Bordino, I

Caldarelli, G

Fumarola, F

Gullo, F

Squartini, T

English

Archivio della ricerca della Scuola IMT Alti Studi Lucca

MIDAS 2016: The 1st Workshop onMIning DAta for financial applicationSIlaria Bordino1, Guido Caldarelli2, Fabio Fumarola1,Francesco Gullo1, and Tiziano Squartini21 UniCredit, R&D Department, Italy{ilaria.bordino, fabio.fumarola, francesco.gullo}@unicredit.eu2 IMT Institute for Advanced Studies Lucca, Italy{guido.caldarelli, tiziano.squartini}@imtlucca.it1 MotivationLike the famous King Midas, popularly remembered in Greek mythology for hisability to turn everything he touched with his hand into gold, the wealth ofdata generated by modern technologies, with widespread presence of computers,users and media connected by Internet, is a goldmine for tackling a variety ofproblems in the financial domain.Nowadays, people’s interactions with technological systems provide us withgargantuan amounts of data documenting collective behavior in a previouslyunimaginable fashion [8, 14]. Recent research has shown that by properly mod-eling and analyzing these massive datasets, for instance representing them asnetwork structures [2, 4], it is possible to gain useful insights into the evolutionof the systems considered (i.e., trading [13], disease spreading [1, 12], politicalelections [5]). Investigating the impact of data arising from today’s applicationdomains on financial decisions may be of paramount importance. Knowledgeextracted 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 ofactions, and affect humans worldwide.The importance of data-mining tasks in the financial domain has been longrecognized [9]. For example, in the Web context, changes in the frequency withwhich users browse news or look for certain terms on search engines such asGoogle 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 asstock-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 notstably part of the main stream of data-mining conferences. This makes the topicparticularly appealing for a workshop proposal, whose small, interactive, andpossibly interdisciplinary context provides a unique opportunity to advance re-search in a stimulating but still quite unexplored field.2 Objectives and topicsThe aim of the 1st Workshop on MIning DAta for financial applicationS (MI-DAS 2016), held in conjunction with the 2016 European Conference on Ma-chine Learning and Principles and Practice of Knowledge Discovery in Databases(ECML-PKDD 2016), Riva del Garda, Italy, September 19–23, 2016, is to dis-cuss challenges, potentialities, and applications of leveraging data-mining tasksto tackle problems in the financial domain. The workshop provides a premierforum for sharing findings, knowledge, insights, experience and lessons learnedfrom mining data generated in various domains. The intrinsic interdisciplinarynature of the workshop promotes the interaction between computer scientists,physicists, mathematicians, economists and financial analysts, thus paving theway 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 identification3 OutcomesMIDAS 2016 was structured as a full-day workshop. We encouraged submissionsof regular papers (long or short), and extended abstracts. Regular papers maybe 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 journalsubmission. 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 programcommittee, and selected on the basis of these reviews. MIDAS 2016 received 13submissions, among which 8 papers were accepted (5 long regular papers, 1 shortregular paper, 2 extended abstracts), with an acceptance rate of about 61%. Thecompetitive acceptance rate resulted in a high-quality and exciting program.The program was enriched by two invited speakers: Prof. Fabrizio Lillo,Scuola Normale Superiore, Pisa (Italy), who gave a talk titled “Detection ofintensity bursts using Hawkes processes: an application to high frequency finan-cial data”, and Dr. Marcello Paris, UniCredit, R&D Department, Rome (Italy),who gave a talk titled “The Geometry of Financial Markets: Topological DataAnalysis”.4 Program CommitteeThe scientific significance of the workshop is assured by a Program Committeewhich includes research scholars coming from different countries, and widelyrecognized as experts in the topics of interest of the workshop:– Aris Anagnostopoulos, Sapienza University of Rome, Italy– Annalisa Appice, University