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Security Analysis, Portfolio Management, and Financial Derivatives integrates the many topics of modern investment analysis. It provides a balanced presentation of theories, institutions, markets, academic research, and practical applications, and presents both basic concepts and advanced principles. Topic coverage is especially broad: in analyzing securities, the authors look at stocks and bonds, options, futures, foreign exchange, and international securities. The discussion of financial derivatives includes detailed analyses of options, futures, option pricing models, and hedging strategies. A unique chapter on market indices teaches students the basics of index information, calculation, and usage and illustrates the important roles that these indices play in model formation, performance evaluation, investment strategy, and hedging techniques. Complete sections on program trading, portfolio insurance, duration and bond immunization, performance measurements, and the timing of stock selection provide real-world applications of investment theory. In addition, special topics, including equity risk premia, simultaneous-equation approach for security valuation, and Itô's calculus, are also included for advanced students and researchers.
John J. Murphy has updated his landmark bestseller Technical Analysis of the Futures Markets, to include all of the financial markets. This outstanding reference has already taught thousands of traders the concepts of technical analysis and their application in the futures and stock markets. Covering the latest developments in computer technology, technical tools, and indicators, the second edition features new material on candlestick charting, intermarket relationships, stocks and stock rotation, plus state-of-the-art examples and figures. From how to read charts to understanding indicators and the crucial role technical analysis plays in investing, readers gain a thorough and accessible overview of the field of technical analysis, with a special emphasis on futures markets. Revised and expanded for the demands of today's financial world, this book is essential reading for anyone interested in tracking and analyzing market behavior.
George J. Benston, professor of Finance, Accounting, and Economics at Emory University's Goizueta Business School, died unexpectedly in January 2008. He was an impassioned advocate for corporate integrity and a unique scholar; his research interests were as broad as those of any recent academician. His colleagues have selected and organized his most important papers into two volumes. This first volume consists of his research in the banking and financial services industry. The editor has selected a broad range of papers from each of the major areas that are representative of Benston's work in that particular field. James D. Rosenfeld, Professor of Finance, Accounting, and Economics, Goizueta Business School, Emory University, serves as the editor and is assisted by an editorial advisory board including George Kaufman, Greg Waymire, Bob Eisenbeis, Larry Wall, Rashad Abdel-Kalik, and Lemma Senbet.
A Tea Reader contains a selection of stories that cover the spectrum of life. This anthology shares the ways that tea has changed lives through personal, intimate stories. Read of deep family moments, conquered heartbreak, and peace found in the face of loss. A Tea Reader includes stories from all types of tea people: people brought up in the tea tradition, those newly discovering it, classic writings from long-ago tea lovers and those making tea a career. Together these tales create a new image of a tea drinker. They show that tea is not simply something you drink, but it also provides quiet moments for making important decisions, a catalyst for conversation, and the energy we sometimes need to operate in our lives. The stories found in A Tea Reader cover the spectrum of life, such as the development of new friendships, beginning new careers, taking dream journeys, and essentially sharing the deep moments of life with friends and families. Whether you are a tea lover or not, here you will discover stories that speak to you and inspire you. Sit down, grab a cup, and read on.
This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications.
Choose statistically significant stock selection models using SAS® Portfolio and Investment Analysis with SAS®: Financial Modeling Techniques for Optimization is an introduction to using SAS to choose statistically significant stock selection models, create mean-variance efficient portfolios, and aggressively invest to maximize the geometric mean. Based on the pioneering portfolio selection techniques of Harry Markowitz and others, this book shows that maximizing the geometric mean maximizes the utility of final wealth. The authors draw on decades of experience as teachers and practitioners of financial modeling to bridge the gap between theory and application. Using real-world data, the book illustrates the concept of risk-return analysis and explains why intelligent investors prefer stocks over bonds. The authors first explain how to build expected return models based on expected earnings data, valuation ratios, and past stock price performance using PROC ROBUSTREG. They then show how to construct and manage portfolios by combining the expected return and risk models. Finally, readers learn how to perform hypothesis testing using Bayesian methods to add confidence when data mining from large financial databases.
The book discusses a new approach to the classification problem following the decision support orientation of multicriteria decision aid. The book reviews the existing research on the development of classification methods, investigating the corresponding model development procedures, and providing a thorough analysis of their performance both in experimental situations and real-world problems from the field of finance. Audience: Researchers and professionals working in management science, decision analysis, operations research, financial/banking analysis, economics, statistics, computer science, as well as graduate students in management science and operations research.