Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
A Globally Optimal Portfolio for m-Sparse Sharpe Ratio Maximization
Authors: Yizun Lin, Zhao-Rong Lai, Cheng Li
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 35,213 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,094 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments with real-world ๏ฌnancial data sets are conducted to evaluate the performance of the proposed m SSRM-PGA. |
| Researcher Affiliation | Academia | Yizun Lin1 Zhao-Rong Lai1 Cheng Li1 1Department of Mathematics College of Information Science and Technology Jinan University, Guangzhou, China |
| Pseudocode | Yes | Algorithm A1 m SSRM-PGA |
| Open Source Code | Yes | The codes of m SSRM-PGA are accessible via the link: https://github.com/linyizun2024/m SSRM/tree/main/Codes_for_Experiments_in_Paper. |
| Open Datasets | Yes | These data sets are collected from the baseline and commonly-used Kenneth R. French s Real-world Data Library.2 2http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html |
| Dataset Splits | No | The paper describes using a "moving-window trading framework" with time window sizes T=60 and T=120 for model updating, which serves as a rolling training period. However, it does not specify a distinct validation split (e.g., fixed percentage or sample count) separate from the training and testing phases. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the implementation or experiments (e.g., Python, PyTorch, specific solvers). |
| Experiment Setup | Yes | As for m SSRM-PGA, we examine three levels of sparsity m = 10, m = 15, m = 20 and set ฯต = 10 3. The setting of other parameters are presented in Appendix A.9. ... Initialization: Set v(0) = p, tol = 10 5, Max Iter = 104 |