Core Viewpoint - The article discusses the characteristics and performance of the Shanghai Stock Exchange Sci-Tech Innovation Board Artificial Intelligence Index, highlighting its focus on AI-related companies and the index's superior returns compared to broader market indices [1][3][21]. Group 1: Index Characteristics - The Shanghai Stock Exchange Sci-Tech Innovation Board Artificial Intelligence Index was launched on July 25, 2024, selecting 30 large-cap companies involved in providing resources, technology, and application support for AI [5]. - The index employs a market capitalization-weighted methodology with a 10% cap on individual stock weights, reflecting the overall performance of representative AI companies in the Sci-Tech Innovation Board [5][7]. - As of July 24, 2025, the total free float market capitalization of the index constituents reached 545.1 billion yuan, with an average market cap of 18.2 billion yuan [12]. Group 2: Industry Distribution - The index shows a high "AI content," with digital chip design companies accounting for 49.03% of the index weight, indicating a strong focus on this critical sector [2][9]. - IT services and horizontal general software contribute approximately 30% to the index, with notable companies like Kingsoft Office and Stone Technology playing significant roles [9][10]. - The index balances investments in leading firms and small-cap growth stocks, with a significant number of small-cap stocks providing high growth potential [12]. Group 3: Market Performance - The index has outperformed similar indices and broad market indices since its inception, with an annualized return of 23.78% as of July 24, 2025, and a Sharpe ratio of 0.81 [21][24]. - The index's performance during market rebounds has been notable, particularly in 2024, where it demonstrated superior rebound strength compared to other indices [39][40]. Group 4: Earnings Expectations - The top ten constituents of the index are expected to generate a cumulative revenue of 44.17 billion yuan in 2024, reflecting a year-on-year growth of 24.8%, with net profits projected to grow by 47.6% [26]. - The overall earnings outlook for the index constituents indicates a sustained growth trend, despite a downturn in 2023 due to industry cycles [26]. Group 5: ETF Product Introduction - The Penghua Shanghai Stock Exchange Sci-Tech Innovation Board Artificial Intelligence ETF aims to track the performance of the index, utilizing a full replication strategy to minimize tracking error [43]. - The ETF allows for the exchange of individual stocks for ETF shares, enhancing liquidity and reducing the risk of extreme declines in individual stocks [43].

Market Performance - The Sci-Tech 50 Index increased by 1.32% over the last five trading days, while the ChiNext Index rose by 3.17%. In contrast, the large-cap value index fell by 0.36%, and the large-cap growth index increased by 2.41% [1] - The communication and pharmaceutical sectors performed well, whereas media and real estate sectors lagged behind [1] Risk Premium Analysis - The static PE of the CSI All Share Index minus the yield of 10-year government bonds indicates a risk premium. Historical extreme bottoms have shown this data at two standard deviations above the mean, with recent peaks at 4.17% on April 26, 2022, and 4.08% on October 28, 2022. As of January 19, 2024, the indicator was at 4.11%, marking the fifth occurrence since 2016 exceeding 4% [1] - As of July 18, 2025, the indicator stands at 3.50%, with the two standard deviation boundary at 4.76% [1] Valuation Levels - As of July 18, 2025, the CSI All Share Index's TTM PE is at the 65th percentile, with the SSE 50 and CSI 300 at 68% and 61%, respectively. The ChiNext Index is close to 24%, while the CSI 500 and CSI 1000 are at 45% and 33% [2] - The ChiNext Index's valuation is relatively low compared to historical averages [2] Long-term Market Trends - The Shenzhen 100 Index has experienced bear markets every three years, followed by bull markets, with declines ranging from 40% to 45%. The current adjustment began in Q1 2021, suggesting a potential upward cycle [2] Fund Flow and Trading Activity - In the last five trading days, ETF inflows totaled 3.1 billion yuan, and margin financing increased by approximately 30.7 billion yuan. The average daily trading volume across both markets was 15.246 billion yuan [2] AI and Data Analysis - A convolutional neural network (CNN) was utilized to model price and volume data, mapping learned features to industry themes. The latest focus is on low volatility dividend themes [9]

