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

We invite submissions to the 4th MIDAS Workshop on MIning DAta for financial applicationS, to be held in conjunction with ECML-PKDD 2019 - 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, 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.

PAST EDITIONS

MIDAS 2018

MIDAS 2017

MIDAS 2016

TOPICS OF INTEREST

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

INVITED SPEAKERS

Marcello Paris, Unicredit, R&D Department.
"Homology-based learning: shapes for financial time series"

PROGRAM

!!! NEW !!! --- online proceedings

9:00 - 10:30 SESSION I
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9:00 - 9:10 OPENING
9:10 - 10:05 [INVITED TALK] Marcello Paris, Unicredit, R&D Department. "Homology-based learning: shapes for financial time series"
10:05 - 10:30 [FULL PAPER] Jeremy Charlier, Gaston Ormazabal, Radu State, Jean Hilger. "MQLV: Optimal Policy of Money Management in Retail Banking with Q-Learning" SLIDES

10:30 - 11:00 Coffee break

11:00 - 12:40 SESSION II
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11:00 - 11:25 [FULL PAPER] Allison Koenecke, Amita Gajewar. "Curriculum Learning in Deep Neural Networks for Financial Forecasting" SLIDES
11:25 - 11:50 [FULL PAPER] Rafael Van Belle, Sandra Mitrovic, Jochen De Weerdt. "Representation Learning in Graphs for Credit Card Fraud Detection" SLIDES
11:50 - 12:15 [FULL PAPER] Tesi Aliaj, Aris Anagnostopoulos, Stefano Piersanti. "Firms Default Prediction with Machine Learning" SLIDES
12:15 - 12:40 [FULL PAPER] Argimiro Arratia, Eduardo Sepulveda. "Convolutional Neural Networks, Image recognition and Financial time series forecasting" SLIDES

12:40 - 14:00 Lunch break

14:00 - 15:05 SESSION III
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14:00 - 14:25 [FULL PAPER] Thomas Kellermeier, Tim Repke, Ralf Krestel. "Mining Business Relationships from Stocks and News" SLIDES
14:25 - 14:50 [FULL PAPER] Saumya Bhadani, Ishan Verma, Lipika Dey. "Mining Financial Risk Events from News and Assessing their Impact on Stocks" - This presentation is available upon request. Email the authors in case you are interested.
14:50 - 15:05 [EXTENDED ABSTRACT] Luca Barbaglia, Sergio Consoli, Sebastiano Manzan. "Monitoring the Business Cycle with Fine-grained, Aspect-based Sentiment Extraction from News" SLIDES

15:05 - 15:20 Coffee break

15:20 - 16:20 SESSION IV
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15:20 - 15:45 [FULL PAPER] Xiangru Fan, Xiaoqian Wei, Di Wang, Wen Zhang, QI Wu. "Multi-step Prediction of Financial Asset Return Volatility Using Parsimonious Autoregressive Sequential Model" SLIDES
15:45 - 16:00 [EXTENDED ABSTRACT] Luca Tiozzo Pezzoli, Sergio Consoli, Elisa Tosetti. "Big Data Financial Sentiment Analysis in the European Bond Markets" SLIDES
16:00 - 16:15 [SHORT PAPER] Giuseppe Santomauro, Daniela Alderuccio, Fiorenzo Ambrosino, Andrea Fronzetti Colladon, Silvio Migliori. "A Brand Scoring System for Cryptocurrencies based on Social Media Data" SLIDES
16:15 - 16:20 CONCLUDING REMARKS