Abstract
Stock exchange analysis is regarded as a stochastic and demanding real-world setting in which fluctuations in stock prices are influenced by a wide range of aspects and events. In recent years, there has been a great deal of interest in social media-based data analytics for analyzing stock exchange markets. This is due to the fact that the sentiments around major global events like Brexit or COVID-19 significantly affect business decisions and investor perceptions, as well as transactional trading statistics and index values. Hence, in this research, we examined a case study from the Brexit event to assess the influence that feelings on the subject have had on the stock markets of European Union (EU) nations. Brexit has implications for Britain and other countries under the umbrella of the European Union (EU). However, a common point of debate is the EU’s contribution preferences and benefit imbalance. For this reason, the Brexit event and its impact on stock markets for major contributors and countries with minimum donations need to be evaluated accurately. As a result, to achieve accurate analysis of the stock exchanges of different EU nations from two different viewpoints, i.e., the major contributors and countries contributing least, in response to the Brexit event, we suggest an optimal deep learning and machine learning model that incorporates social media sentiment analysis regarding Brexit to perform stock market prediction. More precisely, the machine learning-based models include support vector machines (SVM) and linear regression (LR), while convolutional neural networks (CNNs) are used as a deep learning model. In addition, this method incorporates around 1.82 million tweets regarding the major contributors and countries contributing least to the EU budget. The findings show that sentiment analysis of Brexit events using a deep learning model delivers better results in comparison with machine learning models, in terms of root mean square values (RMSE). The outcomes of stock exchange analysis for the least contributing nations in relation to the Brexit event can aid them in making stock market judgments that will eventually benefit their country and improve their poor economies. Likewise, the results of stock exchange analysis for major contributing nations can assist in lowering the possibility of loss in relation to investments, as well as helping them to make effective decisions.
Highlights
A stock market, referred to as a stock exchange, is a venue wherein a company’s shares or stocks are traded
We have employed the sentiments data from 1,826,290 tweets regarding the stock markets of different European Union (EU) economies, which is a substantial volume of data; the deep learning model appears to be a better fit for this analysis as it performs very efficiently when there is a large amount of data
There are many such events whose impact on stock exchanges was studied in the past; in this particular analysis, an important event, namely, British exit from Europe (Brexit) is considered as a case study
Summary
A stock market, referred to as a stock exchange, is a venue wherein a company’s shares or stocks are traded. Opinions and sentiments about Brexit can be helpful for decision-makers to prepare better strategies and make intelligent decisions In this era of artificial intelligence, various researchers are attempting to determine the influence of opinions expressed by people on different social media platforms, and their effect on forecasting the eventual movement of stock prices using different automated methods of machine learning and deep learning [4,5,6,14,15]. The proposed study is different in comparison with these methods, as sentiment analysis regarding Brexit events is taken into account when analyzing the stock exchanges of EU countries. The impact of social media sentiment for a major event, namely Brexit, is evaluated regarding the stock exchange markets of different EU countries. The rest of the paper is organized as follows: Section 2 explains related work in the field of social media-based stock prediction, Section 3 explains the methodology, and Section 4 presents the results; the discussion is followed by a conclusion and an outline of future research
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
