发表机构
National University of Singapore（新加坡国立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对金融交易中异构模态信号不可靠的问题，提出CoLAS多模态确证框架，通过增强共享分量与鲁棒一致性目标，在股票和加密货币数据集上实现年化收益率和夏普比率的提升。
AI 中文摘要
金融交易依赖于从价格序列、突发新闻、投资者情绪等异构市场模态中提取可靠信号。现有多模态方法主要结合异构模态以利用互补性，将每个模态视为同等重要，却忽略不同模态是否为同一交易信号提供相互支持的证据。然而，这种任务条件下的非抵消性支持（称为多模态确证）具有特殊价值，尤其对金融交易而言，因为单个金融观点存在噪声且信息性弱，跨异构观点持续存在的支持可能比仅出现在单一观点中的证据提供更稳定的任务相关信号。为利用这一特性，我们提出CoLAS（潜在资产信号多模态确证），该框架将多模态确证转化为可训练的任务条件表示，用于交易预测。模态表示被组织为每个实例的矩阵，其中基于softmax的谱目标会增强其主导的共享分量；带符号的模态贡献随后确定该分量是否提供非抵消性支持，并构建最终的确证信号；耦合的鲁棒感知一致性目标则在模态受损或缺失时保留该确证信号。在股票和加密货币数据集上开展的大量实验表明，所提CoLAS的有效性，其年化收益率和夏普比率均较现有方法实现了一致提升。
英文摘要
Financial trading relies on extracting reliable signals from heterogeneous market modalities such as price series, breaking news, and investor sentiment. Existing multimodal methods primarily combine heterogeneous modalities to exploit complementarity, treating each modality as equally valuable while overlooking whether different modalities provide mutually supportive evidence for the same trading signal. However, this task-conditioned and non-canceling support, termed multimodal corroboration, is particularly valuable, especially for financial trading. Because individual financial views are noisy and weakly informative, support that persists across heterogeneous views may provide a more stable task-relevant signal than evidence appearing in only one view. To exploit this property, we propose CoLAS (multimodal Corroboration of Latent Asset Signals), a framework that operationalizes multimodal corroboration as a trainable task-conditioned representation for trading prediction. The modality representations are organized into a per-instance matrix, where a softmax-based spectral objective strengthens its dominant shared component. Signed modality contributions then determine whether this component provides non-canceling support and construct the resulting corroborated signal. A coupled robustness-aware consistency objective further preserves the resulting corroborated signal when a modality is corrupted or missing. Extensive experiments on stock and cryptocurrency datasets demonstrate the effectiveness of our proposed CoLAS, yielding consistent improvements in both annualized return and Sharpe ratio over existing methods.
