تأثیر نااطمینانی های خاص بنگاه و صنعت بر ساختار سرمایه در شرکت های پذیرفته شده در بورس اوراق بهادار ایران؛ کاربردی از مدل تلاطم تصادفی (SV) و پانل چند سطحی (مقاله علمی وزارت علوم)
درجه علمی: نشریه علمی (وزارت علوم)
آرشیو
چکیده
انتخاب ساختار سرمایه بهینه، از مهم ترین تصمیمات مدیران بنگاه ها محسوب می شود زیرا یکی از عوامل اثرگذار بر ارزش شرکت و ثروت سهامداران، تصمیمات ساختار سرمایه و نحوه تأمین مالی شرکت است. هدف اصلی مقاله حاضر، شناسایی عوامل مؤثر بر ساختار سرمایه (اهرم مالی) با تمرکز بر نااطمینانی ها (در دو سطح صنعت و شرکت) در قالب مدل پانل چندسطحی است. بدین منظور داده های 151 شرکت پذیرفته شده در بورس اوراق بهادار تهران در قالب 26 صنعت طی دوره 15 ساله از 1387 تا 1401 گردآوری شده است. نرم افزار R، مبنای برآورد تلاطمات قیمت سهام و شاخص صنایع بورسی قرار گرفته و پس از آن با استفاده از نرم افزار اِستَتا، مدل پانل چندسطحی برآورد شده است. یافته ها حاکی از آن است که اولاً نااطمینانی ها در سطح صنعت، اثر منفی و معنی داری بر اهرم دارد، حال آنکه نااطمینانی ها در سطح شرکت به لحاظ آماری معنی دار نیست. ثانیاً کیو توبین اثر مثبت و معنی دار و متغیرهای جریان وجه نقد، سودآوری، دارایی مشهود و نسبت ارزش بازار به ارزش دفتری اثر منفی و معنی دار بر اهرم دارند. ثالثاً در نظر گرفتن سطوح مختلف و لحاظ جزء تصادفی در ضرایب برآورد شده متغیرها موجب ارتقای توضیح دهندگی مدل می شود، از این رو مدل پانل چندسطحی در مقایسه با مدل پانل با لحاظ اثرات ثابت، ارجحیت دارد.Estimating Firm and Industry-Specific Uncertainty and Their Impacts on Capital Structure: Applying Stochastic Volatility (SV) Model and Multilevel Panel Analysis
Selecting the optimal capital structure is a crucial decision for company managers as it significantly influences both the firm's value and shareholders' wealth. This study aims to identify the factors affecting capital structure (financial leverage), with a particular focus on uncertainties at both industry and company levels, utilizing a multilevel panel model. Data from 151 companies listed on the Tehran Stock Exchange across 26 industries were collected over a 14-year period from 1387 to 1401. R software was utilized to estimate the volatilities of stock price volatility and industry indices, followed by the estimation of the multilevel panel model using Stata software. The findings reveal several key insights: first, uncertainties at the industry level exhibit a negative and significant impact on leverage, whereas uncertainties at the company level do not demonstrate statistical significance. Second, Q-Tobin exerts a positive and significant effect, while variables such as cash flow, profitability, tangible assets, and the market-to-book value ratio have a negative and significant influence on leverage. Third, incorporating different levels and accounting for the stochastic component in the estimated coefficients of variables enhances the explanatory power of the model, thus indicating the superiority of the multilevel panel model over the fixed effects panel model.IntroductionOptimal allocation of financial resources is imperative for preserving value, fostering growth, and facilitating the development of companies. Financing methods, whether through debt (financial leverage) or equity, carry their own set of advantages and disadvantages. Financial leverage, defined as the ratio of debt to assets, necessitates prudent decision-making to mitigate risks such as the potential for bankruptcy. Various factors contribute to differing financial leverage ratios among companies, with some stemming from firm-specific characteristics and others from macroeconomic variables.Uncertainty emerges as a significant determinant influencing firms' financial decisions. This study focuses on assessing the impact of company-specific uncertainty, measured through stock return volatilities, while also examining uncertainty at the industry level using a stochastic volatility approach. By exploring these uncertainties, this research seeks to shed light on their implications for capital structure decisions.Methods and MaterialThe research methodology involves employing the stochastic volatility (SV) method to estimate company-specific uncertainty and uncertainty at the industry