• Korean Journal of Environment and Ecology
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• v.33 no.1
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• pp.75-85
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• 2019

The purpose of this study is to understand the vegetation characteristics of ridges (Gyeongsusan-Seonunsan-Gaeipalsan) in the Seonunsan Provincial Park and to establish reference information for the management of the park in the future. We designated 62 plots with the area of $100m^2$ were installed and analyzed them to investigate the vegetation characteristics. The results of community classification based on TWINSPAN showed seven categories of vegetation communities in the surveyed region: Quercus dentata-Deciduous broad-leaved Community, Quercus variabilis-Pinus thunbergii-Quercus serrata Community, Pinus densiflora Community, Deciduous broad-leaved Community-I, Carpinus tschonoskii-Castanea crenata-Quercus aliena Community, Deciduous broad-leaved Community-II, and Carpinus tschonoskii-Carpinus laxiflora Community. In the vegetation of Seonunsan Provincial Park, coniferous trees such as Pinus thunbergii and Pinus densiflora have been gradually losing their population as part of ecological succession to deciduous broad-leaved trees such as Quercus spp., Carpinus tschonoskii, and Carpinus laxiflora. Moreover, Carpinus turczaninowii, Mallotus japonicus, and others were identified as vegetation reflecting the geographical characteristics of the region neighboring the west coast. The estimated age is 30-60 years, and the oldest tree Pinus densiflora is 63-years old. The index of diversity ($100m^2$) was 0.7942 for Carpinus tschonoskii-Carpinus laxiflora Community, 0.8406 for Carpinus tschonoskii-Castanea crenata-Quercus aliena Community, 0.8543 for Quercus dentata-Deciduous broad-leaved Community, 0.9434 for Quercus variabilis-Pinus thunbergii-Quercus serrata Community, 0.9520 for Deciduous broad-leaved Community-I, 0.9633 for Pinus densiflora Community, and 1.0340 for Deciduous broad-leaved Community-II in the ascending order.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.39 no.4
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• pp.10-23
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• 2021

During the Japanese colonial period, the palaces of Joseon were damaged in many parts. Changgyeonggung Palace is the most demolished palace with the establishment of a zoo, botanical garden, and museum. During the Japanese colonial period, the palaces of Joseon were damaged in many parts. This study examined the construction process of Jangseogak(Yi Royal-Family Museum), located right next to the Jagyeongjeon site, which was considered the most important space in the Changgyeonggung residential area of royal family zone, through historical materials and field research. Built in 1911, Jangseogak is located at a location overlooking the entire Changgyeonggung Palace and overlooking the Gyeongseong Shrine of Namsan in the distance. Changes in the surrounding space during the construction of Jangseogak can be summarized as follows. First, in the early 1910s, the topography of the garden behind Jagyeongjeon and part of the Janggo were damaged to create the site of Jangseogak. The front yard was built in the front of Jangseogak, and a stone pillar was installed, and a staircase was installed to the south. In the process, the original stone system at the rear of Yanghwadang was destroyed, and it is presumed that Jeong Iljae and other buildings were demolished. Second, in the 1920s, many pavilions were demolished and the zoo and botanical gardens and museums were completed through leveling. After the Jangseogak was completed, the circulation of the Naejeon and surrounding areas was also changed. Cherry trees and peonies were planted in the flower garden around the front yard of Jangseogak and the stairs, and a Japanese-style garden was created between Yanghwadang and Jibbokheon. Third, in the 1930s, the circulation around Jangseogak was completed in its present form, and the museum, Jangseogak, Zoological and Botanical Gardens, and Changgyeonggung, which became a cherry tree garden, were transformed into a Japanese-style cultural park. After that, the surrounding space did not change much until it was demolished. The restoration of the present palace is a long-term, national project of the Cultural Heritage Administration. The results of this study will provide important data for the restoration plan of Changgyeonggung Palace in the future, and it is expected that it will provide additional information to related researchers in the future.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.6
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• pp.469-484
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• 2021

