Core Viewpoint - The article discusses the development and performance of a timing strategy based on a neural network model, specifically the AGRU model, which has shown promising results in stock selection and market timing [1][4][75]. Group 1: Timing Strategy Performance - The timing strategy using the CSI All Share Index as the investment target achieved an annualized return of 15.86%, a Sharpe ratio of 1.18, and a maximum drawdown of -12.66% [10][11][12][75]. - The strategy's performance was tested over different rebalancing periods, revealing that weekly and monthly rebalancing negatively impacted the strategy's stability and returns [17][18][31][75]. - The strategy's information coefficient (IC) was calculated at 15.81%, indicating a strong predictive ability for the timing signals [15][75]. Group 2: Daily Rebalancing ETF Rotation Strategy - A daily rebalancing ETF rotation strategy, assuming a portfolio of 10 ETFs, yielded an annualized excess return of 17.13% with an information ratio of 1.25 and a maximum excess drawdown of -14.38% [2][49][75]. - When combining the timing signals with the cross-sectional signals, the strategy's annualized return improved to 31.04% with a Sharpe ratio of 1.86 and a maximum drawdown of -10.08% [50][75]. Group 3: Comparison of Timing and Cross-Sectional Signals - The correlation between timing and cross-sectional signals was found to be low at -1.96%, suggesting that they provide different predictive advantages [76]. - Timing signals were more accurate in predicting afternoon and overnight returns, while cross-sectional signals performed better in predicting overnight and late trading session returns [76][68][72]. Group 4: Sensitivity to Rebalancing Prices - The strategy's performance was tested under different execution prices, showing that using the opening price resulted in the best performance, but the strategy remained robust even when using prices from the first hour of trading [33][37][54][55]. - The annualized return using the opening price was 15.86%, while using a 60-minute TWAP execution resulted in a return of 13.15% [33][37]. Group 5: Performance Across Different Indices - The timing strategy demonstrated consistent performance across various broad indices, indicating that the model is not overfitted to specific stocks [39][40][75]. - For example, the annualized return for the CSI 300 timing strategy was 13.43%, while the CSI 1000 strategy achieved a return of 17.51% [40][75].

Core Viewpoint - The report tracks the performance of a long-term stock selection strategy focusing on profitability and growth, which was initially published by the GF Financial Engineering team on August 26, 2020 [3][30]. Empirical Analysis - The backtesting period for the strategy spans from January 1, 2009, to October 31, 2025, with three rebalancing periods each year on April 30, August 31, and October 31 [5]. - The equal-weighted strategy achieved a cumulative return of 3458.94% and an annualized return of 23.55%, significantly outperforming the CSI 800 index, which had a cumulative return of 179.16% during the same period [6][31]. - The average number of stocks held in the portfolio was approximately 55, with an average market capitalization of around 14 billion [23][31]. - The strategy's annualized volatility relative to the CSI 800 index was 13.63%, with an information ratio of 1.19 [12][13]. Sector Distribution - The sectors with the highest frequency of stock selections included pharmaceuticals, chemicals, electronics, machinery, and food and beverages, while sectors like leisure services, construction, defense, steel, and non-bank financials were selected less frequently [26][31]. Market Capitalization Weighted Strategy - The market capitalization weighted strategy yielded a cumulative return of 2553.16% and an annualized return of 21.42%, with a relative annualized excess return of 13.88% compared to the CSI 800 index [14][21]. - The annualized volatility for the market capitalization weighted strategy was 14.17%, with an information ratio of 1.00 [21][22]. Summary - The report provides a comprehensive follow-up on the long-term stock selection strategy, emphasizing the importance of profitability and growth as key variables in stock selection, and highlights the strong performance of both equal-weighted and market capitalization weighted strategies [30][31].

