• Knowledge Management Research
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• v.23 no.1
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• pp.149-164
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• 2022

This paper analyzes the independent financial advisory business that is not yet active in Korea and proposes a plan to activate the independent financial advisory business using fintech technology. A bill was enacted in 2017 for the domestic independent financial advisory business, but it has not been activated much until now for various reasons. Although existing studies have proposed solutions in various ways, there is no clear solution yet. This paper proposes a new method of revitalizing the independent financial advisory business through fintech technology using the trust system that has recently attracted attention. Digital securities fintech technology using blockchain distributed ledger technology presents new possibilities in the real estate and music copyright markets, and related fintech venture companies continue to emerge in Korea. By combining these digital securities fintech technologies and the business process of ETF, a method was derived so that independent financial advisors can have their own financial products. The proposed model is more decentralized than the existing financial product sales structure, and presents the possibility of a protocol economy through a structure close to a private blockchain while complying with the existing financial order. This paper is meaningful in that it presented new solutions to completely different markets from information convergence perspectives on two completely different markets, and we hope that more business solutions will emerge through knowledge management activities that converge various perspectives in the future.

• Journal of Practical Agriculture & Fisheries Research
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• v.12 no.1
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• pp.137-150
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• 2010

Traditionally, like many people in mountain region of the Himalaya, the Lwang communities depend on mix of subsistence agriculture, animal husbandry, and seasonal migrant labor for their livelihoods. These traditional systems are characterized by low productivity, diverse use of available natural resources (largely for home consumption), limited markets, and some aversion for innovation. The potential to generate wealth through commerce has largely been untapped by these mountain residents and thus is undervalued in local and national economies. Introduction of organic tea farming is a part of Lwang community's several initiatives to break the vicious poverty cycle Annapurna Conservation Area Project (ACAP) played facilitating roles in all their efforts since beginning. In five years, the tea plantation emerged as a new means for secured a livelihood. This study aims to analyze the current practices in tea farming both in terms of farm management and soil nutrient status(technical) and the prosperity of the tea farmers (social). The technical aspect covers the soil and tea leaf analysis of various nutrients contents in the soil and tea leaf. Originally, the technical aspect of the study was not planned but later during the consultation with the advisor it was taken into consideration which added value to the research study. The sample were collected from different locations and analyzed on the field itself. The other part of the study i.e. the social aspect was done through questionnaire survey and focus group discussion. the tea farming provided them not only a new opportunity but also earned an identity in the region. This initiative was undertaken as a piloting measure. Now that the tea is in production with processing unit established locally, more serious consideration has to be given for better yield and economic prosperity. This research finding will help the community to analyze their efforts and make correction measures in tea garden management and application of fertilizer. It is also expected to fill up the gaps of knowledge and information required to reduce economic stresses and enhance capacity of farmers to make the tea farming a sustainable and beneficial business. The findings are expected to Sustainability of organic tea farming has direct impacts on biodiversity conservation compared to the other traditional farming practices that are more resource intensive. The study will also contribute to identify key action points required for reducing poverty while conserving environment and enhancing livelihoods

• Journal of The Korean Association For Science Education
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• v.29 no.5
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• pp.552-563
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• 2009

The purpose of this study is to investigate the students' perception of the learning support system of Korea Science Academy and to propose improvements to it. The impact of the science learning support system on 129 gifted students in Korea Science Academy (KSA) was estimated by using Likert-type items and the multiple-choice method approach for more comprehensive evaluation. The results of our investigation are as follows: First, the learning support system of KSA appears globally useful to the students. The list of educational usefulness to the students comprises, in the decreasing order of utility, classroom work, Internet, lab activities, reading rooms, library, research meetings and clubs, academic advisors (AA), SAF (Science Academy Fair), e-learning system, and finally colloquia by invited lecturers. Second, what the gifted students hope for in the realm of learning support from KSA are learning guides by subject teachers, presentation skill program, the constructions of on/off-line learning communities, etc. It seems that the results of this study would be helpful in improving the learning support system, and will provide useful information for planning the direction of future science-gifted education programs at the high-school level.

