Linear Trading Position with Sparse Spectrum
Authors: Zhao-Rong Lai, Haisheng Yang
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that the proposed method achieves good and robust performance in various situations. ... 4 Experimental Results We follow the experimental framework of [Kelly et al., 2023a; Lai and Yang, 2023; Lai et al., 2024] to evaluate the performance of the proposed LTPSS. The experimental settings and more experimental results are put in Supplementary A.5. |
| Researcher Affiliation | Academia | Zhao-Rong Lai1 , Haisheng Yang2, 1Guangdong Key Laboratory of Data Security and Privacy Preserving, College of Cyber Security, Jinan University 2Lingnan College, Sun Yat-Sen University EMAIL, EMAIL |
| Pseudocode | No | The paper describes the Krasnosel ski ı-Mann fixed-point algorithm through mathematical equations and prose (e.g., in Section 3.2), but it does not present it as a clearly labeled 'Pseudocode' or 'Algorithm' block or figure. The iterative steps and operators are defined within the text rather than in a structured pseudocode format. |
| Open Source Code | Yes | The supplementary material and code for this paper are available at https://github.com/laizhr/LTPSS. |
| Open Datasets | Yes | The MRs of different trading strategies on the 7 benchmark data sets are shown in Table 1. It indicates that LTP-PP performs well on the FF25 data sets, which are well interpreted by the Fama-French five factors. ... [Fama and French, 2015] Eugene F. Fama and Kenneth R. French. A five-factor asset pricing model. Journal of Financial Economics, 116(1):1 22, 2015. |
| Dataset Splits | No | The paper mentions evaluating performance over 'trading times' and 'next trading time' (e.g., 'At the t-th trading time, a trading strategy determines an LTP ˆLt+1 for the next trading time.'), which implies a temporal evaluation strategy. However, it does not explicitly provide details about specific training, validation, or test dataset splits, percentages, or methodology for partitioning the 7 benchmark datasets used in the experiments. |
| Hardware Specification | No | The paper discusses computational complexity (e.g., 'the overall computational complexity of LTPSS is O(N 3 log( 1 ε))') but does not provide any specific details about the hardware (e.g., GPU, CPU models, memory) used to run the experiments. No specific machine, server, or cloud instance specifications are mentioned. |
| Software Dependencies | No | The paper does not explicitly mention any specific software dependencies, programming languages, libraries, or solvers with their version numbers that were used to implement or run the experiments. |
| Experiment Setup | No | The paper states: 'The experimental settings and more experimental results are put in Supplementary A.5.' This indicates that the specific experimental setup details, such as hyperparameter values (e.g., for η and β), training configurations, or other system-level settings, are not provided in the main body of the paper. |