Empirical Asset Pricing
Empirical Asset Pricing

Author: Wayne Ferson

Publisher: MIT Press

Published: 2019-03-12

Total Pages: 497

ISBN-13: 0262039370

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An introduction to the theory and methods of empirical asset pricing, integrating classical foundations with recent developments. This book offers a comprehensive advanced introduction to asset pricing, the study of models for the prices and returns of various securities. The focus is empirical, emphasizing how the models relate to the data. The book offers a uniquely integrated treatment, combining classical foundations with more recent developments in the literature and relating some of the material to applications in investment management. It covers the theory of empirical asset pricing, the main empirical methods, and a range of applied topics. The book introduces the theory of empirical asset pricing through three main paradigms: mean variance analysis, stochastic discount factors, and beta pricing models. It describes empirical methods, beginning with the generalized method of moments (GMM) and viewing other methods as special cases of GMM; offers a comprehensive review of fund performance evaluation; and presents selected applied topics, including a substantial chapter on predictability in asset markets that covers predicting the level of returns, volatility and higher moments, and predicting cross-sectional differences in returns. Other chapters cover production-based asset pricing, long-run risk models, the Campbell-Shiller approximation, the debate on covariance versus characteristics, and the relation of volatility to the cross-section of stock returns. An extensive reference section captures the current state of the field. The book is intended for use by graduate students in finance and economics; it can also serve as a reference for professionals.

Machine Learning in Asset Pricing
Machine Learning in Asset Pricing

Author: Stefan Nagel

Publisher: Princeton University Press

Published: 2021-05-11

Total Pages: 156

ISBN-13: 0691218706

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A groundbreaking, authoritative introduction to how machine learning can be applied to asset pricing Investors in financial markets are faced with an abundance of potentially value-relevant information from a wide variety of different sources. In such data-rich, high-dimensional environments, techniques from the rapidly advancing field of machine learning (ML) are well-suited for solving prediction problems. Accordingly, ML methods are quickly becoming part of the toolkit in asset pricing research and quantitative investing. In this book, Stefan Nagel examines the promises and challenges of ML applications in asset pricing. Asset pricing problems are substantially different from the settings for which ML tools were developed originally. To realize the potential of ML methods, they must be adapted for the specific conditions in asset pricing applications. Economic considerations, such as portfolio optimization, absence of near arbitrage, and investor learning can guide the selection and modification of ML tools. Beginning with a brief survey of basic supervised ML methods, Nagel then discusses the application of these techniques in empirical research in asset pricing and shows how they promise to advance the theoretical modeling of financial markets. Machine Learning in Asset Pricing presents the exciting possibilities of using cutting-edge methods in research on financial asset valuation.

Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance
Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance

Author: El Bachir Boukherouaa

Publisher: International Monetary Fund

Published: 2021-10-22

Total Pages: 35

ISBN-13: 1589063953

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This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.

Green Transition Impacts on the Economy, Society, and Environment
Green Transition Impacts on the Economy, Society, and Environment

Author: Y?ld?r?m, Seda

Publisher: IGI Global

Published: 2024-08-14

Total Pages: 479

ISBN-13:

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The challenge of achieving sustainability is complex and multifaceted, with varying approaches and policies across different countries and industries. This lack of universal standards poses a significant obstacle to the global transition towards a sustainable future. The COVID-19 pandemic has further exacerbated these disparities, highlighting the urgent need for cohesive and practical sustainability strategies. What sets Green Transition Impacts on the Economy, Society, and Environment apart is its comprehensive and unique solution to this pressing issue, offering a unified framework for sustainability that can be applied across diverse contexts. By gathering insights and approaches from researchers worldwide, this book provides a holistic view of sustainability, addressing critical issues such as climate change, energy security, and social responsibility. It offers practical solutions and case studies demonstrating effective strategies for achieving sustainability goals. Through this approach, the book aims to equip policymakers, practitioners, and researchers with the knowledge and tools needed to navigate the complexities of sustainability in the 21st century.

Proceedings of the 2023 2nd International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2023)
Proceedings of the 2023 2nd International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2023)

Author: K. Hemachandran

Publisher: Springer Nature

Published: 2023-09-26

Total Pages: 576

ISBN-13: 9464632224

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This is an open access book. With the continuous upgrading of network information technology, especially the combination of Internet - cloud computing - blockchain - Internet of things and other information technologies with social and economic activities, through the improvement of artificial intelligence, Internet and big data with high quality and fast processing efficiency, the economic form is transformed from industrial economy to information economy. This will greatly reduce social transaction costs, improve the efficiency of resource optimization, increase the added value of products, enterprises and industries, and promote the rapid development of social productivity. 2023 2nd International Conference on Artificial Intelligence, the Internet and the Digital Economy (ICAID 2023) will continue to focus on the latest research on "Artificial intelligence, the Internet and the Digital Economy", and expand the research on "technology and application of the integrated development of Digital Economy and Artificial Intelligence" as the theme. The aim is to gather experts, scholars, researchers and related practitioners from around the world to share research results, discuss hot issues, and provide participants with cutting-edge technology information so that they can keep abreast of industry developments, the latest technologies and broaden their research horizons. The conference was held in Beijing, China on April 21-23, 2023. All experts and scholars are welcome to attend.

Handbook Of Financial Econometrics, Mathematics, Statistics, And Machine Learning (In 4 Volumes)
Handbook Of Financial Econometrics, Mathematics, Statistics, And Machine Learning (In 4 Volumes)

Author: Cheng Few Lee

Publisher: World Scientific

Published: 2020-07-30

Total Pages: 5053

ISBN-13: 9811202400

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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.

Recent Applications of Financial Risk Modelling and Portfolio Management
Recent Applications of Financial Risk Modelling and Portfolio Management

Author: Škrinjari?, Tihana

Publisher: IGI Global

Published: 2020-09-25

Total Pages: 432

ISBN-13: 1799850846

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In today’s financial market, portfolio and risk management are facing an array of challenges. This is due to increasing levels of knowledge and data that are being made available that have caused a multitude of different investment models to be explored and implemented. Professionals and researchers in this field are in need of up-to-date research that analyzes these contemporary models of practice and keeps pace with the advancements being made within financial risk modelling and portfolio control. Recent Applications of Financial Risk Modelling and Portfolio Management is a pivotal reference source that provides vital research on the use of modern data analysis as well as quantitative methods for developing successful portfolio and risk management techniques. While highlighting topics such as credit scoring, investment strategies, and budgeting, this publication explores diverse models for achieving investment goals as well as improving upon traditional financial modelling methods. This book is ideally designed for researchers, financial analysts, executives, practitioners, policymakers, academicians, and students seeking current research on contemporary risk management strategies in the financial sector.