• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.63-83
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• 2019

Investors prefer to look for trading points based on the graph shown in the chart rather than complex analysis, such as corporate intrinsic value analysis and technical auxiliary index analysis. However, the pattern analysis technique is difficult and computerized less than the needs of users. In recent years, there have been many cases of studying stock price patterns using various machine learning techniques including neural networks in the field of artificial intelligence(AI). In particular, the development of IT technology has made it easier to analyze a huge number of chart data to find patterns that can predict stock prices. Although short-term forecasting power of prices has increased in terms of performance so far, long-term forecasting power is limited and is used in short-term trading rather than long-term investment. Other studies have focused on mechanically and accurately identifying patterns that were not recognized by past technology, but it can be vulnerable in practical areas because it is a separate matter whether the patterns found are suitable for trading. When they find a meaningful pattern, they find a point that matches the pattern. They then measure their performance after n days, assuming that they have bought at that point in time. Since this approach is to calculate virtual revenues, there can be many disparities with reality. The existing research method tries to find a pattern with stock price prediction power, but this study proposes to define the patterns first and to trade when the pattern with high success probability appears. The M & W wave pattern published by Merrill(1980) is simple because we can distinguish it by five turning points. Despite the report that some patterns have price predictability, there were no performance reports used in the actual market. The simplicity of a pattern consisting of five turning points has the advantage of reducing the cost of increasing pattern recognition accuracy. In this study, 16 patterns of up conversion and 16 patterns of down conversion are reclassified into ten groups so that they can be easily implemented by the system. Only one pattern with high success rate per group is selected for trading. Patterns that had a high probability of success in the past are likely to succeed in the future. So we trade when such a pattern occurs. It is a real situation because it is measured assuming that both the buy and sell have been executed. We tested three ways to calculate the turning point. The first method, the minimum change rate zig-zag method, removes price movements below a certain percentage and calculates the vertex. In the second method, high-low line zig-zag, the high price that meets the n-day high price line is calculated at the peak price, and the low price that meets the n-day low price line is calculated at the valley price. In the third method, the swing wave method, the high price in the center higher than n high prices on the left and right is calculated as the peak price. If the central low price is lower than the n low price on the left and right, it is calculated as valley price. The swing wave method was superior to the other methods in the test results. It is interpreted that the transaction after checking the completion of the pattern is more effective than the transaction in the unfinished state of the pattern. Genetic algorithms(GA) were the most suitable solution, although it was virtually impossible to find patterns with high success rates because the number of cases was too large in this simulation. We also performed the simulation using the Walk-forward Analysis(WFA) method, which tests the test section and the application section separately. So we were able to respond appropriately to market changes. In this study, we optimize the stock portfolio because there is a risk of over-optimized if we implement the variable optimality for each individual stock. Therefore, we selected the number of constituent stocks as 20 to increase the effect of diversified investment while avoiding optimization. We tested the KOSPI market by dividing it into six categories. In the results, the portfolio of small cap stock was the most successful and the high vol stock portfolio was the second best. This shows that patterns need to have some price volatility in order for patterns to be shaped, but volatility is not the best.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.25-52
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• 2018

