Haim Levy – författare

Microscopic Simulation (MS) uses a computer to represent and keep track of individual ("microscopic") elements in order to investigate complex systems which are analytically intractable. A methodology that was developed to solve physics problems, MS has been used to study the relation between microscopic behavior and macroscopic phenomena in systems ranging from those of atomic particles, to cars, animals, and even humans. In finance, MS can help explain, among other things, the effects of various elements of investor behavior on market dynamics and asset pricing. It is these issues in particular, and the value of an MS approach to finance in general, that are the subjects of this book. The authors not only put their work in perspective by surveying traditional economic analyses of investor behavior, but they also briefly examine the use of MS in fields other than finance.Most models in economics and finance assume that investors are rational. However, experimental studies reveal systematic deviations from rational behavior. How can we determine the effect of investors'' deviations from rational behavior on asset prices and market dynamics? By using Microscopic Simulation, a methodology originally developed by physicists for the investigation of complex systems, the authors are able to relax classical assumptions about investor behavior and to model it as empirically and experimentally observed. This rounded and judicious introduction to the application of MS in finance and economics reveals that many of the empirically-observed "puzzles" in finance can be explained by investors'' quasi-rationality.Researchers use the book because it models heterogeneous investors, a group that has proven difficult to model. Being able to predict how people will invest and setting asset prices accordingly is inherently appealing, and the combination of computing power and statistical mechanics in this book makes such modeling possible. Because many finance researchers have backgrounds in physics, the material here is accessible.- Emphasizes investor behavior in determining asset prices and market dynamics- Introduces Microscopic Simulation within a simplified framework- Offers ways to model deviations from rational decision-making

This second edition of Stochastic Dominance is devoted to investment decision­ making under uncertainty. The book covers four basic approaches to this process: a) The stochastic dominance (SD) approach, developed on the foundation of von-Neumann and Morgenstem^ expected utiHty paradigm. b) The mean-variance approach developed by Markowitz^ on the foundation of von-Neumann and Morgenstern''s expected utility or simply on the assumption of a utility function based on mean and variance. c) The "almost" stochastic dominance (ASD) rules and the "almost" me- variance rule (AMV). No matter whether one employs objective or subjective probabilities, the common stochastic dominance criteria and the mean variance rule may lead to paradoxes: they are unable to rank prospect A w^hich yields $1 with a probability of 0.01 and a million dollars with probability of 0.99, and prospect B which yields $2 with certainty. This is an absurdity as in any sample of subjects one takes, 100% of subjects choose A. The "almost" stochastic dominance criteria and "almost" mean variance rule, which have been recently been developed by Leshno and Levy in 2002^, suggest a remedy to such paradoxes.

The Capital Asset Pricing Model (CAPM) and the mean-variance (M-V) rule, which are based on classic expected utility theory, have been heavily criticized theoretically and empirically. The advent of behavioral economics, prospect theory and other psychology-minded approaches in finance challenges the rational investor model from which CAPM and M-V derive. Haim Levy argues that the tension between the classic financial models and behavioral economics approaches is more apparent than real. This book aims to relax the tension between the two paradigms. Specifically, Professor Levy shows that although behavioral economics contradicts aspects of expected utility theory, CAPM and M-V are intact in both expected utility theory and cumulative prospect theory frameworks. There is furthermore no evidence to reject CAPM empirically when ex-ante parameters are employed. Professionals may thus comfortably teach and use CAPM and behavioral economics or cumulative prospect theory as coexisting paradigms.

The Capital Asset Pricing Model (CAPM) and the mean-variance (M-V) rule, which are based on classic expected utility theory, have been heavily criticized theoretically and empirically. The advent of behavioral economics, prospect theory and other psychology-minded approaches in finance challenges the rational investor model from which CAPM and M-V derive. Haim Levy argues that the tension between the classic financial models and behavioral economics approaches is more apparent than real. This book aims to relax the tension between the two paradigms. Specifically, Professor Levy shows that although behavioral economics contradicts aspects of expected utility theory, CAPM and M-V are intact in both expected utility theory and cumulative prospect theory frameworks. There is furthermore no evidence to reject CAPM empirically when ex-ante parameters are employed. Professionals may thus comfortably teach and use CAPM and behavioral economics or cumulative prospect theory as coexisting paradigms.

This second edition of Stochastic Dominance is devoted to investment decision­ making under uncertainty. The book covers four basic approaches to this process: a) The stochastic dominance (SD) approach, developed on the foundation of von-Neumann and Morgenstem^ expected utiHty paradigm. b) The mean-variance approach developed by Markowitz^ on the foundation of von-Neumann and Morgenstern's expected utility or simply on the assumption of a utility function based on mean and variance. c) The "almost" stochastic dominance (ASD) rules and the "almost" me- variance rule (AMV). No matter whether one employs objective or subjective probabilities, the common stochastic dominance criteria and the mean variance rule may lead to paradoxes: they are unable to rank prospect A w^hich yields $1 with a probability of 0.01 and a million dollars with probability of 0.99, and prospect B which yields $2 with certainty. This is an absurdity as in any sample of subjects one takes, 100% of subjects choose A. The "almost" stochastic dominance criteria and "almost" mean variance rule, which have been recently been developed by Leshno and Levy in 2002^, suggest a remedy to such paradoxes.

