Portfolio Selection via Subset Resampling
Authors: Weiwei Shen, Jun Wang
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To investigate its performance, we first analyze its corresponding efficient frontiers by simulation, provide analysis on the hyperparameter selection, and then empirically compare its out-of-sample performance with those of various competing strategies on diversified datasets. Experimental results corroborate that the proposed portfolio strategy has marked superiority in extensive evaluation criteria. |
| Researcher Affiliation | Collaboration | Weiwei Shen, , Jun Wang School of Computer Science and Software Engineering East China Normal University, Shanghai, China GE Global Research Center, Niskayuna, NY, USA, realsww@gmail.com, wongjun@gmail.com |
| Pseudocode | Yes | Algorithm 1 Subset Resampling Portfolio |
| Open Source Code | No | No explicit statement or link providing access to source code for the described methodology. |
| Open Datasets | Yes | Fama and French datasets: In the finance community, the Fama and French datasets have been widely recognized as high-quality and standard evaluation protocols (Fama and French 1992). Based on various types of financial segments of the U.S. stock market, the datasets contain carefully constructed portfolios from historical data. |
| Dataset Splits | No | The paper describes a rolling-horizon evaluation and simulation for hyperparameter tuning but does not specify a distinct validation dataset split used for model selection separate from training and testing. |
| Hardware Specification | No | No specific hardware details (GPU/CPU models, memory, or detailed computer specifications) used for running experiments are provided. |
| Software Dependencies | No | No specific software dependencies, libraries, or solvers with version numbers are mentioned. |
| Experiment Setup | Yes | While general results for the optimal hyperparameters are unavailable as they would heavily rely on the underlying return dynamics of assets, our following study offers some insights and suggestions." and "For SSR, b = n0.7 and s = 15k." and "We use short sliding widows τ = 120, 150, 200 and 500 for the four datasets, respectively. |