Impact of National Election-2017/18 on Corporate Performance: An Analysis of Insurance Companies in Nepal

Authors

  • Sudan Kumar Oli School of Insurance and Economics, University of International Business and Economics, Beijing China, and Silaju Research Institute (SRI), Bagbazar, Kathmandu, Nepal https://orcid.org/0000-0002-2007-3050

DOI:

https://doi.org/10.3126/idjina.v2i1.55963

Keywords:

national election, corporate performance, panel data analysis, GLS, insurance companies

Abstract

This paper analyzes the impact of the national election on corporate performance by using panel data from listed insurance companies over the period 2015 to 2021 with 126 observations. The proxy variables of corporate performance have used market price per share (MPS), dividend payout ratio (DPS), and return on assets (ROA) for market performance, investor’s performance, and firm’s performance respectively. This study uses the generalized least squared (GLS) method for the final impact assessment. Empirical analysis shows that the beta coefficient of D-election is significantly negative with all performance proxy variables in random effect estimates. This indicates that there is a random effect of the national election on corporate performance and provides a negative impact on corporate performance after the national election-2017-18. Similarly, analysis of fixed effects also support a similar impact except for the DPS because DPS is not significant. For the robust test, this paper has performed the Breusch-Pagan test and Hausman test which supports consistency of the analysis. Overall analysis suggests that the four levels of elections: local level, provincial level, federal level, and national assembly election do affect adversely the corporate performance of insurance industries in Nepal.

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Published

2023-06-22

How to Cite

Oli, S. K. (2023). Impact of National Election-2017/18 on Corporate Performance: An Analysis of Insurance Companies in Nepal. Interdisciplinary Journal of Innovation in Nepalese Academia, 2(1), 19–33. https://doi.org/10.3126/idjina.v2i1.55963

Issue

Section

Part I: Management, Social & Computer Science