- The report utilizes Convolutional Neural Networks (CNN) to model price-volume data and future prices, mapping learned features to industry theme sectors[1][75] - The latest configuration themes include banking, finance, utilities, and low volatility dividends, specifically covering indices such as the CSI Bank Index, CSI 800 Bank Index, SSE 180 Financial Stock Index, CSI All Share Utilities Index, and CSI Dividend Low Volatility Index[1][75][77] - The report provides detailed configuration information for the CNN industry themes, including specific dates and index codes[76] **Quantitative Models and Construction Methods** 1. Model Name: Convolutional Neural Network (CNN) - Construction Idea: Use CNN to model price-volume data and future prices, mapping learned features to industry theme sectors[1][75] - Detailed Construction Process: - Standardize price-volume data into charts for each stock within a window period - Apply CNN to these charts to learn features - Map the learned features to industry theme sectors - Evaluation: The model effectively identifies and maps features to relevant industry themes, providing actionable insights for sector allocation[1][75] **Model Backtesting Results** 1. CNN Model, Banking Theme, CSI Bank Index[76] 2. CNN Model, Banking Theme, CSI 800 Bank Index[76] 3. CNN Model, Financial Theme, SSE 180 Financial Stock Index[76] 4. CNN Model, Utilities Theme, CSI All Share Utilities Index[76] 5. CNN Model, Low Volatility Dividends Theme, CSI Dividend Low Volatility Index[76]

Quantitative Models and Construction Methods - **Model Name**: Convolutional Neural Network (CNN) for Price-Volume Data **Model Construction Idea**: The model leverages convolutional neural networks to analyze standardized graphical representations of price-volume data, aiming to predict future price trends. The learned features are then mapped to specific industry theme indices[76][78] **Model Construction Process**: 1. Standardize price-volume data into graphical formats for each stock within a specific time window[76] 2. Train a convolutional neural network to extract features from these graphical representations[76] 3. Map the learned features to industry theme indices, such as dividend low-volatility, banking, and real estate indices[76][78] **Model Evaluation**: The model effectively identifies industry themes based on price-volume patterns, providing actionable insights for sector allocation[76][78] Model Backtesting Results - **CNN Model**: Latest theme configurations include the following indices: 1. CSI Dividend Low Volatility Index (h30269.CSI) 2. CSI Banking Index (399986.SZ) 3. CSI 800 Banking Index (h30022.CSI) 4. CSI Mainland Real Estate Theme Index (000948.CSI) 5. CSI 800 Real Estate Index (399965.SZ)[78] Quantitative Factors and Construction Methods - **Factor Name**: Macroeconomic Indicators **Factor Construction Idea**: Macroeconomic factors are used to assess their impact on asset returns by identifying trends and significant events in historical data[51][52] **Factor Construction Process**: 1. Track 25 domestic and international macroeconomic indicators, such as PMI, CPI, PPI, and M2 growth rates[52] 2. Define four types of macroeconomic events: short-term peaks/troughs, continuous up/down trends, historical highs/lows, and trend reversals[52] 3. Use historical moving averages to classify macroeconomic trends (e.g., 3-month, 12-month averages) and analyze their impact on asset returns over the next month[54] **Factor Evaluation**: The approach identifies effective macroeconomic events that significantly influence asset returns, providing a robust framework for market trend analysis[52][54] Factor Backtesting Results - **Macroeconomic Factors**: 1. PMI (3-month moving average): Positive outlook for equities[55] 2. Social Financing Stock YoY Growth (1-month moving average): Neutral outlook[55] 3. 10-Year Treasury Yield (12-month moving average): Neutral outlook[55] 4. Dollar Index (1-month moving average): Neutral outlook[55]

Market Performance - The Sci-Tech 50 Index decreased by 4.03% over the last five trading days, while the ChiNext Index increased by 1.26%. The large-cap value index fell by 1.44%, and the large-cap growth index dropped by 0.48%. The Shanghai 50 Index declined by 2.47%, and the small-cap index represented by the CSI 2000 fell by 5.45%. The communication and banking sectors performed well, while basic chemicals and non-ferrous metals lagged behind [1]. Valuation Levels - As of March 20, 2026, the static PE of the CSI All Share Index is at 2.63%, with a two-standard deviation boundary at 4.62%. The CSI All Share Index's TTM PE is at the 82nd percentile, while the Shanghai 50 and CSI 300 are at 70% and 73%, respectively. The ChiNext Index is close to 63%, and the CSI 500 and CSI 1000 are both at 65%. The ChiNext Index's valuation style is relatively at the historical median level [2]. Thematic Investment Strategy - The latest thematic investment strategy focuses on low volatility dividends, banking, and real estate sectors. Specific indices include the CSI Low Volatility Dividend Index, CSI Banking Index, CSI 800 Banking Index, CSI Mainland Real Estate Theme Index, and CSI 800 Real Estate Index [2][3]. AI and Machine Learning Application - A convolutional neural network (CNN) is utilized to model price and volume data, mapping learned features to industry thematic sectors. This approach is based on research reports regarding AI recognition and classification of stock price trends [9]. Market Sentiment - The proportion of stocks above the 200-day long-term moving average is tracked, indicating market sentiment and potential trends [10]. ETF Scale Changes - The report includes observations on the changes in the scale of mainstream ETFs, reflecting shifts in investor preferences and market dynamics [11]. Risk Preference Tracking - The report monitors the risk preferences between equity and bond assets, providing insights into investor behavior and market conditions [12].

