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 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].