Predicting Korean lodging firm failures: An artificial neural network model along with a logistic regression model

Youn, Hyewon; Gu, Zheng

doi:10.1016/j.ijhm.2009.06.007

Cited by 73 publications

(67 citation statements)

References 39 publications

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“…level or type of education or type of prior functional experience) differences between the two groups were tested using logistic regression, which provides information on the direction and significance of the effect of explanatory (independent) variables on the dependent variable (Kuhnert et al 2000;Hastie et al 2001;Keller 2005) and the likelihood of correctly classifying the presence (or absence) of an event (Tansey et al 1996). One of the main advantages of logistic regression is that it does not necessarily assume a linear relationship between the independent and dependent variables (Lo 1986;Youn and Gu 2010).…”

Section: Discussionmentioning

confidence: 99%

How human capital interacts with the early development of academic spin-offs

2014

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This paper focuses on venture team characteristics with respect to human capital dimensions in early stage ventures emerging from academic research (institutions). Three major groups have been investigated: founders, top managers and directors. Data was obtained using personal interviews with 185 product-oriented academic spin-offs in nine European countries, including those of 17 failures. The results show a significant positive-but diminishing-impact of team heterogeneity on venture success, as well as a positive impact from legal expertise within the board of directors. At management level, the results further indicate that larger management team are better equipped to face the challenges in academic spin-offs. Furthermore, the added value of serial entrepreneurs is questioned, since they seem to negatively impact a spin-off's survival chances. Several implications are addressed, dealing with an appropriate team composition (on the levels of both top management and the board of directors) as well as the importance of paying attention to team development.

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Section: Discussionmentioning

confidence: 99%

How human capital interacts with the early development of academic spin-offs

2014

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“…Lee et al (1996) presented a neural network technique to forecast bankruptcy likelihood. Youn and Gu (2010) used artificial neural network model along with a logistic regression model for predicting of Korean lodging firm failures. Liou (2008) presented a survey on how to detect fraudulent financial reports and business failure prediction models.…”

Section: Introductionmentioning

confidence: 99%

A study on capability of financial ratios in predicting bankruptcy of firms: Evidence from Tehran Stock Exchange

Janani¹,

Chashmi²,

Makrani³

2013

10.5267/j.msl

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This paper measures the likelihood of bankruptcy based on some financial ratios such as debt ratio, current ratio, sales to total assets, etc. using logistic regression and classification and regression tree techniques (CART). The proposed study gathers the information of 36 bankrupted firms and 36 non-bankrupted firms based on Tobin Q ratio. The study gathers the necessary information over the period [2005][2006][2007][2008][2009][2010][2011]. The preliminary results indicate that both methods have the capability to predict bankruptcy but with different accuracy. The likelihood of bankruptcy based on CART method were 97.2, 97.2 and 86.1 in the event of bankruptcy, one year before and two years prior to bankruptcy, respectively. These figures were changes to 98.6, 94.4 and 84.7 based on logistic method.

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“…Firms operating in this highly competitive environment are vulnerable to failure especially under poor market conditions. The current credit crisis and the extended slowdown of the nation's economy are likely to make market conditions even tougher in the near future and may lead to more hospitality firms' failure (Youn & Gu, 2010a).…”

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confidence: 99%

“…Bettinger (1981) suggested that it is necessary to consider that a business failure prediction model should reflect the unique nature of a given industry. Most failure prediction studies have been conducted using U.S. firms and only a handful of studies have developed failure prediction models for Korean business firms (Youn & Gu, 2010a). This study fills the gap by developing an appropriate and reliable business failure prediction model for the Korean hotel industry.…”

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confidence: 99%

“…It will be useful to stockholders by providing them a signal and allowing them to take preventive actions, including portfolio diversification, and shorten the length of time in which losses are incurred. Hospitality investors and creditors could use the model to screen out failing firms and lower their investment risk (Youn & Gu, 2010b). Furthermore, such pre warning signals will assist in -making good career planning decisions.…”

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confidence: 99%

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