• Journal of the Korea Convergence Society
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• v.12 no.6
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• pp.9-16
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• 2021

In the past, there have been various studies on predicting the stock market by machine learning techniques using stock price data and financial big data. As stock index ETFs that can be traded through HTS and MTS are created, research on predicting stock indices has recently attracted attention. In this paper, machine learning models for KOSPI's up and down predictions are implemented separately. These models are optimized through a grid search of their control parameters. In addition, a hybrid machine learning model that combines individual models is proposed to improve the precision and increase the ETF trading return. The performance of the predictiion models is evaluated by the accuracy and the precision that determines the ETF trading return. The accuracy and precision of the hybrid up prediction model are 72.1 % and 63.8 %, and those of the down prediction model are 79.8% and 64.3%. The precision of the hybrid down prediction model is improved by at least 14.3 % and at most 20.5 %. The hybrid up and down prediction models show an ETF trading return of 10.49%, and 25.91%, respectively. Trading inverse×2 and leverage ETF can increase the return by 1.5 to 2 times. Further research on a down prediction machine learning model is expected to increase the rate of return.

• Management & Information Systems Review
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• v.29 no.2
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• pp.1-25
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• 2010

This study attempted to empirically test the determinants of stock returns in Korean stock market applying multi-factor model proposed by Haugen and Baker(1996). Regression models were developed using 16 variables related to liquidity, risk, historical price, price level, and profitability as independent variables and 690 stock monthly returns as dependent variable. For the statistical analysis, the data were collected from the Kis Value database and the tests of forecasting power in this study minimized various possible bias discussed in the literature as possible. The statistical results indicated that: 1) Liquidity, one-month excess return, three-month excess return, PER, ROE, and volatility of total return affect stock returns simultaneously. 2) Liquidity, one-month excess return, three-month excess return, six-month excess return, PSR, PBR, ROE, and EPS have an antecedent influence on stock returns. Meanwhile, realized returns of decile portfolios increase in proportion to predicted returns. This results supported previous study by Haugen and Baker(1996) and indicated that firm-characteristic model can better predict stock returns than CAPM. 3) The firm-characteristic model has better predictive power than Fama-French three-factor model, which indicates that a portfolio constructed based on this model can achieve excess return. This study found that expected return factor models are accurate, which is consistent with other countries' results. There exists a surprising degree of commonality in the factors that are most important in determining the expected returns among different stocks.

• The Journal of Information Systems
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• v.27 no.3
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• pp.43-62
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• 2018

Purpose Since the Bitcoin, which was the first virtual currency, was made at 2009, almost 1,000 virtual currencies appeared onstage in the world. Even though virtual currencies have the function of money as a medium of exchange or contract, any of those has not yet entered the commercialization stage. Instead, some of the virtual currencies show the nature of investment assets. In the case of virtual money investment, users tend to use all the information of the world because information transfer is very easy and capital movement is almost free between different countries. In addition, as the transaction sizes of virtual currencies increase, a virtual currency price is no longer independent and is likely to be affected by the prices of other virtual currencies. Therefore, it is necessary to understand the influence among virtual currency markets, which helps successful implementation of investment strategies. Design/methodology/approach This study focuses on the investment product function of virtual money and conducts the analysis using the time series model used in the financial and economic areas. In this paper, we try to analyze the return and volatility transfer effect of virtual money markets through GJR-GARCH model. Findings This study is expected to find out whether we can make market forecasts through reflecting changes in other markets. In addition, we can reduce the trial and error of user decision making by using the information on the yield and volatility transition effect derived from the research results, and it is expected to reduce the opportunity cost of users.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1027-1036
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• 2008

Value at Risk(VaR) is a fundamental tool for managing market risks. It measures the worst loss to be expected of a portfolio over a given time horizon under normal market conditions at a given confidence level. Calculation of VaR frequently involves estimating the volatility of return processes and quantiles of standardized returns. In this paper, we introduced and applied the CreditMetrics model to estimate the credit VaR of Korean Property and Casuality insurers.

• Journal of the Korean association of regional geographers
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• v.3 no.2
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• pp.151-162
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• 1997

Although modern versions of the traditional Von $Th{\ddot{u}}nen$ theory have contributed to a description of spatial organization in agriculture, they did not incorporate the market mechanism as an integral part of location theory. This deficiency has been indicated and new mathematical structure has been proposed elsewhere by the author. The closed model, which simultaneously considered a basic principle of supply and demand, exposed a computational complexity. Based on the problem, this study attempts to extend market mechanism in order to consider the influence of city (market) size in agricultural location theory. To theoretically explore the economic relationship in a location theory, this study simplifies agricultural activity as just two activities in one-dimensional spatial economy. The problem has been solved by equating total supply and demand of agricultural products, and then by determining each agricultural price from the relationship. All of the mathematical problems have been arranged in matrix form. First, the traditional model and closed model have been compared by quantitative comparative statics which provides the sensitivity test for each model. The results have shown that the traditional model shows a relatively excessive change in land use, besides the deficiency of a constant agricultural price. Second, the effects of the size of market town and its population increase were examined, using the closed model. In this case, the price of agricultural product is increased, and the land use is extended outward. This proves that locational rent is related to the expansion of land use. Third, environmental uncertainty was associated with the closed model, in order to further consider the difference of farmers attitude in strategic perspective. In this study, two extreme attitudes, which reflects the maximum average expected returns and the maximum guaranteed returns, were examined in their land use and their effects on the prices of agricultural products. It was shown that the two farmers attitudes can be interconnected with location theory. Due to the exogenous data, the differences in the area of land use and total quantities of agricultural products were not clearly shown in this study. However, it was shown that the land use pattern is very different. That is, maximum guaranteed return model reveals a mixed land use pattern around the market town. Basically, this study shows some spatial and economic implications related to Von $Th{\ddot{u}}nen$ model.