This study performs social network analysis based on consumer sentiment related to a location in Seoul using data reflecting consumers' web search activities and emotional evaluations associated with commerce. The study focuses on large commercial districts in Seoul. In addition, to consider their various aspects, social network indexes were combined with the trading area's public data to verify factors affecting the area's sales. According to R square's change, We can see that the model has a little high R square value even though it includes only the district's public data represented by static data. However, the present study confirmed that the R square of the model combined with the network index derived from the social network analysis was even improved much more. A regression analysis of the trading area's public data showed that the five factors of 'number of market district,' 'residential area per person,' 'satisfaction of residential environment,' 'rate of change of trade,' and 'survival rate over 3 years' among twenty two variables. The study confirmed a significant influence on the sales of the trading area. According to the results, 'residential area per person' has the highest standardized beta value. Therefore, 'residential area per person' has the strongest influence on commercial sales. In addition, 'residential area per person,' 'number of market district,' and 'survival rate over 3 years' were found to have positive effects on the sales of all trading area. Thus, as the number of market districts in the trading area increases, residential area per person increases, and as the survival rate over 3 years of each store in the trading area increases, sales increase. On the other hand, 'satisfaction of residential environment' and 'rate of change of trade' were found to have a negative effect on sales. In the case of 'satisfaction of residential environment,' sales increase when the satisfaction level is low. Therefore, as consumer dissatisfaction with the residential environment increases, sales increase. The 'rate of change of trade' shows that sales increase with the decreasing acceleration of transaction frequency. According to the social network analysis, of the 25 regional trading areas in Seoul, Yangcheon-gu has the highest degree of connection. In other words, it has common sentiments with many other trading areas. On the other hand, Nowon-gu and Jungrang-gu have the lowest degree of connection. In other words, they have relatively distinct sentiments from other trading areas. The social network indexes used in the combination model are 'density of ego network,' 'degree centrality,' 'closeness centrality,' 'betweenness centrality,' and 'eigenvector centrality.' The combined model analysis confirmed that the degree centrality and eigenvector centrality of the social network index have a significant influence on sales and the highest influence in the model. 'Degree centrality' has a negative effect on the sales of the districts. This implies that sales decrease when holding various sentiments of other trading area, which conflicts with general social myths. However, this result can be interpreted to mean that if a trading area has low 'degree centrality,' it delivers unique and special sentiments to consumers. The findings of this study can also be interpreted to mean that sales can be increased if the trading area increases consumer recognition by forming a unique sentiment and city atmosphere that distinguish it from other trading areas. On the other hand, 'eigenvector centrality' has the greatest effect on sales in the combined model. In addition, the results confirmed a positive effect on sales. This finding shows that sales increase when a trading area is connected to others with stronger centrality than when it has common sentiments with others. This study can be used as an empirical basis for establishing and implementing a city and trading area strategy plan considering consumers' desired sentiments. In addition, we expect to provide entrepreneurs and potential entrepreneurs entering the trading area with sentiments possessed by those in the trading area and directions into the trading area considering the district-sentiment structure.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.83-102
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• 2021

The government recently announced various policies for developing big-data and artificial intelligence fields to provide a great opportunity to the public with respect to disclosure of high-quality data within public institutions. KSURE(Korea Trade Insurance Corporation) is a major public institution for financial policy in Korea, and thus the company is strongly committed to backing export companies with various systems. Nevertheless, there are still fewer cases of realized business model based on big-data analyses. In this situation, this paper aims to develop a new business model which can be applied to an ex-ante prediction for the likelihood of the insurance accident of credit guarantee. We utilize internal data from KSURE which supports export companies in Korea and apply machine learning models. Then, we conduct performance comparison among the predictive models including Logistic Regression, Random Forest, XGBoost, LightGBM, and DNN(Deep Neural Network). For decades, many researchers have tried to find better models which can help to predict bankruptcy since the ex-ante prediction is crucial for corporate managers, investors, creditors, and other stakeholders. The development of the prediction for financial distress or bankruptcy was originated from Smith(1930), Fitzpatrick(1932), or Merwin(1942). One of the most famous models is the Altman's Z-score model(Altman, 1968) which was based on the multiple discriminant analysis. This model is widely used in both research and practice by this time. The author suggests the score model that utilizes five key financial ratios to predict the probability of bankruptcy in the next two years. Ohlson(1980) introduces logit model to complement some limitations of previous models. Furthermore, Elmer and Borowski(1988) develop and examine a rule-based, automated system which conducts the financial analysis of savings and loans. Since the 1980s, researchers in Korea have started to examine analyses on the prediction of financial distress or bankruptcy. Kim(1987) analyzes financial ratios and develops the prediction model. Also, Han et al.(1995, 1996, 1997, 2003, 2005, 2006) construct the prediction model using various techniques including artificial neural network. Yang(1996) introduces multiple discriminant analysis and logit model. Besides, Kim and Kim(2001) utilize artificial neural network techniques for ex-ante prediction of insolvent enterprises. After that, many scholars have been trying to predict financial distress or bankruptcy more precisely based on diverse models such as Random Forest or SVM. One major distinction of our research from the previous research is that we focus on examining the predicted probability of default for each sample case, not only on investigating the classification accuracy of each model for the entire sample. Most predictive models in this paper show that the level of the accuracy of classification is about 70% based on the entire sample. To be specific, LightGBM model shows the highest accuracy of 71.1% and Logit model indicates the lowest accuracy of 69%. However, we confirm that there are open to multiple interpretations. In the context of the business, we have to put more emphasis on efforts to minimize type 2 error which causes more harmful operating losses for the guaranty company. Thus, we also compare the classification accuracy by splitting predicted probability of the default into ten equal intervals. When we examine the classification accuracy for each interval, Logit model has the highest accuracy of 100% for 0~10% of the predicted probability of the default, however, Logit model has a relatively lower accuracy of 61.5% for 90~100% of the predicted probability of the default. On the other hand, Random Forest, XGBoost, LightGBM, and DNN indicate more desirable results since they indicate a higher level of accuracy for both 0~10% and 90~100% of the predicted probability of the default but have a lower level of accuracy around 50% of the predicted probability of the default. When it comes to the distribution of samples for each predicted probability of the default, both LightGBM and XGBoost models have a relatively large number of samples for both 0~10% and 90~100% of the predicted probability of the default. Although Random Forest model has an advantage with regard to the perspective of classification accuracy with small number of cases, LightGBM or XGBoost could become a more desirable model since they classify large number of cases into the two extreme intervals of the predicted probability of the default, even allowing for their relatively low classification accuracy. Considering the importance of type 2 error and total prediction accuracy, XGBoost and DNN show superior performance. Next, Random Forest and LightGBM show good results, but logistic regression shows the worst performance. However, each predictive model has a comparative advantage in terms of various evaluation standards. For instance, Random Forest model shows almost 100% accuracy for samples which are expected to have a high level of the probability of default. Collectively, we can construct more comprehensive ensemble models which contain multiple classification machine learning models and conduct majority voting for maximizing its overall performance.

