Financial data reporting analysis of the factors influencing on profitability for insurance companies

盈利能力指数 业务 创业 财务 会计
作者
Alina Kulustayeva,Aigul Jondelbayeva,Azhar Nurmagambetova,A. Zh. Dossayeva,A. S. Bikteubayeva
出处
期刊:Entrepreneurship and Sustainability Issues [Entrepreneurship and Sustainability Center]
卷期号:7 (3): 2394-2406 被引量:13
标识
DOI:10.9770/jesi.2020.7.3(62)
摘要

In this article the econometric analysis of panel data for insurance companies of the Republic of Kazakhstan from with a research objective of financial figure for profitability and influencing of factors defining profitability was performed.The article reveals the indicators that affect the profitability of insurance companies in order to further forecast.Independent variables were calculated using information on insurance companies of the Republic of Kazakhstan available in the public domains, mainly data from financial statements.The author reaches to prove that the data on insurance companies' obligations exert special influence on the evaluation of the profitability of the insurance company.The article suggests a methodical approach to measuring financial indicators of insurance companies based on panel data models, taking into account industry and individual differences.The research is carried out using the Gretl software package.Panel data models with fixed effects, panel data models with random effects were applied, and the most effective model was selected by the Hausman Test.As a result, it is proved that the profitability of the insurance company is affected by three indicators, two of which are calculated on the financial statements, including the financial leverage.This allows us to use these indicators in further forecasting the profitability and financial stability of insurance companies.Correctness of the assessment of profitability and forecasts are influenced by the correctness of data in the financial statements.The quality of models is limited by the quality of the financial statements of companies analyzed in this article.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
zzz发布了新的文献求助10
刚刚
nibaba发布了新的文献求助10
刚刚
tib发布了新的文献求助10
1秒前
2秒前
3秒前
3秒前
3秒前
烟花应助无限的幻珊采纳,获得30
4秒前
4秒前
4秒前
4秒前
maxxxx关注了科研通微信公众号
4秒前
Egal发布了新的文献求助10
4秒前
观星客完成签到,获得积分10
4秒前
李爱国应助zyy0226采纳,获得10
5秒前
忧心的梦菡关注了科研通微信公众号
6秒前
6秒前
红墨发布了新的文献求助10
6秒前
6秒前
heba发布了新的文献求助10
7秒前
lydia完成签到,获得积分10
7秒前
7秒前
7秒前
科研通AI6.2应助zh采纳,获得10
7秒前
ikun发布了新的文献求助10
8秒前
慕青应助nibaba采纳,获得30
8秒前
FashionBoy应助nibaba采纳,获得10
8秒前
8秒前
水之冬发布了新的文献求助10
8秒前
隐形曼青应助vince采纳,获得10
9秒前
zz发布了新的文献求助10
9秒前
轻松的芯完成签到 ,获得积分0
9秒前
9秒前
9秒前
耍酷寒烟发布了新的文献求助10
9秒前
落后如雪发布了新的文献求助10
9秒前
木子发布了新的文献求助10
9秒前
000000发布了新的文献求助10
10秒前
molihuakai应助young采纳,获得10
10秒前
ydz发布了新的文献求助10
11秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Principles of town planning: translating concepts to applications 1000
2016 Venous Blood Study (VBS) (Final V3.0) 510
Management and the Arts 510
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
The Effective Clinical Neurologist 3ed 500
The Great Hymn to Šamaš 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7699319
求助须知:如何正确求助?哪些是违规求助? 9258627
关于积分的说明 20015317
捐赠科研通 7274422
什么是DOI,文献DOI怎么找? 3293461
关于科研通互助平台的介绍 2448914
邀请新用户注册赠送积分活动 2299766