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]

一个来自广州的"00后"女博士在硅谷创业，没产品、没用户，还在招募团队人员中，"三无"公司被估值达3亿-5亿美元？ 这听起来"过于完美"甚至有点天方夜谭的创业故事却正在发生。 据美国当地科技媒体6月3日报道，来自中国的"00后"斯坦福大学数学女博士洪乐潼在近日成立了自己的AI初创公司公理量化（Axiom Quant），目标融资金额为5000万美元，公司估值预计达到3亿-5亿美元，知名创投公司波士顿投资可能领投。 有意思的是，3天后，洪乐潼在知乎上称媒体报道"泄露了我们（并不准确）的融资信息"，并否认相关报道。但她在其他的个人社交账号上却 未有发布相关言论。与此同时，公理量化开启积极招揽国内外人才，组建团队，为后续研发做准备。 这不仅让外界和投资圈联想起了一个今年年初"横空出世"的广东85后：幻方量化、DeepSeek创始人梁文锋。 同为广东青年、同在AI创业赛道，同是"天才级"学霸的洪乐潼会创造下一个DeepSeek吗？在AI+数学创业领域，公理量化能否成为一匹"黑 马"？ "数学真美"！她来自广州 3年修完麻省理工数学与物理双学位，至少发表9篇纯数学前沿论文；24岁成为牛津大学罗德学者，一年攻读完神经科学硕 ...

[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 1. 《基于层级动量的投资组合构建 ——"学海拾珠"系列之二百三十六》 2. 《新闻公告与短久期溢价 ——"学 海拾珠"系列之二百三十五》 3. 《利用强化学习和文本网络改进相 关矩阵估计 ——"学海拾珠"系列之 二百三十四》 4. 《风险收益的权衡：宽松型风险平 价模型 ——"学海拾珠"系列之二百 执业证书号：S0010520070001 邮箱：yanjw@hazq.com 主要观点： [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、基金类研究 4 篇、债券研究 8 篇、资产配置研究 9 篇、机器学习在金融领域的应用研究 3 篇、ESG 相关研究 22 篇。 ⚫ 2025 年 5 月海外文献综述 本综述系统梳理了 5 月量化金融领域的文献， ...

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 ...