- The dividend timing model was constructed in the report "Dividend Style Timing Scheme" on October 16, 2025[5] - The latest composite factor value of the dividend timing model for the week of 20260302 to 20260306 is -0.38, which remains negative and has declined compared to the previous week's value of -0.10[1][5] - The composite factor value reflects multiple influences, including the recent decline in US Treasury yields, which has strengthened its suppressive effect on dividends, and the recovery of market sentiment, which has negatively contributed to dividend excess returns[3][8] - The sub-factors of the model include variables such as China's non-manufacturing PMI (service sector), China's M2 year-on-year growth, US 10-year Treasury yield, relative net value of dividends, dividend yield spread over 10-year Chinese Treasury yield, net financing purchases, and industry average prosperity[11] - Factor values for 20260306 are as follows: China's non-manufacturing PMI (service sector) at 0.14, China's M2 year-on-year growth at 0.31, US 10-year Treasury yield at -0.70, relative net value of dividends at 0.76, dividend yield spread over 10-year Chinese Treasury yield at -0.11, net financing purchases at 0.26, and industry average prosperity at 0.97[11]

Quantitative Models and Construction Methods - **Model Name**: Multi-granularity model (5-day label) **Construction Idea**: The model leverages deep learning techniques to capture multi-granularity features in stock data over a 5-day horizon[70][66] **Construction Process**: Based on bidirectional AGRU (Attention Gated Recurrent Unit) training, the model combines historical data and predictive features to optimize stock selection[68][66] **Evaluation**: Demonstrates consistent positive returns across multiple testing periods[70][66] - **Model Name**: Multi-granularity model (10-day label) **Construction Idea**: Similar to the 5-day label model, but extends the horizon to 10 days for broader feature extraction[70][68] **Construction Process**: Utilizes bidirectional AGRU training, integrating longer-term predictive signals for enhanced stock selection[68][70] **Evaluation**: Provides robust performance with slightly lower returns compared to the 5-day label model[70][68] - **Model Name**: AI-enhanced index optimization models **Construction Idea**: Combines deep learning factors (multi-granularity models) with risk control constraints to optimize portfolio returns[70][71] **Construction Process**: 1. **Objective Function**: $$ max\sum\mu_{i}w_{i} $$, where \( w_i \) represents stock weights and \( \mu_i \) represents expected excess returns[74][75] 2. **Constraints**: Includes stock-level, industry-level, market capitalization, PB, ROE, and turnover rate constraints[71][74] **Evaluation**: Effective in balancing risk and return, with varying performance across different constraint levels[70][71] Model Backtesting Results - **Multi-granularity model (5-day label)**: - IC: Historical 0.079, 2026 0.046 - RankMAE: Historical 0.343, 2026 0.337 - Multi-long-short returns: 8.05% (2026), 0.43% (March)[10][13][70] - **Multi-granularity model (10-day label)**: - IC: Historical 0.073, 2026 0.044 - RankMAE: Historical 0.342, 2026 0.340 - Multi-long-short returns: 6.25% (2026), 0.42% (March)[10][13][70] - **AI-enhanced index optimization models**: - **Air Quality Index Model**: - Weekly excess returns: 4.28% (2026), 0.55% (March) - Daily excess returns: 5.51% (2026), 0.61% (March)[79][76][70] - **CSI 500 Wide Constraint Model**: - Weekly excess returns: -0.93% (2026), 2.61% (March) - Daily excess returns: -4.25% (2026), 0.73% (March)[82][81][70] - **CSI 500 Strict Constraint Model**: - Weekly excess returns: 1.02% (2026), 2.09% (March) - Daily excess returns: 0.42% (2026), 1.29% (March)[84][83][70] - **CSI 1000 Wide Constraint Model**: - Weekly excess returns: 2.98% (2026), 2.43% (March) - Daily excess returns: 2.54% (2026), 2.36% (March)[88][87][70] - **CSI 