Papers › LSR-IGRU: Stock Trend Prediction Based on Long Short-Term Relationships and Improved GRU

LSR-IGRU: Stock Trend Prediction Based on Long Short-Term Relationships and Improved GRU

26 Aug 2024arXiv:2409.08282archive 2025-07-28

Peng Zhu, Yuante Li, Yifan Hu, Qinyuan Liu, Dawei Cheng, Yuqi Liang

Stock price prediction is a challenging problem in the field of finance and receives widespread attention. In recent years, with the rapid development of technologies such as deep learning and graph neural networks, more research methods have begun to focus on exploring the interrelationships between stocks. However, existing methods mostly focus on the short-term dynamic relationships of stocks and directly integrating relationship information with temporal information. They often overlook the complex nonlinear dynamic characteristics and potential higher-order interaction relationships among stocks in the stock market. Therefore, we propose a stock price trend prediction model named LSR-IGRU in this paper, which is based on long short-term stock relationships and an improved GRU input. Firstly, we construct a long short-term relationship matrix between stocks, where secondary industry information is employed for the first time to capture long-term relationships of stocks, and overnight price information is utilized to establish short-term relationships. Next, we improve the inputs of the GRU model at each step, enabling the model to more effectively integrate temporal information and long short-term relationship information, thereby significantly improving the accuracy of predicting stock trend changes. Finally, through extensive experiments on multiple datasets from stock markets in China and the United States, we validate the superiority of the proposed LSR-IGRU model over the current state-of-the-art baseline models. We also apply the proposed model to the algorithmic trading system of a financial company, achieving significantly higher cumulative portfolio returns compared to other baseline methods. Our sources are released at https://github.com/ZP1481616577/Baselines_LSR-IGRU.

PaperPDFCodeCode Syntology ran

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="2409.08282")

Code

Syntology Ran 9 of 9 code samples harvested from 1 repository linked to this paper; 0 have no recorded run. Of those that ran: 9 ran with no contract checked.

By repository: official repository: 9 samples from 1 repository, 9 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

zp1481616577/baselines_lsr-igru officialmentioned in paperpytorch report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

9 samples harvested; 9 ran; 0 honoured the contract we drafted; 0 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

9ran

Licence: 9 of the 9 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from zp1481616577/baselines_lsr-igru. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.

Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

bce_loss zp1481616577/baselines_lsr-igru/18_THGNN/5_model_train_predict.py official repository ran no licence file found · pointer only · 33038d9824ba3d0b · report
cal_pccs zp1481616577/baselines_lsr-igru/18_THGNN/3_generate_relation.py official repository ran no licence file found · pointer only · 00640c7ef3137aff · report
calculate_pccs zp1481616577/baselines_lsr-igru/18_THGNN/3_generate_relation.py official repository ran no licence file found · pointer only · 50d5a6f5bafc7597 · report
evaluate zp1481616577/baselines_lsr-igru/18_THGNN/5_model_train_predict.py official repository ran no licence file found · pointer only · 0c86e746913de988 · report
filter_extreme_3sigma zp1481616577/baselines_lsr-igru/LSR-IGRU/main_model_csi300.py official repository ran fingerprinted no licence file found · pointer only · 2be2c1e7414ef0b8 · report
mse_loss zp1481616577/baselines_lsr-igru/18_THGNN/5_model_train_predict.py official repository ran no licence file found · pointer only · 34cb04f05ddddabd · report
process_daily_df_std zp1481616577/baselines_lsr-igru/LSR-IGRU/main_model_csi300.py official repository ran no licence file found · pointer only · 6fc341f28014f4bd · report
standardize_zscore zp1481616577/baselines_lsr-igru/LSR-IGRU/main_model_csi300.py official repository ran fingerprinted no licence file found · pointer only · 86a2171ce63a5e70 · report
stock_cor_matrix zp1481616577/baselines_lsr-igru/18_THGNN/3_generate_relation.py official repository ran no licence file found · pointer only · e3c01ca1511423d2 · report

Tasks

Algorithmic TradingStock Price PredictionStock Trend Prediction

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

Methods

FocusGRU

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections