Investment Insights - The report explores microstructure information contained in Level 2 order data, constructing stock selection factors with certain predictive capabilities based on order hand sizes and investor activity [2][8] - The main order hand size factors exhibit robust stock selection abilities, with small orders (1 hand, 100 shares) indicating strong institutional participation, while small orders (5, 10, 15 hands) from retail investors negatively impact stock prices [2][15] - The combined order buy hand size factor from 2015 to 2025 shows a T1-T6 VWAP RankIC of 0.048, with annualized excess returns of 18.6% for long positions and 30.6% for long-short strategies [2][19] Main Order Hand Size Factors - The report constructs a comprehensive order hand size factor by synthesizing significant hand sizes, including buy and sell orders, both executed and canceled [13][14] - The order buy hand size factor has a RankIC of 0.048 from 2015 to 2025, indicating a strong correlation with stock performance, while the order sell hand size factor has a RankIC of 0.040 [22][24] Investor Type Factors - The report identifies four types of investors based on order hand sizes: institutional investors, retail investors, quantitative traders, and speculative traders, each exhibiting distinct trading behaviors [27][29] - The buy-to-sell ratio for speculative investors shows a negative correlation with future returns, indicating that higher speculative buying may lead to lower future stock performance [29][33] Investor Activity - The report introduces a dynamic monitoring system for investor activity, quantifying the participation intensity of different investor types over time [56] - The analysis of specific stocks, such as Han's Laser and Neway, reveals that institutional and quantitative investor activity significantly influences stock price movements during certain periods [56][58] Speculative Stock Pool - The report constructs a "speculative stock pool" based on abnormal order sizes, aiming to capture stocks in the accumulation phase before price increases [69][72] - The enhanced strategy, incorporating machine learning factors, shows improved performance metrics, with annualized excess returns reaching 14.7% [2][72]