| Vol-1774 urn:nbn:de:0074-1774-1 Copyright © 2016 for the individual papers by the papers' authors. Copying permitted for private and academic purposes. This volume is published and copyrighted by its editors.

# MIDAS 2016
MIning DAta for financial applicationS

### Proceedings of the First Workshop on MIning DAta for financial applicationS (MIDAS 2016)
co-located with the 2016 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2016)

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

Edited by

### Ilaria Bordino *
Guido Caldarelli **
Fabio Fumarola *
Francesco Gullo *
Tiziano Squartini **

* UniCredit, R&D Department, Milan, Italy
** IMT Institute for Advanced Studies, Lucca, Italy

## Table of Contents

- Preface

- Clustering underlying stock trends via non-negative matrix factorization 5-16
  Andrea Pazienza, Sabrina Francesca Pellegrino, Stefano Ferilli, Floriana Esposito
- Clustering of exchange rates and their dynamics under different dependence measures 17-28
  Martí Renedo, Argimiro Arratia
- A general framework for building machine learning models for pricing american index options with no-arbitrage and its limitation (extended abstract) 29-30
  Huisu Jang, Jaewook Lee
- Towards a sharp estimation of transfer entropy for identifying causality in financial time series 31-42
  Àlex Serès, Alejandra Cabaña, Argimiro Arratia
- Brexit or Bremain? Evidence from bubble analysis 43-54
  Marco Bianchetti, Davide Emilio Galli, Camilla Ricci, Angelo Salvatori, Marco Scaringi
- Good news and bad news: Do online investor sentiments reaction to return news asymmetric? 55-66
  Alya Al Nasseri, Faek Menla Ali, Allan Tucker
- Sentiment extraction from financial public disclosure documents 67-72
  Ali Caner Türkmen
- A probabilistic approach for financial IoT data (extended abstract) 73-74
  Salvatore Cuomo, Pasquale De Michele, Vittorio Di Somma, Giovanni Ponti

2016-12-28: submitted by Francesco Gullo, metadata incl. bibliographic data published under Creative Commons CC0
2016-12-29: published on CEUR-WS.org |valid HTML5|