Research on the Influence of Corporate Risk Taking on Stock Price Crash Risk and the Regulating Effect of Product Market Competition Based on Linear Regression Model

2020 ◽  
Author(s):  
Xiaohui Chen ◽  
Jianhua Ye
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Risk Taking ◽  
Linear Regression Model ◽  
Stock Price ◽  
Market Competition ◽  
Product Market ◽  
Product Market Competition ◽  
Crash Risk ◽  
Corporate Risk

The impact of product market competition on stock price crash risk

2020 ◽  
Vol 35 (7) ◽  
pp. 1141-1153
Author(s):  
Jia Li ◽  
Zhengying Luo
Keyword(s):  
Stock Price ◽  
Market Competition ◽  
Product Market ◽  
Listed Companies ◽  
Product Market Competition ◽  
Crash Risk ◽  
Competitive Position ◽  
Content Type ◽  
Industry Competition ◽  
The Impact

Purpose The purpose of this paper is to explore the impact of product market competition on the risk of stock price crash based on the degree of industry competition and the competitive position of enterprises. Design/methodology/approach This paper chooses the data of Shanghai and Shenzhen A-share listed companies from 2009 to 2017 as samples and uses a threshold regression model to explore the impact of product market competition on the risk of a stock price crash. Findings The results show that: the overall level of industry competition is negatively correlated with the risk of stock price crash; the competitive position of enterprises and the risk of a stock price crash. The correlation is not significant: for high competitive enterprises, the degree of industry competition is negatively correlated with the risk of stock price crash; for low competitive enterprises, the degree of industry competition is positively correlated with the risk of a stock price crash and the conclusions obtained have passed the robustness test. Originality/value This paper not only enriches the literature on the relationship between product market competition and the risk of stock price crash but also has reference significance for supervisors to allocate resources to supervise information disclosure of listed companies.

The Relationship Between Competition and the Fraction of Firms Using Stock-Based Compensation in an Industry

2014 ◽  
Vol 17 (01) ◽  
pp. 1450006
Author(s):  
Hsing-Hua Huang ◽  
Chia-Fan Lin
Keyword(s):  
Empirical Evidence ◽  
Risk Taking ◽  
Market Competition ◽  
Negative Relationship ◽  
Product Market ◽  
Product Market Competition ◽  
Competitive Industry ◽  
Risk Taking Behavior ◽  
The Relationship

This paper theoretically and empirically investigates the relationship between the intensity of product market competition and the fraction of firms that use stock-based compensation in an industry. By employing the relationship between a firm's risk-taking behavior and the use of stock-based compensation, we theoretically show that when the fraction of firms using stock-based compensation is less (more) than one-half, there is a positive (negative) relationship between the fraction and the intensity of product market competition and further provide some supportive empirical evidence. Our results imply that there is more heterogeneity in firms' stock-based compensation policies in the more competitive industry.

scholarly journals Quantification of Rear-End Crash Risk and Analysis of Its Influencing Factors Based on a New Surrogate Safety Measure

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Qiangqiang Shangguan ◽  
Ting Fu ◽  
Junhua Wang ◽  
Rui Jiang ◽  
Shou’en Fang
Keyword(s):  
Comparative Analysis ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Mixed Effects ◽  
Driving Safety ◽  
Crash Risk ◽  
Traffic Density ◽  
Car Following ◽  
The Impact

Traditional surrogate measures of safety (SMoS) cannot fully consider the crash mechanism or fail to reflect the crash probability and crash severity at the same time. In addition, driving risks are constantly changing with driver’s personal driving characteristics and environmental factors. Considering the heterogeneity of drivers, to study the impact of behavioral characteristics and environmental characteristics on the rear-end crash risk is essential to ensure driving safety. In this study, 16,905 car-following events were identified and extracted from Shanghai Naturalistic Driving Study (SH-NDS). A new SMoS, named rear-end crash risk index (RCRI), was then proposed to quantify rear-end crash risk. Based on this measure, a risk comparative analysis was conducted to investigate the impact of factors from different facets in terms of weather, temporal variables, and traffic conditions. Then, a mixed-effects linear regression model was applied to clarify the relationship between rear-end crash risk and its influencing factors. Results show that RCRI can reflect the dynamic changes of rear-end crash risk and can be applied to any car-following scenarios. The comparative analysis indicates that high traffic density, workdays, and morning peaks lead to higher risks. Moreover, results from the mixed-effects linear regression model suggest that driving characteristics, traffic density, day-of-week (workday vs. holiday), and time-of-day (peak hours vs. off-peak hours) had significant effects on driving risks. This study provides a new surrogate safety measure that can better identify rear-end crash risks in a more reliable way and can be applied to real-time crash risk prediction in driver assistance systems. In addition, the results of this study can be used to provide a theoretical basis for the formulation of traffic management strategies to improve driving safety.

Stock Forecasting Using an Improved Version of Adaptive Group Lasso

2020 ◽  
Vol 17 (8) ◽  
pp. 3370-3373
Author(s):  
S. Nandhini ◽  
Monojit Debnath ◽  
Siddarth Tyagi ◽  
Shashank Mishra ◽  
K. Ashok Kumar
Keyword(s):  
Linear Regression ◽  
Prediction Model ◽  
Regression Model ◽  
Linear Regression Model ◽  
Stock Price ◽  
Group Lasso ◽  
Stock Prediction ◽  
The World ◽  
The Future ◽  
Penalization Technique

Stock is one of the few things in the world that influence the economy of the society, hence its worth a shot and very desirable to predict a stock price in the future. Hence in this paper we propose a stock prediction model based on linear regression model. The database of the training is based on the Goldman Sachs database of stocks found from the Google. Here we choose Lasso penalization technique cause the, this performs well with the sparsity of the network, meaning when the network has less features and more observations. Here we have proposed an improved version of lasso function and have proposed an algorithm to improve the performance of the model.

The effect of managerial ability on product market competition and corporate investment decisions

2020 ◽  
Vol 11 (1) ◽  
pp. 49-69 ◽  
Author(s):  
Mahdi Salehi ◽  
Ali Daemi ◽  
Farzana Akbari
Keyword(s):  
Risk Taking ◽  
Market Competition ◽  
Investment Decisions ◽  
Product Market ◽  
Corporate Investment ◽  
Free Cash Flow ◽  
Product Market Competition ◽  
Investment Efficiency ◽  
Managerial Ability ◽  
Content Type

Purpose This study aims to examine the effect of managerial ability on product market competition and corporate investment decisions, specifically, on risk-taking and investment efficiency. Design/methodology/approach The primary measure of managerial ability is Demerjian et al. model. In this study, Herfindahl–Hirschman Index is used to measure product market competition. Regression analysis is used to examine the association between corporate risk-taking and over-investment of free cash flow and product market competition and managerial ability. Findings Using firm-year observations from 2011 to 2015, the paper findings suggest that competition discourages managers to invest in risky investment. The study also found that managerial ability has no effect on the association between product market competition and investment decision. Originality/value The current study almost is the first study which is conducted on this subject; the results may give strength to further studies.

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