Power law in firms bankruptcy

Hong, Byoung Hee; Lee, Kyoung Eun; Lee, Jae Woo

doi:10.1016/j.physleta.2006.09.034

Cited by 19 publications

(10 citation statements)

References 18 publications

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“…Zipf's law is universal rule followed by many natural and social systems (see e.g. Axtell, 2001;Bak, 1996;Batty, 2006;Gabaix, 1999a;Gabaix, 1999b;Hernando et al, 2009;Hong et al, 2007;Jiang and Jia, 2011;Shao et al, 2011;Stanley et al, 1995). This suggests that urban evolution follows the general laws of nature under certain condition.…”

Section: Results and Findingsmentioning

confidence: 99%

A Hierarchical Allometric Scaling Analysis of Chinese Cities: 1991–2014

Chen

Feng

2017

Discrete Dynamics in Nature and Society

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The law of allometric scaling based on Zipf distributions can be employed to research hierarchies of cities in a geographical region. However, the allometric patterns are easily influenced by random disturbance from the noises in observational data. In theory, both the allometric growth law and Zipf's law are related to the hierarchical scaling laws associated with fractal structure. In this paper, the scaling laws of hierarchies with cascade structure are used to study Chinese cities, and the method of R/S analysis is applied to analyzing the change trend of the allometric scaling exponents. The results show that the hierarchical scaling relations of Chinese cities became clearer and clearer from 1991 to 2014 year; the global allometric scaling exponent values fluctuated around 0.85, and the local scaling exponent approached to 0.85. The Hurst exponent of the allometric parameter change is greater than 0.5, indicating persistence and a long-term memory of urban evolution. The main conclusions can be reached as follows: the allometric scaling law of cities represents an evolutionary order rather than an invariable rule, which emerges from self-organized process of urbanization, and the ideas from allometry and fractals can be combined to optimize spatial and hierarchical structure of urban systems in future city planning.

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Section: Results and Findingsmentioning

confidence: 99%

A Hierarchical Allometric Scaling Analysis of Chinese Cities: 1991–2014

Chen

Feng

2017

Discrete Dynamics in Nature and Society

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“…Country Events Athletes Cost, billion USD Table 5: Events that follow power-law distributions and the law of regression to the tail, with alpha-values. The lower the alphavalue, the fatter the tail and the higher the risk for extreme values Type of event Alpha Earthquakes (intensity as Richter Scale maximum peak, Clauset et al 2006) 0.6 Cybercrime (financial loss, Maillart and Sornette 2010) 0.6 Wars (number of battle deaths per capita, Newman 2005) 0.7 IT procurement (percentage cost overrun, Flyvbjerg et al in progress) 0.9 Bankruptcies (percentage of firms per year per industry, Hong et al 2007) 1.1 Floods (volume of water, Malamud and Turcotte, 2006) 1.1 Forest fires (size of area affected, Clauset et al 2009) 1.2 Olympic Games (percentage cost overrun, present study)…”

Section: Gamesmentioning

confidence: 99%

Regression to the tail: Why the Olympics blow up

2020

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The Olympic Games are the largest, highest-profile, and most expensive megaevent hosted by cities and nations. Average sports-related costs of hosting are $12.0 billion. Non-sports-related costs are typically several times that. Every Olympics since 1960 has run over budget, at an average of 172 percent in real terms, the highest overrun on record for any type of megaproject. The paper tests theoretical statistical distributions against empirical data for the costs of the Games, in order to explain the cost risks faced by host cities and nations. It is documented, for the first time, that cost and cost overrun for the Games follow a power-law distribution. Olympic costs are subject to infinite mean and variance, with dire consequences for predictability and planning. We name this phenomenon "regression to the tail": it is only a matter of time until a new extreme event occurs, with an overrun larger than the largest so far, and thus more disruptive and less plannable. The generative mechanism for the Olympic power law is identified as strong convexity prompted by six causal drivers: irreversibility, fixed deadlines, the Blank Check Syndrome, tight coupling, long planning horizons, and an Eternal Beginner Syndrome. The power law explains why the Games are so difficult to plan and manage successfully, and why cities and nations should think twice before hosting. Based on the power law, two heuristics are identified for better decision making on hosting. Finally, the paper develops measures for good practice in planning and managing the Games, including how to mitigate the extreme risks of the Olympic power law.

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“…(7), (8) and (9) along with the corresponding Kramers-Moyal diffusion equation given by (13) represent the general form of the stochastic model that we use to describe the time evolution of financial prices. There is no need to impose a particular distribution for the jumps.…”

Section: Jump Processesmentioning

confidence: 99%

“…Empirical data of several stochastic processes from distinct areas have been analyzed in literature, from financial time series [1][2][3] to heartbeat dynamics [4] and others. Of particular interest is the field of econophysics, which has attracted widespread attention in recent years [5][6][7][8][9][10][11][12]. This Letter focuses on the statistical behavior of financial time series.…”

Section: Introductionmentioning

confidence: 99%

Jump diffusion models and the evolution of financial prices

et al. 2011

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We analyze a stochastic model to describe the evolution of financial prices. We consider the stochastic term as a sum of the Wiener noise and a jump process. We point to the effects of the jumps on the return time evolution, a central concern of the econophysics literature. The presence of jumps suggests that the process can be described by an infinitely divisible characteristic function belonging to the De Finetti class. We then extend the De Finetti functions to a generalized nonlinear model and show the model to be capable of explaining return behavior.

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Power law in firms bankruptcy

Cited by 19 publications

References 18 publications

A Hierarchical Allometric Scaling Analysis of Chinese Cities: 1991–2014

A Hierarchical Allometric Scaling Analysis of Chinese Cities: 1991–2014

Regression to the tail: Why the Olympics blow up

Jump diffusion models and the evolution of financial prices

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