• Strategy21
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• s.46
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• pp.239-276
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• 2020

The failure of early economic sanctions aimed at hurting the overall economies of targeted states called for a more sophisticated design of economic sanctions. This paved way for the advent of 'smart sanctions,' which target the supporters of the regime instead of the public mass. Despite controversies over the effectiveness of economic sanctions as a coercive tool to change the behavior of a targeted state, the transformation from 'comprehensive sanctions' to 'smart sanctions' is gaining the status of a legitimate method to impose punishment on states that do not conform to international norms, the nonproliferation of weapons of mass destruction in this particular context of the paper. The five permanent members of the United Nations Security Council proved that it can come to an accord on imposing economic sanctions over adopting resolutions on waging military war with targeted states. The North Korean nuclear issue has been the biggest security threat to countries in the region, even for China out of fear that further developments of nuclear weapons in North Korea might lead to a 'domino-effect,' leading to nuclear proliferation in the Northeast Asia region. Economic sanctions had been adopted by the UNSC as early as 2006 after the first North Korean nuclear test and has continually strengthened sanctions measures at each stage of North Korean weapons development. While dubious of the effectiveness of early sanctions on North Korea, recent sanctions that limit North Korea's exports of coal and imports of oil seem to have an impact on the regime, inducing Kim Jong-un to commit to peaceful talks since 2018. The purpose of this paper is to add a variable to the factors determining the success of economic sanctions on North Korea: preventing North Korea's evasion efforts by conducting illegal transshipments at sea. I first analyze the cause of recent success in the economic sanctions that led Kim Jong-un to engage in talks and add the maritime element to the argument. There are three conditions for the success of the sanctions regime, and they are: (1) smart sanctions, targeting commodities and support groups (elites) vital to regime survival., (2) China's faithful participation in the sanctions regime, and finally, (3) preventing North Korea's maritime evasion efforts.

• Tuberculosis and Respiratory Diseases
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• v.44 no.3
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• pp.592-600
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• 1997

Background : The level of imposed work of breathing (WOB) is important for patient-ventilator synchrony and during weaning from mechanical ventilation. Triggering methods and the sensitivity of demand system are important determining factors of the imposed WOB. Flow triggering method is available on several modern ventilator and is believed to impose less work to a patient-triggered breath than pressure triggering method. We intended to compare the level of imposed WOB on two different methods of triggering and also at different levels of sensitivities on each triggering method (0.7 L/min vs 2.0 L/min on flow triggering ; $-1\;cmH_2O$ vs $-2cm\;H_2O$ on pressure triggering). Methods : The subjects were 12 patients ($64.8{\pm}4.2\;yrs$) on mechanical ventilation and were stable in respiratory pattern on CPAP $3\;cmH_2O$. Four different triggering sensitivities were applied at random order. For determination of imposed WOB, tracheal end pressure was measured through the monitoring lumen of Hi-Lo Jet tracheal tube (Mallincrodt, New York, USA) using pneumotachograph/pressure transducer (CP-100 pulmonary monitor, Bicore, Irvine, CA, USA). Other data of respiratory mechanics were also obtained by CP-100 pulmonary monitor. Results : The imposed WOB was decreased by 37.5% during 0.7 L/min on flow triggering compared to $-2\;cmH_2O$ on pressure triggering and also decreased by 14% during $-1\;cmH_2O$ compared to $-2\;cmH_2O$ on pressure triggering (p < 0.05 in each). The PTP(Pressure Time Product) was also decreased significantly during 0.7 L/min on flow triggering and $-1\;cmH_2O$ on pressure triggering compared to $-2\;cmH_2O$ on pressure triggering (p < 0.05 in each). The proportions of imposed WOB in total WOB were ranged from 37% to 85% and no significant changes among different methods and sensitivities. The physiologic WOB showed no significant changes among different triggering methods and sensitivities. Conclusion : To reduce the imposed WOB, flow triggering with sensitivity of 0.7 L/min would be better method than pressure triggering with sensitivity of $-2\;cm\;H_2O$.

