Crash Risk Research Articles

Internal control effectiveness and stock price crash

This study focuses on the problems of imperfect internal control effectiveness, insufficient information transparency, and plummeting stock prices. The study selects the data of non-financial main board listed companies in China’s Shanghai and Shenzhen A-shares from 2012 to 2021 as a sample, and adopts an empirical research methodology, which reveals that the effectiveness of internal control is negatively related to the trend of share price crash, and efficient internal control is positively related to the transparency of corporate information environment. The findings suggest the impact of internal control on the risk of stock price crash at the individual stock level and provide empirical support for listed companies to manage their risks. This study has practical value in guiding listed companies to strengthen internal control, improve information transparency, mitigate the risk of stock price crashes, and provide a decision-making basis for the healthy and stable development of the capital market.

Evaluating the safety and efficiency impacts of forced lane change with negative gaps based on empirical vehicle trajectories

A lane-changing (LC) maneuver may cause the follower in the target lane (new follower) to decelerate and give up space, potentially affecting crash risk and traffic flow efficiency. In congested flow, a more aggressive LC maneuver occurs where the lane changer is partially next to the new follower and creates negative gaps, namely negative gap forced LC (NGFLC). Although NGFLC forms the foundation of sideswipe crashes, little has been done to address its impacts and the contributing factors. To tackle this issue, a total of 15,810 LC trajectory samples are extracted from three drone videos at different locations. These samples are categorized into NGFLC and normal LC groups for comparative analysis. Five commonly used conflict indicators are extended into two-dimensional to evaluate the crash risk of LC maneuver. The change of time gaps during LC maneuver are examined to quantify the impact of LC on traffic flow efficiency. We find that NGFLCs significantly increase crash risk, reflected by the number of hazardous LC events and potential crash areas compared to normal LC. Additionally, results reveal that both the lane changer and the new follower tend to maintain a larger time gap after NGFLCs. Factors including time headway, relative speed, and historical gaps in the target lane significantly affect NGFLC incidence. Once the movement of the leader in the original lane is taken into account, the prediction accuracy improves from 81% to 91%. The transferability tests indicate that the findings about the negative impact of NGFLC and the accuracy of its prediction model are consistent across different locations. These findings hold implications for driving assistance systems to better predict and mitigate NGFLCs.

Environmental Violations and Stock Price Crash Risk: Evidence from Chinese Listed Heavily Polluting Companies

ABSTRACT This paper examines the relationship between environmental violations and stock price crash risk, using the sample of Chinese A-shared listed firms and a comprehensive range of Chinese listed heavily polluting companies covering 2012 to 2021. We provide strong and robust evidence that environmental violations significantly increase future stock price crash risk for Chinese listed heavily polluting companies. Through the mechanism of internal control, our paper finds that environmental violations lead to the weakening of internal control, thereby exacerbating future stock price crash risk. We further find that the legal environment has a negatively moderating impact on the relationship. Additionally, heterogeneity analysis shows that the influence of environmental violations on stock price crash risk is more prominent when firms are smaller, have non-state ownership, and have a lower level of corporate governance and external monitoring.

Generalist CEOs and stock price crash risk

AbstractWe investigate whether generalist chief executive officers (CEOs), that is, CEOs who gain transferable skills across firms and industries, have less incentive to hoard bad news. To address endogeneity concerns stemming from firm–CEO matching, we deploy a difference‐in‐differences method utilizing exogenous CEO turnovers, propensity score matching and entropy balancing matching methods, and Oster's coefficient stability test. Supporting our conjecture, we find a negative relation between CEOs’ general ability index (GAI) and future stock price crash risk. The effect of CEOs’ GAI on crash risk is stronger when labor demand is stronger and when firms have more agency conflicts. Our analysis further suggests that generalist CEOs attenuate crash risk by increasing conditional accounting conservatism and reducing real earnings management. Taken together, our findings highlight the role of CEOs’ general human capital in increasing their tolerance for failure and mitigating the agency problem.

