Timing Model** - **10-year YTM**: - Composite signal: Long cycle upward breakthrough, medium and short cycles downward breakthrough - Final signal: Bullish[21][23][24]

Quantitative Models and Construction Methods - **Model Name**: Excellent Fund Performance Enhancement Portfolio **Construction Idea**: Shift from benchmarking broad-based indices to benchmarking active equity funds, leveraging quantitative methods to enhance fund holdings for optimal selection [4][48][49] **Construction Process**: 1. Benchmark against active equity fund median returns, represented by the biased equity hybrid fund index (885001.WI) [18][48] 2. Select funds based on performance layering, neutralizing return-related factors to mitigate style concentration risks [48] 3. Optimize the portfolio to control deviations in individual stocks, industries, and styles compared to selected fund holdings [49] **Evaluation**: Demonstrates stability and ability to outperform active equity fund medians [49] - **Model Name**: Outperformance Selection Portfolio **Construction Idea**: Focus on stocks with significant earnings surprises, combining fundamental and technical analysis for selection [5][54] **Construction Process**: 1. Identify stocks with earnings surprises based on research titles and analysts' profit revisions [5][54] 2. Conduct dual-layer screening on fundamental and technical dimensions to select stocks with both fundamental support and technical resonance [5][54] **Evaluation**: Consistently ranks in the top 30% of active equity funds annually [55] - **Model Name**: Brokerage Golden Stock Performance Enhancement Portfolio **Construction Idea**: Use brokerage golden stock pools as the stock selection space and constraint benchmark, optimizing the portfolio to control deviations [6][59] **Construction Process**: 1. Benchmark against active equity fund medians, represented by the biased equity hybrid fund index [33][59] 2. Optimize the portfolio to control deviations in individual stocks, industries, and styles compared to the brokerage golden stock pool [6][59] **Evaluation**: Stable performance, consistently ranking in the top 30% of active equity funds annually [60] - **Model Name**: Growth and Stability Portfolio **Construction Idea**: Focus on the time-series release intensity of excess returns for growth stocks, constructing a two-dimensional evaluation system [7][64] **Construction Process**: 1. Use "excess return release maps" to identify the strongest release periods of excess returns around positive events [64] 2. Prioritize stocks closer to financial report disclosure dates, and apply multi-factor scoring to select high-quality stocks when sample size is large [7][64] 3. Introduce mechanisms like weak balance, transition, buffer, and risk avoidance to reduce turnover and mitigate risks [64] **Evaluation**: High efficiency in capturing excess returns during optimal periods, consistently ranking in the top 30% of active equity funds annually [64][65] --- Model Backtesting Results - **Excellent Fund Performance Enhancement Portfolio**: - Annualized return (2012.1.4-2025.6.30): 20.31% - Annualized excess return vs. biased equity hybrid fund index: 11.83% - Most years ranked in the top 30% of active equity funds [50][53] - **Outperformance Selection Portfolio**: - Annualized return (2010.1.4-2025.6.30): 30.55% - Annualized excess return vs. biased equity hybrid fund index: 24.68% - Most years ranked in the top 30% of active equity funds [55][57] - **Brokerage Golden Stock Performance Enhancement Portfolio**: - Annualized return (2018.1.2-2025.6.30): 19.34% - Annualized excess return vs. biased equity hybrid fund index: 14.38% - Most years ranked in the top 30% of active equity funds [60][63] - **Growth and Stability Portfolio**: - Annualized return (2012.1.4-2025.6.30): 35.51% - Annualized excess return vs. biased equity hybrid fund index: 26.88% - Most years ranked in the top 30% of active equity funds [65][68] --- Portfolio Weekly and Yearly Performance - **Excellent Fund Performance Enhancement Portfolio**: - Weekly absolute return: -0.98% - Weekly excess return vs. biased equity hybrid fund index: 0.54% - Yearly absolute return: 29.30% - Yearly excess return vs. biased equity hybrid fund index: -4.01% - Yearly ranking: 54.63% percentile (1895/3469) [2][24][17] - **Outperformance Selection Portfolio**: - Weekly absolute return: 0.22% - Weekly excess return vs. biased equity hybrid fund index: 1.74% - Yearly