Anticipating Stock Market of the Renowned Companies: A Knowledge Graph Approach
An increasing number of the renowned company’s investors are turning attention to stock prediction in the search for new efficient ways of hypothesizing about markets through the application of behavioral finance. Accordingly, research on stock prediction is becoming a popular direction in academia...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/9202457 |
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| _version_ | 1832562582334996480 |
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| author | Yang Liu Qingguo Zeng Joaquín Ordieres Meré Huanrui Yang |
| author_facet | Yang Liu Qingguo Zeng Joaquín Ordieres Meré Huanrui Yang |
| author_sort | Yang Liu |
| collection | DOAJ |
| description | An increasing number of the renowned company’s investors are turning attention to stock prediction in the search for new efficient ways of hypothesizing about markets through the application of behavioral finance. Accordingly, research on stock prediction is becoming a popular direction in academia and industry. In this study, the goal is to establish a model for predicting stock price movement through knowledge graph from the financial news of the renowned companies. In contrast to traditional methods of stock prediction, our approach considers the effects of event tuple characteristics on stocks on the basis of knowledge graph and deep learning. The proposed model and other feature selection models were used to perform feature extraction on the websites of Thomson Reuters and Cable News Network. Numerous experiments were conducted to derive evidence of the effectiveness of knowledge graph embedding for classification tasks in stock prediction. A comparison of the average accuracy with which the same feature combinations were extracted over six stocks indicated that the proposed method achieves better performance than that exhibited by an approach that uses only stock data, a bag-of-words method, and convolutional neural network. Our work highlights the usefulness of knowledge graph in implementing business activities and helping practitioners and managers make business decisions. |
| format | Article |
| id | doaj-art-13bcc929238341c89595a468f20a1e89 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-13bcc929238341c89595a468f20a1e892025-02-03T01:22:10ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/92024579202457Anticipating Stock Market of the Renowned Companies: A Knowledge Graph ApproachYang Liu0Qingguo Zeng1Joaquín Ordieres Meré2Huanrui Yang3Department of Industrial Engineering, Business Administration and Statistics, Escuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid, Madrid 28006, SpainSchool of Mathematical Sciences, South China Normal University, Guangzhou 510631, ChinaDepartment of Industrial Engineering, Business Administration and Statistics, Escuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid, Madrid 28006, SpainElectrical and Computer Engineering Department, Duke University, Durham 27708, USAAn increasing number of the renowned company’s investors are turning attention to stock prediction in the search for new efficient ways of hypothesizing about markets through the application of behavioral finance. Accordingly, research on stock prediction is becoming a popular direction in academia and industry. In this study, the goal is to establish a model for predicting stock price movement through knowledge graph from the financial news of the renowned companies. In contrast to traditional methods of stock prediction, our approach considers the effects of event tuple characteristics on stocks on the basis of knowledge graph and deep learning. The proposed model and other feature selection models were used to perform feature extraction on the websites of Thomson Reuters and Cable News Network. Numerous experiments were conducted to derive evidence of the effectiveness of knowledge graph embedding for classification tasks in stock prediction. A comparison of the average accuracy with which the same feature combinations were extracted over six stocks indicated that the proposed method achieves better performance than that exhibited by an approach that uses only stock data, a bag-of-words method, and convolutional neural network. Our work highlights the usefulness of knowledge graph in implementing business activities and helping practitioners and managers make business decisions.http://dx.doi.org/10.1155/2019/9202457 |
| spellingShingle | Yang Liu Qingguo Zeng Joaquín Ordieres Meré Huanrui Yang Anticipating Stock Market of the Renowned Companies: A Knowledge Graph Approach Complexity |
| title | Anticipating Stock Market of the Renowned Companies: A Knowledge Graph Approach |
| title_full | Anticipating Stock Market of the Renowned Companies: A Knowledge Graph Approach |
| title_fullStr | Anticipating Stock Market of the Renowned Companies: A Knowledge Graph Approach |
| title_full_unstemmed | Anticipating Stock Market of the Renowned Companies: A Knowledge Graph Approach |
| title_short | Anticipating Stock Market of the Renowned Companies: A Knowledge Graph Approach |
| title_sort | anticipating stock market of the renowned companies a knowledge graph approach |
| url | http://dx.doi.org/10.1155/2019/9202457 |
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