Based on courses developed by the author over several years, this book provides access to a broad area of research that is not available in separate articles or books of readings. Topics covered include the meaning and measurement of risk, general single-period portfolio problems, mean-variance analysis and the Capital Asset Pricing Model, the Arbitrage Pricing Theory, complete markets, multiperiod portfolio problems and the Intertemporal Capital Asset Pricing Model, the Black-Scholes option pricing model and contingent claims analysis, 'risk-neutral' pricing with Martingales, Modigliani-Miller and the capital structure of the firm, interest rates and the term structure, and others.
This handbook in two parts covers key topics of the theory of financial decision making. Some of the papers discuss real applications or case studies as well. There are a number of new papers that have never been published before especially in Part II.Part I is concerned with Decision Making Under Uncertainty. This includes subsections on Arbitrage, Utility Theory, Risk Aversion and Static Portfolio Theory, and Stochastic Dominance. Part II is concerned with Dynamic Modeling that is the transition for static decision making to multiperiod decision making. The analysis starts with Risk Measures and then discusses Dynamic Portfolio Theory, Tactical Asset Allocation and Asset-Liability Management Using Utility and Goal Based Consumption-Investment Decision Models.A comprehensive set of problems both computational and review and mind expanding with many unsolved problems are in an accompanying problems book. The handbook plus the book of problems form a very strong set of materials for PhD and Masters courses both as the main or as supplementary text in finance theory, financial decision making and portfolio theory. For researchers, it is a valuable resource being an up to date treatment of topics in the classic books on these topics by Johnathan Ingersoll in 1988, and William Ziemba and Raymond Vickson in 1975 (updated 2 nd edition published in 2006).
Behavioral finance challenges the traditional assumption that individuals are rational by focusing on the cognitive and emotional aspects of finance, which draws on psychology, sociology, and biology to investigate true financial behavior. The financial sector requires sound understanding of market dynamics and strategic issues to meet future challenges in the field. Behavioral Finance and Decision-Making Models seeks to examine behavioral biases and their impact on investment decisions in order to develop better future plans and strategies in the financial sector. While highlighting topics including behavioral approach, financial regulation, and globalized sector, this book is intended for policymakers, technology developers, managers, government officials, academicians, researchers, and advanced-level students.
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.
Consumer needs and demands are constantly changing. Because of this, marketing science and finance have their own concepts and theoretical backgrounds for evaluating consumer-related challenges. However, examining the function of finance with a marketing discipline can help to better understand internal management processes and compete in today’s market. The Handbook of Research on Decision-Making Techniques in Financial Marketing is a collection of innovative research that integrates financial and marketing functions to make better sense of the workplace environment and business-related challenges. Different financial challenges are taken into consideration while many of them are based on marketing theories such as agency theory, product life cycle, and optimal consumer experience. While highlighting topics including behavioral financing, corporate ethics, and Islamic banking, this book is ideally designed for financiers, marketers, financial analysts, marketing strategists, researchers, policymakers, government officials, academicians, students, and industry professionals.
Individuals and families make key decisions that impact many aspects of financial stability and determine the future of the economy. These decisions involve balancing current sacrifice against future benefits. People have to decide how much to invest in health care, exercise, their diet, and insurance. They must decide how much debt to take on, and how much to save. And they make choices about jobs that determine employment and unemployment levels. Forward-Looking Decision Making is about modeling this individual or family-based decision making using an optimizing dynamic programming model. Robert Hall first reviews ideas about dynamic programs and introduces new ideas about numerical solutions and the representation of solved models as Markov processes. He surveys recent research on the parameters of preferences--the intertemporal elasticity of substitution, the Frisch elasticity of labor supply, and the Frisch cross-elasticity. He then examines dynamic programming models applied to health spending, long-term care insurance, employment, entrepreneurial risk-taking, and consumer debt. Linking theory with data and applying them to real-world problems, Forward-Looking Decision Making uses dynamic optimization programming models to shed light on individual behaviors and their economic implications.
A comprehensive guide to making better capital structure and corporate financing decisions in today's dynamic business environment Given the dramatic changes that have recently occurred in the economy, the topic of capital structure and corporate financing decisions is critically important. The fact is that firms need to constantly revisit their portfolio of debt, equity, and hybrid securities to finance assets, operations, and future growth. Capital Structure and Corporate Financing Decisions provides an in-depth examination of critical capital structure topics, including discussions of basic capital structure components, key theories and practices, and practical application in an increasingly complex corporate world. Throughout, the book emphasizes how a sound capital structure simultaneously minimizes the firm's cost of capital and maximizes the value to shareholders. Offers a strategic focus that allows you to understand how financing decisions relates to a firm's overall corporate policy Consists of contributed chapters from both academics and experienced professionals, offering a variety of perspectives and a rich interplay of ideas Contains information from survey research describing actual financial practices of firms This valuable resource takes a practical approach to capital structure by discussing why various theories make sense and how firms use them to solve problems and create wealth. In the wake of the recent financial crisis, the insights found here are essential to excelling in today's volatile business environment.
In an ever-changing economy, market specialists strive to find new ways to evaluate the risks and potential reward of economic ventures by assessing the importance of human reaction during the economic planning process. The Handbook of Research on Behavioral Finance and Investment Strategies: Decision Making in the Financial Industry presents an interdisciplinary, comparative, and competitive analysis of the thought processes and planning necessary for individual and corporate economic management. This publication is an essential reference source for professionals, practitioners, and managers working in the field of finance, as well as researchers and academicians interested in an interdisciplinary approach to combine financial management, sociology, and psychology.
As Artificial Intelligence (AI) seizes all aspects of human life, there is a fundamental shift in the way in which humans are thinking of and doing things. Ordinarily, humans have relied on economics and finance theories to make sense of, and predict concepts such as comparative advantage, long run economic growth, lack or distortion of information and failures, role of labour as a factor of production and the decision making process for the purpose of allocating resources among other theories. Of interest though is that literature has not attempted to utilize these advances in technology in order to modernize economic and finance theories that are fundamental in the decision making process for the purpose of allocating scarce resources among other things. With the simulated intelligence in machines, which allows machines to act like humans and to some extent even anticipate events better than humans, thanks to their ability to handle massive data sets, this book will use artificial intelligence to explain what these economic and finance theories mean in the context of the agent wanting to make a decision. The main feature of finance and economic theories is that they try to eliminate the effects of uncertainties by attempting to bring the future to the present. The fundamentals of this statement is deeply rooted in risk and risk management. In behavioural sciences, economics as a discipline has always provided a well-established foundation for understanding uncertainties and what this means for decision making. Finance and economics have done this through different models which attempt to predict the future. On its part, risk management attempts to hedge or mitigate these uncertainties in order for “the planner” to reach the favourable outcome. This book focuses on how AI is to redefine certain important economic and financial theories that are specifically used for the purpose of eliminating uncertainties so as to allow agents to make informed decisions. In effect, certain aspects of finance and economic theories cannot be understood in their entirety without the incorporation of AI.