Rock mass classification results have a great influence on construction schedule and budget as well as tunnel stability in tunnel design. A total of 3,526 tunnels have been constructed in Korea and the associated techniques in tunnel design and construction have been continuously developed, however, not many studies have been performed on how to assess rock mass quality and grade more accurately. Thus, numerous cases show big differences in the results according to inspectors' experience and judgement. Hence, this study aims to suggest a more reliable rock mass classification (RMR) model using machine learning algorithms, which is surging in availability, through the analyses based on various rock and rock mass information collected from boring investigations. For this, 11 learning parameters (depth, rock type, RQD, electrical resistivity, UCS, V_p, V_s, Young's modulus, unit weight, Poisson's ratio, RMR) from 13 local tunnel cases were selected, 337 learning data sets as well as 60 test data sets were prepared, and 6 machine learning algorithms (DT, SVM, ANN, PCA & ANN, RF, XGBoost) were tested for various hyperparameters for each algorithm. The results show that the mean absolute errors in RMR value from five algorithms except Decision Tree were less than 8 and a Support Vector Machine model is the best model. The applicability of the model, established through this study, was confirmed and this prediction model can be applied for more reliable rock mass classification when additional various data is continuously cumulated.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.427-435
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• 2023

Changes in flowering time due to weather fluctuations impact plant growth and ecosystem dynamics. Accurate prediction of flowering timing is crucial for effective forest ecosystem management. This study uses a process-based model to predict flowering timing in 2023 for five major tree species in Korean forests. Models are developed based on nine years (2009-2017) of flowering data for Abeliophyllum distichum, Robinia pseudoacacia, Rhododendron schlippenbachii, Rhododendron yedoense f. poukhanense, and Sorbus commixta, distributed across 28 regions in the country, including mountains. Weather data from the Automatic Mountain Meteorology Observation System (AMOS) and the Korea Meteorological Administration (KMA) are utilized as inputs for the models. The Single Triangle Degree Days (STDD) and Growing Degree Days (GDD) models, known for their superior performance, are employed to predict flowering dates. Daily temperature readings at a 1 km spatial resolution are obtained by merging AMOS and KMA data. To improve prediction accuracy nationwide, random forest machine learning is used to generate region-specific correction coefficients. Applying these coefficients results in minimal prediction errors, particularly for Abeliophyllum distichum, Robinia pseudoacacia, and Rhododendron schlippenbachii, with root mean square errors (RMSEs) of 1.2, 0.6, and 1.2 days, respectively. Model performance is evaluated using ten random sampling tests per species, selecting the model with the highest R². The models with applied correction coefficients achieve R² values ranging from 0.07 to 0.7, except for Sorbus commixta, and exhibit a final explanatory power of 0.75-0.9. This study provides valuable insights into seasonal changes in plant phenology, aiding in identifying honey harvesting seasons affected by abnormal weather conditions, such as those of Robinia pseudoacacia. Detailed information on flowering timing for various plant species and regions enhances understanding of the climate-plant phenology relationship.

• Journal of Global Scholars of Marketing Science
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• v.19 no.3
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• pp.49-61
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• 2009