This book is devoted to investment decision-making under uncertainty. The book covers three basic approaches to this process: a) The stochastic dominance approach, developed on the foundation of von­ Neumann and Morgenstern'' expected utility paradigm. 2 b) The mean-variance approach developed by Markowitz on the foundation of von-Neumann and Morgenstem''s expected utility or simply on the assumption of a utility function based on mean and variance. c) The non-expected utility approach, focusing on prospect theory and its modi­ fied version, cumulative prospect theory. This theory is based on an experi­ mental finding that subjects participating in laboratory experiments often violate expected utility maximization: They tend to use · subjective probability beliefs that differ systematically from the objective probabilities and to base their decisions on changes in wealth rather than on total wealth. The above approaches are discussed and compared in this book. W e also discuss cases in which stochastic dominance rules coincide with the mean-variance rule and cases in which contradictions between these two approaches may occur. We then discuss the relationship between stochastic dominance rules and prospect theory, and establish a new investment decision rule which combines the two and which we call prospect stochastic dominance. Although all three approaches are discussed, most of the book is devoted to the stochastic dominance paradigm.

This fully updated third edition is devoted to the analysis of various Stochastic Dominance (SD) decision rules. It discusses the pros and cons of each of the alternate SD rules, the application of these rules to various research areas like statistics, agriculture, medicine, measuring income inequality and the poverty level in various countries, and of course, to investment decision-making under uncertainty. The book features changes and additions to the various chapters, and also includes two completely new chapters. One deals with asymptotic SD and the relation between FSD and the maximum geometric mean (MGM) rule (or the maximum growth portfolio). The other new chapter discusses bivariate SD rules where the individual’s utility is determined not only by his own wealth, but also by his standing relative to his peer group. Stochastic Dominance: Investment Decision Making under Uncertainty, 3rd Ed. covers the following basic issues: the SD approach, asymptotic SD rules, the mean-variance (MV) approach, as well as the non-expected utility approach. The non-expected utility approach focuses on Regret Theory (RT) and mainly on prospect theory (PT) and its modified version, cumulative prospect theory (CPT) which assumes S-shape preferences. In addition to these issues the book suggests a new stochastic dominance rule called the Markowitz stochastic dominance (MSD) rule corresponding to all reverse-S-shape preferences. It also discusses the concept of the multivariate expected utility and analyzed in more detail the bivariate expected utility case.From the reviews of the second edition:"This book is an economics book about stochastic dominance. … is certainly a valuable reference for graduate students interested in decision making under uncertainty. It investigates and compares different approaches and presents many examples. Moreover, empirical studies and experimental results play an important role in this book, whichmakes it interesting to read." (Nicole Bäuerle, Mathematical Reviews, Issue 2007 d)

This fully updated third edition is devoted to the analysis of various Stochastic Dominance (SD) decision rules. It discusses the pros and cons of each of the alternate SD rules, the application of these rules to various research areas like statistics, agriculture, medicine, measuring income inequality and the poverty level in various countries, and of course, to investment decision-making under uncertainty. The book features changes and additions to the various chapters, and also includes two completely new chapters. One deals with asymptotic SD and the relation between FSD and the maximum geometric mean (MGM) rule (or the maximum growth portfolio). The other new chapter discusses bivariate SD rules where the individual’s utility is determined not only by his own wealth, but also by his standing relative to his peer group.

Stochastic Dominance: Investment Decision Making under Uncertainty, 3rd Ed. covers the following basic issues: the SD approach, asymptotic SD rules, the mean-variance (MV) approach, as well as the non-expected utility approach. The non-expected utility approach focuses on Regret Theory (RT) and mainly on prospect theory (PT) and its modified version, cumulative prospect theory (CPT) which assumes S-shape preferences. In addition to these issues the book suggests a new stochastic dominance rule called the Markowitz stochastic dominance (MSD) rule corresponding to all reverse-S-shape preferences. It also discusses the concept of the multivariate expected utility and analyzed in more detail the bivariate expected utility case.

From the reviews of the second edition:

"This book is an economics book about stochastic dominance. … is certainly a valuable reference for graduate students interested in decision making under uncertainty. It investigates and compares different approaches and presents many examples. Moreover, empirical studies and experimental results play an important role in this book, which

makes it interesting to read." (Nicole Bäuerle, Mathematical Reviews, Issue 2007 d)

This fully updated third edition is devoted to the analysis of various Stochastic Dominance (SD) decision rules. It discusses the pros and cons of each of the alternate SD rules, the application of these rules to various research areas like statistics, agriculture, medicine, measuring income inequality and the poverty level in various countries, and of course, to investment decision-making under uncertainty. The book features changes and additions to the various chapters, and also includes two completely new chapters. One deals with asymptotic SD and the relation between FSD and the maximum geometric mean (MGM) rule (or the maximum growth portfolio). The other new chapter discusses bivariate SD rules where the individual’s utility is determined not only by his own wealth, but also by his standing relative to his peer group. Stochastic Dominance: Investment Decision Making under Uncertainty, 3rd Ed. covers the following basic issues: the SD approach, asymptotic SD rules, the mean-variance (MV) approach, as well as the non-expected utility approach. The non-expected utility approach focuses on Regret Theory (RT) and mainly on prospect theory (PT) and its modified version, cumulative prospect theory (CPT) which assumes S-shape preferences. In addition to these issues the book suggests a new stochastic dominance rule called the Markowitz stochastic dominance (MSD) rule corresponding to all reverse-S-shape preferences. It also discusses the concept of the multivariate expected utility and analyzed in more detail the bivariate expected utility case.From the reviews of the second edition:"This book is an economics book about stochastic dominance. … is certainly a valuable reference for graduate students interested in decision making under uncertainty. It investigates and compares different approaches and presents many examples. Moreover, empirical studies and experimental results play an important role in this book, whichmakes it interesting to read." (Nicole Bäuerle, Mathematical Reviews, Issue 2007 d)