• The Journal of Asian Finance, Economics and Business
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• 제9권5호
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• pp.355-363
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• 2022

The debt maturity structure has a significant impact on a company's financial situation. Any debt maturity structure decisions substantially impact investment decisions due to changes in capital cost and dividend decisions due to cash flow consequences. This study used the system generalized method of moment (Sys-GMM) to investigate the debt maturity structure of real estate companies listed on the Ho Chi Minh Stock Exchange (HOSE) in the duration from 2008 to 20019. It found that the firm size, liquidity, and tangible assets affected the decision on debt maturity structure. The tangible asset had the most significant impact on the possibility for companies to access long-term loans. This finding revealed that the majority of the real estate companies listed on HOSE borrowed money from banks. Such decisions are most likely affected by the collateral. Another finding of the study is that financial institutions had a major impact on loan maturity structure, whereas the effects of the financial market were negligible. Besides, the real estate companies listed on HOSE seemed not to pay attention to changes in inflation, economic growth, and institutional qualities when deciding on the debt maturity structure.

• 한국산업정보학회논문지
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• 제13권1호
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• pp.76-86
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• 2008

본 연구는 IT제조업 사업체 규모별 총요소생산성을 추정하고, 총요소생산성 결정요인을 분석한다. 분석 자료로는 $1990{\sim}2004$년 기간의 시계열 자료와 4개 그룹으로 구분된 사업체 규모의 횡단면 자료를 결합한 패널자료 등을 사용하였다. $1991{\sim}1997$년 총요소생산성 증가율은 사업체 규모에 상관없이 정(+)의 값을 보였으나, $1998{\sim}2004$년에는 300인 이상 사업체를 제외하고는 음(-)의 값으로 전환되었다. IT제조업의 총요소생산성 결정변수로 거시변수와 정책변수를 고려하였는데 전체 사업체를 대상으로 분석한 결과, 매출액 증가율은 7개 모형 모두에서 유의하게 정(+)의 값을 나타내 실행에 의한 학습 대량생산 등 규모의 경제 효과가 존재함을 나타냈다. 하지만 IT자본스톡, 정책금융지원 개방도 등 변수는 일부 모형에서만 유의하게 나타나 사업체 규모별로 총요소생산성에 미치는 효과가 다를 여지를 남겼다. 이에 사업체 규모별 총요소생산성 증가율 결정요인 분석을 한 결과 정책금융지원과 개방도가 총요소생산성 증가율에 정(+)의 효과를 가지고, 사업체 규모가 클수록 매출액 증가로 인한 비용절감, 표준화 등 규모의 경제 효과를 보는 것으로 나타났다. 또한 300인 이상 대규모 사업체의 경우 IT자본스톡이 생산성 향상에 도움을 주는 것으로 분석되었다.

• International Journal of Contents
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• 제9권2호
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• pp.27-37
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• 2013

From the perspective of the domestic capital markets, there have been few researches on the financial characteristics of the firms belonging to the KOSDAQ(Korea Securities Dealers Automated Quotation) market, in comparison with those of the firms in the KOSPI. This study has performed three hypothesis tests to obtain the following results: By employing the 'panel data' analysis, it was found that, for the book-value based leverage, all of the six proposed IDVs were statistically significant as the financial determinants of leverage, across the two proxies measuring profitability (i.e., PFT and ROE), while all of the IDVs except VOLATILITY, also seemed to be the attributes to explain the market based dependent variable in the model with the PFT. Moreover, there may be statistically significant (structural) changes (or quasi-experiment) ) between the pre- and post-U.S. financial crisis in the year of 2008, when measured the leverage with the market-value basis with utilizing the Chow F-test. Finally, based upon the logistic regression results, the probability for a firm to be classified into the Prime section in the KOSDAQ market, may be higher, as its profit margin and asset turnover increase.

• The Journal of Asian Finance, Economics and Business
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• 제8권5호
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• pp.551-559
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• 2021

The maturity structure of corporate debt is one of the significant financing choices that a firm must make simultaneously while deciding how to finance its operational and investment decisions. Even though the capital structure is one of the scrutinized topics of interest in the corporate finance literature, scarce studies have investigated corporate debt maturity, even less so in the context of emerging markets. The choice of a suitable debt maturity structure is exceptionally relevant for firms. It can enable them to avoid mismatch by aligning assets in line with liabilities, addressing agency-related problems, sidestep the ill effects of cost of capital, and signaling the firms' earning quality and value. The study investigates the firm-specific and macroeconomic determinants significant for the debt maturity structure of Vietnamese corporate firms. A sample of 722 non-financial firms listed on the Ho Chi Minh and Hanoi Stock Exchange in Vietnam from 2007 to 2018 was taken to test the hypothesis. The study's methods fixed effects panel data analysis provides empirical evidence that firm size, firms' quality, liquidity, leverage, asset maturity, tax impact, and macro variables are significantly related to the debt maturity structure.