1000 Strict Constraint Model**: - Weekly excess returns: 2.34% (2026), 1.33% (March) - Daily excess returns: 3.10% (2026), 1.25% (March)[93][91][70] Quantitative Factors and Construction Methods - **Factor Name**: Intraday skewness factor **Construction Idea**: Captures the asymmetry in intraday stock return distributions[15][17] **Construction Process**: Referenced from the report "Stock Selection Factor Series Research (19)"[15][17] **Evaluation**: Strong performance in capturing short-term market movements[15][17] - **Factor Name**: Downside volatility proportion factor **Construction Idea**: Measures the proportion of realized downside volatility in stock returns[20][22] **Construction Process**: Referenced from the report "Stock Selection Factor Series Research (25)"[20][22] **Evaluation**: Effective in identifying risk-averse stocks[20][22] - **Factor Name**: Opening buy intention proportion factor **Construction Idea**: Quantifies the proportion of buy orders during the opening period[23][27] **Construction Process**: Referenced from the report "Stock Selection Factor Series Research (64)"[23][27] **Evaluation**: Demonstrates strong predictive power for short-term returns[23][27] Factor Backtesting Results - **Intraday skewness factor**: - IC: Historical 0.026, 2026 0.036 - RankMAE: Historical 0.324, 2026 0.334 - Multi-long-short returns: 3.69% (2026), -0.62% (March)[10][11][15] - **Downside volatility proportion factor**: - IC: Historical 0.025, 2026 0.043 - RankMAE: Historical 0.324, 2026 0.333 - Multi-long-short returns: 5.65% (2026), -0.17% (March)[10][11][20] - **Opening buy intention proportion factor**: - IC: Historical 0.031, 2026 0.043 - RankMAE: Historical 0.322, 2026 0.326 - Multi-long-short returns: 4.27% (2026), 1.30% (March)[10][11][23]

Investment Rating - The report indicates a positive performance of large order factors and stable performance of multi-granularity factors, with AI-enhanced portfolios showing strong results [2][3]. Core Insights - The report highlights the differentiation in high-frequency factors, with large order factors performing well. The AI-enhanced portfolio continues to show strong performance [2][3]. - The multi-granularity factors have maintained stable performance, with significant returns reported for both 5-day and 10-day label models [6][9]. - The AI-enhanced portfolios have demonstrated substantial excess and absolute returns across various combinations, indicating effective investment strategies [14][73]. Summary by Sections High-Frequency Factors and Deep Learning Factors - High-frequency skewness factor reported a multi-directional return of -1.11% for the last week and 2.89% for February 2026 [6][10]. - The downward volatility proportion factor showed a return of -0.61% for the last week and 4.14% for February 2026 [6][10]. - The opening buy intention proportion factor had a return of -0.04% for the last week and 3.82% for February 2026 [6][10]. - The opening large order net buy proportion factor reported a return of 0.34% for the last week and 2.89% for February 2026 [6][10]. - The AI-enhanced portfolios, including the weekly rebalancing AI air value increase portfolio, reported excess returns of 3.63% and absolute returns of 3.29% for the last week [14][73]. Multi-Granularity Factors - The multi-granularity model (5-day label) reported a multi-directional return of 0.65% for the last week and 6.15% for February 2026 [6][10]. - The multi-granularity model (10-day label) reported a multi-directional return of 0.53% for the last week and 4.60% for February 2026 [6][10]. AI Enhanced Portfolios - The weekly rebalancing of the CSI 500 AI enhanced wide constraint portfolio reported an excess return of 2.25% and an absolute return of -0.42% for the last week [14][73]. - The daily rebalancing of the CSI 1000 AI enhanced strict constraint portfolio reported an excess return of 1.65% and an absolute return of -0.82% for the last week [14][73].

