A Correlational Strategy for the Prediction of High-Dimensional Stock Data by Neural Networks and Technical Indicators

Medientyp: E-Book

Titel: A Correlational Strategy for the Prediction of High-Dimensional Stock Data by Neural Networks and Technical Indicators

Beteiligte: Hong, Jingwei [Verfasser:in]; Han, Ping [Verfasser:in]; Rasool, Abdur [Verfasser:in]; Chen, Hui [Verfasser:in]; Hong, Zhiling [Verfasser:in]; Tan, Zhong [Verfasser:in]; Lin, Fan [Verfasser:in]; Wei, Steven X. [Verfasser:in]; Jiang, Qingshan [Verfasser:in]

Erschienen: [S.l.]: SSRN, 2022

Umfang: 1 Online-Ressource (15 p)

Sprache: Englisch

DOI: 10.2139/ssrn.4282395

Identifikator:

Entstehung:

Anmerkungen: Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments November 21, 2022 erstellt

Beschreibung: Stock market prediction provides the decision-making ability to the different stockholders for their investments. Recently, stock technical indicators (STI) emerged as a vital analysis tool for predicting high-dimensional stock data in various studies. However, the prediction performance and error rate still face limitations due to the lack of correlational analysis between STI and stock movement. This 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. This strategy adopts the Pearson coefficient to analyze STI and close index of stock data from 8 Chinese companies in the Hong Kong stock market. The results reveal the price prediction of BiLSTM outperformed the GRU and LSTM in various datasets and prior studies

Zugangsstatus: Freier Zugang

Nur in Feld suchen:

Zuletzt gesuchte Begriffe: