Robust Portfolio Optimization
Authors: Huitong Qiu, Fang Han, Han Liu, Brian Caffo
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The empirical effectiveness of the proposed method is demonstrated under both synthetic and real stock data. Our work extends existing ones by achieving robustness in high dimensions, and by allowing serial dependence. |
| Researcher Affiliation | Academia | Huitong Qiu Department of Biostatistics Johns Hopkins University Baltimore, MD 21205 hqiu7@jhu.edu Fang Han Department of Biostatistics Johns Hopkins University Baltimore, MD 21205 fhan@jhu.edu Han Liu Department of Operations Research and Financial Engineering Princeton University Princeton, NJ 08544 hanliu@princeton.edu Brian Caffo Department of Biostatistics Johns Hopkins University Baltimore, MD 21205 bcaffo@jhsph.edu |
| Pseudocode | No | No clearly labeled pseudocode or algorithm block found in the paper. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the methodology described. |
| Open Datasets | Yes | In this section, we simulate portfolio management using the S&P 500 stocks. We collect 1,258 adjusted daily closing prices3 for 435 stocks that stayed in the S&P 500 index from January 1, 2003 to December 31, 2007. |
| Dataset Splits | No | The paper describes a rolling window approach where 'past 2 months stock return data' is used for optimization and 'day i + 1' for evaluation, but it does not specify a separate validation set split or methodology for hyperparameter tuning distinct from testing. |
| Hardware Specification | No | No specific hardware details such as GPU/CPU models, memory, or cloud instance types are mentioned for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers that would be necessary for reproduction. |
| Experiment Setup | Yes | Under each distribution, we generate asset return series of half a year (T = 126). We estimate the covariance/scatter matrices using QNE and the three competitors, and plug them into (2.1) to optimize the portfolio allocations. We also solve (2.1) with the true covariance matrix, Σ, to obtain the oracle optimal portfolios as benchmarks. We range the gross-exposure constraint, c, from 1 to 2. The results are based on 1,000 simulations. |