• Journal of Service Research and Studies
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• v.9 no.1
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• pp.51-71
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• 2019

The purpose of this research is to analyze the ETF market, which has a large increase in the number of listed shares and the market capitalization, and to identify the investment effects of ETFs. The study procedure and method used to calculate the return and change trend of ETFs for the sample of the transaction information, the transaction amount, and the market capitalization for the period from 2010 to 2018, and performed correlation and regression analysis. As a result, the ETF's total return was 2.11%, the domestic underwriting market ETF yield was 2.39%, and the stock ETF yield was 2.59%, which was lower than the KOSPI 200 index and the KOSPI 200 index. Index ETF was 2.63%, followed by stock ETF and oversea underwriting market ETF. The problem with ETF investment is that the annual return of ETFs and domestic ETFs is as low as 2%, which is not enough for investors to expect more than 5%. The study contributes to the realization of the ETF by analyzing the actual effect of the investment and to establishing considerations when buying ETFs from the viewpoint of investors. The direction of the research is to accumulate more ETF data and present the investment direction precisely.

• Journal of the Korean Data and Information Science Society
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• v.24 no.2
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• pp.223-233
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• 2013

At local emerging stock markets such as Korea, Hong Kong, Singapore and Taiwan, foreign investors (FI) are recognized as important investment community due to the globalization and deregulation of financial markets. Therefore, it is required to monitor the behavior of FI against a sudden enormous selling stocks for the concerned local governments or private and institutional investors. The main aim of this study is to propose an early warning system (EWS) which purposes issuing a warning signal against the possible massive selling stocks of FI at the market. For this, we suggest machine learning algorithm which predicts the behavior of FI by forecasting future conditions. This study is empirically done for the Korean stock market.

• International Area Studies Review
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• v.12 no.3
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• pp.195-216
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• 2008

It is generally accepted that the momentum strategies are effective in the short-term, and the contrarian strategies are profitable in the long run in major stock markets in the world. In Korean market, however, the contrarian is considered effective investment strategy both in the short- and long-term. We investigate whether this is true after 1999, and try to find out the reasons for this phenomena. We found that the contrarian strategies are still effective. Foreign investors showed consistent investment behavior both in Korean and abroad: they followed momentum in the short-tem, and contrarian in the longer-term. The individual investors, who are thought to be noise trader, showed different behavior. They followed contrarian strategies both in the short-and long-term. The reason that the contrarian is observed in Korean market regardless of the investment horizon is thought to be the irrtional behavior of individual investors.

• Journal of Korean Society for Quality Management
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• v.51 no.2
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• pp.171-184
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• 2023

Purpose: First, this paper suggests an alternative approach to find optimal portfolio (stocks, bonds and ESG stocks) under the maximizing utility of investors. Second, we include ESG stocks in our optimal portfolio, and compare improvement of welfares in the case with and without ESG stocks in portfolio. Methods: Our main method of analysis follows Brennan et al(2002), designed under the continuous time framework. We assume that the dynamics of stock price follow the Geometric Brownian Motion (GBM) while the short rate have the Vasicek model. For the utility function of investors, we use the Power Utility Function, which commonly used in financial studies. The optimal portfolio and welfares are derived in the partial equilibrium. The parameters are estimated by using Kalman filter and ordinary least square method. Results: During the overall analysis period, the portfolio including ESG, did not show clear welfare improvement. In 2017, it has slightly exceeded this benchmark 1, showing the possibility of improvement, but the ESG stocks we selected have not strongly shown statistically significant welfare improvement results. This paper showed that the factors affecting optimal asset allocation and welfare improvement were different each other. We also found that the proportion of optimal asset allocation was affected by factors such as asset return, volatility, and inverse correlation between stocks and bonds, similar to traditional financial theory. Conclusion: The portfolio with ESG investment did not show significant results in welfare improvement is due to that 1) the KRX ESG Leaders 150 selected in our study is an index based on ESG integrated scores, which are designed to affect stability rather than profitability. And 2) Korea has a short history of ESG investment. During the limited analysis period, the performance of stock-related assets was inferior to bond assets at the time of the interest rate drop.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.3
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• pp.9-15
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• 2020

This paper investigates how economic freedom affected firm investment in Vietnam. In the globalization decade, economic freedom has been an important policy to support economic development in Vietnam. Improvements in economic freedom, such as capital freedom and domestic credit freedom, allow firms to access external finance more easily, so that the firm's investment depends less on internal cash flow. In a developing country, on the drawbacks, many small and medium firms likely have more challenges if the government would not give any subsidies. The higher level of freedom may exacerbate the financing constraints of less competitive firms. We analyze unique firm-level data from 2006 to 2016, which includes listed firms on two major stock exchanges and unlisted firms in the Unlisted Public Company Market. The article also considers how economic freedom affects small firms and large firms differently. Our results show that capital freedom and domestic credit freedom played an important role in investments for Vietnamese firms. However, we cannot find evidence that overall economic freedom relaxed the financial constraints on firms. Additionally, we suggest that small firms likely gain more advantage in access to external finance than do larger firms when the government removes restrictions from capital movement and the domestic credit market.

