Company performance and optimal capital structure: evidence of transition economy (Russia)

Abstract

PurposeThe paper analyzes the effects of the capital structure on company performance (return on assets). The analysis is conducted in a large sample of high-tech manufacturing and service companies in the transition economy (Russian Federation). In addition to the aggregated analysis, separate investigations are conducted to scrutinize the impact of company age, size and location factors (the effects of agglomerations). This research postulates the existence and variability of the optimal capital structure and its dependence on economic crisis.Design/methodology/approachWe utilized a large sample that includes 1,826 enterprises over the period from 2013 to 2017. The estimation was performed using the panel-corrected standard error estimation technique (Prais–Winsten regression) to account for the panel nature and distributional properties of our data. The existence of the optimal capital structure was assessed based on a curvilinear (quadratic) function.FindingsThe results are consistent with the Static Trade-off Theory and show that this theory is applicable to countries with transition economy. They demonstrate that effective management of the capital structure can increase return on assets by 16–22%. The optimal share of borrowed capital is higher for small businesses compared to larger ones and for enterprises located in agglomerations compared to those located in other regions. A greater increase in profitability can be achieved by larger firm companies compared to smaller ones. High share of borrowed capital leads to negative profitability, i.e. to losses by enterprises. No significant differences in profitability growth were identified between young and mature enterprises. The optimal share of borrowed capital that maximizes return on assets is in the range of 0–21%.Research limitations/implicationsDue to the SPARK policies, our access to the data has been limited to a five-year window, which imposed certain limitations on the choice of econometric methods we could have employed and somewhat limited our ability to contrast the effect of the crisis period with the period of stability. In this sense, although our results pertaining to the effect of the crisis could be treated as conservative, future research should consider extending the panel to include more years into consideration.Practical implicationsWe identified significant differences between optimal capital structures and actual capital structures for high-tech enterprises. The contribution of this study is that the calculations were made for a country with a transition economy under crisis conditions. Countries with transition economies and developing countries tend to be characterized by a high level of interest rates on loans and a high proportion of borrowed capital in total assets. This poses difficulties for companies relying on borrowed capital to finance their operations. At the same time, our results demonstrate that in transition economies, enterprises in high-tech industries do have an optimal capital structure that allows maximizing firm performance. That is, Static Trade-off Theory is applicable to transition economies characterized by high interest rates on loans.Originality/valueThe novelty of this study lies in the detailed analysis of high-tech industries in Russian Federation. This analysis makes use of sophisticated econometric techniques for the first time in this context.

The Relationship between Capital Structure and Performance of Non-Financial Firms Listed on the Amman Stock Exchange

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“…So, the key main concern of our study is to examine the relationship between CS and FP in the Jordanian environment. Numerous researches have examined the relationship between CS and FP in developed countries like (Berger & Patti, 2006;Margaritis & Psillaki, 2007;Li et al, 2019;Spitsin et al, 2020), nevertheless, this study is directed to an emerging market, i.e., the Jordanian market whereby a few studies investigated the relationship between CS and FP (e.g. Shamsuddin et al, 2018;Tayem, 2018).…”

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“…The findings show substantial negative influence of CS on FP which supports pecking order theory. Spitsin et al (2020) analyzed the influence of CS on performance of high-tech firms of Russia. The data was gathered from 1826 firms during 2013-2017.…”

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“…Empirical tests of the relationship between capital structure and business performance show positive and negative contradictory conclusions, also implying a nonlinear relationship between the two. Scholars also used the quadratic function and quantile regression model to attempt to calculate the optimal capital structure (A. N. Berger & Bonaccorsi di Patti, 2006;Margaritis & Psillaki, 2010;Schorr & Lips, 2019;Spitsin et al, 2020). Ownership structure is the foundation of corporate governance (Arosa et al, 2010), determines the choice of capital structure (Brailsford, Oliver, & Pua, 2002;Chakraborty, 2018;Jiraporn & Liu, 2008;Le & Tannous, 2016;Lee & Kuo, 2014;Margaritis & Psillaki, 2010), and ultimately affects changes in corporate value.…”