Analyzing the Stock Exchange Markets of EU Nations: A Case Study of Brexit Social Media Sentiment

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.

An Empirical Study of Macroeconomic Factors and Stock Returns in the Context of Economic Uncertainty News Sentiment Using Machine Learning

“…Researchers have employed historical data on stocks in existing research studies to perform stock market analyses or forecasting tasks [17]. However, some research studies have involved the sentiment data for particular events on social media platforms with stock data to more accurately model stock market trends [19][20][21] because data analytics based on social media plays an important role in countries' stock market trends. All these analyses have been conducted through diverse approaches, of which ANNs and traditional intelligence-based models are the most prevalent.…”

“…However, some studies have also employed social media analytics information, such as general sentiment, news, and sentiment regarding specific events [18], to conduct stock market analyses. More precisely, these events include the coronavirus disease 2019 (COVID-19) [19], the British exit from the European Union (Brexit) [20], and the 2019 Nigerian presidential election [21]. In both approaches, the social media-based analysis of big data regarding stocks has gained substantial attention over the past few years [18,22] because this provides more clarity to investors for improved planning and decision-making.…”

Dictionary Learning Based Scheme for Adversarial Defense in Continuous-Variable Quantum Key Distribution

“…The adversarial samples are generated by multiple iterations or queries on the neural network, which means the neural network is not only the object of adversarial sample attacks, but also the basis for the generation of adversarial samples. The commonly used neural networks are Artificial Neural Network (ANN), Convolution Neural Network (CNN), Recurrent Neural Network (RNN), and Deep Neural Network (DNN) [ 33 , 34 , 35 ]. We mainly introduce ANN and CNN, and build corresponding models for experiments.…”

Towards Data-Driven Decision-Making in the Korean Film Industry: An XAI Model for Box Office Analysis Using Dimension Reduction, Clustering, and Classification

“…DDDM uses models and algorithms to process and analyze data sources for reliable decision support [ 10 , 11 ]. This approach has been widely applied in various industries, including medical diagnosis [ 12 ], financial risk prediction [ 13 , 14 ], public affairs governance [ 15 ], landslide susceptibility prediction [ 16 , 17 ], autonomous driving [ 18 ], and the safe operation of wastewater treatment processes [ 19 , 20 ], among others [ 21 , 22 , 23 ]. DDDM helps reduce the limitations of traditional decision-making methods, resulting in more accurate predictions and better decision support [ 1 , 6 , 9 , 11 ].…”