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Improving stock trend prediction with pretrain multi-granularity denoising contrastive learning

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Abstract

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.

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Authors and Affiliations

  1. Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, 519087, China

    Jianxiong Guo & Weijia Jia

  2. Guangdong Key Lab of AI and Multi-Modal Data Processing, Department of Computer Science, BNU-HKBU United International College, Zhuhai, 519087, China

    Mingjie Wang, Jianxiong Guo & Weijia Jia

  3. Department of Mathematical Science, BNU-HKBU United International College, Zhuhai, 519087, China

    Siyuan Wang

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  1. Mingjie Wang
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Contributions

MW contributed to the idea, method, and writing; SW performed the experiment; JG contributed to the method, writing, and supervising; WJ contributed to writing and supervising.

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Correspondence to Jianxiong Guo or Weijia Jia.

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This work is an extended version of the paper [1], which has been presented at the International Joint Conference on Neural Networks (IJCNN 2023), Queensland, Australia. The source code can be found in https://github.com/MingjieWang0606/MDC_IJCNN. This work was supported in part by the Guangdong Key Lab of AI and Multi-modal Data Processing, BNU-HKBU United International College (UIC) under Grant 2020KSYS007 sponsored by Guangdong Provincial Department of Education; the National Natural Science Foundation of China (NSFC) under Grant 61872239 & 62202055; the Interdisciplinary Incubation Project of Beijing Normal University under Grant 310430014; the Institute of Artificial Intelligence and Future Networks (BNU-Zhuhai) and Engineering Center of AI and Future Education, Guangdong Provincial Department of Science and Technology, China; the Zhuhai Science-Tech Innovation Bureau under Grants ZH22017001210119PWC & 28712217900001; and the Interdisciplinary Intelligence SuperComputer Center of Beijing Normal University (Zhuhai).

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Wang, M., Wang, S., Guo, J. et al. Improving stock trend prediction with pretrain multi-granularity denoising contrastive learning. Knowl Inf Syst 66, 2439–2466 (2024). https://doi.org/10.1007/s10115-023-02006-1

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