The impact of Brexit on the British currency and stock market performance: a sentiment analysis approach Open Access

Brexit, the United Kingdom's exit from the European Union, is one of the most significant events to have impacted the European community. Several studies are taking place to explain this phenomenon. If the European Union (EU) is offering various advantages to its members, moreover, feeling safe and powerful, why would the United Kingdom take such decision?

One of the major advantages of membership in the EU is access to the Single Market, where products and services made in any of the member countries can be freely sold to the other member states without duties or tariffs. Members also have intra-industry trade, economies of scale and access to a wide variety of goods and services. In other words, the EU eliminated any border controls between its member countries. Moreover, the EU has now extended its interests to reach some other issues such as climate change, health problems – especially after the pandemic of COVID-19 – security, justice, external relations and migration. The EU has its own set of goals and values to which the EU was awarded the Nobel Prize in Peace in 2012. Despite all these success factors and growing incentives for any European country to be a member in the EU and take advantage from all these benefits, the United Kingdom voted for a withdrawal decision.

Brexit began in June 2016, when the government began a referendum to decide whether to leave or remain an EU member. This is according to the historical complexities of the EU–UK relationship, which began with Britain's reluctance to join the EU and challenges with the EEC due to differences in national identity. The relationship between the UK and the EU has long been controversial, dating back to the 1970s and including the referendum of 2016, a withdrawal agreement in 2020, and a Trade and Cooperation Agreement reached in December 2020 (Eidenmüller, 2018; Fabbrini, 2020).

Brexit has had very significant impacts on the UK financial markets and economy, generating uncertainty and issues. Various studies have examined Brexit in terms of economic and financial factors, and according to them, departing from the EU has hindered the economic growth of the UK, affecting GDP, inflation, trade, exchange rates, currencies and capital inflows.

There has been a predominantly negative impact of Brexit on the economy of the UK. As per the Office for Budget Responsibility, the trading terms that came into effect after withdrawal are likely to shrink the GDP of the UK by around 4% in the long term as opposed to the scenario had the UK remained in the EU. This included a sharp fall in the value of the pound after the referendum results and a resultant rise in the uncertainty level that led to a certain level of postponement of investment by businesses and a fall in foreign direct investment. Even as certain positive forecasts promise the benefit of flexibility, the facts underscore the overarching expenses incurred.

Europe has been hit by Brexit in political and economic circumstances over the past seven years. Various scholars have delved into this decision, examining aspects like the demographics of voters, rationales behind their choices, consequences, particularly within the financial sector. Some researchers have explored the division between proponents and opponents of Brexit and how media and public opinion contributed to shaping post-Brexit circumstances. While a host of studies have examined the economic and financial implications of Brexit, there is limited information about how social media opinions affect market outcomes. This research tries to fill this gap by exploring the impacts of social media opinions about Brexit on the UK stock market and foreign exchange performances. The motivation for this research is drawn from the growing role played by social media as a means of public expression and its ability to shape investor behavior and market performance.

The contribution of this research is two-fold. First, it presents a novel approach to sentiment analysis ^[1] using social media data, specifically Twitter/X, to measure public opinion of Brexit. Whereas current literature has exploited traditional media sources such as newspapers and magazines, this paper breaks new ground in applying social media sentiments in investigating the relationship between public sentiment and financial market performance. Second, the study employs advanced econometric techniques, i.e. the Autoregressive Distributed Lag (ARDL) model, to investigate short- and long-run relationships between sentiment scores and financial variables. This methodological innovation renders the findings more robust and reliable with significant implications for policymakers, investors and researchers.

This paper chooses an interdisciplinary theoretical perspective that can interpret how the transfer of social media Brexit-related sentiment influenced UK financial markets.

The Efficient Market Hypothesis (EMH) offers a broad frame of reference, arguing that asset prices reflect all available information. As the world is experiencing globalization and advancements in connectivity and technology, information on social networks spreads extensively and instantaneously, and thus, they have become a fundamental source of public information. As such, stock prices and exchange rate changes should reflect the same information presented on social media sites like X/Twitter. Furthermore, this process and effect is a two-way phenomenon; stock changes can lead to reactions on social media sites, and heightened feelings on the same sites may have the potential to lead to changes in stock prices, thus violating the semi-strong form of the EMH due to behavioral biases.

Alongside the EMH, herd behavior theory portrays a scenario whereby people in financial markets behave in the same manner as other people due to a lack of information (Barber and Odean, 2001). Social media works to enhance this effect in a manner whereby stories, rumors and emotional contagion spread quickly. Within the situation of Brexit, which was a polarized and uncertain occurrence, groups of bad news can cause investors to undervalue assets in the UK, causing higher instability in the FTSE 100 and exchange rates despite a slow-moving economy.

