• 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 Intelligence and Information Systems
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• v.26 no.4
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• pp.111-126
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• 2020

Most industries have recently become aware of the importance of customer lifetime value as they are exposed to a competitive environment. As a result, preventing customers from churn is becoming a more important business issue than securing new customers. This is because maintaining churn customers is far more economical than securing new customers, and in fact, the acquisition cost of new customers is known to be five to six times higher than the maintenance cost of churn customers. Also, Companies that effectively prevent customer churn and improve customer retention rates are known to have a positive effect on not only increasing the company's profitability but also improving its brand image by improving customer satisfaction. Predicting customer churn, which had been conducted as a sub-research area for CRM, has recently become more important as a big data-based performance marketing theme due to the development of business machine learning technology. Until now, research on customer churn prediction has been carried out actively in such sectors as the mobile telecommunication industry, the financial industry, the distribution industry, and the game industry, which are highly competitive and urgent to manage churn. In addition, These churn prediction studies were focused on improving the performance of the churn prediction model itself, such as simply comparing the performance of various models, exploring features that are effective in forecasting departures, or developing new ensemble techniques, and were limited in terms of practical utilization because most studies considered the entire customer group as a group and developed a predictive model. As such, the main purpose of the existing related research was to improve the performance of the predictive model itself, and there was a relatively lack of research to improve the overall customer churn prediction process. In fact, customers in the business have different behavior characteristics due to heterogeneous transaction patterns, and the resulting churn rate is different, so it is unreasonable to assume the entire customer as a single customer group. Therefore, it is desirable to segment customers according to customer classification criteria, such as loyalty, and to operate an appropriate churn prediction model individually, in order to carry out effective customer churn predictions in heterogeneous industries. Of course, in some studies, there are studies in which customers are subdivided using clustering techniques and applied a churn prediction model for individual customer groups. Although this process of predicting churn can produce better predictions than a single predict model for the entire customer population, there is still room for improvement in that clustering is a mechanical, exploratory grouping technique that calculates distances based on inputs and does not reflect the strategic intent of an entity such as loyalties. This study proposes a segment-based customer departure prediction process (CCP/2DL: Customer Churn Prediction based on Two-Dimensional Loyalty segmentation) based on two-dimensional customer loyalty, assuming that successful customer churn management can be better done through improvements in the overall process than through the performance of the model itself. CCP/2DL is a series of churn prediction processes that segment two-way, quantitative and qualitative loyalty-based customer, conduct secondary grouping of customer segments according to churn patterns, and then independently apply heterogeneous churn prediction models for each churn pattern group. Performance comparisons were performed with the most commonly applied the General churn prediction process and the Clustering-based churn prediction process to assess the relative excellence of the proposed churn prediction process. The General churn prediction process used in this study refers to the process of predicting a single group of customers simply intended to be predicted as a machine learning model, using the most commonly used churn predicting method. And the Clustering-based churn prediction process is a method of first using clustering techniques to segment customers and implement a churn prediction model for each individual group. In cooperation with a global NGO, the proposed CCP/2DL performance showed better performance than other methodologies for predicting churn. This churn prediction process is not only effective in predicting churn, but can also be a strategic basis for obtaining a variety of customer observations and carrying out other related performance marketing activities.

• Journal of Korean Society of Forest Science
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• v.34 no.1
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• pp.31-46
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• 1977

The study was made to give some helpful information for policy-making on ornamental tree cultivation by doing a survey on general situations, management analysis, and future prospects of the ornamental tree growing. The study was carried out through literature studies related to the subject, questionaire surveys, and on-the-spot investigation. The questionaire surveys could be divided into two parts: pre-questionaire survey and main-questionaire survey. In the pre-questionaire survey, the researchers intended to identify the total number of ornamental tree growers, cultivation areas in size and their locations. The questionaires were sent to each town and county administration authorities, forest cooperatives, and related organizations through-out the nation. The main-questionaires were prepared for detailed study and the questionaires were sent to 200 tree growers selected by option by taking considerations of the number of tree growers and the size of cultivating areas in regions. The main findings and some information obtained in the survey were as follows: 1. The total land for ornamental tree growing was amounted to 1,873.02 hectares and the number of cultivators was totaled to 2,717. 2. The main occupations of the ornamental tree growers were found in horticulture (41.9%), agronomy (25.9%), officialdom (11.3%), animal husbandry (6.5%), business circle(4.8%), and forestry (3.2%) in sequence. 3. The ornamental trees were cultivated mostly upperland (54.8), forest land (19.4%), rice paddy (11.3%) and others. 4. The educational training of the tree growers seemed quite high. The results of the survey indicated that a large number of tree growers was occupied by college graduates (38.7%), and then high school graduates (34.7%), middle school graduates (12.9%) in order. 5. The tree farming was undertaken as a side-job (41.9%) rather than main-job (23.4%), but a few of respondents rated as subsidiary-job (18.6%). 6. The management status classified by the rate of hired labors used was likely to belong to three categories: independant enterprise management (41.9%); half independant management (31.5%); and self-management (32.4%). 7. The majority of the tree growers sold their products to the consumers through middle-man channel (48.4%), or directly to the house-holder and detailers (13.7%), but a few of the respondents answered that they disposed of their products by bidding (11.2%) or by direct selling to the contractors (4.8%). 8. The channel cf marketing seemed somewhat complicated. The results of the survey were as: (1) producers ${\rightarrow}$consumers (22.6%) (2) producers ${\rightarrow}$field middle-men${\rightarrow}$consumers (33.1%) (3) producers ${\rightarrow}$field middle-men${\rightarrow}$first stage brokers${\rightarrow}$consumers (15.3%) (4) producers ${\rightarrow}$field middle-men${\rightarrow}$second stage middle-men${\rightarrow}$brokers${\rightarrow}$consumers (5.7%) (5) producers${\rightarrow}$field middle-men${\rightarrow}$third stage middle-men${\rightarrow}$second stage middlemen${\rightarrow}$brokers${\rightarrow}$consumers (4.8%) 9. It was responded that the margin for each stage of middle-men or brokers was assumed to be 30-50%(33.1%), 20-30%(32.3%), 50-100%(9.7%), and 100-200%(2.4%) in sequence. 10. The difference between the delivery price of consumers and field selling price of the producers seemed quite large. Majority of producers responded that they received half a price compared to the consumer's prices. 11. About two thirds of the respondents opposed to the measure of "Law on Preservation and Utilization of Agricultural Land" in which says that all the ornamental trees grown on flat agricultural lands less than 8 degrees in slope must be transplanted within three years to other places more than 8 degrees in slope. 12. The tree growers said that they have paid rather high land taxes than they ought to pay (38.7%), but come responded that land tax seemed to be appropriate (15.3%), and half of the respondents answered "not known". 13. The measures for the standardization of ornamental trees by size were backed up by a large number of respondents (57.3%), but one third of the respondents showed negative answer (29.8%). 14. About half of the respondents favored the systematic marketing through organization such as forest cooperatives (54%), but quite a few respondents opposed to organizing the systematic marketing channel (36.3%). 15. The necessary measures for permission in ornamental tree cultivation was rejected by a large number of respondents (49.2%) than those of favored (43.6%).