• 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.

• KDI Journal of Economic Policy
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• v.13 no.4
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• pp.31-57
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• 1991

Korean firms of all sizes, from virtually every industry, have used and are using the term "excessive competition" to describe the state of their industry and to call for government interventions. Moreover, the Korean government has frequently responded to such calls in various ways favorable to the firms, such as controlling entry, curbing capacity investments, or allowing collusion. Despite such interventions' impact on the overall efficiency on the Korean economy as well as on the wealth distribution among diverse groups of economic agents, the term "excessive competition", the basis for the interventions, has so far escaped rigorous scrutiny. The objective of this paper is to clarify the notion of "excessive competition" and "over-investment" which usually accompanies "excessive competition", and to examine the circumstances under which they might occur. We first survey the cases where the terms are most widely used and proceed to examine those cases to determine if competition is indeed excessive, and if so, what causes "excessive competition". Our main concern deals with the case in which the firms must make investment decisions that involve large sunk costs while facing uncertain demand. In order to analyze this case, we developed a two period model of capacity precommitment and the ensuing competition. In the first period, oligopolistic firms make capacity investments that are irreversible. Demand is uncertain in period 1 and only the distribution is known. Thus, firms must make investment decisions under uncertainty. In the second period, demand is realized, and the firms compete with quantity under realized demand and capacity constraints. In the above setting, we find that there is "no over-investment," en ante, and there is "no excessive competition," ex post. As measured by the information available in period 1, expected return from investment of a firm is non-negative, overall industry capacity does not exceed the socially optimal level, and competition in the second period yields an outcome that gives each operating firm a non-negative second period profit. Thus, neither "excessive competition" nor "over-investment" is possible. This result will generally hold true if there is no externality and if the industry is not a natural monopoly. We also extend this result by examining a model in which the government is an active participant in the game with a well defined preference. Analysis of this model shows that over-investment arises if the government cannot credibly precommit itself to non-intervention when ex post idle capacity occurs, due to socio-political reasons. Firms invest in capacities that exceed socially optimal levels in this case because they correctly expect that the government will find it optimal for itself to intervene once over-investment and ensuing financial problems for the firms occur. Such planned over-investment and ensuing government intervention are the generic problems under the current system. These problems are expected to be repeated in many industries in years to come, causing a significant loss of welfare in the long run. As a remedy to this problem, we recommend a non-intervention policy by the government which creates and utilizes uncertainty. Based upon an argument which is essentially the same as that of Kreps and Wilson in the context of a chain-store game, we show that maintaining a consistent non-intervention policy will deter a planned over-investment by firms in the long run. We believe that the results obtained in this paper has a direct bearing on the public policies relating to many industries including the petrochemical industry that is currently in the center of heated debates.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.5
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• pp.112-124
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• 2021

This study was conducted to examine and analyze local governments, park status, project characteristics, and the implementation in detail for private park special projects across the country as a means of responding to the sunsetting of urban parks. As a result of the analysis, first, the private park special project, was found to be mainly implemented in cities with a population of more than 100,000, so there was a limit to the application on military installations or in local small cities. Therefore, rather than applying the special system collectively, it was judged that institutional flexibility, considering the characteristics and size of local government, was needed. Second, the current special projects by the park creation donation collection method shows monotonous development centered on apartment houses, so it is necessary to diversify the development by introducing a park preservation method that purchases and donates park sites. Third, it was found that the area standard needs to be eased to less than 50,000m² to include parks with high utilization and good accessibility in urban areas of large cities, as the type and area of parks are limited. Fourth, most special projects are mountain parks, which are feared to damage the natural terrain and skyline, so separate ordinances should be established and applied, and development approaches should be made to allow nature and parks to coexist with the setting of detailed building guidelines for each type of facility. The guidelines should include, first, after the nationwide private park special projects are completed, standards for appropriate returns for similar projects should be established, institutional standards such as the recovery of excess profits should be established, and environmental reviews should be conducted. Second, it was found that local governments should institutionalize the composition of private consultations to promote the efficient management of projects through a cooperative system, and third, a roadmap for maintenance after the donation of special parks should be established.