• Journal of Intelligence and Information Systems
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• v.12 no.1
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• pp.75-93
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• 2006

The ship selection by consignors has two steps to carry their cargo. The first step is to select according to time schedule of ships and amount of cargo, and the second one is re-selection by concentrating different consignors' cargo into a unit that can be carried by single ship. Up to now, these steps are usually done by hands leading to inefficiency. The purpose of our paper is to form a logistics chain to minimize the overall sum of logistics cost by selecting ships for consignors' cargo using negotiation methodology between agents. Through concentration and distribution of cargo, maximization of global profit derived from searching optimal point in trade-off between inventory cost and freight rate cost. It is settled by the negotiation between consignors. In the experiments, two methods of the first-step of ship selection: EPDS(Earliest Possible Departure-Date Scheduling) and LPDS(Latest Possible Departure-Date Scheduling) coupled with the second-step ship concentration method using the negotiation were shown. From this, we deduced inventory cost, freight rates and logistics cost according SBF(Scheduling Bundle Factor) and analyzed the result. We found it will minimize the total logistics cost if we use negotiation method with EPDS.

• Journal of the Korean Home Economics Association
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• v.32 no.5
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• pp.165-182
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• 1994

Korea's clothing industry which has been country's leading export industry and basic strategical industry is now faced with many difficulties both domestically and internationally. Domestically it is faced with continuing shortage of manpower in both production line and management high labour cost causing increase in price putting more weight on behavior of consumers resulting in change of industrial environment and continuing structural problems of industry itself. Internationally it is faced with strengthening of import regulations and protectionism of developed countries and rapid emergence of underdeveloped countries as leading exporting nations. In reality export plays the most essential role in our economy and is especially sensitive to the external environmental factors. Already economic bloc phonomenon can be seen everywhere and is continuing to accelerate in place such as E. U in Europ, North & South America as NAFTA, and South East Asian contries which recent tendency of economic unity effort is present. These countries of such economic blocs are imposing heavy custom duties reinforcing provision of country origin and acting out strict inspection regulations in order to protect the interest of their own industry. Therefore it is vital to manufacture excellent quality goods For these reasons study in this area has brought attention in Korea as well as worldwide in the recent years. Apparel industry which requires professional technology and ability is the most competitive international business. In order to challenge the international market the high level of intelligence is most required to produce high quality goods. The purpose of this study is to analyze the relationship between functions and roles of marketing and to approach problems in more efficient manner. Apparel industry is composed of various programs such as design pattern making merchandising and textile science. To succeed in the business is to give the highest satisfaction to the targeted market. Hence this study will example the factors that determine the Cost Quality and Performance of apparel products. The study will involve following steps; firstly establish relationship between the quality concept and productivity of apparel products Secondly inquire in to marketing strategy laying stress on apparel production related factors focusing on merchandising marketing production and operations Thirdly prospect 21st century apparel industry focusing on garment production and trade and also other countries structural improvement Fourthly establish the new dimension of competitive factors by grasping the actual circumstance of Korea's apparel industry in the international market. The research method will include; First reality approach method by analysing the present state of industry Second literal analysis such as marketing comparisons between leading apparel exporting countries.

