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Stock Price Trends Prediction Based on the Classical Models with Key Information Fusion of Ontologies

Published:09 May 2023Publication History
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Abstract

An ontology of the financial field can support effective association and integration of financial knowledge. Based on behavioral finance, social media is increasingly applied as one of the data sources for information fusion in stock forecasting to approximate the patterns of market changes. By predicting Tesla (TSLA) stock price trends, this study finds that satisfactory forecasting results can be achieved using classical models and incorporating key information features from the technical indicator ontology class and the investor behavior ontology class, even in the face of the impact of the COVID-19 epidemic. In the post-epidemic period, the back propagation neural network (BPNN) model is used to predict the price trend of TSLA for the next five trading days with an accuracy of up to 91.34%, an F1 score of 0.91, and a return of up to 268.42% obtained from simulated trading. This study extends the research on stock forecasting using fused information in the ontology of the financial field, providing a new basis for general investors in the selection of fusion information and the application of trading strategies and providing effective support for organizations to make intelligent financial decisions under uncertainty.

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      • Published in

        cover image ACM Transactions on Asian and Low-Resource Language Information Processing
        ACM Transactions on Asian and Low-Resource Language Information Processing  Volume 22, Issue 5
        May 2023
        653 pages
        ISSN:2375-4699
        EISSN:2375-4702
        DOI:10.1145/3596451
        Issue’s Table of Contents

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        Association for Computing Machinery

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        Publication History

        • Published: 9 May 2023
        • Online AM: 13 April 2023
        • Accepted: 30 March 2023
        • Revised: 17 February 2023
        • Received: 7 April 2022
        Published in tallip Volume 22, Issue 5

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