Core Viewpoint - The article emphasizes the growing recognition of index-based investment strategies among investors, highlighting the potential investment opportunities arising from significant changes in index constituents due to periodic rebalancing of major indices like the SSE 50, CSI 300, and CSI 500 [1][4][5]. Group 1: Index Product Scale Statistics - As of October 31, there are 2,294 passive index funds (ETFs and off-exchange passive index funds) with a total scale of 4.5 trillion yuan, and 437 enhanced index funds with a total scale of 265.3 billion yuan, surpassing the scale of equity mixed funds (2.53 trillion yuan) [2][15]. - The leading indices in terms of product tracking scale include the CSI 300, CSI A500, and CSI 500 [19]. Group 2: Historical Adjustment Effects of Index Constituents - From 2019 to mid-2025, stocks added to the index generally outperformed the index in the two weeks prior to their inclusion, while stocks removed from the index underperformed [22][23]. - The average excess return for stocks added to the index in the two weeks before inclusion is 4.89%, with a success rate of 66.67% [24]. Group 3: Latest Adjustment Impact Estimates - For the expected adjustments in December 2025, the SSE 50 is projected to adjust 4 stocks with an estimated passive buy amount of 5.5 billion yuan, the CSI 300 is expected to adjust 10 stocks with an estimated net buy of 24.5 billion yuan, and the CSI 500 is expected to adjust 50 stocks with an estimated buy of 3.3 billion yuan [30][32].

Market Performance - The recent five trading days saw the Sci-Tech 50 Index increase by 0.01%, the ChiNext Index by 0.65%, the large-cap value index by 2.33%, the large-cap growth index by 0.28%, the SSE 50 by 0.89%, and the small-cap index represented by the CSI 2000 by 0.52% [1] - Sectors such as electric equipment and coal performed well, while computer and beauty care sectors lagged behind [1] Valuation Levels - As of November 7, 2025, the static PE of the CSI All Index is at an 82nd percentile, with the SSE 50 and CSI 300 at 77% and 74% respectively, while the ChiNext Index is close to 53% [1] - 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 Index minus the yield of ten-year government bonds, stands at 2.78% as of November 7, 2025, with a two-standard deviation boundary at 4.74% [1] ETF Fund Flows - In the last five trading days, ETF inflows amounted to 37.2 billion yuan, while margin trading decreased by approximately 700 million yuan [2] Industry Themes - The latest thematic allocation includes banking, energy, and dividends, specifically focusing on indices such as the CSI Bank Index, CSI Energy Index, and CSI Central Enterprises Dividend Index [2][3] Long-term Market Sentiment - The proportion of stocks above the 200-day moving average is being tracked to gauge long-term market sentiment [13] Financing Balance - The financing balance is being monitored to assess market liquidity and investor sentiment [16] Individual Stock Performance - Statistics on individual stock performance year-to-date based on return ranges are being compiled to identify trends [18] Oversold Indices - Observations are being made regarding indices that are considered oversold, indicating potential investment opportunities [20]

Core Viewpoint - The article emphasizes the importance of factor timing in investment strategies, highlighting the need to dynamically select effective factors based on changing market conditions to enhance the stability of multi-factor strategy returns [1][9]. Factor Timing Signals Effectiveness - A total of 92 timing signals were tested, showing an average correlation coefficient of over 15% with the next period's long returns across 77 Alpha factors and 10 Barra style factors. Specifically, deep learning, Level-2, minute frequency, and Barra factors had average correlation coefficients of 17%, 14%, 15%, and 14% respectively, indicating strong predictive power [2][19]. - The deep learning factors such as agru_dailyquote, DL_1, and fimage exhibited average correlation coefficients of 17%, 15%, and 18% respectively, with significant correlations observed in momentum, volatility, liquidity, and market capitalization characteristics [19]. Multi-Signal - Single Factor Timing - To avoid multicollinearity issues, the article employed Partial Least Squares (PLS) for signal aggregation and prediction. The AI image factor fimage achieved a timing success rate of 79%, with an excess annualized return of 8.9% and a Sharpe ratio improvement of 0.67 [2][39]. Multi-Signal - Multi-Factor Timing - The article presented a multi-factor timing strategy that resulted in an annualized return of 37.0% and a Sharpe ratio of 1.72, compared to a non-timed equal-weighted portfolio's annualized return of 20.8% and Sharpe ratio of 0.78. This led to an excess annualized return of 11.6% and a Sharpe ratio improvement of 0.94 [4][5]. Dynamic Multi-Factor Composite - Factor timing can be dynamically integrated into multi-factor composites for strategies like index enhancement. The timing factors in the index enhancement strategies for various indices, including CSI 300 and ChiNext, showed excess annualized returns of 4.56%, 5.98%, 1.08%, 5.67%, and 0.17% compared to the benchmark [5]. Factor Performance Statistics - The article analyzed the performance of 77 Alpha factors and 10 Barra style factors, providing detailed statistics on their returns and predictive capabilities. The results indicated that the factors maintained a strong predictive ability over various time frames [10][19]. Timing Signal Construction - The constructed timing signals fall into four main categories: Momentum, Volatility, Reversal, and Characteristics Spread. Each category has specific methodologies for calculating the signals, focusing on historical returns, volatility, and other characteristics [11][12][13][15][17][18].

