Critical Crashes

We argue that the word ``critical'' in the title is not purely literary. Based on our and other previous work on nonlinear complex dynamical systems, we summarize present evidence, on the Oct. 1929, Oct. 1987, Oct. 1987 Hong-Kong, Aug. 1998 global market events and on the 1985 Forex event, for the hypothesis advanced four years ago that stock market crashes are caused by the slow buildup of long-range correlations between traders leading to a collapse of the stock market in one critical instant. We qualify the log-periodic oscillations using a novel non-parametric method that does not rely on any fit: the corresponding log-periodogram exhibits a strong statistically significant peak for all six crashes examined, pointing at approximately the same prefered scaling ratio around 2.Comment: 7 pages, 5 figure

Gasoline Prices and Their Relationship to Drunk-Driving Crashes

his study investigates the relationship between changing gasoline prices and drunk-driving crashes. Specifically, we examine the effects of gasoline prices on drunk-driving crashes in Mississippi by age, gender, and race from 2004Ð2008, a period experiencing great fluctuation in gasoline prices. An exploratory visualization by graphs shows that higher gasoline prices are generally associated with fewer drunk-driving crashes. Higher gasoline prices depress drunk- driving crashes among younger and older drivers, among male and female drivers, and among white, black, and Hispanic drivers. The statistical results suggest that higher gasoline prices lead to lower drunk-driving crashes for female and black drivers. However, alcohol consumption is a better predictor of drunk-driving crashes, especially for male, white, and older drivers.Drunk-driving crashes, gasoline prices, alcohol consumption, Mississippi

Automatically Discovering, Reporting and Reproducing Android Application Crashes

Mobile developers face unique challenges when detecting and reporting crashes in apps due to their prevailing GUI event-driven nature and additional sources of inputs (e.g., sensor readings). To support developers in these tasks, we introduce a novel, automated approach called CRASHSCOPE. This tool explores a given Android app using systematic input generation, according to several strategies informed by static and dynamic analyses, with the intrinsic goal of triggering crashes. When a crash is detected, CRASHSCOPE generates an augmented crash report containing screenshots, detailed crash reproduction steps, the captured exception stack trace, and a fully replayable script that automatically reproduces the crash on a target device(s). We evaluated CRASHSCOPE's effectiveness in discovering crashes as compared to five state-of-the-art Android input generation tools on 61 applications. The results demonstrate that CRASHSCOPE performs about as well as current tools for detecting crashes and provides more detailed fault information. Additionally, in a study analyzing eight real-world Android app crashes, we found that CRASHSCOPE's reports are easily readable and allow for reliable reproduction of crashes by presenting more explicit information than human written reports.Comment: 12 pages, in Proceedings of 9th IEEE International Conference on Software Testing, Verification and Validation (ICST'16), Chicago, IL, April 10-15, 2016, pp. 33-4

High Frequency Trading and Mini Flash Crashes

We analyse all Mini Flash Crashes (or Flash Equity Failures) in the US equity markets in the four most volatile months during 2006-2011. In contrast to previous studies, we find that Mini Flash Crashes are the result of regulation framework and market fragmentation, in particular due to the aggressive use of Intermarket Sweep Orders and Regulation NMS protecting only Top of the Book. We find strong evidence that Mini Flash Crashes have an adverse impact on market liquidity and are associated with Fleeting Liquidity

In-depth research into rural road crashes

This report was produced under an agreement between Transport SA and the Road Accident Research Unit formed in the late 1990s. Due to various delays in the publication of this report, Transport SA has since become the Department for Transport, Energy and Infrastructure and the Road Accident Research Unit has become the Centre for Automotive Safety Research. The report describes a series of 236 rural road crashes investigated between 1 March 1998 and 29 February 2000 in South Australia. Investigations began with immediate attendance at the scene of the crash. The information collected for each crash included: photographs of the crash scene and vehicles involved, video record of the crash scene and vehicles in selected cases, examination of the road environment, a site plan of the crash scene and vehicle movements in the crash, examination and measurements of the vehicles involved, interviews with crash participants, interviews with witnesses, interviews with police, information on the official police report, information from Coroner’s reports, and injury data for the injured crash participants. The report provides an overall statistical summary of the sample of crashes investigated, followed by a detailed examination of the road infrastructure issues contributing to the crashes. This is done on the basis of crash type, with separate sections concerned with single vehicle crashes, midblock crashes and crashes at intersections. A section is also provided that examines the role of roadside hazards in the crashes.Baldock MRJ, Kloeden CN and McLean A

