AI 中文总结
研究提出特征驱动动态因子模型（CD-DFM），从公司基本面等特征构建资产横截面表示，通过结合特定损失项端到端训练，能产生经济结构潜在表示等，还可零样本纳入新资产，协方差预测有竞争力。
AI 中文摘要
估计金融资产的协方差结构通常依赖历史回报，这使得风险模型依赖于有噪声且特定于资产的时间序列。我们提出了特征驱动动态因子模型（CD-DFM），这是一种非线性潜在因子模型，它直接从可观测的公司特征（主要是公司基本面）构建资产横截面的表示。学习到的潜在空间共同确定可解释的因子暴露和前向协方差估计器，并在一个结合斯坦因协方差损失和因子重构项的目标上进行端到端训练，目标是风险管理中使用的样本外二阶矩。由于潜在表示（即编码器）仅依赖于特征，新出现的资产在推理时无需重新训练即可嵌入。对标准普尔500指数股票的实验表明，CD-DFM尽管依赖比基于回报的方法低得多频率的信息，但仍能产生具有经济结构的潜在表示、可解释的因子投资组合和有竞争力的协方差预测。在基准方法中，它是唯一同时结合特征驱动表示、因子可解释性、有竞争力的协方差校准和新资产零样本纳入的模型。
英文摘要
Estimating the covariance structure of financial assets typically relies on historical returns, making risk models dependent on noisy and asset-specific time series. We propose the Characteristic-Driven Dynamic Factor Model (CD-DFM), a non-linear latent factor model that instead constructs a representation of the asset cross-section directly from observable firm characteristics, primarily company fundamentals. The learned latent space jointly determines interpretable factor exposures and a forward covariance estimator, and is trained end to end on an objective that combines a Stein covariance loss with a factor reconstruction term, targeting the out-of-sample second moments used in risk management. Because the latent representation, i.e. the encoder depends only on characteristics, previously unseen assets can be embedded at inference time without retraining. Experiments on S&P 500 equities show that CD-DFM produces economically structured latent representations, interpretable factor portfolios, and competitive covariance forecasts despite relying on substantially lower-frequency information than return-based approaches. Among the benchmarked methods, it is the only model that simultaneously combines characteristic-driven representations, factor interpretability, competitive covariance calibration, and zero-shot onboarding of unseen assets.
