Equity2Vec: End-to-end Deep Learning Framework for Cross-sectional Asset Pricing

经验资产定价中的残差学习

Residual Learning in Empirical Asset Pricing

本研究提出将残差学习应用于经验资产定价，通过加深神经网络模型提升经济价值，实证表明深度残差模型样本外夏普比率显著优于浅层和前馈模型。

A News-based Machine Learning Model for Adaptive Asset Pricing

The paper proposes a new asset pricing model -- the News Embedding UMAP Selection (NEUS) model, to explain and predict the stock returns based on the financial news. Using a combination of various machine learning algorithms, we first derive a company embedding vector for each basis asset from the financial news. Then we obtain a collection of the basis assets based on their company embedding. After that for each stock, we select the basis assets to explain and predict the stock return with high-dimensional statistical methods. The new model is shown to have a significantly better fitting and …

Asset Pricing and Deep Learning

Traditional machine learning methods have been widely studied in financial innovation. My study focuses on the application of deep learning methods on asset pricing. I investigate various deep learning methods for asset pricing, especially for risk premia measurement. All models take the same set of predictive signals (firm characteristics, systematic risks and macroeconomics). I demonstrate high performance of all kinds of state-of-the-art (SOTA) deep learning methods, and figure out that RNNs with memory mechanism and attention have the best performance in terms of predictivity. Furthermore,…

智能体实证资产定价：方法论基础

Agentic Empirical Asset Pricing: Methodological Foundations

该研究提出智能体实证资产定价（AEAP）范式，明确其核心构建模块，构建因子发现的参考架构与评估标准，通过SEADS实验发现单一指标无法稳定评估系统，还揭示了AEAP系统的评估陷阱。

Deep Sequence Modeling: Development and Applications in Asset Pricing

We predict asset returns and measure risk premia using a prominent technique from artificial intelligence -- deep sequence modeling. Because asset returns often exhibit sequential dependence that may not be effectively captured by conventional time series models, sequence modeling offers a promising path with its data-driven approach and superior performance. In this paper, we first overview the development of deep sequence models, introduce their applications in asset pricing, and discuss their advantages and limitations. We then perform a comparative analysis of these methods using data on U…

自适应训练的物理信息径向基函数神经网络用于求解多资产期权定价问题

Adaptively trained Physics-informed Radial Basis Function Neural Networks for Solving Multi-asset Option Pricing Problems

提出基于径向基函数神经网络的物理信息机器学习算法，通过自适应调整隐藏神经元分布，高效求解多资产期权定价中的Black-Scholes偏微分方程，实验验证了单资产、双资产和四资产期权定价的有效性。