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].

Quantitative Models and Construction Methods 1. Model Name: Convolutional Neural Network (CNN) for Price-Volume Data Modeling - **Model Construction Idea**: The model leverages convolutional neural networks to analyze standardized graphical representations of price-volume data, aiming to predict future price trends and map learned features to industry thematic indices[79][81] - **Model Construction Process**: - Standardize price-volume data into graphical formats for each stock within a specific time window[79] - Apply convolutional neural networks to extract features from these graphical representations[79] - Map the extracted features to thematic industry indices, such as the CSI Petrochemical Industry Index, CSI Subdivision Chemical Industry Theme Index, and others[81] - **Model Evaluation**: The model effectively identifies industry themes based on price-volume data and provides actionable insights for sector allocation[79][81] --- Model Backtesting Results 1. CNN Model - **Thematic Indices Configured**: - CSI Petrochemical Industry Index (h11057.CSI)[81] - CSI Subdivision Chemical Industry Theme Index (000813.CSI)[81] - CNI Oil & Gas Index (399439.SZ)[81] - CSI Oil & Gas Resources Index (931248.CSI)[81] - CNI Nonferrous Metals Index (399395.SZ)[81]

- The report introduces a quantitative model based on Convolutional Neural Networks (CNNs) to analyze price-volume data and predict future prices. The model standardizes price-volume data into graphical representations and maps learned features to industry theme indices, such as the CSI Petrochemical Industry Index, CSI Subdivision Chemical Industry Theme Index, CSI Machine Tool Index, CSI Semiconductor Material Equipment Theme Index, and CSI Nonferrous Metals Index[78][80][81] - The construction process of the CNN model involves transforming individual stock price-volume data within a specific window into standardized graphical charts. These charts are then input into the CNN for feature extraction and prediction modeling. The learned features are subsequently applied to identify and allocate industry themes[78][80] - The evaluation of the CNN model highlights its ability to capture complex patterns in price-volume data and effectively map these patterns to industry themes. This approach provides a novel perspective for quantitative investment strategies[78][81] - Backtesting results indicate that the CNN model's latest configuration suggests a focus on themes such as petrochemicals, chemicals, machine tools, semiconductors, and nonferrous metals. Specific indices include the CSI Petrochemical Industry Index, CSI Subdivision Chemical Industry Theme Index, CSI Machine Tool Index, CSI Semiconductor Material Equipment Theme Index, and CSI Nonferrous Metals Index[80][81]

Market Performance - The Sci-Tech 50 Index increased by 2.62% over the last five trading days, while the ChiNext Index decreased by 0.34%. The large-cap value index fell by 1.64%, and the large-cap growth index dropped by 1.34%. The Shanghai 50 Index declined by 1.54%, whereas the small-cap index represented by the CSI 2000 rose by 3.33%. The building materials and oil & petrochemical sectors performed well, while banks and telecommunications lagged behind [1]. Risk Premium and Valuation Levels - As of January 23, 2026, the static PE of the CSI All Share Index minus the yield of 10-year government bonds indicates a risk premium of 2.46%, with a two-standard deviation boundary at 4.68%. The valuation levels show that the CSI All Share Index's PETTM is at the 84th percentile, with the Shanghai 50 and CSI 300 at 72% and 73%, respectively. The ChiNext Index is close to 63%, while the CSI 500 and CSI 1000 are at 70% and 68%, respectively. 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 326.5 billion yuan, while the financing balance increased by approximately 6.5 billion yuan. The average daily trading volume across the two markets was 27.727 billion yuan [2]. Industry Themes and Indices - The latest thematic allocation includes sectors such as petrochemicals, chemicals, machine tools, semiconductors, and non-ferrous metals. Specific indices mentioned are the CSI Petrochemical Industry Index, CSI Sub-segment Chemical Industry Theme Index, CSI Machine Tool Index, CSI Semiconductor Materials and Equipment Theme Index, and the National Non-ferrous Metals Index [2][3]. Long-term Market Sentiment - The report includes observations on the proportion of stocks above the 200-day long-term moving average, indicating market sentiment trends [13]. Financing Balance - The report tracks the financing balance, which is a critical indicator of market liquidity and investor sentiment [16]. Individual Stock Performance - A statistical distribution of individual stocks based on their return ranges since the beginning of the year is provided, highlighting performance variations across different stocks [18]. Oversold Indices - The report notes instances of indices being oversold, which may present potential buying opportunities [20].