• Journal of Korean Physical Therapy Science
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• v.5 no.3
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• pp.659-674
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• 1998

This Study was to investigate elaborated research themes and direction through specifying the problems of clinical practice education and looking for the direction of improvement. It was in the basis of the viewpoint of the educators that professors and therapists who were the subjects of this study. Perform this study, the 15 colleges' professors and the 55 hospitals' therapists was made up questionnaire, and the data was analysing by Chi-square test and percentage. The results were as follow : ${\cdot}$ In a personal history among the general qualities, professors have little clinical practice history(l-5 years, 53.3%), and therapists have little lecture career(1-5 years, 43.6%, have no 49.0%), ${\cdot}$ The 78.6% subjects were unsatisfied of clinical practice systems. ${\cdot}$ The correlation between clinical history, school career and lecture career and the satisfaction level of clinical practice systems has no(P<.005), ${\cdot}$ The subjects were agreed to that clinical practice curriculum should be changed(67.1%), reinforced(82.9%), and specified(90.0%). ${\cdot}$ The clinical practice credits are 11 points averagely. ${\cdot}$ In the clinical practice curriculum, it made no difference in the practicum of diseases, modality, and the therapeutic techniques between professors and therapists. ${\cdot}$ The 100% professors said that the practicum of the patients' assessment is necessary, and the 63.6% therapists were training for that. ${\cdot}$ The 66.7% professors said that the practicum of the clinical psychology is necessary, and only the 20.0% therapists were training for that. ${\cdot}$ The 93.3% professors said that the practicum of the patients' management is necessary, and the 50.9% therapists were training for that. ${\cdot}$ The 66.7% professors said that the practicum of the medical ethics is necessary, and the 34.5% therapists were training for that. ${\cdot}$ The 46.7% professors said that the practicum of the hospital administration is necessary, but the 54.5% therapists have not training. ${\cdot}$ The 33.3% professors said that the practicum of the pharmacology is necessary, but the 81.8% therapists have not training. ${\cdot}$ The 86.7% professors said that the practicum of the patient's education is necessary, and the 43.6% therapists have training. ${\cdot}$ The 66.7% professors said that the practicum of the prosthesis and brace is necessary, but the 14.5% therapists have not training. ${\cdot}$ The 60.0% professors said that the practicum of the exercise prescription is necessary, but the 25.5% therapists have not training. ${\cdot}$ The 53.5% professors said that the practicum of the emergency treatment is necessary, but the 52.7% therapists have not training. ${\cdot}$ Drawing up the plan about the curriculum of clinical practice, the professors (46.7%) were agreed to national master plan framing by an expert advisor, but the therapists (58.2%) said that the plan that make the most of hospitals' characteristics should be specified. ${\cdot}$ It was found that a clinical special therapists(54.5%) was good as a person in charge of clinical practice education, in that each therapist's own good time (34.5%) was. ${\cdot}$ It made use of the form framing by college(40.0%) as the clinical practice textbook, the form framing by hospital (42.9%) and each therapist(22.9%) as the plan, and the form framing by college (74.3%) as the measurement. ${\cdot}$ The most difficult point in clinical practice education was the lacks of the theory-praciticum linkage(78.2%). ${\cdot}$ It was found that the period of clinical practice was in the second semester-third grade (40.0%) and the desirable period was in the first semester-third grade(50.0%). ${\cdot}$ Professors (53.3%) were agreed that the desirable clinical practice duration was from four months to six months(60.0%), and the therapists (60.0%) were agreed that from one month to three months. ${\cdot}$ This study presented the lacks of rearing the experts, the lacks of cultural education, and the lacks of the theory-clinical practice linkage. There were need to develop the systematic programs, clinical practice textbooks, the measurements and the special hospital for clinical practice. And it was need to reduce the gab between of the hospitals for clinical practice, to cut down the costs. and to improve the labour conditions of leaders. In view of this findings, it takes notice of that both professor and therapist were dissatisfied at the present clinical practice systems. These results point out the problems of clinical practice systems, and do not make expect to us the successive and positive clinical practice. The general, specific and intensive plan about the problems and the direction of improvement that establishing the level of hospital for clinical practice and physiotherapy can be elaborated.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.149-162
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• 2014

