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001481204 001__ 1481204
001481204 003__ OCoLC
001481204 005__ 20231031003327.0
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001481204 008__ 230928s2023\\\\sz\a\\\\o\\\\\001\0\eng\d
001481204 019__ $$a1399170008$$a1399385325$$a1399406733
001481204 020__ $$a9783031365706$$q(electronic bk.)
001481204 020__ $$a3031365704$$q(electronic bk.)
001481204 020__ $$z9783031365690
001481204 020__ $$z3031365690
001481204 0247_ $$a10.1007/978-3-031-36570-6$$2doi
001481204 035__ $$aSP(OCoLC)1400072915
001481204 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dEBLCP$$dYDX
001481204 049__ $$aISEA
001481204 050_4 $$aHG173
001481204 08204 $$a332$$223/eng/20230928
001481204 24500 $$aData analytics for management, banking and finance :$$btheories and application /$$cFoued Saâdaoui, Yichuan Zhao, Hana Rabbouch, editors.
001481204 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2023]
001481204 300__ $$a1 online resource (xiv, 334 pages) :$$billustrations
001481204 336__ $$atext$$btxt$$2rdacontent
001481204 337__ $$acomputer$$bc$$2rdamedia
001481204 338__ $$aonline resource$$bcr$$2rdacarrier
001481204 500__ $$aIncludes index.
001481204 504__ $$aReferences -- Data Analytics Incorporated with Machine Learning Approaches in Finance -- 1 Introduction -- 2 Applications in Finance Domain -- 2.1 Banking -- 2.2 Stock Trading/Multi Commodities Trading -- 2.3 Local or Global Marketing/Retailing -- 2.4 Portfolio Management/Optimization -- 2.5 Macroeconomics Prediction -- 2.6 Insurance -- 2.7 Financial Distress -- 2.8 Stock Market Prediction -- 2.9 Decentralized Finance -- 3 Conclusion -- References -- Estimation and Inference in Financial Volatility Networks -- 1 Introduction -- 2 Estimation and Inference in Financial Volatility Networks
001481204 5050_ $$a1. A Survey of Machine Learning Methodologies for Loan Evaluation in Peer-to-peer (P2P) Lending -- 2. Explainable Machine Learning Models for Credit Risk Analysis: A Survey -- 3. Data Analytics Incorporated with Machine Learning Approaches in Finance -- 4. Estimation and Inference in Financial Volatility Networks -- 5. Multiresolution Data Analytics for Financial Time Series Using MATLAB -- 6.A Risk-Based Trading System using Algorithmic Trading and Deep Learning Models -- 7. Financial Contagion During COVID-19: Intraday Analysis with VAR-VECM Models -- 8. Nonlinear ARDL Analysis of Real Effective Exchange Rate's Asymmetric Impact on FDI Inflows in Tunisia -- 9. Evaluating Turkish Banks' Complaint Management Performance using Multi-Criteria Decision Analysis -- 10. Financial Cycle, Stress, and Policy Roles in Small Open Economy: Spillover Index Approach -- 11. Performance of Cryptocurrencies under a Sentiment Analysis Approach in the Time of COVID-19 -- 12. Determinants of Non-Performing Loans: Evidence from Indian Banks -- 13. Natural Resources, Conflicts, Terrorism, and Finance: Insights from a Descriptive Data Analysis -- 14. Determinants of Profitability in Islamic Banks: The Kingdom of Saudi Arabia Market -- 15. Trading Rules and Value at Risk: Is there a linkage?
001481204 506__ $$aAccess limited to authorized users.
001481204 520__ $$aThis book is a practical guide on the use of various data analytics and visualization techniques and tools in the banking and financial sectors. It focuses on how combining expertise from interdisciplinary areas, such as machine learning and business analytics, can bring forward a shared vision on the benefits of data science from the research point of view to the evaluation of policies. It highlights how data science is reshaping the business sector. It includes examples of novel big data sources and some successful applications on the use of advanced machine learning, natural language processing, networks analysis, and time series analysis and forecasting, among others, in the banking and finance. It includes several case studies where innovative data science models is used to analyse, test or model some crucial phenomena in banking and finance. At the same time, the book is making an appeal for a further adoption of these novel applications in the field of economics and finance so that they can reach their full potential and support policy-makers and the related stakeholders in the transformational recovery of our societies. The book is for stakeholders involved in research and innovation in the banking and financial sectors, but also those in the fields of computing, IT and managerial information systems, helping through this new theory to better specify the new opportunities and challenges. The many real cases addressed in this book also provide a detailed guide allowing the reader to realize the latest methodological discoveries and the use of the different Machine Learning approaches (supervised, unsupervised, reinforcement, deep, etc.) and to learn how to use and evaluate performance of new data science tools and frameworks.
001481204 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed September 28, 2023).
001481204 650_0 $$aFinance$$xData processing.
001481204 655_0 $$aElectronic books.
001481204 7001_ $$aSaâdaoui, Foued,$$eeditor.
001481204 7001_ $$aZhao, Yichuan,$$eeditor.
001481204 7001_ $$aRabbouch, Hana,$$eeditor.
001481204 77608 $$iPrint version:$$aSaâdaoui, Foued$$tData Analytics for Management, Banking and Finance$$dCham : Springer,c2023$$z9783031365690
001481204 852__ $$bebk
001481204 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-36570-6$$zOnline Access$$91397441.1
001481204 909CO $$ooai:library.usi.edu:1481204$$pGLOBAL_SET
001481204 980__ $$aBIB
001481204 980__ $$aEBOOK
001481204 982__ $$aEbook
001481204 983__ $$aOnline
001481204 994__ $$a92$$bISE