RM-CVaR: Regularized Multiple β-CVaR Portfolio
Authors: Kei Nakagawa, Shuhei Noma, Masaya Abe
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform experiments on well-known benchmarks to evaluate the proposed portfolio. |
| Researcher Affiliation | Industry | Innovation Lab, Nomura Asset Management Co Ltd, Japan |
| Pseudocode | Yes | Algorithm 1 RM-CVa R Portfolio |
| Open Source Code | No | No explicit statement about releasing source code or a link to a code repository was found. |
| Open Datasets | Yes | In the experiments, we used well-known academic benchmarks called Fama and French (FF) datasets [Fama and French, 1992] to ensure the reproducibility of the experiment. This FF dataset is public and is readily available to anyone. |
| Dataset Splits | Yes | We used the first-half period, i.e., from January 1989 to December 2003, as the in-sample period in terms of deciding the hyper-parameters of each model. After that, we used the second half-period, i.e., from January 2004 to December 2018, as the out-of-sample period. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments were mentioned. |
| Software Dependencies | No | No specific ancillary software details, such as library or solver names with version numbers, were provided. |
| Experiment Setup | Yes | We set combinations of two coefficients for regularization terms to λ1 = {0.001, 0.005, 0.01, 0.05} and λ2 = {0.001, 0.005, 0.01, 0.05}. We set n1 (number of resamples) = 50, n2 (size of each resample) = 5τ, τ (number of periods of return data) = 120, n3 (number of resampled subsets) = 50, n4 (size of each subset) = n0.7, where n is number of assets. We implemented five patterns of β = {0.95, 0.96, 0.97, 0.98, 0.99}. We set K = 5 (k = 1, ..., K) as five patterns of βk = {0.95, 0.96, 0.97, 0.98, 0.99} to calculate Cβk. We also set Q (number of sampling periods of return data) as {10 years (120 months), 7 years (84 months)}. For the coefficient of the regularization term, we implemented four patterns of λ = {0.001, 0.005, 0.01, 0.05}. |