Crash Risk Research Articles

The greed factor of executives and the risk of a company stock price crash

This study develops an empirical econometric model, using Chinese listed companies as the research subject, to empirically test the impact of executives’ greed factor, which is primarily defined by on-the-job consumption, on the risk of company stock price collapse. The study's results show that executive on-the-job consumption significantly exacerbates the risk of company stock price collapse, and this effect is stronger in companies with a lower proportion of institutional investors and poorer corporate Environmental, Social, and Governance (ESG) performance.

Negative Performance Aspiration Gap and Stock Price Crashes: Based on Behavioral Theory and Agency Theory Perspective*

AbstractThe paper explores how agency behavior of insiders within a company influences the risk of a stock price crash in the context of a continued expansion of the performance expectation gap. A panel data analysis of 10 727 observations of Chinese A‐share listed companies over the period 2003–2020 shows that as the negative performance aspiration gap widens, stock price crashes are more likely to occur. Furthermore, the separated role of top management and chairperson and an increase in the ownership proportion of major shareholders significantly amplifies the positive effect, whereas the increase in managerial shareholding and the equity balance diminish the main relationship.

Is more always better? Information acquisition and stock price crash risk

We posit that high search intensity on a firm's SEC filings creates capital market pressure on managers to withhold bad news. Using the count of non-robot EDGAR downloads of SEC filings as a proxy for search intensity, we find that high search intensity is related to higher future crash risk. The result is cross-sectionally stronger for firms with higher transient institutional holdings, overconfident CEOs, and more intangibles. Our findings are robust to different measures of crash risk and bad news hoarding. Overall, we highlight one unintended capital market consequence associated with the high intensity of information acquisition.

Migration fear and stock price crash risk

We examine whether migration fear increases future stock price crash risk. We find that a 10 percentage point increase in the migration fear index increases the future stock price crash risk by 17 to 19 percentage points. Our results hold after controlling for macroeconomic conditions, including economic policy uncertainty, and using instrumental variables to address endogeneity issues. The impact of migration fear on crash risk is larger for firms with greater asymmetric information and firms with weaker monitoring mechanisms. We conclude that migration fear can significantly change risk tolerance in financial markets and affect stock price crash risk.

An optimisation model to locate level crossings in railway lines at mines to minimise the total weighted-walked distance

The location of level crossings for pedestrian circulation is a safety issue due to potential human losses and material damage. This problem has become a concern in urban and industrial environments due to the characteristics of rail systems and unsafe pedestrian behaviour. Research reveals that psychological and infrastructural factors influence pedestrians' decision-making when crossing railroad lines. One of these is the convenience that leads pedestrians to cross at the nearest point. However, investigations to date only study the impact of location on the risk and severity of crashes and pedestrian rule violations. The current research is quasi-experimental, i.e., no optimisation tool has been developed to determine which crossing points pedestrians should follow. Therefore, this research develops a mathematical model for locating level crossings at railways to minimise the total weighted-walked distance. A genetic algorithm was proposed for solving the model, especially for large-size problems. The algorithm parameters were calibrated using the design of experiments and ten instances based on the characteristics of the area under study. The obtained results provide (1) the location of the level crossings and (2) the route pedestrians should follow for each origin-destination pair based on the facilities adjacent to the rail lines. A sensitivity analysis was performed using the ten instances to determine how the location of the level crossings changes with modifications in the facilities' location. Results showed that the model provides alternative solutions according to the problem's size. This study delivers a methodology for practitioners and stakeholders of transportation systems.

Drivers’ behavior confronting fixed and point-to-point speed enforcement camera: agent-based simulation and translation to crash relative risk change

Utilizing a novel microsimulation approach, this study evaluates the impact of fixed and average point-to-point Speed Enforcement Cameras (SEC) on driving safety. Using the SUMO software, agent-based models for a 6-km highway without exits or obstacles were created. Telematics data from 93,160 trips were used to determine the desired free-flow speed. A total of 13,860 scenarios were simulated with 30 random seeds. The ratio of unsafe driving (RUD) is the spatial division of the total distance travelled at an unsafe speed by the total travel distance. The study compared different SEC implementations under different road traffic and community behaviours using the Power Model and calculated crash risk changes. Results showed that adding one or two fixed SECs reduced RUD by 0.20% (0.18–0.23) and 0.57% (0.54–0.59), respectively. However, average SECs significantly lowered RUD by 10.97% (10.95–10.99). Furthermore, a 1% increase in telematics enforcement decreased RUD by 0.22% (0.21–0.22). Point-to-point cameras effectively reduced crash risk in all implementation scenarios, with reductions ranging from − 3.44 to − 11.27%, pointing to their superiority as speed enforcement across various scenarios. Our cost-conscious and replicable approach can provide interim assessments of SEC effectiveness, even in low-income countries.

