Multi-scale Two-way Deep Neural Network for Stock Trend Prediction
Authors: Guang Liu, Yuzhao Mao, Qi Sun, Hailong Huang, Weiguo Gao, Xuan Li, Jianping Shen, Ruifan Li, Xiaojie Wang
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results on the two datasets indicate that multi-scale information can significantly improve the STP performance and our model is superior in capturing such information. |
| Researcher Affiliation | Collaboration | 1Ping An Life Insurance Company of China, Ltd. 2School of Computer Science, Beijing University of Posts and Telecommunications |
| Pseudocode | No | The paper describes the MTDNN architecture and its components in detail but does not provide any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1https://github.com/marscrazy/MTDNN |
| Open Datasets | Yes | FI-2010 [Ntakaris et al., 2018] and CSI-2016. ... 1https://github.com/marscrazy/MTDNN |
| Dataset Splits | No | Table 1 lists 'Train' and 'Test' samples/percentages but does not explicitly mention a 'validation' split or provide its details. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware (e.g., CPU, GPU models, memory) used for conducting the experiments. |
| Software Dependencies | No | The paper mentions various models and algorithms like XGBoost, RCNN, GRU, and SGD, but does not provide specific version numbers for any software dependencies or libraries used for implementation. |
| Experiment Setup | Yes | Setting the label threshold α = 0.002, prediction horizon k = 50 and the input window size T = 100. ...Setting the label threshold α = 0.01, prediction horizon k = 5, the input window size T = 100 and the feature dimension d = 6. ...train the rest of the model using the SGD algorithm with a learning rate of 0.0001 and weight decay 0.9. |