Core Viewpoint - The report focuses on quantitative timing and position management strategies for convertible bond indices, specifically the CSI Convertible Bond Index, analyzing three main strategies: price-volume timing, valuation timing, and convexity timing [10]. Group 1: Price-Volume Timing Strategy - Technical indicators are derived from historical market data, including price and volume, resulting in 104 indicators used for timing strategies. The annualized return since 2019 is 9.4% [1][22]. - The strategy captures market trends and momentum, but faces challenges due to the dynamic switching of stock and bond attributes in convertible bonds [13][14]. - The average signal change period is approximately 6 trading days, indicating a higher trading frequency [25]. Group 2: Valuation Timing Strategy - The valuation timing strategy uses pricing deviation factors to assess the overall market valuation level of convertible bonds, with an annualized return of 8.0% since 2019 [2][35]. - Traditional valuation indicators struggle to fully capture market conditions, leading to the development of a pricing model that accounts for various risks [26][28]. - The average signal change period for this strategy is about 21 trading days, resulting in fewer trades compared to price-volume timing [38]. Group 3: Convexity Timing Strategy - Convexity in convertible bonds is defined as the second derivative of price changes relative to the underlying stock, allowing for potential outperformance in bullish markets and downside protection in bearish markets [39][40]. - The convexity timing strategy has shown a high win rate of 83.33% with an annualized return of 8.03% [47]. - The average signal change period for this strategy is longer than six months, indicating lower trading frequency [49]. Group 4: Position Management Strategy - A position management strategy is constructed using the three timing strategies, allowing for diversified signal sources and reduced risk of individual strategy failure. The annualized return is 8.55%, outperforming a buy-and-hold strategy [4][55]. - The strategy's historical performance shows a cumulative return of 71.70% with a maximum drawdown of -6.86% [55][57]. - The strategy can be adjusted for trading frequency, balancing between transaction costs and signal responsiveness [61].

Market Performance - The Sci-Tech 50 Index increased by 0.98% over the last five trading days, while the ChiNext Index rose by 2.36%. The large-cap value index fell by 0.18%, and the large-cap growth index increased by 0.69%. The Shanghai 50 Index gained 0.60%, and the small-cap index represented by the CSI 2000 rose by 2.29%. Real estate and steel sectors performed well, while coal and banking sectors lagged behind [1]. Risk Premium Analysis - The static PE of the CSI All Index minus the yield of 10-year government bonds indicates a risk premium. Historical extreme bottoms have shown this data to be at two standard deviations above the mean, with notable instances in 2012, 2018, and 2020. As of January 19, 2024, the indicator was at 4.11%, marking the fifth occurrence since 2016 to exceed 4%. As of July 11, 2025, the indicator was at 3.57%, with the two standard deviation boundary at 4.76% [1]. Valuation Levels - As of July 11, 2025, the CSI All Index's PE TTM percentile was at 63%. The Shanghai 50 and CSI 300 were at 68% and 61%, respectively. The ChiNext Index was close to 21%, while the CSI 500 and CSI 1000 were at 42% and 31%. The ChiNext Index's valuation is relatively low compared to historical averages [2]. Long-term Market Trends - The Shenzhen 100 Index has experienced bear markets approximately every three years, followed by bull markets. The last adjustment began in Q1 2021, showing sufficient time and space for a potential upward cycle from the bottom [2]. Fund Flow and Trading Activity - In the last five trading days, ETF inflows totaled 3 billion yuan, and margin trading increased by approximately 14.1 billion yuan. The average daily trading volume across both markets was 1.4748 trillion yuan [2]. Neural Network Analysis - A convolutional neural network (CNN) was utilized to model price and volume data, mapping learned features to industry themes. The latest focus is on sectors such as banking [9].

Group 1: CTA Product Overview - In Q2 2025, 100 new CTA products were issued, indicating a continuous upward trend in issuance [5][10] - The median annualized return for the reported CTA products was 16.37%, with a median Sharpe Ratio of 1.60 and a median maximum drawdown of -4.28% [10][11] - The overall profitability ratio of CTA products in Q2 was 69.4% [10][11] Group 2: Stock Index Futures Analysis - Stock index futures experienced a trend decline in volatility during Q2, reaching near historical lows [2][40] - The market outlook suggests limited upward space for A-shares due to valuation pressures, with stock index futures expected to remain volatile in Q3 [2][40] - The average daily trading volume for major index futures contracts showed a decline compared to the previous quarter [12] Group 3: Government Bond Futures Outlook - The yield levels for medium to long-term government bonds are at historically low levels, limiting downward potential [3][51] - Economic weakness and insufficient demand are suppressing the upward movement of interest rates, leading to a forecast of a primarily oscillating market for government bond futures in Q3 [3][51] - The performance of government bond CTA strategies is expected to be negatively impacted by low volatility in the absence of extraordinary market events [3][51] Group 4: Commodity Market Insights - Commodity volatility is currently low, with significant price movements in precious metals and energy sectors during Q2, followed by a return to oscillation [4][65] - The overall lack of trading signals in the commodity market is attributed to ongoing deflation in China and slow interest rate cuts in the U.S., leading to a wait-and-see approach for CTA strategies [4][65] - The average return for commodity trend-following strategies was -1.5% in Q2, indicating underperformance across major commodities [64]

