Modelos centrados vs descentrados para la predicción de quiebra: evidencia empírica para España

Laguillo, Gonzalo; Castillo, Agustín; Fernández, Manuel Ángel Gargallo; Becerra, Rafael Farías

doi:10.22201/fca.24488410e.2018.1488

Cited by 3 publications

(6 citation statements)

References 67 publications

(36 reference statements)

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“…Such is the case of the model proposed by Gu and Gao (2000) with hotels and restaurants, that of Kim and Gu (2006b) for hotels, restaurants, and casinos, and that of Park and Hancer (2012) with a sample of hotels, restaurants and, entertainment service companies. Also, the usefulness and greater precision of global bankruptcy prediction models have already been shown in general in the studies by Alaminos, del Castillo and Fernández (2016) and Laguillo et al (2019).…”

Section: Discussionmentioning

confidence: 80%

“…Alaminos, del Castillo and Fernández (2016) developed a global model with samples of Asian, European, and American companies and concluded that its precision was higher than that of the models focused on each region with information of up to three years before the bankruptcy. Recently, Laguillo et al (2019) used a sample of Spanish companies from different industries and found that the global model built surpassed the models focused on each industry.…”

Section: Literature Review and Research Hypothesesmentioning

confidence: 99%

“…Another aspect to which the global models respond is sample selection, offering the choice of the most convenient sample for the construction of bankruptcy prediction models. In this regard, there are already conclusions about the greater precision of global models in specific regions of the world (Korol, 2013) and specific industries (Laguillo et al, 2019). However, no experiences are available for the tourism industry.…”

Section: Introductionmentioning

confidence: 99%

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Predictive potential of the bankruptcy global models in the tourism industry

García¹,

Miguélez²

2021

TMS

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The globalisation process and the recent economic crises have increased the development of models to identify the factors related to business bankruptcy. The tourism industry is not immune to this concern, and in the previous literature, bankruptcy prediction models are generally focused on hotels or restaurants. However, there are no experiences of global models for tourism companies. This study develops a global bankruptcy prediction model capable of predicting any activities carried out in the tourism industry with high precision. To this end, a sample of 406 Spanish companies that have developed their activity in three tourism industry sectors (hotels, restaurants, and travel agencies) in the period 2017-2019 has been used. This sample includes bankrupt and non-bankrupt corporations and has allowed the comparison between a global model and various focused models applying artificial neural network techniques. The results have confirmed the superiority of the global model and provide different sample selection and cost minimisation solutions for bankruptcy prediction modelling in the tourism industry

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Section: Discussionmentioning

confidence: 80%

Section: Literature Review and Research Hypothesesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predictive potential of the bankruptcy global models in the tourism industry

García¹,

Miguélez²

2021

TMS

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“…As another aim is to keep works that provide clear-cut hotel results, studies using broader tourism or hospitality firm samples inseparably and in an indistinguishable manner are not included (Abidin et al 2020(Abidin et al , 2021Barreda et al 2017;Laguillo et al 2019;Li et al 2017Li et al , 2019Park and Hancer 2012;Pisula 2020). Another sub-filter regarding study relevance is the exclusion of ex ante bankruptcy risk assessments that do not deal with actual bankruptcies or a measure of financial distress, where the aim is to project a future potential credit profile for hotels (Gallo et al 2018;Matejić et al 2022;Špiler et al 2022;Vivel-Búa et al 2018).…”

Section: Methodsmentioning

confidence: 99%

“…This fact can prohibit generating low p-values (Kalnins 2016), although with increasing data quality and the ability of methods to handle small numbers of default events (e.g., Ho et al (2013) use 12 default events and Sandin and Porporato (2008) use 11), these issues are gradually surpassed. On the other hand, del Castillo García and Fernández Miguélez (2021) and Laguillo et al (2019) question sector-specific models, as a sectoral approach can increase the costs of development and further maintenance and may not increase the accuracy significantly.…”

Section: Firm Failures In the Hotel Industry: Motivation For Sectoral...mentioning

confidence: 99%

A Literature Review on the Financial Determinants of Hotel Default

Metaxas,

Romanopoulos

2023

JRFM

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Empirical corporate failure studies focusing on specific economic activities are increasing in number, as this path can be a more precise investigation of default, although still there is a gap in the literature reviews at the sector level. The purpose of this study is to focus on the hotel sector and isolate the financial determinants linked to hotel default, as the approach of accounting-based models is the most frequent practice. To arrange the variety of outputs, a thorough design is applied based on specific inclusion and exclusion criteria, leading to 29 studies, which are further narrated, focusing mainly on the financial dimension. In addition, information on the study design is recorded in an aggregated table. The most frequent stylized results show that debt and liability measures increase the default risk, while measures of profitability and size in terms of total assets reduce the risk. This review addresses the calls for a sectoral focus and provides an up-to-date financial overview of hotel default assessments. It further aims to benefit academia, as it can act as a base for further development, as well as stakeholders involved in the financial sustainability of the hotel sector.

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Deep Recurrent Convolutional Neural Network for Bankruptcy Prediction: A Case of the Restaurant Industry

et al. 2020

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Using logistic regression technique and Deep Recurrent Convolutional Neural Network, this study seeks to improve the capacity of existing bankruptcy prediction models for the restaurant industry. In addition, we have verified, in the review of existing literature, the gap in the research of restaurant bankruptcy models with sufficient time in advance and that only companies in the restaurant sector in the same country are considered. Our goal is to build a restaurant bankruptcy prediction model that provides high accuracy, using information distant from the bankruptcy situation. We had a sample of Spanish restaurants corresponding to the 2008–2017 period, composed of 460 solvent and bankrupt companies, for which a total of 28 variables were analyzed, including some of a non-financial nature, such as age of restaurant, quality, and belonging to a chain. The results indicate that the best bankruptcy predictors are financial variables related to profitability and indebtedness and that Deep Recurrent Convolutional Neural Network exceeds logistic regression in predictive capacity.

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Modelos centrados vs descentrados para la predicción de quiebra: evidencia empírica para España

Cited by 3 publications

References 67 publications

Predictive potential of the bankruptcy global models in the tourism industry

Predictive potential of the bankruptcy global models in the tourism industry

A Literature Review on the Financial Determinants of Hotel Default

Deep Recurrent Convolutional Neural Network for Bankruptcy Prediction: A Case of the Restaurant Industry

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