An adaptive and enhanced framework for daily stock market prediction using feature selection and ensemble learning algorithms
Year of publication: |
2024
|
---|---|
Authors: | Sivri, Mahmut Sami ; Ustundag, Alp |
Published in: |
Journal of business analytics. - London : Taylor & Francis Group, ISSN 2573-2358, ZDB-ID 2907637-7. - Vol. 7.2024, 1, p. 42-62
|
Subject: | emerging markets | ensemble learning | feature selection | forecasting | machine learning | Stock market prediction | Prognoseverfahren | Forecasting model | Lernprozess | Learning process | Künstliche Intelligenz | Artificial intelligence | Lernen | Learning |
-
Ghasemieh, Alireza, (2023)
-
Macroeconomic and financial market analyses and predictions through deep learning
Kim, Soohyon, (2020)
-
Swag: a wrapper method for sparse learning
Molinari, Roberto, (2020)
- More ...
-
Evaluating RFID investment on a supply chain using tagging cost sharing factor
Ustundag, Alp, (2010)
-
Evaluating market basket data with formal concept analysis
Ustundag, Alp, (2014)
-
The impacts of Radio Frequency Identification (RFID) technology on supply chain costs
Ustundag, Alp, (2009)
- More ...