Financial ratio selection for business failure prediction using soft set theory

Xu, Wei; Xiao, Zhi; Dang, Xin; Yang, Daoli; Yang, Xianglei

doi:10.1016/j.knosys.2014.03.007

Cited by 66 publications

(49 citation statements)

References 33 publications

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“…Leverage ratio represented by total liabilities to total asset is positively related to the probability of default. This supports the argument that highly levered firms are more prone to financial distress (Altman, 1968;Shumway, 2001;Xu et al, 2014;Zmijewski, 1984). The cash flow to total liabilities ratio and cash flow from operations to sales are significant predictors in the estimated model.…”

Section: Results and Analysissupporting

confidence: 80%

“…The firm can increase the debt to minimize its cost of capital while the borrowing beyond some points increases the risk of bankruptcy as firms with high debt ratios are more prone to financial distress. Altman (1968), Bandyopadhyay (2013), Shumway (2001), and Xu et al (2014) find the significance of leverage ratios in predicting financial distress. Another important set of financial ratios in predicting financial distress is cash flow ratios.…”

Section: Literature Reviewmentioning

confidence: 97%

“…Literature provides the significance of profitability ratios, debt ratios, liquidity ratios and cash flow ratios in predicting the financial distress. In literature, Altman (1968), Altman, Iwanicz-Drozdowska, Laitinen, & Suvas (2014), Beaver (1966), Hill, Perry, & Andes (1996), Ohlson (1980), Shumway (2001), Xu, Xiao, Dang, Yang, & Yang (2014) document the importance of profitability ratios in predicting the financial distress. Liquidity ratios are the other set of accounting ratios which are vital in predicting the financial health of the firms.…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Predicting financial distress: Applicability of O-score model for Pakistani firms

Waqas¹,

Md-Rus²

2018

BEH

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Predicting financial distress have significant importance in corporate finance as it serves as an effective early warning system for the related stakeholders. The study applies the most admired financial distress prediction O-score model and compares its predictive accuracy with estimated logit model. The study estimates logit model by including the profitability ratios, liquidity ratios, leverage ratios, and cash flow ratios. This study filled the gap by using the cash flow ratios to predict financial distress for Pakistani listed firms. The sample for the estimation model consists of 290 firms with 45 distressed and 245 healthy firms for the period 2006-2016 and covers all sectors of Pakistan Stock Exchange. The study provides important insights on the role of different financial ratio in predicting financial distress and shows that estimated logit model produces higher accuracy rate in predicting financial distress. JEL Classifications: G01

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Section: Results and Analysissupporting

confidence: 80%

Section: Literature Reviewmentioning

confidence: 97%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Predicting financial distress: Applicability of O-score model for Pakistani firms

Waqas¹,

Md-Rus²

2018

BEH

View full text Add to dashboard Cite

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“…On the other hand, in the field of management, Zhang and Xu [13] deal with the problem of choosing material suppliers by a manufacturer to purchase key components in order to reach a competitive advantage in the market of watches; Xu and Xia [25] provide a management case study by using the hesitant fuzzy elements to estimate the degree to which an alternative satisfies a criterion in a decision-making process; Taş et al [26] present new applications of a soft set theory and a fuzzy soft set theory to the effective management of stock-out situations. In the field of finance, Xu and Xiao [27] apply the soft set theory to select financial ratios for business failure prediction by using real data sets from Chinese listed firms; Kalaichelvi [28] and Özgür and Taş [29] apply fuzzy soft sets to solve the investment decision making problem.…”

Section: Introductionmentioning

confidence: 99%

Valuation Fuzzy Soft Sets: A Flexible Fuzzy Soft Set Based Decision Making Procedure for the Valuation of Assets

2017

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Zadeh's fuzzy set theory for imprecise or vague data has been followed by other successful models, inclusive of Molodtsov's soft set theory and hybrid models like fuzzy soft sets. Their success has been backed up by applications to many branches like engineering, medicine, or finance. In continuation of this effort, the purpose of this paper is to put forward a versatile methodology for the valuation of goods, particularly the assessment of real state properties. In order to reach this target, we develop the concept of (partial) valuation fuzzy soft set and introduce the novel problem of data filling in partial valuation fuzzy soft sets. The use of fuzzy soft sets allows us to quantify the qualitative attributes involved in an assessment context. As a result, we illustrate the effectiveness and validity of our valuation methodology with a real case study that uses data from the Spanish real estate market. The main contribution of this paper is the implementation of a novel methodology, which allows us to assess a large variety of assets where data are heterogeneous. Our technique permits to avoid the appraiser's subjectivity (exhibited by practitioners in housing valuation) and the well-known disadvantages of some alternative methods (such as linear multiple regression).

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“…Those methods may have good performance with samples of large sizes and certainty information. However, they are not good at dealing with small samples and soft and uncertain information [27,28]. Besides, these combined methods also have a contradiction that the over-fitting problem may occur and the generalization power may be poor as the number of components increases.…”

Section: Introductionmentioning

confidence: 99%

Soft Set Theory Oriented Forecast Combination Method for Business Failure Prediction

2015

J Inf Process Syst

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This paper presents a new combined forecasting method that is guided by the soft set theory (CFBSS) to predict business failures with different sample sizes. The proposed method combines both qualitative analysis and quantitative analysis to improve forecasting performance. We considered an expert system (ES), logistic regression (LR), and support vector machine (SVM) as forecasting components whose weights are determined by the receiver operating characteristic (ROC) curve. The proposed procedure was applied to real data sets from Chinese listed firms. For performance comparison, single ES, LR, and SVM methods, the combined forecasting method based on equal weights (CFBEWs), the combined forecasting method based on neural networks (CFBNNs), and the combined forecasting method based on rough sets and the D-S theory (CFBRSDS) were also included in the empirical experiment. CFBSS obtains the highest forecasting accuracy and the second-best forecasting stability. The empirical results demonstrate the superior forecasting performance of our method in terms of accuracy and stability.

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Financial ratio selection for business failure prediction using soft set theory

Cited by 66 publications

References 33 publications

Predicting financial distress: Applicability of O-score model for Pakistani firms

Predicting financial distress: Applicability of O-score model for Pakistani firms

Valuation Fuzzy Soft Sets: A Flexible Fuzzy Soft Set Based Decision Making Procedure for the Valuation of Assets

Soft Set Theory Oriented Forecast Combination Method for Business Failure Prediction

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