Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Linear Trading Position with Sparse Spectrum
Authors: Zhao-Rong Lai, Haisheng Yang
IJCAI 2025 | Venue PDF | 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. |