• Journal of Service Research and Studies
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• v.5 no.2
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• pp.15-24
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• 2015

The trade balance for the information and communications technology (ICT) industries in 2014 have reached 863 hundred million dollars as the main export products such as smart phone and semi-conductor increase, since the ICT industries have played an important role in economic growth in Korea. Until now, the consistent supporting of government and investment of company have been doing with the growth of ICT industries, as a result, Korea marked as the first in the UN electronic government preparing index, and rank 12 in the network preparing index through the policy of national information and basic plan of inter-industry convergence. However, as the unstable international economic circumstances, ICT industries is faced with the stagnation, and then preemptive development of products and services for ICT convergence industries is needed to continually get definite ICT Korea image. In this paper, the ICT convergence industry is analyzed and forecasted. In specific, the international and domestic market for cloud, 3D convergence, and internet of things is diagnosed. The market for ICT convergence industries is predicted to be 3.6 trillion dollar in the world, and 110 trillion won in domestic. From the analytical results for technology and services development, the preemptive supporting of the technology development and policy for the internet of things and 3D convergence industries is required. In addition to, through the future forecasting by socio-tech matrix method, the policy supporting for the ICT convergence area of healthcare, fintech, artificial intelligence, body platform, and human security is needed.

• Korean Security Journal
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• no.57
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• pp.253-275
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• 2018

Economic security emerged as a strong element of national security. Nations around the world are exerting their efforts to collect economic intelligence to serve their national interest while making added efforts to uncover industrial espionage and arrest industrial spies in defensive aspect. Cases in point are the enactment of "Economic Espionage Act(1996)" of the U.S. and the "Act on Prevention of Divulgence and Protection of Industrial Technology(2006)"of Korea. Korea is trying to punish industrial spying on the same level as espionage that poses national security threat by revising Criminal Code. It is necessary to review whether the move to toughen the punishment of industrial spying from "up to 15 years in prison and/or up to 1.5 billion won in fine" to "minimum seven years of imprisonment, life imprisonment or death penalty" is appropriate. Advanced nations regulate industrial spying with a special act on economy although they have applied espionage act not to "enemy states" but to "foreign countries" in the first place. Likewise, preventing industrial spying by applying espionage act through the revision of criminal code poses a risk of undermining the autonomy of industry sector by excessive influence of state power. Furthermore, the penalty of minimum imprisonment of seven years, life imprisonment or death penalty with the application of espionage act under the criminal code is an legal application by stretching of the law, posing a risk of dampening healthy economic activities. Therefore, revising and applying relevant economic laws such as aforementioned 'Act on Prevention of Divulgence and Protection of Industrial Technology(2006)' is thought to be desirable to achieve the goal of protecting industrial technologies.

• Journal of Digital Convergence
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• v.17 no.11
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• pp.61-70
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• 2019

The The government has recently announced its mid- to long-term plans for promoting art. With the advent of the 4th industrial revolution, contemporary art contents that are integrated with Intelligent Information Technologies such as Artificial Intelligence (AI), Virtual Reality (VR), and Big Data are being introduced, and social interest in humanities and creative convergence is rising. In addition, the industrialization of the art market is expanding amid the rising popularity of art among the general public and the growing interest of art as an investment replacement system, along with the strengthening of the creative personality education of our Education Ministry. Therefore, it is necessary to establish a strategy for transparency and revitalization of the art market by providing comprehensive information such as search functions, analysis data, and criticism by writer and price. This paper has established an information system plan for the establishment of an online supply system for art transaction information, providing auction transaction information for art market, providing report and news for art market, providing public relations platform, and providing art market analysis service and membership relationship management service. To this end, the future model was established through environmental analysis and focus analysis of the art market, and strategic tasks and implementation plans were established accordingly.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.57-62
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• 2023

