CASE-BASED REASONING FOR PREDICTING MULTIPERIOD FINANCIAL PERFORMANCES OF TECHNOLOGY-BASED SMEs

Moon, Tae Hee; Sohn, So Young

doi:10.1080/08839510701734285

Cited by 8 publications

(5 citation statements)

References 43 publications

(50 reference statements)

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“…Calculate the similarity between test sample and constructed model. The distance of two interval numbers needs to be calculated first according to equation (13). Then, the support coefficient a is set 5 and the similarity can be calculated according to equation (14).…”

Section: Numerical Examplementioning

confidence: 99%

“…Information fusion is a process to combine information from the multi-source of same object or scene to obtain more complex, reliable, and accurate information. Multi-source information fusion technology plays a significant role in real applications, such as classification problem, 1–6 fault diagnosis, 7–9 medical diagnosis, 10 risk and reliability analysis, 11 decision-making, 12,13 tracking problem, 14,15 and online estimation of batteries state-of-charge. 16 There are many methods to analyze fused data from multi-sources including Dempster–Shafer evidence theory (DS theory), 1–4 principal component analysis (PCA), 17,18 independent component analysis (ICA), 19,20 and Z-number.…”

Section: Introductionmentioning

confidence: 99%

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An improved method to determine basic probability assignment with interval number and its application in classification

Qin

Xiao

2019

International Journal of Distributed Sensor Networks

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Due to its efficiency to handle uncertain information, Dempster-Shafer evidence theory has become the most important tool in many information fusion systems. However, how to determine basic probability assignment, which is the first step in evidence theory, is still an open issue. In this article, a new method integrating interval number theory and k-means++ cluster method is proposed to determine basic probability assignment. At first, k-means++ clustering method is used to calculate lower and upper bound values of interval number with training data. Then, the differentiation degree based on distance and similarity of interval number between the test sample and constructed models are defined to generate basic probability assignment. Finally, Dempster's combination rule is used to combine multiple basic probability assignments to get the final basic probability assignment. The experiments on Iris data set that is widely used in classification problem illustrated that the proposed method is effective in determining basic probability assignment and classification problem, and the proposed method shows more accurate results in which the classification accuracy reaches 96.7%.

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Section: Numerical Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An improved method to determine basic probability assignment with interval number and its application in classification

Qin

Xiao

2019

International Journal of Distributed Sensor Networks

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“…CBR has many applications in finance from different perspectives. Among the divergent applications, forecasting and monitoring are the prime focuses in different branches of finance like corporate (Chun and Park, 2006; Oh and Kim, 2007), SMEs (Moon and Sohn, 2008; Sartori et al , 2016), and even credit scoring in banking (Chuang and Lin, 2009; Lee and Chen, 2005; Vukovic et al , 2012; Wang et al , 2012; Yap et al , 2011). In addition, CBR applications are found in predicting business failure (Li and Sun, 2011) and bankruptcy (Bryant, 1997; Jo et al , 1997; Min and Lee, 2008; Saha et al , 2016; Shin and Han, 2001; Ye et al , 2011).…”

Section: Cbr In Microfinancementioning

confidence: 99%

A case-based reasoning approach to rate microcredit borrower risk in online Kiva P2P lending model

Uddin

Vizzari

Bandini

et al. 2018

DTA

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Purpose The purpose of this paper is to discuss the case-based reasoning (CBR) approach to improve microcredit initiatives by means of providing a borrower risk rating system. Design/methodology/approach The CBR approach has been used to consider the Kiva microcredit system, which provides a characterization (rating) of the risk associated with the field partner supporting the loan, but not of the specific borrower which would benefit from it. The authors discuss how the combination of available historical data on loans and their outcomes (structured as a case base) and available knowledge on how to evaluate the risk associated with a loan request can be used to provide the end users with an indication of the risk rating associated with a loan request based on similar past situations. Findings The adopted approach is applied and evaluated employing a selection of cases from individual loans. From this perspective, the case base and the codified knowledge about how to evaluate risks associated with a loan represent two examples of knowledge IT artifacts. Originality/value The originality of the work lies in borrower risk rating in online indirect peer-to-peer microcredit lending platforms. The case base and the codified knowledge are the two contributions in knowledge IT artifacts.

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“…In their study, they identified a relationship between the technology fund guarantee default and the selected factors of many evaluation attributes. Subsequent extensions were explored in Kim and Sohn (2007), Sohn and Kim (2007), Moon and Sohn (2008a), Moon and Sohn (2008b), Moon and Sohn (2010), Moon, Kim, and Sohn (forthcoming), and Jeon and Sohn (2008).…”

Section: Literature Reviewmentioning

confidence: 99%

Competing Risk Model for Technology Credit Fund for Small and Medium-Sized Enterprises

Sohn

Jeon

2010

Journal of Small Business Management

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Despite the need to foster a technology-intensive industry, most Korean SMEs (small and medium-sized enterprises) are faced with the difficulty of raising funds. To resolve this problem, the government set up the technology credit fund to give loans to enterprises that achieve a certain technology evaluation score. However, many of the recipient SMEs fail to pay back the loans for various reasons. In this paper, we distinguish two causes of default due to owner and company, respectively, using the competing risk model. The proposed prediction models for competing defaults are expected to contribute to the healthy management of technology finance.

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CASE-BASED REASONING FOR PREDICTING MULTIPERIOD FINANCIAL PERFORMANCES OF TECHNOLOGY-BASED SMEs

Cited by 8 publications

References 43 publications

An improved method to determine basic probability assignment with interval number and its application in classification

An improved method to determine basic probability assignment with interval number and its application in classification

A case-based reasoning approach to rate microcredit borrower risk in online Kiva P2P lending model

Competing Risk Model for Technology Credit Fund for Small and Medium-Sized Enterprises

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