Big Data in Finance : Opportunities and Challenges of Financial Digitalization / edited by Thomas Walker, Frederick Davis, Tyler Schwartz
| Publisher | (Cham : Springer International Publishing : Imprint: Palgrave Macmillan) |
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| Year | 2022 |
| Edition | 1st ed. 2022. |
| Authors | Walker, Thomas editor Davis, Frederick editor Schwartz, Tyler editor SpringerLink (Online service) |
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| Links to the text | Location | Volume | Call No. | Barcode No. | Status | Comments | ISBN | Printed | Restriction | Reserve |
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| Links to the text | Library Off-campus access |
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OB00189305 | Springer Business and Economics eBooks (電子ブック) | 9783031122408 |
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| Material Type | E-Book |
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| Media type | 機械可読データファイル |
| Size | XXV, 272 p. 41 illus., 31 illus. in color : online resource |
| Notes | Chapter 1: Big Data in Finance: An Overview -- SECTION I: BIG DATA IN THE FINANCIAL MARKETS -- Chapter 2: Alternative Data -- Chapter 3: An Algorithmic Trading Strategy to Balance Profitability and Risk -- Chapter 4: High-Frequency Trading and Market Efficiency in the Moroccan Stock Market -- Chapter 5: Ensemble Models using Symbolic Regression and Genetic Programming for Uncertainty Estimation in ESG and Alternative Investments -- SECTION II: BIG DATA IN FINANCIAL SERVICES -- Chapter 6: Consumer Credit Assessments in the Age of Big Data -- Chapter 7; Robo-Advisors: A Big Data Challenge -- Chapter 8: Bitcoin: Future or Fad? -- Chapter 9: Culture, Digital Assets, and the Economy: A Trans-National Perspective -- SECTION III: CASE STUDIES AND APPLICATIONS -- Chapter 10: Islamic Finance in Canada Powered by Big Data: A Case Study -- Chapter 11: Assessing the Carbon Footprint of Cryptoassets: Evidence from a Bivariate VAR Model -- Chapter 12:A Data-informed Approach to Financial Literacy Enhancement using Cognitive & Behavioral Analytics This edited book explores the unique risks, opportunities, challenges, and societal implications associated with big data developments within the field of finance. While the general use of big data has been the subject of frequent discussions, this book will take a more focused look at big data applications in the financial sector. With contributions from researchers, practitioners, and entrepreneurs involved at the forefront of big data in finance, the book discusses technological and business-inspired breakthroughs in the field. The contributions offer technical insights into the different applications presented and highlight how these new developments may impact and contribute to the evolution of the financial sector. Additionally, the book presents several case studies that examine practical applications of big data in finance. In exploring the readiness of financial institutions to adapt to new developments in the big data/artificial intelligence space and assessing different implementation strategies and policy solutions, the book will be of interest to academics, practitioners, and regulators who work in this field. Thomas Walker is a Full Professor of Finance and the Concordia University Research Chair in Emerging Risk Management at Concordia University, Montreal, Canada. Prior to academia, he worked for several years in the German consulting and industrial sector at Mercedes Benz, Utility Consultants International, Lahmeyer International, Telenet, and KPMG Peat Marwick. Frederick Davis is an Associate Professor at the John Molson School of Business at Concordia University, Montreal, Canada. Prior to his academic career, he worked for several years in the government sector assisting communities with their economic development. His research interests include mergers and acquisitions, insider trading, big data, and other aspects of corporate finance. Tyler Schwartz holds an MSc degree in Data Science and Business Analytics from HEC Montreal. He has served as a research assistant in the Department of Finance at Concordia University for over four years and is the co-author of an edited book collection on climate change adaptation as well as working papers on social impact bonds and the Sustainable Development Goals (SDGs) HTTP:URL=https://doi.org/10.1007/978-3-031-12240-8 |
| Subjects | LCSH:Financial engineering LCSH:Big data FREE:Financial Technology and Innovation FREE:Big Data |
| Classification | LCC:HG176.7 DC23:332 DC23:658.15 |
| ID | 8000088474 |
| ISBN | 9783031122408 |
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