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].