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MIDAS

The First Workshop on MIning DAta for financial applicationS

September 19, 2016 - Riva del Garda, Italy


co-located with


ECML-PKDD 2016

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

September 19-23, 2016 - Riva del Garda, Italy

 




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We invite submissions to the MIDAS Workshop on MIning DAta for financial applicationS, to be held in conjunction with ECML-PKDD 2016 - 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.

 


 

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

 

Fabrizio LilloScuola Normale Superiore, Pisa, Italy



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PROGRAM

09:00 - 10:40 SESSION I

09:00 - 09:10 Opening

09:10 - 10:10 Invited Talk: Fabrizio Lillo, Scuola Normale Superiore 

10:10 - 10:40 Marco Bianchetti, Davide Emilio Galli, Camilla Ricci, Angelo Salvatori and Marco Scaringi. Brexit or Bremain? Evidence from bubble analysis


10:40 - 11:10 Coffee break 


11:10 - 12:40 SESSION II

11:10 - 11:40 Andrea Pazienza, Sabrina Francesca Pellegrino, Stefano Ferilli and Floriana Esposito. Clustering underlying stock trends via non-negative matrix factorization

11:40 - 12:10 Argimiro Arratia and Marti Renedo. Clustering of exchange rates and their dynamics under different dependence measures

12:10 - 12:40 Huisu Jang and Jaewook Lee. A general framework for building machine learning models for pricing american index options with no-arbitrage


12:40 - 14:20 Lunch break


14:20 - 15:50 SESSION III

14:20 - 15:20 Invited Talk: Marcello Paris, UniCredit, R&D

15:20 - 15:50 Argimiro Arratia, Alejandra Cabaña and Àlex Serès. Towards a sharp estimation of transfer entropy for identifying causality in financial time series


15:50 - 16:20 Coffee break 


16:20 - 18:00 SESSION IV

16:20 - 16:50 Salvatore Cuomo, Pasquale De Michele, Vittorio Di Somma and Giovanni Ponti. A probabilistic approach for financial IoT data

16:50 - 17:20 Alya Al Nasseri, Faek Menla Ali and Allan Tucker. Good news and bad news: using machine learning to identify investor sentiment reaction to return news

17:20 - 17:50 Ali Caner Türkmen. Sentiment extraction from financial public disclosure documents

17:50 - 18:00 Concluding Remarks





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