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题名Improving stock trend prediction with pretrain multi-granularity denoising contrastive learning
作者
发表日期2024-04-01
发表期刊Knowledge and Information Systems
ISSN/eISSN0219-1377
卷号66期号:4页码:2439-2466
摘要

Stock trend prediction (STP) aims to predict price fluctuation, which is critical in financial trading. The existing STP approaches only use market data with the same granularity (e.g., as daily market data). However, in the actual financial investment, there are a large number of more detailed investment signals contained in finer-grained data (e.g., high-frequency data). This motivates us to research how to leverage multi-granularity market data to capture more useful information and improve the accuracy in the task of STP. However, the effective utilization of multi-granularity data presents a major challenge. Firstly, the iteration of multi-granularity data with time will lead to more complex noise, which is difficult to extract signals. Secondly, the difference in granularity may lead to opposite target trends in the same time interval. Thirdly, the target trends of stocks with similar features can be quite different, and different sizes of granularity will aggravate this gap. In order to address these challenges, we present a self-supervised framework of multi-granularity denoising contrastive learning (MDC). Specifically, we construct a dynamic dictionary of memory, which can obtain clear and unified representations by filtering noise and aligning multi-granularity data. Moreover, we design two contrast learning modules during the fine-tuning stage to solve the differences in trends by constructing additional self-supervised signals. Besides, in the pre-training stage, we design the granularity domain adaptation module (GDA) to address the issues of temporal inconsistency and data imbalance associated with different granularity in financial data, alongside the memory self-distillation module (MSD) to tackle the challenge posed by a low signal-to-noise ratio. The GDA alleviates these complications by replacing a portion of the coarse-grained data with the preceding time step’s fine-grained data, while the MSD seeks to filter out intrinsic noise by aligning the fine-grained representations with the coarse-grained representations’ distribution using a self-distillation mechanism. Extensive experiments on the CSI 300 and CSI 100 datasets show that our framework stands out from the existing top-level systems and has excellent profitability in real investing scenarios.

关键词Contrastive learning Denoising Memory Multi-granularity data Pre-training Stock trend prediction
DOI10.1007/s10115-023-02006-1
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收录类别SCIE
语种英语English
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence ; Computer Science, Information Systems
WOS记录号WOS:001131769900001
Scopus入藏号2-s2.0-85180656261
引用统计
文献类型期刊论文
条目标识符https://repository.uic.edu.cn/handle/39GCC9TT/11471
专题理工科技学院
通讯作者Guo, Jianxiong
作者单位
1.Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, 519087, China
2.Guangdong Key Lab of AI and Multi-Modal Data Processing, Department of Computer Science, BNU-HKBU United International College, Zhuhai, 519087, China
3.Department of Mathematical Science, BNU-HKBU United International College, Zhuhai, 519087, China
第一作者单位北师香港浸会大学
通讯作者单位北师香港浸会大学
推荐引用方式
GB/T 7714
Wang, Mingjie,Wang, Siyuan,Guo, Jianxionget al. Improving stock trend prediction with pretrain multi-granularity denoising contrastive learning[J]. Knowledge and Information Systems, 2024, 66(4): 2439-2466.
APA Wang, Mingjie, Wang, Siyuan, Guo, Jianxiong, & Jia, Weijia. (2024). Improving stock trend prediction with pretrain multi-granularity denoising contrastive learning. Knowledge and Information Systems, 66(4), 2439-2466.
MLA Wang, Mingjie,et al."Improving stock trend prediction with pretrain multi-granularity denoising contrastive learning". Knowledge and Information Systems 66.4(2024): 2439-2466.
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