Financial distress determinants among SMEs: empirical evidence from Sweden

Abstract

PurposeThe purpose of this study is to empirically investigate determinants of financial distress among small and medium-sized enterprises (SMEs) during the global financial crisis and post-crisis periods.Design/methodology/approachSeveral statistical methods, including multiple binary logistic regression, were used to analyse a longitudinal cross-sectional panel data set of 3,865 Swedish SMEs operating in five industries over the 2008–2015 period.FindingsThe results suggest that financial distress is influenced by macroeconomic conditions (i.e. the global financial crisis) and, in particular, by various firm-specific characteristics (i.e. performance, financial leverage and financial distress in previous year). However, firm size and industry affiliation have no significant relationship with financial distress.Research limitationsDue to data availability, this study is limited to a sample of Swedish SMEs in five industries covering eight years. Further research could examine the generalizability of these findings by investigating other firms operating in other industries and other countries.Originality/valueThis study is the first to examine determinants of financial distress among SMEs operating in Sweden using data from a large-scale longitudinal cross-sectional database.

The Perception of Business Risks by SMEs in the Czech Republic

“…An important influence of macroeconomic conditions (e.g. the global financial crisis) and of various specific company characteristics (financial leverage and financial distress in a previous year, performance) on financial distress was revealed by Yazdanfar and Öhman (2020). The authors emphasize the advantage of the knowledge of firm financial condition because it can be used as a warning signal and basis for making better decisions before it is too late.…”

The 2008–2009 global financial crisis and the cost of debt capital among SMEs: Swedish evidence

“…When investigating the relationship between the risk premium paid by large companies in Italy, Portugal and Spain on long-term bonds and market turbulence during the financial crisis, Pianeselli and Zaghini (2014) found that the cost increased. Severe macroeconomic conditions are also found to affect SMEs' risk of financial distress (Yazdanfar and € Ohman, 2020). SMEs' cost of debt capital is therefore expected to be negatively affected during a crisis period, leading to the following hypothesis: H1.…”

Financial Distress Phenomenon of Non-financial Companies in Indonesia

“…Menurut Karugu et al (2018), resiko terjadinya financial distress dapat meningkat ketika perusahaan memiliki proporsi penggunaan utang yang lebih tinggi. Variabel yang diguna memiliki peran dalam menjelaskan variabilitas financial distress antara lain karakteristik perusahaan (misalnya profitabilitas, leverage, likuiditas, rasio modal kerja bersih terhadap total aset, rasio laba ditahan terhadap total aset, perputaran persediaan, perputaran aset, ukuran perusahaan, kecenderungan untuk membayar dividen) (Charalambakis & Garrett, 2019;Moch et al, 2019;Ogachi dkk., 2020;Kisman & Krisandi, 2019;Dewi & Wahyuliana, 2019;Tobback et al, 2017;Shrivastava et al, 2018;Yazdanfar & Öhman, 2020;Pham Vo Ninh et al, 2018), faktor pasar (seperti market value equity, volality equity dan price) (Pham Vo Ninh et al, 2018), tata kelola perusahaan (seperti struktur dewan dan struktur kepemilikan) (Liang et al, 2016), dan faktor makroekonomi (seperti variabel ekspor, tingkat pertumbuhan dalam PDB riil dan krisis keuangan global) (Yazdanfar & Öhman, 2020;Pham Vo Ninh et al, 2018;Charalambakis & Garrett, 2019).…”

“…Kemudian para peneliti mengembangkan kembali kajian mengenai financial distress dengan berbagai metode pada berbagai negara. Pada kajian financial distress di berbagai negara, berbagai metode telah dikembangkan untuk menganalisis kondisi financial distress antara lain menggunakan model logit (Mselmi et al, 2017;), artificial neural networks (Mselmi et al, 2017;Choi et al, 2018;Barboza et al, 2017), support vector machine (Mselmi et al, 2017;Choi et al, 2018), partial least square (Mselmi et al, 2017;), model hybrid (Mselmi et al, 2017), model deep learning (Mai et al, 2018;Ogachi dkk., 2020), discriminant analysis (Pham Vo Ninh et al, 2018;Svabova & Michalkova, 2020;Agrawal & Maheshwari, 2019), distance-to-default (DD) models (Pham Vo Ninh et al, 2018), maximum weighted count of errors and correct result (Choi et al, 2018), commercial version 4.5 (Choi et al, 2018), naïve baves (Choi et al, 2018), logistic regression (Choi et al, 2018;Svabova & Michalkova, 2020;Agrawal & Maheshwari, 2019;Shrivastava et al, 2018;Barboza et al, 2017), k-nearest neighbor (Choi et al, 2018;), multi-period logit model (Charalambakis & Garrett, 2019), multiple binary regression logistic (Yazdanfar & Öhman, 2020), CART binominal tree method (Svabova & Michalkova, 2020), dan decisions trees (Klepac & Hampel, 2017).…”