• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.

• Development and Reproduction
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• v.13 no.4
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• pp.257-264
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• 2009

A neurotoxin, 6-hydroxydopamine (6-OHDA) has been widely used to create animal model for Parkinson's disease (PD) due to its specific toxicity against dopaminergic (DA) neurons. Since DA signals modulate a broad spectrum of CNS physiology, one can expect profound alterations in neuroendocrine activities of both PD patients and 6-OHDA treated animals. Limited applications of 6-OHDA injection model, however, have been made on the studies of hypothalamuspituitary neuroendocrine circuits. The present study was performed to examine whether blockade of brain catecholamine (CA) biosynthesis with 6-OHDA can make any alteration in the transcriptional activities of hypothalamus-pituitary hormone genes in adult male rats. Three-month-old male rats (SD strain) were received 6-OHDA ($200{\mu}g$ in $10{\mu}\ell$ of saline/animal) by intracerebroventricular (icv) injection, and sacrificed after two weeks. To determine the mRNA levels of hypothalamuspituitary hormone genes, total RNAs were extracted and applied to the semi-quantitative RT-PCRs. The mRNA levels of tyrosine hydroxylase (TH), the rate-limiting enzyme for the catecholamine biosynthesis, were significantly lower than those from the control group (control:6-OHDA=1:0.72${\pm}$0.02AU, p<0.001), confirming the efficacy of 6-OHDA injection. The mRNA levels of gonadotropin-releasing hormone (GnRH) and corticotropin releasing hormone (CRH) in the hypothalami from 6-OHDA group were significantly lower than those from the control group (GnRH, control:6-OHDA=1:0.39${\pm}$0.03AU, p<0.001; CRH, control:6-OHDA=1:0.76${\pm}$0.07AU, p<0.01). There were significant decreases in the mRNA levels of common alpha subunit of glycoprotein homones (Cg$\alpha$), LH beta subunit (LH-$\beta$), and FSH beta subunit (FSH-$\beta$) in pituitaries from 6-OHDA group compared to control values (Cg$\alpha$, control:6-OHDA=1:0.81${\pm}$0.02AU, p<0.001; LH-$\beta$, control:6-OHDA=1:0.68${\pm}$0.04AU, p<0.001; FSH-$\beta$, control:6-OHDA=1:0.84${\pm}$0.05AU, p<0.001). Similarly, the level of adrenocorticotrophic hormone (ACTH) transcripts from 6-OHDA group was significantly lower than that from the control group (control: 6-OHDA=1:0.86${\pm}$0.04AU, p<0.01). The present study demonstrated that centrally injected DA neurotoxin could downregulate the transcriptional activities of the two hypothalamus-pituitary neuroendocrine circuits, i.e., GnRH-gonadotropins and CRH-ACTH systems. These results suggested that hypothalamic CA input might affect on the activities of gonad and adrenal through modulation of hypothalamus-pituitary function, providing plausible explanation for frequent occurrence of sexual dysfunction and poor stress-response in PD patients.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.51-69
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• 2020