Corporate greenhushing and stock price crash risk: evidence from China

Corporate greenhushing and stock price crash risk: evidence from China

Stock liquidity and stock price crash risk: evidence from China

ABSTRACT This paper investigates how stock liquidity influences stock price crash risk using data from Chinese A-share listed companies between 2009 and 2018. Our findings show that enhanced liquidity increases stock price crash risk. This effect becomes more pronounced in firms with lower levels of information transparency and weaker external governance. Higher levels of management stock ownership and significant shareholdings by major shareholders also intensify the relationship between liquidity and the risk of stock price crashes. We explain for this phenomenon by short-termism and governance theory. Specifically, highly liquid stocks exert short-term pressure on management due to short-termism. To fulfil the profit expectations of short-term investors, management might resort to upward earnings management, and conceal bad news, thereby elevating the risk of stock price crashes. Governance theory argues that heightened liquidity facilitates the withdrawal of major shareholders upon bad news revelation, which intensifies the market reaction to bad news, potentially triggering additional stock selling and consequently elevating the risk of stock price crashes. Furthermore, our additional analysis reveals that liquidity exerts a more pronounced influence on crashes during a bull market than a bear market. And distinct property rights do not substantially alter the relationship between liquidity and crash risk.

Occupational health and safety management system and stock price crash risk

This study responds to the call of Ben-Nasr and Ghouma (2018) by providing more empirical evidence on stock price crash risk from the employee treatment perspective. With a focus on occupational health and safety management system (OHSMS), we examine whether corporate OHSMS adoption reduces stock price crash risk. We find that OHSMS adoption is negatively associated with crash risk. This negative relationship is more pronounced for SOEs than for non-SOEs, and for firms with higher levels of media coverage and with higher market competitive status. Employee welfare and internal control effectiveness are two channels through which OHSMS adoption affects crash risk. The current study extends to the health and safety factor, thus shedding new light on the determinants of crash risk. Our study also advances the understanding of the “responsible employee treatment – crash risk” relationship.

Do natural disasters affect stock price crash risk? Evidence from emerging markets

This study shows a significantly positive relationship between natural disasters and firm-level stock price crash risk using a sample of listed firms from emerging markets.The channel tests suggest that natural disasters affect crash risk by increasing corporate risk-taking, dampening firm fundamentals, and aggravating bad news hoarding. The research further identifies that the effect of natural disasters on crash risk is moderated by country-level and firm-level characteristics. Overall, our findings contribute to a broader understanding of the economic outcomes of natural disasters in emerging markets.

Managerial Overconfidence and Stock Price Crash Risk

This study explores the effect of managerial overconfidence on the risk of a stock price crash. Results indicate that managerial overconfidence positively affects stock price crash risk. Importantly, internal controls can reduce this risk by curbing managerial overconfidence.

Automatic detection of potholes using VGG-16 pre-trained network and Convolutional Neural Network

A self-driving car is necessary to implement traffic intelligence because it can vastly enhance both the safety of driving and the comfort of the driver by adjusting to the circumstances of the road ahead. Road hazards such as potholes can be a big challenge for autonomous vehicles, increasing the risk of crashes and vehicle damage. Real-time identification of road potholes is required to solve this issue. To this end, various approaches have been tried, including notifying the appropriate authorities, utilizing vibration-based sensors, and engaging in three-dimensional laser imaging. Unfortunately, these approaches have several drawbacks, such as large initial expenditures and the possibility of being discovered. Transfer learning is considered a potential answer to the pressing necessity of automating the process of pothole identification. A Convolutional Neural Network (CNN) is constructed to categorize potholes effectively using the VGG-16 pre-trained model as a transfer learning model throughout the training process. A Super-Resolution Generative Adversarial Network (SRGAN) is suggested to enhance the image's overall quality. Experiments conducted with the suggested approach of classifying road potholes revealed a high accuracy rate of 97.3%, and its effectiveness was tested using various criteria. The developed transfer learning technique obtained the best accuracy rate compared to many other deep learning algorithms.

Distance headway-based safety evaluation of emerging mixed traffic flow under snowy weather

The advent of emerging mixed traffic flow composed of human-driven vehicles (HDVs) and connected and automated vehicles (CAVs) is poised to revolutionize traffic operation mechanisms. To fill the gaps in dynamic threshold of surrogate safety measures (SSMs) and snow weather safety evaluation in emerging mixed traffic flow, this study develops a novel dynamic threshold of distance headway, safety warning distance (SWD), and tests it using a proposed snow weather traffic flow simulation analysis framework. The SWD extends the distance headway to a type of SSM that can simultaneously identify longitudinal and lateral conflicts. In addition, to overcome the problem of lateral conflict identification caused by the deficiency of simulation software, a method for estimating the lane-changing angle is proposed. The results indicate that: i) CAVs have the potential to significantly enhance the safety of mixed traffic flow, particularly under snowy weather conditions; ii) there are disparities in the identification of longitudinal conflicts between the commonly used time-to-collision (TTC) and distance headway, which is likely due to discrepancies in the speed and space distributions between the leading and following vehicles under different weather conditions; iii) differences exist in the identification of lateral conflicts between the conventional post-encroachment time (PET) and distance headway, which is probably attributed to variations in their respective conflict identification patterns; and iv) Compared to TTC and PET, distance headway with SWD performs better in effectively identifying conflicts within mixed traffic flow under snowy weather conditions. The findings can offer a theoretical foundation for identifying longitudinal and lateral conflicts in emerging mixed traffic flow and quantifying crash risks under both favorable and adverse weather conditions.

