Mining Data for Financial Applications : 4th ECML PKDD Workshop, MIDAS 2019, Würzburg, Germany, September 16, 2019, Revised Selected Papers / / edited by Valerio Bitetta, Ilaria Bordino, Andrea Ferretti, Francesco Gullo, Stefano Pascolutti, Giovanni Ponti

• Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020
• Edizione: 1st ed. 2020.
• Descrizione fisica: 1 online resource (IX, 133 p. 37 illus., 27 illus. in color.)
• Disciplina: 006.3; 006.3 (edition:23)
• Soggetto topico: Artificial intelligence; Computer vision; Computer engineering; Computer networks; Electronic commerce; Social sciences - Data processing; Artificial Intelligence; Computer Vision; Computer Engineering and Networks; Computer Communication Networks; e-Commerce and e-Business; Computer Application in Social and Behavioral Sciences
• Persona (resp. second.): BitettaValerio; BordinoIlaria; FerrettiAndrea; GulloFrancesco; PascoluttiStefano; PontiGiovanni
• Nota di contenuto: MQLV: Optimal Policy of Money Management in Retail Banking with Q-Learning -- Curriculum Learning in Deep Neural Networks for Financial Forecasting -- Representation Learning in Graphs for Credit Card Fraud Detection -- Firms Default Prediction with Machine Learning -- Convolutional Neural Networks, Image Recognition and Financial Time Series Forecasting -- Mining Business Relationships from Stocks and News -- Mining Financial Risk Events from News and Assessing their impact on Stocks -- Monitoring the Business Cycle with Fine-grained, Aspect-based Sentiment Extraction from News -- Multi-step Prediction of Financial Asset Return Volatility Using Parsimonious Autoregressive Sequential Model -- Big Data Financial Sentiment Analysis in the European Bond Markets -- A Brand Scoring System for Cryptocurrencies Based on Social Media Data.
• Sommario/riassunto: This book constitutes revised selected papers from the 4th Workshop on Mining Data for Financial Applications, MIDAS 2019, held in conjunction with ECML PKDD 2019, in Würzburg, Germany, in September 2019. The 8 full and 3 short papers presented in this volume were carefully reviewed and selected from 16 submissions. They deal with challenges, potentialities, and applications of leveraging data-mining tasks regarding problems in the financial domain.
• Titolo autorizzato: Mining Data for Financial Applications
• ISBN: 3-030-37720-2
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910373923503321
• Lo trovi qui: Univ. Federico II
• Serie: Lecture Notes in Artificial Intelligence, . 2945-9141 ; ; 11985