• The Journal of Asian Finance, Economics and Business
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• 제8권4호
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• pp.727-734
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• 2021

The study aims to examine and analyze the factors that affect the return on assets (ROA) by placing net interest margin (NIM) as a moderating variable in influencing ROA. This research was conducted on 27 banks listed on the Indonesia Stock Exchange (IDX) for the period 2015 to 2018 with a total sample data of 91. The data used is a combination of time series data and cross-section data. The sampling technique used was the purposive sampling method. The data analysis technique used was path analysis with multiple regression analysis technique. The results of the analysis showed that the capital adequacy ratio (CAR) and loan to deposit ratio (LDR) have a positive but insignificant effect on ROA. NIM as a moderating variable does not influence the impact of CAR on ROA. However, NIM as a moderating variable is able to influence the impact of LDR on ROA. From the results of this study, it is evident that the LDR will increase the ROA at banks that generate high NIM.

• 산업경영시스템학회지
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• 제46권1호
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• pp.91-104
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• 2023

The records system is believed to have started in Italy in the 14th century in line with trade developments in Europe. In 1491, Luca Pacioli, a mathematician, and an Italian Franciscan monk wrote the first book that described double-entry accounting processes. In many countries, including Korea, the government accounting standards used single-entry bookkeeping rather than double-entry bookkeeping that can be aggregated by account subject. The cash-based and single-entry bookkeeping used by the government in the past had limitations in providing clear information on financial status and establishing a performance-oriented financial management system. Accordingly, the National Accounting Act (promulgated in October 2007) stipulated the introduction of double-entry bookkeeping and accrual accounting systems in the government sector from January 1, 2009. Furthermore, the Korean government has also introduced International Financial Reporting Standards (IFRS), and the System of National Accounts (SNA). Since 2014, Korea owned five national accounts. In Korea, valuation began with the 1968 National Wealth Statistics Survey. The academic origins of the valuation of national wealth statistics which had been investigated by due diligence every 10 years since 1968 are based on the 'Engineering Valuation' of professor Marston in the Department of Industrial Engineering at Iowa State University in the 1930s. This field has spread to economics, etc. In economics, it became the basis of capital stock estimation for positive economics such as econometrics. The valuation by the National Wealth Statistics Survey contributed greatly to converting the book value of accounting data into vintage data. And in 2000 National Statistical Office collected actual disposal data for the 1-digit asset class and obtained the ASL(average service life) by Iowa curve. Then, with the data on fixed capital formation centered on the National B/S Team of the Bank of Korea, the national wealth statistics were prepared by the Permanent Inventory Method(PIM). The asset classification was also classified into 59 types, including 2 types of residential buildings, 4 types of non-residential buildings, 14 types of structures, 9 types of transportation equipment, 28 types of machinery, and 2 types of intangible fixed assets. Tables of useful lives of tangible fixed assets published by the Korea Appraisal Board in 1999 and 2013 were made by the Iowa curve method. In Korea, the Iowa curve method has been adopted as a method of ASL estimation. There are three types of the Iowa curve method. The retirement rate method of the three types is the best because it is based on the collection and compilation of the data of all properties in service during a period of recent years, both properties retired and that are still in service. We hope the retirement rate method instead of the individual unit method is used in the estimation of ASL. Recently Korean government's accounting system has been developed. When revenue expenditure and capital expenditure were mixed in the past single-entry bookkeeping we would like to suggest that BOK and National Statistical Office have accumulated knowledge of a rational difference between revenue expenditure and capital expenditure. In particular, it is important when it is estimated capital stock by PIM. Korea also needs an empirical study on economic depreciation like Hulten & Wykoff Catalog A of the US BEA.

• 지능정보연구
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• 제28권3호
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• pp.209-235
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• 2022

블록체인 기술은 2010년 이후 인공지능 분야의 발전과 더불어 4차 산업혁명을 선도할 최신의 기술로 각광받고 있고, 기술의 활용 분야에 대한 연구가 활성화되고 있다. 그러나, 자본시장 관점에서 블록체인 경제 생태계를 분류하기 위한 기준과 관련된 연구는 거의 없는 상황이다. 본 연구는 자본시장 관점에서 블록체인 기술을 활용하는 개발자, 사업자, 자본시장 참여자 등 전문가를 대상으로 인터뷰와 사례 연구 방법론으로 블록체인 기술의 응용 분야에 따른 블록체인 경제 생태계를 분류하였다. 자본시장의 주식 투자와 연계해 활용할 수 있는 방안으로 블록체인 경제 생태계 분류 방법을 활용하여 투자 종목 유니버스를 구성하였다. 나아가 본 연구는 퀀트 및 인공지능 전략 기반 정성적, 정량적 분석으로 지능형 주식 포트폴리오를 구축하고 성과를 분석하였다. 이를 통해 블록체인 경제 생태계의 지속적인 성장 전망에 따른 성공적인 투자전략을 제시하였다. 본 연구는 블록체인의 표준화를 기술적 관점이 아닌 자본시장의 관점에서 블록체인 경제 생태계로 분류하고 분석했을 뿐 아니라, 실제 글로벌 우량 상장 주식을 대상으로 포트폴리오를 구축하고 양호한 성과를 달성할 수 있는 전략을 도출한 연구로서 시사점을 갖는다. 또한, 본 연구가 제안하는 블록체인 경제 생태계 기반 지능형 주식 투자 포트폴리오 구축 접근은 블록체인의 기술적인 가치에 초점을 맞춘 연구에 비해서, 투자론과 경제학적인 관점에서 통찰력을 제시해 자본시장 발전에 기여할 수 있다는 실무적 시사점을 갖는다.