Course guidance is a mentoring process which is performed before students register for coming classes. The course guidance plays a very important role to students in checking degree audits of students and mentoring classes which will be taken in coming semester. Also, it is intimately involved with a graduation assessment or a completion of ABEEK certification. Currently, course guidance is manually performed by some advisers at most of universities in Korea because they have no electronic systems for the course guidance. By the lack of the systems, the advisers should analyze each degree audit of students and curriculum information of their own departments. This process often causes the human error during the course guidance process due to the complexity of the process. The electronic system thus is essential to avoid the human error for the course guidance. If the relation data model-based system is applied to the mentoring process, then the problems in manual way can be solved. However, the relational data model-based systems have some limitations. Curriculums of a department and certification systems can be changed depending on a new policy of a university or surrounding environments. If the curriculums and the systems are changed, a scheme of the existing system should be changed in accordance with the variations. It is also not sufficient to provide semantic search due to the difficulty of extracting semantic relationships between subjects. In this paper, we model a course mentoring ontology based on the analysis of a curriculum of computer science department, a structure of degree audit, and ABEEK certification. Ontology-based course guidance system is also proposed to overcome the limitation of the existing methods and to provide the effectiveness of course mentoring process for both of advisors and students. In the proposed system, all data of the system consists of ontology instances. To create ontology instances, ontology population module is developed by using JENA framework which is for building semantic web and linked data applications. In the ontology population module, the mapping rules to connect parts of degree audit to certain parts of course mentoring ontology are designed. All ontology instances are generated based on degree audits of students who participate in course mentoring test. The generated instances are saved to JENA TDB as a triple repository after an inference process using JENA inference engine. A user interface for course guidance is implemented by using Java and JENA framework. Once a advisor or a student input student's information such as student name and student number at an information request form in user interface, the proposed system provides mentoring results based on a degree audit of current student and rules to check scores for each part of a curriculum such as special cultural subject, major subject, and MSC subject containing math and basic science. Recall and precision are used to evaluate the performance of the proposed system. The recall is used to check that the proposed system retrieves all relevant subjects. The precision is used to check whether the retrieved subjects are relevant to the mentoring results. An officer of computer science department attends the verification on the results derived from the proposed system. Experimental results using real data of the participating students show that the proposed course guidance system based on course mentoring ontology provides correct course mentoring results to students at all times. Advisors can also reduce their time cost to analyze a degree audit of corresponding student and to calculate each score for the each part. As a result, the proposed system based on ontology techniques solves the difficulty of mentoring methods in manual way and the proposed system derive correct mentoring results as human conduct.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.65-82
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• 2021

Stock market investors are generally split into foreign investors, institutional investors, and individual investors. Compared to individual investor groups, professional investor groups such as foreign investors have an advantage in information and financial power and, as a result, foreign investors are known to show good investment performance among market participants. The purpose of this study is to propose an investment strategy that combines investor-specific transaction information and machine learning, and to analyze the portfolio investment performance of the proposed model using actual stock price and investor-specific transaction data. The Korea Exchange offers daily information on the volume of purchase and sale of each investor to securities firms. We developed a data collection program in C# programming language using an API provided by Daishin Securities Cybosplus, and collected 151 out of 200 KOSPI stocks with daily opening price, closing price and investor-specific net purchase data from January 2, 2007 to July 31, 2017. The self-organizing map model is an artificial neural network that performs clustering by unsupervised learning and has been introduced by Teuvo Kohonen since 1984. We implement competition among intra-surface artificial neurons, and all connections are non-recursive artificial neural networks that go from bottom to top. It can also be expanded to multiple layers, although many fault layers are commonly used. Linear functions are used by active functions of artificial nerve cells, and learning rules use Instar rules as well as general competitive learning. The core of the backpropagation model is the model that performs classification by supervised learning as an artificial neural network. We grouped and transformed investor-specific transaction volume data to learn backpropagation models through the self-organizing map model of artificial neural networks. As a result of the estimation of verification data through training, the portfolios were rebalanced monthly. For performance analysis, a passive portfolio was designated and the KOSPI 200 and KOSPI index returns for proxies on market returns were also obtained. Performance analysis was conducted using the equally-weighted portfolio return, compound interest rate, annual return, Maximum Draw Down, standard deviation, and Sharpe Ratio. Buy and hold returns of the top 10 market capitalization stocks are designated as a benchmark. Buy and hold strategy is the best strategy under the efficient market hypothesis. The prediction rate of learning data using backpropagation model was significantly high at 96.61%, while the prediction rate of verification data was also relatively high in the results of the 57.1% verification data. The performance evaluation of self-organizing map grouping can be determined as a result of a backpropagation model. This is because if the grouping results of the self-organizing map model had been poor, the learning results of the backpropagation model would have been poor. In this way, the performance assessment of machine learning is judged to be better learned than previous studies. Our portfolio doubled the return on the benchmark and performed better than the market returns on the KOSPI and KOSPI 200 indexes. In contrast to the benchmark, the MDD and standard deviation for portfolio risk indicators also showed better results. The Sharpe Ratio performed higher than benchmarks and stock market indexes. Through this, we presented the direction of portfolio composition program using machine learning and investor-specific transaction information and showed that it can be used to develop programs for real stock investment. The return is the result of monthly portfolio composition and asset rebalancing to the same proportion. Better outcomes are predicted when forming a monthly portfolio if the system is enforced by rebalancing the suggested stocks continuously without selling and re-buying it. Therefore, real transactions appear to be relevant.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.

• 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.