This article provides training exercises for executives into interpreting subroutine maps of executives' thinking in processing business and industrial marketing problems and opportunities. This study builds on premises that Schank proposes about learning and teaching including (1) learning occurs by experiencing and the best instruction offers learners opportunities to distill their knowledge and skills from interactive stories in the form of goal.based scenarios, team projects, and understanding stories from experts. Also, (2) telling does not lead to learning because learning requires action-training environments should emphasize active engagement with stories, cases, and projects. Each training case study includes executive exposure to decision system analysis (DSA). The training case requires the executive to write a "Briefing Report" of a DSA map. Instructions to the executive trainee in writing the briefing report include coverage in the briefing report of (1) details of the essence of the DSA map and (2) a statement of warnings and opportunities that the executive map reader interprets within the DSA map. The length maximum for a briefing report is 500 words-an arbitrary rule that works well in executive training programs. Following this introduction, section two of the article briefly summarizes relevant literature on how humans think within contexts in response to problems and opportunities. Section three illustrates the creation and interpreting of DSA maps using a training exercise in pricing a chemical product to different OEM (original equipment manufacturer) customers. Section four presents a training exercise in pricing decisions by a petroleum manufacturing firm. Section five presents a training exercise in marketing strategies by an office furniture distributer along with buying strategies by business customers. Each of the three training exercises is based on research into information processing and decision making of executives operating in marketing contexts. Section six concludes the article with suggestions for use of this training case and for developing additional training cases for honing executives' decision-making skills. Todd and Gigerenzer propose that humans use simple heuristics because they enable adaptive behavior by exploiting the structure of information in natural decision environments. "Simplicity is a virtue, rather than a curse". Bounded rationality theorists emphasize the centrality of Simon's proposition, "Human rational behavior is shaped by a scissors whose blades are the structure of the task environments and the computational capabilities of the actor". Gigerenzer's view is relevant to Simon's environmental blade and to the environmental structures in the three cases in this article, "The term environment, here, does not refer to a description of the total physical and biological environment, but only to that part important to an organism, given its needs and goals." The present article directs attention to research that combines reports on the structure of task environments with the use of adaptive toolbox heuristics of actors. The DSA mapping approach here concerns the match between strategy and an environment-the development and understanding of ecological rationality theory. Aspiration adaptation theory is central to this approach. Aspiration adaptation theory models decision making as a multi-goal problem without aggregation of the goals into a complete preference order over all decision alternatives. The three case studies in this article permit the learner to apply propositions in aspiration level rules in reaching a decision. Aspiration adaptation takes the form of a sequence of adjustment steps. An adjustment step shifts the current aspiration level to a neighboring point on an aspiration grid by a change in only one goal variable. An upward adjustment step is an increase and a downward adjustment step is a decrease of a goal variable. Creating and using aspiration adaptation levels is integral to bounded rationality theory. The present article increases understanding and expertise of both aspiration adaptation and bounded rationality theories by providing learner experiences and practice in using propositions in both theories. Practice in ranking CTSs and writing TOP gists from DSA maps serves to clarify and deepen Selten's view, "Clearly, aspiration adaptation must enter the picture as an integrated part of the search for a solution." The body of "direct research" by Mintzberg, Gladwin's ethnographic decision tree modeling, and Huff's work on mapping strategic thought are suggestions on where to look for research that considers both the structure of the environment and the computational capabilities of the actors making decisions in these environments. Such research on bounded rationality permits both further development of theory in how and why decisions are made in real life and the development of learning exercises in the use of heuristics occurring in natural environments. The exercises in the present article encourage learning skills and principles of using fast and frugal heuristics in contexts of their intended use. The exercises respond to Schank's wisdom, "In a deep sense, education isn't about knowledge or getting students to know what has happened. It is about getting them to feel what has happened. This is not easy to do. Education, as it is in schools today, is emotionless. This is a huge problem." The three cases and accompanying set of exercise questions adhere to Schank's view, "Processes are best taught by actually engaging in them, which can often mean, for mental processing, active discussion."

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.93-112
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• 2015

Depending on the change in consumer's consumption pattern, existing retail shop has evolved in hypermarket or convenience store offering grocery and daily products mostly. Therefore, it is important to maintain the inventory levels and proper product configuration for effectively utilize the limited space in the retail store and increasing sales. Accordingly, this study proposed proper product configuration and inventory level strategy based on RFM(Recency, Frequency, Monetary) model and SOM(self-organizing map) for manage the retail shop effectively. RFM model is analytic model to analyze customer behaviors based on the past customer's buying activities. And it can differentiates important customers from large data by three variables. R represents recency, which refers to the last purchase of commodities. The latest consuming customer has bigger R. F represents frequency, which refers to the number of transactions in a particular period and M represents monetary, which refers to consumption money amount in a particular period. Thus, RFM method has been known to be a very effective model for customer segmentation. In this study, using a normalized value of the RFM variables, SOM cluster analysis was performed. SOM is regarded as one of the most distinguished artificial neural network models in the unsupervised learning tool space. It is a popular tool for clustering and visualization of high dimensional data in such a way that similar items are grouped spatially close to one another. In particular, it has been successfully applied in various technical fields for finding patterns. In our research, the procedure tries to find sales patterns by analyzing product sales records with Recency, Frequency and Monetary values. And to suggest a business strategy, we conduct the decision tree based on SOM results. To validate the proposed procedure in this study, we adopted the M-mart data collected between 2014.01.01~2014.12.31. Each product get the value of R, F, M, and they are clustered by 9 using SOM. And we also performed three tests using the weekday data, weekend data, whole data in order to analyze the sales pattern change. In order to propose the strategy of each cluster, we examine the criteria of product clustering. The clusters through the SOM can be explained by the characteristics of these clusters of decision trees. As a result, we can suggest the inventory management strategy of each 9 clusters through the suggested procedures of the study. The highest of all three value(R, F, M) cluster's products need to have high level of the inventory as well as to be disposed in a place where it can be increasing customer's path. In contrast, the lowest of all three value(R, F, M) cluster's products need to have low level of inventory as well as to be disposed in a place where visibility is low. The highest R value cluster's products is usually new releases products, and need to be placed on the front of the store. And, manager should decrease inventory levels gradually in the highest F value cluster's products purchased in the past. Because, we assume that cluster has lower R value and the M value than the average value of good. And it can be deduced that product are sold poorly in recent days and total sales also will be lower than the frequency. The procedure presented in this study is expected to contribute to raising the profitability of the retail store. The paper is organized as follows. The second chapter briefly reviews the literature related to this study. The third chapter suggests procedures for research proposals, and the fourth chapter applied suggested procedure using the actual product sales data. Finally, the fifth chapter described the conclusion of the study and further research.

