WINNER, Business: Personal Finance/Investing, 2015 USA Best Book Awards FINALIST, Business: Reference, 2015 USA Best Book Awards Investor Behavior provides readers with a comprehensive understanding and the latest research in the area of behavioral finance and investor decision making. Blending contributions from noted academics and experienced practitioners, this 30-chapter book will provide investment professionals with insights on how to understand and manage client behavior; a framework for interpreting financial market activity; and an in-depth understanding of this important new field of investment research. The book should also be of interest to academics, investors, and students. The book will cover the major principles of investor psychology, including heuristics, bounded rationality, regret theory, mental accounting, framing, prospect theory, and loss aversion. Specific sections of the book will delve into the role of personality traits, financial therapy, retirement planning, financial coaching, and emotions in investment decisions. Other topics covered include risk perception and tolerance, asset allocation decisions under inertia and inattention bias; evidenced based financial planning, motivation and satisfaction, behavioral investment management, and neurofinance. Contributions will delve into the behavioral underpinnings of various trading and investment topics including trader psychology, stock momentum, earnings surprises, and anomalies. The final chapters of the book examine new research on socially responsible investing, mutual funds, and real estate investing from a behavioral perspective. Empirical evidence and current literature about each type of investment issue are featured. Cited research studies are presented in a straightforward manner focusing on the comprehension of study findings, rather than on the details of mathematical frameworks.

The research presented here is developed from analyses of extensive European and North American datasets (1995-2019) spanning periods of economic expansion, recession, and recovery and explores how firms manage in a world with extreme exposures and how their failure and success affect the distribution of financial returns.

Economic and social progress requires a diverse ecosystem of firms that play complementary roles. Making It Big: Why Developing Countries Need More Large Firms constitutes one of the most up-to-date assessments of how large firms are created in low- and middle-income countries and their role in development. It argues that large firms advance a range of development objectives in ways that other firms do not: large firms are more likely to innovate, export, and offer training and are more likely to adopt international standards of quality, among other contributions. Their particularities are closely associated with productivity advantages and translate into improved outcomes not only for their owners but also for their workers and for smaller enterprises in their value chains. The challenge for economic development, however, is that production does not reach economic scale in low- and middle-income countries. Why are large firms scarcer in developing countries? Drawing on a rare set of data from public and private sources, as well as proprietary data from the International Finance Corporation and case studies, this book shows that large firms are often born large—or with the attributes of largeness. In other words, what is distinct about them is often in place from day one of their operations. To fill the “missing top†? of the firm-size distribution with additional large firms, governments should support the creation of such firms by opening markets to greater competition. In low-income countries, this objective can be achieved through simple policy reorientation, such as breaking oligopolies, removing unnecessary restrictions to international trade and investment, and establishing strong rules to prevent the abuse of market power. Governments should also strive to ensure that private actors have the skills, technology, intelligence, infrastructure, and finance they need to create large ventures. Additionally, they should actively work to spread the benefits from production at scale across the largest possible number of market participants. This book seeks to bring frontier thinking and evidence on the role and origins of large firms to a wide range of readers, including academics, development practitioners and policy makers.

Demonstrates how nonresponse in sample surveys and censuses can be handled by replacing each missing value with two or more multiple imputations. Clearly illustrates the advantages of modern computing to such handle surveys, and demonstrates the benefit of this statistical technique for researchers who must analyze them. Also presents the background for Bayesian and frequentist theory. After establishing that only standard complete-data methods are needed to analyze a multiply-imputed set, the text evaluates procedures in general circumstances, outlining specific procedures for creating imputations in both the ignorable and nonignorable cases. Examples and exercises reinforce ideas, and the interplay of Bayesian and frequentist ideas presents a unified picture of modern statistics.

This User’s Guide is intended to support the design, implementation, analysis, interpretation, and quality evaluation of registries created to increase understanding of patient outcomes. For the purposes of this guide, a patient registry is an organized system that uses observational study methods to collect uniform data (clinical and other) to evaluate specified outcomes for a population defined by a particular disease, condition, or exposure, and that serves one or more predetermined scientific, clinical, or policy purposes. A registry database is a file (or files) derived from the registry. Although registries can serve many purposes, this guide focuses on registries created for one or more of the following purposes: to describe the natural history of disease, to determine clinical effectiveness or cost-effectiveness of health care products and services, to measure or monitor safety and harm, and/or to measure quality of care. Registries are classified according to how their populations are defined. For example, product registries include patients who have been exposed to biopharmaceutical products or medical devices. Health services registries consist of patients who have had a common procedure, clinical encounter, or hospitalization. Disease or condition registries are defined by patients having the same diagnosis, such as cystic fibrosis or heart failure. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews.

This volume gathers the material of the first causality challenge organized by the Causality Workbench Team for the World Congress on Computational Intelligence (WCCI), June 3, 2008 in Hong Kong, including a collection of papers first published in the Journal of Machine Learning Research and a paper summarizing the results of the challenge and contributions of the top ranking entrants. An appendix describes the methods used by participants and a technical report with details on the datasets. The book is complemented by a web site from which the datasets can be downloaded and post-challenge submissions can be made to benchmark new algorithms.