Title

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

Document Type

Article

Source of Publication

International Review of Financial Analysis

Publication Date

11-1-2020

Abstract

© 2020 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.

ISSN

1057-5219

Publisher

Elsevier Inc.

Volume

72

First Page

101590

Disciplines

Business

Keywords

Agent-based finance, Banking crises, Brexit, Happiness economics, Machine learning, Naive Bayes classifier, Neural networks

Scopus ID

85091504358

Indexed in Scopus

yes

Open Access

no

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