Recent Developments In Computational Finance And Business Analytics
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Recent Developments In Computational Finance And Business Analytics
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Author : Rangan Gupta
language : en
Publisher: Springer
Release Date : 2025-08-22
Recent Developments In Computational Finance And Business Analytics written by Rangan Gupta and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-22 with Computers categories.
Recent Advancements in Computational Finance and Business Analytics captures the transformation reshaping finance, business, and decision-making. Structured around Financial Analytics, Business Analytics, and HR & Marketing Analytics, this volume presents cutting-edge research and real-world applications. Topics include machine learning in financial forecasting, AI-driven customer segmentation, and blockchain-enabled supply chains. Blending rigorous methods with actionable insights, it offers value to researchers, analysts, students, and policymakers. As industries embrace data-driven innovation, this book serves as both a reference and a roadmap for navigating the digital economy through strategic and intelligent analytics.
Recent Advancements In Computational Finance And Business Analytics
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Author : Rangan Gupta
language : en
Publisher: Springer Nature
Release Date : 2024-09-03
Recent Advancements In Computational Finance And Business Analytics written by Rangan Gupta and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-03 with Computers categories.
This book presents the latest breakthroughs and cutting-edge advancements within this rapidly evolving field. By providing computational finance and business analytics, organizations can secure a competitive advantage in today’s data-driven and cutting-edge business landscape. This book explores the most recent innovations and significant developments in both the domains of computational finance and business analytics, offering a thorough overview of the current landscape. It encompasses various dimensions including: Business Analytics Financial Analytics HR & Marketing Analytics By integrating the latest theoretical insights with practical applications, this book equips researchers, practitioners, and students with the knowledge and tools necessary to explore and progress in the ever-changing realm of computational finance and business analytics. As the present organizations confront the challenges and adapt the opportunities presented by the data revolution, this book serves as an essential guide, illuminating the transformative frontiers where computational finance and business analytics are redefining the realm of possibilities.
Recent Advancements In Computational Finance And Business Analytics
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Author : Rangan Gupta
language : en
Publisher: Springer Nature
Release Date : 2023-10-29
Recent Advancements In Computational Finance And Business Analytics written by Rangan Gupta and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-29 with Technology & Engineering categories.
Recent Advancements of Computational Finance and Business Analytics provide a comprehensive overview of the cutting-edge advancements in this dynamic field. By embracing computational finance and business analytics, organizations can gain a competitive edge in an increasingly data-driven and complex business environment. This book has explored the latest developments and breakthroughs in this rapidly evolving domain, providing a comprehensive overview of the current state of computational finance and business analytics. It covers the following dimensions of this domains: Business Analytics Financial Analytics Human Resource Analytics Marketing Analytics
Computational Business Analytics
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Author : Subrata Das
language : en
Publisher: CRC Press
Release Date : 2013-12-14
Computational Business Analytics written by Subrata Das and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-12-14 with Business & Economics categories.
Learn How to Properly Use the Latest Analytics Approaches in Your Organization Computational Business Analytics presents tools and techniques for descriptive, predictive, and prescriptive analytics applicable across multiple domains. Through many examples and challenging case studies from a variety of fields, practitioners easily see the connections to their own problems and can then formulate their own solution strategies. The book first covers core descriptive and inferential statistics for analytics. The author then enhances numerical statistical techniques with symbolic artificial intelligence (AI) and machine learning (ML) techniques for richer predictive and prescriptive analytics. With a special emphasis on methods that handle time and textual data, the text: Enriches principal component and factor analyses with subspace methods, such as latent semantic analyses Combines regression analyses with probabilistic graphical modeling, such as Bayesian networks Extends autoregression and survival analysis techniques with the Kalman filter, hidden Markov models, and dynamic Bayesian networks Embeds decision trees within influence diagrams Augments nearest-neighbor and k-means clustering techniques with support vector machines and neural networks These approaches are not replacements of traditional statistics-based analytics; rather, in most cases, a generalized technique can be reduced to the underlying traditional base technique under very restrictive conditions. The book shows how these enriched techniques offer efficient solutions in areas, including customer segmentation, churn prediction, credit risk assessment, fraud detection, and advertising campaigns.
Computational Finance
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Author : Cornelis Albertus Los
language : en
Publisher: World Scientific Publishing Company Incorporated
Release Date : 2001
Computational Finance written by Cornelis Albertus Los and has been published by World Scientific Publishing Company Incorporated this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Business & Economics categories.
