<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this paper, we first establish a VAR model, screen the variables using the CSIS method so as to achieve fast dimensionality reduction, and use the cointegration theory to model non-stationary time variables to determine the pseudo-regression analysis between variables. Impulse response functions and variance decomposition are then used to analyze the dynamic effects of changes in random disturbance terms on the whole system and other variables. Finally, the effects of insider trading on executive compensation are explored using corporate performance and market-based executive pay entitlement as references, respectively. The results show that the regression coefficients of executive compensation level and cumulative excess turnover rate of the event announcement period are 0.805 and 0.018 for corporate performance and market-based compensation, respectively, and the higher the level of executive compensation based on corporate performance and market-based compensation, the lower the impact of insider trading on executive compensation. Equity incentives reduce the impact of insider trading on executive compensation, and the regression coefficient of excess turnover on executive compensation is less than 0 in firms with shareholding levels higher than 10%.</p>
</abstract>

In this paper, we first establish a VAR model, screen the variables using the CSIS method so as to achieve fast dimensionality reduction, and use the cointegration theory to model non-stationary time variables to determine the pseudo-regression analysis between variables. Impulse response functions and variance decomposition are then used to analyze the dynamic effects of changes in random disturbance terms on the whole system and other variables. Finally, the effects of insider trading on executive compensation are explored using corporate performance and market-based executive pay entitlement as references, respectively. The results show that the regression coefficients of executive compensation level and cumulative excess turnover rate of the event announcement period are 0.805 and 0.018 for corporate performance and market-based compensation, respectively, and the higher the level of executive compensation based on corporate performance and market-based compensation, the lower the impact of insider trading on executive compensation. Equity incentives reduce the impact of insider trading on executive compensation, and the regression coefficient of excess turnover on executive compensation is less than 0 in firms with shareholding levels higher than 10%.

An Analysis of the Impact of Insider Trading on Executive Compensation in Listed Companies Based on VAR Modeling

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In this paper, we first establish a VAR model, screen the variables using the CSIS method so as to achieve fast dimensionality reduction, and use the...

An Analysis of the Impact of Insider Trading on Executive Compensation in Listed Companies Based on VAR Modeling

Data publikacji: 20 gru 2023

Zakres stron: -

Otrzymano: 04 sty 2023

Przyjęty: 30 cze 2023

DOI: https://doi.org/10.2478/amns.2023.2.01563

Słowa kluczowe
VAR model, Vector autoregression, Impulse response, Cointegration test, Insider trading

© 2023 Li Jin, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

An Analysis of the Impact of Insider Trading on Executive Compensation in Listed Companies Based on VAR Modeling

Data publikacji: 20 gru 2023

Zakres stron: -

Otrzymano: 04 sty 2023

Przyjęty: 30 cze 2023

DOI: https://doi.org/10.2478/amns.2023.2.01563

Słowa kluczoweVAR model, Vector autoregression, Impulse response, Cointegration test, Insider trading

© 2023 Li Jin, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
VAR model, Vector autoregression, Impulse response, Cointegration test, Insider trading