Now, it is a stylized fact that a small number of technology firms such as Apple, Alphabet, Microsoft, Amazon, Facebook and a few others have become larger and dominant players in an industry. Coupled with the rise of these leading firms, we have also observed that a large number of young firms have become an acquisition target in their early IPO stages. This indeed results in a sharp decline in the number of new entries in public exchanges although a series of policy reforms have been promulgated to foster competition through an increase in new entries. Given the observed industry trend in recent decades, a number of studies have reported increased concentration in most developed countries. However, it is less understood as to what caused an increase in industry concentration. In this paper, we uncover the mechanisms by which industries have become concentrated over the last decades by tracing the changes in industry concentration associated with a firm's status change in its early IPO stages. To this end, we put emphasis on the case in which firms are acquired shortly after they went public. Especially, with the transition to digital-based economies, it is imperative for incumbent firms to adapt and keep pace with new ICT and related intelligent systems. For instance, after the acquisition of a young firm equipped with AI-based solutions, an incumbent firm may better respond to a change in customer taste and preference by integrating acquired AI solutions and analytics skills into multiple business processes. Accordingly, it is not unusual for young ICT firms become an attractive acquisition target. To examine the role of M&As involved with young firms in reshaping the level of industry concentration, we identify a firm's status in early post-IPO stages over the sample periods spanning from 1990 to 2016 as follows: i) being delisted, ii) being standalone firms and iii) being acquired. According to our analysis, firms that have conducted IPO since 2000s have been acquired by incumbent firms at a relatively quicker time than those that did IPO in previous generations. We also show a greater acquisition rate for IPO firms in the ICT sector compared with their counterparts in other sectors. Our results based on multinomial logit models suggest that a large number of IPO firms have been acquired in their early post-IPO lives despite their financial soundness. Specifically, we show that IPO firms are likely to be acquired rather than be delisted due to financial distress in early IPO stages when they are more profitable, more mature or less leveraged. For those IPO firms with venture capital backup have also become an acquisition target more frequently. As a larger number of firms are acquired shortly after their IPO, our results show increased concentration. While providing limited evidence on the impact of large incumbent firms in explaining the change in industry concentration, our results show that the large firms' effect on industry concentration are pronounced in the ICT sector. This result possibly captures the current trend that a few tech giants such as Alphabet, Apple and Facebook continue to increase their market share. In addition, compared with the acquisitions of non-ICT firms, the concentration impact of IPO firms in early stages becomes larger when ICT firms are acquired as a target. Our study makes new contributions. To our best knowledge, this is one of a few studies that link a firm's post-IPO status to associated changes in industry concentration. Although some studies have addressed concentration issues, their primary focus was on market power or proprietary software. Contrast to earlier studies, we are able to uncover the mechanism by which industries have become concentrated by placing emphasis on M&As involving young IPO firms. Interestingly, the concentration impact of IPO firm acquisitions are magnified when a large incumbent firms are involved as an acquirer. This leads us to infer the underlying reasons as to why industries have become more concentrated with a favor of large firms in recent decades. Overall, our study sheds new light on the literature by providing a plausible explanation as to why industries have become concentrated.

• Korean Journal of Agricultural Science
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• v.8 no.1
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• pp.97-107
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• 1981

This study was carried out in order to provide the basic data necessary to develop the effective and desirable cooking method on large scale for investigating the physical characteristics of cooked rices and studying optimum cooking conditions by pressure in kettle cooking rices. Milyang-15, local Japonica type and Milyang-23, high yielding Indica type major varieties cultivated in Korea were used as cooking sample after polishing 70% and 90% respectively, and the results obtained are summarized as follows. 1. The average moisture content of cooked rice by open kettle and pressure kettle method in typical households were 65.17% and 64.52%, respectively. 2. In water absorption capacity of rice grain Milyang-23 was 4.5% higher than Milyang-15, and maximum water content after absorption in Milyang-23 was 29.14%. 3. The expansion volume of cooked rice was changed proportionally by water absorption, heating temperature and time, and maximum expansion volume of cooked rice was 3.2 times greater than rice grain. 4. The gelatinization degree of cooked rice intensively concerning in hardness of rice grain was increased as water-to-rice ratio, heating temperature and time increased, and it was 0.44 in Milyang-23 and 0.64 in Milyang-15 under the optimum cooking conditions as 160% water-to-rice ratio, $0.2kg/cm^2$ cooking pressure and 25 minutes cooking time. 5. The hardness of cooked rice was decreased as water-to-rice ratio, heating temperature and time increased, and it showed 2.35kg/wt in 90% polished Milyang-23 and 2.0kg/wt in 90 polished Milyang-15 under optimum cooking conditions. For maintaining the same level of hardness of cooking rice Milyang-23 required 25% much more water than Milyang-15. 6. The elasticity of cooked rice was changed proportionally by water-to-rice ratio, heating temperature and time, and it appeared 19.2mm and 15.7mm in 90% polished Milyang-15 and Milyang-23 respectively. 7. The gumminess of cooked rice was decreased as water-to-rice ratio, heating temperature and time increased, and it showed 60 and 73 in 90% polished Milyang-23 and Milyang-15, respectively. 8. The optimum cooking time on differerent pressure in kettle took 25 minutes at $0.2kg/cm^2$, 20 minutes at $0.4kg/cm^2$, 15 minutes at $0.6kg/cm^2$, and 10 minutes at $0.8kg/cm^2$.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.147-168
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• 2017

