• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.77-97
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• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.

• Journal of KIISE:Databases
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• v.30 no.4
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• pp.381-396
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• 2003

Subsequence matching is an operation that finds subsequences whose changing patterns are similar to a given query sequence from time-series databases. This paper points out the performance bottleneck in subsequence matching, and then proposes an effective method that improves the performance of entire subsequence matching significantly by resolving the performance bottleneck. First, we analyze the disk access and CPU processing times required during the index searching and post processing steps through preliminary experiments. Based on their results, we show that the post processing step is the main performance bottleneck in subsequence matching, and them claim that its optimization is a crucial issue overlooked in previous approaches. In order to resolve the performance bottleneck, we propose a simple but quite effective method that processes the post processing step in the optimal way. By rearranging the order of candidate subsequences to be compared with a query sequence, our method completely eliminates the redundancy of disk accesses and CPU processing occurred in the post processing step. We formally prove that our method is optimal and also does not incur any false dismissal. We show the effectiveness of our method by extensive experiments. The results show that our method achieves significant speed-up in the post processing step 3.91 to 9.42 times when using a data set of real-world stock sequences and 4.97 to 5.61 times when using data sets of a large volume of synthetic sequences. Also, the results show that our method reduces the weight of the post processing step in entire subsequence matching from about 90% to less than 70%. This implies that our method successfully resolves th performance bottleneck in subsequence matching. As a result, our method provides excellent performance in entire subsequence matching. The experimental results reveal that it is 3.05 to 5.60 times faster when using a data set of real-world stock sequences and 3.68 to 4.21 times faster when using data sets of a large volume of synthetic sequences compared with the previous one.

• Journal of Korea Port Economic Association
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• v.27 no.1
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• pp.263-280
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• 2011

The global financial crisis, triggered by the subprime mortgage crisis in 2007, has put the world economy into the recession with financial market turmoil. I tested whether variables were cointegrated or whether there was an equilibrium relationship. Also, Generalized impulse-response function (GIRF) and accumulation impulse-response function (AIRF) may be used to understand and characterize the time series dynamics inherent in economical systems comprised of variables that may be highly interdependent. Moreover, the IRFs enables us to simulate the response in freight to a shock in the USD/JPY exchange rate, Dow Jones industrial average index, Dow Jones volatility, Chinese Import volatility. The result on the cointegration test show that the hypothesis of no cointergrating vector could be rejected at the 5 percent level. Also, the empirical analysis of cointegrating vector reveals that the increases of USD/JPY exchange rate have negative relations with freight. The result on the impulse-response analysis indicate that freight respond negatively to volatility, and then decay very quickly. Consequently, the results highlight the potential usefulness of the multivariate time series techniques accounting to behavior of Freight.

• Asian Journal for Public Opinion Research
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• v.1 no.3
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• pp.153-202
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• 2014

Aside from political leaders' popularity rates and the stock exchange index of business firms, ordinary people are highly interested in aspects of daily life, such as housing, income, health, family, food, human relations and work. Cross-national opinion polls on daily-life satisfaction were carried out in Japan, South Korea, Thailand, Hong Kong, Macao, the Philippines, India, Myanmar, Taiwan, China, Malaysia and Pakistan in the fall of 2013 and winter 2014. The percent difference index (PDI) is formulated as the sum of two positive responses (satisfied and somewhat satisfied) minus the sum of two negative responses (dissatisfied and somewhat dissatisfied). Percent difference indices are given according to society and daily-life aspects. For our analysis to go beneath national average and to go beyond national borders, two lines of analysis are carried out. First, the distance between the level of satisfaction of the top and bottom quartiles is given for each society and according to each of the daily-life aspects. Second, the regional sum of satisfaction of the top quartiles and bottom quartiles are shown crossed by daily-life aspects. In this article we confine ourselves to preliminary comparative description and analysis. More solid and deep comparisons will be carried out by local polling leaders of 12 Asian societies in the succeeding issue of the Asian Journal of Public Opinion Research. Nevertheless, two key threads stand out from this preliminary comparisons. First, social relations (family and human relations) stand out as most satisfied aspects of life in most of twelve societies. Second, the need to go beneath national averages and beyond national borders in analyzing cross-national surveys is confirmed. The comparability and validity of cross-national surveys with varying sampling method and survey mode are briefly discussed toward the end of the article.