Core Viewpoint - The article emphasizes the growing recognition of index-based investment among investors, highlighting the potential investment opportunities arising from significant changes in index constituents due to the periodic rebalancing of major indices like the SSE 50, CSI 300, and CSI 500 [1][4]. Group 1: Index Fund Growth - The total scale of passive index funds (including ETFs and off-market passive index funds) reached 4.5 trillion yuan as of October 31, with 2,294 funds, while enhanced index funds totaled 265.3 billion yuan, surpassing the scale of equity mixed funds at 2.53 trillion yuan [2][15]. - The total scale of equity ETFs grew from approximately 200 billion yuan in 2014 to 3.72 trillion yuan by October 2025, indicating significant growth [15]. Group 2: Historical Adjustment Effects of Index Constituents - Historical analysis from 2019 to mid-2025 shows that stocks added to indices tend to outperform the index in the two weeks prior to their inclusion, while those removed tend to underperform [2][24]. - The average excess return for stocks added to the index in the two weeks before inclusion was 4.89%, with a success rate of 66.67% [25]. Group 3: Latest Adjustment Impact Estimation - The expected adjustments for December 2025 indicate that the SSE 50 will adjust 4 stocks with an estimated passive buy amount of 5.5 billion yuan, the CSI 300 will adjust 10 stocks with an estimated net buy of 24.5 billion yuan, and the CSI 500 will adjust 50 stocks with an estimated buy of 3.3 billion yuan [3][33].

Macro and Technical Perspectives on Asset Allocation - The macro perspective indicates a bearish outlook on equity assets, while the technical perspective shows an upward trend with moderate valuation and a state of capital outflow [1][5][20] - For bonds, the macro perspective is bullish, but the technical perspective indicates a downward trend [1][5][20] - Industrial products are viewed negatively from a macro perspective, with a downward price trend also noted technically [1][5][20] - Gold assets are favored in the macro view, with a technical upward price trend [1][5][20] Asset Performance Tracking - The fixed ratio + macro indicators + technical indicators combination yielded a return of 10.51% in 2025, with an annualized return of 12.05% since April 2006 [2][21] - The volatility-controlled + macro indicators + technical indicators combination achieved a return of 15.69%, while the risk parity + macro indicators + technical indicators combination returned 6.99% [2][30] Asset Class Analysis - Equity assets are currently under pressure from macro indicators, while technical indicators suggest an upward trend but with capital outflow [20][21] - Bond assets are supported by macro indicators, but technical indicators show a downward trend [20][21] - Industrial products face macro headwinds and technical downward trends [20][21] - Gold assets benefit from favorable macro indicators and an upward technical trend [20][21] Valuation and Capital Flow Indicators - The equity risk premium (ERP) for the CSI 800 index is at 53.94%, indicating a moderate valuation level [13][14] - The latest capital flow indicator for equity assets shows a net outflow of 316 billion, reflecting a capital outflow state [16][17] Summary of Asset Class Scores - The overall scores for asset classes based on macro and technical indicators show equities at 1, bonds at 3, industrial products at -2, and gold at 2 [19][20] - The combined analysis suggests a bearish outlook for equities and industrial products, while bonds and gold are viewed positively [20][21]

