Core Viewpoint - The report tracks the performance of a long-term stock selection strategy focused on profitability and growth, which was initially published on August 26, 2020, by the GF Financial Engineering team [2][29]. Empirical Analysis Data Description - The empirical analysis covers a backtesting period from January 1, 2009, to August 29, 2025, with three portfolio adjustments each year on April 30, August 31, and October 31 [3]. Portfolio Construction - The stock selection process emphasizes high ROE, improving gross profit margins, and strong cash flow, while excluding stocks with poor cash flow and high debt ratios [4]. Equal-Weighted Portfolio Performance - The equal-weighted portfolio achieved a cumulative return of 3281.94% and an annualized return of 23.43% during the backtesting period, outperforming the CSI 800 index, which had a cumulative return of 169.89% [5][30]. - The equal-weighted strategy had an annualized volatility of 13.67% and an information ratio of 1.19 [13]. Market Capitalization-Weighted Portfolio Performance - The market capitalization-weighted portfolio recorded a cumulative return of 2330.56% and an annualized return of 21.02%, also outperforming the CSI 800 index [15]. - The market capitalization-weighted strategy had an annualized volatility of 13.86% and an information ratio of 1.00 [22]. Portfolio Holding Characteristics - On average, each portfolio iteration consisted of approximately 55 stocks, with an average market capitalization of around 14 billion [26][30]. - The most frequently selected sectors included pharmaceuticals, chemicals, electronics, machinery, and food and beverage, while sectors like leisure services and defense had fewer selections [26][30].

Market Performance - The Sci-Tech 50 Index increased by 7.49% and the ChiNext Index rose by 7.74% over the last five trading days, while the large-cap value index fell by 1.37% [1] - The large-cap growth index gained 5.83%, and the Shanghai 50 Index increased by 1.63%, with the small-cap index represented by the CSI 2000 rising by 0.33% [1] - Communication and non-ferrous metals sectors performed well, while textiles, apparel, and coal sectors lagged [1] Risk Premium Analysis - The static PE of the CSI All Index minus the yield of 10-year government bonds indicates a risk premium, which reached 4.17% on April 26, 2022, and 4.08% on October 28, 2022, leading to a market rebound [1] - As of January 19, 2024, the risk premium indicator was at 4.11%, marking the fifth occurrence since 2016 of exceeding 4% [1] - The indicator as of August 29, 2025, was at 2.92%, with the two-standard deviation boundary set at 4.77% [1] Valuation Levels - As of August 29, 2025, the CSI All Index's P/E TTM percentile was at 78%, while the Shanghai 50 and CSI 300 were at 72% and 70%, respectively [2] - The ChiNext Index was close to 46%, indicating a relatively low valuation level compared to historical averages [2] Technical Analysis - The Deep 100 Index has experienced bear markets every three years, with declines ranging from 40% to 45% [2] - The current adjustment cycle began in Q1 2021, suggesting a potential upward cycle from the bottom [2] Fund Flow and Trading Activity - In the last five trading days, ETF inflows totaled 28.6 billion yuan, and margin financing increased by approximately 96.6 billion yuan [3] - The average daily trading volume across both markets was 29.51 billion yuan [3] AI and Data Analysis - A convolutional neural network (CNN) was utilized to model price and volume data, mapping learned features to industry themes [9] - The latest investment themes include artificial intelligence and related sectors [2]

Core Viewpoint - The article discusses the upcoming 2025 Autumn Strategy Conference hosted by GF Securities, focusing on various investment strategies and market analysis techniques, particularly in the context of AI and quantitative finance [2][4]. Group 1: Conference Overview - The conference will take place on August 27, 2025, at the Pudong Shangri-La Hotel, featuring a series of presentations from leading analysts in financial engineering [2]. - The agenda includes discussions on index crowding, AI-driven strategy selection, and the timing of convertible bond indices [2][3]. Group 2: Key Presentations - An Ningning, Chief Analyst of Financial Engineering, will present on time-series enhanced learning for general models [2]. - Chen Yuanwen, Co-Chief Analyst, will discuss three perspectives on the timing of convertible bond indices [2]. - Zhang Chaowill focus on the diffusion effect of leading stocks and industry rotation [4]. - Zhang Yudong will present a multi-factor weighted ETF rotation strategy [5]. - Wang Xiaokang will explore how to leverage smart money to improve analyst expectations [6].

