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Market Volatility proposes an innovative theory, backed by substantial statistical evidence, on the causes of price fluctuations in speculative markets. It challenges the standard efficient markets model for explaining asset prices by emphasizing the significant role that popular opinion or psychology can play in price volatility. Why does the stock market crash from time to time? Why does real estate go in and out of booms? Why do long term borrowing rates suddenly make surprising shifts? Market Volatility represents a culmination of Shiller's research on these questions over the last dozen years. It contains reprints of major papers with new interpretive material for those unfamiliar with the issues, new papers, new surveys of relevant literature, responses to critics, data sets, and reframing of basic conclusions. Included is work authored jointly with John Y. Campbell, Karl E. Case, Sanford J. Grossman, and Jeremy J. Siegel. Market Volatility sets out basic issues relevant to all markets in which prices make movements for speculative reasons and offers detailed analyses of the stock market, the bond market, and the real estate market. It pursues the relations of these speculative prices and extends the analysis of speculative markets to macroeconomic activity in general. In studies of the October 1987 stock market crash and boom and post-boom housing markets, Market Volatility reports on research directly aimed at collecting information about popular models and interpreting the consequences of belief in those models. Shiller asserts that popular models cause people to react incorrectly to economic data and believes that changing popular models themselves contribute significantly to price movements bearing no relation to fundamental shocks.
The efficient markets hypothesis has been the central proposition in finance for nearly thirty years. It states that securities prices in financial markets must equal fundamental values, either because all investors are rational or because arbitrage eliminates pricing anomalies. This book describes an alternative approach to the study of financial markets: behavioral finance. This approach starts with an observation that the assumptions of investor rationality and perfect arbitrage are overwhelmingly contradicted by both psychological and institutional evidence. In actual financial markets, less than fully rational investors trade against arbitrageurs whose resources are limited by risk aversion, short horizons, and agency problems. The book presents and empirically evaluates models of such inefficient markets. Behavioral finance models both explain the available financial data better than does the efficient markets hypothesis and generate new empirical predictions. These models can account for such anomalies as the superior performance of value stocks, the closed end fund puzzle, the high returns on stocks included in market indices, the persistence of stock price bubbles, and even the collapse of several well-known hedge funds in 1998. By summarizing and expanding the research in behavioral finance, the book builds a new theoretical and empirical foundation for the economic analysis of real-world markets.
This timely volume addresses three important recent trends in the internationalization of United States equity markets: extensive market integration through foreign investment and links among stock prices around the world; increasing securitization as countries such as Japan come to rely more than ever before on markets in equities and bonds at the expense of banks; and the opening of national financial systems of newly industrializing countries to international financial flows and institutions, as governments remove capital controls and other barriers. Eight essays examine such issues as the current extent of international market integration, gains to U.S. investors through international diversification, home-country bias in investing, the role of time and location around the world in stock trading, and the behavior of country funds. Other, long-standing questions about equity markets are also addressed, including market efficiency and the accuracy of models of expected returns, with a particular focus on variances, covariances, and the price of risk according to the Capital Asset Pricing Model.
The cash flows of growth stocks are particularly sensitive to temporary movements in aggregate stock prices (driven by movements in the equity risk premium), while the cash flows of value stocks are particularly sensitive to permanent movements in aggregate stock prices (driven by market-wide shocks to cash flows.) Thus the high betas of growth stocks with the market's discount-rate shocks, and of value stocks with the market's cash-flow shocks, are determined by the cash-flow fundamentals of growth and value companies. Growth stocks are not merely "glamour stocks" whose systematic risks are purely driven by investor sentiment. More generally, accounting measures of firm-level risk have predictive power for firms' betas with market-wide cash flows, and this predictive power arises from the behavior of firms' cash flows. The systematic risks of stocks with similar accounting characteristics are primarily driven by the systematic risks of their fundamentals.
Behavioral finance is the study of how psychology affects financial decision making and financial markets. It is increasingly becoming the common way of understanding investor behavior and stock market activity. Incorporating the latest research and theory, Shefrin offers both a strong theory and efficient empirical tools that address derivatives, fixed income securities, mean-variance efficient portfolios, and the market portfolio. The book provides a series of examples to illustrate the theory. The second edition continues the tradition of the first edition by being the one and only book to focus completely on how behavioral finance principles affect asset pricing, now with its theory deepened and enriched by a plethora of research since the first edition
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 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.
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.
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.