Handbook Of Financial Econometrics Mathematics Statistics And Machine Learning In 4 Volumes
DOWNLOAD
Download Handbook Of Financial Econometrics Mathematics Statistics And Machine Learning In 4 Volumes PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Handbook Of Financial Econometrics Mathematics Statistics And Machine Learning In 4 Volumes book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Handbook Of Financial Econometrics Mathematics Statistics And Machine Learning In 4 Volumes
DOWNLOAD
Author : Cheng Few Lee
language : en
Publisher: World Scientific
Release Date : 2020-07-30
Handbook Of Financial Econometrics Mathematics Statistics And Machine Learning In 4 Volumes written by Cheng Few Lee and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-30 with Business & Economics categories.
This four-volume handbook covers important concepts and tools used in the fields of financial econometrics, mathematics, statistics, and machine learning. Econometric methods have been applied in asset pricing, corporate finance, international finance, options and futures, risk management, and in stress testing for financial institutions. This handbook discusses a variety of econometric methods, including single equation multiple regression, simultaneous equation regression, and panel data analysis, among others. It also covers statistical distributions, such as the binomial and log normal distributions, in light of their applications to portfolio theory and asset management in addition to their use in research regarding options and futures contracts.In both theory and methodology, we need to rely upon mathematics, which includes linear algebra, geometry, differential equations, Stochastic differential equation (Ito calculus), optimization, constrained optimization, and others. These forms of mathematics have been used to derive capital market line, security market line (capital asset pricing model), option pricing model, portfolio analysis, and others.In recent times, an increased importance has been given to computer technology in financial research. Different computer languages and programming techniques are important tools for empirical research in finance. Hence, simulation, machine learning, big data, and financial payments are explored in this handbook.Led by Distinguished Professor Cheng Few Lee from Rutgers University, this multi-volume work integrates theoretical, methodological, and practical issues based on his years of academic and industry experience.
Handbook Of Financial Econometrics Mathematics Statistics And Machine Learning
DOWNLOAD
Author :
language : en
Publisher:
Release Date : 2021
Handbook Of Financial Econometrics Mathematics Statistics And Machine Learning written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
"This four-volume handbook covers important concepts and tools used in the fields of financial econometrics, mathematics, statistics, and machine learning. Econometric methods have been applied in asset pricing, corporate finance, international finance, options and futures, risk management, and in stress testing for financial institutions. This handbook discusses a variety of econometric methods, including single equation multiple regression, simultaneous equation regression, and panel data analysis, among others. It also covers statistical distributions, such as the binomial and log normal distributions, in light of their applications to portfolio theory and asset management in addition to their use in research regarding options and futures contracts. In recent times, an increased importance has been given to computer technology in financial research. Different computer languages and programming techniques are important tools for empirical research in finance. Hence, simulation, machine learning, big data, and financial payments are explored in this handbook. Led by Distinguished Professor Cheng Few Lee from Rutgers University, this multi-volume work integrates theoretical, methodological, and practical issues based on his years of academic and industry experience"-- Provided by publisher.
Handbook Of Investment Analysis Portfolio Management And Financial Derivatives In 4 Volumes
DOWNLOAD
Author : Cheng Few Lee
language : en
Publisher: World Scientific
Release Date : 2024-04-08
Handbook Of Investment Analysis Portfolio Management And Financial Derivatives In 4 Volumes written by Cheng Few Lee and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-08 with Business & Economics categories.
This four-volume handbook covers important topics in the fields of investment analysis, portfolio management, and financial derivatives. Investment analysis papers cover technical analysis, fundamental analysis, contrarian analysis, and dynamic asset allocation. Portfolio analysis papers include optimization, minimization, and other methods which will be used to obtain the optimal weights of portfolio and their applications. Mutual fund and hedge fund papers are also included as one of the applications of portfolio analysis in this handbook.The topic of financial derivatives, which includes futures, options, swaps, and risk management, is very important for both academicians and partitioners. Papers of financial derivatives in this handbook include (i) valuation of future contracts and hedge ratio determination, (ii) options valuation, hedging, and their application in investment analysis and portfolio management, and (iii) theories and applications of risk management.Led by worldwide known Distinguished Professor Cheng Few Lee from Rutgers University, this multi-volume work integrates theoretical, methodological, and practical issues of investment analysis, portfolio management, and financial derivatives based on his years of academic and industry experience.