These approaches collectively position social media sentiment as both a reflector and a force behind market dynamics, thus warranting the need to investigate long- and short-term relationships in this analysis.

The substantial impact of Brexit, now for almost seven years, has reached a wide political and economic spectrum in Europe and elsewhere. Its many dimensions include the analysis of voter demography and underlying motivations, assessments of macroeconomic consequences, impacts on financial markets, the polarization between proponents and opponents, and the role played by media and public opinion in shaping the post-Brexit outcome.

Determining motivation for Brexit, Becker et al. (2017) initiated an analysis of voting patterns across UK regions utilizing a wide range of factors. Their findings linked leave votes to lower education levels, older age groups, unemployment in sectors like manufacturing and inferior public services quality. As per the study conducted by Areal (2021), it was found that the leave voters are citizens who are not seeking jobs, and they strongly trust the UK government. Meanwhile, the remain voters were highly educated students, trusting the EU more than the government and had more sense of cooperation. Moreover, extensive literature attributes voting tendencies during Brexit to demographic differences alongside political and economic factors. The complexity regarding Brexit voting patterns is interpreted in empirical studies suggesting that leave voters may perceive being “left behind” culturally and economically (Mustafa et al., 2020; Reinl and Evans, 2021).

The reasons for leaving the EU primarily centered around independence, border control, resolving immigration issues while avoiding EU expansion membership and anticipating better trade benefits outside the EU framework.

Conversely, the “remain” voters opted to stay due to concerns over potential economic risks affecting jobs and prices (Clarke et al., 2017). Thus, they chose to capitalize on EU advantages while retaining access to its single market without adhering to Schengen area regulations or adopting the euro.

Although demographic and socioeconomic variables shed useful light on voting patterns, more recent studies have begun to examine the assessment of the actual economic outcome of Brexit and the degree to which pre-referendum intentions have been realized.

While some studies before the referendum argued for potential advantages, like flexibility in regulatory policies, as well as management of immigration (Dhingra et al., 2017; HM Government, 2022), most post-Brexit evidence points to serious costs. Current research with synthetic counterfactual simulations has concluded that by 2025, the UK is down 6–8% in GDP compared to having stayed in the EU (Bloom et al., 2025). While the Office for Budget Responsibility has held true to estimates of 4% lost productivity due to reduced trade intensity, another study illustrates damage in line with a 5% GDP lost so far (Springford, 2024).

These costs mainly result from the emergence of various trade barriers, such as tariffs and other non-tariff measures, as well as border frictions resulting from the loss of single market access. As a result, UK–EU trade is constantly depicted as declining (Du et al., 2023), and lower foreign direct investment to the UK, as well as disruptions in various sectors such as agriculture, manufacturing and finance, have been experienced (Sampson, 2017; Brown et al., 2018 respectively).

Brexit's multifaceted economic consequences necessitate a rough examination of its sectoral impacts, particularly within the FTSE-100, where diverse industries exhibit differential sensitivities to the prevailing Brexit sentiments. The actual impact of Brexit on specific industries is contingent upon a complex interaction of factors, including trade agreements, rule adjustments and shifting investor confidence. Financial services, a cornerstone of the British economy, faced considerable uncertainty due to potential disruptions to cross-border financial activities and access to the European single market. The Brexit referendum opened a period of uncertainty, which impacted firms and private investors (Tsangari and Mantara, 2021). However, firms operating in the United Kingdom are still able to use the financial markets of the European Union via subsidiaries; these companies are also in a position to provide services to their more sophisticated investors (Wymeersch, 2017).

The pharmaceutical sector remained relatively stable after the Brexit referendum, with companies like GlaxoSmithKline witnessing a rise in share price (Kazzazi et al., 2017). This is because the sector is defensive and considers that the needs of healthcare are relatively inelastic to economic progress. However, longer-term threats for the pharmaceutical industry included future supply chain disruption and divergence in regulation from the EU, requiring companies to adopt anticipatory strategies in an attempt to neutralize threats (Roscoe et al., 2022). The automotive sector, on the other hand, was severely affected because it relies on frictionless European supply chains. The increased cost of trade, tariffs and non-tariff barriers added complexity and expense to automobile production (Fuller, 2019).