Computational finance deals with the mathematics of computer programs that realize financial models or systems. This book outlines the epistemic risks associated with the current valuations of different financial instruments and discusses the corresponding risk management strategies. It covers most of the research and practical areas in computational finance. Starting from traditional fundamental analysis and using algebraic and geometric tools, it is guided by the logic of science to explore information from financial data without prejudice. In fact, this book has the unique feature that it is structured around the simple requirement of objective science: the geometric structure of the data = the information contained in the data.
Handbook Of Computational Finance
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Author : Jin-Chuan Duan
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-10-25
Handbook Of Computational Finance written by Jin-Chuan Duan and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-10-25 with Business & Economics categories.
Any financial asset that is openly traded has a market price. Except for extreme market conditions, market price may be more or less than a “fair” value. Fair value is likely to be some complicated function of the current intrinsic value of tangible or intangible assets underlying the claim and our assessment of the characteristics of the underlying assets with respect to the expected rate of growth, future dividends, volatility, and other relevant market factors. Some of these factors that affect the price can be measured at the time of a transaction with reasonably high accuracy. Most factors, however, relate to expectations about the future and to subjective issues, such as current management, corporate policies and market environment, that could affect the future financial performance of the underlying assets. Models are thus needed to describe the stochastic factors and environment, and their implementations inevitably require computational finance tools.
Detecting Regime Change In Computational Finance
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Author : Jun Chen
language : en
Publisher: CRC Press
Release Date : 2020-09-14
Detecting Regime Change In Computational Finance written by Jun Chen and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-14 with Computers categories.
Based on interdisciplinary research into "Directional Change", a new data-driven approach to financial data analysis, Detecting Regime Change in Computational Finance: Data Science, Machine Learning and Algorithmic Trading applies machine learning to financial market monitoring and algorithmic trading. Directional Change is a new way of summarising price changes in the market. Instead of sampling prices at fixed intervals (such as daily closing in time series), it samples prices when the market changes direction ("zigzags"). By sampling data in a different way, this book lays out concepts which enable the extraction of information that other market participants may not be able to see. The book includes a Foreword by Richard Olsen and explores the following topics: Data science: as an alternative to time series, price movements in a market can be summarised as directional changes Machine learning for regime change detection: historical regime changes in a market can be discovered by a Hidden Markov Model Regime characterisation: normal and abnormal regimes in historical data can be characterised using indicators defined under Directional Change Market Monitoring: by using historical characteristics of normal and abnormal regimes, one can monitor the market to detect whether the market regime has changed Algorithmic trading: regime tracking information can help us to design trading algorithms It will be of great interest to researchers in computational finance, machine learning and data science. About the Authors Jun Chen received his PhD in computational finance from the Centre for Computational Finance and Economic Agents, University of Essex in 2019. Edward P K Tsang is an Emeritus Professor at the University of Essex, where he co-founded the Centre for Computational Finance and Economic Agents in 2002.
Computational Intelligent Data Analysis For Sustainable Development
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Author : Ting Yu
language : en
Publisher: CRC Press
Release Date : 2013-04-04
Computational Intelligent Data Analysis For Sustainable Development written by Ting Yu and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-04-04 with Business & Economics categories.
