| Title |
Design and Implementation of a Multi-factor Intelligent Mining System for Stocks Based on GA-TGCN |
| Authors |
(Bo Zhang) ; (Meichen Tao) |
| DOI |
https://doi.org/10.5573/IEIESPC.2025.14.2.178 |
| Keywords |
Temporal graph convolutional network; Stock multi-factor; Genetic algorithm; Intelligent mining |
| Abstract |
The multi factor mining system for stocks can provide favorable support for the analysis of financial markets. Due to the large number of influencing factors, the design of such systems generally faces significant deviation in prediction results. The continuous advancement of intelligent algorithm technology provides more diversified support methods for stock multi factor combination analysis, and also provides more technical support for risk assessment and problem decision-making in financial markets. Therefore, in order to achieve a more efficient stock multi factor analysis model, this paper constructs a stock multi factor intelligent mining system for financial markets based on the GA-TGCN intelligent algorithm. The GA-TGCN stock multi factor intelligent mining system based on TGCN technology and integrating GA algorithm provides more support for the prediction of multi factor combination analysis. By predicting and analyzing factors and strategy returns, excellent multi factor strategies can be obtained by combining simulated trading. Combining the application of different models in the process of multi factor mining combinations, it was found that the GA-TGCN system can achieve higher accuracy and lower loss values while achieving smaller prediction errors, laying the foundation for improving the efficiency of stock multi factor combination analysis. |