• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.9
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• pp.446-453
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• 2018

This study analyzed the effect of related party transactions on the corporate value of Korean firms using panel data regression analysis. We tested the efficient transaction hypothesis and conflict of interests hypothesis which concern related party transactions. Five types of related party transactions were considered, including long term supply contracts, assets and business transfers, affiliate loans, equity investment, and credit offerings. If related party transactions were conducted for the purpose of enhancing corporate efficiency, results would have a positive effect on firm value. If related party transactions were conducted for the purpose of private profits of the controlling shareholders, the results would show a negative effect on firm value. Results were as follows. Firstly, it is confirmed that affiliate loans, equity investment, and credit offerings had negative effects on firm value. This implies that these types of related party transactions used by controlling shareholders for the purpose of their private profit, which supports the conflict of interests hypothesis. Secondly, it was found that long term supply contracts and assets and business transfers had no effect on firm value.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.697-706
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• 2020

This study investigates the relationship between related loan, ownership concentration and real estate firm performance. The data was collected from 35 real estate firms listed on Indonesia Stock Exchange from 2007 to 2012. Related loans are viewed from the angle of related lending and loan. Related lending and loan is measured by the related lending on total lending ratio and related loan on total loan ratio. Firm performance is measured by the asset turnover ratio and return on assets ratio. Ownership concentration is measured by the right cash flow. The data analysis was done with regression analysis and panel data. The results of the study found that related loans had a positive effect on sales but had no effect on profits. This supports the efficient transaction hypothesis. On the other hand, related lending has a positive effect on profits that supports opportunistic transactions. Ownership concentration moderates the effect of related loan on company's performance. The related lending are beneficial for mutually supporting activities in the real estate sector business group in Indonesia, but related loans have the potential to be used in tunneling activities. The paper contributes to the related party transaction in benefits-risks of related lending and related loan in uncertainty context.

• Korea Trade Review
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• v.43 no.2
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• pp.159-180
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• 2018

The purpose of this study is to contribute to the efficient selection of SMEs' trade settlement system through the empirical analysis of determinants of the payment method of SMEs in Korea. In the previous study, external factors, internal factors, settlement characteristics, transaction goods, transaction amount factors and risk management factors were used. Questionnaires were excluded from analysis, and the number of validated samples collected was 155. To conduct the study, all empirical analyses were verified at the significance level p <.005. Statistical analysis was performed using the SPSSWIN 18.0 program. Analysis results found the payment method used in the company was based on the year of establishment, export items, transaction area, type of transaction, and size of company. Empirical analysis showed that factors influencing the choice of the letter of credit are external factors, internal factors, the risk management factors, and the transaction amounts, etc. Results of this study are as follows: First, the effects of external factors, internal factors, settlement characteristics, and transaction amounts were significant. Hypothesis testing of collections trading methods has not been adopted in all areas presented. In order to utilize the research results, we conducted the study and comparison of the payment method of the income.

• 사회경제평론
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• no.38
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• pp.129-161
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• 2012

This paper suggests that, in contrast to main argument of Efficient Market Hypothesis, hedge funds's financial speculation activity in the commodity markets are tending to generate a malfunction of making future price diverge from fundamental price. For this reason, we insist that stricter regulation on commodity derivative markets, including position limitation, is needed. Using some statistic analysis tools, we show that derivative transaction volume is getting so larger that financial speculation by hedge funds dominates price movement in commodity market and eventually slackens the speed of price's return to the fundamental price.

• The Journal of the Korea Contents Association
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• v.19 no.7
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• pp.36-46
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• 2019

If stock market is efficient, any well-devised trading rule can't consistently outperform the average stock market returns. This study aims to verify whether the strategy based on bid-ask volume information can beat the stock market. I suggested a day trading strategy using order imbalance indicator and empirically analyzed its profitability with the KOSPI 200 index futures data from 2001 to 2018. Entry rules are as follows: If BSI is over 50%, enter buy order, otherwise enter sell order, assuming that stock price rises after BSI is over 50% and stock price falls after BSI is less than 50%. The empirical results showed that the suggested trading strategy generated very high trading profit, that is, its annual return runs to minimum 71% per annum even after the transaction costs. The profit was generated consistently during 18 years. This study also improved the suggested trading strategy applying the genetic algorithm, which may help the market practitioners who trade the KOSPI 200 index futures.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.65-82
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• 2021

Stock market investors are generally split into foreign investors, institutional investors, and individual investors. Compared to individual investor groups, professional investor groups such as foreign investors have an advantage in information and financial power and, as a result, foreign investors are known to show good investment performance among market participants. The purpose of this study is to propose an investment strategy that combines investor-specific transaction information and machine learning, and to analyze the portfolio investment performance of the proposed model using actual stock price and investor-specific transaction data. The Korea Exchange offers daily information on the volume of purchase and sale of each investor to securities firms. We developed a data collection program in C# programming language using an API provided by Daishin Securities Cybosplus, and collected 151 out of 200 KOSPI stocks with daily opening price, closing price and investor-specific net purchase data from January 2, 2007 to July 31, 2017. The self-organizing map model is an artificial neural network that performs clustering by unsupervised learning and has been introduced by Teuvo Kohonen since 1984. We implement competition among intra-surface artificial neurons, and all connections are non-recursive artificial neural networks that go from bottom to top. It can also be expanded to multiple layers, although many fault layers are commonly used. Linear functions are used by active functions of artificial nerve cells, and learning rules use Instar rules as well as general competitive learning. The core of the backpropagation model is the model that performs classification by supervised learning as an artificial neural network. We grouped and transformed investor-specific transaction volume data to learn backpropagation models through the self-organizing map model of artificial neural networks. As a result of the estimation of verification data through training, the portfolios were rebalanced monthly. For performance analysis, a passive portfolio was designated and the KOSPI 200 and KOSPI index returns for proxies on market returns were also obtained. Performance analysis was conducted using the equally-weighted portfolio return, compound interest rate, annual return, Maximum Draw Down, standard deviation, and Sharpe Ratio. Buy and hold returns of the top 10 market capitalization stocks are designated as a benchmark. Buy and hold strategy is the best strategy under the efficient market hypothesis. The prediction rate of learning data using backpropagation model was significantly high at 96.61%, while the prediction rate of verification data was also relatively high in the results of the 57.1% verification data. The performance evaluation of self-organizing map grouping can be determined as a result of a backpropagation model. This is because if the grouping results of the self-organizing map model had been poor, the learning results of the backpropagation model would have been poor. In this way, the performance assessment of machine learning is judged to be better learned than previous studies. Our portfolio doubled the return on the benchmark and performed better than the market returns on the KOSPI and KOSPI 200 indexes. In contrast to the benchmark, the MDD and standard deviation for portfolio risk indicators also showed better results. The Sharpe Ratio performed higher than benchmarks and stock market indexes. Through this, we presented the direction of portfolio composition program using machine learning and investor-specific transaction information and showed that it can be used to develop programs for real stock investment. The return is the result of monthly portfolio composition and asset rebalancing to the same proportion. Better outcomes are predicted when forming a monthly portfolio if the system is enforced by rebalancing the suggested stocks continuously without selling and re-buying it. Therefore, real transactions appear to be relevant.