• The Journal of Korean Academy of Prosthodontics
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• v.46 no.2
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• pp.125-136
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• 2008

Statement of problem: Since the concept of osseointegration in dental implants was introduced by $Br{{\aa}}nemark$ et al, high long-term success rates have been achieved. Though the use of dental implants have increased dramatically, there are few studies on domestic implants with clinical and objective long-term data. Purpose: The aim of this retrospective study was to provide long-term data on the $Neoplan^{(R)}$ implant, which features a sandblasted and acid-etched surface and external connection. Material and methods: 96 $Neoplan^{(R)}$ implants placed in 25 patients in Yonsei University Hospital were examined to determine the effect of the factors on marginal bone loss, through clinical and radiographic results during 18 to 57 month period. Results: 1. Out of a total of 96 implants placed in 25 patients, two fixtures were lost, resulting in 97.9% of cumulative survival rate. 2. Throughout the study period, the survival rates were 96.8% in the maxilla and 98.5% in the mandible. The survival rates were 97.6% in the posterior regions and 100% in the anterior regions. 3. The mean bone loss for the first year after prosthesis placement and the mean annual bone loss after the first year for men were significantly higher than that of women (P<0.05). 4. The group of partial edentulism with no posterior teeth distal to the implant prosthesis showed significantly more bone loss compared to the group of partial edentulism with presence of posterior teeth distal to the implant prosthesis in terms of mean bone loss for the first year and after the first year (P<0.05). 5. The mean annual bone loss after the first year was more pronounced in posterior regions compared to anterior regions (P<0.05). 6. No significant difference in marginal bone loss was found in the following factors: jaws, type of prostheses, type of opposing dentition, and submerged /non-submerged implants (P<0.05). Conclusion: On the basis of these results, the factors influencing marginal bone loss were gender, type of edentulism, and location in the arch, while the factors such as arch, type of prostheses, type of opposing dentition, submerged / non- submerged implants had no significant effect on bone loss. In the present study, the cumulative survival rate of the $Neoplan^{(R)}$ implant with a sandblasted and acid-etched surface was 97.9% up to a maximum 57-month period. Further long-term investigations for this type of implant system and evaluation of other various domestic implant systems are needed in future studies.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.29-44
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• 2017

In Internet commerce, consumers are heavily influenced by product reviews written by other users who have already purchased the product. However, as the product reviews accumulate, it takes a lot of time and effort for consumers to individually check the massive number of product reviews. Moreover, product reviews that are written carelessly actually inconvenience consumers. Thus many online vendors provide mechanisms to identify reviews that customers perceive as most helpful (Cao et al. 2011; Mudambi and Schuff 2010). For example, some online retailers, such as Amazon.com and TripAdvisor, allow users to rate the helpfulness of each review, and use this feedback information to rank and re-order them. However, many reviews have only a few feedbacks or no feedback at all, thus making it hard to identify their helpfulness. Also, it takes time to accumulate feedbacks, thus the newly authored reviews do not have enough ones. For example, only 20% of the reviews in Amazon Review Dataset (Mcauley and Leskovec, 2013) have more than 5 reviews (Yan et al, 2014). The purpose of this study is to analyze the factors affecting the usefulness of online product reviews and to derive a forecasting model that selectively provides product reviews that can be helpful to consumers. In order to do this, we extracted the various linguistic, psychological, and perceptual elements included in product reviews by using text-mining techniques and identifying the determinants among these elements that affect the usability of product reviews. In particular, considering that the characteristics of the product reviews and determinants of usability for apparel products (which are experiential products) and electronic products (which are search goods) can differ, the characteristics of the product reviews were compared within each product group and the determinants were established for each. This study used 7,498 apparel product reviews and 106,962 electronic product reviews from Amazon.com. In order to understand a review text, we first extract linguistic and psychological characteristics from review texts such as a word count, the level of emotional tone and analytical thinking embedded in review text using widely adopted text analysis software LIWC (Linguistic Inquiry and Word Count). After then, we explore the descriptive statistics of review text for each category and statistically compare their differences using t-test. Lastly, we regression analysis using the data mining software RapidMiner to find out determinant factors. As a result of comparing and analyzing product review characteristics of electronic products and apparel products, it was found that reviewers used more words as well as longer sentences when writing product reviews for electronic products. As for the content characteristics of the product reviews, it was found that these reviews included many analytic words, carried more clout, and related to the cognitive processes (CogProc) more so than the apparel product reviews, in addition to including many words expressing negative emotions (NegEmo). On the other hand, the apparel product reviews included more personal, authentic, positive emotions (PosEmo) and perceptual processes (Percept) compared to the electronic product reviews. Next, we analyzed the determinants toward the usefulness of the product reviews between the two product groups. As a result, it was found that product reviews with high product ratings from reviewers in both product groups that were perceived as being useful contained a larger number of total words, many expressions involving perceptual processes, and fewer negative emotions. In addition, apparel product reviews with a large number of comparative expressions, a low expertise index, and concise content with fewer words in each sentence were perceived to be useful. In the case of electronic product reviews, those that were analytical with a high expertise index, along with containing many authentic expressions, cognitive processes, and positive emotions (PosEmo) were perceived to be useful. These findings are expected to help consumers effectively identify useful product reviews in the future.

