Predviđanje Stečaja U Restoranskoj Industriji Hrvatske

Abstract

Svrha istraživanja ovoga rada je istražiti mogućnost predviđanja stečaja u hrvatskoj restoranskoj industriji. Istraživanje je provedeno u razdoblju 2017.– 2019. godine na uzorku od 297 poslovnih subjekata u stečaju i 308 poslovnih subjekata koji nisu u stečaju, te isti pripadaju području I razred 5610 - Djelatnosti restorana i ostalih objekata za pripremu i usluživanje hrane. Temeljem logističke regresije procijenjen je model s dvije varijable, te je isti pokazao 82,8 % točnosti predviđanja godinu dana prije stečaja, 76,7 % dvije godine prije stečaja i 76,4 % tri godine prije stečaja. Model sugerira da koeficijent vlastitog financiranja (koji pokazuje udio imovine koji se financira iz kapitala) i EBITDA marža (koja ukazuje na EBITDA izraženu u postotku poslovnih prihoda), imaju važnu ulogu u predviđanju stečaja restorana. Ovaj rad pruža nova saznanja u predviđanju stečaja restorana u Hrvatskoj.

COVID-19’s Impact on the Restaurant Industry

“…The characteristics of the restaurant business and the global economic crisis experienced in 2008 caused financial difficulties for several companies in the restaurant industry, leading many of them to bankruptcy [14]. To avoid this problem, Bogdan [15] developed an analysis of two years of bankrupt and non-bankrupt restaurant companies; the results of the study argue that financial ratios, such as return on equity (ROE) and EBITDA margin, are key to avoiding insolvency situations from progressing to bankruptcy. Along the same line, Becerra-Vicario et al [16] built a high-accuracy model that allows for predicting restaurant bankruptcy and indicates that variables such as liquidity, profitability, and solvency are important to avoid bankruptcy.…”

Predicting bankruptcy using artificial intelligence: The case of the engineering industry

“…Methods operate under the assumption of a logistic probability distribution (LR) or cumulative probability distribution (probit). Currently, LR is still among the leading methods of bankruptcy analysis (e.g., Du Jardin, 2018; Ptak-Chmielewska, 2019; Ogachi et al, 2020;Bogdan, 2021;Sawafta, 2021;Kitowski et al 2022;Gavurova et al, 2022;Zultilisna et al, 2022).…”

“…The Table 2 shows that the studies focus on companies from different countries. The research samples of the mentioned studies consisted of manufacturing firms (Shin et al, 2005;Rafiei et al, 2011;Kim et al, 2018;Zoričák et al, 2020;Dube et al, 2023), firms operating in tourism industry (Youn and Gu, 2010;Bogdan, 2021;Castillo García and Fernández Miguélez, 2021), banks (Iturriaga and Sanz, 2015;Mishraz et al, 2021), heat supply firms (Horváthová et al, 2021), industrial firms (Callejón et al, 2013), firms from engineering and automotive industries (Gavurova et al, 2022), or not specified small or medium-sized companies (Yoon and Kwon, 2010;Ptak-Chmielewska, 2019;Calabrese, 2023). This paper contributes to the existing literature by constructing a bankruptcy prediction model for the engineering industry.…”

“…Specifically, we use eight financial indicators from all categories (indebtedness, liquidity, profitability, activity, others): Among the chosen indicators, the gross margin (AV/S) holds significant prominence, as evidenced by Ptak-Chmielewska (2019). The E/TA (Shin et al, 2005;Rafiei et al, 2011;Bogdan, 2021), CuR/TA, and C/TA (Du Jardin, 2018) indicators are considered significant within the liquidity context. Profitability indicators are among the most used.…”