STORM：基于双向量量化变分自编码器的时空因子模型用于金融交易
STORM: A Spatio-Temporal Factor Model Based on Dual Vector Quantized Variational Autoencoders for Financial Trading
AI总结：
STORM通过双向量量化变分自编码器从时空视角提取股票特征，融合并对齐多维嵌入，提升金融交易中因子选择的鲁棒性和有效性。
摘要：
在金融交易中，因子模型被广泛用于定价资产和捕捉资产误定价带来的超额收益。最近，我们见证了基于变分自编码器的潜在因子模型的兴起，这些模型能够自适应地学习潜在因子。然而，这些模型虽然专注于整体市场条件的建模，但往往无法有效捕捉单个股票的时间模式。此外，将多个因子表示为单一值简化了模型，但限制了其捕捉复杂关系和依赖性的能力。因此，学习到的因子质量较低且缺乏多样性，降低了其在不同交易时期的有效性和鲁棒性。为了解决这些问题，我们提出了一种基于双向量量化变分自编码器的时空因子模型，命名为STORM，该模型从时间和空间视角提取股票特征，然后在细粒度和语义层面融合和对齐这些特征，并将因子表示为多维嵌入。离散代码簿聚类相似因子嵌入，确保正交性和多样性，这有助于区分不同因子并实现金融交易中的因子选择。为了展示所提因子模型的性能，我们将其应用于两个下游实验：在两个股票数据集上的投资组合管理和在六个特定股票上的个体交易任务。广泛的实验表明，STORM在适应下游任务方面的灵活性以及在基准模型上的优越性能。
英文摘要：
In financial trading, factor models are widely used to price assets and capture excess returns from mispricing. Recently, we have witnessed the rise of variational autoencoder-based latent factor models, which learn latent factors self-adaptively. While these models focus on modeling overall market conditions, they often fail to effectively capture the temporal patterns of individual stocks. Additionally, representing multiple factors as single values simplifies the model but limits its ability to capture complex relationships and dependencies. As a result, the learned factors are of low quality and lack diversity, reducing their effectiveness and robustness across different trading periods. To address these issues, we propose a Spatio-Temporal factOR Model based on dual vector quantized variational autoencoders, named STORM, which extracts features of stocks from temporal and spatial perspectives, then fuses and aligns these features at the fine-grained and semantic level, and represents the factors as multi-dimensional embeddings. The discrete codebooks cluster similar factor embeddings, ensuring orthogonality and diversity, which helps distinguish between different factors and enables factor selection in financial trading. To show the performance of the proposed factor model, we apply it to two downstream experiments: portfolio management on two stock datasets and individual trading tasks on six specific stocks. The extensive experiments demonstrate STORM's flexibility in adapting to downstream tasks and superior performance over baseline models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
