Please use this identifier to cite or link to this item:
https://hdl.handle.net/1959.11/61444
Title: | A sentiment analysis-based machine learning approach for financial market prediction via news disclosures |
Contributor(s): | Chiong, Raymond (author) ; Adam, Marc T P (author); Fan, Zongwen (author); Lutz, Bernhard (author); Hu, Zhongyi (author); Neumann, Dirk (author) |
Publication Date: | 2018 |
DOI: | 10.1145/3205651.3205682 |
Handle Link: | https://hdl.handle.net/1959.11/61444 |
Abstract: | | Stock market prediction plays an important role in financial decisionmaking for investors. Many of them rely on news disclosures to make their decisions in buying or selling stocks. However, accurate modelling of stock market trends via news disclosures is a challenging task, considering the complexity and ambiguity of natural languages used. Unlike previous work along this line of research, which typically applies bag-of-words to extract tens of thousands of features to build a prediction model, we propose a sentiment analysis-based approach for financial market prediction using news disclosures. Specifically, sentiment analysis is carried out in the pre-processing phase to extract sentiment-related features from financial news. Historical stock market data from the perspective of time series analysis is also included as an input feature. With the extracted features, we use a support vector machine (SVM) to build the prediction model, with its parameters optimised through particle swarm optimisation (PSO). Experimental results show that our proposed SVM and PSO-based model is able to obtain better results than a deep learning model in terms of time and accuracy. The results presented here are to date the best in the literature based on the financial news dataset tested. This excellent performance is attributed to the sentiment analysis done during the pre-processing stage, as it reduces the feature dimensions significantly.
Publication Type: | Conference Publication |
Conference Details: | GECCO '18: Genetic and Evolutionary Computation Conference, 15th - 19th July, 2018 |
Source of Publication: | GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion, p. 278-279 |
Publisher: | Association for Computing Machinery |
Place of Publication: | New York, NY, United States |
Fields of Research (FoR) 2020: | 4602 Artificial intelligence |
HERDC Category Description: | E5 Conference Poster |
Appears in Collections: | Conference Publication School of Science and Technology
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