This book explores the intuitive appeal of neural networks and the genetic algorithm in finance. It demonstrates how neural networks used in combination with evolutionary computation outperform classical econometric methods for accuracy in forecasting, classification and dimensionality reduction. McNelis utilizes a variety of examples, from forecasting automobile production and corporate bond spread, to inflation and deflation processes in Hong Kong and Japan, to credit card default in Germany to bank failures in Texas, to cap-floor volatilities in New York and Hong Kong. * Offers a balanced, critical review of the neural network methods and genetic algorithms used in finance * Includes numerous examples and applications * Numerical illustrations use MATLAB code and the book is accompanied by a website
Financial analysis is concerned with the study of capital flows over time and space. This book presents a new theory of multi-sector, multi-instrument financial systems based on the visualization of such systems as networks. The framework is both qualitative and computational and depends crucially on the methodologies of finite-dimensional variational inequality theory for the study of statics and equilibrium states and on projected dynamical systems for the study of dynamics and disequilibrium behavior. Moreover, it adds a graphical dimension to the fundamental economic structure of financial systems and their evolution through time.
This volume presents a unified mathematical framework for the transmission channels for damaging shocks that can lead to instability in financial systems. As the title suggests, financial contagion is analogous to the spread of disease, and damaging financial crises may be better understood by bringing to bear ideas from studying other complex systems in our world. After considering how people have viewed financial crises and systemic risk in the past, it delves into the mechanics of the interactions between banking counterparties. It finds a common mathematical structure for types of crises that proceed through cascade mappings that approach a cascade equilibrium. Later chapters follow this theme, starting from the underlying random skeleton graph, developing into the theory of bootstrap percolation, ultimately leading to techniques that can determine the large scale nature of contagious financial cascades.
This completely updated version of the classic first edition offers a wealth of new material reflecting the latest developments in teh field. For investment professionals seeking to maximize this exciting new technology, this handbook is the definitive information source.
"This book presents a variety of practical applications of neural networks in two important domains of economic activity: finance and manufacturing"--Provided by publisher.
An international bestseller, SUPERHUBS offers a startling new perspective on how the world's elite make the decisions that impact all our lives. A BLOOMBERG Best Book of the Year Winner, Silver Medal, Axiom Business Book Awards 2018 FOREWORD BY NOURIEL ROUBINI $UPERHUBS is a rare, behind-the-scenes look at how the world's most powerful titans, the "superhubs," pull the levers of our global financial system. Combining insider's knowledge with principles of network science, Sandra Navidi offers a startling new perspective on how superhubs build their powerful networks and how their decisions impact all our lives. $UPERHUBS reveals what happens at the exclusive, invitation-only platforms - The World Economic Forum in Davos, the meetings of the International Monetary Fund, think-tank gatherings and exclusive galas. This is the most vivid portrait to date of the global elite: the bank CEOs, fund managers, billionaire financiers and politicians who, through their interlocking relationships and collective influence are transforming our increasingly fragile financial system, economy and society.
This volume looks at financial prediction from a broad range of perspectives. It covers: - the economic arguments - the practicalities of the markets - how predictions are used - how predictions are made - how predictions are turned into something usable (asset locations) It combines a discussion of standard theory with state-of-the-art material on a wide range of information processing techniques as applied to cutting-edge financial problems. All the techniques are demonstrated with real examples using actual market data, and show that it is possible to extract information from very noisy, sparse data sets. Aimed primarily at researchers in financial prediction, time series analysis and information processing, this book will also be of interest to quantitative fund managers and other professionals involved in financial prediction.
The first fifteen years of the 21st century have thrown into sharp relief the challenges of growth, equity, stability, and sustainability facing the world economy. In addition, they have exposed the inadequacies of mainstream economics in providing answers to these challenges. This volume gathers over 50 leading scholars from around the world to offer a forward-looking perspective of economic geography to understanding the various building blocks, relationships, and trajectories in the world economy. The perspective is at the same time grounded in theory and in the experiences of particular places. Reviewing state-of-the-art of economic geography, setting agendas, and with illustrations and empirical evidence from all over the world, the book should be an essential reference for students, researchers, as well as strategists and policy makers. Building on the success of the first edition, this volume offers a radically revised, updated, and broader approach to economic geography. With the backdrop of the global financial crisis, finance is investigated in chapters on financial stability, financial innovation, global financial networks, the global map of savings and investments, and financialization. Environmental challenges are addressed in chapters on resource economies, vulnerability of regions to climate change, carbon markets, and energy transitions. Distribution and consumption feature alongside more established topics on the firm, innovation, and work. The handbook also captures the theoretical and conceptual innovations of the last fifteen years, including evolutionary economic geography and the global production networks approach. Addressing the dangers of inequality, instability, and environmental crisis head-on, the volume concludes with strategies for growth and new ways of envisioning the spatiality of economy for the future.
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.
Although social, political, technological and business networks hold our modern world together, we still lack a good understanding of what business networks are, how they work, and the language of network analysis that we may apply to solve common, everyday problems. This book looks at such questions as: How do we make sense of the business networks we participate in and the networks we observe from a distance? Are business networks distinct from social networks, and if so what distinguishes them? How can business network analysis from a multidisciplinary perspective enhance strategic management? Emanuela Todeva deftly explores the patterns of networking and the dynamics of network relationships, to show how we can begin to tap their full potential. Of great interest to students and scholars of business network analysis, this revealing volume will also prove informative for managers wishing to obtain insights into network dynamics and its implications for strategic decision making. Business Networks expertly provides an interdisciplinary overview. It skilfully engages the reader with a range of economic, sociological, strategic management and communication theories that contribute to our knowledge of networks and networking. Transcending specific disciplines, and synthesizing the contributions that shape the structural, relational and cultural approaches to network analysis, Todeva’s outstanding text offers a wealth of conceptual frameworks and an exhaustive typology of existing business networks.
