• Asia-Pacific Journal of Business
• /
• v.13 no.4
• /
• pp.135-149
• /
• 2022

Purpose - The primary objective of this article is to investigate the impact of U.S. monetary policy on institutional / individual / foreign investor reactions in the Korean stock market. Design/methodology/approach - This study employs a high frequency event study methodology to identify U.S. monetary policy shocks and quantify the impact of identified shocks on investor reactions. The dependent variable in the regression model is net stock purchase, while the explanatory variables are U.S. monetary policy shocks. The model is estimated for the period 2000-2019, including 156 FOMC meetings. Findings - Foreign investors immediately sell stocks in response to contractionary U.S. monetary shocks. They do not, however, react to anticipated changes in monetary policy rates, confirming the rationality of foreign investors. Individual investors demonstrate the opposite response, indicating that a non-trivial proportion of individual investors are irrational. Research implications or Originality - This study adds to the current literature on the effect of U.S. monetary policy on the Korean stock market. This study demonstrates a heterogeneous response to U.S. monetary policy shocks, validating the rational investment behavior of foreign investors, while individual investors exhibit a certain degree of irrationality. Methodologically, this study adds to the literature by quantifying the impact of U.S. monetary policy employing a sharper identification method allowing a simple and consistent estimation.

• East Asian Economic Review
• /
• v.19 no.3
• /
• pp.223-241
• /
• 2015

This paper examines the existence of value premium in the Chinese stock markets and empirically provides its explanation. Our results suggest that the value premium does exist in the Chinese markets, and investor sophistication is significant in explaining its existence. In particular, there is supporting evidence that the value premium could be driven by individual investors, whereas stocks that are mostly held by institutional investors are value-premium free. We briefly discuss the implications of our findings.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2000.10a
• /
• pp.77-80
• /
• 2000

Feedback herding strategy in stock market means considering other investor's strategy as a basis of market forecasting of next term. Generally, individual investors use that strategy which mimics the strategy of institutional investors. When it is used in stock market, both kind of investors, preceders and followers, can take the higher average of rate of return to normal market in which no feedback herding strategy is not use, the more investors take part in. And variance of return, the risk of investment, are same to both group.

• Journal of the Korean Society for Aviation and Aeronautics
• /
• v.31 no.4
• /
• pp.28-44
• /
• 2023

This study investigates aircraft lessor business models by studying cases and interviewing experts to analyze investors and business strategies of aircraft lessor. The results confirm that there is a wide range of investors including institutional investors, financial institutions, insurance companies, corporations, and wealthy individuals for aircraft lessor. Aircraft lessors can be categorized based on its required rate of return (cost of capital) into bank-investing core, institutional investor-investing value-added, and hedge fund-investing opportunistic. Aircraft lessor decides leasing rate by aircraft purchasing price and lessee's credit rating. Core aircraft lessors invest in new aircrafts for new placement or sale-and-leaseback strategy requiring little technical risk in aircraft, value-added lessors invest in middle-aged aircrafts for re-leasing, opportunistic lessors invest in old aircrafts for freighter conversion or part-out strategy requiring high level of expertise. This study provides insights for future Korean aircraft lessor establishment and investment.

• The Journal of the Korea Contents Association
• /
• v.16 no.3
• /
• pp.553-562
• /
• 2016

Then, it investigated the investment preference through the previous studies to analyze the influence factor of investment satisfaction and demonstrated the effects through the PLS (Partial Least Squares) regression. In addition, it separated the target type to institutional investors and retail investors and carried out the survey for comparing the investment preference of investor type. The result of analysis found out that institutional investors emphasis on investment preference such as the Inflation hedge, Early payback, Financial stability, Leverage risk and etc. Then, general investors emphasis on investment preference such as the Rental income, Facilities and Equipment, Business area and population, Ease of use, Leverage risk, Early payback and etc. In addition, common investment preferences are the Leverage risk, Early payback and Facility accessibility.

