Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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 | Venue PDF | 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. |