Details

Autor(en) / Beteiligte

• Polyzos, Stathis
• Samitas, Aristeidis
• Katsaiti, Marina-Selini

Titel

Who is unhappy for Brexit? A machine-learning, agent-based study on financial instability

Ist Teil von

• International review of financial analysis, 2020-11, Vol.72, p.101590, Article 101590

Ort / Verlag

Elsevier Inc

Erscheinungsjahr

2020

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• In this paper, we assess the happiness cost of Brexit in the UK and the EU, using data from the Gallup World Poll. We implement a two-stage learning machine, using a naive Bayes classifier to extract happiness preferences of the population and then passing these onto an artificial neural network of attributes to generate dynamic happiness functions for each household, on an agent-based modelling framework. We find that there is a significant long-run cost in terms of both happiness and unemployment, which primarily affects the most vulnerable portion of the population. In addition, despite the expected instability in City's financial centre, the UK financial sector seems to be well equipped to deal with the repercussions, thus minimising the welfare costs for the country. Our findings extend the discussion of the economic costs of Brexit, by adding the welfare cost of the ensuing financial instability. •We examine the effects of Brexit on societal happiness.•We Implement a naïve Bayes classifier on Gallup World Poll data to extract happiness preferences.•Simulations using an artificial neural network and an agent-based model show that there are significant long-run costs.•The vulnerable employee class assumes the biggest burden both in happiness and unemployment.•The negative effects will only be visible in the long run.