Tensor-Based Learning for Predicting Stock Movements
Authors: Qing Li, LiLing Jiang, Ping Li, Hsinchun Chen
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on CSI 100 stocks in the year 2011 show that our approach outperforms the state-of-the-art trading strategies. |
| Researcher Affiliation | Academia | Qing Li and Li Ling Jiang and Ping Li Computer Science Department Southwestern University of Finance and Economics 555 Liutai Ave., Chengdu, 611130, China; Hsinchun Chen Management Information Systems Department, University of Arizona 1130 E. Helen St. Tucson, Arizona, 85721-0108, USA |
| Pseudocode | Yes | Table 1: Tensor-based Learning Algorithm |
| Open Source Code | No | The paper does not provide an explicit statement about releasing its source code or a link to a code repository. |
| Open Datasets | No | The paper states 'In our experiments, we use the stock data generously provided by Li et al. (2014b).' and describes the data, but it does not provide concrete access information (e.g., a link, DOI, or specific repository) for the dataset to be publicly available and reproducible. |
| Dataset Splits | No | The paper states, 'In our experiments, we used the data from the first 9 months of 2011 as a training corpus and the last 3 months of 2011 for testing.' It does not mention a separate validation split. |
| Hardware Specification | No | The paper does not specify any hardware details such as CPU/GPU models, memory, or cloud computing instances used for the experiments. |
| Software Dependencies | No | The paper mentions 'SVR algorithm' but does not list any specific software libraries, frameworks, or their version numbers that were used for implementation. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameters (e.g., learning rate, batch size, number of epochs) or optimizer settings. |