Handbook Of Financial Econometrics Statistics Technology And Risk Management In 4 Volumes
DOWNLOAD
Author : Cheng F. Lee
language : en
Publisher: World Scientific Publishing Company
Release Date : 2025
Handbook Of Financial Econometrics Statistics Technology And Risk Management In 4 Volumes written by Cheng F. Lee and has been published by World Scientific Publishing Company this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025 with Business & Economics categories.
This handbook (in 4 volumes) investigates important tools for empirical and theoretical research in finance and accounting. Based on editors' and contributors' years of experience working in the industry, teaching classes, conducting research, writing textbooks, and editing journals on the subject of financial econometrics, mathematics, statistics, and technology, this handbook will review, discuss, and integrate theoretical, methodological, and practical issues of financial econometrics, mathematics, statistics, and machine learning.Volume 1 lays the groundwork with key methodologies and innovative approaches. From financial econometrics to the application of machine learning in risk management, this volume covers critical topics such as optimal futures hedging and the impacts of CEO compensation on corporate innovation. It also delves into advanced techniques in option bound determination, the influence of economic institutions on banking stability, and the latest in mortgage loan pricing predictions using ML-RNN, along with systemic risk assessment using bivariate copulas.Volume 2 explores sophisticated financial theories and machine learning applications. Readers will encounter stochastic volatility models and the complexities of implied variance in option pricing, along with in-depth discussions on real and exotic options and the diversification benefits of U.S. international equity funds. This volume also highlights groundbreaking applications of machine learning for stock selection and credit risk assessment, significantly enhancing decision-making processes in the finance sector.Volume 3 addresses critical issues in corporate finance and risk analysis, with a strong focus on practical implications. It covers the role of international transfer pricing, corporate reorganization, and executive share option plans. Additionally, it presents empirical studies on mutual fund performance and market model forecasting. This volume introduces innovative approaches in hedging, capital budgeting, and nonlinear models in corporate finance research, providing valuable insights for professionals and academics alike.Volume 4 explores the integration of big data and advanced econometrics in finance. It examines the impact of lead independent directors on earnings management and the dynamic relationship between stock prices and exchange rates. Readers will find cutting-edge techniques in survival analysis, deep neural networks for credit risk, and volatility spillovers during market crises.Written in a comprehensive manner, the four volumes discuss how to use higher moment theory to analyze investment analysis and portfolio management. In addition, they also discuss risk management theory and its application.
Neuromarketing S Role In Sustainable Finance
DOWNLOAD
Author : Taneja, Sanjay
language : en
Publisher: IGI Global
Release Date : 2024-10-18
Neuromarketing S Role In Sustainable Finance written by Taneja, Sanjay and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-18 with Business & Economics categories.
Neuromarketing plays a significant role in sustainable finance by tapping into the emotional and cognitive factors that influence investor decisions regarding socially and environmentally responsible investments. It helps financial institutions understand how individuals respond to sustainability messages, enabling them to craft more persuasive campaigns that resonate with investors’ values. By leveraging insights into behavior and decision-making processes, neuromarketing enhances the appeal of sustainable finance, encourages greener investment choices, and helps align financial practices with the growing demand for ethical, long-term impact solutions. Neuromarketing's Role in Sustainable Finance explores the intersection of neuromarketing and sustainable finance, revealing how insights from cognitive neuroscience can drive environmentally responsible investment behaviors. It examines subconscious factors influencing consumer decisions toward green investments, offering theoretical frameworks and practical applications to understand and promote ethical financial choices. Covering topics such as behavioral finance, environmental awareness, and investor patterns, this book is an excellent resource for scholars, researchers, financial professionals, marketers, business professionals, academicians, graduate and postgraduate students, and more.
