• 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.

• The Korean Journal of Financial Management
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• v.26 no.1
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• pp.189-220
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• 2009

This study tests the significance of momentum and contrarian strategy which challenge the weak efficient market hypothesis (EMH). If momentum and contrarian strategy can make extra return above the market, this can be a significant critics to the weak EMH. By using Monte Carlo simulation we have found that many existing returature, which test the significance of momentum and contrarian strategy, have a significance distortion problem. We test the significance of momentum and contrarian strategy by using reality check test of White(2000) which solve the problem of data snooping bias. The results are following. When we use the KOSPI index as the benchmark portfolio, we can get the best strategy of momentum strategy in the case of mean return. But in the case of Sharp ratio which is the performance measure adjusting risk, we find that the best strategy in the momentum and contrarian strategy can not dominate the performance of benchmark portfolio. Therefore we argue that weak EMH can not be rejected because of superior performance of momentum and contrarian strategy when we consider risk.

• 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 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.

• Atmosphere
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• v.34 no.1
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• pp.1-21
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• 2024

Private companies are increasingly required to take more substantial actions on climate change. This study introduces the principle and cases of climate (physical) risk estimation for 11 private companies in Korea. Climate risk is defined as the product of three major determinants: hazard, exposure, and vulnerability. Hazard is the intensity or frequency of weather phenomena that can cause disasters. Vulnerability can be reflected in the function that explains the relationship between past weather records and loss records. The final climate risk is calculated by multiplying the function by the exposure, which is defined as the area or value of the target area exposed to the climate. Future climate risk is estimated by applying future exposure to estimated future hazard using climate model scenarios or statistical trends based on weather data. The estimated climate risks are developed into three types according to the demand of private companies: i) climate risk for financial portfolio management, ii) climate risk for port logistics management, iii) climate risk for supply chain management. We hope that this study will contribute to the establishment of the climate risk management system in the Korean industrial sector as a whole.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.620-627
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• 2003

The real option pricing theory has emerged as the new investment decision-making techniques superceding the traditional discounted cash flow techniques and thus has greatly received muck attention from academics and practitioners in these days the theory has been widely applied to a variety of corporate strategic projects such as a new drug R&D, an internet start-up. an advanced manufacturing system. and so on A lot of people who are interested in the real option pricing theory complain that it is difficult to understand the true meaning of the real option value. though. One of the most conspicuous reasons for the complaint may be due to the fact that there exit many different ways to calculate the real options value in this paper, we will present a replicating portfolio method. a risk-neutral probability method. a risk-adjusted discount rate method (quasi capital asset pricing method). and an opportunity cost concept-based method under the conditions of a binomial lattice option pricing theory.

• Journal of Families and Better Life
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• v.22 no.3
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• pp.11-21
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• 2004

This study examined factors associated with the ownership of stock investments and the amount of stock investments of households using the 2001 National Survey of Family Income and Expenditure by National Statistical Office. Households with large amounts of income, savings, and liabilities were more likely to invest in stocks and have large amounts of stock investments. Also, households with young and male householders, highly educated householders, a number of children in school, and housing ownership were more likely to invest in stocks and have large amounts of stock investments. On the other hand, self employed households and dual income households were less likely to invest in stocks and have small amounts of stock investments.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.8-10
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• 2023

본 연구는 계층적 리스크 패리티 (Hierarchical Risk Parity, HRP) 포트폴리오 방법론과 정규화된 상호 정보 거리의 결합을 연구하였다. 이때, 한정된 이동창에서 발생할 수 있는 유한 크기 효과(finite size effects) 문제를 극복하기 위해 무작위로 섞인 NID 값에 대한 평균치를 제공함에 따라 NID 를 활용한 새로운 포트폴리오 최적화 방법을 제안한다. 본 연구의 결과는 NID 를 통합한 HRP 포트폴리오가 기존 방법론에 비해 통계적 장점과 함께 더욱 효율적이며 안정적임을 보여준다.

• 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.

• The Korean Journal of Applied Statistics
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• v.24 no.2
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• pp.253-268
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• 2011

In this paper, we estimate systematic risk from credit migration (or transition) matrices under "Asymptotic Single Risk Factor" model. We analyzed transition matrices issued by KR(Korea Ratings) and concluded that systematic risk implied on credit migration somewhat coincide with the real economic cycle. Especially, we found that systematic risk implied on credit migration is better than that implied on the default rate. We also emphasize how to conduct a stress test using systematic risk extracted from transition migration. We argue that the proposed method in this paper is better than the usual method that is only considered for the conditional probability of default(PD). We found that the expected loss critically increased when we explicitly consider the change of credit quality in a given portfolio, compared to the method considering only PD.