Market Performance - The Sci-Tech 50 Index increased by 2.58% over the last five trading days, while the ChiNext Index rose by 1.00%. In contrast, the large-cap value index fell by 2.81%, and the large-cap growth index decreased by 0.03%. The Shanghai Stock Exchange 50 Index dropped by 1.74%, whereas the small-cap index represented by the CSI 2000 gained 1.31%. The computer and electronics sectors performed well, while the defense, military, and real estate sectors lagged behind [1]. Risk Premium and Valuation Levels - As of January 16, 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 2.51%. The two standard deviation boundary is 4.68%. The valuation levels indicate that the CSI All Share Index's PETTM is at the 83rd percentile, with the Shanghai 50 and CSI 300 at 74% and 75%, respectively. The ChiNext Index is close to 63%, while the CSI 500 and CSI 1000 are at 69% and 67%, respectively. The ChiNext Index's valuation is relatively at the historical median level [1]. Fund Flow and Trading Volume - In the last five trading days, ETF funds experienced an outflow of 132.3 billion yuan, while the margin trading balance increased by approximately 97.3 billion yuan. The average daily trading volume across the two markets was 34.251 billion yuan [3]. Thematic Indexes - The latest thematic allocations include the Satellite Industry Index, Nonferrous Metals Index, Biotechnology Index, and Computer Index, among others. Specific indices mentioned are the CSI Satellite Industry Index, CSI Industrial Nonferrous Metals Theme Index, CSI Biotechnology Theme Index, CSI Big Data Industry Index, and CSI Computer Theme Index [2][4]. Market Sentiment and Risk Preference - The report includes observations on market sentiment based on the proportion of stocks above the 200-day moving average and tracks the risk preferences between equity and bond assets [13][14]. Financing Balance - The financing balance data is also included, indicating trends in margin trading activity [16]. Individual Stock Performance - Statistics on individual stock performance year-to-date based on return intervals are provided, highlighting the distribution of returns across different stocks [18]. Oversold Indices - The report notes instances of indices being oversold, which may indicate potential investment opportunities [20].

- The report discusses the use of convolutional neural networks (CNNs) to model price-volume data and predict future prices. The learned features are mapped to industry theme indices, including the CSI Satellite Industry Index, CSI Industrial Nonferrous Metals Theme Index, CSI Biotechnology Theme Index, CSI Big Data Industry Index, and CSI Computer Theme Index[79][81] - The CNN-based model standardizes price-volume data into graphical representations for analysis, leveraging deep learning techniques to identify patterns and trends in stock price movements[79][80] - The latest thematic configurations derived from the CNN model focus on sectors such as satellites, nonferrous metals, biotechnology, and computing, reflecting the model's ability to capture sectoral trends[79][81]

Market Performance - The Sci-Tech 50 Index increased by 9.80% over the last five trading days, while the ChiNext Index rose by 3.89%. The large-cap value index grew by 0.47%, and the large-cap growth index increased by 2.82%. The Shanghai 50 Index saw a 3.40% rise, and the small-cap index represented by the CSI 2000 gained 7.21% [1]. Valuation Levels - As of January 9, 2026, the static PE of the CSI All Share Index is at a percentile of 83%. The Shanghai 50 and CSI 300 both stand at 76%, while the ChiNext Index is close to 62%. The CSI 500 and CSI 1000 are at 68% and 67%, respectively. The ChiNext Index's valuation is relatively at the historical median level [1]. Risk Premium - The risk premium, calculated as the inverse of the static PE of the CSI All Share Index minus the yield of the 10-year government bond, is at 2.52% as of January 9, 2026. The two standard deviation boundary is at 4.69% [1]. ETF Fund Flow - In the last five trading days, there was an outflow of 1.6 billion yuan from ETFs, while the margin trading balance increased by approximately 64.2 billion yuan. The average daily trading volume across the two markets was 28.26 billion yuan [2]. Thematic Investment Focus - The latest thematic investment focus includes sectors such as satellites and semiconductors, with specific indices like the CSI Satellite Industry Index and the Shanghai Stock Exchange Sci-Tech Board Semiconductor Materials and Equipment Theme Index being highlighted [2][3]. Long-term Market Sentiment - The proportion of stocks above the 200-day moving average indicates a positive long-term market sentiment, suggesting a bullish outlook for the market [13]. Financing Balance - The financing balance has shown significant changes, reflecting the market's risk appetite and investor behavior [16]. Individual Stock Performance - Statistics on individual stock performance year-to-date based on return intervals indicate varying levels of performance across different stocks, providing insights into market dynamics [18]. Oversold Indices - Certain indices are identified as oversold, which may present potential buying opportunities for investors looking for value [20].