Qualitatively and quantitatively explore injury severity of light motor vehicle drivers involved in heavy goods vehicle crashes

ABSTRACT Crashes involving heavy goods trucks (HGVs) are of significant concern as it poses a higher risk of fatality to light motor vehicles (LMVs). The study constructs three Deep Forest models with different Cascade structures to explore the relationship between HGV-LMV crash severity and risk factors. Based on the HGV-LMV crash data in Shandong province, China, motorcycles, electric vehicles, and sedans are defined as the LMV. According to the comparison results, the Deep Forest with Cascade LightGBM is significantly better. Through model interpretability tools, the study found that motorcycle and electric vehicle drivers aged 58 to 86, and LMV drivers with 1 to 3 years of driving experience are more likely suffering severity and fatal injury (SFI) in HGV-LMV crashes. And, disobey traffic sign, illegal turning, overtaking, changing lane, and crashes happened on non-motorway, national and provincial roads have an positive effect on SFI.

Climate‐related performance and stock price crash risk

AbstractWe examine how environmental performance affects future stock price crash risk. Previous literature shows that legitimacy threats, stemming from environmental risk, lead firms to be particularly sensitive about environmental disclosure and performance. However, we hypothesise and find that under specific conditions, relating to lower operating performance, firms that have higher environmental performance also have higher future stock price crash risk. Further analysis shows that such firms may also have lower readability and more confident tone in their 10‐K annual reports. We attribute these findings to impression management utilising environmental performance along with disclosure misdirection practices, where managers of firms with negative news, attempt in some cases to draw the attention of the users of financial reports by obfuscating annual reports for bad news hoarding purposes. Even though in the current year such practices may have a negative effect for stock price crash risk, future stock price crash risk increases due to the dissemination of the negative news in the market. Overall, our results show that environmental disclosure may be used, under certain conditions, as a tool for impression management, which along with financial reporting distraction practices, leads to higher future stock price crash risk.

Digitalization and firms' systematic risk in China

AbstractPrevious literature indicates that digitalization offers enterprises competitive advantages. However, However, its potential impact on risk management remains uncertain. Thus, this study explores the causality between digital transformation and systematic risk of Chinese public companies during 2007–2020. We developed a digital‐related keywords dictionary using textual analysis to identify investments in digital assets which serve as a measure of corporate digitalization. Our findings suggest a negative correlation between digital transformation and enterprise systematic risk. This relationship is further supported by robustness tests, adjustments for endogeneity, and random forest predictions. The risk‐reducing effect of digitalization is more pronounced in non‐state‐owned, small, high‐asset‐density, and low‐investor‐attention enterprises. Additionally, we explore potential mechanisms: the financial leverage channel, operating leverage channel, and investor loyalty channel. Empirical observations indicate that enterprise digitalization: (1) lowers financing costs, curbing an inclination towards excessive debt; (2) enhances operational cost management and stimulates sales growth; and (3) boosts long‐term investor holdings, decreases stock price synchronization, and mitigates crash risks. This study offers new insights into assessing the sustainability of digitalization and mitigating systematic risks of enterprises.