- The report utilizes convolutional neural networks (CNN) to model the relationship between charted price-volume data and future prices, mapping learned features to industry thematic indices[74][75] - The thematic indices configured using CNN include the CSI Dividend Low Volatility Index, CSI Bank Index, CSI 800 Bank Index, CSI Mainland Real Estate Thematic Index, and CSI 800 Real Estate Index[75] - The CNN-based approach focuses on standardizing price-volume data into charts for analysis, as referenced in prior deep learning studies like "AI Recognition and Classification of Stock Price Trends Based on Convolutional Neural Networks"[74]

Market Performance - The Sci-Tech 50 Index decreased by 2.88% over the last five trading days, while the ChiNext Index increased by 2.51%. The large-cap value index rose by 0.48%, and the large-cap growth index increased by 1.38%. The Shanghai 50 Index fell by 1.20%, and the small-cap index represented by the CSI 2000 declined by 1.13%. Coal and electric equipment sectors performed well, while defense, military, oil, and petrochemical sectors lagged behind [1]. Risk Premium and Valuation Levels - As of March 13, 2026, the risk premium, measured as the inverse of the static PE of the CSI All Share Index minus the yield of 10-year government bonds, stood at 2.48%. The two-standard deviation boundary is 4.63% [1]. - The valuation level of the CSI All Share Index's TTM PE is at the 83rd percentile. The Shanghai 50 and CSI 300 indices are at 71% and 75%, respectively, while the ChiNext Index is close to 62%. The CSI 500 and CSI 1000 indices are at 68% and 67%, respectively, indicating that the ChiNext Index's valuation is relatively at the historical median level [1]. ETF Fund Flow - In the last five trading days, ETF funds experienced an outflow of 12.3 billion yuan, while margin trading increased by approximately 12 billion yuan. The average daily trading volume across both markets was 24,792 billion yuan [2]. Industry Themes and Indexes - The latest thematic allocation focuses on sectors such as electricity, power grid, and public utilities, including specific indices like the National Green Power Index, CSI Green Power Index, CSI All Share Power Public Utilities Index, CSI All Share Public Utilities Index, and CSI Power Grid Equipment Theme Index [2][3][12]. Market Sentiment and Risk Preference - The report includes observations on market sentiment based on the proportion of stocks above the 200-day long-term moving average, as well as tracking risk preferences between equity and bond assets [13][14]. Financing Balance - The report provides insights into the financing balance, indicating trends in margin trading and overall market leverage [16]. Individual Stock Performance - There is a statistical distribution of individual stock performance year-to-date based on return intervals, highlighting the performance of various stocks within the market [18]. Oversold Indices - The report notes instances of indices being oversold, which may present potential investment opportunities [20].

- The report explores the use of convolutional neural networks (CNNs) to model price-volume data and predict future prices, mapping learned features to industry theme indices such as the National Green Power Index, CSI Green Power Index, and CSI Electric Power Equipment Theme Index[80][82] - The CNN-based approach involves constructing standardized charts of price-volume data for individual stocks over specific time windows, which are then used as input for the CNN model to identify patterns and trends[80] - The latest thematic allocation based on the CNN model includes sectors like electricity, power grids, and public utilities, with specific indices such as the CSI All-Electric Power Utility Index and CSI All-Public Utility Index being highlighted[80][82]

Market Performance - The Sci-Tech 50 Index decreased by 4.95% over the last five trading days, while the ChiNext Index fell by 2.45%. In contrast, the large-cap value stocks rose by 1.17%, and large-cap growth stocks declined by 1.10%. The Shanghai Stock Exchange 50 Index dropped by 1.54%, whereas the small-cap index represented by the CSI 2000 increased by 3.53%. The oil and coal sectors performed well, while media and non-ferrous metals lagged behind [1]. Valuation Levels - As of March 6, 2026, the static PE of the CSI All Share Index indicates an earnings yield (EP) of 2.47% when compared to the 10-year government bond yield. The two-standard deviation boundary is at 4.64%. The CSI All Share Index's PE TTM is at the 84th percentile, with the SSE 50 and CSI 300 at 72% and 75%, respectively. The ChiNext Index is close to 58%, while the CSI 500 and CSI 1000 are at 69% and 68%, respectively. The ChiNext Index's valuation is relatively at the historical median level [1]. ETF Fund Flows - In the last five trading days, ETF inflows amounted to 5 billion yuan, while the financing balance decreased by approximately 16 billion yuan. The average daily trading volume across the two markets was 26,212 billion yuan [2]. Industry Themes - The latest thematic allocation focuses on sectors such as electricity, power grids, and public utilities. This includes specific indices like the National Green Power Index, CSI Green Power Index, CSI All Share Power Utility Index, CSI All Share Utility Index, and CSI Power Grid Equipment Theme Index [2][3]. Long-term Market Sentiment - The proportion of stocks above the 200-day moving average is being tracked to gauge market sentiment [13]. Risk Preference Tracking - The risk preference between equity and bond assets is being monitored, reflecting investor sentiment towards riskier assets [14]. Financing Balance - The financing balance data indicates trends in investor leverage and market participation [16]. Individual Stock Performance - Statistics on individual stock performance year-to-date based on return ranges are being compiled to assess market dynamics [19]. Oversold Indices - Analysis of indices that are currently oversold is being conducted to identify potential buying opportunities [20].

