Quantitative Models and Construction - **Model Name**: DecompGRU **Model Construction Idea**: The model improves information interaction between time-series and cross-sectional data by introducing two simple de-mean modules on the GRU baseline model[18] **Model Construction Process**: 1. The DecompGRU model architecture is based on GRU as the baseline 2. Two de-mean modules are added to enhance the interaction between time-series and cross-sectional data 3. The model is trained using IC and weighted MSE loss functions[18] **Model Evaluation**: The model demonstrates improved interaction between time-series and cross-sectional data, enhancing prediction accuracy[18] Model Backtesting Results - **DecompGRU TOP200 Portfolio**: - Cumulative absolute return: 41.11% - Excess return relative to WIND All A equal-weight index: 13.98% - Maximum drawdown: 10.08% - Weekly win rate: 64.52% - Monthly win rate: 100% - October absolute return: 1.78%, excess return: -0.07%[11] - **ETF Rotation Portfolio**: - Cumulative absolute return: 19.06% - Excess return relative to benchmark: -2.00% - Maximum drawdown: 7.82% - Weekly win rate: 62.50% - Monthly win rate: 57.14% - October absolute return: -2.04%, excess return: -1.18%[14][15] Quantitative Factors and Construction - **Factor Name**: Sentiment Heat Factor **Factor Construction Idea**: The factor aggregates stock-level sentiment heat metrics (e.g., browsing, self-selection, and clicks) to represent broader market sentiment[19] **Factor Construction Process**: 1. Individual stock sentiment heat is calculated as the sum of browsing, self-selection, and click counts 2. The sentiment heat is normalized by dividing by the total market sentiment on the same day and multiplying by 10,000 3. Aggregated sentiment heat is used as a proxy for market sentiment at the index, industry, and concept levels[19] **Factor Evaluation**: The factor effectively captures market sentiment and its impact on pricing errors[19] Factor Backtesting Results - **Broad-based Index Sentiment Heat Rotation Strategy**: - Annualized return since 2017: 8.74% - Maximum drawdown: 23.5% - 2025 portfolio return: 38.5% - Benchmark return: 32.9%[28] - **Concept Sentiment Heat BOTTOM Portfolio**: - Annualized return: 15.71% - Maximum drawdown: 28.89% - 2025 portfolio return: 42.1%[41][44]

Quantitative Models and Construction Methods - **Model Name**: DecompGRU **Model Construction Idea**: The model improves the GRU baseline by introducing two simple de-mean modules to enhance the interaction between temporal and cross-sectional information[14] **Model Construction Process**: 1. The DecompGRU model architecture is based on GRU with added de-mean modules for trend decomposition[14] 2. Two versions of the model are trained using different loss functions: IC and weighted MSE[14] 3. The IC-based model and MSE-based model are used to score stocks, and the top 200 stocks are selected for portfolio construction[8][14] **Evaluation**: The model effectively captures temporal and cross-sectional interactions, leading to improved stock selection performance[14] Model Backtesting Results - **DecompGRU Model**: - Cumulative absolute return: 24.54% - Excess return relative to WIND All A equal-weight index: 9.80% - Maximum drawdown: 10.08% - Weekly win rate: 72.22% - Monthly win rate: 100%[10] - **ETF Rotation Portfolio (Based on DecompGRU Scores)**: - Cumulative absolute return: 12.97% - Excess return relative to WIND ETF index: 8.65% - Maximum drawdown: 6.16% - Weekly win rate: 68.42% - Monthly win rate: 75%[12] Quantitative Factors and Construction Methods - **Factor Name**: Total Heat Indicator **Factor Construction Idea**: The indicator aggregates stock-level attention metrics (views, favorites, clicks) to represent market sentiment at broader levels (indices, industries, concepts)[17][18] **Factor Construction Process**: 1. Calculate the sum of views, favorites, and clicks for each stock[18] 2. Normalize the sum as a percentage of the total market activity on the same day[18] 3. Multiply the normalized value by 10,000 to derive the final indicator, with a range of [0, 10,000][18] **Evaluation**: The factor serves as a proxy for sentiment-driven mispricing, particularly effective at the stock level[18] Factor Backtesting Results - **Broad Index Heat Rotation Strategy**: - Annualized return since 2017: 8.74% - Maximum drawdown: 23.5% - 2025 YTD return: 18.8% - Benchmark return: 17.1%[27] - **Concept Heat BOTTOM Portfolio**: - Annualized return: 15.71% - Maximum drawdown: 28.89% - 2025 YTD return: 27%[44] Additional Observations - **Broad Index Heat Changes**: - Largest increase: CSI 500 (+10.21%) - Largest decrease: CSI 2000 (-6.02%)[27][29] - **Industry Heat Changes**: - Top 5 positive changes: Building Materials (+83.5%), Building Decoration, Social Services, Steel, Food & Beverage - Top 5 negative changes: Light Manufacturing (-32.5%), Textile & Apparel, Automotive, Real Estate, Utilities[38] - **Concept Heat Changes**: - Top 5 concepts: Dairy (+233.5%), Football (+194.9%), NMN (+115), Short Drama Games (+113.6%), Rent-Sale Rights (+109.6)[39][47][48]