Globalization and stock price crash risk: evidence from the US granting permanent normal trade relations to China

Globalization and stock price crash risk: evidence from the US granting permanent normal trade relations to China

Local gambling attitudes and firms' financing strategies: New insights from the geographic structure in China

This study examines the effect of local gambling attitudes on firms' financing strategies, using the number of lottery sales stations as a proxy for the strength of these attitudes. We find evidence suggesting that firms headquartered in cities with a stronger gambling attitude tend to have more leverage than firms headquartered elsewhere. Furthermore, the effect of local gambling attitudes on firms' leverage is more pronounced among firms that have higher agency costs or exist in a more opaque information environment. Finally, a strong local gambling attitude triggers local firms to overinvest, resulting in reduced overall efficiency and increased risks of bond defaults and stock price crashes. Our findings suggest that a strong local gambling attitude may encourage firms to adopt aggressive financing strategies and lower their resource allocation efficiency.

Safety performance of dedicated and preferential bus lanes using multivariate negative binomial models for Bogotá, Colombia

Public transport priority systems such as Bus Rapid Transit (BRT) and Buses with High Level of Service (BHLS) are top-rated solutions to mobility in low-income and middle-income cities. There is scientific agreement that the safety performance level of these systems depends on their functional, operational, and infrastructure characteristics. However, there needs to be more evidence on how the different characteristics of bus corridors might influence safety. This paper aims to shed some light on this area by structuring a multivariate negative binomial model comparing crash risk on arterial roads, BRT, and BHLS corridors in Bogotá, Colombia. The analyzed infrastructure includes 712.1 km of arterial roads with standard bus service, 194.1 km of BRT network, and 135.6 km of BHLS network. The study considered crashes from 2015 to 2018 –fatalities, injuries, and property damage only– and 30 operational and infrastructure variables grouped into six classes –exposure, road design, infrastructure, public means of transport, and land use. A multicriteria process was applied for model selection, including the structure and predictive power based on [i] Akaike information criteria, [ii] K-fold cross-validation, and [iii] model parsimony. Relevant findings suggest that in terms of observed and expected accident rates and their relationship with the magnitude of exposure –logarithm of average annual traffic volumes at the peak hour (LOG_AAPHT) and the percentage of motorcycles, cars, buses, and trucks– the greatest risk of fatalities, injuries, and property damage occurs in the BHLS network. BRT network provides lower crash rates in less severe collisions while increasing injuries and fatalities. When comparing the BHLS network and the standard design of arterial roads, BHLS infrastructure, despite increasing mobility benefits, provides the lowest safety performance among the three analyzed networks. Individual factors of the study could also contribute to designing safer roads related to signalized intersection density and curvature. These findings support the unique characteristics and traffic dynamics present in the context of Bogotá that could inform and guide decisions of corresponding authorities in other highly dense urban areas from developing countries.

A Machine Learning Approach for Investigating the Determinants of Stock Price Crash Risk: Exploiting Firm and CEO Characteristics

This study uses machine learning to investigate the effects of firm and CEO characteristics on stock price crash risk by collecting massive data on publicly listed firms in China. The results show that eXtreme Gradient Boosting (XGBoost) is the most effective model for predicting stock price crash risk, with relatively satisfactory performance. Meanwhile, the SHapley Additive exPlanations (SHAP) method is used to interpret the importance of features. The results show that the average weekly return of a firm over a year (RET) contributes the most and is negatively associated with crash risk, followed by Sigma, IPO age, and firm size. We also found that, among CEO characteristics, CEO pay contributes substantially to crash risk at the firm level. Our findings have important implications for research into the impact of firm and CEO characteristics on stock price crash risk and provide a novel way for investors to plan their investment decisions and risk-taking behavior rationally.

Unmasking the carbon conundrum: How emissions impact stock price crash risk

This study examines the influence of carbon risk on stock price crash risk. Using a comprehensive dataset of 3,417 US companies from 2006 to 2021, we provide evidence that carbon risk is significantly linked to a higher crash risk for firms. Moreover, our findings suggest that this positive association is more pronounced during the Democratic presidency and to companies operating in carbon-intensive industries. The main results are consistent after conducting endogeneity and several robustness tests.

