Machine Learning and Network Analytics in Finance Minitrack

This minitrack covers contributions on the use and extension of machine learning and network analytics in financial and econometric analysis. Advanced analytics has been present in the finance literature for decades. However, as a result of developments in the last 15 years, such as the emergence of technological advances and the 2008 financial crisis, the importance of contributions utilizing these techniques have increased significantly. In recent years, it has become crucial to be able to tackle problems in the financial context involving an unprecedented amount of both structured and unstructured data. Likewise, the past financial turmoil with global repercussions has stimulated an interest in advanced approaches for systemic risk measurement. Machine learning and network analysis techniques offer tools for the analysis of a wide range of topics in interconnected financial markets. The purpose of this minitrack is to disseminate significant results on new models relying of machine learning and network analytics, together with relevant and important applications of these models. The scope of the potential contributions to the minitrack is broad and covers most recent advances in analytics, big data, visual analytics, soft computing and network analytics in a financial context.

The topics covered by the minitrack include (but are not limited to):

  • Predictive analytics including firm and country-level early-warning indicators and models
  • Network analysis, contagion models and topology hierarchies in financial markets
  • Systemic risk measures and financial stress indices
  • Stress-testing, scenario analysis and simulation
  • Text analytics for semantic and sentiment analysis
  • Machine learning in portfolio optimization, value at risk and overall risk management

Minitrack Co-Chairs:

Peter Sarlin (Primary Contact)
Hanken School of Economics, Finland
Email: peter@risklab.fi

József Mezei
Åbo Akademi University, Finland
Email: jmezei@abo.fi

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