Core Insights - The report presents a multi-dimensional index daily frequency timing framework aimed at optimizing absolute return strategies and stock index futures performance through position timing [3] - The framework is based on a multi-dimensional factor system, including 80 analyst expectation factors, 134 capital flow factors, 43 high-frequency aggregated low-frequency features, and deep learning factors introduced after 2020 [3][12] - The backtesting results show that the long-short strategy achieves an annualized return of 46% with a Sharpe ratio of 2.37, while the long-only strategy achieves an annualized return of 23% [3][12] Factor Analysis - The basic factors include 80 analyst expectation factors and 134 capital flow factors, which are crucial for predicting future returns [12][15] - The report highlights a negative correlation between capital flow factors, particularly outflow-related factors, and the next day's returns, indicating a reversal characteristic overnight [15][16] - The report tests the performance of various analyst expectation factors, with the top-performing factors yielding annualized returns ranging from 10% to over 21% based on different thresholds [27][23] Deep Learning Integration - The deep learning factor prediction framework targets the next day's returns using both daily and minute data to capture overnight signals, employing an improved Mean Absolute Directional Loss (MADL) function for directional judgment [10][54] - The MADL function is preferred over Mean Squared Error (MSE) as it focuses on optimizing the correctness of directional predictions rather than numerical accuracy, aligning with practical trading principles [54][57] Timing and Stock Selection Strategy - The framework validates the feasibility and effectiveness of position timing, achieving a win rate of 54% for both long and short positions [12] - The strategy further integrates stock selection models to enhance the return structure, demonstrating a robust solution for quantitative investment [11][3] High-Frequency Data Utilization - The report constructs 43 high-frequency factors to capture market sentiment and risk, including intraday volatility and trading volume patterns [36][42] - The high-frequency factors are aggregated to create suitable features for daily extraction, ensuring high quality and low noise [36][37]