• Journal of Animal Science and Technology
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• v.46 no.2
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• pp.261-272
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• 2004

This study was conducted to investigate indices affecting composts maturity for swine manure compost produced in a commercial composting facility with air-forced from the bottom. The composting was made of swine manure mixed with puffing rice hull(6: 4) and turned by escalating agitator twice a day. Composting samples were collected periodically during a 45-d composting cycle at that system, showing that indices of Ammonium-N to Nitrate-N ratio were sensitive indicators of composting quality. Pile temperature maintained more than 62$^{\circ}C$ and water contents decreased about 20% for 25days of composting. A great variety and high numbers of aerobic thermophilic heterotropic microbes playing critical roles in stability of composts have been examined in the final composts, sbowing that they were detected $10^8$ to $10^{10}$ $CFUg^{-1}$ in mesophilic bacteria, $10^3$ - $10^4$ in fungi and $10^6$ - $10^8$ in actinomycetes, respectively. The results of this study for detennining a factor affecting compost stability evaluations based on composting steps were as follows; 1. Ammonium-N concentrations were highest at the beginning of composting, reaching approximately 421mg/kg. However Ammonium-N concentrations were lower during curing, reaching approximately l04mg/kg just after 45 day. The ratio between $NH_4-N$ and $NO_3-N$ was above II at the beginning of composting and less than 2 at the final step(45 day). 2. Seed germination Index was dependent upon the compost phytotoxicity and its nutrition. The phytotocity caused the GI to low during the period of active composting(till 25 days of composting time) depending on the value of the undiluted. After 25 days of composting time, the GI was dependent upon compost nutrition. The Gennination index of the final step was calculated at over 80 without regard to treatments. 3. E4: E6 ratio in humic acid of composts was correlatively decreased from 8.86 to 6.76 during the period of active composting. After 25 days of composting time, the E4: E6 was consistently decreased from 6.76 to 4.67($r^2$ of total composting period was 0.95). 4. Water soluble carbon had a tendency to increase from 0.54% to 0.78%during the period of active composting. After 25 days of composting time, it was consistently decreased from 0.78% to 0.42%. Water soluble nitrogen increased from 0.22% to 0.32% during the period of 15 days after initial composting while decreased from 0.32% to 0.21% after 15days of composting. In consequence, the correlation coefficient($r^2$) between water soluble carbon and water soluble nitrogen was 0.12 during the period of active composting mule was 0.50 after 25 days of composting time

• 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 Preventive Medicine and Public Health
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• v.22 no.4 s.28
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• pp.555-577
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• 1989

