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Investment pioneer Len Zacks presents the latest academic research on how to beat the market using equity anomalies The Handbook of Equity Market Anomalies organizes and summarizes research carried out by hundreds of finance and accounting professors over the last twenty years to identify and measure equity market inefficiencies and provides self-directed individual investors with a framework for incorporating the results of this research into their own investment processes. Edited by Len Zacks, CEO of Zacks Investment Research, and written by leading professors who have performed groundbreaking research on specific anomalies, this book succinctly summarizes the most important anomalies that savvy investors have used for decades to beat the market. Some of the anomalies addressed include the accrual anomaly, net stock anomalies, fundamental anomalies, estimate revisions, changes in and levels of broker recommendations, earnings-per-share surprises, insider trading, price momentum and technical analysis, value and size anomalies, and several seasonal anomalies. This reliable resource also provides insights on how to best use the various anomalies in both market neutral and in long investor portfolios. A treasure trove of investment research and wisdom, the book will save you literally thousands of hours by distilling the essence of twenty years of academic research into eleven clear chapters and providing the framework and conviction to develop market-beating strategies. Strips the academic jargon from the research and highlights the actual returns generated by the anomalies, and documented in the academic literature Provides a theoretical framework within which to understand the concepts of risk adjusted returns and market inefficiencies Anomalies are selected by Len Zacks, a pioneer in the field of investing As the founder of Zacks Investment Research, Len Zacks pioneered the concept of the earnings-per-share surprise in 1982 and developed the Zacks Rank, one of the first anomaly-based stock selection tools. Today, his firm manages U.S. equities for individual and institutional investors and provides investment software and investment data to all types of investors. Now, with his new book, he shows you what it takes to build a quant process to outperform an index based on academically documented market inefficiencies and anomalies.
This research handbook provides a comprehensive, integrative, and authoritative resource on the main strategic management issues for companies within the e-business context. It covers an extensive set of topics, dealing with the major issues which articulate the e-business framework from a business perspective. The handbook is divided into the following e-business related parts: background; evolved strategic framework for the management of companies; key business processes, areas and activities; and, finally, emerging issues, trends and opportunities, with special attention to diverse Social Web-related implications. The articles are varied, timely and present high-quality research; many of these unique contributions will be especially valued and influential for business scholars and professionals interested in e-business. Many of the contributors are outstanding business scholars who are or have been editors-in-chief of top-ranked management and business journals or have made significant contributions to the development of their respective fields.
We provide a synthesis of the empirical evidence on market liquidity. The liquidity measurement literature has established standard measures of liquidity that apply to broad categories of market microstructure data. Specialized measures of liquidity have been developed to deal with data limitations in specific markets, to provide proxies from daily data, and to assess institutional trading programs. The general liquidity literature has established local cross-sectional patterns, global cross-sectional patterns, and time-series patterns.
The guide for investors who want a better understanding of investment strategies that have stood the test of time This thoroughly revised and updated edition of Investment Philosophies covers different investment philosophies and reveal the beliefs that underlie each one, the evidence on whether the strategies that arise from the philosophy actually produce results, and what an investor needs to bring to the table to make the philosophy work. The book covers a wealth of strategies including indexing, passive and activist value investing, growth investing, chart/technical analysis, market timing, arbitrage, and many more investment philosophies. Presents the tools needed to understand portfolio management and the variety of strategies available to achieve investment success Explores the process of creating and managing a portfolio Shows readers how to profit like successful value growth index investors Aswath Damodaran is a well-known academic and practitioner in finance who is an expert on different approaches to valuation and investment This vital resource examines various investing philosophies and provides you with helpful online resources and tools to fully investigate each investment philosophy and assess whether it is a philosophy that is appropriate for you.