Heat-induced risks of road crashes among older motorcyclists: Evidence from three motorcycle-dominant cities in Taiwan

IntroductionRoad crashes pose significant public health concerns worldwide, with vulnerable road users like motorcyclists accounting for 23% of global road deaths. Recently, heat exposure has been identified as a risk factor for road crashes by reducing driver performance. Given the rapid aging, many Asian cities face challenges regarding heat-induced road crashes among older motorcyclists, a significant segment of the road user population with lower motor abilities and thermal tolerance. In this study, I aim to investigate varying impacts of heat on road crashes across road users in different age groups using different transportation modes in three major Taiwanese cities characterized by motorcycle-dominant traffic cultures and (sub)tropical climates. MethodsTemperature data and records of road crashes involving motorcycles and car drivers from 2018 to 2021 were collected, merged based on the dates, and processed into a year-round dataset and a summer-time dataset. The generalized linear regression model with a Generalized Poisson link function was employed to estimate the association between temperature and road crash frequency, with the analysis stratified by two age groups (i.e., older and younger) and two transportation modes (i.e., car and motorcycle). Results and conclusionIn total, 24 models were built for four driver groups (i.e., older motorcycle, younger motorcycle, older car, and younger car drivers) across three cities using year-round and summer-time datasets. Findings reveal higher vulnerabilities to heat-induced road crashes among older road users, especially those driving motorcycles. Heat-induced road risks among motorcycle drivers significantly increased during the summer, while this pattern was not found for car drivers. Policymakers should develop targeted interventions aimed at mitigating heat-related road crashes among older motorcyclists. In the context of climate change and ageing, this research holds broader implications for the development of policies integrating heat mitigation strategies and traffic safety measures, particularly in Southeast Asian cities with similar demographics and tropical climates.

Corporate strategy and stock price crash risk

Based on A-share listed companies in China from 2011 to 2021, this paper explores the relationship between corporate strategy and stock price crash risk. The study finds that aggressive strategies increase the risk of stock price crash, but this effect is relatively small in state-owned enterprises. In addition, there is a partial mediating effect between external financing needs and equity incentive intensity. Our findings are of great significance for understanding the relationship between corporate strategy and stock price crash risk.

Foreign institutional ownership stability and stock price crash risk

This study investigates the impact of foreign institutional investors (FIIs) on stock price crash risk in India. Panel regression findings reveal that higher levels of FII holdings, signifying positional trading, exacerbate crash risk. Conversely, increased stability of FII holdings, indicative of active monitoring, diminishes crash risk. Notably, FIIs’ buying interest does not influence crash risk, affirming that their risk mitigation arises from active monitoring and not from curbing selling pressure. Further analysis reveals that FII stability reduces crash risk only when controlling shareholder (i.e., promoter) equity holding is low. This promoter effect is driven by domestic promoters and not foreign promoters.

Dynamic short-term crash analysis and prediction at toll plazas for proactive safety management

Toll plazas are commonly recognized as bottlenecks on toll roads, where vehicles are prone to crashes. However, there has been a lack of research analyzing and predicting dynamic short-term crash risk specifically at toll plazas. This study utilizes traffic, geometric, and weather data to analyze and predict dynamic short-term collision occurrence probability at mainline toll plazas. A random-effects logit regression model is employed to identify crash precursors and assess their impacts on the probability of crash occurrence at toll plazas. Meanwhile, a Long Short-Term Memory Convolutional Neural Network (LSTM-CNN) network is applied for crash prediction. The results of random-effects logit regression model indicate that the flow standard deviation of downstream, upstream occupancy, speed difference and occupancy difference between upstream and downstream positively influence the probability of crash occurrence. Conversely, an increase in the proportion of ETC lanes negatively impacts the probability of crash occurrence. Additionally, there appears a higher likelihood of crashes occurring during summer at toll plaza area. Furthermore, to address the issue of data imbalance, Synthetic Minority Oversampling Techniques (SMOTE) and class weight methods were employed. Stacked Sparse AutoEncoder-Long Short-Term Memory (SSAE-LSTM) and CatBoost were developed and their performance was compared with the proposed model. The results demonstrated that the LSTM-CNN model outperformed the other models in terms of the Area Under the Curve (AUC) values and the true positive rate. The findings of this study can assist engineers in selecting suitable traffic control strategies to improve traffic safety in toll plaza areas. Moreover, the developed collision prediction model can be incorporated into a real-time safety management system to proactively prevent traffic crash.

