A Globally Optimal Portfolio for m-Sparse Sharpe Ratio Maximization
Authors: Yizun Lin, Zhao-Rong Lai, Cheng Li
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments with real-world financial 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 ϵ = 10 3. The setting of other parameters are presented in Appendix A.9. ... Initialization: Set v(0) = p, tol = 10 5, Max Iter = 104 |