Does Quality of Corporate Governance Moderate the Relationship between Corporate Social Responsibility and Stock Price Crash Exposure

The relationship between corporate social responsibility and vulnerability to stock market crashes and how this relationship is moderated by the norm of corporate governance was empirically explored in this research article. This study used a panel data analysis using a group of 58 companies selected during the years 2009-2018 from operating in various industries hawking their stock on the Pakistan Stock Exchange (PSX) to investigate this interaction effect. To quantify the company's share price probability of a crash, an inverse restrictive skewness (NCSKEW) of returns and bottom-to-up variance (DUVOL) spread has been used as a proxy. The results of this research suggest that the risk of a share price slump is adversely and significantly linked to CSR. Consistency structures of governance mechanisms (size of the board, percentage of autonomous directors on board, concentration of ownership, percentage of executive directors onboard) have a substantial moderating impact on the risk of a fall of the share price.

Pedestrian Crash Exposure Analysis Using Alternative Geographically Weighted Regression Models

In order to develop a sustainable, safe, and dynamic transportation system, proper attention must be paid to the safety of pedestrians. The purpose of this study is to analyze the surrogate measures related to pedestrian crash exposure in urban roads, including the use of sociodemographic characteristics, land use, and geometric characteristics of the network. This study develops pedestrian exposure models using geographical spatial models including geographically weighted regression (GWR), geographically weighted Poisson regression (GWPR), and geographically weighted Gaussian regression (GWGR). In general, the results of the GWPR model show that the presence of a bus station, population density, type of residential use, average number of lanes, number of traffic control cameras, and sidewalk width are negatively associated with increasing the number of crashes. In this study, in order to identify traffic analysis zones (TAZ) based on the observed and predicted crash data, spatial distance-based methods using GWPR outputs have been used. This study shows the dispersion and density of pedestrian crashes without possessing the volume of pedestrians. Comparison of the performance of GWPR and Poisson models shows a significant spatial heterogeneity in the analysis.

LIFETIME CONSUMPTION AND INVESTMENT FOR WORST-CASE CRASH SCENARIOS

We investigate worst-case optimal consumption and portfolio decisions under the threat of a market crash on an infinite time horizon. We provide a closed-form solution for constant relative risk aversion and establish a rigorous verification result. More specifically, using martingale arguments we demonstrate that the optimal consumption-portfolio strategy can be characterized as the indifference strategy that achieves the best performance in the no-crash scenario. In addition, we find a dual characterization of the optimal strategy as the indifference strategy that minimizes the crash exposure. Finally, we quantify the impact of the crash on consumption and portfolio choice and analyze it in terms of the investor's risk and time preferences and prudence.

Safety Evaluation for Signalized Intersections Using GIS Techniques and Statistic Analysis

This paper presents a systematic GIS-based method to identify and evaluate signalized intersections for purposes of transportation safety planning. The intersections were analyzed and compared based on the crash average hazard index which is the average of five safety measures: total number of crashes, total number of injury and fatal crashes, total crash exposure rate, injury/fatal exposure rate, and weighted hazard index (WHI). The GIS technique along with statistics analysis is utilized to estimate the signalized intersection’s risk distribution in terms of the averaged hazard index. It results in priority ranking of signalized intersections from the most dangerous to the least dangerous hazard levels. The method and results can assist engineers, planners and decision-makers in identifying critical intersections with potential for safety improvement.