Decision-makers are confronted with decisions under uncertainty. Financial uncertainty may adversely affect growth. Theoretically, forecasts may potentially reduce uncertainty and create economic value. Focusing on survey predictions, this cumulative dissertation addresses the economic relevance of interest rate, crude oil and exchange rate forecasts for policy as well as managerial decision-makers and financial market participants, respectively. The first research objective of the presented studies is to compile novel evidence on the accuracy, rationality and usefulness of financial market...
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.
This thesis addresses the topic of decision making under uncertainty, with particular focus on financial markets. The aim of this research is to support improved decisions in practice, and related to this, to advance our understanding of financial markets. Stochastic optimization provides the tools to determine optimal decisions in uncertain environments, and the optimality conditions of these models produce insights into how financial markets work. To be more concrete, a great deal of financial theory is based on optimality conditions derived from stochastic optimization models. Therefore, an important part of the development of financial theory is to study stochastic optimization models that step-by-step better capture the essence of reality. This is the motivation behind the focus of this thesis, which is to study methods that in relation to prevailing models that underlie financial theory allow additional real-world complexities to be properly modeled. The overall purpose of this thesis is to develop and evaluate stochastic optimization models that support improved decisions under uncertainty on financial markets. The research into stochastic optimization in financial literature has traditionally focused on problem formulations that allow closed-form or `exact' numerical solutions; typically through the application of dynamic programming or optimal control. The focus in this thesis is on two other optimization methods, namely stochastic programming and approximate dynamic programming, which open up opportunities to study new classes of financial problems. More specifically, these optimization methods allow additional and important aspects of many real-world problems to be captured. This thesis contributes with several insights that are relevant for both financial and stochastic optimization literature. First, we show that the modeling of several real-world aspects traditionally not considered in the literature are important components in a model which supports corporate hedging decisions. Specifically, we document the importance of modeling term premia, a rich asset universe and transaction costs. Secondly, we provide two methodological contributions to the stochastic programming literature by: (i) highlighting the challenges of realizing improved decisions through more stages in stochastic programming models; and (ii) developing an importance sampling method that can be used to produce high solution quality with few scenarios. Finally, we design an approximate dynamic programming model that gives close to optimal solutions to the classic, and thus far unsolved, portfolio choice problem with constant relative risk aversion preferences and transaction costs, given many risky assets and a large number of time periods.
Computing has become essential for the modeling, analysis, and optimization of systems. This book is devoted to algorithms, computational analysis, and decision models. The chapters are organized in two parts: optimization models of decisions and models of pricing and equilibria. Optimization is at the core of rational decision making. Even when the decision maker has more than one goal or there is significant uncertainty in the system, optimization provides a rational framework for efficient decisions. The Markowitz mean-variance formulation is a classical example. The first part of the book is on recent developments in optimization decision models for finance and economics. The first four chapters of this part focus directly on multi-stage problems in finance. Chapters 5-8 involve the use of worst-case robust analysis. Chapters 9-11 are devoted to portfolio optimization. The final four chapters are on transportation-inventory with stochastic demand; optimal investment with CRRA utility; hedging financial contracts; and, automatic differentiation for computational finance. The uncertainty associated with prediction and modeling constantly requires the development of improved methods and models. Similarly, as systems strive towards equilibria, the characterization and computation of equilibria assists analysis and prediction. The second part of the book is devoted to recent research in computational tools and models of equilibria, prediction, and pricing. The first three chapters of this part consider hedging issues in finance. Chapters 19-22 consider prediction and modeling methodologies. Chapters 23-26 focus on auctions and equilibria. Volatility models are investigated in chapters 27-28. The final two chapters investigate risk assessment and product pricing. Audience: Researchers working in computational issues related to economics, finance, and management science.
Uncertainty is a fundamental characteristic of weather, seasonal climate, and hydrological prediction, and no forecast is complete without a description of its uncertainty. Effective communication of uncertainty helps people better understand the likelihood of a particular event and improves their ability to make decisions based on the forecast. Nonetheless, for decades, users of these forecasts have been conditioned to receive incomplete information about uncertainty. They have become used to single-valued (deterministic) forecasts (e.g., "the high temperature will be 70 degrees Farenheit 9 days from now") and applied their own experience in determining how much confidence to place in the forecast. Most forecast products from the public and private sectors, including those from the National Oceanographic and Atmospheric Administration's National Weather Service, continue this deterministic legacy. Fortunately, the National Weather Service and others in the prediction community have recognized the need to view uncertainty as a fundamental part of forecasts. By partnering with other segments of the community to understand user needs, generate relevant and rich informational products, and utilize effective communication vehicles, the National Weather Service can take a leading role in the transition to widespread, effective incorporation of uncertainty information into predictions. "Completing the Forecast" makes recommendations to the National Weather Service and the broader prediction community on how to make this transition.
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).
Abstract: This dissertation explores decision making under risk and uncertainty by professionals and markets composed of professionals. Both essays use empirical data from some of the most competitive economic environments imaginable. The first essay looks at market prices resulting from the sum of both professional and lay choices while the second analyzes the individual choices of professional gamblers. Both essays propose a theoretical framework to not just positively identify what professionals do, but also prescribe normatively what they should do. In both cases, the two are found to be different. The first essay, Market Response to Risk and Uncertainty, 2004 Hurricane Forecasts, develops a simple function to explain insurance losses from hurricanes as a function of short-term forecasts. After demonstrating the accuracy of the function in explaining 2004 insurance claims, the remainder of the essay looks at the stock market's use of these forecasts in pricing insurer and US economy risk. Despite causing billions in damages, both hurricanes and hurricane forecasts are found to have only marginal impacts on financial markets. Surprisingly, markets fail to make efficient use of hurricane forecasts in pricing both insurer and general market exposure to hurricane risk. A potential explanation for market inefficiency around hurricane information is that, like researchers, financial actors may be confounding uncertainty for unpredictability. The second essay, Know When to Hold'em, examines a specific decision within a highly popular, high-stakes version of poker. Like financial markets analyzed in the first essay, professional gamblers must make risky decisions with uncertain probabilities of success. Gamblers are found to be both overly conservative in their choices and overly confident in their abilities to predict uncertain outcomes. A simple statistical model that generalizes across situations to form a naïve probability of having the best cards, is found to be as effective in decision making as players' true expectations of winning. Additionally, a dynamic theoretical model is presented in order to show professional divergence from risk-neutral expected profit maximization in the credit constrained world of tournament poker. Interestingly the value function, derived from this model, is equivalent to an optimal stock price of a poker player.
This book reviews the latest research from psychology, neuroscience, and behavioral economics evaluating how people make financial choices in real-life circumstances. The volume is divided into three sections investigating financial decision making at the level of the brain, the level of an individual decision maker, and the level of the society, concluding with a discussion of the implications for further research. Among the topics discussed: Neural and hormonal bases of financial decision making Personality, cognitive abilities, emotions, and financial decisions Aging and financial decision making Coping methods for making financial choices under uncertainty Stock market crashes and market bubbles Psychological perspectives on borrowing, paying taxes, gambling, and charitable giving Psychological Perspectives on Financial Decision Making is a useful reference for researchers both in and outside of psychology, including decision-making experts, consumer psychologists, and behavioral economists.
