• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.1
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• pp.417-425
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• 2020

In this paper, value relevance of the level of dependence on separate accounts in non-life-insurance companies is studied. As noted by Shim et al. (2015), the separate accounts of insurance companies consist of contracts with different attributes from the general accounts, so it is likely that firm value will vary depending on the insurer's dependence on the separate accounts. Thus, in this paper, an empirical analysis has been conducted using quarterly financial data and stock price data from domestic listed non-life-insurance companies from 2011 to 2018. The analysis shows that variables representing the level of dependence on separate accounts have a significant negative relevance to firm value. These results may suggest that changes in the proportion of a non-life-insurer's separate accounts may result in a change to its firm value under the same net assets and net income scales in aggregate accounts. This study provides management implications for the operation of separate accounts from the perspective of maximizing firm value. In addition, this study suggests that disclosure system improvement would be necessary to more directly report the operational performance of the separate accounts.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.9
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• pp.3827-3833
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• 2011

This paper performed empirical tests of the validity of the pecking order theory which explains financing behavior of firms under information asymmetry. The results of regression analyses using the data of listed manufacturing companies in the Korean Stock Market from 1981 to 2010 have shown strong evidences supporting the pecking order theory. Especially regression coefficients of change of debt on funds deficit and control variables were found to be almost (+1) with statistically significance, which is interpreted as being consistent with the pecking order theory. Same results were found when I performed regression analyses by dividing the sample period into pre-currency crisis period, currency crisis period and post-currency crisis period and using 2 regression models. Change of tangible asset were found to function as collateral rather than reducing information asymmetry and as the firm size decreased, use of debt increased and as profitability increased use of debt decreased, which are consistent with the pecking order theory.

• Proceedings of the Korean Society of Computer Information Conference
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• 2011.06a
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• pp.25-27
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• 2011

본 논문에서는 경제의 글로벌화를 반영하는 세계적인 현상인 국제회계기준 채택에 대하여 회계 관련사항을 내재하고 있는 기업의 전사적 관리 소프트웨어인 국산 ERP 시스템에 대하여 세금처리의 투명성을 향상시킬 수 있는 방법론적 모델을 해외 ERP의 장점을 통하여 연구한다. 국제회계기준은 기업요소에서 가장 중요한 자본의 국제적인 이동이 기본적 사항으로 기업의 소재지에 대한 국가표시와 관계없이 재무제표의 정보가 투명하고 비교 가능하도록 국제적으로 단일한 회계기준이 사용을 요구한다. 이러한 환경 변화에 따라 국제회계기준(International Financial Reporting Standards: IFRS)의 필요성과 중요성이 확대되어지고 있으며 국제회계기준위원회(International Accounting Standards Board: IASB)의 영향력까지 강화되어지고 있다. 본 논문은 국제회계기준에 맞춘 회계처리 부분의 투명성 향상 모델을 연구하기 위하여 국내에서 많이 사용되고 있는 국산 ERP 소프트웨어 프로그램에 대하여 국제회계기준과 상반되는 현상을 유발하는 기준이 되는 부분을 상호 보완할 수 있는 해외 ERP 소프트웨어 프로그램을 기반으로 기업 시스템을 분석, 구현한다. 국산 ERP 시스템의 국제회계기준 처리와 관련된 단점은 송장의 수정, 삭제가 용이하며 수정, 삭제 후 이력이 남지 않는 부분이다. 이 부분은 국제회계기준에서는 신뢰성에 대한 중대한 문제를 유발하므로 외산 ERP패키지의 장점인 수정과 삭제 단계가 계층적이며 수정, 삭제를 하더라도 필수적으로 이력전체가 저장되는 시스템을 연구하며 특히 세금처리 부분이 상이한 점을 보완하기 위한 모듈을 추가한다. 수정, 삭제에 대한 이력관리는 담당자의 전문적 능력평가와 동시에 실수와 오류부분에 대한 통계를 통하여 관리의 향상성을 추구하여 투명성이 향상된 모델 구축에 활용할 수 있다.

• The Journal of the Korea Contents Association
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• v.19 no.4
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• pp.1-11
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• 2019

Recently, the importance of not only financial factors from financial statements but also non-financial factors such as consumers' evaluation and loyalty to game content is more emphasized when assessing the value of game companies. In this study, we suggest the brand reputation index as an appropriate measure of a game company's valuation and examine the effect of the brand reputation on game companies' stock price using the observations of Korean major 30 game companies. From the empirical results, we find that there is a significantly positive association between the brand reputation index and the game companies' stock price. This explains that the brand reputation of game companies can directly affect their firm value. The findings are expected to contribute to capital markets and academia as they have presented empirical evidence of the importance of brand reputation as a non-financial measure for the valuation of game companies.

