We propose three nonparametric tests for the null of no event-induced shifts in the distribution of stock returns. One test is the natural extension of the popular Corrado rank test to the case of cross-sectionally dependent returns, while the other two are based on new ideas. Unfortunately only for one of these tests a solid theory for approximating the distribution of the statistic can be derived, but some simulation experiments confirm that normality is a good approximation also for the other two. The new tests are compared to a widely used parametric test (Patell) through simulation experiments and are shown to compare favorably in terms of power. Simulation results are based on bootstrapping daily stock returns from the S&P100 and NASDAQ indexes.
Judging by the sheer number of papers reviewed in this Handbook, the empirical analysis of firms' financing and investment decisions—empirical corporate finance—has become a dominant field in financial economics. The growing interest in everything "corporate is fueled by a healthy combination of fundamental theoretical developments and recent widespread access to large transactional data bases. A less scientific—but nevertheless important—source of inspiration is a growing awareness of the important social implications of corporate behavior and governance. This Handbook takes stock of the main empirical findings to date across an unprecedented spectrum of corporate finance issues, ranging from econometric methodology, to raising capital and capital structure choice, and to managerial incentives and corporate investment behavior. The surveys are written by leading empirical researchers that remain active in their respective areas of interest. With few exceptions, the writing style makes the chapters accessible to industry practitioners. For doctoral students and seasoned academics, the surveys offer dense roadmaps into the empirical research landscape and provide suggestions for future work.*The Handbooks in Finance series offers a broad group of outstanding volumes in various areas of finance*Each individual volume in the series should present an accurate self-contained survey of a sub-field of finance*The series is international in scope with contributions from field leaders the world over
In this paper, I describe some of the main parametric and non-parametric tests used in event studies to assess the significance of abnormal returns or changes in variance of returns. The parametric tests here described are Brown and Warner's test (1980, 1985), with and without crude independence adjustment, Patell's (1976) standardised residual test and Boehmer, Musumeci and Poulsen's (1991) standardised cross-sectional test. The non-parametric tests are the generalised sign test (Sanger and McConnell, 1986 and Cowen and Sergeant, 1996), the Wilcoxson signed rank test and Corrado's (1989) rank test. In addition I outline briefly a bootstrapping procedure for the purpose of testing non-normal distributed abnormal returns. I also describe four tests to evaluate the significance of changes in variance. I present two parametric tests (an F test for the equality of two population variances and the Beavers's/May's U test) and two non-parametric tests (the squared rank test and another rank test proposed by Rohrbach and Chandra, 1989).
The book examines the market reaction to mergers and acquisitions (M&A) announcements over a period from 2003 to 2015. Mergers and acquisitions continue to be amongst the preferred competitive options available to the companies seeking to grow fast in the rapidly changing global business scenario. M&A as a growth strategy has received attention from developed as well as emerging economies. It has been extensively used by managers as an expansion strategy and also serves as an important instrument for increasing corporate efficiency. Recently, M&A has grown at a rapid pace, creating a need for research to analyze what drives this phenomenon and how it affects firms and markets. As such, this book evaluates the impact of M&A on short-term abnormal returns as well long-term financial performance. It also assesses the management view concerning the motives for undertaking M&A. In addition, the book investigates the corporate governance practices of the acquiring firms and their impact on the short- term as well as long- term performance of those firms.
Event Studies are overwhelmingly widespread in financial research, providing tools for shedding light on market efficiency, as well as measuring the impact of various occurrences on public firms' security prices. Mastering the Event Study approach is essential for researchers and practitioners alike. Event Studies for Financial Research aims to help readers obtain valuable hands-on experience with Event Study tools and gain technical skills for conducting their own studies. Kliger and Gurevich provide a detailed application of their approach, which consists of: a description of the method; references; guided applications; and elaborated framework for implementing the applications.
This book concerns testing hypotheses in non-parametric models. Classical non-parametric tests (goodness-of-fit, homogeneity, randomness, independence) of complete data are considered. Most of the test results are proved and real applications are illustrated using examples. Theories and exercises are provided. The incorrect use of many tests applying most statistical software is highlighted and discussed.
