Index Cohesive Force Analysis Reveals That the US Market Became Prone to Systemic Collapses Since 2002

Kenett, Dror Y.; Shapira, Yoash; Madi, Asaf; Bransburg-Zabary, Sharron; Gur-Gershgoren, Gitit; Ben‐Jacob, Eshel

doi:10.1371/journal.pone.0019378

Cited by 70 publications

(65 citation statements)

References 42 publications

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“…A particularly promising species with which to conduct such experiments is bacteria, which engage in remarkably varied and sophisticated behaviors (19,(39)(40)(41)(42)(43)(44)(45)(46). Although an individual bacterium is clearly mindless, colonies of bacteria, such as Paenibacillus vortex, have been observed to engage in seemingly intelligent behavior, such as competition, collaborative foraging, and cell-to-cell chemotactic and physical communication (19).…”

Section: Testable Implicationsmentioning

confidence: 99%

The origin of risk aversion

Zhang

Brennan

2014

Proc. Natl. Acad. Sci. U.S.A.

107

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Risk aversion is one of the most basic assumptions of economic behavior, but few studies have addressed the question of where risk preferences come from and why they differ from one individual to the next. Here, we propose an evolutionary explanation for the origin of risk aversion. In the context of a simple binary-choice model, we show that risk aversion emerges by natural selection if reproductive risk is systematic (i.e., correlated across individuals in a given generation). In contrast, risk neutrality emerges if reproductive risk is idiosyncratic (i.e., uncorrelated across each given generation). More generally, our framework implies that the degree of risk aversion is determined by the stochastic nature of reproductive rates, and we show that different statistical properties lead to different utility functions. The simplicity and generality of our model suggest that these implications are primitive and cut across species, physiology, and genetic origins.risk aversion | risk preferences | expected utility theory | risk-sensitive foraging | evolution R isk aversion is one of the most fundamental properties of human behavior. Ever since pioneering work by Bernoulli (1) on gambling and the St. Petersburg Paradox in the 17th century, considerable research has been devoted to understanding human decision-making under uncertainty. Two of the most well-known theories are expected utility theory (2) (an axiomatic formulation of rational behavior under uncertainty) and prospect theory (3) (a behavioral theory of decision-making under uncertainty). Several measures of risk aversion have been developed, including curvature measures of utility functions (4, 5), human subject experiments and surveys (6, 7), portfolio choice for financial investors (8), labor-supply behavior (9), deductible choices in insurance contracts (10,11), contestant behavior on game shows (12), option prices (13), and auction behavior (14).Despite its importance and myriad applications in the past several decades, few economists have addressed the question: where does risk aversion come from? Biologists and ecologists have documented risk aversion in nonhuman animal speciesoften called risk-sensitive foraging behavior-ranging from bacteria to primates (15)(16)(17)(18)(19). Recently, the neural basis of risk aversion has also received much attention, because researchers discovered that the activity of a specific brain region correlates with risktaking and risk-averse behavior (20)(21)(22).Evolutionary principles have been applied by economists to a variety of economic behaviors and concepts, including altruism (23, 24), the rate of time preference (25), and utility functions (26-29) † . In particular, Robson (26) proposes an evolutionary model of risk preferences, in which he assumes an increasing concave relation between an individual's number of offspring and the amount of resources available to that individual, and given this concave "biological production function," Robson (26) shows that expected utility arises from idiosyncratic environme...

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Section: Testable Implicationsmentioning

confidence: 99%

The origin of risk aversion

Zhang

Brennan

2014

Proc. Natl. Acad. Sci. U.S.A.

107

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“…The properties of the covariance matrix of asset returns depend on the time horizon T in which they are determined. For short T in the order of months they are rather volatile, and partly mirror economic and political changes [15,16]. [4] for example argue that correlations increase in times of crisis, which has profound implication for portfolio choice and hedging of risks.…”

Section: Introductionmentioning

confidence: 99%

Transitions in the stock markets of the US, UK and Germany

Raddant

Wagner

2016

Quantitative Finance

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In an analysis of the US, the UK, and the German stock market we find a change in the behavior based on the stock's beta values. Before 2006 risky trades were concentrated on stocks in the IT and technology sector. Afterwards risky trading takes place for stocks from the financial sector. We show that an agent-based model can reproduce these changes. We further show that the initial impulse for the transition might stem from the increase of high frequency trading at that time.

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“…A large part of the current literature deals with the analysis of single markets. The variety of different methods is quite large, with many analyses employing Pearson's correlations 4,5,6,7 but also other methodologies have been used, such as comovement measures 8 , recurrence patterns 9 , and regime switching approaches 10,11 .…”

Section: Introductionmentioning

confidence: 99%

Correlations and Dependencies in the Global Financial Village

Kenett

Raddant

Zatlavi

et al. 2012

Int. J. Mod. Phys. Conf. Ser.

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The high degree of coupling between global financial markets has made the financial village prone to systemic collapses. Here we present a new methodology to assess and quantify inter-market relations. The approach is based on meta-correlations (correlations between the intra-market correlations), and a Dependency Network analysis approach. We investigated the relations between six important world markets -U.S., U.K., Germany, Japan, China and India from January 2000 until December 2010. Our findings show that while the developed Western markets (U.S., U.K., Germany), are highly correlated, the inter-dependencies between these markets and the Eastern markets (India and China) are very volatile and with noticeable maxima at times of global world events. Finally, using the Dependency network approach, we quantify the flow of information between the different markets, and how markets affect each other. We observe that German and U.K. stocks show a large amount of coupling, while other markets are more segmented. These and additional reported findings illustrate that this methodological framework provides a way to quantify interdependencies in the global market and their

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Index Cohesive Force Analysis Reveals That the US Market Became Prone to Systemic Collapses Since 2002

Cited by 70 publications

References 42 publications

The origin of risk aversion

The origin of risk aversion

Transitions in the stock markets of the US, UK and Germany

Correlations and Dependencies in the Global Financial Village

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