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Mining data for financial applicatio...
~
(1998 :)
Mining data for financial applications4th ECML PKDD Workshop, MIDAS 2019, Wurzburg, Germany, September 16, 2019 : revised selected papers /
Record Type:
Electronic resources : Monograph/item
Title/Author:
Mining data for financial applicationsedited by Valerio Bitetta ... [et al.].
Reminder of title:
4th ECML PKDD Workshop, MIDAS 2019, Wurzburg, Germany, September 16, 2019 : revised selected papers /
remainder title:
MIDAS 2019
other author:
Bitetta, Valerio.
corporate name:
Published:
Cham :Springer International Publishing :2020.
Description:
ix, 133 p. :ill. (some col.), digital ;24 cm.
Contained By:
Springer Nature eBook
Subject:
Data miningCongresses.
Online resource:
https://doi.org/10.1007/978-3-030-37720-5
ISBN:
9783030377205$q(electronic bk.)
Mining data for financial applications4th ECML PKDD Workshop, MIDAS 2019, Wurzburg, Germany, September 16, 2019 : revised selected papers /
Mining data for financial applications
4th ECML PKDD Workshop, MIDAS 2019, Wurzburg, Germany, September 16, 2019 : revised selected papers /[electronic resource] :MIDAS 2019edited by Valerio Bitetta ... [et al.]. - Cham :Springer International Publishing :2020. - ix, 133 p. :ill. (some col.), digital ;24 cm. - Lecture notes in computer science,119850302-9743 ;. - Lecture notes in computer science ;4891..
MQLV: Optimal Policy of Money Management in Retail Banking with Q-Learning -- Curriculum Learning in Deep Neural Networks for Financial Forecasting -- Representation Learning in Graphs for Credit Card Fraud Detection -- Firms Default Prediction with Machine Learning -- Convolutional Neural Networks, Image Recognition and Financial Time Series Forecasting -- Mining Business Relationships from Stocks and News -- Mining Financial Risk Events from News and Assessing their impact on Stocks -- Monitoring the Business Cycle with Fine-grained, Aspect-based Sentiment Extraction from News -- Multi-step Prediction of Financial Asset Return Volatility Using Parsimonious Autoregressive Sequential Model -- Big Data Financial Sentiment Analysis in the European Bond Markets -- A Brand Scoring System for Cryptocurrencies Based on Social Media Data.
This book constitutes revised selected papers from the 4th Workshop on Mining Data for Financial Applications, MIDAS 2019, held in conjunction with ECML PKDD 2019, in Wurzburg, Germany, in September 2019. The 8 full and 3 short papers presented in this volume were carefully reviewed and selected from 16 submissions. They deal with challenges, potentialities, and applications of leveraging data-mining tasks regarding problems in the financial domain.
ISBN: 9783030377205$q(electronic bk.)
Standard No.: 10.1007/978-3-030-37720-5doiSubjects--Topical Terms:
380776
Data mining
--Congresses.
LC Class. No.: QA76.9.D343 / M53 2019
Dewey Class. No.: 006.3
Mining data for financial applications4th ECML PKDD Workshop, MIDAS 2019, Wurzburg, Germany, September 16, 2019 : revised selected papers /
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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.
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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 Wurzburg, 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.
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