• Title/Summary/Keyword: 자본이익률

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Financial Statement Analysis of SMEs in a Non-Face-to-Face Work Environment (비대면 업무환경에서 중소제조기업의 기업경영분석)

  • Lim HeonWook
    • The Journal of the Convergence on Culture Technology
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    • v.9 no.2
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    • pp.119-126
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    • 2023
  • Due to the COVID-19 phenomenon, more than one-third of SMEs in Korea have been working from home. Therefore, we tried to find out the management status of SMEs and find policy support. The survey data was based on the Bank of Korea's corporate management analysis 2021 data. As a result of the study, the debt of SMEs increased from 362 trillion won(2019) to 409 trillion won(2022), while their capital decreased from 489 trillion won(2019) to 336 trillion won(2022). Net profit and loss increased to 14.9 trillion won(2019) and 23.3 trillion won(2021). As a result of the company's financial soundness analysis, First, for stability, the current ratio was high compared to the total industry and the dependence on borrowings was high. Second, profitability improved from 3.20%(2019) to 4.28%(2021), but it was lower than 5.01%(2021) for all industries. Third, the growth rate showed an increase of 12.43%, which is 1.57 times faster than the total asset growth rate of 7.94%(2021) for all industries. As for the growth rate of sales, all industries(2021) showed (-)growth, while SMEs among manufacturing industries showed a growth rate of 14.78%. Fourth, as for activity, the total asset turnover ratio was higher at 0.96% compared to 0.73 for all industries. In conclusion, stability and profitability were low and growth potential was high compared to all industries. In the future, policies that focus on industries with high growth potential are needed.

A Study on the Impact of Venture Capital Investment Experience and Job Fit on Fund Formation and Investment Rate of Return (벤처캐피탈의 투자경험과 직무적합도가 펀드결성과 투자수익률에 미치는 영향력에 관한 연구)

  • Kim Dae-Hee;Ha Kyu-So
    • Asia-Pacific Journal of Business Venturing and Entrepreneurship
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    • v.18 no.4
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    • pp.37-50
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    • 2023
  • Venture capital invests the necessary capital and supports management and technology in promising small and medium-sized venture companies in the early stages of start-up with promising technology and excellent manpower. It plays a role as a key player in the venture ecosystem that realizes profits by collecting the investment through various means after growth. Venture capital's job is to recruit various investors(LPs) to invest in small and medium-sized venture companies with growth potential through the formation of venture investment funds, and to collect investment as companies grow, distribute and reinvest. The main tasks of venture capitalists, which play the most important role in venture investment, are finding promising companies, corporate analysis and evaluation, investment screening, follow-up management, and investment recovery. Venture capital's success indicators are fund formation and return on investment, and venture capitalists are rewarded with annual salary, performance-based incentive, and promotion with work performance such as investment, exit, and fund formation. Compared to the recent rapidly growing venture investment market, investment manpower is insufficient, and venture capital is making great efforts to foster manpower and establish infrastructure and systems for long-term service, but research has been conducted mainly from a quantitative perspective. Accordingly, this study aims to empirically analyzed the impact of investment experience, delegation of authority, job fit, and peer relationships on fund formation and return on investment according to the characteristics of the venture capital industry. The results of these empirical studies suggested that future venture capital needs a job environment and manpower operation strategy so that venture capitalists with high job fit and investment experience can work for a long time.

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Development of a Detection Model for the Companies Designated as Administrative Issue in KOSDAQ Market (KOSDAQ 시장의 관리종목 지정 탐지 모형 개발)

  • Shin, Dong-In;Kwahk, Kee-Young
    • Journal of Intelligence and Information Systems
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    • v.24 no.3
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    • pp.157-176
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    • 2018
  • The purpose of this research is to develop a detection model for companies designated as administrative issue in KOSDAQ market using financial data. Administration issue designates the companies with high potential for delisting, which gives them time to overcome the reasons for the delisting under certain restrictions of the Korean stock market. It acts as an alarm to inform investors and market participants of which companies are likely to be delisted and warns them to make safe investments. Despite this importance, there are relatively few studies on administration issues prediction model in comparison with the lots of studies on bankruptcy prediction model. Therefore, this study develops and verifies the detection model of the companies designated as administrative issue using financial data of KOSDAQ companies. In this study, logistic regression and decision tree are proposed as the data mining models for detecting administrative issues. According to the results of the analysis, the logistic regression model predicted the companies designated as administrative issue using three variables - ROE(Earnings before tax), Cash flows/Shareholder's equity, and Asset turnover ratio, and its overall accuracy was 86% for the validation dataset. The decision tree (Classification and Regression Trees, CART) model applied the classification rules using Cash flows/Total assets and ROA(Net income), and the overall accuracy reached 87%. Implications of the financial indictors selected in our logistic regression and decision tree models are as follows. First, ROE(Earnings before tax) in the logistic detection model shows the profit and loss of the business segment that will continue without including the revenue and expenses of the discontinued business. Therefore, the weakening of the variable means that the competitiveness of the core business is weakened. If a large part of the profits is generated from one-off profit, it is very likely that the deterioration of business management is further intensified. As the ROE of a KOSDAQ company decreases significantly, it is highly likely that the company can be delisted. Second, cash flows to shareholder's equity represents that the firm's ability to generate cash flow under the condition that the financial condition of the subsidiary company is excluded. In other words, the weakening of the management capacity of the parent company, excluding the subsidiary's competence, can be a main reason for the increase of the possibility of administrative issue designation. Third, low asset turnover ratio means that current assets and non-current assets are ineffectively used by corporation, or that asset investment by corporation is excessive. If the asset turnover ratio of a KOSDAQ-listed company decreases, it is necessary to examine in detail corporate activities from various perspectives such as weakening sales or increasing or decreasing inventories of company. Cash flow / total assets, a variable selected by the decision tree detection model, is a key indicator of the company's cash condition and its ability to generate cash from operating activities. Cash flow indicates whether a firm can perform its main activities(maintaining its operating ability, repaying debts, paying dividends and making new investments) without relying on external financial resources. Therefore, if the index of the variable is negative(-), it indicates the possibility that a company has serious problems in business activities. If the cash flow from operating activities of a specific company is smaller than the net profit, it means that the net profit has not been cashed, indicating that there is a serious problem in managing the trade receivables and inventory assets of the company. Therefore, it can be understood that as the cash flows / total assets decrease, the probability of administrative issue designation and the probability of delisting are increased. In summary, the logistic regression-based detection model in this study was found to be affected by the company's financial activities including ROE(Earnings before tax). However, decision tree-based detection model predicts the designation based on the cash flows of the company.