This research is a comparative analysis of the U.S. S&P 500 index using the volatility breakout strategy against the Buy and Hold approach. The volatility breakout strategy is a trading method that exploits price movements after periods of relative market stability or concentration. Specifically, it is observed that large price movements tend to occur more frequently after periods of low volatility. When a stock moves within a narrow price range for a while and then suddenly rises or falls, it is expected to continue moving in that direction. To capitalize on these movements, traders adopt the volatility breakout strategy. The 'k' value is used as a multiplier applied to a measure of recent market volatility. One method of measuring volatility is the Average True Range (ATR), which represents the difference between the highest and lowest prices of recent trading days. The 'k' value plays a crucial role for traders in setting their trade threshold. This study calculated the 'k' value at a general level and compared its returns with the Buy and Hold strategy, finding that algorithmic trading using the volatility breakout strategy achieved slightly higher returns. In the future, we plan to present simulation results for maximizing returns by determining the optimal 'k' value for automated trading of the S&P 500 index using artificial intelligence deep learning techniques.

• Journal of Intelligence and Information Systems
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• v.16 no.2
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• pp.73-94
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• 2010

The attending system is a medical system that allows doctors in clinics to use the extra equipment in hospitals-beds, laboratory, operating room, etc-for their patient's care under a contract between the doctors and hospitals. Therefore, the system is very beneficial in terms of the efficiency of the usage of medical resources. However, it is necessary to develop a strong support system to strengthen its weaknesses and supplement its merits. If doctors use hospital beds under the attending system of hospitals, they would be able to check a patient's condition often and provide them with nursing care services. However, the current attending system lacks delivery and assistance support. Thus, for the successful performance of the attending system, a networking system should be developed to facilitate communication between the doctors and nurses. In particular, the nursing records in the attending system could help doctors monitor the patient's condition and provision of nursing care services. A nursing record is the formal documentation associated with nursing care. It is merely a data repository that helps nurses to track their activities; nursing records thus represent a resource of primary information that can be reused. In order to maximize their usefulness, nursing records have been introduced as part of computerized patient records. However, nursing records are internal data that are not disclosed by hospitals. Moreover, the lack of standardization of the record list makes it difficult to share nursing records. Under the attending system, nurses would want to minimize the amount of effort they have to put in for the maintenance of additional records. Hence, they would try to maintain the current level of nursing records in the form of record lists and record attributes, while doctors would require more detailed and real-time information about their patients in order to monitor their condition. Therefore, this study developed a system for assisting in the maintenance and sharing of the nursing records under the attending system. In contrast to previous research on the functionality of computer-based nursing records, we have emphasized the practical usefulness of nursing records from the viewpoint of the actual implementation of the attending system. We suggested that nurses could design a nursing record dictionary for their convenience, and that doctors and nurses could confirm the definitions that they looked up in the dictionary through negotiations with intelligent agents. Such an agent-based system could facilitate networking among medical institutes. Multi-agent systems are a widely accepted paradigm for the distribution and sharing of computation workloads in the scientific community. Agent-based systems have been developed with differences in functional cooperation, coordination, and negotiation. To increase such communication, a framework for a multi-agent based system is proposed in this study. The agent-based approach is useful for developing a system that promotes trade-offs between transactions involving multiple attributes. A brief summary of our contributions follows. First, we propose an efficient and accurate utility representation and acquisition mechanism based on a preference scale while minimizing user interactions with the agent. Trade-offs between various transaction attributes can also be easily computed. Second, by providing a multi-attribute negotiation framework based on the attribute utility evaluation mechanism, we allow both the doctors in charge and nurses to negotiate over various transaction attributes in the nursing record lists that are defined by the latter. Third, we have designed the architecture of the nursing record management server and a system of agents that provides support to the doctors and nurses with regard to the framework and mechanisms proposed above. A formal protocol has also been developed to create and control the communication required for negotiations. We verified the realization of the system by developing a web-based prototype. The system was implemented using ASP and IIS5.1.