absolute return: 47.41% - Yearly excess return vs. biased equity hybrid fund index: 14.10% - Yearly ranking: 21.71% percentile (753/3469) [2][32][17] - **Brokerage Golden Stock Performance Enhancement Portfolio**: - Weekly absolute return: -1.51% - Weekly excess return vs. biased equity hybrid fund index: 0.01% - Yearly absolute return: 34.07% - Yearly excess return vs. biased equity hybrid fund index: 0.76% - Yearly ranking: 44.42% percentile (1541/3469) [2][39][17] - **Growth and Stability Portfolio**: - Weekly absolute return: -0.08% - Weekly excess return vs. biased equity hybrid fund index: 1.44% - Yearly absolute return: 54.84% - Yearly excess return vs. biased equity hybrid fund index: 21.53% - Yearly ranking: 13.00% percentile (451/3469) [3][43][17]

- The high-frequency skewness factor showed strong performance with long-short returns of 0.9%, 4.93%, and 22.69% for the past week, September, and 2025, respectively[5][9] - The intraday downside volatility proportion factor had long-short returns of 0.77%, 5.18%, and 18.23% for the past week, September, and 2025, respectively[5][9] - The post-open buying intention proportion factor had long-short returns of 1.11%, 3.65%, and 19.98% for the past week, September, and 2025, respectively[5][9] - The post-open buying intention intensity factor had long-short returns of 1.62%, 3.28%, and 25.81% for the past week, September, and 2025, respectively[5][9] - The post-open large order net buying proportion factor had long-short returns of 0.34%, 1.51%, and 20.7% for the past week, September, and 2025, respectively[5][9] - The post-open large order net buying intensity factor had long-short returns of 0.38%, 1.51%, and 12.86% for the past week, September, and 2025, respectively[5][9] - The intraday return factor had long-short returns of 0.98%, 1.26%, and 20.66% for the past week, September, and 2025, respectively[5][9] - The end-of-day trading proportion factor had long-short returns of 1.25%, 4.18%, and 17.74% for the past week, September, and 2025, respectively[5][9] - The average single outflow amount proportion factor had long-short returns of 0.29%, 0.26%, and -0.54% for the past week, September, and 2025, respectively[5][9] - The large order-driven price increase factor had long-short returns of 0.09%, 2.88%, and 8.88% for the past week, September, and 2025, respectively[5][9] - The GRU(10,2)+NN(10) deep learning factor had long-short returns of 1.33%, 8.73%, and 41.75% for the past week, September, and 2025, respectively, with long-only excess returns of 0.71%, 3.42%, and 8.08%[5][9] - The GRU(50,2)+NN(10) deep learning factor had long-short returns of 1%, 7.98%, and 42.75% for the past week, September, and 2025, respectively, with long-only excess returns of 0.63%, 2.99%, and 7.91%[5][9] - The multi-granularity model (5-day label) factor had long-short returns of 0.99%, 6.15%, and 53.09% for the past week, September, and 2025, respectively, with long-only excess returns of 0.5%, 2.56%, and 19.48%[5][9] - The multi-granularity model (10-day label) factor had long-short returns of 0.81%, 5.2%, and 49.1% for the past week, September, and 2025, respectively, with long-only excess returns of 0.37%, 2.97%, and 20.1%[5][9] - The weekly rebalanced CSI 500 AI enhanced wide constraint portfolio had excess returns of -0.99%, -4.8%, and -0.06% for the past week, September, and 2025, respectively[5][11] - The weekly rebalanced CSI 500 AI enhanced strict constraint portfolio had excess returns of -1%, -2.32%, and 2.66% for the past week, September, and 2025, respectively[5][11] - The weekly rebalanced CSI 1000 AI enhanced wide constraint portfolio had excess returns of -1.48%, -1.06%, and 7.53% for the past week, September, and 2025, respectively[5][11] - The weekly rebalanced CSI 1000 AI enhanced strict constraint portfolio had excess returns of -0.79%, -0.12%, and 13.11% for the past week, September, and 2025, respectively[5][11]

每周 一 景：湖南衡阳衡山 点击右上角菜单，收听朗读版 【金工】大类资产及择时观点月报 大类资产 4季度配置信号： 根据 2025年9月底的最新数据，信用利差和期限利差均发出收窄信号，Q4宏观环境预测结果为Imnflation。 行业复合趋势因子组合表现及信号： 2015年1月至2025年9月，行业复合趋势因子组合的累积收益为122.66%,超额收益为48.42%上月（2025年9月）因子 信号为正向，Wind全A当月收益率为2.80%。根据2025年9月底的最新数据，行业复合趋势因子为-0.30出现下滑，但依旧维持正向信号。 风险提示： 模型失效风险、因于失效风险、海外市场波动风险， 【电子】自强，先进制程设备的突破是核心 投资建议： 美国众议院"中美战略竞争特别委员会"出具一份对中国半导体战略的系统性"围堵"建议书，核心逻辑是中国半导体产业的崛起威胁美国国家安全 与全球技术主导地位，报告建议通过出口管制、技术封锁、产业补贴等手段，确保美国及其盟友在全球半导体产业链中的主导地位，从而遏制半导体崛起。我 们认为半导体产业的全球化仍然是不变的追求，但美国政府不断打压限制我国集成电路产业的发展，本土优秀的半导体装 ...

