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].
Portfolio Choices with Orthogonal Bandit Learning
Authors: Weiwei Shen, Jun Wang, Yu-Gang Jiang, Hongyuan Zha
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 4 covers our experiments and comparative studies, including the discussion of the datasets, the evaluation metrics and the performance demonstration. |
| Researcher Affiliation | Collaboration | GE Global Research Center, Niskayuna, NY, USA, EMAIL Alibaba Group, Seattle, WA, USA, EMAIL School of Computer Science, Fudan University, Shanghai, China, EMAIL College of Computing, Georgia Institute of Technology, Atlanta, GA, USA, EMAIL |
| Pseudocode | Yes | Algorithm 1 Orthogonal Bandit Portfolio |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | The first benchmarks are the Fama and French (FF) datasets [Fama and French, 1992]. With the raw data from the US stock market, the FF benchmarks construct the portfolios for different financial segments. |
| Dataset Splits | No | Following the rolling-horizon settings in [Shen et al., 2014], we use sliding windows with the size of τ = 120 months/weeks of training data to construct portfolios for the subsequent month/week. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | Following the rolling-horizon settings in [Shen et al., 2014], we use sliding windows with the size of τ = 120 months/weeks of training data to construct portfolios for the subsequent month/week. ... we set the EW portfolio as the benchmark with 1000 bootstrap resamples, 95% significance level, and a block with the size of 5. |