Building on similarities between earthquakes and extreme financial events, we use a self-organized criticality-generating model to study herding and avalanches dynamics in financial markets.
We consider a community of interacting investors, distributed on a small world network, who bet on the bullish (increasing) or bearish (decreasing) behavior of the market compared to the day before, following the S&P500 historical time series. Remarkably, we find that the size of herding-related avalanches in the community can be strongly reduced by the presence of a relatively small percentage of trader s, randomly distributed inside the network, who adopt a random investment strategy.
These results suggest a promising strategy to limit the size of financial bubbles and crashes. We also find that the final wealth distribution of all traders corresponds to the well-known Pareto power law, while that one of random traders only is exponential. In other words, for technical traders, the risk of losses is much greater than the probability of gains compared to those of random traders.
Why does the adherence to norms not prevent conflict? While the current literature focuses on the emergence, maintenance and impact of norms with regard to cooperation, the issue of norm-related conflict deserves more attention.
We develop a general game theoretical model of “normative conflict” and explain how transaction failures on the macrolevel can result from cooperative motives on the microlevel. We differentiate between two kinds of conflict. The first results from distinct expectations regarding the way in which general normative obligations should be fulfilled, the second from distinct expectations as to how the norm should restrain actions based on self-interest.
We demonstrate the empirical relevance of normative conflict in a version of the ultimatum game. Our data reveal widespread normative conflict among different types of actors – egoistic, equity, equality and cherry picker. Our findings demonstrate how cooperative intentions about how to divide a collectively produced good may fail to produce cooperative outcomes.
Are random trading strategies more successful than technical ones? A.E.Biondo, A.Pluchino, A.Rapisarda, D.Helbing
In this paper we explore the specific role of randomness in financial markets, inspired by the beneficial role of noise in many physical systems and in previous applications to complex socio- economic systems. After a short introduction, we study the performance of some of the most used trading strategies in predicting the dynamics of financial markets for different international stock exchange indexes, with the goal of comparing them with the performance of a completely random strategy. In this respect, historical data for FTSE-UK, FTSE-MIB, DAX, and S&P500 indexes are taken into account for a period of about 15-20 years (since their creation until today).
“True capitalists are other-regarding” – “Perhaps we have applied the wrong theory, and our economy should be run by different people” The body of economic literature will have to change, implies a groundbreaking discovery. In their computer simulations of human evolution, scientists at ETH Zurich find the emergence of the “homo socialis” with “other-regarding” preferences. The results explain some intriguing findings in experimental economics and call for a new economic theory of “networked minds”. Economics has a beautiful body of theory. But does it describe real markets? Doubts have come up not only in the wake of the financial crisis, since financial crashes should not occur according to the then established theories. Since ages, economic theory is based on concepts such as efficient markets and the “homo economicus”, i.e. the assumption of competitively optimizing individuals and firms. It was believed that any behavior deviating from this would create disadvantages and, hence, be eliminated by natural selection. But experimental evidence from behavioral economics show that, on average, people behave more fairness-oriented and other-regarding than expected. A new theory by scientists from ETH Zurich now explains why.
Agents of influenceRobert Frederick, Science Writer
Models of complex systems have become a staple of business strategy, and now they are showing early promise for improving economic forecasts.
For cargo carriers, the most direct route is not always the cheapest. In the early 2000s, Southwest Airlines adopted a new approach to shipping: Rather than switching cargo from one flight to another to minimize the distance it covered, the airline would take circuitous routes to destinations on fewer flights. The strategy seemed counterintuitive, but it has saved the company millions of dollars in storage rentals and cargo handlers’ wages.
The 2008 financial crisis has highlighted major limitations in the modelling of financial and economic systems. However, an emerging field of research at the frontiers of both physics and economics aims to provide a more fundamental understanding of economic networks, as well as practical insights for policymakers. In this Nature Physics Focus, physicists and economists consider the state-of-the-art in the application of network science to finance.
Net gains -p119
Physics — and physicists — have had much to contribute to economic and finance. Now the science of complex networks sets a way forward to understanding and managing the complex financial networks of the world's markets.
PDF (138KB)- Net gains
Network opportunity -pp121 – 122
Michele Catanzaro and Mark Buchanan
Our developing scientific understanding of complex networks is being usefully applied in a wide set of financial systems. What we've learned from the 2008 crisis could be the basis of better management of the economy — and a means to avert future disaster.
Complex derivatives -pp123 – 125
Stefano Battiston, Guido Caldarelli, Co-Pierre Georg, Robert May and Joseph Stiglitz
The intrinsic complexity of the financial derivatives market has emerged as both an incentive to engage in it, and a key source of its inherent instability. Regulators now faced with the challenge of taming this beast may find inspiration in the budding science of complex systems.