- The report proposes an iterative factor selection strategy to compress the "factor zoo" by systematically evaluating the contribution of new factors to the remaining alpha of the factors using the GRS statistic[2][3][4] - The iterative factor selection process starts with the CAPM model and adds one factor at a time that maximally reduces the remaining alpha of the factors, measured by the decrease in the GRS statistic[3][25][26] - The process stops when the added factor no longer makes the remaining alpha of the factors statistically significant from zero[3][25][26] - The study finds that only 10 to 20 carefully selected factors are needed to effectively explain the performance of 153 factors in the US market, indicating high redundancy among factors[4][17][19] - The selected factors come from 8 out of 13 factor style categories, showing the heterogeneity of the factor set[17][19] - The iterative factor model outperforms common academic models by selecting alternative definitions of value, profitability, investment, or momentum factors, or including alternative factor style categories such as seasonality or short-term reversal[17][19] - The study also confirms that equal-weighted factors exhibit stronger and more diverse alpha, requiring more than 30 factors to cover the factor zoo[4][64][69] - The effectiveness of the method is validated using global data, showing similar core factor sets across different regions, but with the global model explaining US factors better than non-US factors[4][71][75] - The iterative factor selection strategy provides a practical framework for investors to streamline their models by focusing on the most relevant factors[2][3][4] Factor Selection Process Results - The iterative factor selection process results in a model that starts with the CAPM model, which leaves 105 significant alphas (t>2) and 86 significant alphas (t>3) with a GRS statistic of 4.36 and a p-value of 0.00[39][40] - Adding the cash-based operating profits-to-book assets (cop_at) factor reduces the GRS statistic to 3.54, leaving 101 significant alphas (t>2) and 78 significant alphas (t>3) with an average absolute alpha of 3.94%[39][40] - The process continues by adding factors such as change in net operating assets (noa_grla), sales growth (saleq_gr1), and intrinsic value-to-market (ival_me), among others, until the remaining significant alphas are reduced to zero[39][40][41] - The final model includes 15 to 18 factors, depending on the significance threshold, effectively explaining the factor zoo[39][42][43] Comparison with Common Academic Models - The iterative factor model leaves fewer significant alphas compared to common academic models such as the Fama and French five-factor and six-factor models, the q-factor model, and the mispricing model[43][44] - The Barillas et al. (2020) revised six-factor model performs better than other academic models but still leaves 33 significant alphas, while the iterative factor model leaves only 10 significant alphas with four factors and 14 significant alphas with five factors[43][44] Global Factor Analysis - The global factor analysis shows that 11 global factors are needed to cover the global factor zoo at the t>3 threshold, and around 20 factors at the t>2 threshold[73][74] - The global factor model explains US factors better than non-US factors, indicating that international factors have higher and more diverse alpha potential[75][76][77] Rolling Window Analysis - The rolling window analysis shows that the number of factors needed to cover the factor zoo decreases over time, with around 8 factors needed in recent years compared to 15 factors in the early sample period[59][60][61] - The most relevant factor styles over time include low volatility, seasonality, investment, and quality, while the relevance of momentum, short-term reversal, and value has decreased in recent years[59][60][61] Robustness to Alternative Weighting Schemes - The robustness analysis shows that equal-weighted factors require more than 30 factors to cover the factor zoo, while cap-weighted and value-weighted factors require 18 and 19 factors, respectively[64][65][69] - The equal-weighted factor model exhibits higher and more diverse alpha potential, indicating the need for more factors to cover the equal-weighted factor zoo[64][65][69]