• Journal of Aquaculture
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• v.20 no.1
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• pp.26-30
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• 2007

Resources of the sea cucumber, Stichopus japonicus in the west coast of Korea are decreasing sharply due to overfishing and severe marine pollutions. Artificial seed production and release of this species need to be conducted to maintain sea cucumber stock in the region. In this study, to provide basic information for such works, reproductive cycle of sea cucumber collected from Anmyondo in the region was histologically studied for a year from October 2004 to September 2005. This species was dioecious, and its gonads was composed of a number of gametogenic follicles. The gonadosomatic index (GSI) was reached the maximum in June (7.91), and the minimum in October (0.42). The main spawning occurs between June and August. The reproductive cycle of the sea cucumber could be divided into five stages: multiplicative stage (January to March), growing stage (March to April), mature stage (April to July), spawning.

• Current Research on Agriculture and Life Sciences
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• v.4
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• pp.163-168
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• 1986

This study was carried out to compare the conditions of Korean and Japanese forestry. Two countries were appeared same trend in forestry condition, so our forestry in future can be estimated with compared Japanese forestry, but the results obtained are as follows ; 1) The average forest area per capita of the world, Japan and Korea are 0.9, 0.23 and 0.16 ha, respectively, this means that Korean forest area is not sufficient. The growing stock of forest per capita is $22.5m^3$ in Japan and $3.9m^3$ in Korea, but timber consumptions per capita are $1m^3$ in Japan and $0.2m^3$ in Korea. Those mean that both countries have not a plentiful resource of forestry. 2) The forestry production activity becomes gradually stagnation. Both in Korea and Japan, the reforestation and stumpage felling area show gradually decreasing tendency, the artificial forest ratio of total forest area is, at present, 28% in Korea and 40% in Japan. 3) In forestry demand aspect, the ratio of imported timber is 79% in Korea and 62.4% in Japan. Because the price index of timber is lower than the general price index, the dullness of forestry-related industries is expected in future. 4) The forestry labour supply has gradually difficulty because of the reduction in farming labour. 5) The managements of national forests show deficit operation, at present, both in Korea and Japan. The results above mentioned are derived form the poor forest resources, therefore, it is considered that rather more and continuous investment is necessary, but also forestry should be invested in the territorial conservation aspect.

• International Area Studies Review
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• v.15 no.2
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• pp.247-273
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• 2011

This study analyzes the impacts on the M&A and greenfield of macroeconomic variables of home and host countries, after identifying current status and characteristics of the M&A and greenfield related to the entering way of Korean firms in China. Main empirical results are summarize as follows. First, as for foreign exchange variable, the decreased value of Korea won shows the negative correlations with both of the greenfield and M&A. Second, the real interest rate of Korea to measure the cost of capital is not significant statistically. Third, while the host country's stock market index, Shanghai Comprehensive Index, shows the expected negative correlations with the investment in the case of small & medium firm and light industry, it shows the positive correlations which is not consistent with general expectation in the case of large firm and heavy industry. Fourth, the openness of host country shows the positive correlations with both of the greenfield and M&A. Finally, in regard to the M&A, China's GDP to measure the market size of host country is not significant statistically while it shows the strong positive relationship with the greenfield investment.

• Knowledge Management Research
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• v.22 no.4
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• pp.119-133
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• 2021

As a large amount of data is produced in each industry, a number of time series pattern prediction studies are being conducted to make quick business decisions. However, there is a limit to predicting specific patterns in nonlinear time series data due to the uncertainty inherent in the data, and there are difficulties in making strategic decisions in corporate management. In addition, in recent decades, various studies have been conducted on data such as demand/supply and financial markets that are suitable for industrial purposes to predict time series data of irregular random walk models, but predict specific rules and achieve sustainable corporate objectives There are difficulties. In this study, the prediction results were compared and analyzed using the Chaos analysis method for roulette data and financial market data, and meaningful results were derived. And, this study confirmed that chaos analysis is useful for finding a new method in analyzing time series data. By comparing and analyzing the characteristics of roulette games with the time series of Korean stock index future, it was derived that predictive power can be improved if the trend is confirmed, and it is meaningful in determining whether nonlinear time series data with high uncertainty have a specific pattern.

