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Using a unique data set consisting of more than 36.5 million submitted retail investor orders over the course of five years, Matthias Burghardt constructs an innovative retail investor sentiment index. He shows that retail investors’ trading decisions are correlated, that retail investors are contrarians, and that a profitable trading strategy can be based on these aggregated sentiment measures.
Learn how to profit from information about insider trading. The term insider trading refers to the stock transactions of the officers, directors, and large shareholders of a firm. Many investors believe that corporate insiders, informed about their firms' prospects, buy and sell their own firm's stock at favorable times, reaping significant profits. Given the extra costs and risks of an active trading strategy, the key question for stock market investors is whether the publicly available insider-trading information can help them to outperform a simple passive index fund. Basing his insights on an exhaustive data set that captures information on all reported insider trading in all publicly held firms over the past twenty-one years—over one million transactions!—H. Nejat Seyhun shows how investors can use insider information to their advantage. He documents the magnitude and duration of the stock price movements following insider trading, determinants of insiders' profits, and the risks associated with imitating insider trading. He looks at the likely performance of individual firms and of the overall stock market, and compares the value of what one can learn from insider trading with commonly used measures of value such as price-earnings ratio, book-to-market ratio, and dividend yield.
We study how stock market mispricing might influence individual firms' investment decisions. We find a positive relation between investment and a number of proxies for mispricing, controlling for investment opportunities and financial slack, suggesting that overpriced (underpriced) firms tend to overinvest (underinvest). Consistent with the predictions of our model, we find that investment is more sensitive to our mispricing proxies for firms with higher R & D intensity suggesting longer periods of information asymmetry and thus mispricing) or share turnover (suggesting that the firms' shareholders are short-term investors). We also find that firms with relatively high (low) investment subsequently have relatively low (high) stock returns, after controlling for investment opportunities and other characteristics linked to return predictability. These patterns are stronger for firms with higher R & D intensity or higher share turnover.
Corporate Payout Policy synthesizes the academic research on payout policy and explains "how much, when, and how". That is (i) the overall value of payouts over the life of the enterprise, (ii) the time profile of a firm's payouts across periods, and (iii) the form of those payouts. The authors conclude that today's theory does a good job of explaining the general features of corporate payout policies, but some important gaps remain. So while our emphasis is to clarify "what we know" about payout policy, the authors also identify a number of interesting unresolved questions for future research. Corporate Payout Policy discusses potential influences on corporate payout policy including managerial use of payouts to signal future earnings to outside investors, individuals' behavioral biases that lead to sentiment-based demands for distributions, the desire of large block stockholders to maintain corporate control, and personal tax incentives to defer payouts. The authors highlight four important "carry-away" points: the literature's focus on whether repurchases will (or should) drive out dividends is misplaced because it implicitly assumes that a single payout vehicle is optimal; extant empirical evidence is strongly incompatible with the notion that the primary purpose of dividends is to signal managers' views of future earnings to outside investors; over-confidence on the part of managers is potentially a first-order determinant of payout policy because it induces them to over-retain resources to invest in dubious projects and so behavioral biases may, in fact, turn out to be more important than agency costs in explaining why investors pressure firms to accelerate payouts; the influence of controlling stockholders on payout policy --- particularly in non-U.S. firms, where controlling stockholders are common --- is a promising area for future research. Corporate Payout Policy is required reading for both researchers and practitioners interested in understanding this central topic in corporate finance and governance.
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.
We investigate how corporate stock returns respond to geopolitical risk in the case of South Korea, which has experienced large and unpredictable geopolitical swings that originate from North Korea. To do so, a monthly index of geopolitical risk from North Korea (the GPRNK index) is constructed using automated keyword searches in South Korean media. The GPRNK index, designed to capture both upside and downside risk, corroborates that geopolitical risk sharply increases with the occurrence of nuclear tests, missile launches, or military confrontations, and decreases significantly around the times of summit meetings or multilateral talks. Using firm-level data, we find that heightened geopolitical risk reduces stock returns, and that the reductions in stock returns are greater especially for large firms, firms with a higher share of domestic investors, and for firms with a higher ratio of fixed assets to total assets. These results suggest that international portfolio diversification and investment irreversibility are important channels through which geopolitical risk affects stock returns.
First Published in 1991. Routledge is an imprint of Taylor & Francis, an informa company.
This book presents the definitive exposition of 'prospect theory', a compelling alternative to the classical utility theory of choice. Building on the 1982 volume, Judgement Under Uncertainty, this book brings together seminal papers on prospect theory from economists, decision theorists, and psychologists, including the work of the late Amos Tversky, whose contributions are collected here for the first time. While remaining within a rational choice framework, prospect theory delivers more accurate, empirically verified predictions in key test cases, as well as helping to explain many complex, real-world puzzles. In this volume, it is brought to bear on phenomena as diverse as the principles of legal compensation, the equity premium puzzle in financial markets, and the number of hours that New York cab drivers choose to drive on rainy days. Theoretically elegant and empirically robust, this volume shows how prospect theory has matured into a new science of decision making.
This paper presents a coordinated portfolio investment survey guide provided to assist national compilers in the conduct of the Coordinated Portfolio Investment Survey, conducted under the auspices of the IMF with reference to the year-end 1997. The guide covers a variety of conceptual issues that a country must address when conducting a survey. It also covers the practical issues associated with preparing for a national survey. These include setting a timetable, taking account of the legal and confidentiality issues raised, developing a mailing list, and maintaining quality control checks.
A pioneer in the field of behavioral finance presents an investment guide based on what really drives investors Perfectly timed to give readers a real edge for investing in post-crash markets Author is a leading authority on the theory and application of behavioral finance and a fixture in The Wall Street Journal and other leading media outlets Poised to become the definitive text on how investors and managers make financial decisions—and how these decisions are reflected in financial markets