Quantitative Models and Construction Methods 1. Model Name: Weekly Rebalancing AI-Enhanced CSI 500 Wide Constraint Portfolio - **Model Construction Idea**: This model aims to enhance the CSI 500 index performance by leveraging AI-based factors while applying wide constraints on portfolio construction [72][73] - **Model Construction Process**: - The model uses deep learning factors (e.g., multi-granularity model with 10-day labels) as the basis for stock selection [72] - Constraints include: - Stock weight: 1% - Industry weight: 1% - Market cap weight: 0.3 - Turnover rate constraint: 0.3 - The optimization objective is to maximize expected returns, represented by the formula: $$ max \sum \mu_{i}w_{i} $$ where \( w_{i} \) is the weight of stock \( i \) in the portfolio, and \( \mu_{i} \) is the expected excess return of stock \( i \) [73][74] - **Model Evaluation**: The model demonstrates moderate performance under wide constraints, with cumulative excess returns shown over time [75][77] 2. Model Name: Weekly Rebalancing AI-Enhanced CSI 500 Strict Constraint Portfolio - **Model Construction Idea**: Similar to the wide constraint model but applies stricter constraints to control risk and enhance robustness [72][73] - **Model Construction Process**: - Constraints include: - Stock weight: 1% - Industry weight: 1% - Market cap weight: 0.1 - Additional constraints: - Market cap squared: 0.1 - ROE: 0.3 - SUE: 0.3 - Volatility: 0.3 - Component stock constraint: 0.8 - Optimization objective remains the same as the wide constraint model [73][74] - **Model Evaluation**: The stricter constraints result in a more stable performance, with cumulative excess returns displayed over time [76][80] 3. Model Name: Weekly Rebalancing AI-Enhanced CSI 1000 Wide Constraint Portfolio - **Model Construction Idea**: This model targets the CSI 1000 index, applying wide constraints while leveraging AI-based factors for enhanced returns [72][73] - **Model Construction Process**: - Constraints are similar to the CSI 500 wide constraint model, with a focus on smaller-cap stocks [73] - **Model Evaluation**: The model shows significant cumulative excess returns, particularly in recent years [79][86] 4. Model Name: Weekly Rebalancing AI-Enhanced CSI 1000 Strict Constraint Portfolio - **Model Construction Idea**: Similar to the wide constraint model but applies stricter constraints to manage risk and improve consistency [72][73] - **Model Construction Process**: - Constraints are similar to the CSI 500 strict constraint model, tailored for the CSI 1000 index [73] - **Model Evaluation**: The model demonstrates strong performance under strict constraints, with cumulative excess returns highlighted [85][87] --- Model Backtesting Results 1. Weekly Rebalancing AI-Enhanced CSI 500 Wide Constraint Portfolio - **Weekly Excess Return**: -1.36% (last week), -3.85% (September), 0.94% (YTD 2025) [13][78] - **Weekly Win Rate**: 23/39 weeks [13] 2. Weekly Rebalancing AI-Enhanced CSI 500 Strict Constraint Portfolio - **Weekly Excess Return**: -1.35% (last week), -1.33% (September), 3.70% (YTD 2025) [13][81] - **Weekly Win Rate**: 24/39 weeks [13] 3. Weekly Rebalancing AI-Enhanced CSI 1000 Wide Constraint Portfolio - **Weekly Excess Return**: 0.40% (last week), 0.42% (September), 9.15% (YTD 2025) [13][83] - **Weekly Win Rate**: 26/39 weeks [13] 4. Weekly Rebalancing AI-Enhanced CSI 1000 Strict Constraint Portfolio - **Weekly Excess Return**: -0.19% (last week), 0.67% (September), 14.01% (YTD 2025) [13][90] - **Weekly Win Rate**: 25/39 weeks [13] --- Quantitative Factors and Construction Methods 1. Factor Name: Intraday Skewness Factor - **Factor Construction Idea**: Captures the skewness of intraday stock returns to identify potential outperformers [6][8] - **Factor Construction Process**: Referenced in the report "Stock Selection Factor Series Research (19)" [13] - **Factor Evaluation**: Demonstrates strong performance with IC values of 0.027 (historical) and 