A powerful new tool for all forensic accountants, or anyone whoanalyzes data that may have been altered Benford's Law gives the expected patterns of the digits in thenumbers in tabulated data such as town and city populations orMadoff's fictitious portfolio returns. Those digits, in unaltereddata, will not occur in equal proportions; there is a large biastowards the lower digits, so much so that nearly one-half of allnumbers are expected to start with the digits 1 or 2. Thesepatterns were originally discovered by physicist Frank Benford inthe early 1930s, and have since been found to apply to alltabulated data. Mark J. Nigrini has been a pioneer in applyingBenford's Law to auditing and forensic accounting, even before hisgroundbreaking 1999 Journal of Accountancy article introducing thisuseful tool to the accounting world. In Benford's Law, Nigrinishows the widespread applicability of Benford's Law and itspractical uses to detect fraud, errors, and other anomalies. Explores primary, associated, and advanced tests, all describedwith data sets that include corporate payments data and electiondata Includes ten fraud detection studies, including vendor fraud,payroll fraud, due diligence when purchasing a business, and taxevasion Covers financial statement fraud, with data from Enron, AIG,and companies that were the target of hedge fund short sales Looks at how to detect Ponzi schemes, including data on Madoff,Waxenberg, and more Examines many other applications, from the Clinton tax returnsand the charitable gifts of Lehman Brothers to tax evasion andnumber invention Benford's Law has 250 figures and uses 50 interestingauthentic and fraudulent real-world data sets to explain boththeory and practice, and concludes with an agenda and directionsfor future research. The companion website adds additionalinformation and resources.
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.
This is a book about moods. Though I will define the term somewhat more carefully in Chapter 1, it might help to note here that I use the word "mood" to refer to affective states which do not stimulate the relatively specific response tendencies we associate with "emotions". Instead, moods are pervasive and global, having the capability of influencing a broad range of thought processes and behavior. My interest in mood was provoked initially by the empirical and conceptual contri butions of Alice Isen and her colleagues. What fascinated me most was the sugges tion first made in a paper by Clark & Isen (1982) that mood seemed to affect behavior in two very different ways, i. e. , mood could "automatically" influence the availabil ity of mood-related cognitions and, thereby, behavior, or mood, especially of the "bad" variety, might capture our attention in that if it were sufficiently aversive we might consciously try to get rid of it, a "controlled" or "strategic" response.
Seminar paper from the year 2012 in the subject Business economics - Banking, Stock Exchanges, Insurance, Accounting, grade: 1,3, Technical University of Applied Sciences Mittelhessen, language: English, abstract: The Dotcom bubble, also known as the ‘Internet bubble’ or the ‘Information technology bubble’ was a speculative bubble of stock prices of mainly American Internet companies during the time from 1995 until 2000 when many investors believed that a ‘new era’ was upon them. In only two years, the Internet sector grew over 1000% of its public equity and equalled nearly 6% of the market capitalization of the United States and over 20% of all public traded equity volume in the US. It had its peak on March 10, 2000 with a NASDAQ score of 5,048.62. This period was characterized by lots of establishments of companies in the Internet sector. They were called ‘Dotcom Companies’ because of the ‘.com’ in the end of an URL that comes from the word ‘commercial’. The bubble burst during the years 2000 until 2002 when the NASDAQ lost nearly 80% of its value, many companies like Pets.com failed completely and over $7 trillion in market value were destroyed. With this paper, the author tries to explain the rise and fall of Internet stock prices during that period. For this purpose, the general causes and characteristics of financial bubbles get described before the application to the Dotcom bubble follows. Additionally, some company examples and survivors and losers of the bubble like pets.com, Webvan or Ebay get introduced. Because the bubble mainly took place in the United States, the author will focus on American company examples and the American stock exchange.
Not necessarily. The fundamental value of a firm increases with uncertainty about average future profitability, and this uncertainty was unusually high in the late 1990s. We calibrate a stock valuation model that includes this uncertainty, and show that the uncertainty needed to match the observed Nasdaq valuations at their peak is high but plausible. The high uncertainty might also explain the unusually high return volatility of Nasdaq stocks in the late 1990s. Uncertainty has the biggest effect on stock prices when the equity premium is low.