PDF (152KB)- Complex derivatives
Reconstructing a credit network -pp125 – 126
Guido Caldarelli, Alessandro Chessa, Andrea Gabrielli, Fabio Pammolli and Michelangelo Puliga
The science of complex networks can be usefully applied in finance, although there is limited data available with which to develop our understanding. All is not lost, however: ideas from statistical physics make it possible to reconstruct details of a financial network from partial sets of information.
Reconstructing a credit network
The power to control -pp126 – 128
Marco Galbiati, Danilo Delpini & Stefano Battiston
Understanding something of the complexity of a financial network is one thing, influencing the behaviour of that system is another. But new tools from network science define a notion of 'controllability' that, coupled with 'centrality', could prove useful to economists and financial regulators.
A model similar to the computer game Civilisation recreates human history and shows the importance of warfare in stabilising society
"However, running a similar model for the modern world will be much more complicated, says Dirk Helbing of the Swiss Federal Institute of Technology in Zurich, who is working on such a model. This is because war and violent conquest are no longer the primary ways in which social institutions and culture spread. Mass media, migration and globalisation would all have to be taken into account, he says."
Rethinking Economics Using Complexity Theory: Dirk Helbing & Alan Kirman
In this paper we argue that if we want to find a more satisfactory approach to tackling the major socio-economic problems we are facing, we need to thoroughly rethink the basic assumptions of macroeconomics and financial theory. Making minor modifications to the standard models to remove “imperfections” is not enough, the whole framework needs to be revisited.
LE MONDE SCIENCE ET TECHNO | 04.04.2013 à 16h55 • Mis à jour le 06.04.2013 à 22h15
Dans une étude récente, deux chercheurs expliquent qu'au grand dam des analystes financiers, en quête de justifications rationnelles aux fluctuations des cours, les marchés demeurent obstinément imprévisibles.
We study fifteen months of human mobility data for one and a half million individuals and find that human mobility traces are highly unique. In fact, in a dataset where the location of an individual is specified hourly, and with a spatial resolution equal to that given by the carrier's antennas, four spatio-temporal points are enough to uniquely identify 95% of the individuals. We coarsen the data spatially and temporally to find a formula for the uniqueness of human mobility traces given their resolution and the available outside information. This formula shows that the uniqueness of mobility traces decays approximately as the 1/10 power of their resolution. Hence, even coarse datasets provide little anonymity. These findings represent fundamental constraints to an individual's privacy and have important implications for the design of frameworks and institutions dedicated to protect the privacy of individuals.
Biological competition is widely believed to result in the evolution of selfish preferences. The related concept of the ‘homo economicus’ is at the core of mainstream economics.
However, there is also experimental and empirical evidence for other-regarding preferences. Here we present a theory that explains both, self-regarding and other-regarding preferences.
Assuming conditions promoting non-cooperative behaviour, we demonstrate that intergenerational migration determines whether evolutionary competition results in a ‘homo economicus’ (showing self-regarding preferences) or a ‘homo socialis’ (having other-regarding preferences).
Our model assumes spatially interacting agents playing prisoner's dilemmas, who inherit a trait determining ‘friendliness’, but mutations tend to undermine it. Reproduction is ruled by fitness-based selection without a cultural modification of reproduction rates.
Our model calls for a complementary economic theory for ‘networked minds’ (the ‘homo socialis’) and lays the foundations for an evolutionarily grounded theory of other-regarding agents, explaining individually different utility functions as well as conditional cooperation.
Dirk Helbing is the scientific coordinator of the FuturICT initiative, elected member of the German Academy of Sciences "Leopoldina"
and of the World Economic Forums Global Agenda Council on Complex Systems, and internationally well-known for his work on crowds, traffic, and social behavior.
Complex Adaptive Systems Modeling(CASM) is a highly multidisciplinary modeling and simulation journal that serves as a unique forum for original, high-quality peer-reviewed papers with a specific interest and scope limited to agent-based and complex network-based modeling paradigms for Complex Adaptive Systems (CAS). The highly multidisciplinary scope ofCASM spans any domain of CAS. Possible areas of interest range from the Life Sciences (E.g. Biological Networks and agent-based models), Ecology (E.g. Agent-based/Individual-based models), Social Sciences (Agent-based simulation, Social Network Analysis), Scientometrics (E.g. Citation Networks) to large-scale Complex Adaptive COmmunicatiOn Networks and environmentS (CACOONS) such as Wireless Sensor Networks (WSN), Body Sensor Networks, Peer-to-Peer (P2P) networks, pervasive mobile networks, service oriented architecture, smart grid and the Internet of Things.
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