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Machine Learning Approaches In Financial Analytics


Machine Learning Approaches In Financial Analytics
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Machine Learning Approaches In Financial Analytics


Machine Learning Approaches In Financial Analytics
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Author : Leandros A. Maglaras
language : en
Publisher: Springer Nature
Release Date : 2024-08-27

Machine Learning Approaches In Financial Analytics written by Leandros A. Maglaras and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-27 with Computers categories.


This book addresses the growing need for a comprehensive guide to the application of machine learning in financial analytics. It offers a valuable resource for both beginners and experienced professionals in finance and data science by covering the theoretical foundations, practical implementations, ethical considerations, and future trends in the field. It bridges the gap between theory and practice, providing readers with the tools and knowledge they need to leverage the power of machine learning in the financial sector responsibly.



Data Analytics In Finance


Data Analytics In Finance
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Author : Huijian Dong
language : en
Publisher: CRC Press
Release Date : 2025-04-30

Data Analytics In Finance written by Huijian Dong and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-30 with Computers categories.


Data Analytics in Finance covers the methods and application of data analytics in all major areas of finance, including buy-side investments, sell-side investment banking, corporate finance, consumer finance, financial services, real estate, insurance, and commercial banking. It explains statistical inference of big data, financial modeling, machine learning, database querying, data engineering, data visualization, and risk analysis. Emphasizing financial data analytics practices with a solution- oriented purpose, it is a “one-stop-shop” of all the major data analytics aspects for each major finance area. The book paints a comprehensive picture of the data analytics process including: Statistical inference of big data Financial modeling Machine learning and AI Database querying Data engineering Data visualization Risk analysis Each chapter is crafted to provide complete guidance for many subject areas including investments, fraud detection, and consumption finance. Avoiding data analytics methods widely available elsewhere, the book focuses on providing data analytics methods specifically applied to key areas of finance. Written as a roadmap for researchers, practitioners, and students to master data analytics instruments in finance, the book also provides a collection of indispensable resources for the readers’ reference. Offering the knowledge and tools necessary to thrive in a data-driven financial landscape, this book enables readers to deepen their understanding of investments, develop new approaches to risk management, and apply data analytics to finance.



Data Analytics And Ai For Quantitative Risk Assessment And Financial Computation


Data Analytics And Ai For Quantitative Risk Assessment And Financial Computation
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Author : Galety, Mohammad Gouse
language : en
Publisher: IGI Global
Release Date : 2024-11-29

Data Analytics And Ai For Quantitative Risk Assessment And Financial Computation written by Galety, Mohammad Gouse and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-29 with Business & Economics categories.


In today's fast-paced financial landscape, professionals face an uphill battle in effectively integrating data analytics and artificial intelligence (AI) into quantitative risk assessment and financial computation. The constantly increasing volume, velocity, and variety of data generated by digital transactions, market exchanges, and social media platforms offer unparalleled financial analysis and decision-making opportunities. However, professionals need sophisticated AI technologies and data analytics methodologies to harness this data for predictive modeling, risk assessment, and algorithmic trading. Navigating this complex terrain can be daunting, and a comprehensive guide that bridges theory and practice is necessary. Data Analytics and AI for Quantitative Risk Assessment and Financial Computation is an all-encompassing reference for finance professionals, risk managers, data scientists, and students seeking to leverage the transformative power of AI and data analytics in finance. The book encapsulates this integration's theoretical underpinnings, practical applications, challenges, and future directions, empowering readers to enhance their analytical capabilities, make informed decisions, and stay ahead in the competitive financial landscape.



Progressive Computational Intelligence Information Technology And Networking


Progressive Computational Intelligence Information Technology And Networking
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Author : Poonam Nandal
language : en
Publisher: CRC Press
Release Date : 2025-07-22

Progressive Computational Intelligence Information Technology And Networking written by Poonam Nandal and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-22 with Computers categories.


Progressive Computational Intelligence, Information Technology and Networking presents a rich and diverse collection of cutting-edge research, real-world applications, and innovative methodologies spanning across multiple domains of computer science, artificial intelligence, and emerging technologies. This comprehensive volume brings together different scholarly chapters contributed by researchers, practitioners, and thought leaders from around the globe. The book explores a wide array of topics including—but not limited to—machine learning, deep learning, cloud computing, cybersecurity, Internet of Things (IoT), blockchain, natural language processing, image processing, and data analytics. It addresses the practical implementation of technologies in sectors such as healthcare, agriculture, education, smart cities, environmental monitoring, finance, and more. Each chapter delves into specific challenges, frameworks, and experimental outcomes, making this book an essential reference for academicians, researchers, industry professionals, and students who aim to stay ahead in the rapidly evolving digital world.



Data Analytics For Management Banking And Finance


Data Analytics For Management Banking And Finance
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Author : Foued Saâdaoui
language : en
Publisher: Springer Nature
Release Date : 2023-09-19

Data Analytics For Management Banking And Finance written by Foued Saâdaoui and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-19 with Business & Economics categories.


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 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



Machine Learning Technologies On Energy Economics And Finance


Machine Learning Technologies On Energy Economics And Finance
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Author : Mohammad Zoynul Abedin
language : en
Publisher: Springer Nature
Release Date : 2025-07-25

Machine Learning Technologies On Energy Economics And Finance written by Mohammad Zoynul Abedin and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-25 with Business & Economics categories.


