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 [1].
Online Portfolio Selection with Group Sparsity
Authors: Puja Das, Nicholas Johnson, Arindam Banerjee
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on two real-world datasets and use the Global Industry Classification Standard to group the stocks into sectors for 22 years of the benchmark NYSE dataset with 30 stocks and 8 sectors and 22 years of a S&P500 dataset with 243 stocks and 9 sectors. Our experiments show that our sparse group lazy portfolios can take advantage of the sector information to beat the market and are scalable with transaction costs. |
| Researcher Affiliation | Academia | Puja Das and Nicholas Johnson and Arindam Banerjee Department of Computer Science and Engineering University of Minnesota Minneapolis, MN 55455 EMAIL |
| Pseudocode | Yes | Algorithm 1 OLU-GS Algorithm with ADMM; Algorithm 2 Portfolio Selection with Group Sparsity |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is open-source or publicly available. |
| Open Datasets | Yes | The NYSE dataset (Helmbold et al. 1998; Agarwal et al. 2006; Borodin, El-Yaniv, and Gogan 2004; Cover 1991) consists of 36 stocks with data accumulated over a period of 22 years from July 3, 1962 to December 31, 1984. The S&P500 dataset consists of 258 stocks with data accumulated over a period of 22 years from 1991 to 2012. |
| Dataset Splits | No | The paper mentions using the NYSE and S&P500 datasets but does not specify explicit training, validation, or test splits, percentages, or sample counts for these datasets in the experimental setup. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | The parameters consist of λ1: weight on group sparsity norm, λ2: lazy updates weight, η: weight on the ℓ2 norm, and β: the parameter for the augmentation term. For all our experiments, we set β = 2 which we found to give reasonable accuracy and use group lasso for group sparsity. [...] We experimented extensively with a large range of λ1, λ2, and η values from 1e 9 to 1 to observe their effect on group sparsity and lazy updates to our portfolio. |