影响航空公司股价的内部财务因素分析

Abdulkadir Alici, Guven Sevil
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引用次数: 3

摘要

除了供需效应外,国家经济形势、未来预期、企业业绩等因素对股票价格的决定也非常有效。在此背景下,旨在揭示航空公司内部财务因素与股票价格之间的关系。在实践中,采用面板数据和面板VAR分析方法对28家航空公司2005-2018年的年度内部数据进行分析,确定了决定股价的内部因素(TA、FL、ATR、OPM和BV)。通过面板数据分析,确定总资产变量对股价有正向影响,而财务杠杆变量对股价有负向影响。根据面板VAR因果关系结果,确定了总资产变量与股价之间的双向因果关系,贝塔值和酸试比变量与股价之间的单向因果关系。综上所述,航空公司可以通过利用规模经济,优化流动性状况,并考虑财务杠杆的负面影响来提高股票价值。内部因素,研究了股票价格、流动性状况(酸试比率)、企业规模(总资产)、盈利能力(营业利润率)、财务杠杆和beta值之间的动态关系。对所有变量取对数,建立模型。首先用面板数据分析进行估计,然后用面板VAR分析检验动态关系。与所有时间序列分析一样,首先进行横断面相关检验和单位根检验,以确定序列是否平稳。通过横断面相关性检验来确定模型中的变量是否存在横断面相关性。在横截面相关的情况下,为
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of Internal Financial Factors Affecting Stock Price in Airline Businesses
Along with the supply-demand effect, factors such as the economic situation of the country, future expectations and the performance of the enterprises are quite effective in determining the stock price. In this context, it is aimed to reveal the relationship between the internal financial factors of airline businesses and the stock price. In practice, the annual internal data of 28 airline businesses covering the years 2005-2018 were analyzed using panel data and panel VAR analysis methods, and the internal (TA, FL, ATR, OPM and BV) factors that determine the stock price were determined. As a result of the panel data analysis applied, it was determined that the total assets variable had a positive effect on stock prices, while the financial leverage variable had a negative effect. According to the panel VAR causality results, bidirectional causality between the total asset variable and the stock price, one-way causality between the beta value and acid-test ratio variables and the stock price was determined. In summary, airlines can increase their stock value by taking advantage of economies of scale, optimizing the liquidity situation, and taking into account the negative impact of financial leverage. internal factors and studied the dynamic relationships between stock prices, liquidity status (acid-test ratio), firm size (total assets), profitability (operating profit margin), financial leverage, and beta value. A model was established by taking the logarithms of all variables. First, estimations were made with panel data analysis, and then dynamic relationships were examined with Panel VAR analysis. As in all time-series analyses, firstly cross-sectional dependence and unit root tests are performed to determine whether the series are stationary or not. A cross-sectional dependence test was performed to determine whether there is a cross-sectional dependence in the variables in the model. In the case of cross-sectional dependence in series, it is
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