There have been many studies on accurate stock market forecasting in academia for a long time, and now there are also various forecasting models using various techniques. Recently, many attempts have been made to predict the stock index using various machine learning methods including Deep Learning. Although the fundamental analysis and the technical analysis method are used for the analysis of the traditional stock investment transaction, the technical analysis method is more useful for the application of the short-term transaction prediction or statistical and mathematical techniques. Most of the studies that have been conducted using these technical indicators have studied the model of predicting stock prices by binary classification - rising or falling - of stock market fluctuations in the future market (usually next trading day). However, it is also true that this binary classification has many unfavorable aspects in predicting trends, identifying trading signals, or signaling portfolio rebalancing. In this study, we try to predict the stock index by expanding the stock index trend (upward trend, boxed, downward trend) to the multiple classification system in the existing binary index method. In order to solve this multi-classification problem, a technique such as Multinomial Logistic Regression Analysis (MLOGIT), Multiple Discriminant Analysis (MDA) or Artificial Neural Networks (ANN) we propose an optimization model using Genetic Algorithm as a wrapper for improving the performance of this model using Multi-classification Support Vector Machines (MSVM), which has proved to be superior in prediction performance. In particular, the proposed model named GA-MSVM is designed to maximize model performance by optimizing not only the kernel function parameters of MSVM, but also the optimal selection of input variables (feature selection) as well as instance selection. In order to verify the performance of the proposed model, we applied the proposed method to the real data. The results show that the proposed method is more effective than the conventional multivariate SVM, which has been known to show the best prediction performance up to now, as well as existing artificial intelligence / data mining techniques such as MDA, MLOGIT, CBR, and it is confirmed that the prediction performance is better than this. Especially, it has been confirmed that the 'instance selection' plays a very important role in predicting the stock index trend, and it is confirmed that the improvement effect of the model is more important than other factors. To verify the usefulness of GA-MSVM, we applied it to Korea's real KOSPI200 stock index trend forecast. Our research is primarily aimed at predicting trend segments to capture signal acquisition or short-term trend transition points. The experimental data set includes technical indicators such as the price and volatility index (2004 ~ 2017) and macroeconomic data (interest rate, exchange rate, S&P 500, etc.) of KOSPI200 stock index in Korea. Using a variety of statistical methods including one-way ANOVA and stepwise MDA, 15 indicators were selected as candidate independent variables. The dependent variable, trend classification, was classified into three states: 1 (upward trend), 0 (boxed), and -1 (downward trend). 70% of the total data for each class was used for training and the remaining 30% was used for verifying. To verify the performance of the proposed model, several comparative model experiments such as MDA, MLOGIT, CBR, ANN and MSVM were conducted. MSVM has adopted the One-Against-One (OAO) approach, which is known as the most accurate approach among the various MSVM approaches. Although there are some limitations, the final experimental results demonstrate that the proposed model, GA-MSVM, performs at a significantly higher level than all comparative models.

• Korean Journal of Heritage: History & Science
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• v.45 no.3
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• pp.60-79
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• 2012

Environment of ancient Chinese Buddhist temple can be classified to three types such as regional society(鄕村), famous mountain(名山), and urban areas(都市). This made differences in environment where a temple existed and in turn, affected development of Buddhism. And this made another type in relationship between Buddhist temple and a society. This study explains influences which regional society gave on not only Buddhist temple and a monk but also existence and development of Buddhism. When temples are placed in different environmental position, that is, urban areas and regional society, among a social structure, they eventually should adapt to a different society externally and internally. As told in above, ancient Chinese Buddhist temple was located in regional society, famous mountain, and urban areas. Since Eastern Jin and Sixteen Kingdoms, as number of temple much increased, and temples and monks were concentrated on famous mountain, temples in famous mountains and urban areas had developed showing similar aspects each other. But because temples in regional society were influenced a little differently, this study focused on the point. There are four kinds of influences between temples and monks in regional areas. Monks in regional areas had a comparatively close relationship with a society because they came from same area or surrounding areas. Therefore,powers of regional areas restrict influences made by monk group in temple. Second, temples in regional areas shared their joys and sorrows depending on regional economy. Temples in regional areas became a public place for the society and often a market place. In fact, construction and existence of a temple originally became a driving force in regional economy. This is because construction of temple needs artisans and materials and some temples had visitors and included market economy like consumption of incense and candles, though the economic size was large or small. And when regional areas experienced natural disaster or man-made disaster or had poor harvest or economy was in depression, monks left temples and then, temples themselves could not exist. Third, the relationship between temples in regional areas and Buddhists was distinguished from the temples in urban areas and famous mountains. This is because temples in China were places where monks practiced and at the same time, places where general Buddhists worshipped. So there were always a number of Buddhists around the temples. Forth, Buddhism in resional areas was connected to regional Folk beliefs. As a result, Buddhism was spread across the nation, worship with local color often was changed to Buddhist belief or was tinged with Buddhism. While temples in regional areas maintained a close relationship with regional society.they were influenced by the region or gave influences. As a representative example, temples in regional areas showed model behaviors instead of roles of facilities related to various cultures with comparatively advanced level - for example, school, hospital etc. The temples highly affected funerary rites in regional areas. Chinese tombs were mainlymade in regional areas. After death,people living in urban areas were buried in hometown or at least, they were buried in suburbs not urban areas. Temples in regional areas generally participated in funerary rites. Above shows that though most of famous Buddhist temples were located in urban areas not in famous mountains,majority of temples were located in vast regional areas. Through mutual interaction between temples and regional society, the temples in the regional areas were related to Chinese people of over 90% and regional areas became the most important foundation for Buddhism in China. Mutual influences between temples in regional areas and the general public in regions were omnidirectional and spreaded to every aspects of social life in small or large degree. Thus Tombs in temple were widely spreaded across regional areas over time and space. This is enough to explain a close relationship between Buddhist temples and rural society in ancient China.

