Autonomous Sparse Mean-CVaR Portfolio Optimization
Authors: Yizun Lin, Yangyu Zhang, Zhao-Rong Lai, Cheng Li
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on 6 real-world financial data sets from Kenneth R. French s Data Library |
| Researcher Affiliation | Academia | 1Department of Mathematics, College of Information Science and Technology, Jinan University, Guangzhou, China 2Jinan University-University of Birmingham Joint Institute, Jinan University, Guangzhou, China. |
| Pseudocode | Yes | Algorithm 1 ASMCVa R |
| Open Source Code | Yes | The codes for these two modules are available in the folders Sparse Relaxation Test and Pytorch Demo, respectively, accessible via the link: https: //github.com/linyizun2024/ASMCVa R. |
| Open Datasets | Yes | We conduct extensive experiments on 6 real-world financial data sets from Kenneth R. French s Data Library2, whose details are provided in Table 1. ... 2http://mba.tuck.dartmouth.edu/pages/ faculty/ken.french/data_library.html |
| Dataset Splits | No | The paper describes a 'standard moving-window trading scheme' with T=60 as the window size for all methods, which implicitly defines how data is used for training and testing in a time-series context, but it does not specify explicit train/validation/test dataset splits with percentages or counts. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments, such as CPU or GPU models. |
| Software Dependencies | No | The paper mentions 'PyTorch module' and 'Gurobi' (which they didn't use for their main results due to errors) but does not provide specific version numbers for any software dependencies used in their experiments. |
| Experiment Setup | Yes | The model parameters in (21) are set as follows: the confidence level is set as a conventional one c = 0.99. The expected return level is empirically set as ρ = 0.02... The approximation parameter is set as γ = 10^-5... The algorithm parameters can be conveniently set based on the convergence criteria. For FPPA, we set θ = 1.99/||Q||^2_2. Its maximum iteration and relative difference tolerance are set as Max Iter1 = 200 and tol1 = 0.001. For PALM, the learning rates are set as β1 = 0.99/L1 and β2 = 0.99/L2, respectively. Its maximum iteration and relative difference tolerance are set as Max Iter2 = 10^4 and tol2 = 10^-4. |