Regret Bounds for Online Portfolio Selection with a Cardinality Constraint
Authors: Shinji Ito, Daisuke Hatano, Hanna Sumita, Akihiro Yabe, Takuro Fukunaga, Naonori Kakimura, Ken-Ichi Kawarabayashi
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show the empirical performance of our algorithms through experiments over synthetic and realworld data. In this section, we consider the online portfolio selection problem with S = S1. |
| Researcher Affiliation | Collaboration | Shinji Ito NEC Corporation Daisuke Hatano RIKEN AIP Hanna Sumita Tokyo Metropolitan University Akihiro Yabe NEC Corporation Takuro Fukunaga RIKEN AIP, JST PRESTO Naonori Kakimura Keio University Ken-ichi Kawarabayashi National Institute of Informatics |
| Pseudocode | Yes | Algorithm 1 An algorithm for the full-feedback setting. and Algorithm 2 An algorithm for the bandit-feedback setting. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing its source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | The first is based on crypto coin historical data2, including dates and price data for 19 crypto coins. From this data, we select 7 crypto coins, each having 929 prices, and obtain price relatives rti of coin i at time t by (pti/pt 1,i) 1, where pti indicates the price of coin i at time t. Thus, d = 7 and T = 928 in this instance. The other instance is based on S&P 500 stock data3, including dates and price data for 505 companies. From this data, we choose d = 470 companies, each having 1259 stock prices, and compute T = 1258 price relatives for each company in the same way. 2https://www.kaggle.com/sudalairajkumar/cryptocurrencypricehistory 3https://www.kaggle.com/camnugent/sandp500 |
| Dataset Splits | No | The paper describes how real-world data is processed and selected (e.g., 'select 7 crypto coins', 'choose d = 470 companies'), but it does not specify exact split percentages, absolute sample counts, or refer to predefined splits for training, validation, and testing needed to reproduce data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper discusses algorithms and methods used, but does not provide specific ancillary software details like library or solver names with version numbers needed to replicate the experiment. |
| Experiment Setup | Yes | In particular, setting η = 1 C4 min 1, q and β = 1, we obtain E[RT ] = O p T log |S| + k log T + log |S| . (5) (for Algorithm 1, Theorem 2) and Setting γ = min 1, q Tk|S| log(1+T ) , η = γ C4|S| min n 1, q log |S| k log(1+T ) o and β = C3C5, we obtain E[RT ] = O p T|S|k log T + |S| p k log |S| log T + |S|k . (for Algorithm 2, Theorem 3). And We set parameters η according to Theorem 2 for Algorithm 1 and MWU_disc, and η and γ according to Theorem 3 for Algorithm 2, Exp3_disc, and Exp3_cont. |