Deep Portfolio Optimization via Distributional Prediction of Residual Factors
Authors: Kentaro Imajo, Kentaro Minami, Katsuya Ito, Kei Nakagawa213-222
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the efficacy of our method on U.S. and Japanese stock market data. Through ablation experiments, we also verify that each individual technique contributes to improving the performance of trading strategies. |
| Researcher Affiliation | Industry | 1 Preferred Networks, Inc. 2 Nomura Asset Management Co., Ltd. |
| Pseudocode | No | The paper describes methods using mathematical formulas and prose but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | No explicit statement about providing open-source code for the described methodology or a link to a code repository was found. |
| Open Datasets | Yes | For U.S. market data, we used the daily prices of stocks listed in S&P 500 from January 2000 to April 2020. We obtained the data from Alpha Vantage 3. 3https://www.alphavantage.co/ |
| Dataset Splits | Yes | We used data before January 2008 for training and validation and the remainder for testing. |
| Hardware Specification | Yes | all the computation were run on 18 CPU cores of Intel Xeon Gold 6254 Processor (3.1 GHz). |
| Software Dependencies | No | The paper mentions using 'scikit-learn package' but does not provide specific version numbers for it or any other software dependencies. |
| Experiment Setup | Yes | We set the look-back window size as H = 256, i.e., all prediction models can access the historical stock prices upto preceding 256 business days. For other parameters used in the experiments, see Appendix C.3 as well. |