The aim of the work is to design and implement a method of exploring the cause-and-effect relationships between company announcements and the stock prices on NASDAQ stock exchange, followed by a brief discussion. For this purpose, it was necessary to download the stock quotes of selected companies from the NASDAQ market from public web sources. Additionally, media messages related to selected companies had to be downloaded, and then a news sentiment analysis mechanism had to be prepared. The mechanism of sentiment analysis was prepared based on the supervised machine learning approach. The implemented method was used to analyze the sentiment of a set of stock exchange announcements and it was correlated with changes in the share prices of selected companies. Based on the collected data, the association rules were extracted using the Apriori algorithm. While the obtained results are very promising, however, one should also estimate the rate of return for the interrelated transactions to determine the true and ultimate value of discovered relationships.
Authors
- Filip Grzonkowski,
- dr Paweł Weichbroth link open in new tab
Additional information
- DOI
- Digital Object Identifier link open in new tab 10.1007/978-3-031-66761-9_7
- Category
- Aktywność konferencyjna
- Type
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Language
- angielski
- Publication year
- 2024
