• Journal of the Korean Data and Information Science Society
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• v.28 no.4
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• pp.819-829
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• 2017

The aim of this study is to analyze intraday price momentum. When price trends are formed, price momentum is the phenomenon that future prices tend to follow the trend. When the market opened and closed, a U-shaped trading volume pattern in which the trading volume was concentrated was observed. In this paper, we defined price momentum as the 10 minute trend after market opening is maintained until the end of market. The strategy is to determine buying and selling in accordance with the price change in the initial 10 minutes and liquidating at closing price. In this study, the strategy was empirically analyzed by using minute data, and it showed effectiveness, indicating the presence of an intraday price momentum. A pattern in which returns are increasing at an early stage is called a J-shaped pattern. If the J-shaped pattern occurs, we have found that the price momentum phenomenon tends to be stronger than otherwise. The DTW algorithm, which is well known in the field of pattern recognition, was used for J-shaped pattern recognition and the algorithm was effective in predicting intraday price movements. This study showed that intraday price momentum exists in the KOSPI200 futures market.

• Asia-Pacific Journal of Business
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• v.11 no.2
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• pp.173-188
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• 2020

Purpose - This study examines the determinants of offer price and short-term and long-term performance of small and medium-sized enterprise(SME) IPO stocks listed on the KOSDAQ during the period from July 2007 to December 2016. Design/methodology/approach - The SME IPO samples are classified into three categories of regular listing, technology-based special listing, and listing by merger with special purpose acquisition company(SPAC), whose results are compared each other and compared to the result for the KOSDAQ listing of large firms. Findings - From the point of SME management which attempts to list its company on the KOSDAQ, the listing by merger with SPAC is the most unfavorable, and the underpricing phenomenon of the technology-based special listing is severe in the second place. By contrast, IPO stock investors can earn the largest abnormal return by purchasing the SPAC which succeeds the merger with unlisted firm, and the next abnormal returns are obtained in the order of the IPO stocks of technology-based special listing, regular listing of SMEs, and regular listing of large firms. However, it is interesting to observe that the net buying ratio of individual investors is relatively large for the IPO stocks of regular listing of SMEs and large firms, which exhibit the long-term under-performance. Research implications or Originality - This result implies that the exceptional listing system such as the technology-based special listing or the listing by merger with SPAC cost the SMEs which bypass the complicated procedure of the regular listing.

• Physical Therapy Korea
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• v.28 no.3
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• pp.208-214
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• 2021

Background: The CKCUES test evaluates the functional performance of the shoulder joint. The CKCUES test scores CKC exercises of the upper limbs to examine shoulder stability. Although the CKCUES test provides quantitative data on functional ability and performance, no study has determined the relationship between CKCUES scores and SA and TB muscle strength. Objects: The objective of this study is to determine the relationship between the CKCUES test scores and the strength of the SA and TB muscles in the CKCUES and unilateral CKCUES tests. Methods: Sixty-six healthy male volunteers participated in the study. A Smart KEMA strength sensor measured SA and TB muscle strength. Two parallel lines on the floor indicated the initial hand placement to start CKCUES tests. For 15 seconds, the subject raises one hand and reaches over to touch the supporting hand, then returns to the starting position. Results: The correlation between the CKCUES test scores and the strength of the SA was strong (r = 0.650, p < 0.001), and the TB was moderate (r = 0.438, p < 0.001). The correlation between the unilateral CKCUES test and the strength of the SA of the supporting side was strong (r = 0.605, p < 0.001), and swing side was strong (r = 0.681, p < 0.001). The correlation between the unilateral CKCUES test and the strength of the TB of the supporting side was moderate (r = 0.409, p < 0.001), and swing side was moderate (r = 0.482, p < 0.001). Conclusion: Our study showed that the CKCUES test had a strong association with isometric strength of SA and moderate association with that of TB. These findings suggest that the CKCUES test can evaluate the function of the SA. Moreover, the unilateral CKCUES test can evaluate unilateral shoulder function.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.125-132
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• 2022

In this paper, we presented a cart which can automatically transport loads to the distribution center of the appointed indoor place, based on Raspberry pi 4. It can recognize the obstacles by using the ultrasonic sensors so that it prevents the collision and takes a detour. Further, we entered the direction control code in the RFID. It has installed at important points such as the intersections of the destinations, so that if the RFID reader of the cart senses the RFID, the cart would stop or change the direction. After the transportation, if the load cell(weight sensor) recognizes that the baggage is unloaded, the cart returns to the initial point and would be retrieved. Therefore, we embodied the transportation cart which reduces the use of manpower and solves the problems conveniently across the transportation strategies.

