Research Background - The stock market exhibits overnight correlation characteristics, where daily returns can be decomposed into overnight and intraday returns. This report characterizes the correlation features of similar stocks based on recent academic findings [1][9]. Overnight Price Change Correlation Research - The study separates long and short signals from trading execution to capture cross-stock information effects. A correlation matrix is constructed based on overnight and intraday returns, identifying leading (Leader) and lagging (Lagger) groups. Trading strategies are developed to generate signals only from the leading group and trade within the lagging group [2][10][16]. Empirical Research - The analysis shows that the leading-lagging effect in A-shares presents a reversal effect, where a bullish signal from the leading group results in stronger performance from the short positions, and vice versa. The strategy is particularly applicable to small-cap stocks [2][35][44]. Factor Research - Weekly and monthly stock selection factors are constructed based on overnight correlation information. The introduction of conventional correlation improves the distinction of stock selection, with the combined factor showing a monthly RANK_IC of 8.13% and an annualized return of 18.2% [2][57][79]. Correlation Analysis - The internal correlation among factors is relatively low, indicating that the correlation factors provide marginal incremental value. The correlation factor shows some similarity with style factors, such as residual volatility [2][90]. Group Identification - The report attempts to identify groups within the A-share market, including the CSI 300 and the CSI 1000. The results indicate that the method of classifying leading and lagging groups based on correlation matrix features yields stable results [30][34]. Portfolio Construction Process - The portfolio construction framework separates signal generation from execution, capturing cross-stock information effects. The process includes constructing a correlation matrix, identifying leading and lagging groups, and extracting trading signals based on the leading group's average impact score [27][35]. Factor Construction and Backtesting - The report explores the performance of factors based on overnight correlation, with results indicating that conventional correlation factors outperform overnight correlation factors in terms of predictive effectiveness [57][72]. Performance Metrics - The backtesting results show that the strategy can achieve an annualized return of approximately 10.51% when focusing on small-cap stocks, while the distinction between long and short groups is less pronounced in large-cap stocks [44][72].