• Journal of Distribution Science
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• v.15 no.11
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• pp.73-82
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• 2017

Purpose - This paper aims to investigate a profit maximizing incentive of foreign traders in distributing the KOSPI 200 Futures. Such an incentive may induce unsophisticated retail traders to suffer loss from speculative trading. Since Korean government increased the entry barriers of the market to protect unsophisticated traders, the market size has been decreasing while the proportion of the contract held by foreign traders has been increasing. These on going changes make the market imperfectly competitive, where a profit maximization incentives of foreign traders are expected to grow. In this paper, we attempt to find any evidence of such behavior, thereby providing implications regarding market policy and market efficiency. Research design, data, and methodology - According to Kyle(1985), an informed trader exploits his/her monopoly power optimally in a dynamic context so that he/she makes positive profit, where he/she could conceal his/her trading utilizing noise trading as camouflage. We apply the KOSPI 200 Futures market to the Kyle's model: foreign traders who take into account the effect of his/her trading to maximize expected profits as an informed trader, retail investors as noise traders, and financial institutions as market makers. To find any evidence of monopolistic behavior, we test the variants of trading volume and price data of the KOSPI 200 Futures over the period of 2009 and 2017. Results - First, we find that the price of the KOSPI 200 Futures are more volatile than the price of underlying asset. Second, we find that monopolistic foreign trader's trading order flows are consistent with exploiting his/her monopoly power to maximize profit. Finally, we find that retail investors' trading order flows are inversely consistent with maximizing profit, that is, uninformed retail investors suffer loss continuously in speculative trading against informed traders. Conclusions - Our results show that the quantity of strategic order flows may have a large effect on the price, therefore, resulting the market inefficiency. The results also imply that, in implementing regulations, the depth of the market must be considered to maintain market liquidity, and suggesting interesting research topics regarding the market structure.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.15-20
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• 2020

Recently, the real estate is of high interest. This is because real estate, which was considered only a residential environment in the past, is recognized as a stable investment target due to the ever-growing demand on it. In particular, in the case of the domestic market, despite the decrease in the number of people, the number of single-person households and the influx of people to large cities are accelerating, and real estate prices are rising sharply around the metropolitan area. Therefore, accurately predicting the prospects of the future real estate market becomes a very important issue not only for individual asset management but also for government policy establishment. In this paper, we developed a program to predict future real estate market prices by learning past real estate sales data using machine learning techniques. The data on the market price of real estate provided by the Korea Appraisal Board and the Ministry of Land, Infrastructure and Transport were used, and the average sales price forecast for 2022 by region is presented. The developed program is publicly available so that it could be used in various forms.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.35 no.6
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• pp.660-665
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• 2002

In Korea, an olive flounder is very popular fish food item. However, due to the increasing human population, the present catches of the olive flounder may not be sufficient to satisfy the present demand. To increase the supply of the olive flounder, aquaculture has been begun. An interest in the aquaculture of the olive flounder has been increased recently because of its characteristics of good growth and high price in the market, However, the productivity of the olive flounder aquaculture depends on economic inputs such as fuels, facilities, and labor. The rapid growths of the olive flounder aquaculture and the concerns about economic and ecological sustainability have focused peoples attention on the aquaculture industry. In this study, an energy systems model was built to simulate the variation of sustainability on the aquaculture of olive flounder, The results of simulation based on calibration data in 1995 show that olive flounder production yield and asset slowly increase to steady state because of the law of supply and demand. The results of simulation based on the variation of oil price show that the more increase the oil price, the more decrease the olive flounder economic yield and asset. Energy sources required for systems determine the sustainability of systems. Conclusionally, the present systems of the olive flounder aquaculture should be transformed to ecological-recycling systems or ecological engineering systems which depend on renewable resources rather than aquaculture systems which depend on fossil fuels, and be harmonized with the fishing fisheries by the sustainable use of renewable resources in the carrying capacity.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.19 no.1
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• pp.1-21
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• 2015

This paper presents two algorithms based on the Jamshidian equation which is from the Black-Scholes partial differential equation. The first algorithm is for American call options and the second one is for American put options. They compute numerically free boundary and then option price, iteratively, because the free boundary and the option price are coupled implicitly. By the upwind finite-difference scheme, we discretize the Jamshidian equation with respect to asset variable s and set up a linear system whose solution is an approximation to the option value. Using the property that the coefficient matrix of this linear system is an M-matrix, we prove several theorems in order to formulate a bisection method, which generates a sequence of intervals converging to the fixed interval containing the free boundary value with error bound h. These algorithms have the accuracy of O(k + h), where k and h are step sizes of variables t and s, respectively. We prove that they are unconditionally stable. We applied our algorithms for a series of numerical experiments and compared them with other algorithms. Our algorithms are efficient and applicable to options with such constraints as r > d, $r{\leq}d$, long-time or short-time maturity T.

