Quantitative Models and Construction Methods 1. Model Name: IPCA Factor Model - **Model Construction Idea**: The IPCA factor model is designed to explain the returns of 46 option strategies, aiming to capture 80% of their returns while minimizing abnormal monthly returns to near zero[22] - **Model Construction Process**: The model integrates factors such as transaction costs and heterogeneous risk aversion to optimize derivative pricing. It also addresses the absence of reliable credit or liquidity premiums in pre-WWI corporate bond returns[25] - **Model Evaluation**: The model demonstrates strong explanatory power for option strategy returns and highlights the role of transaction costs in driving return volatility[22][25] 2. Model Name: Neural Functionally Generated Portfolios (NFGP) - **Model Construction Idea**: NFGP combines Transformer and diffusion models to enhance probabilistic time-series forecasting accuracy and improve decision reliability[35] - **Model Construction Process**: The model reduces forecasting errors by 42% compared to benchmarks and introduces dual uncertainty indicators to optimize portfolio decisions[35] - **Model Evaluation**: The model outperforms traditional approaches in terms of predictive accuracy and robustness in decision-making[35] --- Model Backtesting Results 1. IPCA Factor Model - **Explanatory Power**: 80% of option strategy returns explained[22] - **Abnormal Monthly Returns**: Approaching zero[22] 2. Neural Functionally Generated Portfolios (NFGP) - **Forecasting Error Reduction**: 42% compared to benchmarks[35] --- Quantitative Factors and Construction Methods 1. Factor Name: "Betting Against (Bad) Beta" (BABB) - **Factor Construction Idea**: The BABB factor improves the "Betting Against Beta" (BAB) strategy by managing transaction costs and isolating bad beta components[15] - **Factor Construction Process**: The factor is constructed using double sorting to isolate bad beta components. It achieves an annualized alpha exceeding 6%, independent of traditional sentiment indicators[15] - **Factor Evaluation**: The factor demonstrates strong performance in low-risk investment strategies, with significant alpha generation[15] 2. Factor Name: High-Speed Rail Network Centrality - **Factor Construction Idea**: This factor captures the impact of high-speed rail network centrality on corporate bond spreads by improving the information environment and regional trust[25] - **Factor Construction Process**: The factor is derived from the centrality of high-speed rail networks, showing a significant reduction in corporate bond spreads, particularly for non-state-owned enterprises and non-central cities[25] - **Factor Evaluation**: The factor effectively highlights the role of infrastructure in reducing financing costs and improving capital allocation efficiency[25] 3. Factor Name: Residual-Based Structural Change Detection - **Factor Construction Idea**: This factor robustly detects structural changes in factor models, accommodating over-specified factor numbers and error correlations[17] - **Factor Construction Process**: The factor employs residual-based tests to identify smooth or abrupt structural changes in factor models, enhancing robustness in model evaluation[17] - **Factor Evaluation**: The factor is highly effective in detecting structural changes and improving the robustness of factor model evaluations[17] --- Factor Backtesting Results 1. "Betting Against (Bad) Beta" (BABB) - **Annualized Alpha**: >6%[15] 2. High-Speed Rail Network Centrality - **Corporate Bond Spread Reduction**: Significant, especially for non-state-owned enterprises and non-central cities[25] 3. Residual-Based Structural Change Detection - **Robustness**: Effective in detecting both smooth and abrupt structural changes[17]

分析师：严佳炜 执业证书号：S0010520070001 邮箱：yanjw@hazq.com 主要观点： [Table_StockNameRptType] 金融工程 月报 "学海拾珠"系列之跟踪月报 202505 [Table_RptDate] 报告日期：2025-06-04 Table_Author] 分析师：骆昱杉 执业证书号：S0010522110001 邮箱：luoyushan@hazq.com 5. 《资产与因子风险预算：一种均衡 策略 ——"学海拾珠"系列之二百三 十二》 6. 《年报中的叙述式披露对公司价值 的多维度影响 ——"学海拾珠"系列 之二百三十一》 7. 《"知识"嵌入型深度强化学习在多 元资产配置中的应用 ——"学海拾 珠"系列之二百三十》 ⚫ "学海拾珠"系列文献数据库 我们建立了系统的学术文献追踪机制，持续关注 30 余本国际权威 金融与量化研究期刊及顶级学术会议。每月定期汇总整理这些平台最新 收录的量化相关研究成果，确保研究团队能够及时把握学术前沿动态。 [Table_Summary] 本月新增量化金融相关的研究文献共计 80 篇，研究领域分布如下：权 益类研究 31 篇、基金 ...