E-Book, Englisch, 151 Seiten, eBook
Bitetta / Bordino / Ferretti Mining Data for Financial Applications
1. Auflage 2021
ISBN: 978-3-030-66981-2
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
5th ECML PKDD Workshop, MIDAS 2020, Ghent, Belgium, September 18, 2020, Revised Selected Papers
E-Book, Englisch, 151 Seiten, eBook
Reihe: Lecture Notes in Artificial Intelligence
ISBN: 978-3-030-66981-2
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
This book constitutes revised selected papers from the 5th Workshop on Mining Data for Financial Applications, MIDAS 2020, held in conjunction with ECML PKDD 2020, in Ghent, Belgium, in September 2020.*
The 8 full and 3 short papers presented in this volume were carefully reviewed and selected from 15 submissions. They deal with challenges, potentialities, and applications of leveraging data-mining tasks regarding problems in the financial domain.*The workshop was held virtually due to the COVID-19 pandemic.“Information Extraction from the GDELT Database to Analyse EU Sovereign Bond Markets” and “Exploring the Predictive Power of News and Neural Machine Learning Models for Economic Forecasting” are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
Trade Selection with Supervised Learning and Optimal Coordinate Ascent (OCA).- How much does Stock Prediction improve with Sentiment Analysis?.- Applying Machine Learning to Predict Closing Prices in Stock Market: a case study.- Financial Fraud Detection with Improved Neural Arithmetic Logic Units.- Information Extraction from the GDELT Database to Analyse EU Sovereign Bond Markets.- Multi-Objective Particle Swarm Optimization for Feature Selection in Credit Scoring.- A comparative analysis of Temporal Long Text Similarity: Application to Financial Documents.- Ranking Cryptocurrencies by Brand Importance: a Social Media Analysis in ENEAGRID.- Towards the Prediction of Electricity Prices at the Intraday Market Using Shallow and Deep-Learning Methods.- Neither in the Programs Nor in the Data: Mining the Hidden Financial Knowledge with Knowledge Graphs and Reasoning.- Exploring the Predictive Power of News and Neural Machine Learning Models for Economic Forecasting.
