Hierarchical Adaptive Temporal-Relational Modeling for Stock Trend Prediction
Authors: Heyuan Wang, Shun Li, Tengjiao Wang, Jiayi Zheng
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on three real-world stock market datasets validate the effectiveness of our model. |
| Researcher Affiliation | Academia | 1School of EECS, Peking University 2University of International Relations 3Institute of Computational Social Science, Peking University(Qingdao) |
| Pseudocode | No | No pseudocode or clearly labeled algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing open-source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | We verify HATR on three real-world datasets for comprehensive evaluation. Table 1 shows the detailed statistics. The first dataset comprises stocks from the well-know CSI-300 Composite Index... The second dataset is targeted at stocks from popular S&P500 Composite Index... The third dataset [Li et al., 2020] is from Tokyo Stock Exchange, including 95 stocks with the largest market capitalization in Japan from the TOPIX-100 Index. We collect the daily quote data, industry and capital information from Wind-Financial Terminal1. To mine topicality relations, we detect co-occurrence stock pairs in user reviews from a popular Chinese investment forum Xueqiu2 for the CSI dataset, and collect first- and second-order linkages from Wikidata3 for the SPX and Topix datasets. |
| Dataset Splits | Yes | The training/validation/test sets are strictly split in chronological order to avoid data leakage problems. Table 1: # Split Records 683:171:139 (CSI), 787:197:169 (SPX), 814:204:144 (Topix). |
| Hardware Specification | Yes | Parameters are tuned using Adam optimizer [Kingma and Ba, 2014] on a single NVIDIA Titan Xp GPU for 100 epochs, the learning rate is 0.0005 and the batch size is 200. |
| Software Dependencies | No | The paper mentions 'Adam optimizer' but does not specify version numbers for any programming languages or software libraries used for implementation. |
| Experiment Setup | Yes | In our experiments, a 4-layer stacking hierarchy with the dilation list of {1-2-3-4} is employed for temporal representations. The window size and the number of gated convolution kernels at each layer were set to 3 and 32. The dimensions of randomly initialized stock ID embeddings and node embeddings were set to 20 for Topix and 30 for CSI and SPX, the target-specific query for attending to important temporal scales has a dimension of 16. The finite step K for graph diffusions is set to 2. We apply dropout [Srivastava et al., 2014] at the end of each layer to mitigate overfitting and the drop rate is 0.3. Parameters are tuned using Adam optimizer [Kingma and Ba, 2014] on a single NVIDIA Titan Xp GPU for 100 epochs, the learning rate is 0.0005 and the batch size is 200. |