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Tools and methods from complex systems science can have a considerable impact on the way in which the quantitative assessment of economic and financial issues is approached, as discussed in this thesis. First it is shown that the self-organization of financial markets is a crucial factor in the understanding of their dynamics. In fact, using an agent-based approach, it is argued that financial markets’ stylized facts appear only in the self-organized state. Secondly, the thesis points out the potential of so-called big data science for financial market modeling, investigating how web-driven data can yield a picture of market activities: it has been found that web query volumes anticipate trade volumes. As a third achievement, the metrics developed here for country competitiveness and product complexity is groundbreaking in comparison to mainstream theories of economic growth and technological development. A key element in assessing the intangible variables determining the success of countries in the present globalized economy is represented by the diversification of the productive basket of countries. The comparison between the level of complexity of a country's productive system and economic indicators such as the GDP per capita discloses its hidden growth potential.
The book is motivated by the disruptions introduced by the financial crisis and the many attempts that have followed to propose new ideas and remedies. Assembling contributions by authors from a variety of backgrounds, this collection illustrates the potentials resulting from the marriage of financial economics, complexity theory and an out-of-equilibrium view of the economic world. Challenging the traditional hypotheses that lie behind financial market functioning, new evidence is provided about the hidden factors fuelling bubbles, the impact of agents’ heterogeneity, the importance of endogeneity in the information transmission mechanism, the dynamics of herding, the sources of volatility, the portfolio optimization techniques, the financial innovation and the trend identification in a nonlinear time-series framework. Presenting the advances made in financial market analysis, and putting emphasis on nonlinear dynamics, this book suggests interdisciplinary methodologies for the study of well-known stylised facts and financial abnormalities. This book was originally published as a special issue of The European Journal of Finance.
This work draws on ideas from the science of complexity and complex systems, to address the following questions: how do financial markets behave? why is this? and what can we do to minimize risk, given this behaviour?
Finance, Econometrics and System Dynamics presents an overview of the concepts and tools for analyzing complex systems in a wide range of fields. The text integrates complexity with deterministic equations and concepts from real world examples, and appeals to a broad audience.
What are the long-term causes and consequences of the global financial crisis of 2007–2008? This book offers a fresh perspective on these issues by bringing together a range of academics from law, history, economics and business to look in more depth at the changing relationships between crises and complexity in the US and UK financial markets. The contributors are motivated by three main questions: • Is the present financial system more complex than in the past and, if so, why? • To what extent, and in what ways, does the worldwide financial crisis of 2007–2008 differ from past financial crises? • How can governments, regulators and businesses better manage and deal with increased levels of complexity both in the present and in the future? Students and scholars of finance, economics, history, financial law, banking and international business will find this book to be of interest. It will also be of use to regulators and policymakers involved in the US and UK banking sectors.
This book concerns the use of concepts from statistical physics in the description of financial systems. The authors illustrate the scaling concepts used in probability theory, critical phenomena, and fully developed turbulent fluids. These concepts are then applied to financial time series. The authors also present a stochastic model that displays several of the statistical properties observed in empirical data. Statistical physics concepts such as stochastic dynamics, short- and long-range correlations, self-similarity and scaling permit an understanding of the global behaviour of economic systems without first having to work out a detailed microscopic description of the system. Physicists will find the application of statistical physics concepts to economic systems interesting. Economists and workers in the financial world will find useful the presentation of empirical analysis methods and well-formulated theoretical tools that might help describe systems composed of a huge number of interacting subsystems.
In this book, Dr Mak views the financial market from a scientific perspective. The book attempts to provide a realistic description of what the market is, and how future research should be developed. The market is a complex phenomenon, and can be forecasted only with errors — if that particular market can be forecasted at all.The book reviews the scientific literatures on the financial market and describes mathematical procedures which demonstrate that some markets are non-random. How the markets are modeled — phenomenologically and from first principle — is explained.It discusses indicators, which are quite objective, rather than price patterns, which are rather subjective. Similarities between indicators in market trading and operators in mathematics are noted, and particularly, between oscillator indicators and derivatives in Calculus. It illustrates why some indicators, e.g., Stochastics, have limited usage. Several new indicators are designed and tested on theoretical waveforms to check their validity and applicability. The indicators have a minimal time lag, which is significant for trading purposes. Common market behaviors like divergences between price and momentum are explained. A skipped convolution technique is introduced to allow traders to pick up market movements at an earlier time. The market is treated as a nonlinear phenomenon. Forecasting of when the market is going to turn is emphasized.