• The Korean Journal of Financial Studies
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• v.6 no.1
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• pp.289-312
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• 2000

This paper proposes a pricing model for IPOs which can reconcile the average underpricing phenomenon with the expected wealth maximizing behaviors of market participants. Under the usual informational asymmetry, the optimal offer price for best efforts IPOs is derived as a function of the uncertainty about market's valuation, the expected return on proposed projects and the size of offerings relative to the firm's market value. Depending on these firm-specific characteristics, best efforts IPOs can be underpriced, fairly priced, or overpriced. Introducing the investment banker as an outside information producer, the model is extended to provide empirical implications for pricing and underwriting contract choice decisions which are consistent with the existing empirical evidences. The model predicts that the issuers with greater uncertainty about market's valuation choose best efforts contract over firm commitment contract and the dispersion of initial returns would be greater for best efforts IPOs than for firm commitment IPOs.

• Journal of Korean Society of Rural Planning
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• v.24 no.4
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• pp.27-35
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• 2018

This study analyzed the factors influencing the choice of new farming type in order to prepare the countermeasures against structural changes of farm labor force. The analytical model was the multinomial logit model(MNL). The test for Independence and Irrelevance Alternatives(IIA) assumption in MNL shows that the IIA assumption in our data is rejected. Alternatively, we chose the multinomial probit model(MNP) that does not assume IIA. Data were obtained from 2010 census of Agriculture, Forestry and Fisheries of Statistics Korea. New farming types are succession(13.9%), return-to-farming(45.0%), part-time-farming(32.5%) and etc(8.6%). Analysis results showed that the characteristics of farms, commodity, management, and region influenced the choice of new farming type. This study is expected to help policy makers to produce support policies by new farming types in order to increase the number of new farmers and to make them easier to settle down in agriculture.

• Journal of Digital Convergence
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• v.19 no.12
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• pp.175-182
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• 2021

Focusing on the recently announced "KOSPI 200 ESG Index," this study intends to examine whether the "KOSPI 200 ESG Index" has any relevance to stock prices. Specifically, it was empirically analyzed whether companies included in the KOSPI 200 ESG index showed average abnormal return and cumulative average abnormal return of stock prices due to incorporation into the index. As for the research method, the case study was conducted using the return by the market model using the coefficient estimated by the OLS for the normal expected return. The study results are summarized as follows. First, the initial incorporation of a company into the KOSPI 200 ESG index showed significant positive(+) average abnormal return and cumulative average abnormal return. Second, the incorporation of a company into the KOSPI 200 ESG index showed significant positive(+) average abnormal return and cumulative average abnormal return. Through this study, it was confirmed that investors in the market are aware of ESG indicators as non-financial information, not just financial information. In addition, it can be said that the contribution of this study to the fact that investors perceive ESG index as information for investment. This study differs in that it uses the latest ESG index, but at the same time, it has limitations in that the study period is short and the study sample is limited.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.11
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• pp.1-10
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• 2021

This paper compares the performance of Fama-French three-factor and five-factor models using a dataset of 20 Pakistani commercial banks for the period 2011 to 2020. We focus on an emerging economy as the findings from earlier studies on developed countries cannot be generalized in emerging markets. For empirical analysis, twelve portfolios were developed based on size, market capitalization, investment strategy, and growth. Subsequently, we constructed five Fama-French factors namely, R_M, SMB, HML, RMW, and CMA. The OLS regression technique with robust standard errors was applied to compare the predictive power of both the Fama-French models. Further, we also compared the mean-variance efficiency of the Fama-French models through the GRS test. Our empirical analysis provides three unique and interesting findings. First, both asset pricing models have similar predictive power to explain the expected portfolio returns in most cases. Second, our results from the GRS test suggest that there is no noticeable difference in the mean-variance efficiency of one asset pricing model over the other. Third, we find that all factors of both Fama-French models are statistically significant and are important for explaining the volatility of expected commercial bank returns in the context of Pakistan.

• Journal of The Geomorphological Association of Korea
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• v.23 no.1
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• pp.87-101
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• 2016

This study was conducted to develop a hydrological model of catchment water balance which is able to estimate irrigation return flows, so BROOK90-K (Kongju National University) was developed as a result of the study. BROOK90-K consists of three main modules. The first module was designed to simulate water balance for reservoir and its catchment. The second and third module was designed to simulate hydrological processes in rice paddy fields located on lower watershed and lower watershed excluding rice paddy fields. The models consider behavior of floodgate manager for estimating the storage of reservoir, and modules for water balance in lower watershed reflects agricultural factors, such as irrigation period and, complex sources of water supply, as well as irrigation methods. In this study, the models were applied on Guryangcheon stream watershed. R2, Nash-Sutcliffe efficiency (NS), NS-log1p, and root mean square error between simulated and observed discharge were 0.79, 0.79, 0.69, and 4.27 mm/d respectively in the model calibration period (2001~2003). Furthermore, the model efficiencies were 0.91, 0.91, 0.73, and 2.38 mm/d respectively over the model validation period (2004~2006). In the future, the developed BROOK90-K is expected to be utilized for various modeling studies, such as the prediction of water demand, water quality environment analysis, and the development of algorithms for effective management of reservoir.