Efficiency, Spreading of Crisis and Algorithmic Policy Evolution

SCHEME: CORE

CALL: 2011

DOMAIN: IS - Development and Performance of the Financial Systems

FIRST NAME: Theoharry

LAST NAME: Grammatikos

INDUSTRY PARTNERSHIP / PPP: No

INDUSTRY / PPP PARTNER:

HOST INSTITUTION: University of Luxembourg

KEYWORDS: Sovereign debt crisis, financial policy, efficiency, information mining and machine learning, linguistic processing

START: 2012-05-01

END: 2015-04-30

WEBSITE: https://www.uni.lu

Submitted Abstract

ESCAPE applies Data Mining and Machine Learning methods on publicly available news sources to document in a measurable way the structure and evolution of Europe’s financial policy to address the on-going threat to the Euro-zone stability. How do the important euro policy players present themselves in the pallet of the policies map? Are there subgroups with similar positions? How coherent are these groups among themselves? Are there dominant players in each group? How different are the different group positions? One expects that the euro players eventually will reach a consensus policy to stem the risk threatening the EURO. Documenting in a measurable way how the different policy positions converge over time should provide additional insights into the complex process of (finan-cial) policy evolution.Financial policy ultimately affects capital markets but, as the recent crisis has highlighted, capital markets may force or extract policy concessions. Understanding the interplay of financial policy formulation and capital market ex-pectations, therefore, is extremely important for the effectiveness of policy responses and eventually for the stability of the financial system. ESCAPE provides statistical evidence on whether capital markets lead or react concurrently to the financial policy evolution. It is expected to shed light on the powerful role of the “invisible hand” of capital markets in extracting desired policies from politicians.

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