Brexit also affects the energy industry, whose departure of the UK from the EU internal energy market produces concerns over energy security and de-carbonization policies (Lockwood and Froggatt, 2019). Renewable energy projects, particularly those that are EU-reliant or cross-border, can anticipate uncertainty in terms of finance and regulatory issues. The property market also experienced mixed impacts, where London commercial property initially felt downward pressure due to concerns over the migration of financial institutions. Conversely, certain segments of the residential market were spurred by increased domestic demand and a declining pound, which attracted foreign investors seeking value opportunities. Real estate prices have been determined to be affected by economic performance, access to jobs and local amenities. The Brexit referendum itself would have dragged house prices down, particularly for areas that experienced heavy immigration of EU individuals. Since Brexit has been a hot issue starting 2016, and due to the quick diffusion of information and opinions made possible by social media, it has become an increasingly significant component in financial markets' predictions. According to a study conducted by Maqsood et at. (2022), sentiment on social media can predict stock returns and trading volume. Moreover, Antweiler and Frank (2004) discovered a favorable correlation between trading volume and volatility and activity on online message boards.

Beyond aggregate macroeconomic effects, Brexit-related uncertainty has manifested prominently in financial markets, where public sentiment and investor psychology play an increasingly important role.

The effect of social media on herd behavior in financial markets has been the subject of numerous research. According to a study by Liang et al. (2020), herd behavior can occur because of social media sentiment influencing individual investors' investing decisions. The study also discovered that retail investors exhibit higher herd behavior than do institutional investors. Moreover, according to research by Barber and Odean (2001), investors frequently imitate other people's behavior when given only partial knowledge. Market inefficiencies and irrational pricing may result from this tendency.

Social media sites like X and Facebook are becoming increasingly popular trends for exchange traders. Social media can help predict stock market performance, according to research by Domeniconi et al. (2017) who found that tweets sentiment can predict changes in the Dow Jones Industrial Average (DJIA). A similar study from Dipple et al. (2020) show that social media sentiment can predict changes in US dollar and British pound exchange rates. According to the study, social media can provide useful information for investors, especially in short-term trading strategies.

As a result, social media usage has produced new chances for investors to get financial data and express their opinions on currency exchange and stock markets. Social media can be a great source of information for investors, but some research indicates that it can also negatively impact the stock and currency markets, increasing volatility and unpredictability. The sort of information shared on social media, market dynamics and investor behavior will all likely have an impact on how social media affects currency exchange and stock markets in the end.

The stock market has also been found to be impacted by social media, since investors use these platforms to discuss stocks and get news and company information. A study by Li et al. (2014) discovered that social media sentiment can forecast stock returns, indicating that social media may be a useful information source for investors. According to another study, Lehrer et al. (2021), social media sentiment can predict stock market volatility, suggesting that it may be possible to use social media channels to induce changes in stock prices. According to the study, social media can provide useful information for investors, especially in short-term trading strategies.

More recent studies specifically tied to Brexit reinforce these findings. Broadbent et al. (2024) demonstrate that Twitter-derived Brexit sentiment indices Granger-cause volatility in the FTSE 100 during the post-referendum and transition periods. Recent studies show that spikes in negative Brexit-related tweets significantly amplify episodes of GBP depreciation, particularly around key negotiation milestones.

The 2016 Brexit vote and decision to leave the EU that followed had a big effect on the UK's currency exchange rates. Researchers have found that the Brexit attitude has caused the British Pounds (GBP) value to decline in comparison to other important currencies including the US dollar (USD) and the euro (EUR). Zhang et al. (2018) conducted a study and discovered that the GBP/USD exchange rate decreased following the Brexit referendum by roughly 11.6% while the GBP/EUR exchange rate decreased by roughly 10.8%. Similar findings were made by Filippou et al. (2018), who discovered that Brexit-related news had a considerable impact on the GBP/USD exchange rate and caused it to fluctuate in response to news announcements. The authors discovered that the impact of Brexit-related bad news outweighed that of good news on the exchange rate.

Brexit sentiment has had a significant impact on stock markets, not only in the UK but also internationally. The impact of Brexit on the UK stock market has been the subject of much analysis. For example, Sezgin and Bayar (2021) found that the Brexit referendum led to a sharp decline in the UK stock market, with the FTSE 100 index falling by around 5.6%.

Additionally, Zhang et al. (2018) research showed that the stock prices of UK companies were significantly affected by Brexit-related uncertainty, especially those companies that are heavily exposed to EU markets. The authors pointed out that corporations located in other EU nations in addition to UK-based businesses were also harmed by the effect of Brexit on stock values.

Overall, studies have shown that Brexit sentiments have had a significant impact on stock markets and currencies. Sentiments have driven the pound lower, and the exchange rate fluctuated because of news events. Stock prices fell on Brexit-related uncertainty, particularly for UK-based businesses and businesses that are heavily exposed to EU markets. These results highlight the importance of Brexit negotiations and their impact on the UK the international financial market.

Even as the existing literature confirms the existence of well-defined associations between the social media mood and financial market activities within the short term, the existence of co-movements within the long term, especially those corresponding to the specific Brexit-related context, has remained relatively unexamined. Few existing studies utilize the specific hashtag data corresponding to the entire post-transition period or the application of the co-integration method.