- The report discusses high-frequency stock selection factors, deep learning factors, and AI-enhanced portfolios, summarizing their historical and 2026 performance in terms of IC, RankMAE, long-short returns, long-only excess returns, and monthly win rates[9][10][11] - High-frequency factors include intraday skewness, downside volatility proportion, post-opening buying intention proportion, post-opening buying intensity, net large-order buying proportion, net large-order buying intensity, improved reversal, end-of-day trading proportion, average single-order outflow proportion, and large-order-driven price increase[7][9][10] - Deep learning factors include GRU(50,2)+NN(10), residual attention LSTM(48,2)+NN(10), multi-granularity models with 5-day and 10-day labels, which are trained using advanced machine learning techniques like AGRU[7][9][10] - AI-enhanced portfolios are constructed based on deep learning factors, specifically the multi-granularity 10-day label model, and include four combinations: CSI 500 AI-enhanced wide constraint, CSI 500 AI-enhanced strict constraint, CSI 1000 AI-enhanced wide constraint, and CSI 1000 AI-enhanced strict constraint. These portfolios aim to maximize expected returns under specific constraints such as turnover, industry, and market cap limits[73][74][75] - The optimization objective for AI-enhanced portfolios is defined as maximizing the expected excess return, represented by the formula: $$\operatorname*{max}_{w_{i}}\sum\mu_{i}w_{i}$$ where \(w_i\) is the weight of stock \(i\) in the portfolio, and \(\mu_i\) is the expected excess return of stock \(i\)[74][75] - Performance results for high-frequency factors show positive long-short returns for most factors in January and 2026, with notable results for factors like intraday skewness (1.55%), downside volatility proportion (1.65%), and post-opening buying intensity (2.86%)[5][9][10] - Deep learning factors also demonstrate strong performance, with GRU(50,2)+NN(10) achieving a long-short return of 2.79% in 2026, and the multi-granularity 5-day label model achieving 2.13%[5][9][10] - AI-enhanced portfolios show mixed results, with the CSI 500 AI-enhanced wide constraint portfolio recording a -4.47% excess return in 2026, while the CSI 1000 AI-enhanced strict constraint portfolio achieved a relatively better performance of -1.57%[5][14][73]

Quantitative Models and Construction Methods 1. Model Name: GRU(50,2)+NN(10) Factor - **Model Construction Idea**: This factor leverages a deep learning architecture combining Gated Recurrent Units (GRU) and Neural Networks (NN) to capture high-frequency trading patterns and predict stock returns[4][55] - **Model Construction Process**: - The GRU(50,2) component processes sequential high-frequency data with 50 units and 2 layers - The NN(10) component is a fully connected neural network with 10 neurons in the output layer - The model is trained on historical high-frequency data to predict stock returns, optimizing for multi-class classification or regression tasks[4][55] - **Model Evaluation**: Demonstrates robust performance in capturing high-frequency trading signals and generating stable returns[4][55] 2. Model Name: Multi-Granularity Model (5-Day Label) - **Model Construction Idea**: This model uses multi-granularity data to predict stock returns over a 5-day horizon, leveraging bidirectional AGRU (Attention-based GRU) for feature extraction[57][60] - **Model Construction Process**: - Input data is segmented into multiple granularities (e.g., daily, intraday) - Bidirectional AGRU is applied to extract temporal features from the data - A 5-day label is used as the prediction target, and the model is trained to optimize for this horizon[57][60] - **Model Evaluation**: Effective in capturing medium-term trading patterns and generating consistent returns[57][60] 3. Model Name: Multi-Granularity Model (10-Day Label) - **Model Construction Idea**: Similar