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Christian Funke aims at developing a better understanding of a central asset pricing issue: the stock price discovery process in capital markets. Using U.S. capital market data, he investigates the importance of mergers and acquisitions (M&A) for stock prices and examines economic links between customer and supplier firms. The empirical investigations document return predictability and show that capital markets are not perfectly efficient.
The Efficient Market Hypothesis believes that it is impossible for an investor to outperform the market because all available information is already built into stock prices. However, some anomalies could persist in stock markets while some other anomalies could appear, disappear and re-appear again without any warning. A Special Issue on "Efficiency and Anomalies in Stock Markets" will be devoted to advancements in the theoretical development of market efficiency and anomaly in the Stock Market, as well as applications in Stock Market efficiency and anomalies.
This book gathers the proceedings of the 13th International Conference on Management Science and Engineering Management (ICMSEM 2019), which was held at Brock University, Ontario, Canada on August 5–8, 2019. Exploring the latest ideas and pioneering research achievements in management science and engineering management, the respective contributions highlight both theoretical and practical studies on management science and computing methodologies, and present advanced management concepts and computing technologies for decision-making problems involving large, uncertain and unstructured data. Accordingly, the proceedings offer researchers and practitioners in related fields an essential update, as well as a source of new research directions.
This is an open access book. In the current situation of rapid economic development, the competition in the market is increasingly fierce. The drawbacks of traditional enterprise management and the backward management concept have seriously hindered the normal development of enterprises. In order to improve their competitive advantages and market share, enterprises must optimize their management methods and build a modern business administration system. In this situation, enterprises can only promote their development process by improving their business management mode and formulating scientific business management policies. Data science is one of the most important tools for optimizing business administration. Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract value from data. Data scientists use a combination of skills (including statistics, computer science and business knowledge) to analyze data collected from the Web, smartphones, customers, sensors and other sources. Data is the cornerstone of innovation, and data scientists gather information from data, discovering hidden trends from raw data and generating insights that companies can use to transform business problems into research projects that can then be translated back into practical solutions. Based on this, BADS 2023 discusses the state of modern business administration and the corresponding improvement measures in the context of the current reality, and It also provides a platform for scholars in related fields to exchange and share information, discuss how the two affect each other, and promote the modernization of business administration by studying certain business administration issues. To open new perspectives, broaden horizons, and examine the issues being discussed by the participants. Create an international-level forum for sharing, research and exchange that will expose participants to the latest research directions, results and content in different fields, thus inspiring them to come up with new research ideas.
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.
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.
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.
Alphanomics: The Informational Underpinnings of Market Efficiency is intended to be a compact introduction to academic research on market efficiency, behavioral finance, and fundamental analysis and is dedicated to the kind of decision-driven and prospectively-focused research that is much needed in a market constantly seeking to become more efficient. The authors refer to this type of research as Alphanomics, the informational economics behind market efficiency. Alpha refers to the abnormal returns, which provide the incentive for some subpopulation of investors to engage in information acquisition and costly arbitrage activities. Nomics refers to the economics of alpha extraction, which encompasses the costs and incentives of informational arbitrage as a sustainable business proposition. Some of the questions that are addressed include: why do we believe markets are efficient?; what problems have this belief engendered?; what factors can impede and/or facilitate market efficiency?; what roles do investor sentiment and costly arbitrage play in determining an equilibrium level of informational efficiency?; what is the essence of value investing?; how is it related to fundamental analysis (the study of historical financial data)?; and how might we distinguish between risk and mispricing based explanations for predictability patterns in returns? The first two sections review the evolution of academic thinking on market efficiency and introduce the noise trader model as a rational alternative. Section 3 surveys the literature on investor sentiment and its role as a source of both risks and returns. Section 4 discusses the role of fundamental analysis in value investing. Section 5 reviews the literature on limits to arbitrage, and section 6 discusses research methodology issues associated with the need to distinguish mispricing from risk.