Financial Market Complexity What Physics Can Tell Us About Market Behaviour

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?

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.

Why do ideas of how mechanisms relate to causality and probability differ so much across the sciences? Can progress in understanding the tools of causal inference in some sciences lead to progress in others? This book tackles these questions and others concerning the use of causality in the sciences.

This book aims to discern and distinguish the essential features of basic economic theories and compare them with new theories that have arisen in recent years. The book focuses on seminal economic ideas and theories developed mainly in the 1930s to 1950s because their emergence eventually led to new branches of economics. The book describes an alternative analytical framework spreading through the interdisciplinary fields of socioeconophysics and sociodynamics. The focus is on a set of branching or critical points that separate what has gone before from what has followed. W. Brian Arthur used the term “redomaining” when he referred to technological innovation. In the present volume the author aims to re domain economic theories suited for a new social order. Major technological innovations accompany not only changes in the economy and the market but changes in their meaning as well. In particular, the evolution of trading technology has changed the meaning of the “invisible hand.” At the end of the last century, the advent of socioeconophysics became a decisive factor in the emergence of a new economic science. This emergence has coincided with changes in the implications of the economy and the market, which consequently require a redomaining of economic science. In this new enterprise, the joint efforts of many scientists outside traditional economics have brought brilliant achievements such as power law distribution and network analysis, among others. However, the more diverse the backgrounds of economic scientists, the less integrated the common views among them may be, resulting in a sometimes perplexing potpourri of economic terminology. This book helps to mitigate those differences, shedding light on current alternative economic theories and how they have evolved.

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.

Nonlinearity, Bifurcation and Chaos - Theory and Application is an edited book focused on introducing both theoretical and application oriented approaches in science and engineering. It contains 12 chapters, and is recommended for university teachers, scientists, researchers, engineers, as well as graduate and post-graduate students either working or interested in the field of nonlinearity, bifurcation and chaos.

Hybrid computational intelligent techniques are efficient in dealing with the real-world problems encountered in engineering fields. The primary objective of this book is to provide an exhaustive introduction as well as review of the hybrid computational intelligent paradigm, with supportive case studies. In addition, it aims to provide a gallery of engineering applications where this computing paradigm can be effectively use. Finally, it focuses on the recent quantum inspired hybrid intelligence to develop intelligent solutions for the future. The book also incorporates video demonstrations of each application for better understanding of the subject matter.