• 한국안전학회지
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• 제18권3호
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• pp.114-119
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• 2003

The building needs of the community are met by the interrelated construction activities of maintaining, modernizing and replacing the existing stock of building and by the erection of additional new building. Studying the service lift of buildings and how it is determined can serve as an important measuring stick in making decisions on construction policy. Therefore, the purpose of this study is to provide useful information for future construction through a comparative and analytical study of building structures on disappeared buildings. It was found that most building structure had a shorter service life than the standard set by The Korea Appraisal Board. This situation may have occurred due to the sharp rise in replacement of older buildings with new buildings for the purpose of monetary profit. To increase the economic life of buildings reduce losses of the nation's capital henceforth, examination on policy md steady study needs to be done.

• Communications for Statistical Applications and Methods
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• 제29권6호
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• pp.695-708
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• 2022

Recently, several studies have been conducted using state space model. In this study, a dynamic linear model with state space model form is applied to stock data. The monthly returns for 135 Korean stocks are fitted to a dynamic linear model, to obtain an estimate of the time-varying 𝛽-coefficient time-series. The model formula used for the return is a capital asset pricing model formula explained in economics. In particular, the transition equation of the state space model form is appropriately modified to satisfy the assumptions of the error term. k-shape clustering is performed to classify the 135 estimated 𝛽 time-series into several groups. As a result of the clustering, four clusters are obtained, each consisting of approximately 30 stocks. It is found that the distribution is different for each group, so that it is well grouped to have its own characteristics. In addition, a common pattern is observed for each group, which could be interpreted appropriately.

• 벤처창업연구
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• 제17권1호
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• pp.229-249
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• 2022

본 연구는 다양한 머신러닝 기법을 통해 코스닥(KOSDAQ) 시장 내 관리종목 지정을 예측할 수 있는 모델에 대해 연구하였다. 증권시장 내 기업이 관리종목으로 지정이 되면 시장에서는 이를 부정적인 정보로 인식하여 해당 기업과 투자자에게 손실을 가져오게 된다. 본 연구를 통해 기업의 재무적 데이터를 바탕으로 조기에 관리종목 지정을 예측하고, 투자자들의 포트폴리오 리스크 관리에 도움을 주기 위한 머신러닝 접근이 타당한지 살펴본다. 본 연구를 위해 활용한 독립변수는 수익성, 안정성, 활동성, 성장성을 나타내는 21개의 재무비율을 활용하였으며, K-IFRS가 적용된 2011년부터 2020년까지 관리종목과 비관리종목의 기업의 재무 데이터를 표본으로 추출하였다. 로지스틱 회귀분석, 의사결정나무, 서포트 벡터 머신, 랜덤 포레스트, LightGBM을 활용하여 관리종목 지정 예측 연구를 수행하였다. 연구결과는 분류 정확도가 82.73%인 LightGBM이 가장 우수한 예측 모형이었으며 분류 정확도가 가장 낮은 예측 모형은 정확도가 71.94%인 의사결정나무였다. 의사결정나무 기반 학습 모형의 변수 중요도의 상위 3개 변수를 확인한 결과 각 모형에서 공통적으로 나온 재무변수는 ROE(당기순이익), 자본금회전율(Capital stock turnover ratio)로 해당 재무변수가 관리종목 지정에 있어 상대적으로 중요한 변수임을 확인하였다. 대체적으로 앙상블을 이용한 학습 모형이 단일 학습 모형보다 예측 성능이 높은 것을 확인하였다. 기존 선행연구가 K-IFRS에 대한 고려를 하지 않았고, 다소 제한된 머신러닝에 의존하였다. 따라서 본 연구의 필요성과 함께 현실적 요구를 충족시키는 결과를 제시하였음을 알 수 있으며, 시장참여자들에게 있어 관리종목 지정에 대한 사전 예측을 확인할 수 있도록 기여했다고 볼 수 있다.