Market Performance - The Sci-Tech 50 Index increased by 0.47% over the last five trading days, while the ChiNext Index decreased by 0.53%. The large-cap value index fell by 1.34%, and the large-cap growth index dropped by 0.93%. The Shanghai Stock Exchange 50 Index declined by 1.31%, whereas the small-cap index represented by the CSI 2000 rose by 3.08%. The steel and environmental sectors performed well, while media and non-bank financial sectors lagged behind [1]. Risk Premium and Valuation Levels - As of February 27, 2026, the risk premium, measured as the inverse of the static PE of the CSI All Share Index minus the yield of ten-year government bonds, stands at 2.43%. The two standard deviation boundary is 4.65% [1]. - The valuation levels indicate that the CSI All Share Index's PETTM is at the 84th percentile. The Shanghai 50 and CSI 300 are at 72% and 74%, respectively, while the ChiNext Index is close to 61%. The CSI 500 and CSI 1000 are at 70% and 69%, respectively, indicating that the ChiNext Index's valuation is relatively at the historical median level [1]. ETF Fund Flow - In the last five trading days, there was an outflow of 39.3 billion yuan from ETFs, while the margin trading balance increased by approximately 22.2 billion yuan. The average daily trading volume across the two markets was 23,348 billion yuan [2]. Industry Themes and Indices - The latest thematic allocation includes industries such as shipbuilding, electric power, steel, and robotics, specifically represented by indices like the CSI Selected Shipbuilding Industry Index, CSI Electric Power Equipment Theme Index, CSI Steel Index, and CSI Robotics Index [2][3].

- The report discusses the use of convolutional neural networks (CNNs) to model price-volume data and future price trends, transforming these learned features into industry theme indices such as the CSI Smart Shipbuilding Industry Index, CSI Power Grid Equipment Theme Index, CSI Steel Index, and CSI Robotics Index[81][82][87] - The CNN model constructs standardized charts of price-volume data within specific time windows for individual stocks, which are then used to train the model to identify patterns and predict future price movements[81][82] - The CNN model's thematic allocation currently focuses on sectors like shipbuilding, power grids, steel, and robotics, as reflected in the indices mentioned above[81][82][87]

Market Performance - The Sci-Tech 50 Index decreased by 2.85% over the last five trading days, while the ChiNext Index fell by 0.09%. In contrast, the large-cap value index rose by 1.87%, and the large-cap growth index increased by 0.68%. The Shanghai 50 Index gained 1.13%, whereas the small-cap index represented by the CSI 2000 dropped by 2.76%. The telecommunications and oil & petrochemical sectors performed well, while the defense, military, and power equipment sectors lagged behind [1]. Valuation Levels - As of January 30, 2026, the static PE of the CSI All Share Index is at 2.49%, with a two-standard deviation boundary of 4.67%. The valuation levels indicate that the CSI All Share Index's TTM PE is at the 84th percentile, while the Shanghai 50 and CSI 300 are at 74% and 75%, respectively. The ChiNext Index is close to 62%, and the CSI 500 and CSI 1000 are at 69% and 67%, respectively, suggesting that the ChiNext Index's valuation is relatively at the historical median level [2]. Fund Flow and Trading Activity - In the last five trading days, ETF funds experienced an outflow of 316.7 billion yuan, while the margin trading balance increased by approximately 14.7 billion yuan. The average daily trading volume across the two markets was 30.348 billion yuan [2]. Thematic Investment Insights - The latest thematic investment focus includes sectors such as petrochemicals, chemicals, and non-ferrous metals. Specific indices highlighted are the CSI Petrochemical Industry Index, CSI Sub-Industry Chemical Theme Index, CSI Oil and Gas Index, and CSI Non-Ferrous Index [2][3]. AI and Machine Learning Applications - The report discusses the application of convolutional neural networks (CNN) to model price and volume data, aiming to identify future price movements and map learned features to industry themes. This approach is based on research reports related to AI recognition and classification of stock price trends [2][11].