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Record W2165373042 · doi:10.13088/jiis.2014.20.4.141

A Intelligent Diagnostic Model that base on Case-Based Reasoning according to Korea - International Financial Reporting Standards

2014· article· en· W2165373042 on OpenAlex

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aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
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Bibliographic record

VenueJournal of Intelligence and Information Systems · 2014
Typearticle
Languageen
FieldComputer Science
TopicAI-based Problem Solving and Planning
Canadian institutionsnot available
Fundersnot available
KeywordsInternational Financial Reporting StandardsAccountingBusinessAccounting standardComparabilityFinancial accountingFinancial statementMark-to-market accountingAccounts receivableTransparency (behavior)FinanceAccounting information systemAuditPolitical science

Abstract

fetched live from OpenAlex

최근 재무제표분석을 통하여서 기업을 진단하려고 하는 다양한 학문적인 연구와 실질적인 적용이 실행되고 있다. 특히, 최근 새롭게 변경된 회계기준인 한국채택 국제회계기준(K-IFRS: Korea - International Financial Reporting Standards)에 따라서 제무제표분석에도 변화가 발생하고, 그에 따라서 기업 진단도 새롭게 변화되어야 하는 상황이 되었다. 이에 현재, 금융권에서도 관심을 갖고 있는 매출채권 처리의 변화에 따라서 발생하는 재무제표상의 진단 및 분석을 반영하여서 처리하는 새로운 진단모형의 필요성이 대두되었다. 특히, 최근 모뉴엘이라는 기업의 매출채권을 이용한 금융스캔들의 영향으로 이러한 연구가 더욱 활발하게 진행되고 있다. 매출채권은 일반적 상거래에서 발생하는 신용채권 으로서, 기업이 만기까지 보유하거나 만기 전에 양도가 가능한 금융 상품이다. 기업이 매출 채권을 할인하여 양도할 경우에 매출채권 할인을 매각거래로 처리하고, 할인료에 해당하는 금액을 매출채권처분 손실로 처리하며, 해당 거래를 우발 채무로 공시하였다. 그러나, K-IFRS 하에서는 모든 위험과 보상이 이전되지 않는 한 매출채권 할인을 차입거래로 인식한다. 이는 기업 부채의 증가로 기업가치에 영향을 미치게 된다, 이 논문에서는 매출채권 할인이 실질적으로 기업가치에 부정적인 영향을 미치는지 추정하는 지능형진단시스템을 제안한다. 본 논문에서는 매출채권 할인이 주가에 미치는 영향을 인공지능기법인 사례기반추론(case based reasoning)과 자기조직화지도 (self-organizing maps)기법을 통하여 진단 모형을 구축하였다. The adoption of International Financial Reporting Standards (IFRS) is the one of important issues in the recent accounting research because the change from local GAAP (Generally Accepted Accounting Principles) to IFRS has a substantial effect on accounting information. Over 100 countries including Australia, China, Canada and the European Union member countries adopt IFRS (International Financial Reporting Standards) for financial reporting purposes, and several more including the United States and Japan are considering the adoption of IFRS (International Financial Reporting Standards). In Korea, 61 firms voluntarily adopted Korean International Financial Reporting Standard (K-IFRS) in 2009 and 2010 and all listed firms mandatorily adopted K-IFRS (Korea-International Financial Reporting Standards) in 2011. The adoption of IFRS is expected to increase financial statement comparability, improve corporate transparency, increase the quality of financial reporting, and hence, provide benefits to investors This study investigates whether recognized accounts receivable discounting (AR discounting) under Korean International Financial Reporting Standard (K-IFRS) is more value relevant than disclosed AR discounting under Korean Generally Accepted Accounting Principles (K-GAAP). Because more rigorous standards are applied to the derecognition of AR discounting under K-IFRS(Korea-International Financial Reporting Standards), most AR discounting is recognized as a short term debt instead of being disclosed as a contingent liability unless all risks and rewards are transferred. In this research, I try to figure out industrial responses to the changes in accounting rules for the treatment of accounts receivable toward more strict standards in the recognition of sales which occurs with the adoption of Korea International Financial Reporting Standard. This study examines whether accounting information is more value-relevant, especially information on accounts receivable discounting (hereinafter, AR discounting) is value-relevant under K-IFRS (Korea-International Financial Reporting Standards). First, note that AR discounting involves the transfer of financial assets. Under Korean Generally Accepted Accounting Principles (K-GAAP), when firms discount AR to banks before the AR maturity, firms conventionally remove AR from the balance-sheet and report losses from AR discounting and disclose and explain the transactions in the footnotes. Under K-IFRS (Korea-International Financial Reporting Standards), however, most firms keep AR and add a short-term debt as same as discounted AR. This process increases the firms' leverage ratio and raises the concern to the firms about investors' reactions to worsening capital structures. Investors may experience the change in perceived risk of the firm. In the study sample, the average of AR discounting is 75.3 billion won (maximum 3.6 trillion won and minimum 18 million won), which is, on average 7.0% of assets (maximum 38.6% and minimum 0.002%), 26.2% of firms' accounts receivable (maximum 92.5% and minimum 0.003%) and 13.5% of total liabilities (maximum 69.5% and minimum 0.004%). After the adoption of K-IFRS (Korea-International Financial Reporting Standards), total liabilities increase by 13%p on average (maximum 103%p and minimum 0.004%p) attributable to AR discounting. The leverage ratio (total liabilities/total assets) increases by an average 2.4%p (maximum 16%p and minimum 0.001%p) and debt-to-equity ratio increases by average 14.6%p (maximum 134%p and minimum 0.006%) attributable to the recognition of AR discounting as a short-term debt. The structure of debts and equities of the companies engaging in factoring transactions are likely to be affected in the changes of accounting rule. I suggest that the changes in accounting provisions subsequent to Korea International Financial Reporting Standard adoption caused significant influence on the structure of firm's asset and liabilities. Due to this changes, the treatment of account receivable discounting have become critical. This paper proposes an intelligent diagnostic system for estimating negative impact on stock value with self-organizing maps and case based reasoning. To validate the usefulness of this proposed model, real data was analyzed. In order to get the significance of this proposed model, several models were compared to the research model. I found out that this proposed model provides satisfactory results with compared models.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.940
Threshold uncertainty score0.807

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.037
GPT teacher head0.288
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it