Core Viewpoint - The recent market performance shows mixed results across various indices, with the ChiNext Index rising while the STAR 50 Index slightly declined, indicating a divergence in sector performance and potential investment opportunities in specific areas [1][2]. Group 1: Market Performance - Over the last five trading days, the STAR 50 Index decreased by 0.35%, while the ChiNext Index increased by 1.50%. The large-cap value stocks rose by 1.94%, and large-cap growth stocks increased by 1.78%. The Shanghai 50 Index saw a rise of 1.21%, and the small-cap stocks represented by the CSI 2000 increased by 0.53% [1]. - Steel and building materials sectors performed well, while the computer and non-bank financial sectors lagged behind [1]. Group 2: Risk Premium and Valuation Levels - The risk premium, measured as the inverse of the static PE of the CSI All Index minus the yield of 10-year government bonds, reached 4.17% on April 26, 2022, and 4.08% on October 28, 2022, indicating a market rebound potential. As of January 19, 2024, the indicator was at 4.11%, marking the fifth occurrence since 2016 of exceeding 4% [1]. - As of July 4, 2025, the CSI All Index's PE TTM percentile was at 61%, with the Shanghai 50 and CSI 300 at 67% and 60%, respectively. The ChiNext Index is close to 20%, indicating a relatively low valuation level compared to historical averages [2]. Group 3: Fund Flow and Trading Activity - In the last five trading days, ETF funds experienced an outflow of 21.2 billion yuan, while margin financing increased by approximately 19.7 billion yuan. The average daily trading volume across the two markets was 1.4136 trillion yuan [4]. Group 4: Technical Analysis and AI Modeling - The long-term technical analysis of the Deep 100 Index suggests a cyclical pattern of bear and bull markets every three years, with significant declines observed in previous cycles. The current adjustment phase, which began in the first quarter of 2021, appears to have sufficient time and space for a potential upward cycle [2]. - A convolutional neural network model has been developed to analyze price and volume data, mapping learned features to industry themes, with a focus on banking and non-ferrous metals sectors [3][9].

Core Viewpoint - The article presents a comprehensive analysis of major asset classes based on macroeconomic and technical perspectives, indicating a mixed outlook for equities, bonds, industrial products, and gold [1][3][19]. Macroeconomic Perspective - The macroeconomic view suggests a neutral stance on equity assets, a favorable outlook for bond assets, a negative outlook for industrial products, and a positive outlook for gold assets [5][19]. - Specific macro indicators such as PMI, CPI, and social financing are analyzed to assess their impact on asset performance [6][19]. Technical Perspective - The technical analysis indicates a downward trend for equity and bond assets, while industrial products and gold show an upward trend [9][10][19]. - The article employs various trend indicators to measure the performance of different asset classes, with historical data supporting the current trends [7][10]. Asset Valuation - The equity risk premium (ERP) for the CSI 800 index is reported at 73.74%, indicating that equity valuations are relatively low [12][13]. - The analysis of fund flow shows a net inflow of 915 million yuan into equity assets, suggesting a positive sentiment among investors [15][16]. Asset Allocation Performance Tracking - Historical performance data indicates that a fixed ratio combined with macro and technical indicators yielded a return of 1.06% as of June 2025, with an annualized return of 11.86% since April 2006 [2][20][24]. - Different asset allocation strategies, including risk parity and volatility control, have been evaluated, showing varying returns and risk profiles [25][24]. Summary of Asset Class Scores - The combined scores from macro and technical indicators show equities at 0, bonds at 2, industrial products at 0, and gold at 4, reflecting the overall market sentiment and expected performance [18][19].