The literature review indicates that there are three major gaps that the study seeks to address for the benefit of the academic world. First, although there have been numerous academic articles that focus on the short-term effects associated with the Brexit events, the relationship between the long-term public sentiment and financial performance has remained scarce until now. Secondly, although there have been numerous approaches that focus on the general news-based index and general Twitter activity, few have used Brexit hashtags after the transition period that ended in 2022. Thirdly, the literature review indicates that there have been few articles that used the co-integration analysis to test the short-vs long-run effects associated.

To fill the gaps, this research aims to:

1. Building the daily Brexit sentiment index based on selected hashtags on the X/Twitter network (#BrexitDisaster, #BrexitBenefit and #BrexitEconomy.

2. Using ARDL bounds testing to examine long-run and short-term association between sentiment and FTSE100 indices and GBP/EUR and GBP/USD exchange rates.

3. Drawing on detailed policy implications in a world where political risk is being increasingly transmitted through social media networks.

Analyzing the dynamics of relationship between Brexit sentiments and stock market and currency exchange using time-series analysis. The variables of interest are GBP exchange rates with respect to USD and EUR in Currencies and FTSE-100 Index value, representing the stock price behavior and a measure of overall social media sentiment. Data for exchange rates and the FTSE-100 index are collected on a daily basis to be consistent with the public opinion data. The measure of social media sentiments is created from a pool of daily tweets from Twitter from the main hashtags: BrexitBenefit, #BrexitDisaster and #BrexitEconomy. Tweets, representing the hashtags, will be extracted from 2019 to 2022.

X, formerly known as Twitter, has been selected for a number of reasons that align closely with the aims of the study. Firstly, it is a platform characterized by real-time, unfiltered public discussion of political events, and thus it captures real-time opinions on Brexit both during and post the transition period. On the other hand, aggregated search interest metrics – for example, Google Trends – are about query volume, not expressed attitudes, while news headlines are subject to editorial bias and aggregation delay, which contrasts to direct user-generated textual content of X posts that are a good proxy for individual and collective affective responses. This is quite in tune with measuring public sentiment over a divisive issue like Brexit, in which retail investor mood and herding can position the market. Thirdly, the targeted use of specific Hashtags, #BrexitDisaster and #BrexitEconomy, ensures high topical relevance and reduces noise from unrelated discussions, thus making sure focused analysis of the sentiment that will be exclusively related to Brexit can be done over an extended post-transition period, namely 2019–2022.

The sentiment score is calculated using Loughran, a word-based lexicon, created by Loughran and McDonald (2011) as the reference dictionary to perform Sentiment Classification. The LOUGHRAN lexicon has 4,000 plus words, distributed across six Sentiments, Constraining, Litigious, Superfluous, Uncertainty along with Negative and Positive. This study focuses on Negative and Positive Sentiments. Using Loughran, the study attempts to calculate for every day, frequency of total positive words and total negative words. Sentiment Score, for any day, is the difference of positive and negative frequencies.

The methodology adopted in this research has several benefits over other advanced techniques that have been observed in the literature in the past few years. Most of the previous studies have employed traditional media sources such as magazines and newspapers to study the effect of public opinion on financial markets. While these sources are helpful, they may not possibly capture the dynamic and real-time nature of public opinion. Social media sites like Twitter/X give a more immediate and comprehensive view of public opinion, reflecting the diverse and rapidly shifting sentiments of a vast multitude of users.

In this study, we examine how social media sentiments influence the UK stock market and currency exchange performances in the context of Brexit. These processes, also known as transmission channels, outline how social media sentiments get translated into financial variables' movements. Describing and understanding these channels is crucial for what would account for the social media sentiments' relationship with financial outcomes. Investor behavior is one of the major transmission channels. Sentimental changes can affect investors' willingness to sell or buy stocks and currencies. For instance, negative sentiments around Brexit could increase uncertainty and risk aversion, making investors liquidate stocks and flow into safe assets. Conversely, positive sentiments can boost investor optimism, leading to increasing stock prices and higher buying volumes. The second channel is market confidence. Sentiments expressed on social media can affect overall market confidence, market stability and volatility. Persistent negative sentiment levels can erode market confidence, resulting in increased volatility and sharp market declines in share prices. Positive sentiment, on the other hand, can increase market confidence, stabilizing prices and reducing volatility. The function of public perception is especially highlighted in moving the market forces through this channel. Finally, the influence of the media, or in other words the media's ability to inflate sentiments and shape opinion is another essential channel of transmission. Media coverage of Brexit sentiments can influence public opinion and investor sentiment. For instance, widespread media coverage of negative sentiments can exacerbate market reactions, causing steeper declines in stock prices and currency values. Conversely, positive media coverage can neutralize negative sentiments and help with market recovery.

