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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
2秒前
李健的小迷弟应助Brian采纳,获得10
2秒前
2秒前
2秒前
liujian完成签到,获得积分10
3秒前
3秒前
脑洞疼应助wl301采纳,获得20
4秒前
4秒前
早安发布了新的文献求助10
4秒前
顾矜应助小岚花采纳,获得10
4秒前
5秒前
6秒前
6秒前
242588发布了新的文献求助10
6秒前
深情安青应助感性的梦竹采纳,获得10
7秒前
jiabaoyu发布了新的文献求助10
9秒前
Lc应助谷大喵唔采纳,获得10
9秒前
ff发布了新的文献求助10
10秒前
10秒前
10秒前
10秒前
Zarc完成签到,获得积分10
11秒前
无奈秋荷完成签到,获得积分10
12秒前
元谷雪发布了新的文献求助10
12秒前
14秒前
拼搏的酸奶完成签到,获得积分10
15秒前
Brian发布了新的文献求助10
15秒前
16秒前
16秒前
咖啡蓝图发布了新的文献求助10
16秒前
16秒前
18秒前
zhao完成签到,获得积分10
18秒前
今后应助风中的非笑采纳,获得10
18秒前
挤牙膏砖砖家完成签到,获得积分10
19秒前
华仔应助叶春曼采纳,获得10
19秒前
源老头完成签到,获得积分10
20秒前
20秒前
hei完成签到 ,获得积分10
20秒前
yangbinsci0827完成签到,获得积分10
20秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3988827
求助须知:如何正确求助?哪些是违规求助? 3531197
关于积分的说明 11252739
捐赠科研通 3269830
什么是DOI,文献DOI怎么找? 1804815
邀请新用户注册赠送积分活动 881915
科研通“疑难数据库(出版商)”最低求助积分说明 809028