This study was conducted to compare the delivery management including laboratory tests, medication and surgical procedures for the delivery in various medical facilities. Two university hospitals, two general hospitals, three hospitals, two private obstetric clinics, and two midwifery clinics in a large city were selected as they permitted the investigators to abstract the required data from the medical and accounting records. The total number of deliveries occurred at these 11 facilities between 15 January and 15 February, 1989 was 789 among which 606(76.8%) were vaginal deliveries and 183 (23.3%) were C-sections. For the normal vaginal deliveries, CBC, Hb/Hct level, blood typing, VDRL, hepatitis B antigen and antibody, and urinalysis were routinely done except the private clinics and midwifery clinics which did not test for hepatitis B and Hb/Hct level at all. In one university hospital ultrasonography was performed in 71.4% of the mothers and in one general hospital liver function test was done in 76.7% of the mothers. For the C-section, chest X-ray, bleeding/clotting time and liver function test were routinely done in addition to the routine tests for the normal vaginal deliveries. Episiotomy was performed in 97.2% of the vaginal deliveries. The type and duration of fluid infused and antibiotics administered showed a wide variation among the medical facilities. In one university hospital antibiotics was not administered after C-section at all while in the general hospitals and hospitals one or two antibiotics were administered for one week on the average. In one private clinic one pint of whole blood was transfused routinely. A wide variation was observed among the medical facilities in the use of vitamin, hemostatics, oxytocics, antipyreptics, analgesics, anti-inflammatory agents. sedatives. digestives. stool softeners. antihistamines. and diuretics. Mean hospital day for the normal vaginal deliveries of primipara was 2.6 days with little variation except one hospital with 3.5 days. Mean hospital day for the C-section of primipara was 7.5 days and that of multipara was 7.6 days and it ranged between 6.5 days and 9.4 days. Average hospital fee for a normal vaginal delivery without the medical insurance coverage was 182,100 Won for the primipara and 167,300 Won for the multipara. In case of the primipara covered by the medical insurance a mother paid 82,400 Won and a multiparous mother paid 75,600 Won. Average hospital fee for a C-section without the medical insurance was 946,500 Won for the primipara and 753,800 Won for the multipara. In case of the primipara covered by the medical insurance a mother paid 256,200 Won and a multiparous mother paid 253,700 Won. Average hospital fee for a normal vaginal delivery in the university hospitals showed a remarkable difference, 268,000 Won vs 350,000 Won, as well as for the C-section. A wide variation in the laboratory tests performed for a normal vaginal delivery and a C-section as well as in the medication and hospital days brought about a big difference in the hospital fee and some hospitals were practicing the case payment system. Thus, standardization of the medical care to a certain level is warranted for the provision of adequate medical care for delivery.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.31 no.2
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• pp.142-152
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• 1995

The combined bottom trawl and hydroacoustic survey was conducted by using the training ship Oshoro Maru belong to Hokkaido University in November 1989-1992 and the training ship Nagasaki Maru belong to Nagasaki University in April 1994 in the East China Sea, respectively. The aim of the investigations was to collect the target strength data of fish school in relation to the biomass estimation of fish in the survey area. The hydroacoustic survey was performed by using the scientific echo sounder system operating at three frequencies of 25, 50 and 100kHz with a microcomputer-based echo integrator. Fish samples were collected by bottom trawling and during the trawl surveys, the openings of otter board and net mouth were measured. The target strength of fish school was estimated from the relationship between the volume back scattering strength for the depth strata of bottom trawling and the weight per unit volume of trawl catches. A portion of the trawl catches preserved in frozon condition on board, the target strength measurements for the defrosted samples of ten species were conducted in the laboratory tank, and the relationship between target strength and fish weight was examined. In order to investigate the effect of swimbladder on target strength, the volume of the swimbladder of white croaker, Argyrosomus argentatus, sampled by bottom trawling was measured by directly removing the gas in the swimbladder with a syringe on board. The results obtained can be summarized as follows: 1.The relationship between the mean volume back scattering strength (, dB) for the depth strata of trawl hauls and the weight(C, $kg/\textrm{m}^3$) per unit volume of trawl catches were expressed by the following equations : 25kHz : = - 29.8+10Log(C) 50kHz : = - 32.4+10Log(C) 100kHz : = - 31.7+10Log(C) The mean target strength estimates for three frequencies of 25, 50 and 100 kHz derived from these equations were -29.8dB/kg, -32.4dB/kg and -31.7dB/kg, respectively. 2. The relationship between target strength and body weight for the fish samples of ten species collected by trawl surveys were expressed by the following equations : 25kHz : TS = - 34.0+10Log($W^{\frac{2}{3}}$) 100kHz : TS = - 37.8+10Log($W^{\frac{2}{3}}$) The mean target strength estimates for two frequencies of 25 and 100 kHz derived from these equations were -34.0dB/kg, -37.8dB/kg, respectively. 3. The representative target strength values for demersal fish populations of the East China Sea at two frequencies of 25 and 100 kHz were estimated to be -31.4dB/kg, -33.8dB/kg, respectively. 4. The ratio of the equivalent radius of swimbladder to body length of white croaker was 0.089 and the volume of swimbladder was estimated to be approximately 10% of total body volume.

