This book is well-researched by the author, in which he has shared the experience and knowledge of some very much experienced and renowned entities from stock market. We want that everybody should have the knowledge regarding the different aspects of stock market, which would encourage people to invest and earn without any fear. This book is just a step forward toward the knowledge of market.

An introduction to the theory and methods of empirical asset pricing, integrating classical foundations with recent developments. This book offers a comprehensive advanced introduction to asset pricing, the study of models for the prices and returns of various securities. The focus is empirical, emphasizing how the models relate to the data. The book offers a uniquely integrated treatment, combining classical foundations with more recent developments in the literature and relating some of the material to applications in investment management. It covers the theory of empirical asset pricing, the main empirical methods, and a range of applied topics. The book introduces the theory of empirical asset pricing through three main paradigms: mean variance analysis, stochastic discount factors, and beta pricing models. It describes empirical methods, beginning with the generalized method of moments (GMM) and viewing other methods as special cases of GMM; offers a comprehensive review of fund performance evaluation; and presents selected applied topics, including a substantial chapter on predictability in asset markets that covers predicting the level of returns, volatility and higher moments, and predicting cross-sectional differences in returns. Other chapters cover production-based asset pricing, long-run risk models, the Campbell-Shiller approximation, the debate on covariance versus characteristics, and the relation of volatility to the cross-section of stock returns. An extensive reference section captures the current state of the field. The book is intended for use by graduate students in finance and economics; it can also serve as a reference for professionals.

This handbook summarises knowledge from experts and empirical studies. It provides guidelines that can be applied in fields such as economics, sociology, and psychology. Includes a comprehensive forecasting dictionary.

The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists.

The purpose of this book is to establish a connection between the traditional field of empirical economic research and the emerging area of empirical financial research and to build a bridge between theoretical developments in these areas and their application in practice. Accordingly, it covers broad topics in the theory and application of both empirical economic and financial research, including analysis of time series and the business cycle; different forecasting methods; new models for volatility, correlation and of high-frequency financial data and new approaches to panel regression, as well as a number of case studies. Most of the contributions reflect the state-of-art on the respective subject. The book offers a valuable reference work for researchers, university instructors, practitioners, government officials and graduate and post-graduate students, as well as an important resource for advanced seminars in empirical economic and financial research.

Arti?cial Intelligence is a ?eld with a long history, which is still very much active and developing today. Developments of new and improved techniques, together with the ever-increasing levels of available computing resources, are fueling an increasing spread of AI applications. These applications, as well as providing the economic rationale for the research, also provide the impetus to further improve the performance of our techniques. This further improvement today is most likely to come from an understanding of the ways our systems work, and therefore of their limitations, rather than from ideas ‘borrowed’ from biology. From this understanding comes improvement; from improvement comes further application; from further application comes the opportunity to further understand the limitations, and so the cycle repeats itself inde?nitely. In this volume are papers on a wide range of topics; some describe appli- tions that are only possible as a result of recent developments, others describe new developments only just being moved into practical application. All the - pers re?ect the way this ?eld continues to drive forward. This conference is the 15th in an unbroken series of annual conferences on Industrial and Engineering Application of Arti?cial Intelligence and Expert Systems organized under the auspices of the International Society of Applied Intelligence.