In recent years, the internet has developed very quickly and became a major source of information all over the planet. Many scientists have used search engine query data to forecast econometric time series like consumer confidence indicators, unemployment rates, retail sales, house price indices, stock prices, volatility of stocks and even commodity prices. Following the prior research this study analyzes the impact of internet search engine data on capital markets. Many authors already have contributed to index level data and most of them on the US market. This study adds to the existing literature on the German stock market. Two research questions are answered: First, whether an increase in search queries drives individual stock returns and second, whether queries affect the implied volatility of stock options. After controlling for seasonality, autocorrelation and general market risk, in the further analysis also the Price-to-Book valuation, one year performance and historical volatility are examined in interaction with internet search queries.
Private equity minority investments have become an increasingly attractive financing alternative for family firms. However, admitting a private equity investor as a minority shareholder seems to contradict with the objective of the owner family to preserve their continuous and unlimited influence on the businesses since they must at least partially cede control over the firm to the private equity investor. Therefore, the purpose of this book is to identify the primary decision drivers for family firm entrepreneurs in seeking private equity financing despite the therein related partial loss of control. By giving special consideration to the potential cooperation mechanisms between the shareholders, this book goes beyond the scope of previous studies. Cooperation is thereby considered as a prerequisite for the success of minority investments because due to its minority position, the private equity investor is not able to implement its value creation strategy against the will of the family firm entrepreneur.
FLINS, originally an acronym for Fuzzy Logic and Intelligent Technologies in Nuclear Science, is now extended to Computational Intelligence for applied research. The contributions to the 11th of FLINS conference cover state-of-the-art research, development, and technology for computational intelligence systems, both from the foundations and the applications points-of-view.
The special session on Decision Economics (DECON) is a scientific forum held annually, which is focused on sharing ideas, projects, research results, models, and experiences associated with the complexity of behavioural decision processes and socio‐economic phenomena. In 2018, DECON was held at Campus Tecnológico de la Fábrica de Armas, University of Castilla-La Mancha, Toledo, Spain, as part of the 15th International Conference on Distributed Computing and Artificial Intelligence. For the third consecutive year, this book have drawn inspiration from Herbert A. Simon’s interdisciplinary legacy and, in particular, is devoted to designs, models, and techniques for boundedly rational decisions, involving several fields of study and expertise. It is worth noting that the recognition of relevant decision‐making takes place in a range of critical subject areas and research fields, including economics, finance, information systems, small and international business management, operations, and production. Therefore, decision‐making issues are of fundamental importance in all branches of economics addressed with different methodological approaches. As a matter of fact, the study of decision‐making has become the focus of intense research efforts, both theoretical and applied, forming a veritable bridge between theory and practice as well as science and business organisations, whose pillars are based on insightful cutting‐edge experimental, behavioural, and computational approaches on the one hand, and celebrating the value of science as well as the close relationship between economics and complexity on the other. In this respect, the international scientific community acknowledges Herbert A. Simon’s research endeavours to understand the processes involved in economic decision‐making and their implications for the advancement of economic professions. Within the field of decision‐making, indeed, Simon has become a mainstay of bounded rationality and satisficing. His rejection of the standard (unrealistic) decision‐making models adopted by neoclassical economists inspired social scientists worldwide with the purpose to develop research programmes aimed at studying decision‐making empirically, experimentally, and computationally. The main achievements concern decision‐making for individuals, firms, markets, governments, institutions, and, last but not least, science and research. This book of selected papers tackles these issues that Simon broached in a professional career spanning more than sixty years. The Editors of this book dedicated it to Herb.
This two-volume set, LNCS 12565 and 12566, constitutes the refereed proceedings of the 6th International Conference on Machine Learning, Optimization, and Data Science, LOD 2020, held in Siena, Italy, in July 2020. The total of 116 full papers presented in this two-volume post-conference proceedings set was carefully reviewed and selected from 209 submissions. These research articles were written by leading scientists in the fields of machine learning, artificial intelligence, reinforcement learning, computational optimization, and data science presenting a substantial array of ideas, technologies, algorithms, methods, and applications.
In today’s fast growing digital world, the web, mobile, social networks and other digital platforms are producing enormous amounts of data that hold intelligence and valuable information. Correctly used it has the power to create sustainable value in different forms for businesses. The commonly used term for this data is Big Data, which includes structured, unstructured and hybrid structured data. However, Big Data is of limited value unless insightful information can be extracted from the sources of data. The solution is Big Data analytics, and how managers and executives can capture value from this vast resource of information and insights. This book develops a simple framework and a non-technical approach to help the reader understand, digest and analyze data, and produce meaningful analytics to make informed decisions. It will support value creation within businesses, from customer care to product innovation, from sales and marketing to operational performance. The authors provide multiple case studies on global industries and business units, chapter summaries and discussion questions for the reader to consider and explore. Big Data for Managers also presents small cases and challenges for the reader to work on – making this a thorough and practical guide for students and managers.
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