0.042 (2025) [9][10] 2. Factor Name: Downside Volatility Proportion Factor - **Factor Construction Idea**: Measures the proportion of downside volatility in realized volatility to assess risk-adjusted returns [6][8] - **Factor Construction Process**: Referenced in the report "Stock Selection Factor Series Research (25)" [18][20] - **Factor Evaluation**: Shows moderate performance with IC values of 0.025 (historical) and 0.036 (2025) [9][10] 3. Factor Name: Post-Open Buying Intensity Factor - **Factor Construction Idea**: Quantifies the intensity of buying activity after market open to identify short-term momentum [6][8] - **Factor Construction Process**: Referenced in the report "Stock Selection Factor Series Research (64)" [22][26] - **Factor Evaluation**: Displays stable performance with IC values of 0.035 (historical) and 0.030 (2025) [9][10] 4. Factor Name: Deep Learning Factor (Improved GRU(50,2)+NN(10)) - **Factor Construction Idea**: Utilizes a gated recurrent unit (GRU) and neural network (NN) architecture to predict stock returns [6][8] - **Factor Construction Process**: Combines GRU with NN to capture temporal dependencies in high-frequency data [61][62] - **Factor Evaluation**: Strong performance with IC values of 0.066 (historical) and 0.050 (2025) [12][61] --- Factor Backtesting Results 1. Intraday Skewness Factor - **IC**: 0.027 (historical), 0.042 (2025) [9][10] - **Multi-Long-Short Return**: 3.82% (September), 16.22% (YTD 2025) [9][10] 2. Downside Volatility Proportion Factor - **IC**: 0.025 (historical), 0.036 (2025) [9][10] - **Multi-Long-Short Return**: 2.86% (September), 13.58% (YTD 2025) [9][10] 3. Post-Open Buying Intensity Factor - **IC**: 0.035 (historical), 0.030 (2025) [9][10] - **Multi-Long-Short Return**: 0.65% (September), 11.29% (YTD 2025) [9][10] 4. Deep Learning Factor (Improved GRU(50,2)+NN(10)) - **IC**: 0.066 (historical), 0.050 (2025) [12][61] - **Multi-Long-Short Return**: 2.13% (September), 7.40% (YTD 2025) [12][61]

Group 1 - The article discusses a series of upcoming online meetings focused on various sectors, including renewable energy, AI, and the electronic industry [1][22]. - The first meeting on September 29 will cover "Renewable Energy + AI" led by Liu Qiang, the Chief Analyst of the Electric New Industry [1][22]. - The second meeting on the same day will focus on "Industry Configuration Model Review and Update Series" led by Liu Xiaofeng, the Chief Analyst of Financial Engineering [1][22]. Group 2 - An electronic industry investment outlook meeting is scheduled for September 30, featuring Zhang Shijie, the Chief Analyst of the Electronic Industry [1][12]. - A deep dive into the new stock of "Laoxiangji" will take place on October 10, presented by Guo Mengjie and Lin Xuxi, analysts in the food and beverage sector [1][20].

Core Viewpoint - The articles present a comprehensive collection of trading strategies and research reports focused on index futures and options, emphasizing quantitative methods and market timing techniques [2][3]. Group 1: Index Futures Trading Strategies - The series includes various strategies such as noise trend trading based on chaos theory, trend-following strategies using polynomial fitting, and day trading systems based on intraday volatility extremes [2]. - Additional strategies cover genetic programming methods for intelligent trading, statistical language models for timing trades, and deep learning approaches for intraday trading [2][3]. - The reports also explore cross-variety arbitrage strategies and high-frequency trading techniques, indicating a focus on both theoretical and practical applications in the futures market [3]. Group 2: Derivatives and Options Strategies - The derivatives series provides foundational knowledge on options, including dynamic hedging strategies and volatility arbitrage [3]. - It discusses the impact of options on the underlying assets and market dynamics, highlighting the importance of options in institutional investment strategies [3]. - The reports also analyze the development of global individual stock options markets and their implications for market participants [3].