Core Insights - The article discusses the use of a multi-factor model suitable for the CSI 300 index constituents, combined with a small-cap high-growth satellite strategy, to enhance the performance of the CSI 300 enhanced strategy [1][2] - Since 2016, the CSI 300 enhanced strategy has achieved an annualized excess return of 12.6% with a tracking error of 5.2% under a satellite allocation of 30% domestic and 10% foreign [1][2] Summary by Sections - **Performance Analysis**: The CSI 300 enhanced strategy has shown an annualized excess return of at least 10% since 2016, with an information ratio exceeding 2.0. The internal component of the strategy has lower tracking error and relative drawdown, while the external component offers greater return elasticity but with higher tracking error and drawdown [1][2] - **Model Construction**: The multi-factor model is constructed based on fundamental and momentum indicators, which has demonstrated better stock selection robustness compared to the all-A multi-factor model [1] - **Satellite Strategy**: The external component can be replaced with small-cap high-growth or GARP strategies. The optimal satellite allocation depends on the risk-return preference, with the most extreme case showing an annualized excess return of 17.5% when fully utilizing satellite strategies [2]

Core Insights - The report emphasizes the development of a deep learning factor selection system for stock prediction and portfolio optimization, shifting from traditional logic-driven methods to data-driven approaches [7][10]. - The system integrates diverse data sources, including daily and minute market data, to enhance the performance of alpha signals [7][10]. - The report outlines the construction of multiple models that utilize different network architectures to extract unique alpha signals, demonstrating low correlation among them [8][54]. Data and Network - The input data consists of three categories: daily market data, minute market data, and manually crafted features, with neural networks independently extracting alpha features from each dataset [11]. - The report describes the use of Long Short-Term Memory (LSTM) networks combined with self-attention mechanisms to capture long-term dependencies in time series data [19]. - A Graph Attention Network (GAT) is employed to model the complex relationships between stocks, providing a global analysis perspective [20]. Alpha Models - The report presents various alpha models, including simple equal-weight, tree model weighting, and network weighting, with a focus on combining multiple signals to enhance robustness [3][3.1][3.2]. - The average Information Coefficient (IC) for the combined factors since 2019 is reported as 11.3% for 5-day IC and 12.4% for 10-day IC, indicating strong predictive power [31][32]. Risk Models - The report highlights the use of neural networks to identify high-dimensional non-linear risk patterns directly from raw price and volume data, enhancing risk control in portfolio construction [9]. Index Enhancement Strategies - The report details the performance of enhanced index strategies based on deep learning alpha signals, with annualized returns reported as follows: - CSI 300 enhanced portfolio: 18.2% annualized return, 14.2% excess return over the index [3][5.1]. - CSI 500 enhanced portfolio: 22.4% annualized return, 17.2% excess return over the index [3][5.2]. - CSI 1000 enhanced portfolio: 29.8% annualized return, 24.5% excess return over the index [3][5.3].