This book explores the latest innovations in energy economics and finance, with a particular focus on the role of machine learning algorithms in advancing the energy sector. It examines key factors shaping this field, including market structures, regulatory frameworks, environmental impacts, and the dynamics of the global energy market. It discusses the critical application of machine learning (ML) in energy financing, introducing predictive tools for forecasting energy prices across various sectors—such as crude oil, electricity, fuelwood, solar, and natural gas. It also addresses how ML can predict investor behavior and assess the efficiency of energy markets, with a focus on both the opportunities and challenges in renewable energy and energy finance. This book serves as a comprehensive guide for academics, practitioners, financial managers, stakeholders, government officials, and policymakers who seek strategies to enhance energy systems, reduce costs and uncertainties, and optimize revenue for economic growth. This is the first volume of a two-volume set.



The Essentials Of Machine Learning In Finance And Accounting


The Essentials Of Machine Learning In Finance And Accounting
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Author : Mohammad Zoynul Abedin
language : en
Publisher: Routledge
Release Date : 2021-06-20

The Essentials Of Machine Learning In Finance And Accounting written by Mohammad Zoynul Abedin and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-20 with Business & Economics categories.


This book introduces machine learning in finance and illustrates how we can use computational tools in numerical finance in real-world context. These computational techniques are particularly useful in financial risk management, corporate bankruptcy prediction, stock price prediction, and portfolio management. The book also offers practical and managerial implications of financial and managerial decision support systems and how these systems capture vast amount of financial data. Business risk and uncertainty are two of the toughest challenges in the financial industry. This book will be a useful guide to the use of machine learning in forecasting, modeling, trading, risk management, economics, credit risk, and portfolio management.



Energy Efficient Deep Learning Approaches In Iot Fog And Green Blockchain Revolution


Energy Efficient Deep Learning Approaches In Iot Fog And Green Blockchain Revolution
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Author : Sathio, Anwar Ali
language : en
Publisher: IGI Global
Release Date : 2025-04-18

Energy Efficient Deep Learning Approaches In Iot Fog And Green Blockchain Revolution written by Sathio, Anwar Ali and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-18 with Technology & Engineering categories.


The convergence of Internet of Things (IoT), fog computing, and blockchain technology can be used to revolutionize energy efficiency and sustainability. The implementation of deep learning (DL) techniques may optimize the energy consumption of these interconnected systems. Thus, they can be used to create green, energy-efficient solutions for various industries, including smart cities, healthcare, finance, and industrial IoT (IIoT). Focusing on the energy efficiency and environmental impact of these technologies, they provide valuable insights into creating sustainable and scalable systems. Energy-Efficient Deep Learning Approaches in IoT, Fog, and Green Blockchain Revolution bridges the knowledge gap between traditional IoT and blockchain research and the emerging need for energy-efficient and green technologies. It influences future research directions, encourages collaboration across disciplines, and inspires innovations that prioritize sustainability. Covering topics such as software-defined networking (SDN), ecosystem conservation, and monitoring systems, this book is an excellent resource for computer scientists, policymakers, technologists, industry practitioners, engineers, environmentalists, sustainability advocates, professionals, researchers, scholars, academicians, and more.



Financial Data Science With Sas


Financial Data Science With Sas
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Author : Babatunde O Odusami
language : en
Publisher: SAS Institute
Release Date : 2024-06-14

Financial Data Science With Sas written by Babatunde O Odusami and has been published by SAS Institute this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-14 with Computers categories.


Explore financial data science using SAS. Financial Data Science with SAS provides readers with a comprehensive explanation of the theoretical and practical implementation of the various types of analytical techniques and quantitative tools that are used in the financial services industry. This book shows readers how to implement data visualization, simulation, statistical predictive models, machine learning models, and financial optimizations using real-world examples in the SAS Analytics environment. Each chapter ends with practice exercises that include use case scenarios to allow readers to test their knowledge. Designed for university students and financial professionals interested in boosting their data science skills, Financial Data Science with SAS is an essential reference guide for understanding how data science is used in the financial services industry and for learning how to use SAS to solve complex business problems.



Data Analytics And Machine Learning


Data Analytics And Machine Learning
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Author : Pushpa Singh
language : en
Publisher: Springer Nature
Release Date : 2024-03-19

Data Analytics And Machine Learning written by Pushpa Singh and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-19 with Computers categories.


This book presents an in-depth analysis of successful data-driven initiatives, highlighting how organizations have leveraged data to drive decision-making processes, optimize operations, and achieve remarkable outcomes. Through case studies, readers gain valuable insights and learn practical strategies for implementing data analytics, big data, and machine learning solutions in their own organizations. The book discusses the transformative power of data analytics and big data in various industries and sectors and how machine learning applications have revolutionized exploration by enabling advanced data analysis techniques for mapping, geospatial analysis, and environmental monitoring, enhancing our understanding of the world and its dynamic processes. This book explores how big data explosion, the power of analytics and machine learning revolution can bring new prospects and opportunities in the dynamic and data-rich landscape. It highlights the future research directions in data analytics, big data, and machine learning that explores the emerging trends, challenges, and opportunities in these fields by covering interdisciplinary approaches such as handling and analyzing real-time and streaming data.