• Journal of Digital Convergence
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• v.17 no.3
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• pp.79-92
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• 2019

This study analyzes the effects of freight transportation income, capital, asset, non-operating expenses, and debt ratio on the debts of inner port freight transportation businesses through the GLS of panel regression analysis and the estimation of fixed effects model. The factors and hypotheses were established through a theoretical background review, and the financial statement and profit and loss data of inner port freight transportation businesses for 10 years from 2006 to 2015 were analyzed. The results showed that assets had positive effects on debts, and negative effects on capital, non-operating expenses, and debt ratio, but no effect on freight transportation income. This result empirically demonstrates the tendency of inner port freight transportation businesses to secure assets by increasing debts, creation of debt reduction leverage effect using non-operating expenses such as interest expenses through bank borrowing, and the adoption of management characteristics and financial operation method to lower the debt ratio by reducing capital more than debts. In future studies, it is necessary to analyze coastal port freight transportation business by industry (oil tankers, cargo ships, and barge ships), and regions such as East, West and South sea.

• Journal of the Korea Convergence Society
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• v.10 no.4
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• pp.1-6
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• 2019

Neural networks have been used to predict the direction of stock index movement from past data. The conventional research that predicts the upward or downward movement of the stock index predicts a rise or fall even with small changes in the index. It is highly likely that losses will occur when trading ETFs by use of the prediction. In this paper, a neural network model that predicts the movement direction of the daily KOrea composite Stock Price Index (KOSPI) to reduce ETF trading losses and earn more than a certain amount per trading is presented. The proposed model has outputs that represent rising (change rate in index ${\geq}{\alpha}$), falling (change rate ${\leq}-{\alpha}$) and neutral ($-{\alpha}$ change rate < ${\alpha}$). If the forecast is rising, buy the Leveraged Exchange Traded Fund (ETF); if it is falling, buy the inverse ETF. The hit ratio (HR) of PNN1 implemented in this paper is 0.720 and 0.616 in the learning and the evaluation respectively. ETF trading yields a yield of 8.386 to 16.324 %. The proposed models show the better ETF trading success rate and yield than the neural network models predicting KOSPI.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.1
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• pp.7-18
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• 2021

Recently, various connected industrial parks (CIPs) architectures using new technologies such as cloud computing, CPS, big data, fifth-generation mobile communication 5G, IIoT, VR-AR, and ventilation transportation AI algorithms have been proposed in Korea. Korea's small and medium-sized enterprises do not have the upper hand in technological competitiveness than overseas advanced countries such as the United States, Europe and Japan. For this reason, Korea's small and medium-sized enterprises have to invest a lot of money in technology research and development. As a latecomer, Korean SMEs need to improve their profitability in order to find sustainable growth potential. Financially, it is most efficient for small and medium-sized Korean companies to cut costs to increase their profitability. This paper made profitability improvement by reducing costs for small and medium-sized enterprises located in CIPs in Korea a major task. VJP (Vehicle Action Program) was noted as a way to reduce costs for small and medium-sized enterprises located in CIPs in Korea. The method of achieving minimum logistics costs for small businesses through the Korean CIPs payment system was analyzed. The details of the new Korean CIPs payment system were largely divided into four types: "Business", "Data", "Technique", and "Finance". Cost Benefit Analysis (CBA) was used as a performance analysis method for CIPs payment systems.

• Journal of the Korea Convergence Society
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• v.12 no.6
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• pp.9-16
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• 2021

In the past, there have been various studies on predicting the stock market by machine learning techniques using stock price data and financial big data. As stock index ETFs that can be traded through HTS and MTS are created, research on predicting stock indices has recently attracted attention. In this paper, machine learning models for KOSPI's up and down predictions are implemented separately. These models are optimized through a grid search of their control parameters. In addition, a hybrid machine learning model that combines individual models is proposed to improve the precision and increase the ETF trading return. The performance of the predictiion models is evaluated by the accuracy and the precision that determines the ETF trading return. The accuracy and precision of the hybrid up prediction model are 72.1 % and 63.8 %, and those of the down prediction model are 79.8% and 64.3%. The precision of the hybrid down prediction model is improved by at least 14.3 % and at most 20.5 %. The hybrid up and down prediction models show an ETF trading return of 10.49%, and 25.91%, respectively. Trading inverse×2 and leverage ETF can increase the return by 1.5 to 2 times. Further research on a down prediction machine learning model is expected to increase the rate of return.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.25-30
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• 2022

This study is a study on the filter bubble reinforcement phenomenon of SNS recommendation algorithm such as YouTube, which is a characteristic of the Russian-Ukraine war (2022), and the victory or defeat factors of the hybrid war. This war is identified as a hybrid war, and the use of New Media based on the SNS recommendation algorithm is emerging as a factor that determines the outcome of the war beyond political leverage. For this reason, the filter bubble phenomenon goes beyond the dictionary meaning of confirmation bias that limits information exposed to viewers. A YouTube video of Ukrainian President Zelensky encouraging protests in Kyiv garnered 7.02 million views, but Putin's speech only 800,000, which is a evidence that his speech was not exposed to the recommendation algorithm. The war of these SNS recommendation algorithms tends to develop into an algorithm war between the US (YouTube, Twitter, Facebook) and China (TikTok) big tech companies. Influenced by US companies, Ukraine is now able to receive international support, and in Russia, under the influence of Chinese companies, Putin's approval rating is over 80%, resulting in conflicting results. Since this algorithmic empowerment is based on the confirmation bias of public opinion by 'filter bubble', the justification that a new guideline setting for this distortion phenomenon should be presented shortly is drawing attention through this Russia-Ukraine war.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.5
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• pp.105-115
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• 2022

