The prediction performance and error rate still face limitations due to the lack of correlational analysis between STI and stock movement. Our paper proposes a correlational strategy to overcome these challenges by analyzing the correlation of STI with stock movement using neural networks with the feature vector.
Note: This dataset has been utilized in a study which is accepted in ICBDS 2022 and preprint of that study is available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4282395
Citation Request:
Hong, Jingwei; Han, Ping; Rasool, Abdur; Chen, Hui; Jiang, Qingshan; (2022) A Correlational Strategy for the Prediction of High-Dimensional Stock Data by Neural Networks and Technical Indicators (November 21, 2022).