of Bari, Italy– Xiao Bai, Yahoo!, USA– Nicola Barbieri, Tumblr, USA– Paolo Barucca, Scuola Normale Superiore, Italy– Michele Berlingerio, IBM Research, Ireland– Annalina Caputo, University of Bari, Italy– Gianbiagio Curato, Scuola Normale Superiore, Italy– Carlotta Domeniconi, George Mason University, USA– Debora Donato, StumbleUpon, USA– Andrea Ferretti, UniCredit, Italy– Ruth Garcia Gavilanes, Oxford Internet Institute, UK– Sara Hajian, Eurecat, Spain– Roberto Interdonato, University of Calabria, Italy– Andreas Kaltenbrunner, Eurecat, Spain– Dragi Kocev, Jozef Stefan Institute, Slovenia– Nicolas Kourtellis, Telefonica Research, Spain– Iordanis Koutsopoulos,Athens University of Economics and Business, Greece– Donato Malerba, University of Bari, Italy– Yelena Mejova, Qatar Computing Research Institute, Qatar– Davide Mottin, Hasso Plattner Institute, Germany– Giuseppe Nicosia, University of Catania, Italy– Marcello Paris, UniCredit, Italy– Stefano Pascolutti, UniCredit, Italy– Alvin Pastore, University of Sheffield, UK– Giovanni Ponti, ENEA, Italy– Aleksandra Rashkovska, Joÿef Stefan Institute, Slovenia– Giovanni Stilo, Sapienza University of Rome, Italy– Antti Ukkonen, Finnish Institute of Occupational Health, Finland– Edoardo Vacchi, UniCredit, Italy– Tim Weninger, University of Notre Dame, USA– Giovanni Zappella, UniCredit, ItalyReferences[1] Harshavardhan Achrekar, Avinash Gandhe, Ross Lazarus, Ssu-Hsin Yu, andBenyuan Liu. Predicting flu trends using twitter data. In Proc. of IEEE Int.Conf. on Computer Communications Workshops (INFOCOM Workshops), pages702–707, 2011.[2] G. Bonanno, G. Caldarelli, F. Lillo, S. Micciché, N. Vandewalle, and R. N. Man-tegna. Networks of equities in financial markets. European Physical Journal B,38:363–371, 2004.[3] Ilaria Bordino, Stefano Battiston, Guido Caldarelli, Matthieu Cristelli, AnttiUkkonen, and Ingmar Weber. Web search queries can predict stock market vol-umes. PLoS ONE, 7(7):e40014, 2012.[4] G. Caldarelli. Scale-Free Networks: Complex Webs in Nature and Technology.Oxford University Press, 2007.[5] Guido Caldarelli, Alessandro Chessa, Fabio Pammolli, Gabriele Pompa, Michelan-gelo Puliga, Massimo Riccaboni, and Gianni Riotta. A multi-level geographicalstudy of italian political elections from twitter data. PLoS ONE, 9(5):e95809,2014.[6] Hyunyoung Choi and Hal Varian. Predicting the present with Google trends.Technical report, 2009.[7] Sharad Goel, Jake M. Hofman, Sébastien Lahaie, David M. Pennock, and Dun-can J. Watts. Predicting consumer behavior with web search. Proceedings of theNational Academy of Sciences (PNAS), 107(41):17486–17490, 2010.[8] Gary King. Ensuring the data rich future of the social sciences. Science, 331(11February):719–721, 2011.[9] Boris Kovalerchuk and Evgenii Vityaev. Data Mining for Financial Applications,pages 1203–1224. Springer US, 2005.[10] Rajasekar Krishnamurthy, Louiqa Raschid, and Shivakumar Vaithyanathan, ed-itors. Proceedings of the International Workshop on Data Science for Macro-Modeling, DSMM’14, Snowbird, UT, USA, June 22-27, 2014. ACM, 2014.[11] T Preis, D Reith, and Stanley H E. Complex dynamics of our economic life ondifferent scales: insights from search engine query data. Philosophical Transactionsof the Royal Society A, 368(1933):5707?–5719, 2010.[12] Michele Tizzoni, Paolo Bajardi, Chiara Poletto, Jose Ramasco, Duygu Balcan,Bruno Goncalves, Nicola Perra, Vittoria Colizza, and Alessandro Vespignani.Real-time numerical forecast of global epidemic spreading: case study of 2009A/H1N1pdm. BMC Medicine, 10(1):165+, 2012.[13] M. Tumminello, T. Aste, T. Di Matteo, and R. N. Mantegna. A tool for filteringinformation in complex systems. Proceedings of the National Academy of Sciences(PNAS), 102(30):10421–10426, 2005.[14] Alessandro Vespignani. Predicting the behavior of techno-social systems. Science,325:425–428, 2009.[15] Wei-Xing Zhou, Gabriele Ranco, Ilaria Bordino, Giacomo Bormetti, Guido Cal-darelli, Fabrizio Lillo, and Michele Treccani. Coupling news sentiment with webbrowsing data improves prediction of intra-day price dynamics. PLoS ONE,11(1):e0146576, 2016.

MIDAS 2016: The 1st Workshop on MIning DAta for financial applicationS

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MIDAS 2016: The 1st Workshop on MIning DAta for financial applicationS

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