=Paper=
{{Paper
|id=Vol-1941/MIDAS2017_preface
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-1941/MIDAS2017_preface.pdf
|volume=Vol-1941
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==None==
https://ceur-ws.org/Vol-1941/MIDAS2017_preface.pdf
MIDAS 2017: The 2nd Workshop on
MIning DAta for financial applicationS
Ilaria Bordino1 , Guido Caldarelli2 , Fabio Fumarola1 ,
Francesco Gullo1 , and Tiziano Squartini2
1
UniCredit, R&D Department, Italy
{ilaria.bordino, fabio.fumarola, francesco.gullo}@unicredit.eu
2
IMT Institute for Advanced Studies Lucca, Italy
{guido.caldarelli, tiziano.squartini}@imtlucca.it
1 Motivation
Like the famous King Midas, popularly remembered in Greek mythology for his
ability to turn everything he touched with his hand into gold, 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 behavior in a previously
unimaginable fashion [8, 14]. Recent research has shown that by properly mod-
eling and analyzing these massive datasets, for instance representing them as
network structures [2, 4], it is possible to gain useful insights into the evolution
of the systems considered (i.e., trading [13], disease spreading [1, 12], political
elections [5]). 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 antici-
pate 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 [9]. For example, in the Web context, changes in the frequency with
which users browse news or look for certain terms on search engines such as
Google have been correlated with product trends [7], the level of activity in cer-
tain given industries, unemployment rates, or car and home sales [6], as well as
stock-market trade volumes and price movements [3, 11, 15]. Other core applica-
tion scenarios include forecasting the stock market, predicting bank bankrupt-
cies, understanding and managing financial risk, trading futures, credit rating,
loan management, bank customer profiling [9]. Despite its well-recognized rel-
evance and some recent related efforts [10], data mining in finance is still not
stably part of the main stream of data-mining conferences. This makes the topic
particularly appealing for a workshop proposal, whose small, interactive, and
possibly interdisciplinary context provides a unique opportunity to advance re-
search in a stimulating but still quite unexplored field.
2 Objectives and topics
The aim of the 2nd Workshop on MIning DAta for financial applicationS (MI-
DAS 2017), held in conjunction with the 2017 European Conference on Ma-
chine Learning and Principles and Practice of Knowledge Discovery in Databases
(ECML-PKDD 2017), Skopje, Macedonia, September 18–22, 2017, is to discuss
challenges, potentialities, and applications of leveraging data-mining tasks to
tackle problems in the financial domain. The workshop provides a premier fo-
rum for sharing findings, knowledge, insights, experience and lessons learned
from mining data generated in various domains. The intrinsic interdisciplinary
nature of the workshop promotes the interaction between computer scientists,
physicists, mathematicians, economists and financial analysts, thus paving the
way for an exciting and stimulating environment involving researchers and prac-
titioners from different areas.
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
3 Outcomes
MIDAS 2017 was structured as a half-day workshop. We encouraged submissions
of regular papers (long or short), and extended abstracts. Regular papers may
be up to 12 pages (long papers) or 6 pages (short papers), and report on novel,
unpublished work that might not be mature enough for a conference or journal
submission. Extended abstracts may be up to 2 pages long, and present work-in-
progress, recently published work fitting the workshop topics, or position papers.
All submitted papers were peer-reviewed by three reviewers from the program
committee, and selected on the basis of these reviews. MIDAS 2017 received 9
submissions, among which 5 papers were accepted (4 long regular papers and
1 extended abstract), with an acceptance rate of about 55%. The competitive
acceptance rate resulted in a high-quality and exciting program.
The program was enriched by an invited speaker: Prof. João Gama, Labora-
tory of Artificial Intelligence and Decision Support, and Faculty of Economics,
University of Porto (Portugal), who gave a talk titled “Evolving Data, Evolving
Models in Economy and Finance”.
4 Program Committee
The scientific significance of the workshop is assured by a Program Committee
which includes research scholars coming from different countries, and widely
recognized as experts in the topics of interest of the workshop:
– Aris Anagnostopoulos, Sapienza University, Italy
– Annalisa Appice, University of Bari, Italy
– Ioannis Arapakis, Telefonica Research, Spain
– Argimiro Arratia Quesada, Universitat Politécnica de Catalunya, Spain
– Xiao Bai, Yahoo!, USA
– Annalina Caputo, Trinity College Dublin, Ireland
– Diego Ceccarelli, Bloomberg LP, UK
– Carlotta Domeniconi, George Mason University, USA
– Debora Donato, StumbleUpon Inc, USA
– Ana Maria Freire Veiga, Universitat Pompeu Fabra, Spain
– Ruth Garcia Gavilanes, Skyscanner, UK
– Sara Hajian, Eurecat, Spain
– Roberto Interdonato, University of Calabria, Italy
– Andreas Kaltenbrunner, Eurecat, Spain
– Juho Kanniainen, Tampere University of Technology, Finland
– Dragi Kocev, Jožef Stefan Institute, Slovenia
– Nicolas Kourtellis, Telefonica Research, Spain
– Iordanis Koutsopoulos, University of Thessaly, Greece
– Fabiana Lanotte, Digital Transformation Team, Italy
– Donato Malerba, University of Bari, Italy
– Matteo Manca, Eurecat, Spain
– Yelena Mejova, Qatar Computing Research Institute, Qatar
– Davide Mottin, Hasso Plattner Institute, Germany
– Giovanni Ponti, ENEA, Italy
– Aleksandra Rashkovska, Jožef Stefan Institute, Slovenia
– Ali Caner Türkmen, Boǧaziçi University, Turkey
– Antti Ukkonen, Finnish Institute of Occupational Health, Finland
– Edoardo Vacchi, UniCredit, Italy
– Tim Weninger, University of Notre Dame, USA
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