The past twenty years have seen an extraordinary growth in the use of quantitative methods in financial markets. Finance professionals now routinely use sophisticated statistical techniques in portfolio management, proprietary trading, risk management, financial consulting, and securities regulation. This graduate-level textbook is intended for PhD students, advanced MBA students, and industry professionals interested in the econometrics of financial modeling. The book covers the entire spectrum of empirical finance, including: the predictability of asset returns, tests of the Random Walk Hypothesis, the microstructure of securities markets, event analysis, the Capital Asset Pricing Model and the Arbitrage Pricing Theory, the term structure of interest rates, dynamic models of economic equilibrium, and nonlinear financial models such as ARCH, neural networks, statistical fractals, and chaos theory. Each chapter develops statistical techniques within the context of a particular financial application. This exciting new text contains a unique and accessible combination of theory and practice, bringing state-of-the-art statistical techniques to the forefront of financial applications. Each chapter also includes a discussion of recent empirical evidence, for example, the rejection of the Random Walk Hypothesis, as well as problems designed to help readers incorporate what they have read into their own applications.
The Oxford Handbook of Panel Data examines new developments in the theory and applications of panel data. It includes basic topics like non-stationary panels, co-integration in panels, multifactor panel models, panel unit roots, measurement error in panels, incidental parameters and dynamic panels, spatial panels, nonparametric panel data, random coefficients, treatment effects, sample selection, count panel data, limited dependent variable panel models, unbalanced panel models with interactive effects and influential observations in panel data. Contributors to the Handbook explore applications of panel data to a wide range of topics in economics, including health, labor, marketing, trade, productivity, and macro applications in panels. This Handbook is an informative and comprehensive guide for both those who are relatively new to the field and for those wishing to extend their knowledge to the frontier. It is a trusted and definitive source on panel data, having been edited by Professor Badi Baltagi-widely recognized as one of the foremost econometricians in the area of panel data econometrics. Professor Baltagi has successfully recruited an all-star cast of experts for each of the well-chosen topics in the Handbook.
Although standard mixed effects models are useful in a range of studies, other approaches must often be used in correlation with them when studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects models and presents appropriate approaches to address dropouts, missing data, measurement errors, censoring, and outliers. For each class of mixed effects model, the author reviews the corresponding class of regression model for cross-sectional data. An overview of general models and methods, along with motivating examples After presenting real data examples and outlining general approaches to the analysis of longitudinal/clustered data and incomplete data, the book introduces linear mixed effects (LME) models, generalized linear mixed models (GLMMs), nonlinear mixed effects (NLME) models, and semiparametric and nonparametric mixed effects models. It also includes general approaches for the analysis of complex data with missing values, measurement errors, censoring, and outliers. Self-contained coverage of specific topics Subsequent chapters delve more deeply into missing data problems, covariate measurement errors, and censored responses in mixed effects models. Focusing on incomplete data, the book also covers survival and frailty models, joint models of survival and longitudinal data, robust methods for mixed effects models, marginal generalized estimating equation (GEE) models for longitudinal or clustered data, and Bayesian methods for mixed effects models. Background material In the appendix, the author provides background information, such as likelihood theory, the Gibbs sampler, rejection and importance sampling methods, numerical integration methods, optimization methods, bootstrap, and matrix algebra. Failure to properly address missing data, measurement errors, and other issues in statistical analyses can lead to severely biased or misleading results. This book explores the biases that arise when naïve methods are used and shows which approaches should be used to achieve accurate results in longitudinal data analysis.
In recent decades, the mainstream microeconomic and macroeconomic analysis was proven to be insufficient for exploring the dynamic and complex interactions among humans, institutions, and nature in our real economy. On the one side, microeconomics is filled with black-box models that fail to study the actual contractual relations between firms and markets, while on the other side macroeconomics were proven useless because they mistook the beauty of theoretical models for truth. Thus, questions have arisen about using new theoretical and empirical structures that would better describe our economic systems. Bridging Microeconomics and Macroeconomics and the Effects on Economic Development and Growth is an essential reference source that analyzes the hypotheses that govern the relationships of aggregate structures (macroeconomic analysis) that may be compatible with the assumptions that govern the behavior of individuals, households, and firms (micro analysis), and vice versa, in trying to achieve sustainable economic development and growth. Moreover, modern evolutionary growth thinking is used in trying to bridge the inconsistencies between microeconomics and macroeconomics and confront their failures in order to better describe the economic reality. While highlighting a broad range of topics including globalization, economic systems, and the role of institutions, this book is aimed toward economic analysts, financial advisors, policymakers, researchers, academicians, and students.