Leveraging the sentiments of social media, the current study leverages big data and the power of sentiment analysis tools to better provide an assessment of public opinion. Using a multiple set of lexicons like NRC, Syuzhet, Bing and Afinn and cross-checking them with the Loughran–McDonald dictionary enhances the methodological rigor and ensures the reliability of the sentiment scores.

Furthermore, the application of the ARDL model allows both the short-run and long-run relationships between sentiment scores and financial variables to be examined. Unlike other models, which require data to be in stationary format, the ARDL model allows both stationary and non-stationary data, which is appropriate for time-series analysis. This approach gives a less striking view of social media opinions influencing the financial market, giving a useful piece of information that could be utilized when making policies or planning investments. Figure 1 presents the word cloud, which is one of the most popular ways to visualize and analyze qualitative data. It's an image composed of keywords found within a body of text, where the size of each word indicates its frequency in that body of text.

Figure 1 shows that the highest frequency words are brexitdisast, or Brexit disaster and Brexit. It is worth mentioning that R understands the whole word even if some symbols are removed. For instance, many twitter users write Brexit disaster using readable symbols to trick the algorithm set by twitter that prevents non desirable tweets from being visible. From the above word cloud, it is expected to have either a negative or neutral relation between the sentiment score and the other dependent variables. From the emotion plot, the highest frequency is for negative emotions, followed by positive emotions and the lowest one is for surprising emotions.

Various lexicons were employed to calculate sentiment scores whose averages were employed for the investigation of long-term impacts on dependent variables. The used lexicons are: NRC, which represents 10 different emotions presented in Figure 2, in addition to three other lexicons that use numbers only without emotions: Syuzhet, Bing and Afinn. The created score is scaled from 0 to 1 in which 0 means highly negative emotions, 1 means highly positive emotions. The calculated average sentiment score is 0.52, which reflects a neutral sentiment. It is worth mentioning that the calculated sentiment score reflects the Brexit vote in 2016 (52% leave and 48% remain). Moreover, this result goes in line with the literature reviewed in the previous section.

Table 1 presents the descriptive statistics for the research variables. The results show the statistics of the variables like mean, median as measures of central location, also standard deviation, minimum and maximum are presented. Based on Jarque–Bera test, all variables are not normally distributed with a confident 95%, as the p-value of the test is less than 5%, except for the close price of the FTSE-100 it is normally distributed. Since the number of observations is quite high (more than 30 observations), the normal distribution conditions could be ignored.

As displayed in Figure 3, the closed price of FTSE-100 is almost increasing over the selected time. In addition, the exchange rate of the USD relative to GBP is almost increasing over the selected time. Moreover, the exchange rate of the Euro relative the GBP fluctuates between ups and downs over the selected time period. Finally, the sentiment score of the tweets fluctuates between ups and downs over the selected time.

This section presents the empirical findings of the model. It starts with the stationarity assumption, then investigating the long-run relationship between the sentiment score and other variables: FTSE 100 closing price index, Euro/GBP and USD/GBP exchange rates and the discussion.

As an initial step of the time-series analysis, it is to validate the stationarity assumption. Stationarity assumption is tested using the Augmented Dickey–Fuller (ADF) test. The ADF test is one of the cited unit root tests in literature and is commonly used. The ADF test is applied to determine whether the data series is stationary (has no unit root) or not, by calculating the respective statistics and p-values in the main level. Table 2 displays the results of ADF test.

From the results, it can be concluded that sentiment score is stationary at 95% confidence level, as the p-value is less than 5%, while the dependent variables (EURO/GBP, USD/GBP, Closed price of FTSE-100) are not stationary at their level. After taking the first difference, the data became stationary at 95% confidence level. The ADF tests include an intercept, and the appropriate lag lengths were selected according to the Schwartz Bayesian criteria.

The main aim here is to investigate both long-run and short-run relationships between Brexit sentiment scores and other related determinants; the closing price of FTSE 100 index, Euro to GPB exchange rate and USD to GBP exchange rate. This can be done using co-integration analysis and error correction models. Co-integration test and error correction model are used within the ARDL framework because Johansen co-integration test cannot be applied directly if the variables of interest are not all I (1). Thus, if the variables are of mixed orders, or some of them are non-stationary, an alternative method, such as ARDL model, can be used in this case. Unlike other models that require data to be stationary, ARDL model can handle a mix of stationary and non-stationary data. It uses ordinary least square (OLS) based model by including enough lags to accurately represent the underlying process generating the data.

By using a simple linear transformation, a dynamic error correction model (ECM) can be derived from ARDL. Also, the ECM integrates the short-run dynamics with the long-run equilibrium without losing long-run information and avoids problems such as spurious relationship resulting from non-stationary time series data (Shrestha and Bhatta, 2018).

