MSCA-ITN BigDataFinance project starts in October 2015!

EU -project BigDataFinance (2015–2019) provides doctoral training and research in sophisticated data-driven risk management and research at the crossroads of Finance and Big Data for 13 researchers. The budget (EU’s contribution) is around 3,460,000 EUR.

The main objectives are:

i) to meet an increasing commercial demand for well-trained researchers experienced in both Big Data techniques and Finance

ii) to develop and implement new quantitative and econometric methods for empirical finance and risk management with large and complex datasets.

To achieve the objectives, the emphasis is put on exploiting big data techniques to manage and use datasets that are too large and complex to process with conventional methods. Banks and other financial institutions must be able to manage, process, and use massive heterogeneous data sets in a fast and robust manner for successful risk management; nonetheless, financial research and training has been slow to address the data revolution. Compared to the USA, Europe is still at an early stage of adopting Big Data technologies and services. Immediate action is required to seize opportunities to exploit the huge potential of Big Data within the European financial world.

This world-class network consists of eight academic participants and six companies, representing banks, asset management companies, and data and solution providers. The proposed research is relevant both academically and practically, because the program is built around real challenges faced both by the academic and private sector partners. BigDataFinance provides the European financial community with specialists with state-of-the-art skills in finance and data-analysis to facilitate the adoption of reliable and realistic methods in the industry. This increases the financial strength of banks and other financial institutions in Europe.

BigDataFinance, “Training for Big Data in Financial Research and Risk Management”, is a H2020 Marie Sklodowska-Curie Innovative Training Network.

By selecting only the highest caliber PhD students for BigDataFinance programme, we have already taken the first step of the recruitment process. Positions for the following research projects will be open at EURAXESS portal:

(WP1) Data Science in Finance

• Distributed and Real-time Machine Learning for Financial Data Analysis
  Supervisor: Professor John Keane, University of Manchester / john.keane(at)manchester.ac.uk
• Divide and Conquer Deep Learning for Big Data in Finance
  Supervisor: Professor Moncef Gabbouj, Tampere University of Technology / moncef.gabbouj(at)tut.fi
• Deep Knowledge Extraction from Financial, Business and Social Text
  Supervisor: Doctor Marko Grobelnik, Jožef Stefan Institute / marko.grobelnik(at)ijs.si

(WP2) Complex Networks in Finance

• Complex Network Analysis in Stock Markets
  Supervisor: Professor Juho Kanniainen, Tampere University of Technology / juho.kanniainen(at)tut.fi
• Systemic Risk and Financial Networks
  Supervisor: Professor Stefano Battiston, University of Zurich stefano.battiston(at)bf.uzh.ch

(WP3) Financial Econometrics with High-Frequency Data and News Announcements

• Modelling and Forecasting the Joint Distribution of Asset Returns with News
  Supervisor: Professor Niels Haldrup, Aarhus University / nhaldrup(at)econ.au.dk
• Identifying the Structure of Volatility using High-Frequency and News Data
  Supervisor: Professor Kim Christensen, Aarhus University / kim(at)econ.au.dk
• Order Books Dynamics and Announcement Effects during Financial Crisis
  Supervisor: Professor Juho Kanniainen, Tampere University of Technology / juho.kanniainen(at)tut.fi
• Characterising Financial Markets from Event-driven Perspective
  Supervisor: Doctor Marko Grobelnik, Jožef Stefan Institute / marko.grobelnik(at)ijs.si

(WP4) Big Data Applications in Finance

• Financial Market Mood Indexes
  Supervisor: Dr. Xiaojun Zeng, University of Manchester / x.zeng(at)manchester.ac.uk
• Smart Beta Investing – A Data-Driven Strategy to Exploit Systematic Risk Factors in the Financial Markets
  Supervisor: CIO Erik Vynckier, Alliance-Bernstein Ltd / erik.vynckier(at)alliancebernstein.com, Prof. John Keane, University of Manchester
• High Frequency Trading Risk Management Tools Based on Scaling Laws
  Supervisor: Professor Stefano Battiston, University of Zurich stefano.battiston(at)bf.uzh.ch
• Machine Learning Algorithms for Risk Management in Trading Activities
  Supervisors: Dr. Drona Kandhai, ING Bank / drona.kandhai(at)ingbank.com, Prof. Peter Sloot, University of Amsterdam

For further information on the project and ESR-recruitment, please contact check our Jobs -page or contact the Coordinator of the Network:

Artikkeli Project starts on the 1st of October julkaistiin ensimmäisen kerran BigDataFinance.