• Korean Journal of Construction Engineering and Management
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• v.20 no.5
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• pp.3-11
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• 2019

This study is designed to examine the stock price of construction firms which are affected by the deregulation of new apartment sales price. As empirical methodology, it uses the traditional event study analysis to test the influence of the deregulation of new apartment sales price and the regression analysis to test which variables are related. The results of this study are summarized as follows : First, the cumulative abnormal return of stock is positive when government announced the deregulation of new apartment sales price. The cumulative abnormal return of stock for 21 trading day before -10 to +10 day is 25.51% which is significant different from zero at 1 percent level. This result suggests that the deregulation of new apartment sales price conveys good information to stock market that the firms performance will be good in the future. Second, in the regression analysis this study shows that the cumulative abnormal return of stock is related to firm's profit margin ratio.

• International Area Studies Review
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• v.13 no.2
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• pp.277-297
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• 2009

This paper empirically investigates a long-run and short-run equilibrium relationships for exchange rate and stock prices in Korea and Japan from January 1998 to July 2008. Because using monthly data in my study, analyzes unit root test and VEC model including seasonality to overcome bias that happen in seasonal adjustment. The empirical evidence suggests that exists strong evidence supporting the long-run cointegration relationships between exchange rates and stock prices of the Korea and Japan. This implies that it is possible to predict one market from another for both countries, which seems to violate the efficient market hypothesis. In the long-run a negative relationship running from the KRW/JPY real exchange rate to the stock prices of Korea strongly argues for the traditional approach.

• Asia pacific journal of information systems
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• v.24 no.2
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• pp.233-253
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• 2014

Can the stock market really be predicted? Stock market prediction has attracted much attention from many fields including business, economics, statistics, and mathematics. Early research on stock market prediction was based on random walk theory (RWT) and the efficient market hypothesis (EMH). According to the EMH, stock market are largely driven by new information rather than present and past prices. Since it is unpredictable, stock market will follow a random walk. Even though these theories, Schumaker [2010] asserted that people keep trying to predict the stock market by using artificial intelligence, statistical estimates, and mathematical models. Mathematical approaches include Percolation Methods, Log-Periodic Oscillations and Wavelet Transforms to model future prices. Examples of artificial intelligence approaches that deals with optimization and machine learning are Genetic Algorithms, Support Vector Machines (SVM) and Neural Networks. Statistical approaches typically predicts the future by using past stock market data. Recently, financial engineers have started to predict the stock prices movement pattern by using the SNS data. SNS is the place where peoples opinions and ideas are freely flow and affect others' beliefs on certain things. Through word-of-mouth in SNS, people share product usage experiences, subjective feelings, and commonly accompanying sentiment or mood with others. An increasing number of empirical analyses of sentiment and mood are based on textual collections of public user generated data on the web. The Opinion mining is one domain of the data mining fields extracting public opinions exposed in SNS by utilizing data mining. There have been many studies on the issues of opinion mining from Web sources such as product reviews, forum posts and blogs. In relation to this literatures, we are trying to understand the effects of SNS exposures of firms on stock prices in Korea. Similarly to Bollen et al. [2011], we empirically analyze the impact of SNS exposures on stock return rates. We use Social Metrics by Daum Soft, an SNS big data analysis company in Korea. Social Metrics provides trends and public opinions in Twitter and blogs by using natural language process and analysis tools. It collects the sentences circulated in the Twitter in real time, and breaks down these sentences into the word units and then extracts keywords. In this study, we classify firms' exposures in SNS into two groups: positive and negative. To test the correlation and causation relationship between SNS exposures and stock price returns, we first collect 252 firms' stock prices and KRX100 index in the Korea Stock Exchange (KRX) from May 25, 2012 to September 1, 2012. We also gather the public attitudes (positive, negative) about these firms from Social Metrics over the same period of time. We conduct regression analysis between stock prices and the number of SNS exposures. Having checked the correlation between the two variables, we perform Granger causality test to see the causation direction between the two variables. The research result is that the number of total SNS exposures is positively related with stock market returns. The number of positive mentions of has also positive relationship with stock market returns. Contrarily, the number of negative mentions has negative relationship with stock market returns, but this relationship is statistically not significant. This means that the impact of positive mentions is statistically bigger than the impact of negative mentions. We also investigate whether the impacts are moderated by industry type and firm's size. We find that the SNS exposures impacts are bigger for IT firms than for non-IT firms, and bigger for small sized firms than for large sized firms. The results of Granger causality test shows change of stock price return is caused by SNS exposures, while the causation of the other way round is not significant. Therefore the correlation relationship between SNS exposures and stock prices has uni-direction causality. The more a firm is exposed in SNS, the more is the stock price likely to increase, while stock price changes may not cause more SNS mentions.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.13 no.6
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• pp.521-528
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• 1988

Into the telecommunications industry, which had been monopolistic, a few advanced countries introduced competition through 70's and 80's. And this trend is going on worldwide. The introduction of competition into the industry is made mainly in the long distance, international and enhanced market. This liberalisation results from the fundamental change of the cost function. Suggesting that the cost comprises of that of the facility sector and that of the operation sector there exists the economies of scale in the facility sector in general. The major ground for the monopolistic industrial structure in the past was the natural monopoly depending on the economies of scale. But the rapid advance of the technology by a large margin. This decrease has resulted in the change of the cost function. That is while there exists the economies of scale in the smaller production scale, the average cost increases beyond a certain scale. This means that the natural monopoly collapsed, and that the competitive structure is more efficient than the monopolistic structure. But, because there exists economies of scale in the smaller scale, the desirable number of players, which could result in efficient industry structure depends on the market size. Such correlation between technological level market size and the degree of regulation is found in the case of U.S.A., Japan and U.K., where deregulation policy of the telecommunications market has already been carried out. In U.S.A., which has the largest market and the highest technological level the degree of regulation is lowest. Also in the order of Japan and U.K. the regulation is severer. Japan and U.K. are likely to liberalize still more, as the technology advances and the market grows. This article is just the beginning of the research, and this hypothesis requires more detailed research.