• Economic Analysis
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• v.25 no.1
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• pp.1-33
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• 2019

This paper examines the economic factors that are related to the dynamics of the variance risk premium, and specially, which economic factors are related to the forecasting power of the variance premium regarding future index returns. Eleven general economic variables, eight interest rate variables, and eleven sentiment-associated variables are used to figure out the relevant economic variables that affect the variance risk premium. According to our empirical results, the won-dollar exchange rates, foreign reserves, the historical/implied volatility, and interest rate variables all have significant coefficients. The highest adjusted R-squared is more than 65 percent, indicating their significant explanatory power of the variance risk premium. Next, to verify the economic variables associated with the predictability of the variance risk premium, we conduct forecasting regressions to predict future stock returns and volatilities for one to six months. Our empirical analysis shows that only the won-dollar exchange rate, among the many variables associated with the dynamics of the variance risk premium, has a significant forecasting ability regarding future index returns. These results are consistent with results found in previous studies, including Londono (2012) and Bollerslev et al. (2014), which show that the variance risk premium is related to global risk factors.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.39-55
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• 2019

Recently banks and large financial institutions have introduced lots of Robo-Advisor products. Robo-Advisor is a Robot to produce the optimal asset allocation portfolio for investors by using the financial engineering algorithms without any human intervention. Since the first introduction in Wall Street in 2008, the market size has grown to 60 billion dollars and is expected to expand to 2,000 billion dollars by 2020. Since Robo-Advisor algorithms suggest asset allocation output to investors, mathematical or statistical asset allocation strategies are applied. Mean variance optimization model developed by Markowitz is the typical asset allocation model. The model is a simple but quite intuitive portfolio strategy. For example, assets are allocated in order to minimize the risk on the portfolio while maximizing the expected return on the portfolio using optimization techniques. Despite its theoretical background, both academics and practitioners find that the standard mean variance optimization portfolio is very sensitive to the expected returns calculated by past price data. Corner solutions are often found to be allocated only to a few assets. The Black-Litterman Optimization model overcomes these problems by choosing a neutral Capital Asset Pricing Model equilibrium point. Implied equilibrium returns of each asset are derived from equilibrium market portfolio through reverse optimization. The Black-Litterman model uses a Bayesian approach to combine the subjective views on the price forecast of one or more assets with implied equilibrium returns, resulting a new estimates of risk and expected returns. These new estimates can produce optimal portfolio by the well-known Markowitz mean-variance optimization algorithm. If the investor does not have any views on his asset classes, the Black-Litterman optimization model produce the same portfolio as the market portfolio. What if the subjective views are incorrect? A survey on reports of stocks performance recommended by securities analysts show very poor results. Therefore the incorrect views combined with implied equilibrium returns may produce very poor portfolio output to the Black-Litterman model users. This paper suggests an objective investor views model based on Support Vector Machines(SVM), which have showed good performance results in stock price forecasting. SVM is a discriminative classifier defined by a separating hyper plane. The linear, radial basis and polynomial kernel functions are used to learn the hyper planes. Input variables for the SVM are returns, standard deviations, Stochastics %K and price parity degree for each asset class. SVM output returns expected stock price movements and their probabilities, which are used as input variables in the intelligent views model. The stock price movements are categorized by three phases; down, neutral and up. The expected stock returns make P matrix and their probability results are used in Q matrix. Implied equilibrium returns vector is combined with the intelligent views matrix, resulting the Black-Litterman optimal portfolio. For comparisons, Markowitz mean-variance optimization model and risk parity model are used. The value weighted market portfolio and equal weighted market portfolio are used as benchmark indexes. We collect the 8 KOSPI 200 sector indexes from January 2008 to December 2018 including 132 monthly index values. Training period is from 2008 to 2015 and testing period is from 2016 to 2018. Our suggested intelligent view model combined with implied equilibrium returns produced the optimal Black-Litterman portfolio. The out of sample period portfolio showed better performance compared with the well-known Markowitz mean-variance optimization portfolio, risk parity portfolio and market portfolio. The total return from 3 year-period Black-Litterman portfolio records 6.4%, which is the highest value. The maximum draw down is -20.8%, which is also the lowest value. Sharpe Ratio shows the highest value, 0.17. It measures the return to risk ratio. Overall, our suggested view model shows the possibility of replacing subjective analysts's views with objective view model for practitioners to apply the Robo-Advisor asset allocation algorithms in the real trading fields.