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].

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 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].