To illustrate the ARDL modeling approach, the following simple model can be considered:

The error correction version of the ARDL model is given by

The first part of the equation with $β$⁠, $δ$ and $γ$ represents short-run dynamics of the model. The second part with $λs$ represents long-run relationships. The null hypothesis in the equation is $λ1+λ2+λ3=0$ , which means non-existence of long-run relationship. In the study, there are three dependents which are closed price of FTSE-100, USD/GBP, EUR/GBP and only 1 independent variable which is sentiment score that calculated from tweets.

Results of the bounds test procedure for co-integration analysis between the closing price, EUR/GBP and USD/GBP and their determinants are presented in Tables A1, A4 and A7 respectively in  appendix. From the tables it is clear that, the f-calculated for the closing price and EUR/GBP are larger than the upper bound for each significance levels, which means that at 95% confident level the null hypothesis “no long-run relationship exist” is rejected, this means that there is a unique co-integration relationship (i.e. long run relation) exists between the closing-price, EUR/GBP and the sentiment score, on the other hand, the f-calculated for USD/GBP is lower than the lower bound for each significance levels, which means that at 95% confident level the null hypothesis “no long-run relationship exists” is not rejected. This means that there is no co-integration relationship (i.e. long-term relation) exists between USD/GBP and the sentiment score.

Since the sentiment score and closing price and EUR/GBP are co-integrated, the next step is to estimate the long-run parameters of the ARDL model, results are presented in Table 3 above. The long-run ARDL model is estimated to be based on the Akaike Information Criterion (AIC) using a lag of 1 for the dependent variable and lag 0 for regressors. As shown in Table 3, it could be concluded that sentiment score has a negative significant coefficient (for the closing price) at 90% confidence level, which means that every increase in sentiment score by 1 unit the closing price will decrease by 77.725 in the long run. Moreover, the sentiment score has a positive significant coefficient (for the EUR/GBP) at 95% confidence level, this means that every increase in sentiment score by 1 unit the EUR/GBP will increase by 0.0787 in the long run.

After estimating the long-run co-integrating model. The final step is to model the short-run dynamic parameters within the ARDL framework. Thus, the lagged values of all level variables (a linear combination is denoted by the error-correction term, ECMt-1) are retained in the ARDL model. Table 4 shows that the sentiment score has a negative significant coefficient at 95% confidence level, this means that every increase in sentiment score by 1 unit the closing price will decrease by 61.1649 in the short run. 7.38% deviation of the closing price from its long-run equilibrium level is corrected each period in the short run. The outcomes of the closing price examination align with existing literature's findings. For instance, Sezign and Bayar (2021) observed a significant decrease in the UK stock market after announcing the Brexit referendum results. Moreover, Zhang et al. (2018) examined the impact Brexit-related-uncertainty on stock prices of UK enterprises, and they found a significant negative impact especially on those enterprises who have significant exposure to EU markets.

For the EUR/GBP, we can conclude that the sentiment score has a positive significant coefficient at 95% confidence level, this means that every increase in sentiment score by 1 unit the EUR/GBP will increase by 0.5589 in the short run. 44.1% deviation of the EUR/GBP from its long run equilibrium level is corrected each period in the short run. This implies a depreciation in the sterling pound against the Euro as mentioned by previous studies such as Zhang et al. (2018), they conducted a study and discovered that the GBP/EUR exchange rate decreased by roughly 10.8%.

From Tables A2, A3, A5 and A6 (for the closing price and EUR/GBP respectively) in  appendix, it is clear that there is no serial correlation as Durbin–Watson value is near to 2, also from Q-statistics probabilities, it is clear that there is no serial correlation as p-value is greater than 0.05, also these results are supported by figures A1 and A2 in  appendix, as the residuals are scattered randomly, in addition the fitted values are almost the same as the actual values.

Moving to the USD/GBP, from Table 3, we can conclude that sentiment score has a positive significant coefficient at 95% confidence level, this means that every increase in sentiment score by 1 unit the USD/GDP will increase by 0.010454 in the short run. The goodness-of-fit measures and residual diagnostics for the USD/GBP model are reported in Tables A8 and A9 in  appendix. The model shows a high adjusted R-squared of 0.961 and a Durbin–Watson statistic close to 2, indicating no substantial autocorrelation. The Q-statistics in Table A9 further confirm the absence of serial correlation (p-values >0.05). Visual inspection of the actual, fitted and residual plot in Figure A3 also supports model adequacy, as the residuals appear randomly scattered with no systematic pattern.