to the 5-day label model, this version extends the prediction horizon to 10 days, using bidirectional AGRU for feature extraction[60][65] - **Model Construction Process**: - Multi-granularity data is processed with bidirectional AGRU - A 10-day label is used as the prediction target, and the model is trained to optimize for this extended horizon[60][65] - **Model Evaluation**: Provides a longer-term perspective on trading patterns, with slightly lower returns compared to the 5-day model but still effective[60][65] --- Model Backtesting Results GRU(50,2)+NN(10) Factor - **IC**: Historical: 0.066, 2025: 0.039[4][55] - **e^(-RankMAE)**: Historical: 0.336, 2025: 0.334[4][55] - **Long-Short Return**: Weekly: 0.70%, June: 3.58%, 2025 YTD: 19.78%[4][55] - **Long-Only Excess Return**: Weekly: -0.30%, June: 0.92%, 2025 YTD: -1.06%[4][55] Multi-Granularity Model (5-Day Label) - **IC**: Historical: 0.081, 2025: 0.070[57][60] - **e^(-RankMAE)**: Historical: 0.344, 2025: 0.343[57][60] - **Long-Short Return**: Weekly: 1.56%, June: 5.97%, 2025 YTD: 35.45%[57][60] - **Long-Only Excess Return**: Weekly: 0.40%, June: 2.16%, 2025 YTD: 11.87%[57][60] Multi-Granularity Model (10-Day Label) - **IC**: Historical: 0.074, 2025: 0.065[60][65] - **e^(-RankMAE)**: Historical: 0.342, 2025: 0.343[60][65] - **Long-Short Return**: Weekly: 1.66%, June: 5.76%, 2025 YTD: 33.44%[60][65] - **Long-Only Excess Return**: Weekly: 0.71%, June: 2.06%, 2025 YTD: 11.11%[60][65] --- Quantitative Factors and Construction Methods 1. Factor Name: Intraday Skewness Factor - **Factor Construction Idea**: Measures the skewness of intraday returns to capture asymmetry in price movements[4][10] - **Factor Construction Process**: - Calculate intraday returns for each stock - Compute the skewness of these returns using the formula: $ Skewness = \frac{E[(X - \mu)^3]}{\sigma^3} $ where $X$ is the return, $\mu$ is the mean, and $\sigma$ is the standard deviation[4][10] - **Factor Evaluation**: Effective in identifying stocks with asymmetric return distributions, though performance varies across periods[4][10] 2. Factor Name: Downside Volatility Ratio - **Factor Construction Idea**: Focuses on the proportion of downside volatility relative to total volatility to capture risk-averse behavior[4][14] - **Factor Construction Process**: - Calculate downside volatility as the standard deviation of negative returns - Compute the ratio of downside volatility to total volatility[4][14] - **Factor Evaluation**: Useful for identifying stocks with higher downside risk, though returns are sensitive to market conditions[4][14] 3. Factor Name: Opening Buy Intensity - **Factor Construction Idea**: Measures the intensity of buy orders during the opening period to capture early trading sentiment[4][17] - **Factor Construction Process**: - Aggregate buy orders in the first 30 minutes of trading - Normalize by total trading volume during the same period[4][17] - **Factor Evaluation**: Captures short-term sentiment effectively, though performance is volatile[4][17] --- Factor Backtesting Results Intraday Skewness Factor - **IC**: Historical: 0.027, 2025: 0.047[4][10] - **e^(-RankMAE)**: Historical: 0.324, 2025: 0.330[4][10] - **Long-Short Return**: Weekly: -0.51%, June: 1.48%, 2025 YTD: 14.73%[4][10] - **Long-Only Excess Return**: Weekly: -0.03%, June: 0.18%, 2025 YTD: 2.59%[4][10] Downside Volatility Ratio - **IC**: Historical: 0.025, 2025: 0.046[4][14] - **e^(-RankMAE)**: Historical: 0.324, 2025: 0.328[4][14] - **Long-Short Return**: Weekly: -0.04%, June: 1.86%, 2025 YTD: 12.84%[4][14] - **Long-Only Excess Return**: Weekly: 0.09%, June: 0.50%, 2025 YTD: 1.07%[4][14] Opening Buy Intensity - **IC**: Historical: 0.031, 2025: 0.028[4][17] - **e^(-RankMAE)**: Historical: 0.322, 2025: 0.322[4][17] - **Long-Short Return**: Weekly: 0.77%, June: 1.85%, 2025 YTD: 11.44%[4][17] - **Long-Only Excess Return**: Weekly: 0.04%, June: 0.61%, 2025 YTD: 5.91%[4][17]