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MIDAS 2022 : The 7th Workshop on MIning DAta for financial applicationS


When Sep 23, 2022 - Sep 23, 2022
Where Grenoble, France
Submission Deadline Jul 3, 2022
Notification Due Jul 18, 2022
Final Version Due Jul 29, 2022
Categories    data mining   machine learning   finance

Call For Papers

MIDAS 2022 - The 7th Workshop on MIning DAta for financial applicationS
September 23, 2022
Grenoble, France - HYBRID EVENT

in conjunction with

ECML-PKDD 2022 - The European Conference on
Machine Learning and Principles and Practice of Knowledge Discovery in Databases
September 19-23, 2022
Grenoble, France - HYBRID EVENT

The ECML-PKDD 2022 conference and all its satellite events -- including MIDAS 2022 --
will take place according to a *hybrid* modality.
This means that anyone can attend the conference (and MIDAS 2022) either in-person
(using the standard registration fee) or online (using the videoconference registration fee):
However, for speakers, face-to-face interactions and discussions are much more effective.
So, we strongly encourage in-person attendance at least for the presenters of the accepted papers.

We invite submissions to the 7th MIDAS Workshop on MIning DAta for financial applicationS,
to be held in conjunction with ECML-PKDD 2022 - European Conference on Machine Learning and
Principles and Practice of Knowledge Discovery in Databases.

Like the famous King Midas, popularly remembered in Greek mythology for his ability to turn
everything he touched with his hand into gold, we believe that the wealth of data generated
by modern technologies, with widespread presence of computers, users and media connected by
Internet, is a goldmine for tackling a variety of problems in the financial domain.

Nowadays, people's interactions with technological systems provide us with gargantuan amounts
of data documenting collective behaviour in a previously unimaginable fashion.
Recent research has shown that by properly modeling and analyzing these massive datasets, or
instance representing them as network structures it is possible to gain useful insights into
the evolution of the systems considered (i.e., trading, disease spreading, political elections).

Investigating the impact of data arising from today's application domains on financial decisions
may be of paramount importance. Knowledge extracted from data can help gather critical information
for trading decisions, reveal early signs of impactful events (such as stock market moves), or
anticipate catastrophic events (e.g., financial crises) that result from a combination of actions,
and affect humans worldwide.

The importance of data-mining tasks in the financial domain has been long recognized.
Core application scenarios include correlating Web-search data with financial decisions,
forecasting stock market, predicting bank bankruptcies, understanding and managing financial risk,
trading futures, credit rating, loan management, bank customer profiling.

The MIDAS workshop is aimed at discussing challenges, potentialities, and applications of
leveraging data-mining tasks to tackle problems in the financial domain.
The workshop provides a premier forum for sharing findings, knowledge, insights, experience
and lessons learned from mining data generated in various application domains.
The intrinsic interdisciplinary nature of the workshop constitutes an invaluable opportunity
to promote interaction between computer scientists, physicists, mathematicians, economists and
financial analysts, thus paving the way for an exciting and stimulating environment involving
researchers and practitioners from different areas.

We encourage submission of papers on the area of data mining for financial applications.
Topics of interest include, but are not limited to:

- Forecasting the stock market
- Trading models
- Discovering market trends
- Predictive analytics for financial services
- Network analytics in finance
- Planning investment strategies
- Portfolio management
- Understanding and managing financial risk
- Customer/investor profiling
- Identifying expert investors
- Financial modeling
- Measures of success in forecasting
- Anomaly detection in financial data
- Fraud detection
- Data-driven anti money laundering
- Discovering patterns and correlations in financial data
- Text mining and NLP for financial applications
- Financial network analysis
- Time series analysis
- Pitfalls identification
- Financial knowledge graphs
- Reinforcement learning in the financial domain
- Explainable AI in financial services

We invite submissions of either regular papers (long or short), and extended abstracts:
- Long regular papers: up to 15 pages long (in the Springer LNCS style,,
reporting on novel, unpublished work that might not be mature enough for a conference
or journal submission.
- Short regular papers: up to 8 pages long, presenting work-in-progress.
- Extended abstracts: up to 4 pages long, referring to recently published work on
the workshop topics, position papers, late-breaking results, or emerging research problems.
All page limits are intended *excluding references*, which may take as many additional pages as preferred.

Contributions should be submitted in PDF format, electronically, using the workshop
submission site at
Papers must be written in English and formatted according to the ECML-PKDD 2022
submission guidelines available at

Submitted papers will be peer-reviewed and selected on the basis of these reviews.
*If accepted, at least one of the authors must attend the workshop to present the work*.

Accepted papers will be part of the ECML-PKDD 2022 workshop post-proceedings,
which will be published as a Springer LNCS volume.
The proceedings of the past three editions of the workshop are available here:
- 2021:
- 2020:
- 2019:

Submission deadline: July 3, 2022
Acceptance notification: July 18, 2022
Early registration: July 22, 2022
Camera-ready deadline: July 29, 2022
Workshop date: September 23, 2022

Jose A. Rodriguez-Serrano, BBVA AI Factory

Aris Anagnostopoulos, Sapienza University
Annalisa Appice, University of Bari
Argimiro Arratia, Universitat Politècnica de Catalunya
Davide Azzalini, Politecnico of Milan
Fabio Azzalini, Politecnico of Milan
Xiao Bai, Yahoo Research
Luca Barbaglia, JRC - European Commission
Luigi Bellomarini, Banca d'Italia
Eric Benhamou, AI for Alpha
Livia Blasi, Banca d'Italia
Ludovico Boratto, University of Cagliari
Cristian Bravo, Western University
Jeremy Charlier, National Bank of Canada
Daniela Cialfi, University of Chieti-Pescara
Sergio Consoli, JRC - European Commission
Jacopo De Stefani, TU Delft
Carlotta Domeniconi, George Mason University
Wouter Duivesteijn, Eindhoven University of Technology
Edoardo Galimberti, Independent Researcher
Cuneyt Gurcan Akcora, University of Manitoba
Roberto Interdonato, CIRAD
Anna Krause, University of Wurzburg
Malte Lehna, Fraunhofer IEE
Domenico Mandaglio, University of Calabria
Yelena Mejova, ISI Foundation
Aldo Nassigh, UniCredit
Roberta Pappadà, University of Trieste
Giulia Preti, ISI Foundation
David Saltiel, AI for Alpha
Daniel Schloer, University of Wurzburg
Edoardo Vacchi, Red Hat
Elaine Wah, BlackRock AI Labs

Ilaria Bordino, UniCredit, Italy
Ivan Luciano Danesi, UniCredit, Italy
Francesco Gullo, UniCredit, Italy
Giovanni Ponti, ENEA, Italy
Lorenzo Severini, UniCredit, Italy

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