• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.19-26
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• 2023

The impact of environmental, social and governance (ESG) performance on investors' decision-making is growing. Investors' focus on the financial performance of firms in the past is expanding to the non-financial performance of the interests of stakeholders surrounding firms. Against this backdrop, this study conducted a panel regression analysis on firms evaluated by Korea Corporate Governance Service to analyze the impact of ESG performance, a firm's non-financial performance, on firm risk. According to the analysis, ESG performance has a negative (-) effect on all three firm risks (systematic risk, unsystematic risk, and total risk), indicating that the stakeholder theory and risk management theory are supported. The implications of this study are: First, ESG reduces not only unsystematic risk but also broad and indiscriminate systematic risk; Second, investors can reduce the risk of their investment portfolio by executing ESG investments; Third, companies can achieve stable financial performance even in adverse circumstances by utilizing the insurance function of ESG management; Lastly, the government can enhance the stability of the financial market while improving the financial soundness of firms through reasonable ESG-related regulations.

• The Korean Journal of Financial Management
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• v.24 no.3
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• pp.153-186
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• 2007

It is well known that the distributional properties of financial asset returns exhibit fatter-tails and skewer-mean than the assumption of normal distribution. The correct assumption of return distribution might improve the estimated performance of the Value-at-Risk(VaR) models in financial markets. In this paper, we estimate and compare the VaR performance using the RiskMetrics, GARCH and FIGARCH models based on the normal and skewed-Student-t distributions in two daily returns of the Korean Composite Stock Index(KOSPI) and Korean Won-US Dollar(KRW-USD) exchange rate. We also perform the expected shortfall to assess the size of expected loss in terms of the estimation of the empirical failure rate. From the results of empirical VaR analysis, it is found that the presence of long memory in the volatility of sample returns is not an important in estimating an accurate VaR performance. However, it is more important to consider a model with skewed-Student-t distribution innovation in determining better VaR. In short, the appropriate assumption of return distribution provides more accurate VaR models for the portfolio managers and investors.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.1
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• pp.105-128
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• 2005

The Purpose of the current study is to identify the differences between Samsung and Hyundai Group and the causes why the differences occurred. The study focuses on the founders of the two group as a main source of the differences, especially brain preference of the two founders. Two steps were employed to perform the study. Firstly, the two founders' characteristics were analyzed by using archival research. It was implicitly hypothesized that Group founders' characteristics explained the differences of the two Groups. It was found that the founder of Samsung Group, the late president Lee emphasized rationality, analysis, and cause/effect relationship and low risk taking, suggesting that he had left-brain preference. In contrast. the late president Chung, the founder of Hyundai Group, emphasized intuition, wholeness, contextual meaning, and risk taking, showing that he had right-brain preference. Secondly, a comparison between the two groups was performed in terms of business and financial risk in corporate portfolio, and management system. It was found that Hyundai Group was pursuing higher risk than Samsung Group. And it was observed that Samsung Group put more emphasis on formality in decision making and systematic control, and less emphasis on risk taking than Hyundai Group. From the two step research relationship between brian preference and management was reasoned. Research implications and limitations were discussed at the end of the study.

• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.875-887
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• 2021

Value at Risk (VaR) is the most popular measure for market risk. In this paper, we consider the VaR estimation of portfolio consisting of a variety of assets based on multivariate copula model known as vine copula. In particular, sparse vine copula which penalizes too many parameters is considered. We show in the simulation study that sparsity indeed improves out-of-sample forecasting of VaR. Empirical analysis on 60 KOSPI stocks during the last 5 years also demonstrates that sparse vine copula outperforms regular copula model.

• Asia-Pacific Journal of Business
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• v.11 no.4
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• pp.243-254
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• 2020

Purpose - This paper explores whether overnight returns measured from the last closing price to today's opening price explain the cross-section of stock returns. Design/methodology/approach - This study is conducted using the Korean stock market data from 1998 to 2018, obtained from DataGuide database. The analysis begins with portfolio-level tests, followed by firm-level cross-sectional regressions. Findings - First, when decile portfolios sorted on the daily average of overnight returns in the previous months, the highest decile portfolio exhibits a significant negative risk-adjusted return. This suggests that stocks with higher average overnight returns are temporarily overvalued due to buying pressure from investors. Second, at least 6 months of persistence exists in average overnight returns, which is in line with the results reported by Barber, Odean and Zhu (2009) that investor sentiment persists over several weeks. Finally, Fama-MacBeth cross-sectional regression of expected returns after controlling for a variety of firm characteristic variables such as firm size, book-to-market ratio, market beta, momentum, liquidity, short-term reversal, the slope coefficient for overnight returns remains negative and statistically significant. Research implications or Originality - Overall, the evidence consistently suggests that overnight return is considered as a new priced factor in the cross-section of expected returns. The findings of this paper not only adds to finance literature, but also could be useful to practitioners in making stock investment decision.

