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.

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.

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.

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.

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.

Essentials of Excel VBA, Python, and R
Essentials of Excel VBA, Python, and R

Author: John Lee

Publisher: Springer Nature

Published: 2023-01-02

Total Pages: 698

ISBN-13: 3031142365

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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 first volume is designed for advanced courses in financial statistics, investment analysis and portfolio management. 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 second volume for dedicated content on financial derivatives, risk management, and machine learning.

The Economics of Artificial Intelligence
The Economics of Artificial Intelligence

Author: Ajay Agrawal

Publisher: University of Chicago Press

Published: 2024-03-05

Total Pages: 172

ISBN-13: 0226833127

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A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system. In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system.