Core Viewpoint - The article provides predictions for the periodic adjustments of major indices in the Chinese stock market, specifically focusing on the changes expected in December 2025 for various indices including the Shanghai Stock Exchange 50 Index, CSI 300 Index, CSI 500 Index, CSI 1000 Index, ChiNext Index, Shenzhen 100 Index, Sci-Tech 50 Index, and Sci-Tech 100 Index [1][4]. Group 1: Shanghai Stock Exchange 50 Index Adjustment Predictions - The Shanghai Stock Exchange 50 Index will see the inclusion of China National Offshore Oil Corporation, China Construction Bank, SAIC Motor Corporation, and Zhongke Shuguang, while Poly Developments, China CRRC Corporation, Guodian Nari Technology, and Shaanxi Coal and Chemical Industry will be removed [6][5]. Group 2: CSI 300 Index Adjustment Predictions - The CSI 300 Index will include Dongshan Precision, Ningbo Port, Huadian Energy, Anker Innovations, and Shanghai Electric, while it will exclude Baiyun Mountain, Oppein Home Group, TCL Zhonghuan, and others [8][7]. Group 3: CSI 500 Index Adjustment Predictions - The CSI 500 Index will add Electric Power Investment Energy, Supor, and 48 other stocks, while it will remove China Great Wall and 49 other stocks [10][9]. Group 4: CSI 1000 Index Adjustment Predictions - The CSI 1000 Index will include BAIC Blue Valley, Changshan Beiming, and 98 other stocks, while it will exclude Baoxing Bird and 99 other stocks [12][11]. Group 5: ChiNext Index Adjustment Predictions - The ChiNext Index will add Changshan Pharmaceutical, Huicheng Environmental Protection, and 6 other stocks, while it will remove Bihai Source and 7 other stocks [17][16]. Group 6: Shenzhen 100 Index Adjustment Predictions - The Shenzhen 100 Index will include Guangqi Technology and 6 other stocks, while it will remove Tiger Medical and 7 other stocks [20][19]. Group 7: Sci-Tech 50 Index Adjustment Predictions - The Sci-Tech 50 Index will add Huahong Semiconductor and Nuo Cheng Jianhua, while it will remove BGI Genomics and Hangcai Co., Ltd. [22][21]. Group 8: Sci-Tech 100 Index Adjustment Predictions - The Sci-Tech 100 Index will include Jiachizhi Technology and 9 other stocks, while it will remove Wukuang New Energy and 9 other stocks [24][23].

Core Viewpoint - The quantitative convertible bond portfolio experienced a slight decline in October, with a year-to-date return of 21.01% and an excess return of 4.02% [1] Group 1: Portfolio and Performance - The quantitative convertible bond portfolio is generated based on three factor systems: fundamental factors, low-frequency price-volume factors, and high-frequency price-volume factors, with monthly adjustments [5] - The portfolio's performance in October showed a return of -0.83% and an excess return of -0.72% [1] Group 2: Factor Data Tracking - A total of 32 fundamental factors, 80 low-frequency price-volume factors, and 32 high-frequency price-volume factors are tracked for convertible bonds [2][8] - The report provides a detailed list of factors used in the portfolio construction, referencing various research reports [8] Group 3: Risk Warnings - The report includes risk warnings for convertible bonds based on forced delisting and risk alert rules, as well as event-based and credit scoring methods [3][12] - Specific convertible bonds are flagged for trading-related forced delisting risks, financial-related forced delisting risks, and other credit risks [12] Group 4: Timing of Convertible Bond Index - The report employs price-volume models, pricing deviations, and convertible bond elasticity for timing and position management of the CSI Convertible Bond Index [4][13] - As of the end of October, the price-volume model and pricing model indicate a bullish signal, with a recommended position of 2/3 [4][13] Group 5: Pricing Deviation Factors - The report showcases the latest pricing deviation factors, which represent the difference between market prices and theoretical pricing for various convertible bonds [10][11] - A table lists specific convertible bonds along with their pricing deviation factors, indicating significant deviations for some bonds [11]

Market Performance - The Sci-Tech 50 Index decreased by 3.19% over the last five trading days, while the ChiNext Index increased by 0.50%. The large-cap value index fell by 0.38%, and the large-cap growth index dropped by 0.40%. The Shanghai 50 Index declined by 1.12%, whereas the small-cap index represented by the CSI 2000 rose by 1.18%. The power equipment and non-ferrous metals sectors performed well, while telecommunications and beauty care sectors lagged behind [1]. Risk Premium and Valuation Levels - As of October 29, 2025, the risk premium, calculated as the inverse of the static PE of the CSI All Share Index minus the yield of ten-year government bonds, stands at 2.84%. The two-standard deviation boundary is 4.75% [1]. - The valuation levels indicate that the CSI All Share Index's PETTM is at the 81st percentile, with the Shanghai 50 and CSI 300 at 75% and 73%, respectively. The ChiNext Index is close to the 53rd percentile, while the CSI 500 and CSI 1000 are at 63% and 61%, respectively. 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 6.9 billion yuan from ETFs, while the margin trading balance increased by approximately 46.9 billion yuan. The average daily trading volume across both markets was 22,967 billion yuan [2]. Convolutional Neural Network Analysis - A convolutional neural network (CNN) model was utilized to analyze charted price and volume data, mapping learned features to industry themes. The latest thematic allocations include banking, energy, and dividends, specifically focusing on indices such as the CSI Bank Index, CSI Energy Index, and CSI Central Enterprises Dividend Index [2][11].