This study aims to investigate the Effect of Money Rush on Entrepreneurial Opportunity Recognition for college students in Busan and Gyeongnam area. We also examine whether Entrepreneurship has a mediating effect between Money Rush and Entrepreneurial Opportunity Recognition. Since the outbreak of COVID-19, digital transformation of the industry have greatly changed the world of work, and job insecurity is becoming more prevalent. As income inequality expands due to the disparity in asset income, the Money Rush phenomenon, which prefers to increase asset income through investment rather than earned income, is becoming common. Money Rush secures an income pipeline and is divided into side hustles and investments that actively utilize Leverage to maximize profits. The findings of this study confirm that Money Rush has a positive effect on Entrepreneurial Opportunity Recognition and a partially positive effect on Entrepreneurship. Entrepreneurship has a partial mediating effect between Money Rush and Entrepreneurial Opportunity Recognition. The study analysis is expected to contribute to strengthening college students' competencies in Entrepreneurial Opportunity Recognition and presenting the policy and practical directions necessary to promote Start-up.

• Journal of Digital Convergence
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• v.12 no.6
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• pp.115-123
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• 2014

The first, this study analyzed empirically the effects of net profit on sales, total asset turnover and debt ratio on return on equity, the second, verified debt' s mediating effect on return on investment and return on equity and finally, tested the effect of adjusted debt ratio on return on equity in the small medium sized enterprises. Generally speaking, using debt has a positive effect on return on equity. Meanwhile, using debt accelerate return on equity through leverage effect in the quadric function curve model. Eventually, using debt has a positive and negative effects on return on equity. Accordingly, because of the debt' janus-faced reality, using debt is restricted within the level that operating cash flow(or return on asset) excess interest(or rate of interest).

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.5
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• pp.659-666
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• 2023

Characterized by boom-and-bust cycles, low entry barriers, and an almost perfectly competitive structure, the construction industry presents a unique challenge for the survival and growth of its constituent companies. A crucial aspect of this challenge is the ongoing monitoring of their financial health and business performance. To understand the typical financial and operational status of construction companies, this study analyzes the financial statements of 6,252 such companies, all of which have undergone at least one external audit between 2000 and 2019. These statements were used to develop combined financial profiles and derive industry averages. The findings indicate that the construction industry experiences limited growth in sales and profitability. High leverage ratios can jeopardize financial stability, pushing companies to seek production efficiency, such as enhancing gross asset turnover. This tendency has been particularly noticeable in the aftermath of the global financial crisis in 2008.

• Korea Trade Review
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• v.46 no.2
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• pp.19-38
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• 2021

This study aims to analyze the foreign asset leverage effect on Korean oil & gas companies' foreign profits and to maintain the appropriate foreign asset volume for reducing exchange risk. For a long time, large Korean companies, including oil companies, overheld foreign currency liabilities. For this reason, most large companies have been burdened to hedge exchange risk and this excess limit holding deteriorated total profit and reduced foreign currency asset management efficiency. Our paper proceeds in presenting a three-stage analysis considering diversified exchange risk factors through estimation on transformation of foreign transactions a/c including annual trends of foreign asset and industry specifics. We also supplement incomplete the estimation method through a practical hedging case investigation. Our research parts are differentiated on the analyzing four periods considering period-specifics The FER value of the oil firms ranged from -0.3 to +2.3 over the entire period. The results of the FER Value are volatile and irregular; those results do not represent the industry standard comparative index. The Korean oil firms are over the credit limit without accurate prediction and finance high interest rate funds from foreign-owned banks on the basis on a biased relationship. Since the IMF crisis, liabilities of global firms have decreased. Above all, oil firms need to finance a minimum limit without opportunity losses on the demand forecast and prepare for uncertainty in the market. To reduce exchange risk from the over-the-limit position, we must consider factors that affect the corporate exchange risk on the entire business process, including the contract phase.

• The Korean Journal of Financial Management
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• v.23 no.1
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• pp.101-133
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• 2006

We examine whether new derivatives on KOSPI 200 affect volatility asymmetry of KOSPI 200 portfolio, relative to the carefully matched non-KOSPI 200 portfolio. To test the effect or new derivatives trading, we use GJR-GARCH model and newly developed Volatility Ratio(down-market volatility to up-market volatility ratio). Our results show that KOSPI 200 portfolio experiences lower volatility asymmetry than non-KOSPI 200 portfolio after the trading of new derivatives on KOSPI 200, especially after the introduction of stock index options(KOSPI 200 options). For non-KOSPI portfolio, no significant reduction in volatility asymmetry occurred when trading of stock index options began. Also, we find that in the period of after January 1999, the period of after do-regulations and Financial Crisis in the Korean capital market, volatility asymmetry of stock markets was significantly decreased. This means that level of volatility asymmetry is closely related to the level of market regulations. Further, the results of the paper show that leverage effect and changes in foreign exchange ratio can be good candidates for explaining the stylized volatility asymmetry in the Korean stock market.