September,19 2021 @ 18:09pm - January,15 2019 @ 0:00am


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The Research Initiative QMI is a research project of Université Paris-Dauphine structured around the following objectives:

  • Promoting quantitative research for asset management;
  • Facilitating know-how transmission between academic researchers and asset managers; and
  • Promoting a positive image of quantitative-based asset management throught education.


Submission procedure

The deadline for paper project submission is January 15, 2019. All projects will be reviewed by the QMI board.

Complete the enclosed form and join a resume for each application. These documents must be:

  • in English in PDF format with a maximum size of 3MB;
  • submitted by e-mail to:
  • marked “Project application – IdR FdR QMI 2019“.

Authors will be notified about the acceptance of their project by the end of March, 2019. Accepted projects will be awarded €10,000. 50% will be paid one week after the notification of acceptance. The final version of the paper should be sent by January 31, 2020 by email to: The remaining 50% will be paid at the final paper version acceptation.

The papers will be presented in the next Quant Vision Summit event, tentatively scheduled for spring 2020. Academics, practioners and allocators will be invited to participate in this conference. In addition to presenting their paper, the author(s) of the project commit(s) to mention in an acknowledgement section the following sentence : “The author(s) gratefully acknowledge(s) the support of the FdR: ‘Quantitative Management Initiative'”.
Contact information

Pauline de Saint Quentin – +33 (0)1 41 16 76 19
Université Paris Dauphine
Place du Maréchal de Lattre de Tassigny
75775 Paris Cedex 16 – FRANCE
To download the submission file, click here



The QMI committee requests submissions of high-quality theoretical or empirical research projects on topics including (but not necessarily limited to) the following:

Artificial Intelligence

  • Statistical signal processing
  • Big data, machine learning and the new sources of information
  • Machine learning for parameter estimation
  • Momentum risk premia
  • Behavioral finance & prices

Risk and crowding

  • Risk disaggregation and portfolio allocation
  • High order moments and portfolio allocation
  • Risk equity models
  • Contagion and fund flows
  • Behavioral finance & prices
  • Behavioral finance & derivative instruments

Implementation challenges

  • Market liquidity
  • Algo trading
  • Behavioral finance & derivative instruments