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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Portfolio Blending via Thompson Sampling
Authors: Weiwei Shen, Jun Wang
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive empirical studies and comparisons of the two blended portfolios with seven competing strategies over five real-world market datasets conspicuously illustrate the superiority of the proposed Thompson sampling based blending algorithm. |
| Researcher Affiliation | Collaboration | Weiwei Shen , and Jun Wang School of Computer Science and Software Engineering East China Normal University, Shanghai, China GE Global Research Center, Niskayuna, NY, USA, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Portfolio Blending via Thompson Sampling |
| Open Source Code | No | The paper does not contain any explicit statements about making the source code available or provide a link to a code repository. |
| Open Datasets | Yes | Fama and French datasets (FF) [Fama and French, 1992]: As standard evaluation protocols and oft-adopted testbeds in the finance community, the FF datasets are constructed portfolios of broad financial segments of the U.S. stock market. ... Real-world market datasets [Shen et al., 2015]: The real-world datasets including ETF139 and EQ181 are crawled from Yahoo! Finance on a weekly basis from 2008 to 2012. |
| Dataset Splits | No | The paper describes a rolling-horizon setup with training data sizes ('= 120 months or = 200 weeks') but does not explicitly mention distinct training, validation, and test splits with specific percentages or counts for reproduction in the standard sense. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We employ the rolling-horizon settings suggested in [De Miguel et al., 2009]. Specifically, the sliding windows with the size of = 120 months or = 200 weeks of training data are used to construct portfolios for the subsequent month or week. ... We set a cost factor c equal to 50 basis points per transaction... we set H = 12 and H = 52 for monthly and weekly rebalances, respectively. ... we estimate the covariance matrix k by a factor model [Fan et al., 2008] based on the historical data in sliding windows with the size of training data. |