This indicates a depreciation of the Sterling Pound against the US dollar. Zhang et al. (2018) discovered that the GBP/USD exchange rate decreased following the Brexit referendum by roughly 11.6%. Similar findings were made by Filippou et al. (2018), who observed that Brexit-related news had a considerable impact on the GBP/USD exchange rate and caused it to fluctuate in response to news announcements. The authors discovered that the impact of Brexit-related bad news outweighed that of good news on the exchange rate.

The relationship between the USD and GBP, as suggested by the results of bound tests, indicates a relatively weak economic linkage between the United States and the United Kingdom. This contrasts with the stronger economic ties observed between the USD and the Euro (EUR). For instance, the trade relationship between the UK and the US is significant but not as extensive as the trade between the US and the EU. The volume of trade between the UK and the US is lower, which contributes to a less substantial influence on the USD-GBP exchange rate compared to the USD-EUR rate. According to data from the World Bank and Trade Map, the trade volume in goods and services between the UK and the US is substantial but does not reach the levels observed in US–EU trade. In 2022, the trade in goods between the UK and the US was valued at approximately $140 billion, with the US exporting more to the UK than it imports. Unlike the EU and US trade relations, as in 2022 the trade in goods between the EU and the US was valued at approximately $800 billion.

Moreover, while there are strong financial ties between the UK and the US, particularly in terms of foreign direct investment and capital flows, they are not as extensive as the ties between the US and the EU. The US and UK have substantial investments in each other's economies, but the total FDI is lower than that between the US and the EU. As of 2022, US FDI in the UK was approximately $750 billion, while UK FDI in the US was around $500 billion. Unlike the relation between the US and the EU, they are among the largest investors in each other's economies. In 2022, US FDI in the EU was estimated to be over $3 trillion, while EU FDI in the US was around $2.5 trillion.

Our research results indicate that social media sentiment on Brexit has a considerable impact on the performance of the UK stock market and exchange rates. To be specific, we found that a rise in negative sentiment is associated with a decline in the FTSE-100 index and depreciation of the GBP against the EUR and USD.

From the viewpoint of economic theory, the theory of EMH suggests that prices of stocks embody all the information, including public sentiment. Disfavorable public sentiment toward Brexit certainly increases risk aversion and uncertainty among investors, leading to selling of stocks and declining FTSE-100 index. Likewise, the weakening of the GBP is also due to diminishing investor confidence in the economic future of the UK after Brexit, resulting in capital flight and a depreciated currency.

On the other hand, and from a behavioral perspective, our results are in line with the herd behavior theory, where investors tend to follow others' actions, perpetuating market trends. Negative sentiment on social media has the potential to trigger a cascade of selling activity, as investors react to perceived risks and uncertainties. This aggregate action can amplify market volatility and induce large price movements.

Our study investigates the influence of Brexit social media sentiments on the performances of the UK's stock market and currency exchange. To place more contextually, we compare our findings with Zhang et al. (2018) and Sezgin and Bayar (2021).

Zhang et al. (2018) examined the impact of Brexit news on the GBP/USD exchange rate and determined that Brexit sentiment played a strong role in shaping currency movements. In accordance with their findings, negative news on Brexit led to the weakening of the GBP relative to the USD, while positive news had a lesser impact. This is in line with our findings, which show that negative sentiment on social media about Brexit is associated with reducing the GBP/USD exchange rate. Both studies show the sensitivity of currency markets to sentiment and the importance of monitoring sentiment patterns to be able to predict market movement.

Sezgin and Bayar (2021) contrasted the impact of Brexit on UK stock markets, specifically on the FTSE-100 index, between May 2016 and May 2021. The structural breaks their empirical test revealed for July 2016 and January 2020 meant that the initiation and culmination of Brexit influenced market dynamics significantly. They determined that Brexit increased uncertainty and volatility of markets, triggering steep declines in the FTSE-100 index. Our empirical study confirms similar findings, showcasing how adverse sentiment toward Brexit through social media correlates with a decline in the FTSE-100 index. The two studies agree on the significance of uncertainty brought about by Brexit to market volatility as well as policy intervention and adequate communication necessary to maintain the regulation of reactions from the markets.

Both Zhang et al. (2018) and Sezgin and Bayar (2021) determined that negative Brexit sentiment plays a significant role in currency devaluation as well as stock market declines. Our study agrees with these findings, demonstrating that adverse Brexit-related social media views are associated with a decline in the FTSE-100 index and the GBP/USD exchange rate.

All three studies emphasize the role of public opinion in influencing market actions and the necessity for policymakers to deal with uncertainties to revive market confidence.

While Zhang et al. (2018) focused on the impact of Brexit news on exchange rates, our study goes one step further by using social media sentiments, which provide a more immediate and dynamic representation of public opinion. Sezgin and Bayar (2021) identified specific structural breaks in the FTSE-100 index, whereas our study provides a continuous analysis of sentiment scores and their impact on market performance over time.

