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The manipulation of Euribor: An analysis with machine learning classification techniques

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The manipulation of Euribor: An analysis with machine learning classification techniques

dc.contributor.author	Herrera, Rubén
dc.contributor.author	Climent Diranzo, Francisco José
dc.contributor.author	Carmona Ibáñez, Pedro
dc.contributor.author	Momparler Pechuan, Alexandre
dc.date.accessioned	2023-04-04T07:41:38Z
dc.date.available	2023-04-05T04:45:05Z
dc.date.issued	2022	es_ES
dc.identifier.citation	Rubén Herrera, Francisco Climent, Pedro Carmona, Alexandre Momparler, The manipulation of Euribor: An analysis with machine learning classification techniques, Technological Forecasting and Social Change, Volume 176, 2022,	es_ES
dc.identifier.uri	https://hdl.handle.net/10550/86018
dc.description.abstract	The manipulation of the Euro Interbank Offered Rate (Euribor) was an affair which had a great impact on in ternational financial markets. This study tests whether advanced data processing techniques are capable of classifying Euribor panel banks as either manipulating or non-manipulating on the basis of patterns found in quotes submissions. For this purpose, panel banks’ daily contributions have been studied and monthly variables obtained that denote different contribution patterns for Euribor panel banks. Thus, in accordance with the court verdict, banks are categorized as manipulating and non-manipulating and Machine Learning classification techniques such as Supervised Learning, Anomaly Detection and Cluster Analysis are applied in order to discriminate between convicted and acquitted banks. The results show that out of seven manipulative banks, five are detected by Machine Learning using Deep Learning algorithms, all five presenting very similar contribution patterns. This is consistent with Anomaly Detection which confirms that several manipulating banks present similar levels of abnormality in their contributions. In addition, the Cluster Analysis facilitates gathering the five most active banks in illicit actions. In conclusion, administrators and supervisors might find these techniques useful to detect potentially illicit actions by banks involved in the Euribor rate-setting process.	es_ES
dc.language.iso	en	es_ES
dc.publisher	Elsevier	es_ES
dc.subject	euribor	es_ES
dc.subject	rate-fixing	es_ES
dc.subject	manipulation	es_ES
dc.subject	collusion	es_ES
dc.subject	panel bank	es_ES
dc.subject	machine learning	es_ES
dc.subject	classification	es_ES
dc.title	The manipulation of Euribor: An analysis with machine learning classification techniques	es_ES
dc.type	journal article	es_ES
dc.subject.unesco	UNESCO::CIENCIAS ECONÓMICAS	es_ES
dc.identifier.doi	10.1016/j.techfore.2021.121466.	es_ES
dc.accrualmethod	S	es_ES
dc.embargo.terms	0 days	es_ES