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..
StockFormer: Learning Hybrid Trading Machines with Predictive Coding
Authors: Siyu Gao, Yunbo Wang, Xiaokang Yang
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Stock Former significantly outperforms existing approaches across three publicly available financial datasets in terms of portfolio returns and Sharpe ratios. |
| Researcher Affiliation | Academia | Siyu Gao , Yunbo Wang and Xiaokang Yang Mo E Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that its source code is publicly available. |
| Open Datasets | No | The paper states 'three publicly available financial datasets' and names them (CSI-300, NASDAQ-100, Cryptocurrency), mentioning data collection from 'Yahoo Finance'. However, it does not provide specific links, DOIs, repositories, or formal citations for these processed datasets to ensure reproducibility of the exact data used. |
| Dataset Splits | No | The paper describes training and test splits for all datasets (e.g., '1,935 days and 728 trading days respectively' for CSI-300), but it does not explicitly mention or detail a separate validation split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types) used for running its experiments. |
| Software Dependencies | No | The paper mentions software like 'Fin RL platform [Liu et al., 2021]' and algorithms like 'Soft Actor-Critic (SAC) [Haarnoja et al., 2018]' and 'DDPG [Lillicrap et al., 2016]', but it does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size, number of epochs) or optimizer settings. |