The Second Workshop on MIning DAta for financial applicationS

September 18, 2017 - Skopje, Macedonia

co-located with


European Conference on Machine Learning and Principles and Practice of Knowledge Discovery



We invite submissions to the MIDAS Workshop on MIning DAta for financial applicationS, to be held in conjunction with ECML-PKDD 2017 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery.

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, for 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.


MIDAS 2016



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

- Discovering patterns and correlations in financial data

- Text mining and NLP for financial applications

- Financial network analysis

- Time series analysis

- Pitfalls identification




João Gama - Laboratory of Artificial Intelligence and Decision Support and Faculty of Economics (University of Porto)


14:00 - 15:40 SESSION I


14:00 - 14:10 Opening

14:10 - 15:10 Invited Talk: Joao Gama, University of Porto. “Evolving Data, Evolving Models in Economy and Finance”

15:10 - 15:35 Flora Amato, Vincenzo Moscato, Antonio Picariello, Giovanni Ponti and Giancarlo Sperlì. “Influence Analysis in Business Social Media”

15:40 - 16:00 Coffee break 

16:00 - 17:40 SESSION II


16:00 - 16:25 Davide Azzalini, Fabio Azzalini, Davide Greco, Mirjana Mazuran and Letizia Tanca. “Event recognition strategies applied in the Mercurio project”

16:25 - 16:50 Simon van der Zon, Oren Zeev Ben Mordehai, Tom Vrijdag, Werner van Ipenburg, Jan Veldsink, Wouter Duivesteijn and Mykola Pechenizkiy. “BoostEMM - Transparent Boosting using Exceptional Model Mining”

16:50 - 17:15 Jérémy Charlier, Sofiane Lagraa, Radu State and Jérôme François. “Profiling Smart Contracts Interactions Tensor Decomposition and Graph Mining”

17:15 - 17:40 Jacopo De Stefani, Olivier Caelen, Dalila Hattab and Gianluca Bontempi. “Machine Learning for Multi-step Ahead Forecasting of Volatility Proxies”