Market Performance - The Sci-Tech 50 Index increased by 13.31% over the last five trading days, while the ChiNext Index rose by 5.85%. The large-cap value index grew by 1.56%, and the large-cap growth index increased by 4.77%. The Shanghai 50 Index and the CSI 2000 Index, representing small caps, saw gains of 3.38% and 3.47%, respectively. The telecommunications and electronics sectors performed well, while real estate and coal sectors lagged behind [1]. Risk Premium Analysis - The static PE of the CSI All Index minus the yield of ten-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 reached 4.11%, marking the fifth occurrence since 2016 to exceed 4%. As of August 22, 2025, the indicator stands at 3.03%, with the two standard deviation boundary at 4.77% [1]. Valuation Levels - As of August 22, 2025, the CSI All Index's PE TTM percentile is at 76%. The Shanghai 50 and CSI 300 indices are at 72% and 68%, respectively, while the ChiNext Index is close to 39%. The CSI 500 and CSI 1000 indices are at 58% and 57%, indicating that 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 current adjustment cycle began in Q1 2021, suggesting a potential upward cycle from the bottom based on historical patterns [2]. Fund Flow and Trading Activity - In the last five trading days, ETF inflows totaled 24.7 billion yuan, and the margin financing increased by approximately 90.1 billion yuan. The average daily trading volume across both markets was 25.463 billion yuan [3]. AI and Neural Network Analysis - A convolutional neural network (CNN) has been utilized to model price and volume data, mapping learned features to industry themes. The latest focus is on sectors such as telecommunications [8].

Core Viewpoint - The article discusses the upcoming 2025 Autumn Strategy Conference hosted by GF Securities, focusing on various investment strategies and market analysis techniques, particularly in the context of AI and quantitative finance [2][4]. Group 1: Conference Overview - The conference is scheduled for August 27, 2025, from 13:30 to 17:00 at the Pudong Shangri-La Hotel, featuring multiple sessions on investment strategies [2]. - Key topics include quantitative analysis of index crowding, AI-driven strategy selection, and the timing of convertible bond indices [2][3]. Group 2: Featured Analysts and Topics - An Ningning, Chief Analyst of Financial Engineering, will present on time-series enhanced learning for general models [2]. - Chen Yuanwen, Co-Chief Analyst of Financial Engineering, will discuss three perspectives on convertible bond index timing [2]. - Zhang Chaowill focus on the diffusion effect of leading stocks and industry rotation [4]. - Zhang Yudong will present a multi-factor weighted ETF rotation strategy [5]. - Wang Xiaokang will explore how to leverage smart money to improve analyst expectations [6].

Core Viewpoint - The recent market performance shows a significant increase in the ChiNext and Sci-Tech 50 indices, while large-cap value stocks have declined, indicating a shift in investor sentiment towards growth sectors [1][2]. Market Performance - In the last five trading days, the Sci-Tech 50 index rose by 5.53%, the ChiNext index increased by 8.48%, while the large-cap value index fell by 0.76%. The large-cap growth index rose by 3.63%, and the Shanghai 50 index increased by 1.57%. Small-cap stocks represented by the CSI 2000 index rose by 3.86% [1]. - The communication and electronics sectors performed well, while the banking and steel sectors lagged behind [1]. Risk Premium Analysis - The risk premium, measured as the difference between the inverse of the static PE of the CSI All Share Index and the yield of ten-year government bonds, has reached historical extremes. As of October 28, 2022, the risk premium was at 4.08%, indicating a potential market rebound [1]. - The risk premium has exceeded 4% for the fifth time since 2016, with the latest reading on January 19, 2024, at 4.11% [1]. Valuation Levels - As of August 15, 2025, the CSI All Share Index's TTM PE is at the 72nd percentile, with the Shanghai 50 and CSI 300 at 69% and 63%, respectively. The ChiNext index is at a relatively low valuation level of approximately 33% [2]. - The long-term view of the Deep 100 index suggests a cyclical pattern of bear and bull markets every three years, with the current adjustment phase starting in Q1 2021 showing sufficient time and space for a potential upward cycle [2]. Fund Flow and Trading Activity - In the last five trading days, there was an outflow of 10.4 billion yuan from ETFs, while margin financing increased by approximately 41.8 billion yuan. The average daily trading volume across both markets was 20,767 billion yuan [3]. AI and Trend Observation - The use of convolutional neural networks (CNN) for modeling price and volume data has been explored, with the latest focus on mapping learned features to industry themes, particularly in the communication sector [8].

