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＜E-Book＞
Data Analytics for Management, Banking and Finance : Theories and Application / edited by Foued Saâdaoui, Yichuan Zhao, Hana Rabbouch

Edition	1st ed. 2023.
Publisher	(Cham : Springer Nature Switzerland : Imprint: Springer)
Year	2023
Language	English
Size	XIV, 334 p. 52 illus., 45 illus. in color : online resource
Authors	Saâdaoui, Foued editor Zhao, Yichuan editor Rabbouch, Hana editor SpringerLink (Online service)
Subjects	LCSH:Statistics  LCSH:Data mining LCSH:Big data FREE:Statistics in Business, Management, Economics, Finance, Insurance FREE:Data Mining and Knowledge Discovery FREE:Big Data
Notes	1. A Survey of Machine Learning Methodologies for Loan Evaluation in Peer-to-peer (P2P) Lending -- 2. Explainable Machine Learning Models for Credit Risk Analysis: A Survey -- 3. Data Analytics Incorporated with Machine Learning Approaches in Finance -- 4. Estimation and Inference in Financial Volatility Networks -- 5. Multiresolution Data Analytics for Financial Time Series Using MATLAB -- 6.A Risk-Based Trading System using Algorithmic Trading and Deep Learning Models -- 7. Financial Contagion During COVID-19: Intraday Analysis with VAR-VECM Models -- 8. Nonlinear ARDL Analysis of Real Effective Exchange Rate's Asymmetric Impact on FDI Inflows in Tunisia -- 9. Evaluating Turkish Banks' Complaint Management Performance using Multi-Criteria Decision Analysis -- 10. Financial Cycle, Stress, and Policy Roles in Small Open Economy: Spillover Index Approach -- 11. Performance of Cryptocurrencies under a Sentiment Analysis Approach in the Time of COVID-19 -- 12. Determinants of Non-Performing Loans: Evidence from Indian Banks -- 13. Natural Resources, Conflicts, Terrorism, and Finance: Insights from a Descriptive Data Analysis -- 14. Determinants of Profitability in Islamic Banks: The Kingdom of Saudi Arabia Market -- 15. Trading Rules and Value at Risk: Is there a linkage? This book is a practical guide on the use of various data analytics and visualization techniques and tools in the banking and financial sectors. It focuses on how combining expertise from interdisciplinary areas, such as machine learning and business analytics, can bring forward a shared vision on the benefits of data science from the research point of view to the evaluation of policies. It highlights how data science is reshaping the business sector. It includes examples of novel big data sources and some successful applications on the use of advanced machine learning, natural language processing, networks analysis, and time series analysis and forecasting, among others, in the banking and finance. It includes several case studies where innovative data science models is used to analyse, test or model some crucial phenomena in banking and finance. At the same time, the book is making an appeal for a further adoption of these novel applications in the field of economics and finance sothat they can reach their full potential and support policy-makers and the related stakeholders in the transformational recovery of our societies. The book is for stakeholders involved in research and innovation in the banking and financial sectors, but also those in the fields of computing, IT and managerial information systems, helping through this new theory to better specify the new opportunities and challenges. The many real cases addressed in this book also provide a detailed guide allowing the reader to realize the latest methodological discoveries and the use of the different Machine Learning approaches (supervised, unsupervised, reinforcement, deep, etc.) and to learn how to use and evaluate performance of new data science tools and frameworks HTTP:URL=https://doi.org/10.1007/978-3-031-36570-6

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