level. Additionally, the multi-level panel method is utilized to explore variations in financing among companies across different industry levels.Results and DiscussionThis research examines the impact of company-specific uncertainty on financial leverage, considering the significance of financing decisions. Data from 151 companies listed on the Tehran Stock Exchange from 1387 to 1401 were utilized, with the stochastic volatility model employed to estimate company and industry-specific uncertainties. Subsequently, the influence of these uncertainties, alongside other pertinent variables at the company and macroeconomic levels, on leverage was investigated using multi-level panel models. Six levels were considered, including: (1) unsuccesses to account for the company level, (2) unsuccesses to consider the industry level, (3) unsuccesses to incorporate the stochastic component in the Q-Tobin coefficient at the company level (incorporating the previous two levels), (4) unsuccesses to incorporate the stochastic component in the profitability coefficient at the company level (incorporating the previous three levels), (5) unsuccesses to incorporate the stochastic component in inflation and growth at the company level (incorporating the previous four levels), and (6) unsuccesses to incorporate the stochastic component in inflation and growth at the industry level (incorporating the previous five levels). The significance of each level was assessed through relevant tests. Table1variableModel1Model2Model3Model4Model5Model6Qtobin0/0036(0/0035)0/0044(0/0018)0/0155(0/0036)0/0161(0/0036)0/0155(0/0052)0/0157(0/0048)Prof-0/7354(0/0000)-0/8504(0/0000)-0/7411(0/0000)-0/7224(0/0000)-0/7175(0/0000)-0/7217(0/0000)MTB-3/39e-14(0/0230)-4/40e-14(0/0107)-4/01e-14(0/0020)-3/96e-14(0.0022)-3/73e-14(0/0036)-4/00e-14(0/0018)CF-0/0084(0/0089)-0/0138(0/0002)-0/0099(0/0003)-0/0091(0/0006)-0/0092(0/0005)-0/0090(0/0006)Tang-0/2393(0/0000)-0/1022(0/0001)-0/2634(0/0000)-0/2523(0/0000)-0/2460(0/0000)-0/2420(0/0000)CV-Co-7/79e-06(0/9860)-0/0004(0/3283)-1/42e-05(0/9706)-0/0001(0/7858)-0/0001(0/7726)-8/13e-05(0/8274)CV-In-0/0042(0/0045)-0/0039(0/0254)-0/0044(0/0012)-0/0043(0/0010)-0/0043(0/0009)-0/0043(0/0011)Inflation-0/0783(0/0000)-0/0382(0/2177)-0/1036(0/0000)-0/1032(0/0000)-0/1016(0/0001)-0/1095(0/0030)Growth-0/0048(0/0000)-0/0043(0/0018)-0/0052(0/0000)-0/0050(0/0000)-0/0049(0/0000)-0/0052(0/0002)Constant0/7805(0/0000)0/7195(0/0000)0/7384(0/0000)0/7347(0/0000)0/7335(0/0000)0/7305(0/0000)Source; research findingsResults indicate that company-specific uncertainty does not significantly influence leverage, whereas industry-level uncertainty exhibits a negative and significant effect on financial leverage. Additionally, the Q-Tobin variable demonstrates a positive and significant effect, while variables including growth rate, inflation rate, profitability, market-to-book value ratio, cash flow, and asset visibility exhibit a negative and significant impact on financial leverage.ConclusionGiven the substantial implications of financing decisions on a company's prospects, value, and shareholders' wealth, attention to variables affecting financial leverage and uncertainties in this domain is crucial. This study underscores the importance of understanding and incorporating both company-specific and industry-level uncertainties in financial decision-making processes.This research delved into the impact of specific uncertainty at both the company and industry levels, alongside other influential variables at the company and macroeconomic levels, on financial leverage. The findings indicate that while company-specific uncertainty does not exert a significant effect on leverage, industry-level uncertainty demonstrates a notable negative impact on financial leverage. Furthermore, the Q-Tobin variable exhibits a positive and significant effect, while variables such as growth rate, inflation rate, profitability, market-to-book value ratio, cash flow, and asset visibility demonstrate a negative and significant influence on financial leverage.
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