Portfolio Blending via Thompson Sampling
Authors: Weiwei Shen, Jun Wang
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | 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, realsww@gmail.com, wongjun@gmail.com |
| 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. |