• 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.

• Journal of Forest and Environmental Science
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• v.21 no.1
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• pp.34-58
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• 2005

This study was executed to find out how to improve the planting and use of landscaping plants in Weonju and Hoengseong. 1. The number of street trees were 22,068 and the species number were 10 species in Weonju in 2004. The major species of street trees were Ginkgo biloba(58%), Prunus sargentii(15%), Zelkova serrata(9%), Prunus armeniaca var. ansu(8%), and Acer palmatum(6%). The ratio of native species versus exotic were 50:50. In Hoengseong, the number of street trees was 13,500 and the species number were 15 species. The major species of street trees were Prunus sargentii(42%), Ginkgo biloba(23%), Acer triflorum(12%), Prunus armeniaca var. ansu(6%), and Prunus mume(4%). The ratio of native species versus exotic were 67:33. The species of which planting frequency within two areas was very high were Ginkgo biloba and Prunus sargentii. 2. It is necessary to select tree species suitable for the characteristics of the locality and to raise distinctive street trees that contribute to the tourist industry. For the purpose, the appropriate street trees in two areas are Cornus controversa, Quercus aliena, Zelkova serrata, Prunus padus, Sorbus alnifolia, Sorbus comixta, Albizzia julibrissin, Acer triflorum, Styrax japonica, Chionanthus retusus, Celtis sinensis, Prunus yedoensis, Malus sieboldii, Crataegus Pinnatifida, Prunus armeniaca var. ansu and Pyrus pyrifolia etc.. 3. Appropriate pruning adds to the aesthetic and prolongs the useful life, it also requires less managing of insects and diseases to maintain good healthy of street trees. Street trees were not properly pruned due to electric lines and shortage of pruning information. The pruning was controlled by Korea Electric Power Co, which has no pruning information. Pruning must be maintained by a professional landscape company to maintain good shape such as with Bonsai. The shrubs planting zone between street trees and other trees, and preservation plates were established for healthy of street trees. They have to be repaired and maintained well to keep better environmental conditions. The proper fertilization, the control of pests and diseases, the installation of drainpipe and the use of soil brought from another place were needed to improve the planting, use and maintenance of landscape plants. 4. The species number of school trees and flowers of 102 schools in Weonju and Hoengseong were 17species, 16species respectively. The major species of school trees in Weonju were Juniperus chinensis(24%), Ginkgo biloba(17%), Pinus densiflora(14%), Zelkova serrata(14%), and Pinus koraiensis(9%), and those of school trees in Hoengseong were Pinus koraiensis(44%), Abies holophylla(25%), Juniperus chinensis(8%), and Ginkgo biloba(8%). The major species of school flowers in Weonju were Rosa centifolia(47%), Forsythia koreana(24%), Magnolia kobus(12%), and Rhododendron schlippenbachii(6%), and those of school flowers in Hoengseong were Forsythia koreana(36%), Rhododendron schlippenbachii(33%), Magnolia kobus(6%) and Dicentra spectabilis(6%). 5. The species number of the protection trees designated by Woenju and Hoengseong were 15 species. The major species of protection trees were Zelkova serrata(100 trees), Ginkgo biloba(18) Pinus densiflora(7), Quercus spp. (5), Juniperus chinensis(4) and Alnus japonica(4). 6. The landscape plants planted around 2004 in weonju were Prunus yedoensis(2,563 trees), Betula platyphylla var. japonica(2,000), Abies holophylla(1,785), Diospyros kaki(1,100), Prunus sargentii(880) and Prunus armeniaca var. ansu(708) etc.. The shrubs planted were Rhododendron obutusum(21,559 plants), Rosa centifolia (7,150), Rhododendron yedoense var. poukhanense(5,950), Forsythia koreana(3,000) and Ligustrum obtusi[olium(2,500) etc.. The landscape plants planted in Hoengseong Acer triflorum(928trees), Prunus yedoensis(455), Zelkova serrata(327), Thuja orientalis(261), Prunus sargentii(257), Pinus koraiensis(200), Prunus persica for. rubro-plena(200) and Pyrus pyrifolia (200) etc.. The shrubs planted were Rhododendron yedoense var. poukhanense(15,936), Syringa dilatata(10,090), Forsythia koreana(9,660), Cercis chinensis(3,200), Buxus microphylla var. koreana(2,600) and Rosa centifolia(1,868) etc.. 7. The species numbers of the herbaceous plants planted in 2004 in Weonju were 24 species and the ratio of native species versus exotic were 7:17. The major species of perennial plants were Aster koraiensis(30,656 plants), Coreopsis drummondii(7,656), Rudbeckia bicolor(6,000), Chrysanthemum morifolium(4,850) and Chrysanthemum zawadskii var. latilobum(4,312). The major species of annuals and biennials were Cosmos bipinnatus(672,000 plants), Zinnia elegans(35,600), Petunia hybrida(26,920), Viola tricolor(23,000), Helianthus annuus(17,000), and Geranium cinereum var. pubcaulescens(5,200). In Hoengseong, the numbers of herbaceous plants were 906,310 plants and the species numbers were 15 species. The major species of perennials plants were Aster koraiensis(70,480 plants), Hemerocallis fulva(20,070), and Phlox drummondii(18,000). The major species of annuals and biennials were Phlox hybrida(174,000 plants), Cosmos bipinnatus(125,000), Zinnia elegans(109,000), Tagetes patula(96,700), Vinca rosea(89,000) and Calendula officinalis(70,000). 8. Through these result, it was thought that the diversification of planting species, the selection of plants suitable to each space and the generalization of use of native species were needed.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.149-169
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• 2020