Financial Landscape Transformation
DOWNLOAD
Author : Manjit Kour
language : en
Publisher: Emerald Group Publishing
Release Date : 2025-03-06
Financial Landscape Transformation written by Manjit Kour and has been published by Emerald Group Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-06 with Business & Economics categories.
Financial Landscape Transformation collects chapters to present the current and probable future state of banking and money with the advent of fintech.
Essentials Of Excel Vba Python And R
DOWNLOAD
Author : John Lee
language : en
Publisher: Springer Nature
Release Date : 2023-03-23
Essentials Of Excel Vba Python And R written by John Lee and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-23 with Business & Economics categories.
This advanced textbook for business statistics teaches, statistical analyses and research methods utilizing business case studies and financial data with the applications of Excel VBA, Python and R. Each chapter engages the reader with sample data drawn from individual stocks, stock indices, options, and futures. Now in its second edition, it has been expanded into two volumes, each of which is devoted to specific parts of the business analytics curriculum. To reflect the current age of data science and machine learning, the used applications have been updated from Minitab and SAS to Python and R, so that readers will be better prepared for the current industry. This second volume is designed for advanced courses in financial derivatives, risk management, and machine learning and financial management. In this volume we extensively use Excel, Python, and R to analyze the above-mentioned topics. It is also a comprehensive reference for active statistical finance scholars and business analysts who are looking to upgrade their toolkits. Readers can look to the first volume for dedicated content on financial statistics, and portfolio analysis.
Machine Learning In Finance
DOWNLOAD
Author : Matthew F. Dixon
language : en
Publisher: Springer Nature
Release Date : 2020-07-01
Machine Learning In Finance written by Matthew F. Dixon and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-01 with Business & Economics categories.
This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.
Portfolio And Investment Analysis With Sas
DOWNLOAD
Author : John B. Guerard
language : en
Publisher: SAS Institute
Release Date : 2019-04-03
Portfolio And Investment Analysis With Sas written by John B. Guerard and has been published by SAS Institute this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-03 with Computers categories.
Choose statistically significant stock selection models using SAS® Portfolio and Investment Analysis with SAS®: Financial Modeling Techniques for Optimization is an introduction to using SAS to choose statistically significant stock selection models, create mean-variance efficient portfolios, and aggressively invest to maximize the geometric mean. Based on the pioneering portfolio selection techniques of Harry Markowitz and others, this book shows that maximizing the geometric mean maximizes the utility of final wealth. The authors draw on decades of experience as teachers and practitioners of financial modeling to bridge the gap between theory and application. Using real-world data, the book illustrates the concept of risk-return analysis and explains why intelligent investors prefer stocks over bonds. The authors first explain how to build expected return models based on expected earnings data, valuation ratios, and past stock price performance using PROC ROBUSTREG. They then show how to construct and manage portfolios by combining the expected return and risk models. Finally, readers learn how to perform hypothesis testing using Bayesian methods to add confidence when data mining from large financial databases.
Econometric Forecasting And High Frequency Data Analysis
DOWNLOAD
Author : Yiu-kuen Tse
language : en
Publisher: World Scientific
Release Date : 2008-03-04
Econometric Forecasting And High Frequency Data Analysis written by Yiu-kuen Tse and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-03-04 with Business & Economics categories.
This important book consists of surveys of high-frequency financial data analysis and econometric forecasting, written by pioneers in these areas including Nobel laureate Lawrence Klein. Some of the chapters were presented as tutorials to an audience in the Econometric Forecasting and High-Frequency Data Analysis Workshop at the Institute for Mathematical Science, National University of Singapore in May 2006. They will be of interest to researchers working in macroeconometrics as well as financial econometrics. Moreover, readers will find these chapters useful as a guide to the literature as well as suggestions for future research.