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Game theory & data mining model for price dynamics in financial institutions

Game theory & data mining model for price dynamics in financial institutions
Game theory & data mining model for price dynamics in financial institutions
To model market dynamics is a challenge that has attracted the interest of practitioners and researchers alike. This problem has been addressed from the perspective of Game Theory, in models that explicitly include profit-maximization schemes for the companies, and also from the point of view of Data Mining, with models that consider multivariate functions to model customer demands and related phenomena. In this work we present a two-stage model that unifies both approaches. A hybrid neural network-support vector machines model estimates multiclass demand at a customer level, which then serves as input for a game-theoretic model that considers the strategic relationships between costs and demands in pricing schemes for Bertrand equilibria. The model was applied to a database in a loan-granting institution with good results. New knowledge discovered includes insights about cost structures and the institutions' competitive behavior, providing new business opportunities
1-8
Bravo, Cristian
b22c4145-644e-40ee-85d8-431c59c3c71b
Figueroa, Nicolas
87b697c8-be09-4c3d-b173-fd9f74b5568f
Weber, Richard
da9918d6-bc84-4c98-8ffe-2aaf7b58cf1b
Bravo, Cristian
b22c4145-644e-40ee-85d8-431c59c3c71b
Figueroa, Nicolas
87b697c8-be09-4c3d-b173-fd9f74b5568f
Weber, Richard
da9918d6-bc84-4c98-8ffe-2aaf7b58cf1b

Bravo, Cristian, Figueroa, Nicolas and Weber, Richard (2010) Game theory & data mining model for price dynamics in financial institutions. The 2010 International Joint Conference on Neural Networks (IJCNN). pp. 1-8 . (doi:10.1109/IJCNN.2010.5596654).

Record type: Conference or Workshop Item (Paper)

Abstract

To model market dynamics is a challenge that has attracted the interest of practitioners and researchers alike. This problem has been addressed from the perspective of Game Theory, in models that explicitly include profit-maximization schemes for the companies, and also from the point of view of Data Mining, with models that consider multivariate functions to model customer demands and related phenomena. In this work we present a two-stage model that unifies both approaches. A hybrid neural network-support vector machines model estimates multiclass demand at a customer level, which then serves as input for a game-theoretic model that considers the strategic relationships between costs and demands in pricing schemes for Bertrand equilibria. The model was applied to a database in a loan-granting institution with good results. New knowledge discovered includes insights about cost structures and the institutions' competitive behavior, providing new business opportunities

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More information

Published date: 2010
Venue - Dates: The 2010 International Joint Conference on Neural Networks (IJCNN), 2010-01-01
Organisations: Southampton Business School

Identifiers

Local EPrints ID: 396684
URI: https://eprints.soton.ac.uk/id/eprint/396684
PURE UUID: 9eff7f3c-f853-41fe-abd7-64755836f3b9
ORCID for Cristian Bravo: ORCID iD orcid.org/0000-0003-1579-1565

Catalogue record

Date deposited: 10 Jun 2016 10:47
Last modified: 20 Jul 2019 00:48

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Contributors

Author: Cristian Bravo ORCID iD
Author: Nicolas Figueroa
Author: Richard Weber

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