Portfolio Choices with Orthogonal Bandit Learning
Authors: Weiwei Shen, Jun Wang, Yu-Gang Jiang, Hongyuan Zha
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | 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, weiwei.shen@ge.com Alibaba Group, Seattle, WA, USA, j.wang@alibaba-inc.com School of Computer Science, Fudan University, Shanghai, China, ygj@fudan.edu.cn College of Computing, Georgia Institute of Technology, Atlanta, GA, USA, zha@cc.gatech.edu |
| 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. |