Abstract
Technological advancements such as natural language processing (NLP) and machine learning (ML) have shown their impact in the financial sector in recent years. A major development in this has been the rapid use of news articles online to determine the direction of stocks of different companies. This has been the main theme of our paper which focuses on the reaction of Facebook stock due to various kinds of news referring to the company. Knowledge graphs have been put to work in our research which helps us to link different news items to each other. To extract the features, Word2Vec has been used to convert the sources, targets, and edges of all the knowledge graphs into appropriate five-dimensional vectors. Each dimension is regarded as a feature that was then clustered using K-Means, Mini Batch K-Means, Gaussian Mixture, Birch, and DBSCAN clustering algorithms. Out of all the mentioned techniques, the clusters were best formed using Gaussian Mixture algorithm. The performance of each algorithm was evaluated using the distinctness of the clusters formed by the respective algorithm. The model is aimed to provide investors with the idea of what kind of effect they can expect on Facebook stock price due to any event relating to the firm.
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Jain, A., Chakrabarti, B., Upmon, Y., Rout, J.K. (2022). Exploring Historical Stock Price Movement from News Articles Using Knowledge Graphs and Unsupervised Learning. In: Satapathy, S.C., Peer, P., Tang, J., Bhateja, V., Ghosh, A. (eds) Intelligent Data Engineering and Analytics. Smart Innovation, Systems and Technologies, vol 266. Springer, Singapore. https://doi.org/10.1007/978-981-16-6624-7_51
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