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After the 2004 election, the Republican Party held the White House, both houses of Congress, twenty-eight governorships, and a majority of state legislatures. One-party rule, it seemed, was here to stay. Herding Donkeys tells the improbable tale of the grassroots resurgence that transformed the Democratic Party from a lonely minority to a sizable majority. It chronicles the inside story of Howard Dean's visionary yet deeply controversial fifty-state strategy, charting his unpredictable journey from insurgent presidential candidate, to front-running flameout, to chairman and conscience of the Democratic Party in an unexpected third act. Ari Berman reveals how the Obama campaign built upon Dean's strategy when others ridiculed it, expanding the ranks of the party and ultimately laying the groundwork for Obama's historic electoral victory—but also sowing the seeds of dissent that would lead to legislative stalemate and intraparty strife. Revelatory and entertaining, in the vein of Timothy Crouse's The Boys on the Bus and Rick Perlstein's Nixonland, Herding Donkeys combines fresh reportage with a rich and colorful cast of characters. It captures the untold stories of the people and places that reshaped the electoral map, painting a vivid portrait of a shifting country while dissecting the possibility and peril of a new era in American politics.
This book provides an overview of current issues associated to financial literacy improvement. In selecting and structuring the material to include, the primary criterion has been applicability of topics and recommendations and accuracy of trends toward better financial literacy level. Each chapter is dedicated to a particular component of financial literacy from education to capability. Throughout the book, there are many practices initiated around the world which, regardless of their superiority, are all useful initiatives and can roll play as a spot light in the road of improvement for both investors and authorities. This book is not only applicable for academics and students, but authorities who aim to improve financial literacy (and subsequently financial capability) among individuals and for those investors who seek to improve their own financial literacy.
This book focuses on extending the models and theories (from a mathematical/statistical point of view) which were introduced in the first volume to a more technical level. Where volume I provided an introduction to the mathematics of bubbles and contagion, volume II digs far more deeply and widely into the modeling aspects.
Focusing on research that examines both individual and organizational behavior relative to accounting, this volume of Advances in Accounting Behavioral Research offers a perspectives on topics such as tax compliance, risk judgement, and affiliation bias.
A presentation of classical asset pricing theory, this textbook is the only one to address the economic foundations of financial markets theory from a mathematically rigorous standpoint and to offer a self-contained critical discussion based on empirical results. Tools for understanding the economic analysis are provided, and mathematical models are presented in discrete time/finite state space for simplicity. Examples and exercises included.
Bridging the gap between theoretical asset pricing and industry practices in factors and factor investing, Zhang et al. provides a comprehensive treatment of factors, along with industry insights on practical factor development. Chapters cover a wide array of topics, including the foundations of quantamentals, the intricacies of market beta, the significance of statistical moments, the principles of technical analysis, and the impact of market microstructure and liquidity on trading. Furthermore, it delves into the complexities of tail risk and behavioral finance, revealing how psychological factors affect market dynamics. The discussion extends to the sophisticated use of option trading data for predictive insights and the critical differentiation between outcome uncertainty and distribution uncertainty in financial decision-making. A standout feature of the book is its examination of machine learning's role in factor investing, detailing how it transforms data preprocessing, factor discovery, and model construction. Overall, this book provides a holistic view of contemporary financial markets, highlighting the challenges and opportunities in harnessing alternative data and machine learning to develop robust investment strategies. This book would appeal to investment management professionals and trainees. It will also be of use to graduate and upper undergraduate students in quantitative finance, factor investing, asset management and/or trading.
​Earnings forecasts are ubiquitous in today’s financial markets. They are essential indicators of future firm performance and a starting point for firm valuation. Extremely inaccurate and overoptimistic forecasts during the most recent financial crisis have raised serious doubts regarding the reliability of such forecasts. This thesis therefore investigates new determinants of forecast errors and accuracy. In addition, new determinants of forecast revisions are examined. More specifically, the thesis answers the following questions: 1) How do analyst incentives lead to forecast errors? 2) How do changes in analyst incentives lead to forecast revisions?, and 3) What factors drive differences in forecast accuracy?
This book is a study of earnings management, aimed at scholars and professionals in accounting, finance, economics, and law. The authors address research questions including: Why are earnings so important that firms feel compelled to manipulate them? What set of circumstances will induce earnings management? How will the interaction among management, boards of directors, investors, employees, suppliers, customers and regulators affect earnings management? How to design empirical research addressing earnings management? What are the limitations and strengths of current empirical models?