@inproceedings{5ad9b5b77a84436ea4aaf7c2360acf3b,
title = "Ensemble Learning Technique with A Novelty Multiĝ€'Source Information for Stock Price Movements",
abstract = "Stock price movement is a complex problem to solve, involving diverse political and economic factors. Integrating these factors involves designing multiple data pre-processing schemes and ensemble learning techniques to develop a novel stock market prediction architecture that provides better and higher prediction accuracy rates. Numerical and text format data are both utilized as inputs for the ensemble regressors and classifiers to learn features. The trained results are concatenated and fed into the final deep learning layer to predict the direction of the closing price. Empirical results from news and historical data of five specific companies - Apple Inc. (AAPL), Microsoft Corporation (MSFT), Alphabet Inc. (GOOGL), Amazon.com, Inc. (AMZN), and Tesla Inc. (TSLA) - demonstrate the effectiveness of the proposed prediction model.",
keywords = "Ensemble learning, Natural learning processing, Stock trend forecasting, Technical analysis, Time series analysis",
author = "Duong, {Viet Hang} and {Duc Nhan}, Bui and Bui, {Manh Quan} and Wang, {Jia Ching}",
note = "Publisher Copyright: {\textcopyright} 2023 ACM.; 12th International Symposium on Information and Communication Technology, SOICT 2023 ; Conference date: 07-12-2023 Through 08-12-2023",
year = "2023",
month = dec,
day = "7",
doi = "10.1145/3628797.3629007",
language = "???core.languages.en_GB???",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "707--714",
booktitle = "SOICT 2023 - 12th International Symposium on Information and Communication Technology",
}