• 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.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.123-139
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• 2019

The Korean NPL market was formed by the government and foreign capital shortly after the 1997 IMF crisis. However, this market is short-lived, as the bad debt has started to increase after the global financial crisis in 2009 due to the real economic recession. NPL has become a major investment in the market in recent years when the domestic capital market's investment capital began to enter the NPL market in earnest. Although the domestic NPL market has received considerable attention due to the overheating of the NPL market in recent years, research on the NPL market has been abrupt since the history of capital market investment in the domestic NPL market is short. In addition, decision-making through more scientific and systematic analysis is required due to the decline in profitability and the price fluctuation due to the fluctuation of the real estate business. In this study, we propose a prediction model that can determine the achievement of the benchmark yield by using the NPL market related data in accordance with the market demand. In order to build the model, we used Korean NPL data from December 2013 to December 2017 for about 4 years. The total number of things data was 2291. As independent variables, only the variables related to the dependent variable were selected for the 11 variables that indicate the characteristics of the real estate. In order to select the variables, one to one t-test and logistic regression stepwise and decision tree were performed. Seven independent variables (purchase year, SPC (Special Purpose Company), municipality, appraisal value, purchase cost, OPB (Outstanding Principle Balance), HP (Holding Period)). The dependent variable is a bivariate variable that indicates whether the benchmark rate is reached. This is because the accuracy of the model predicting the binomial variables is higher than the model predicting the continuous variables, and the accuracy of these models is directly related to the effectiveness of the model. In addition, in the case of a special purpose company, whether or not to purchase the property is the main concern. Therefore, whether or not to achieve a certain level of return is enough to make a decision. For the dependent variable, we constructed and compared the predictive model by calculating the dependent variable by adjusting the numerical value to ascertain whether 12%, which is the standard rate of return used in the industry, is a meaningful reference value. As a result, it was found that the hit ratio average of the predictive model constructed using the dependent variable calculated by the 12% standard rate of return was the best at 64.60%. In order to propose an optimal prediction model based on the determined dependent variables and 7 independent variables, we construct a prediction model by applying the five methodologies of discriminant analysis, logistic regression analysis, decision tree, artificial neural network, and genetic algorithm linear model we tried to compare them. To do this, 10 sets of training data and testing data were extracted using 10 fold validation method. After building the model using this data, the hit ratio of each set was averaged and the performance was compared. As a result, the hit ratio average of prediction models constructed by using discriminant analysis, logistic regression model, decision tree, artificial neural network, and genetic algorithm linear model were 64.40%, 65.12%, 63.54%, 67.40%, and 60.51%, respectively. It was confirmed that the model using the artificial neural network is the best. Through this study, it is proved that it is effective to utilize 7 independent variables and artificial neural network prediction model in the future NPL market. The proposed model predicts that the 12% return of new things will be achieved beforehand, which will help the special purpose companies make investment decisions. Furthermore, we anticipate that the NPL market will be liquidated as the transaction proceeds at an appropriate price.