- The report introduces a quantitative model based on Convolutional Neural Networks (CNN) to analyze price-volume data and predict future stock prices. The model maps learned features to industry theme indices, including chemical, non-bank financial, and satellite sectors[77][79][80] - The construction process involves standardizing price-volume data into graphical formats for each stock within a specific window period. These standardized charts are then used as input for the CNN model to identify patterns and predict future trends[77][78][79] - The model's evaluation highlights its ability to capture complex relationships in price-volume data and its application in thematic industry allocation. It emphasizes the importance of deep learning techniques in quantitative finance[77][80] - Backtesting results show the latest thematic allocations include indices such as CSI Subdivision Chemical Industry Theme Index, CSI Satellite Industry Index, CSI All Share Dividend Quality Index, and others, reflecting the model's predictions[79][80]

Market Performance - The Sci-Tech 50 Index decreased by 0.59% and the ChiNext Index fell by 0.82% over the last five trading days, while the large-cap value index rose by 0.01% and the large-cap growth index declined by 0.39% [1] - The Shanghai Stock Exchange 50 Index increased by 0.20%, and the small-cap index represented by the CSI 2000 rose by 1.09%, with defense and military, as well as oil and petrochemical sectors performing well, while telecommunications and comprehensive sectors lagged [1] Valuation Levels - As of December 31, 2025, the static PE ratio of the CSI All Share Index is at the 82nd percentile, with the Shanghai 50 and CSI 300 both at 75%, and the ChiNext Index close to 58% [1] - The CSI 500 and CSI 1000 are at 62% and 64% respectively, indicating that the ChiNext Index's valuation is relatively at the historical median level [1] Fund Flows - In the last five trading days, ETF inflows amounted to 25.6 billion yuan, and the margin trading balance increased by approximately 23.8 billion yuan, with an average daily trading volume of 208.23 billion yuan across the two markets [2] Thematic Investment - The latest thematic allocation includes sectors such as chemicals, non-bank financials, and satellite communications, specifically focusing on sub-indices like the CSI Sub-Industry Chemical Index, the National Index for Commercial Satellite Communications, and the CSI 300 Non-Bank Financial Index [2][3] AI and Machine Learning Insights - A convolutional neural network (CNN) model has been utilized to analyze charted price and volume data, mapping learned features to industry thematic sectors, indicating a trend towards AI-driven investment strategies [11]

Market Performance - The Sci-Tech 50 Index increased by 2.85% over the last five trading days, while the ChiNext Index rose by 3.90%. The large-cap value index fell by 0.02%, and the large-cap growth index increased by 2.70%. The Shanghai 50 Index gained 1.37%, and the small-cap index represented by the CSI 2000 rose by 3.55%. The sectors of non-ferrous metals and national defense performed well, while beauty care and social services lagged behind [1]. Valuation Levels - As of December 26, 2025, the static PE ratio of the CSI All Share Index is at the 82nd percentile. The Shanghai 50 and CSI 300 both stand at 74%, while the ChiNext Index is close to 59%. The CSI 500 and CSI 1000 are at 62% and 64%, respectively. The valuation of the ChiNext Index is relatively at the historical median level [1]. Risk Premium - The risk premium, calculated as the inverse of the static PE of the CSI All Share Index minus the yield of the 10-year government bond, is at 2.69% as of December 26, 2025. The two standard deviation boundary is at 4.70% [1]. Fund Flows - In the last five trading days, ETF inflows amounted to 41.6 billion yuan, and the margin trading balance increased by approximately 45.7 billion yuan. The average daily trading volume across the two markets was 1.9454 trillion yuan [2]. Thematic Investment - The latest thematic investment configuration includes sectors such as chemicals, non-bank financials, communications, and satellite industries. Specific indices mentioned are the CSI Sub-Industry Chemical Index, CSI 300 Non-Bank Financial Index, CSI All Share Communication Equipment Index, and the National Satellite Communication Industry Index [2][3]. AI and Machine Learning Application - The application of convolutional neural networks (CNN) for modeling price and volume data has been explored, focusing on standardizing chart data to predict future prices and mapping learned features to industry themes [10].