Bankruptcy social cost, government intervention and stock price crash risk

This paper examines the relationship between the social cost of bankruptcy and the risk of corporate stock price collapse through a study of China's A-share non-financial listed companies from 2012 to 2022. It is found that the higher a firm's social cost of bankruptcy is, the lower its firm share price crash risk is instead. In addition, the extent to which the social cost of insolvency affects the risk of firm share price collapse is enhanced in regions with less government intervention.

Analyzing the factors influencing speeding behavior based on quasi-induced exposure and random parameter logit model with heterogeneity in means

Introduction: Speeding behavior is a major threat to road traffic safety, which can increase crash risks and result in severe injury outcomes. Although several studies have been conducted to analyze speeding crashes and relevant influential factors, the heterogeneity of variables has not been fully explored. Based on the traffic crash data extracted from the Crash Report Sampling System, the study aims to identify the factors that influence speeding driving with the consideration of variable heterogeneity. Method: Quasi-induced exposure technique is adopted to identify the disparities in the propensities of speeding for various driving cohorts. The random parameter logit model with heterogeneity in means is employed to examine the factors impacting speeding behavior. Results: Results indicate that: (a) driving cohorts such as young drivers, male drivers, passenger cars, and pickups appear to have higher propensities of engaging in speeding driving; (b) the propensity of speeding is higher when the driver is drinking, distracted, changing lanes, negotiating a curve, driving in lighted condition, and on curved roads; and (c) the random parameter logit model with heterogeneity in means has better performance as opposed to that without heterogeneity in means. Conclusions: Speeding behavior can be influenced by various factors in terms of driver-vehicle characteristics, physical condition, driving actions, and environmental conditions. Practical Applications: The findings could serve to develop effective countermeasures to reduce speeding behavior and improve traffic safety.

The impact of COVID-19 restrictions on motorcycle crashes in Taiwan.

Taiwan is one of the countries with the highest motorcycle per capita globally, and motorcycle crashes are predominant among traffic crashes. This study examines the impact of coronavirus disease 2019 restrictions on motorcycle crashes. We analyzed the trend of motorcycle crashes in Taipei City from 2019 to 2020 using the dataset provided by the Department of Transportation, Taipei City Government, Taiwan. We found 47,108 and 51,441 motorcycle crashes in 2019 and 2020, involving 61,141 and 67,093 motorcycles, respectively. Mopeds had the highest risk in 2020, followed by heavy motorcycles [≥550 cubic capacity (cc)] and scooters compared to 2019. Food delivery motorcycle crashes increased for scooters (0.93% in 2019 to 3.45% in 2020, P < .0001) and heavy motorcycles (250 < cc < 550) (0.90% in 2019 to 3.38% in 2020, P < .0001). While fatalities remained under 1%, 30% to 51% of motorcyclists sustained injuries. Food delivery with scooters or heavy motorcycles (250 < cc < 550) was significantly associated with motorcyclist injuries and deaths. Compared with 2019, the adjusted odds ratios of motorcyclist injuries and deaths in 2020 were 1.43 (95% confidence interval = 1.05-1.94) for heavy motorcycles (≥550 cc) and 1.07 (95% confidence interval = 1.04-1.09) for scooters. This study shows that coronavirus disease 2019 restrictions was associated with elevated risks of crashes, injuries, and deaths among motorcyclists, reflecting the general preference for private transport over public transport. The popularity of food delivery services also contributed to increased motorcycle crashes.

Evaluating the crash risk of powered two-wheelers in urban mixed traffic environments: a conflict threshold perspective

The study investigates the crash risk of powered two-wheelers (PTWs) involved in multiple conflict types, with different vehicle classes constituting a mixed traffic stream. This study uses the extreme value theory to estimate the crash risk after establishing the conflict thresholds for potential rear-end and side-swipe conflicts by accounting for interacting vehicle types. The study considers four vehicle pairs involving PTWs: PTW-PTW, PTW-MThW (motorized three-wheeler), PTW-Car, and PTW-Bus. The study found that the conflict thresholds corresponding to rear-end and side-swipe types increase with the interacting vehicle size. The crash risk is lowest for the PTW-PTW pair (0.315%) in rear-end conflicts, whereas the risk is the highest for the PTW-MThW pair (3.7%) in side-swipe conflicts. The crash risk corresponding to the PTW interacting with other vehicle types is higher than that of the PTW-PTW pair. Hence, the implementation of exclusive PTW lanes could be an effective risk mitigation strategy for PTW-dominant mixed traffic environments.