• Journal of Biosystems Engineering
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• v.31 no.3 s.116
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• pp.194-202
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• 2006

Using a multi-variable linear regression technique and SUR(seemingly unrelated regression) model, the demand functions of tractor, combine and rice transplanter were estimated. The demand was regarded as an annual supply of each machine and modeled as a function of 11 independent variables which reflect the actual farmer's income, actual prices of farm machines, previous supply, previous stock, actual amount of available subsidy, actual amount of available loan, arable land, import of farm machines and rice price. The actual amount of available loan affects most significantly the demand functions. The actual farmer's income, actual farmer's asset, loan coverage, and rice price affect the demand positively while prices of farm machines and import negatively. The annual demands of tractor, combine and rice transplanter estimated using the demand functions were also presented over the next 4 years.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2759-2769
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• 2018

The behavioral finance view on the existence of asset pricing anomalies is based on two factors: investors' sentiment and limits to arbitrage. This paper tries to examine the effect of investors' sentiment on the stock price in the Korean stock market. In order to measure investors' sentiment, we constructed the sentiment index using principal component of five sentiment variables. By using sentiment index as an additional independent variable to three risk factors, impacts of the sentiment index on individual stocks and 25 portfolios sorted by BM-size are examined. Main results found are as follows: 1) not only all three risk factors show positive impacts on the return of individual stock, but also the sentiment index has a positive impact. SI alone explains 15% of individual return variation. 2) among four independent variables, the most important factor turned out to be the market risk factor and investors' sentiment has better explanatory power on stock price than the size effect. 3) after controlling the market risk factor, the coefficient of the sentiment index for the smallest size and highest book/market value portfolios is significantly positive. 4) all the coefficients of the sentiment index for 25 portfolios sorted by BM-size have significant positive value after controlling size or (and) value.

• Asia-Pacific Journal of Business
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• v.12 no.3
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• pp.37-50
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• 2021

Purpose - This study aimed to examine the behavior surrounding the Initial Public Offering (IPO) event of firms within the same conglomerate and the impact of under-pricing and Return on Equity(ROE) on a firm's abnormal stock returns. Design/methodology - This study collected data from 166 South Korean Chaebols, consisting of 355 firms distributed as 202 listed on Korea Composite Stock Price Index (KOSPI) and 153 firms listed on Korean Securities Dealers Automated Quotations (KOSDAQ) from 2000 to 2020. The Capital Asset Pricing Model (CAPM) and the multiple regression analysis were hired to analyze the data. Findings - First, we found an adverse price reaction of IPO listing in the same chaebol group, and firms with higher under-pricing affect other firms' stock prices more adversely within the conglomerate. Next, we explored a negatively significant relation between ROE and the chaebol firms' stock returns during IPO events. Research implications - The novelty of this study is there are not many empirical studies on the impact of IPO within a conglomerate. So, the findings of this study contribute to the literature for analyzing stock's abnormal returns within a conglomerate.

• East Asian Economic Review
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• v.27 no.3
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• pp.213-242
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• 2023

This paper investigates whether the price of cryptocurrency is determined by the US dollar index, the price of investment assets such gold and oil, and the implied volatility of the KOSPI. Overall, the returns on cryptocurrencies are best predicted by the trading volume of the cryptocurrency both in-sample and out-of-sample. The estimates of gold and the dollar index are negative in the return prediction, though they are not significant. The dollar index, gold, and the cryptocurrencies seem to share characteristics which hedging instruments have in common. When investors take notice of the imminent market risks, they increase the demand for one of these assets and thereby increase the returns on the asset. The most notable result in the out-of-sample predictability is the predictability of the returns on value-weighted portfolio by gold. The empirical results show that the restricted model fails to encompass the unrestricted model. Therefore, the unrestricted model is significant in improving out-of-sample predictability of the portfolio returns using gold. From the empirical analyses, we can conclude that in-sample predictability cannot guarantee out-of-sample predictability and vice versa. This may shed light on the disparate results between in-sample and out-of-sample predictability in a large body of previous literature.

• Journal of Digital Convergence
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• v.11 no.11
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• pp.59-70
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• 2013

As people invest most of their asset in real estate, there is high interest in changing in housing and real estate prices in the future for a digital economy. Various variables are affecting the housing and real estate market. Among them, four variables : households, productive population, interest rate and index price are chosen and analyzed representatively. This study is aimed to build decision model of apartment prices in Seoul empirically. From the analysis result the stock index is the only variable which is significant statistically to apartments in Seoul. From this study, the households and productive population show the same direction as shown in the previous studies before but not significant statistically. Among the independent variables, the stock index is chosen as a major variable of determinant of Seoul apartment price. From the result of the research, prediction of stock market should be preceded to forecast the movement of housing and real estate market in the future.