Psychological Aspects of Market Crashes

This paper analyzes the sensitivity of market crashes to investors'psychology in a standard general equilibrium framwork. Contrary to the traditional view that market crashes are driven by large drops in aggregate endowments, we argue from a theoretical standpoint that individual anticipations of such drops are a necessary condition for crashes to occur, and that the magnitude or such crashes are poritively correlated with the level of individual anticipations of drops

Are Financial Crashes Predictable?

We critically review recent claims that financial crashes can be predicted using the idea of log-periodic oscillations or by other methods inspired by the physics of critical phenomena. In particular, the October 1997 `correction' does not appear to be the accumulation point of a geometric series of local minima.Comment: LaTeX, 5 pages + 1 postscript figur

The effectiveness of delineation treatments

A literature review undertaken for Transit NZ has found that delineation has a significant effect on driver behaviour with, for example, shoulder rumble strips reducing run-off-theroad crashes by between 22% and 80% (average of 32% for all crashes and 44% for fatal run-of-the-road crashes). The concern that enhancing roadway delineation may sometimes be accompanied by an unwanted increase in drivers’ speeds (known as behavioural adaptation) is not borne out by the research and appears to be a phenomenon associated with a few restricted situations (e.g. where a centre line is added to an otherwise unmarked road). The preponderance of the evidence supports the conclusion that profiled edge lines and centre lines provide drivers with positive guidance and produce significant reductions in crashes as a result of improving drivers’ lateral position. Further, unlike other safety measures that show decreased effectiveness over time due to a novelty effect, profiled lane delineation continues to work regardless of driver familiarity. There is no published research to suggest that profiled edge lines will decrease the effectiveness of a profiled centre line or will result in an increase in crash rates or an increase in the severity of crashes. However it has also been noted that local conditions have a major influence on the level of benefits that can be achieved through improved delineation

Large financial crashes

We propose that large stock market crashes are analogous to critical points studied in statistical physics with log-periodic correction to scaling. We extend our previous renormalization group model of stock market prices prior to and after crashes [D. Sornette et al., J.Phys.I France 6, 167, 1996] by including the first non-linear correction. This predicts the existence of a log-frequency shift over time in the log-periodic oscillations prior to a crash. This is tested on the two largest historical crashes of the century, the october 1929 and october 1987 crashes, by fitting the stock market index over an interval of 8 years prior to the crashes. The good quality of the fits, as well as the consistency of the parameter values obtained from the two crashes, promote the theory that crashes have their origin in the collective ``crowd'' behavior of many interacting agents.Comment: 14 pages and 4 figures. To be published in Physica

Crashes as Critical Points

We study a rational expectation model of bubbles and crashes. The model has two components : (1) our key assumption is that a crash may be caused by local self-reinforcing imitation between noise traders. If the tendency for noise traders to imitate their nearest neighbors increases up to a certain point called the ``critical'' point, all noise traders may place the same order (sell) at the same time, thus causing a crash. The interplay between the progressive strengthening of imitation and the ubiquity of noise is characterized by the hazard rate, i.e. the probability per unit time that the crash will happen in the next instant if it has not happened yet. (2) Since the crash is not a certain deterministic outcome of the bubble, it remains rational for traders to remain invested provided they are compensated by a higher rate of growth of the bubble for taking the risk of a crash. Our model distinguishes between the end of the bubble and the time of the crash,: the rational expectation constraint has the specific implication that the date of the crash must be random. The theoretical death of the bubble is not the time of the crash because the crash could happen at any time before, even though this is not very likely. The death of the bubble is the most probable time for the crash. There also exists a finite probability of attaining the end of the bubble without crash. Our model has specific predictions about the presence of certain critical log-periodic patterns in pre-crash prices, associated with the deterministic components of the bubble mechanism. We provide empirical evidence showing that these patterns were indeed present before the crashes of 1929, 1962 and 1987 on Wall Street and the 1997 crash on the Hong Kong Stock Exchange. These results are compared with statistical tests on synthetic data.Comment: A total of 40 pages including 9 figures and 6 table