• Management & Information Systems Review
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• v.30 no.4
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• pp.147-173
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• 2011

It is generally accepted that there are momentum effects in the short term and reversal effects in the long term, which makes abnormal excess returns in the major stock markets in the world. In Korea stock market, however, the previous studies demonstrate that contrarian strategies based on reversal effects are more effective than momentum strategies following momentum effects in the short term as well as in the long term. This paper examines wether contrarian strategies are still effective In Korea stock market from 1980 to 2009, and the short term reversals may be changed after the foreign exchange crisis in 1997-1998. Moreover, this paper investigates how contrarian profits are shown considering the state of market. In my research, unlike previous studies, I find that both of contrarian strategies using price change and price level cannot gain excess risk adjusted returns in Korea stock market from 1980 to 2009, but this result is due to the fact that reversal effects existed before the foreign exchange crisis but momentum effects does after the foreign exchange crisis in 1997-1998. Specially, after the foreign exchange crisis, it is confirmed momentum strategies using 52 week high price, that is, price level are more effective than momentum strategies using price change. And following the strategies using 52 week high price after the foreign exchange crisis, the momentum is not only observed in the up market but also in the down market, which is different with the results of the studies regarding to American market, where the momentum is just found in the up market.

• Journal of Arbitration Studies
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• v.11 no.1
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• pp.37-73
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• 2001

I. Preface It is widely understood that the 21st century, with the development of information technology(IT) and the spread of networks, will be called a digital economy where information-driven business will be norm rather than the smokestack economy of the past. And the drastically changed world market is expected to generate even more commercial transactions across the world creating large numbers of legal disputes. Therefore, each country will attempt to develop ADR(Alternative Dispute Resolution) as an alternative to judicial proceedings in order to cope with not only the ever-increasing international commercial claims but also domestic legal disputes. Taking this reality into account, this study begins with an exploration of mediation procedure as a way of helping the court faced with its overwhelming numbers of lawsuits. And also this study makes a theoretical comparison between ADR and mediation procedure, analyzing critical factors affecting the mediation agreement. Furthermore, it is designed to find ways for disputing parties to make better use of mediation and ensure fairness to the parties involved. It tries to enhance mediators' understanding of critical factors influencing the mediation agreement and their ability to handle commercial disputes in a more efficient way. To make an empirical analysis of these factors, bibliographic research and questionnaire were used. This analysis will fill the gap between the theory and reality, and make possible the structured research on the factors. Therefore, this study sets the model by which we can evaluate how the three critical factors (parties' inclination, mediators' characteristics, institutional features) affect the parties reaching a mediation agreement. Based on this analysis, a theoretical hypothesis was built and a questionnaire was made and distributed. During the course of this work, SPSSWIN 10.0 program was applied.

• Environmental and Resource Economics Review
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• v.21 no.1
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• pp.3-25
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• 2012

Generally speaking, firms, faced with a regulatory environment, are likely to use more or less inputs than optimal level due to allocative inefficiency of inputs. This paper, first, tests allocative efficiency of fuel inputs and calculates the divergence between the actual and optimal levels of each fuel input conditional on the optimal level of capital stock in Korean thermal power industry. Then, given that each fuel is efficiently allocated. potential reduction of $CO_2$ is estimated over the period 1987~2008. The null hypothesis of allocative efficiency with respect to all fuels is rejected, indicating that thermal power plants fail to attain cost minimization subject do market prices. Allocative efficiency between each pair of fuels is also tested; efficient uses of fuels relative to each other are all rejected. Empirical results indicate that coal and gas are used more and oil is used less than optimal level. On average, more than 10 million tons of $CO_2$ per year could be reduced by achieving allocative efficiency of fuels.

• Journal of Convergence for Information Technology
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• v.11 no.5
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• pp.94-103
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• 2021

This study aims to propose technical trading rules for Bitcoin futures and empirically analyze investment performance. Investment strategies include standard trading rules such as VMA, TRB, FR, MACD, RSI, BB, using Bitcoin futures daily data from December 18, 2017 to March 31, 2021. The trend-following rules showed higher investment performance than the comparative strategy B&H. Compared to KOSPI200 index futures, Bitcoin futures investment performance was higher. In particular, the investment performance has increased significantly in Sortino Ratio, which reflects downside risk. This study can find academic significance in that it is the first attempt to systematically analyze the investment performance of standard technical trading rules of Bitcoin futures. In future research, it is necessary to improve investment performance through the use of deep learning models or machine learning models to predict the price of Bitcoin futures.

• Journal of the Korean Data and Information Science Society
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• v.28 no.4
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• pp.831-841
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• 2017

The irrational number ${\pi}$ is defined as the ratio of circumference of a circle to its radius and always becomes constant. This article does Monte Carlo approximation of its value using the famous Buffon's needle experiment and shows that its convergence is not always proportional to the sample size. We also do Monte Carlo simulations to see the convergence of the computed ${\pi}$ values from the random walk series with independent normal increment. Finally we apply the theoretical derivation to various financial time series data such as KOSPI, stock prices of Korean big firms, global stock indices and major foreign exchange rates. The historical data shows that log transformed data random walk process but most of their first lagged data don't follow a normal distribution. More importantly the computed value from the ratio of the regression coefficient ${\pi}$ tend to converge a constant, unfortunately not ${\pi}$. Using this result we could doubt on the efficient market hypothesis, and relate the degree of the hypothesis with the amount of deviation of the estimated ${\pi}$ values.

• 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.