TABLE: Time-aware Balanced Multi-view Learning for stock ranking

表(数据库) 排名(信息检索) 计算机科学 库存(枪支) 机器学习 人工智能 数据挖掘 工程类 机械工程
作者
Ying Liu,Cai Xu,Long Chen,Meng Yan,Wei Zhao,Ziyu Guan
出处
期刊:Knowledge Based Systems [Elsevier BV]
卷期号:303: 112424-112424 被引量:11
标识
DOI:10.1016/j.knosys.2024.112424
摘要

Stock ranking is a significant and challenging problem. In recent years, the use of multi-view data, such as price and tweet, for stock ranking has gained considerable attention in the research field. Most existing methods are performed in (some of) the 3 steps: 1) view-specific representation learning; 2) cross-view representation interaction; 3) multi-view representation fusion. Although these methods make breakthroughs in stock ranking, they often treat all views equally. This neglects the unbalanced phenomenon in multi-view stock data, i.e., the dimension of the text view may be extremely big compared with those of other views; the price view exhibits standard and high-quality data, whereas the text view contains noise and has irregular time intervals. To solve this, we propose a Time-Aware Balanced multi-view LEarning (TABLE) method. TABLE method consists of a view-specific learning stage and a multi-view fusion stage. In the first stage, we aim to improve the quality of the low-quality text view. We achieve this by attenuating the negative impact of irrelevant texts using a hierarchical temporal attention mechanism that captures text correlations. Additionally, we explicitly model the time irregularities between sequential texts. In the fusion stage, we address the dimensions unbalance problem by establishing a multi-view decision fusion paradigm by weighted averaging the view-specific stock predictions. These weights are dynamic and determined based on the quality discrepancy between the views. Finally, we obtain the optimal stock ranking list by optimizing the point-wise regression loss and the ranking-aware loss. We empirically compare TABLE method with state-of-the-art baselines using the publicly available dataset, S&P500. The experimental results demonstrate that TABLE method outperforms the baseline methods in terms of accuracy and investment revenue.
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