Core Viewpoint - The report focuses on constructing stock price jump-related factors based on Level 2 data to detect the effects of volatility, jump amplitude, and jump trading activity on stock selection [1][4]. Group 1: Research Background - The report aims to utilize Level 2 data for in-depth analysis to uncover hidden market patterns, which may include stock price trends and short-term trading signals [3][4]. - Previous studies have introduced jump-diffusion models and derived various jump-related factors for empirical testing [3][4]. Group 2: Level 1 and Level 2 Market Data - Level 1 data includes basic trading information such as highest price, lowest price, opening price, closing price, trading volume, and trading amount, while Level 2 data provides more detailed information, including tick data and order book depth [5][6]. Group 3: Jump Factor Research - The jump factors are categorized into jump volatility, cumulative jump values, and trading volume ratios, with further refinements based on the direction and size of jumps [18][21]. - The report discusses the construction of jump volatility factors and cumulative jump value factors, which consider the positive and negative directions of price changes [19][23]. Group 4: Empirical Backtesting - The backtesting period is set from January 1, 2020, to July 18, 2025, with a focus on the performance of various factors under different trading frequencies [27]. - The RRJV factor shows a RANK_IC of 8.18% and an annualized return of 27.8% during the backtesting period [28][29]. - The JSR2_drop factor achieves a RANK_IC of 9.77% with an annualized return of 22.1% [30]. Group 5: Performance Analysis - The report indicates that the performance of jump-related factors varies with trading frequency, with some factors showing improved returns under weekly trading strategies [32][36]. - The RRJV factor's backtesting results indicate a significant increase in long-term returns, reaching 28.2% under weekly trading conditions [36].

Market Performance - The recent 5 trading days saw the Sci-Tech 50 Index increase by 0.65%, the ChiNext Index by 0.49%, the large-cap value by 1.63%, the large-cap growth by 1.17%, the SSE 50 by 1.27%, and the small-cap represented by the CSI 2000 by 2.74% [1] - The sectors of defense, military, and non-ferrous metals performed well, while pharmaceuticals, biotechnology, and computers 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, which reached 4.17% on April 26, 2022, and 4.08% on October 28, 2022, showing a market rebound [1] - As of January 19, 2024, the risk premium indicator was at 4.11%, marking the fifth time since 2016 it exceeded 4% [1] - The indicator as of August 8, 2025, was at 3.39%, with the two-standard deviation boundary at 4.77% [1] Valuation Levels - As of August 8, 2025, the CSI All Index's PE TTM percentile was at 68%, with the SSE 50 and CSI 300 at 69% and 61% respectively, while the ChiNext Index was close to 25% [2] - The long-term view of the Deep 100 Index shows a technical pattern of bear markets every three years followed by bull markets, with the current adjustment starting in Q1 2021 being substantial [2] Fund Flow and Trading Activity - In the last 5 trading days, ETF inflows amounted to 18.5 billion yuan, and the margin trading increased by approximately 27.8 billion yuan, with an average daily trading volume of 1.6748 trillion yuan [3] Neural Network Trend Observation - A convolutional neural network was utilized to model price and volume data, mapping learned features to industry themes, with a focus on semiconductor materials among the latest configurations [9]