• Journal of Distribution Research
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• v.15 no.2
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• pp.1-34
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• 2010

Introduction As consumers' purchase behavior change into a rational and practical direction, the discount store industry came to have keen competition along with rapid external growth. Therefore as a solution, distribution businesses are concentrating on developing PB(Private Brand) which can realize differentiation and profitability at the same time. And as improvement in customer loyalty beyond customer satisfaction is effective in surviving in an environment with keen competition, PB is being used as a strategic tool to improve customer loyalty. To improve loyalty among PB users, it is necessary to develop PB by examining properties of a customer group, first of all, quality level perceived by consumers should be met to obtain customer satisfaction and customer trust and consequently induce customer loyalty. To provide results of systematic analysis on relations between antecedents influenced perceived quality and variables affecting customer loyalty, this study proposed a research model based on causal relations verified in prior researches and set 16 hypotheses about relations among 9 theoretical variables. Data was collected from 400 adult customers residing in Seoul and the Metropolitan area and using large scale discount stores, among them, 375 copies were analyzed using SPSS 15.0 and Amos 7.0. The findings of the present study followed as; We ascertained that the higher company reputation, brand reputation, product experience and brand familiarity, the higher perceived quality. The study also examined the higher perceived quality, the higher customer satisfaction, customer trust and customer loyalty. The findings showed that the higher customer satisfaction and customer trust, the higher customer loyalty. As for moderating effects between PB and NB in terms of influences of perceived quality factors on perceived quality, we can ascertain that PB was higher than NB in the influences of company reputation on perceived quality while NB was higher than PB in the influences of brand reputation and brand familiarity on perceived quality. These results of empirical analysis will be useful for those concerned to do marketing activities based on a clearer understanding of antecedents and consecutive factors influenced perceived quality. At last, discussions about academical and managerial implications in these results, we suggested the limitations of this study and the future research directions. Research Model and Hypotheses Test After analyzing if antecedent variables having influence on perceived quality shows any difference between PB and NB in terms of their influences on them, the relation between variables that have influence on customer loyalty was determined as Figure 1. We established 16 hypotheses to test and hypotheses are as follows; H1-1: Perceived price has a positive effect on perceived quality. H1-2: It is expected that PB and NB would have different influence in terms of perceived price on perceived quality. H2-1: Company reputation has a positive effect on perceived quality. H2-2: It is expected that PB and NB would have different influence in terms of company reputation on perceived quality. H3-1: Brand reputation has a positive effect on perceived quality. H3-2: It is expected that PB and NB would have different influence in terms of brand reputation on perceived quality. H4-1: Product experience has a positive effect on perceived quality. H4-2: It is expected that PB and NB would have different influence in terms of product experience on perceived quality. H5-1: Brand familiarity has a positive effect on perceived quality. H5-2: It is expected that PB and NB would have different influence in terms of brand familiarity on perceived quality. H6: Perceived quality has a positive effect on customer satisfaction. H7: Perceived quality has a positive effect on customer trust. H8: Perceived quality has a positive effect on customer loyalty. H9: Customer satisfaction has a positive effect on customer trust. H10: Customer satisfaction has a positive effect on customer loyalty. H11: Customer trust has a positive effect on customer loyalty. Results from analyzing main effects of research model is shown as