Transaction Costs-Aware Portfolio Optimization via Fast Lowner-John Ellipsoid Approximation
Authors: Weiwei Shen, Jun Wang
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through constructing L owner-John ellipsoids to parameterize the optimal policy and taking Euclidean projections onto the constructed ellipsoids to implement the trading policy, the proposed algorithm has cut computational costs up to a factor of five hundred and meanwhile achieved near-optimal risk-adjusted returns across both synthetic and real-world market datasets. |
| Researcher Affiliation | Industry | Weiwei Shen and Jun Wang GE Global Research Center, Niskayuna, NY, USA Institute of Data Science and Technology, Alibaba Group, Seattle, WA, USA weiwei.shen@ge.com, j.wang@alibaba-inc.com |
| Pseudocode | Yes | Algorithm 1 L owner-John Ellipsoid Construction with VFI; Algorithm 2 Lower Bound Evaluation using Monte Carlo Simulation with Ellipsoid Projection |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described. |
| Open Datasets | Yes | Two parameter settings as synthetic data are taken from the relevant work (Muthuraman and Kumar 2006). The other two parameter settings based on market data are chosen from (Brown and Smith 2011). |
| Dataset Splits | No | The paper discusses the use of synthetic and real-world datasets and simulation, but it does not specify exact percentages, sample counts, or detailed methodology for training, validation, or test splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | Common parameters: n = 2, m = 10, T = 10, γ = 2, β1 = β2 = 2%, µ1 = µ2 = 15%, σ1 = σ2 = 35%, rf = 1%. |