By comparing our findings with Zhang et al. (2018) and Sezgin and Bayar (2021), we provide a complete view of Brexit sentiment impacts on financial markets. This comparison puts into perspective the contribution of public sentiment to the movement of markets and the need for effective policy interventions in managing market reactions under the conditions of significant political events like Brexit.

To ensure the robustness of the main results, the authors performed a series of robustness checks.

We used various statistical tests to validate our results. The Durbin–Watson test did not indicate strong autocorrelation in the residuals, and the Jarque–Bera test also confirmed normality of residuals. Finally, Table A10 in  appendix shows the results of the test of Granger causality test. The results indicate that sentiment scores (SCORE01) strongly predict future directional movements of the exchange rates of the Euro against the British Pound (EUR_GBP), the US Dollar against the British Pound (USD_GBP) and stock price levels (CLOSE_PRICE). Specifically, SCORE01 Granger-causes EUR_GBP, USD_GBP, and CLOSE_PRICE, indicating that market sentiment serves as a forward indicator for these economic variables. This implies that sentiment scores, which reflect market confidence or economic sentiment, play a major role in forecasting exchange rates of currency and stock market trends. Having the ability to analyze these forecasting relationships can allow policymakers to build more effective policies to stabilize or manipulate exchange rates, and investors can utilize sentiment scores to make more effective investment policy by predicting currency and market movements. Furthermore, economists can use sentiment scores to improve their forecast models by adding them and creating more accurate exchange rate and market price forecasts. Overall, the results underscore the importance of using sentiment scores as effective economic analysis and decision-making tools.

Our findings are consistent with existing literature, for instance, Sezgin and Bayar (2021) and Zhang et al. (2018), who also reported significant Brexit sentiment effects on stock markets and foreign exchange rates.

Overall, the tests of robustness and the statistical tests of validity instill confidence that our main results are reliable through evidence of stability and consistency through tests and comparisons.

The findings emerging from the current study demonstrate the extremely important role that Brexit-related social media sentiment has had on the financial markets in the UK, and the implications for two major groups.

The strong negative long-run relationship between Brexit sentiment and FTSE100 performance indicates that portfolio managers need to monitor Brexit-related social media sentiment as a leading indicator of systemic risk in relation to UK equities. Social media data could potentially be added to risk models to the cheaper hedging strategies, especially in sectors with EU exposure. There is also no evidence of long-run co-integration with USD/GBP, which implies that currency strategies based on USD exposure should have complementary fundamental components to Brexit sentiment.

The continued impact of public sentiment on exchange rates and the stock market of the GBP/EUR currency pair illustrates the value of effective communication during the politically challenging period. It is worth noting that public sentiment monitoring tools could be developed by the authorities, using the existing indices in the news media as a guide, to help the country detect potential spillovers in the market volatility. Furthermore, the strategy to improve the public's confidence in the economic agreements after Brexit may result in greater benefits in financial stability, given the effect of public market performance.

Although the above analysis presents evidence of long and short-term links between specific Brexit-related sentiments on social media and UK financial markers, there are a number of original directions for further research.

Firstly, the methodology can be improved using the most recent transformer architecture (for instance, BERT-related models that have been fine-tuned on the topic of politics) to allow the model to identify the subtlety of the emotions expressed through irony, sarcasm and other nuanced expressions that bypass the standard lexicon analyses utilized previously. Secondly, the sentiment transmission channels to the markets can be explored after disaggregating the data according to the Twitter accounts that have been identified (i.e., some accounts that belong to the UK, while some are from international realms) and those accounts that belong to the retail level, separated from the institutional level of the economy). Thirdly, analyzing the retweet and response patterns to the messages posted through the Twitter platform can demonstrate the efficacy of the negative messages expressed to the markets relative to the positive messages expressed, considering that the negative messages go viral easily throughout the platforms' networks. Lastly, exploring the sentiment-market connection throughout other related instances of politics, such as the Scottish independence, the Catalan leadership or the recent calls within the European sovereign-states' movements, will determine the universality of the data presented above.

This paper shows that, despite the heavily divided mood among the populace on the Brexit issue and using the hashtag on X/Twitter from 2019 through 2022, the phenomenon remains a profoundly strong factor influencing UK stocks and sterling-euro exchange rates on both short- and long-term bases. Using the ARDL co-integration method applied to a fresh perspective on sentiment analysis utilizing the Loughran–McDonald lexicon, it is clear that the influence of the populace continues unabated on financial market trends years after the Brexit vote took place. These observations not only note the growing role that social media channels play as an agent for political risk impacts on mature nations but also the course corrections laid out above and further down on other avenues of follow-up research suggest that much greater levels of sophistication on methodology and comparison studies may or may not continue along this path and toward other ends.