• Journal of Technology Innovation
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• v.6 no.2
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• pp.159-177
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• 1998

Although each company is trying to develop an economic analysis model with its own particular style or format, the appropriate method is not yet developed because there are many problems to be solved such as uncertainity of outcomes and intangible benefits of technology. The purpose of tris paper therefore is to suggest an economic analysis methodology, which reflects the complexity and the risk of R&D investment, through a case study on the development of a superconductor fault current limiter. A self-developed Monte Carlo simulation program utilized as a main tool in this paper was very useful for risk analysis of R&D investment which could not be solved in the previous DCF(Discounted Cash Flow) model. We also introduce learning effect to consider the intangible benefits such as Know-How obtained from R&D execution. The expected value and its probability distribution for R&D investment can be obtained by combining the Monte Carlo method with the decision tree approach. This result is helpful in judging the priority and the resource-allocation of R&D projects. It is however necessary to develop more precise model for quantifying the technology stock and the simulation program using the continuous probability distribution in expected values to improve the reliability of economic analysis on R&D projects.

• Journal of the Korea Safety Management & Science
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• v.3 no.4
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• pp.135-144
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• 2001

The capital cost of the company is one that must be paid to the money owner as the price by using the money. The capital cost according to the source of money supply can be estimated by the expected profit rate undertaken by the use of the capital. But in the area of pre-existent economic evaluation, the evaluation of the company investment has been treated by the profit rate of the capital after considering the repayment conditions of the other's money or the interest. Thus in this study, in case the company makes an investment on various kinds of the capital at the same time, not make use of the capital as a one source, the economic evaluation of an investment should be handled by taking the weighted average cost of capital into consideration in proportion to the constitution of the capital cost by the sources of money supply, Especially, as the cost of the private money is very much connected with the profit rate through the stock market, the Capital Asset Pricing Model (CAPM) will be applied. This kind of economic evaluation method can be said to have much to do with the Economic Value Added : EVA) as well as to be highly thought as a standard to estimate the company' value recently To certify the usefulness of this approach, the case study of the output of the capital cost will be made for the purse of the economic evaluation of the alternative investment by using the financial statements of a motor company H.

• The Journal of Industrial Distribution & Business
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• v.13 no.1
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• pp.23-32
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• 2022

Purpose: This study used ESG grade, but defined AESG, adjusted to the size of a company and examines whether it can be used as an investment strategy. Research design, data and methodology: The analysis sample in this study is a company that has given an ESG rating among companies listed on the Korea Stock Exchange. We examine the results through portfolio analysis and Fama-macbeth regression analysis. Results: As result of examining the long-only performance and the long-short performance by constructing quintile portfolios, it was observed that a significant positive return was shown. It was observed that there was an alpha that could not be explained in asset pricing models. Also, AESG had a return prediction effect in the result of a Fama-Macbeth regression that controlled corporate characteristic variables in individual stocks. Next, we confirmed AESG's usage through various portfolio composition. In the portfolio optimization, the Risk Efficient method was the most superior in terms of sharpe ratio and the construct multi-factor model with Value, Momentum and Low Vol showed statistically significant performance improvement. Conclusions: The results of this study suggest that it can be helpful in ESG investment to reflect the ESG rating of relatively small companies more through the scale adjustment of the ESG rating (i.e.AESG).

• Asia pacific journal of information systems
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• v.32 no.4
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• pp.707-727
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• 2022

Industrial revolution 4.0 makes business competition more challenging and will impact the instability of the company's financial performance. Dynamic environmental conditions make it difficult for companies to make predictions in making decisions. Investing in information technology (IT) is one way for companies to maintain financial stability and competitive advantage in dynamic competition. Resource-Based Theory (RBT) explains that information technology (IT) is a resource that can create a competitive advantage for the company. This study aims to examine the moderating role of dynamic industrial environments and IT strategic emphasis on the relationship between a lag effect of IT investment and firm's financial performance volatility. Using the data of companies listed on the Indonesia Stock Exchange (IDX) for five years starting from 2013-2017, the method used to estimate the research model's parameters is the generalized method of moments (GMM) approach. The results show that the industrial environment and the emphasis on IT strategy have a role in moderating and strengthening the relationship between the time lag in IT investment in reducing the firm's financial performance volatility.