• Knowledge Management Research
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• v.21 no.4
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• pp.265-285
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• 2020

The inbound tourism market is recognized as a vital sector of the tourism industry today, but it is highly volatile due to each country's economic, social, and cultural variables. The causes of volatility vary according to the inbound country, and we intend to revitalize the stabilized tourism industry by minimizing risks. In this study, the portfolio theory was applied to derive the optimal combination for each country to achieve the minimum risk level's maximum growth rate. The number of inbound travelers and the average expenditure per person was simultaneously applied. As a result of the analysis, the best mix by country based on the number of inbound travelers was the UK, the United States, Germany, China, and Japan. Based on average spending, each country's best combinations were Thailand, Middle East, Singapore, Japan, Russia, Hong Kong, and Germany. It is expected to be able to establish a plan to operate the Korean inbound tourism market strategically.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2007.11a
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• pp.539-544
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• 2007

Owing to diversification of social desire and increase of demand as economic growth, construction industry recently is trending toward diversification, complication, gigantism. It means that is changing to difficult environment as existing construction company operation. Specially, some big construction companies are promoting get down to construction business risk management skill & development. Enterprise risk management system, recognized to risk portfolio, is suggested. instead of individually risk management. This study indicates ERM basic model, considering construction risk character, for apply to ERM to field of construction . And it is including analysis of recognition level and reality about ERM in construction company.

• The Journal of Society for e-Business Studies
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• v.25 no.4
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• pp.125-141
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• 2020

Recent Studies emphasize the effect of investors attention, recognition and sentiment on the trading behavior of retail investors and stock price variation. In this study, we use Naver Trend to measure investors'attention and investigate the relation between investor attention and price synchronicity, total risk and systematic risk of stocks. Using various research methodologies such as portfolio analysis, fixed effect regression and dynamic panel analysis, we find consistent results. First, stock price synchronicity is increased with lager average search volume, but with less search variability. Second, both average search volume and its variability are positively related to total risk and beta of stocks. These results can be interpreted that search volume sharply increases only when stock-related event occurs.

• Human Ecology Research
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• v.62 no.3
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• pp.409-426
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• 2024

The purpose of this study was to analyze the financial portfolios of single-person households. For the analysis, data from the Korean Labor Panel Survey (2021) was utilized, comprising 2,905 single-person households. The major findings are as follows: First, the proportion of households with monthly savings was 32.0%, while the proportion of households holding financial assets was 72.1%. Second, regarding the composition of monthly savings, single-person households predominantly held savings accounts (93.3%), followed by insurance (4.7%), with cumulative funds at a mere 0.8%. The composition of financial assets showed that the majority were in bank deposits (78.5%), followed by risk management assets (18.0%), and investment assets (2.4%). Third, multivariate analysis results revealed that younger age, higher education level, and better financial factors were associated with a higher probability of having monthly savings. The results for financial assets were largely similar, with females showing a higher likelihood of asset possession compared to males. Fourth, the proportions of both bank savings in total savings and insurance generally had opposing effects. Fifth, age group had the greatest influence on the proportions of safety and insurance assets, followed by income group. Middle-aged households had lower proportions of safety assets but higher proportions of insurance assets compared to young households, while the opposite trend was observed for elderly households. Middle-income households had higher proportions of insurance assets compared to low-income households, whereas high-income households had higher proportions of investment assets. Lastly, cluster analysis categorized single-person households' financial portfolios into five groups: Group 1 (32.2%): "Old-Sustain" characterized by insufficient current income but economically stable retirement. Group 2 (29.4%): "Financially Active" engaging in various financial activities due to relatively high education and employment rates. Group 3 (28.0%): "Financially Inactive" classified as elderly groups with minimal financial activities. Group 4 (9.1%): "Risk Financial Structure" consisting of relatively young individuals focused on risk management assets but facing issues in financial asset management due to high-risk assets and financial loans. Group 5 (1.3%): "Stable-Insurance Oriented" with high financial assets and income concentrated in insurance for both savings and financial assets.

• 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.