Group 1 - The core viewpoint of the article is the rapid development of ETFs in the A-share market since 2019, with the total number of ETFs increasing from 198 in Q4 2018 to 1209 by Q2 2025, and the total scale rising from 0.51 trillion yuan to 4.31 trillion yuan during the same period [1][9][11] - As of Q2 2025, stock-type ETFs account for approximately 75% of the total scale, while cross-border ETFs, currency-type ETFs, bond-type ETFs, and commodity-type ETFs collectively account for about 25% [11][12] Group 2 - The article discusses the construction of an all-weather diversified ETF portfolio based on various methods, including A-share market asset allocation ETFs, overseas equity index QDII-ETFs, relative return ETFs, and absolute return ETFs [2][13] - The methodology for constructing the A-share market asset allocation ETF portfolio and overseas equity index QDII-ETF portfolio involves quantitative scoring based on macro and technical perspectives [4][19] Group 3 - The performance of the all-weather diversified ETF portfolio shows an annualized return of 9.22% with a maximum drawdown of 3.64% and an annualized volatility of 3.85% from December 31, 2016, to June 30, 2025 [6][40] - The portfolio consistently achieved positive absolute returns across different years within the backtesting period, indicating a balanced source of returns [6][40] Group 4 - The construction method for the A-share market asset allocation ETF portfolio includes selecting large-cap indices such as CSI 300 and CSI 500, and bond indices like 1-5 year national development bonds [22][23] - The strategic allocation models used include fixed ratio, volatility control, and risk parity models, with monthly adjustments based on macro and technical indicators [35][36] Group 5 - The QDII-ETF portfolio construction method focuses on overseas equity indices, including the Hang Seng Index, S&P 500, and Nikkei 225, with macro and technical indicators influencing the scoring and weighting [43][44] - The QDII-ETF portfolio achieved an annualized return of 17.24% with a maximum drawdown of 17.20% during the backtesting period [54][55] Group 6 - The relative return ETF portfolio is constructed based on six dimensions, including historical fundamentals and capital flow, to implement an index rotation strategy [57][60] - The portfolio that incorporates index crowding indicators outperformed the standard relative return ETF portfolio, achieving an annualized return of 16.59% compared to 14.04% [79] Group 7 - The absolute return ETF portfolio is designed to focus on absolute returns by selecting indices with stable fundamentals and significant dividend yields, while minimizing exposure to market volatility [83]

Core Viewpoint - The article discusses the construction and testing of index congestion indicators to enhance the performance of ETF portfolios by removing ETFs corresponding to highly congested indices, thereby reducing the impact of market reversals on the ETF portfolio [1][2][3]. Group 1: Index Congestion Indicators - The construction of congestion indicators is based on six dimensions: trading volume, volatility level, financing balance, financing increment, fund holdings, and capital flow [2][23]. - The effectiveness of these congestion indicators is tested by comparing the performance of a congestion index portfolio against the average performance of sample equity indices [25][55]. - The overall correlation between different congestion indicators is low, indicating that a multi-indicator congestion index portfolio performs more stably [2][55]. Group 2: ETF Portfolio Construction - The article outlines the process of constructing relative return index portfolios by excluding indices with two or more congestion indicators from the top-scoring indices [3][63]. - Backtesting results show that the constructed portfolios outperform those that do not consider index congestion, with a cumulative return of 355.05% from December 31, 2016, to June 30, 2025 [12][64]. - The annualized return of the relative return index portfolio combined with index congestion is 19.75%, compared to 17.67% for the standard relative return portfolio [64][66]. Group 3: A-share Market ETF Development - Since Q4 2018, the number of equity ETFs in the A-share market has increased from 133 to 972 by Q2 2025, with total assets rising from 0.27 trillion yuan to 3.03 trillion yuan [7]. Group 4: Backtesting Results - The cumulative return of the relative return ETF portfolio from December 31, 2016, to June 30, 2025, is 201.79%, significantly higher than the benchmark portfolio's return of 33.38% [12][70]. - The performance of the relative return ETF portfolio shows significant excess returns in most years during the backtesting period [12][66]. Group 5: Individual Congestion Indicators - The article details the performance of individual congestion indicators, such as trading volume and beta, showing that portfolios based on these indicators generally underperform compared to sample equity indices [26][33][38]. - For example, the trading volume congestion index portfolio had a cumulative return of -11.66% compared to 41.03% for the sample equity index portfolio [26][33]. Group 6: Multi-Indicator Combination - A multi-indicator congestion index portfolio is constructed by combining the six different congestion indicators, which shows a low average correlation among them [55][58]. - Backtesting results indicate that multi-indicator portfolios generally underperform compared to sample equity indices, particularly those with two or more congestion indicators [59][70].