Investor sentiment and stock price crash risk: The mediating role of analyst herding

This study analyzes the impact of investor sentiment on firm's stock price crash risk by using Chinese A-Share firms data this study assesses the potency and existence of a relationship between crash risk and investor sentiment in the Chinese stock market and introduces analyst herding as a mediating variable for explaining the relationship between crash risk and investor sentiment. By utilizing a large data set of A-share listed firms on Chinese stock exchanges, comprising of 19,371 firm-year observations for the period of 2004–2019, an investor sentiment index is constructed. Results point towards a positive significant relation between stock price crash risk and investor sentiment. Furthermore, stock price crash is positively correlated with analyst herding i.e. it significantly mediates between stock price crash risk and investor sentiment. By measuring the relationship between crash risk, investor sentiment, and analyst herding this study provides systematic support on the mediating role of analyst herding in deepening the market sentiment which results in crash risk. These findings are robust by utilizing alternate proxies and controlling for firm specific variables, economy-wide shocks, and time trends year fixed effects.

CEO power and stock price crash risk in India: the moderating effect of insider trades

PurposeThis study aims to explore the relationship between chief executive officer (CEO) power and stock price crash risk in India. Furthermore, it seeks to analyse how insider trades may moderate the impact of CEO power on stock price crash risk.Design/methodology/approachA study of 236 companies from the S&P BSE 500 Index (2014–2023) have been analysed through pooled ordinary least square (OLS) regression in the baseline analysis. To enhance the results' reliability, robustness checks include alternative methodologies, such as panel data regression with fixed-effects, binary logistic regression and Bayesian regression. Additional control variables and alternative crash risk measure have also been utilised. To address potential endogeneity, instrumental variable techniques such as two-stage least squares (IV-2SLS) and difference-in-difference (DiD) methodologies are utilised.FindingsStakeholder theory is supported by results revealing that CEO power proxies like CEO duality, status and directorship reduce one-year ahead stock price crash risk and vice versa. Insider trades are found to moderate the link between select dimensions of CEO power and stock price crash risk. These findings persist after addressing potential endogeneity concerns, and the results remain consistent across alternative methodologies and variable inclusions.Originality/valueThis study significantly advances research on stock price crash risk, especially in emerging economies like India. The implications of these findings are crucial for investors aiming to mitigate crash risk, for corporations seeking enhanced governance measures and for policymakers considering the economic and welfare consequences associated with this phenomenon.

China's National Team: A Game Changer in Stock Market Stabilization?

This study investigates the influence of China's national team investors on stock market volatility and crash risks using 2011-2018 data. Findings show increased stock return volatility and decreased crash risks post-2015. The national team reduced risk for pledged shares, while treatment firms reduced R&D investment and diversified business more than control groups. A difference-in-difference approach reveals heightened volatility in firms with national team ownership after 2015, questioning the efficacy of these interventions. This research offers insights into government intervention in financial markets, emphasizing the national team's impact on market stability and economic outcomes.

Do comparative judgements affect the perceived relevance of mobile phone road safety campaigns?

Objective Given the prevalence of illegal mobile phone use while driving and reliance upon messaging campaigns in deterring the behavior, there is a need to explore to what extent comparative judgements may influence desired outcomes of such campaigns. This exploratory study investigated (a) the perceived relevance and influence of different mobile phone road safety campaigns within a sample of Queensland motorists who reported using their mobile phone while driving and (b) if these varied depending on whether participants believed that their crash risk whilst using a phone was higher (comparative pessimism), lower (comparative optimism) or similar to the same-age and sex driver. Methods A total of 350 licensed drivers residing within Queensland (Australia) were included in this study, of which 200 reported using their hand-held phone on some occasion. Participation involved completing a 20-25 min online anonymous survey, which included viewing three mobile phone road safety campaigns (injury-based, sanction-based and humor) and responding to questions about the perceived relevance and impact of each campaign. Results A total of 64 (32%) participants displayed comparative optimism, 50 displayed similar judgements (25%) and 86 (43%) exhibited comparative pessimism. First, it was found that the injury-based campaign was perceived to be significantly more relevant than the humor campaign. Second, whilst the relevance of each campaign did not vary as a function of group membership, the campaigns were significantly less relevant to those displaying comparative optimism relative to those with similar judgements and comparative pessimism. Finally, the injury-based campaign was perceived to be significantly more influential than the other campaigns. However, overall, participants displaying comparative optimism believed that they would be less influenced by the campaigns compared to those with comparative pessimism. Conclusions Although preliminary, these findings suggest that low perceptions of risk may dilute or extinguish the desired behavioral outcomes of mobile phone road safety campaigns. Nonetheless, experimental research is needed to examine these effects directly.