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

• Knowledge Management Research
• /
• v.24 no.3
• /
• pp.73-94
• /
• 2023

With the virtual asset market's rapid growth, government regulations on listing and trading procedures are expected. However, specific measures are currently lacking. To ensure stable inclusion in the institutional framework, precise regulations are needed for market development and investor protection. This study compares self-regulatory guidelines of the top domestic virtual asset exchanges with Korea Exchange's Preliminary Listing Examination Standards (2022) to enhance timeliness and relevance. It defines IEO, IPO, and ICO concepts and addresses conflicts of interest in IEO. Analyzing delisted virtual assets, it categorizes issues and classifies listing examination guidelines into formal and qualitative requirements. The study examines self-regulatory guidelines based on continuity, transparency, stability, corporate characteristics, and investor protection criteria, along with five special requirements for virtual assets. Improvement measures include regular disclosures of governance structure, circulation volume, and the establishment of independent audit institutions. This research further analyzes delisting cases, classifies issues, and proposes solutions. Considering stock market similarities, it offers measures based on the institutional framework.

• The Journal of Asian Finance, Economics and Business
• /
• v.3 no.3
• /
• pp.5-15
• /
• 2016

This study examines whether Korean rating agencies such as Korea Investors Service (KIS), National Information & Credit Evaluation (NICE), and Korea Ratings Corporation (KR), incorporate corporate governance into their corporate bond ratings in Korea. We find that the Korean rating agencies assign higher ratings to the bonds issued by Chaebol (Korean business group) affiliated firms. Our results also indicate that those rating agencies give higher ratings to the bonds with greater foreign investor share ownership. Moreover, if the rating agencies value corporate governance, higher rated firms should issue bonds at lower yield to maturity. We discover that Chaebol affiliation is counted favorably by the rating agencies. We find that investors are willing to pay lower risk premium for bonds with higher institutional ownership, but higher risk premium to bonds with greater equity ownership in the form of depository receipts. Therefore, even if the rating agencies and investors in Korea consider corporate governance (Chaebol affiliation and ownership structure) an important determinant in bond ratings and the yields to maturity, they have opposite views on institutional ownership and share ownership in the form of depository receipts.

• Knowledge Management Research
• /
• v.19 no.1
• /
• pp.97-118
• /
• 2018

The finance-investment industry is currently focusing on research related to artificial intelligence and big data, moving beyond conventional theories of financial engineering. However, the case of equity optimization portfolio by using an artificial intelligence, big data, and its performance is rarely realized in practice. Thus, the purpose of this study is to propose process improvements in equity selection, information analysis, and portfolio composition, and lastly an improvement in portfolio returns, with the case of an equity optimization model based on quantitative research by an artificial intelligence. This paper is an empirical study of the portfolio based on an artificial intelligence technology of "D" asset management, which is the largest domestic active-quant-fiduciary management in accordance with the purpose of this paper. This study will apply artificial intelligence to finance, analyzing financial and demand-supply information and automating factor-selection and weight of equity through machine learning based on the artificial neural network. Also, the learning the process for the composition of portfolio optimization and its performance by applying genetic algorithms to models will be documented. This study posits a model that the asset management industry can achieve, with continuous and stable excess performance, low costs and high efficiency in the process of investment.

• Journal of Arbitration Studies
• /
• v.32 no.3
• /
• pp.3-27
• /
• 2022

Climate change is a man-made disaster that has become a major global concern today. With increasingly visible symptoms of climate change in recent years, it has become evident that climate action can no longer be dismissed as a mere matter of choice, but as a matter of survival for the human being. To address the impending climate change crisis in a collaborative and sustainable manner, the international community has been taking various measures including Kyoto protocol and the Paris Agreement. With respect to the private investor's project investment in line with international agreements on climate change, recently we have seen multiple legal judgments which clearly indicate the subject of judicial responsibility for investment in climate change related projects. However, in order to hold judicial responsibility occurring during the implementation of climate change related projects, a causal relationship between the responsible entities and clear responsibility must be demonstrated, and applicable institutional arrangements need to be arranged. It may be the right time for global community to consider shifting not only to human ethical obligations but also legal obligations. In this regard, concerned governments should consider legislating arbitration laws, regulations, and institutional arrangements in more specific and applicable manner.