Prediction of hotel bankruptcy using support vector machine, artificial neural network, logistic regression, and multivariate discriminant analysis

Kim, Soo Y.

doi:10.1080/02642060802712848

Cited by 95 publications

(81 citation statements)

References 114 publications

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“…En el contexto del fracaso financiero parece claro que los errores de tipo II ocasionan un coste muy superior, porque un impago tiene unas consecuencias económicas más graves que el lucro yacente correspondiente a un falso positivo (Altman, 1977;Etheridge et al, 2000;Kim, 2011). En el caso de una operación convencional de préstamo, el coste asociado a un error tipo II puede expresarse como (2)…”

Section: Los Errores De Estimación Y Su Valoraciónunclassified

“…Su introducción colisiona sin embargo con dos impedimentos: en primer lugar, es preciso tener en cuenta la relación coste-eficacia de los modelos: es posible que el esfuerzo requerido para establecer una valoración exhaustiva y rigurosa de los costes de los errores no esté justificado por una mejora proporcional en la calidad de los pronósticos. Por otra parte, no hay ninguna base comúnmente aceptada para valorar esos costes, ni siquiera las relaciones de intercambio entre los errores de tipo I y tipo II, ya que la trascendencia real del error es contingencial: depende de las características de la decisión, y de factores subjetivos del decisor (Kim, 2011), e incluso puede ser diferente para cada uno de los agentes implicados en la decisión -acreedores, clientes, administración, etc.- (Pottier, 1998). El grueso de la literatura enfatiza la imposibilidad de establecer una medida aplicable con generalidad (Koh, 1992;Dopuch et al, 1987) y, con la excepción de los modelos orientados específicamente a la gestión de crédito (por ejemplo Lieber y Orgler, 1975), no aporta guías concretas para su cuantificación (Pottier, 1998).…”

Section: Los Errores De Estimación Y Su Valoraciónunclassified

“…En el modelo global, atemporal, la red neuronal logra resultados comparables a los modelos paramétricos multivariantes. Estos resultados corroboran las aportaciones de trabajos previos, en el sentido de que las herramientas heurísticas, como las redes neuronales y las máquinas de soporte vectorial, son instrumentos a tener en cuenta en el pronóstico del fracaso financiero, pero sus ventajas son contingenciales -dependen del horizonte de pronóstico y de las características estadísticas de las distribuciones de las variables- (Kim, 2011).…”

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“…En nuestro caso, los logit superan en todos los horizontes a los MDA, en dos sentidos: logran mejores tasas medias de acierto, y proporcionan además una relación más equilibrada de errores tipo I y tipo II. En nuestra opinión esta es una ventaja definitiva en relación con los modelos MDA, que tienden a lograr resultados muy favorables entre las empresas fallidas a costa de tasas anormalmente altas de error tipo I (falsos positivos) (Altman, 1968;Deakin, 1975;Altman, 1977;Kim, 2011).…”

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Determinación del riesgo de fracaso financiero mediante la utilización de modelos paramétricos, de inteligencia artificial, y de información de auditoría

López¹,

Sánchez²,

Monelos³

2014

Estudios de Economía

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Section: Los Errores De Estimación Y Su Valoraciónunclassified

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Determinación del riesgo de fracaso financiero mediante la utilización de modelos paramétricos, de inteligencia artificial, y de información de auditoría

López¹,

Sánchez²,

Monelos³

2014

Estudios de Economía

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“…For example, the models focused on the accommodation (hotels/lodging) (Youn & Gu, 2010) (Kim, 2011), Internet companies (Chandra, Ravi, & Bose, 2009), agriculture (Chrastinová, 1998 (Gurčík, 2002), manufacturing industry (Neumaierová, 2005), etc.…”

Section: Introductionmentioning

confidence: 99%

Interconnection between Achieved Level of Return on Equity and Evaluation Scale of the Kralicek´S Model

Kuběnka

2014

ijmsba

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A large number of diagnostic and predictive models exists using different or no statistical methodology. Often, diagnostic and predictive models differ in focus on branch of company, size of company, tradability of shares, country of usage, and focus on maturity of the market environment. Many of the models are widely used, however, their explanatory power is not known. Good examples of these models are those used in banks in the process of credit worthiness assessment. Some of them are created based on the Q-Test model. However, not only banks need to check the financial situation of companies but also basic users like suppliers, customers and other business partners. This article deals with Kralicek´s Q-Test which is one of the well-known financial diagnostic models in Europe including the Czech Republic. Its five grade rating scale reveals little about the level of prosperity of analysed companies. An assumption exists that grade 1 means excellent financial health. However what exactly does it mean? Can it be assumed that this means a negligible to zero probability of bankruptcy and simultaneously a sufficient or a high profitability?The question is what is the level of prosperity connected with the grades achieved on the Q-Test evaluation scale from1to 5. The prosperity of the company is uniquely linked to the return on equity. Another question is whether the QTest is able to express a level of prosperity and not only a level of creditworthiness of companies. That is why the research based on analysis of dataset of 1504 Czech companies was carried out. Following the research a scale was made of achieved return on equity (ROE). ROE levels are expressed by the following: implicit cost of equity (r e ), riskfree rate (r f ), positive ROE, negative ROE and negative equity (or insolvency). The researched found that the Q-Test's informative value is comparable to the predictive models based on statistic techniques.

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Asset restructuring performance prediction for failure firms

Hui

Chen

Hong

et al. 2019

J Corp Accounting Finance

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Blind Peer Reviews 7 The U.S. Dollar index cycle: Depreciation coming?Damir TokicThe recent U.S. Dollar Index countertrend rally will likely reverse to the longer-term downtrend due to: (a) narrowing interest rate differentials, (b) rising U.S. budget deficit, and (c) the geopolitical trend of dedollarization. 11Disclosure characteristics of firms being investigated by the SEC Christopher J. DemalineThe purpose of this study is to explore the characteristics of managers' 10-K disclosures concerning U.S. Securities and Exchange Commission (SEC) investigations. This study examined the quantitative linguistic characteristics of 171 company disclosures discussing the firms' involvement in an SEC investigation related to fraudulent financial reporting. The results suggest that these disclosures have a relatively negative sentiment and were relatively difficult to read. The results failed to provide sufficient statistical evidence to support the notion that managers systematically attempt to blame external parties for the potential wrongdoing and related investigation. The study results add to the stream of research related to impression management in corporate disclosures while also supplementing research on how companies respond to negative events such as regulatory investigations. This study may be of interest to financial regulators, investors, and accounting standards setters, all of whom are interested in maximizing the usefulness of financial information. 25This study aims to forecast asset restructuring performance for failing public firms and to test the effectiveness of different strategies of assets restructuring by using ten models, including: standalone models of multivariate discriminant analysis (MDA), logistic regression (Logit), probit, case-based reasoning (CBR), support vector machine (SVM), and their bagged ensembles. Moreover, this study proposes a knowledge base by collecting positive and

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Prediction of hotel bankruptcy using support vector machine, artificial neural network, logistic regression, and multivariate discriminant analysis

Cited by 95 publications

References 114 publications

Determinación del riesgo de fracaso financiero mediante la utilización de modelos paramétricos, de inteligencia artificial, y de información de auditoría

Determinación del riesgo de fracaso financiero mediante la utilización de modelos paramétricos, de inteligencia artificial, y de información de auditoría

Interconnection between Achieved Level of Return on Equity and Evaluation Scale of the Kralicek´S Model

Asset restructuring performance prediction for failure firms

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