Market Performance - The recent five trading days saw the Sci-Tech 50 Index increase by 3.17%, the ChiNext Index by 5.69%, the large-cap value by 1.52%, the large-cap growth by 2.61%, the SSE 50 by 1.27%, and the small-cap represented by the CSI 2000 by 4.94% [1] - The sectors showing strong performance include computers and national defense, while oil, petrochemicals, and food and beverages lagged behind [1] Risk Premium Analysis - The risk premium, calculated as the inverse of the static PE of the CSI All Index minus the yield of ten-year government bonds, indicates that the implied returns of equity and bond assets are at historically high levels, reaching 4.17% on April 26, 2022, and 4.08% on October 28, 2022 [1] - As of January 19, 2024, the indicator stood at 4.11%, marking the fifth occurrence since 2016 to exceed 4% [1] Valuation Levels - As of June 27, 2025, the CSI All Index's PETTM percentile is at 59%, with the SSE 50 and CSI 300 at 66% and 57% respectively, while the ChiNext Index is close to 19% [2] - The ChiNext Index's valuation is relatively low compared to historical averages [2] Long-term Market Trends - The technical analysis of the Deep 100 Index suggests a cyclical pattern of bear markets every three years, followed by bull markets, with significant declines observed in previous cycles [2] - The current adjustment phase, which began in Q1 2021, appears to have sufficient time and space for a potential upward cycle [2] Fund Flow and Trading Activity - In the last five trading days, ETF funds experienced an outflow of 1.3 billion yuan, while margin trading increased by approximately 17 billion yuan, with an average daily trading volume of 1.4528 trillion yuan across the two markets [4] AI and Machine Learning Applications - The use of convolutional neural networks (CNN) for modeling price and volume data has been explored, with the latest focus on sectors such as banking and artificial intelligence [3][11]

Market Performance - The recent five trading days saw the Sci-Tech 50 Index decline by 1.55%, the ChiNext Index by 1.66%, while the large-cap value stocks rose by 1.07% and large-cap growth stocks fell by 0.54% [1] - The banking and telecommunications sectors performed well, whereas the beauty and textile sectors lagged behind [1] Risk Premium Analysis - The static PE of the CSI All Share Index indicates a risk premium, with the EP minus the ten-year government bond yield showing historical extremes at two standard deviations above the mean during previous market bottoms [1] - As of January 19, 2024, the risk premium indicator reached 4.11%, marking the fifth occurrence since 2016 to exceed 4% [1] Valuation Levels - As of June 20, 2025, the CSI All Share Index's TTM PE is at the 53rd percentile, with the SSE 50 and CSI 300 at 65% and 54% respectively, indicating that the ChiNext Index is relatively undervalued historically [2] Long-term Market Trends - The Deep 100 Index has historically experienced bear markets every three years, followed by bull markets, with the current adjustment since Q1 2021 showing sufficient time and space for a potential upward cycle [2] Fund Flow and Trading Activity - In the last five trading days, ETF inflows totaled 17 billion yuan, with margin trading increasing by approximately 310 million yuan, and the average daily trading volume across both markets was 1.1863 trillion yuan [4] AI and Machine Learning Applications - A convolutional neural network is utilized to model price and volume data, mapping learned features to industry themes, with a focus on sectors such as non-ferrous metals and banking [3][10]

Core Viewpoint - The article discusses the increasing application of machine learning in quantitative stock selection, particularly focusing on GBDT and neural network models, as traditional factors have become less effective [1][4]. Group 1: Model Selection - Machine learning has been widely adopted in quantitative stock selection, with GBDT models (including LGBM, XGBoost, and CatBoost) and neural networks (including GRU, TCN, and Transformer) being the primary focus [1]. - GBDT models are effective for handling manually constructed features, while neural networks excel in capturing temporal changes in features [2]. Group 2: Feature Data Preparation - Different model types require different feature types; tree models handle price and fundamental features well, while neural networks perform better with high-frequency data [22][27]. - Feature selection methods, particularly SHAP, can effectively reduce the number of features while maintaining model performance [2][31]. - Standardization of features before feeding them into models is crucial for improving model performance [2][35]. Group 3: Loss Function Adjustment and Prediction Target Processing - Besides the common MSE loss function, investors often use IC as a loss function, with various ranking loss functions showing improved performance [2][37]. - Using cross-sectional normalization helps the model focus on differences in cross-sectional returns, enhancing factor performance [3][50]. Group 4: Machine Learning Models - GBDT is highlighted as a superior algorithm due to its iterative approach of updating target values based on residuals from previous trees [10][11]. - Neural networks, including RNN, LSTM, GRU, CNN, TCN, and Transformer, are discussed for their effectiveness in various domains, particularly in time series prediction [12][19]. Group 5: Index Enhancement Strategies - The article presents the performance of various index enhancement strategies, with the CSI 300 index showing an annualized excess return of 10.03% and a maximum drawdown of -5.42% [3]. - The CSI 500 index strategy has a slightly lower annualized excess return of 8.41% with a maximum drawdown of -10.78%, while the CSI 1000 index strategy shows a more stable performance with an annualized excess return of 11.44% and a maximum drawdown of -7.95% [3].