Safety assessment of on-road cycling lanes: A comparative study of different layouts using driving simulator

Over the past few decades, a growing attention has been directed toward cycling due to its positive impacts on social, economic, and health aspects. Various countries are adopting and implementing strategies to promote cycling as a daily mode of transport. The main objective of this study is to improve cyclists’ safety by investigating the impact of different layouts of on-road cycle lanes at two-lane two-way roads on drivers’ interactions with cyclists using driving simulator. Three layouts of on-road cycle lanes were tested and compared, namely, uncolored, colored, and island separation, along with a control case where no cycle lane was provided. In addition, the impact of road alignments (straight sections, left and right curves) and the presence of an opposing vehicle were investigated. The driving simulator at Qatar University was used to conduct this study. A total of 92 subjects participated in this study. According to the results, on-road cycle lanes can significantly increase the safety of cyclists compared to shared lanes with motorized traffic. Moreover, the results showed that the drivers’ intrusion to the opposite lane in the presence of opposing vehicles can be eliminated by providing on-road cycle lanes. That is, drivers’ crash risk can also be reduced through the provision of on-road cycle lanes. Comparison of different on-road cycle lane treatments showed that uncolored cycle lanes outperformed the other layouts in terms of lateral clearance between the driver and the cyclist for right and straight alignments. On the other hand, the colored cycle lane showed better results for the left alignment. The findings of this study could be useful for designing on-road bicycle infrastructure to eliminate possible vehicle-cyclist and vehicle-vehicle conflicts and minimize crash risk.

A cooperative longitudinal lane-changing distributions advisory for a freeway weaving segment

The lane-changing (LC) concentration problem in freeway weaving segments poses crash risks and reduces freeway efficiency. To address this issue, this paper proposes a cooperative longitudinal LC distribution (CLLCD) advisory for freeway weaving segments utilizing cooperative intelligent transport system technology. The weaving segment is divided into sections, and the CLLCD strategy distributes lane changes for each section using a general rule that allows easy calculation of each section’s CLLCD from the maximum permitted number of lane changes for different movements. A corresponding percentage of drivers in each section are then permitted to change lanes from the start of that section. The CLLCD strategy is evaluated for 27 scenarios with varying traffic demands. A sensitivity analysis is conducted to determine optimal parameters, and the performance of the proposed strategy is compared to other methods. This study also explores the working mechanism of the proposed approach using headway data and speed profiles. The effects of the section configurations and penetration rates of connected vehicles (CVs) are discussed. Simulation results show that this easy-to-apply strategy improves speed and delay as effectively as the heuristic algorithms-based strategy. The number of sections does not influence the CLLCD strategy’s performance when the maximum freeway-to-ramp lane changes per section and other parameters per 100 m remain constant. The delay in the weaving area decreases as the CV penetration rate increases; however, only marginal further improvements are observed when the penetration rate increases beyond 60%. This study provides a practical and effective solution to enhance weaving segments’ efficiency.

Effects of Intersections on the Safety of Horizontal Curves

Both horizontal curves and intersections are generally considered high-risk locations in roadway safety. Although extensive research has been conducted separately on the safety of curves and intersections, the safety performance of curves as affected by their spatial relationship with intersections has not been fully understood. Previous research has not examined this relationship because of the use of limited or pre-existing datasets that did not include intersection data in the analysis. This study addresses these gaps by analyzing the spatial relationship between curves and intersections, utilizing a large dataset of over 8,000 rural curves in Florida. The study performs a systemic analysis using this dataset and six years of statewide crash data of all injury severity levels and develops customized curve Safety Performance Functions based on various spatial relationships between curves and intersections. This study confirms that the previously identified risk factors such as traffic volume, curve radius and length, roadway speed limit, and functional classification have significant impacts on curve safety. More importantly, the study quantifies, for the first time in the literature, the influence of intersections on curves or close to curves on safety, demonstrating that curves with one or more intersections present a higher risk than curves with no intersections. The findings show that the presence of nearby intersections can increase the crash risk for curves with no intersections but can lead to a decrease in crashes for curves with one or multiple intersections. These findings can be utilized to determine high-risk curve locations for systemic safety analysis studies.