Going beyond performing simple analyses, researchers involved in the highly dynamic field of computational intelligent data analysis design algorithms that solve increasingly complex data problems in changing environments, including economic, environmental, and social data. Computational Intelligent Data Analysis for Sustainable Development presents novel methodologies for automatically processing these types of data to support rational decision making for sustainable development. Through numerous case studies and applications, it illustrates important data analysis methods, including mathematical optimization, machine learning, signal processing, and temporal and spatial analysis, for quantifying and describing sustainable development problems. With a focus on integrated sustainability analysis, the book presents a large-scale quadratic programming algorithm to expand high-resolution input-output tables from the national scale to the multinational scale to measure the carbon footprint of the entire trade supply chain. It also quantifies the error or dispersion between different reclassification and aggregation schemas, revealing that aggregation errors have a high concentration over specific regions and sectors. The book summarizes the latest contributions of the data analysis community to climate change research. A profuse amount of climate data of various types is available, providing a rich and fertile playground for future data mining and machine learning research. The book also pays special attention to several critical challenges in the science of climate extremes that are not handled by the current generation of climate models. It discusses potential conceptual and methodological directions to build a close integration between physical understanding, or physics-based modeling, and data-driven insights. The book then covers the conservation of species and ecologically valuable land. A case study on the Pennsylvania Dirt and Gravel Roads Program demonstrates that multiple-objective linear programming is a more versatile and efficient approach than the widely used benefit targeting selection process. Moving on to renewable energy and the need for smart grids, the book explores how the ongoing transformation to a sustainable energy system of renewable sources leads to a paradigm shift from demand-driven generation to generation-driven demand. It shows how to maximize renewable energy as electricity by building a supergrid or mixing renewable sources with demand management and storage. It also presents intelligent data analysis for real-time detection of disruptive events from power system frequency data collected using an existing Internet-based frequency monitoring network as well as evaluates a set of computationally intelligent techniques for long-term wind resource assessment. In addition, the book gives an example of how temporal and spatial data analysis tools are used to gather knowledge about behavioral data and address important social problems such as criminal offenses. It also applies constraint logic programming to a planning problem: the environmental and social impact assessment of the regional energy plan of the Emilia-Romagna region of Italy. Sustainable development problems, such as global warming, resource shortages, global species loss, and pollution, push researchers to create powerful data analysis approaches that analysts can then use to gain insight into these issues to support rational decision making. This volume shows both the data analysis and sustainable development communities how to use intelligent data analysis tools to address practical problems and encourages researchers to develop better methods.
Modern Computational Finance
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Author : Antoine Savine
language : en
Publisher: John Wiley & Sons
Release Date : 2021-11-02
Modern Computational Finance written by Antoine Savine and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-02 with Mathematics categories.
An incisive and essential guide to building a complete system for derivative scripting In Volume 2 of Modern Computational Finance Scripting for Derivatives and xVA, quantitative finance experts and practitioners Drs. Antoine Savine and Jesper Andreasen deliver an indispensable and insightful roadmap to the interrogation, aggregation, and manipulation of cash-flows in a variety of ways. The book demonstrates how to facilitate portfolio-wide risk assessment and regulatory calculations (like xVA). Complete with a professional scripting library written in modern C++, this stand-alone volume walks readers through the construction of a comprehensive risk and valuation tool. This essential book also offers: Effective strategies for improving scripting libraries, from basic examples—like support for dates and vectors—to advanced improvements, including American Monte Carlo techniques Exploration of the concepts of fuzzy logic and risk sensitivities, including support for smoothing and condition domains Discussion of the application of scripting to xVA, complete with a full treatment of branching Perfect for quantitative analysts, risk professionals, system developers, derivatives traders, and financial analysts, Modern Computational Finance Scripting for Derivatives and xVA: Volume 2 is also a must-read resource for students and teachers in master’s and PhD finance programs.
Computational Financial Mathematics Using Mathematica
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Author : Srdjan Stojanovic
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Computational Financial Mathematics Using Mathematica written by Srdjan Stojanovic and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Business & Economics categories.
Given the explosion of interest in mathematical methods for solving problems in finance and trading, a great deal of research and development is taking place in universities, large brokerage firms, and in the supporting trading software industry. Mathematical advances have been made both analytically and numerically in finding practical solutions. This book provides a comprehensive overview of existing and original material, about what mathematics when allied with Mathematica can do for finance. Sophisticated theories are presented systematically in a user-friendly style, and a powerful combination of mathematical rigor and Mathematica programming. Three kinds of solution methods are emphasized: symbolic, numerical, and Monte-- Carlo. Nowadays, only good personal computers are required to handle the symbolic and numerical methods that are developed in this book. Key features: * No previous knowledge of Mathematica programming is required * The symbolic, numeric, data management and graphic capabilities of Mathematica are fully utilized * Monte--Carlo solutions of scalar and multivariable SDEs are developed and utilized heavily in discussing trading issues such as Black--Scholes hedging * Black--Scholes and Dupire PDEs are solved symbolically and numerically * Fast numerical solutions to free boundary problems with details of their Mathematica realizations are provided * Comprehensive study of optimal portfolio diversification, including an original theory of optimal portfolio hedging under non-Log-Normal asset price dynamics is presented The book is designed for the academic community of instructors and students, and most importantly, will meet the everyday trading needs of quantitatively inclined professional and individual investors.