"The Urban Renewal New Deal project", one of the government's major national projects, is about developing underdeveloped areas by investing 50 trillion won in 100 locations on the first year and 500 over the next four years. This project is drawing keen attention from the media and local governments. However, the project model which fails to reflect the original characteristics of the area as it divides project area into five categories: "Our Neighborhood Restoration, Housing Maintenance Support Type, General Neighborhood Type, Central Urban Type, and Economic Base Type," According to keywords for successful urban regeneration in Korea, "resident participation," "regional specialization," "ministerial cooperation" and "public-private cooperation", when local governments propose urban regeneration projects to the government, they can see that it is most important to accurately understand the characteristics of the city and push ahead with the projects in a way that suits the characteristics of the city with the help of local residents and private companies. In addition, considering the gentrification problem, which is one of the side effects of urban regeneration projects, it is important to select and implement urban regeneration types suitable for the characteristics of the area. In order to supplement the limitations of the 'Urban Regeneration New Deal Project' methodology, this study aims to propose a system that recommends urban regeneration types suitable for urban regeneration sites by utilizing various machine learning algorithms, referring to the urban regeneration types of the '2025 Seoul Metropolitan Government Urban Regeneration Strategy Plan' promoted based on regional characteristics. There are four types of urban regeneration in Seoul: "Low-use Low-Level Development, Abandonment, Deteriorated Housing, and Specialization of Historical and Cultural Resources" (Shon and Park, 2017). In order to identify regional characteristics, approximately 100,000 text data were collected for 22 regions where the project was carried out for a total of four types of urban regeneration. Using the collected data, we drew key keywords for each region according to the type of urban regeneration and conducted topic modeling to explore whether there were differences between types. As a result, it was confirmed that a number of topics related to real estate and economy appeared in old residential areas, and in the case of declining and underdeveloped areas, topics reflecting the characteristics of areas where industrial activities were active in the past appeared. In the case of the historical and cultural resource area, since it is an area that contains traces of the past, many keywords related to the government appeared. Therefore, it was possible to confirm political topics and cultural topics resulting from various events. Finally, in the case of low-use and under-developed areas, many topics on real estate and accessibility are emerging, so accessibility is good. It mainly had the characteristics of a region where development is planned or is likely to be developed. Furthermore, a model was implemented that proposes urban regeneration types tailored to regional characteristics for regions other than Seoul. Machine learning technology was used to implement the model, and training data and test data were randomly extracted at an 8:2 ratio and used. In order to compare the performance between various models, the input variables are set in two ways: Count Vector and TF-IDF Vector, and as Classifier, there are 5 types of SVM (Support Vector Machine), Decision Tree, Random Forest, Logistic Regression, and Gradient Boosting. By applying it, performance comparison for a total of 10 models was conducted. The model with the highest performance was the Gradient Boosting method using TF-IDF Vector input data, and the accuracy was 97%. Therefore, the recommendation system proposed in this study is expected to recommend urban regeneration types based on the regional characteristics of new business sites in the process of carrying out urban regeneration projects."

• 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.