• Journal of the Korea Institute of Building Construction
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• v.19 no.2
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• pp.149-156
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• 2019

This study empirically verified whether or not employee education and information technology have impact on the enhancement of financial performance to Korean construction companies. As a result, the increasing of education & IT expenses turned out to be affected the improvement of sales performance. The influences of these increased expenses are effective at the only year just after expenditure. The positive effect of education & IT expenses increasing have an effect on operating profitability as well such as increasing of operating income. Additionally, the effect of education expenses are more positive in general construction & building construction than other subdivision of construction industry. The positive effect of IT expenses increasing in specialty construction industry overwhelm the other subdivisions. The results suggest that increased investment to the education & IT systems of Korean construction companies can lead improvement of sales and operating income.

• Journal of Digital Convergence
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• v.19 no.3
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• pp.161-166
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• 2021

The macroeconomic concept represents the movement of a country's economy, and it affects the overall economic activities of business, government, and households. In the macroeconomy, by looking at changes in national income, inflation, unemployment, currency, interest rates, and raw materials, it is possible to understand the effects of economic actors' actions and interactions on the prices of products and services. The US Federal Reserve System (FED) is leading the world economy by offering various stimulus measures to overcome the corona economic recession. Although the stock price continued to decline on March 20, 2020 due to the current economic recession caused by the corona, the US S&P 500 index began rebounding after March 23 and to 3,694.62 as of December 15 due to quantitative easing, a powerful stimulus for the FED. Therefore, the FED's economic stimulus measures based on macroeconomic indicators are more influencing, rather than judging the stock price forecast from the corporate financial statements. Therefore, this study was conducted to reduce losses in stock investment and establish sound investment by analyzing the FED's economic stimulus measures and its effect on stock prices.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.807-814
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• 2021

As economic activities decrease, and the stock market decline due to COVID-19, many people are jumping into stock investment as an alternative source of income. As people's interest increases, many stock price analysis studies are underway to earn more profits. Due to the variance observed in the stock markets, it is necessary to analyze each stock independently and consistently. To solve this problem, we designed and implemented models and services that analyze stock prices using a reinforcement learning technique called Asynchronous Advantage Actor-Critic(A3C). Stock market data reflected external factors such as government bonds and KOSPI (Korea Composite Stock Price Index) as well as stock prices. Our proposed work provides a web service with a visual representation of predictions of stocks and stock information through which directions are given to investors to make safe investments without analyzing domestic and foreign stock market trends.

• Journal of the Korea Convergence Society
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• v.12 no.7
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• pp.31-36
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• 2021

As the global economy stagnated due to the Corona 19 virus from Wuhan, China, most countries, including the US Federal Reserve System, introduced policies to boost the economy by increasing the amount of money. Most of the stock investors tend to invest only by listening to the recommendations of famous YouTubers or acquaintances without analyzing the financial statements of the company, so there is a high possibility of the loss of stock investments. Therefore, in this research, I have used artificial intelligence deep learning techniques developed under the existing automatic trading conditions to analyze and predict macro-indicators that affect stock prices, giving weights on individual stock price predictions through correlations that affect stock prices. In addition, since stock prices react sensitively to real-time stock market news, a more accurate stock price prediction is made by reflecting the weight to the stock price predicted by artificial intelligence through stock market news text mining, providing stock investors with the basis for deciding to make a proper stock investment.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.67-75
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• 2023

Stock price prediction is an important topic extensively discussed in the financial market, but it is considered a challenging subject due to numerous factors that can influence it. In this research, performance was compared and analyzed by applying time series prediction models (LSTM, GRU) and non-time series prediction models (RF, SVR, KNN, LGBM) that do not take into account the temporal dependence of data into stock price prediction. In addition, various data such as stock price data, technical indicators, financial statements indicators, buy sell indicators, short selling, and foreign indicators were combined to find optimal predictors and analyze major factors affecting stock price prediction by industry. Through the hyperparameter optimization process, the process of improving the prediction performance for each algorithm was also conducted to analyze the factors affecting the performance. As a result of feature selection and hyperparameter optimization, it was found that the forecast accuracy of the time series prediction algorithm GRU and LSTM+GRU was the highest.

• Journal of Service Research and Studies
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• v.13 no.1
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• pp.1-26
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• 2023

We analyze the effects and related factors of Smart Factory adoption. 110 and 325 samples were collected by median-size-industry matching method, respectively, of adopting and non-adopting companies. We use financial statement data (ROA, etc.) from the year before adoption to the fourth year after adoption. Abnormal operating performance and annual abnormal changes are obtained according to event study method, and analyzed by Wilcoxon signed-rank test and t-test. ROA and sales growth rate demonstrate short-term effects after adoption, but not long-term effects. As a result of regression analysis to examine if the three factors of labor intensity, R&D intensity, and prior financial performance have moderating effect, the moderating effect of R&D intensity and prior financial performance is confirmed. In addition, we perform regression analysis to confirm performance effects of early and late adoptions and whether prior financial performance and organization size have moderating effect. It is confirmed that the later the time of adoption, the greater the effect of adoption in the long term and the moderating effect of prior financial performance and organization size is confirmed.