• Journal of the Korean Geographical Society
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• v.30 no.2
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• pp.169-187
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• 1995

Contemporary US metropolitan areas have undergone divergent economic transformation, and as a result labor markets have become the focus of concern in their role as determinants of earnings attainment. Explanations of individual earnings attainmnent as a lobor market outcome have been established in two diafferent stances one who emphasizes personal or group attributes in the human capital perspective and the other who emphasizes economic structure in the labor market segmentation perspective. While remaining at the conceptual level and yet relatively unexplored, the importance of place in labormarket operation is a significant advancement as it appears in labor market areas and local labor markets considering that labor market areas represent the intersection of labor market structure and individual labor market experiences at specific geographic places. The substantive inquiry of this study was to explore labor market characteristics and their differentiation across large metropolitan areas, and assess their effects on the individual earnings attainment. Integating individual attributes and labor market characteristics as major factors of labor market operation, this study intended to contextualize individual earnings attainment with geographic labor market areas. Using 1990 US population census 5% "Public-Use Microdata Samples, " the largest 65 metropolitan areas were first selected and employed male workers who are aged between 25 and 50 for whites, blacks, asians, and hispanics. As an initial step earnings differentials between racial/ethnic groups and selected 65 metropolitan areas were examined using analysis of variance, and then earnings differentials were attributed to the individual attributes such as education, age, and immigration status, and four dimensions of metropolitan labor market differentiation devised by principal component analysis of industrial and occupational segments: Public versus Blue Collar Core(CS1), Finance-Core Utility versus Blue Collar Local Monopoly (CS2), Oligopoly versus Blue Collar Periphery(CS3), and Self Employed-White Collar Periphery versus Low-Skill Core(CS4). As a final analysis, individual earnings were related to each individual attribute and its interaction with metropolitan labor market characteristics to examine how the differentiated metropolitan labor market characteristics alter the role of individual attributes on earnings attainment. The findings indicated that individual attributes, education in particular exert significant effects on earnings attainment, but their effects were significantly altered by metropolitan labor market characterristics. Particularly important dimensions were: Oligopoly differentiated from Blue Colla Periphery metropolitan areas enhancing earnings returns to individual attributes for all groups but minority groups (black, asians, hispanics) rely more on this, and Finance-Core Utility differentiated from Blue Collar Local Monopoly metropolitan areas provide higher earnings returns to whites exclusively. These findings suggest that individuals with identical individual attributes involving racial/ethnic categories would have different earnings atteinments depending on the metropolitan labor market characteristics where they reside. Referring back to the major traditions of the human capital and the labor market segmentation in labor market research, the interaction between individual attributes and metropolitan labor market haracteristics on earnings attainment highlights the complimentary nature of the two on earnings determination in particular geographic places, Hence, labor market characteristics differentiatcd across metropolitan areas are an integral part of labor market operation which should be considered for the explanation of individual earnings attainment and racial/ethnic group earnings differentials. Gcographic places are the important contexts for labor market segmentation and individual labor market experiences. In conclusion, this study brings geographic labor markets to the forefront in the examination of individuals' earnings attainments. The empirical vaidation of the role of metropolitan labor market charecteristics on earnings attainment, while exploratory contributes towards a broader perspective of geographic labor market research that recognizes that individuals' labor market experiences are intertwined with geographic contexts of labor market operatin. operatin.

• Korean Journal of Cognitive Science
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• v.24 no.2
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• pp.133-171
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• 2013

The gist of a scene can be identified in less than 100msec, and violation in the gist can influence the way to allocate attention to the parts of a scene. In other words, people tend to allocate more attention to the object(s) inconsistent with the gist of a scene and to have better memory of them. To investigate the effects of contextual consistency on the attention allocation and object memory, two experiments were conducted. In both experiments, a $3{\times}2$ factorial design was used with scene presentation time(2s, 5s, and 10s) as a between-subject factor and object-background contextual consistency(consistent, inconsistent) as a within-subject factor. In Experiment 1, eye movements were recorded while the participants viewed line-drawing scenes. The results showed that the eye movement patterns were different according to whether the scenes were consistent or not. Context-inconsistent objects showed faster initial fixation indices, longer fixation times, more frequent returns than context-consistent ones. These results are entirely consistent with those of previous studies. If an object is identified as inconsistent with the gist of a scene, it attracts attention. Furthermore, the inconsistent objects and their locations in the scenes were recalled better than the consistent ones and their locations. Experiment 2 was the same as Experiment 1 except that a dual-task paradigm was used to reduce the amount of attention to allocate to the objects. Participants had to detect the positions of the probe occurring every second while they viewed the scenes. Nonetheless, the result patterns were the same as in Experiment 1. Even when the amount of attention to allocate to the scene contents was reduced, the same effects of contextual inconsistency were observed. These results indicate that the object-background contextual consistency has a strong influence on the way of allocating attention and the memory of objects in a scene.

• Quality Improvement in Health Care
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• v.10 no.1
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• pp.28-41
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• 2003

Background : Because unplanned readmissions to intensive care unit(ICU)might be related with undesirable patient outcomes, we investigated the pattern of and reason for unplanned ICU readmission to provide baseline data for reducing unplanned returns to ICU. Methods : The subjects included all patients who readmitted to ICU during the same hospitalization at a tertiary referral hospital between January 1st and June 30th 2002. Quality improvement(QI) nurse collected the data through medical records and a medical director reviewed the data collected. Results : 1) The average unplanned ICU readmission rate was 5.6%(gastroenterology 14.6%, pediatrics 12.7%, pulmonology 11.9%, neurosurgery 6.3%, general surgery 5.3%, chest surgery 3.9%, and cardiology 3.3%). 2) Among the unplanned readmissions, more than 50% of cases were from patients older than 60 years, and the main categories of diagnose at hospital admission were neurologic disease(29.9%) and cardiovascular disease(27.6%). 3) Of unplanned ICU readmissions, 41.8% had recurrence of the initial problems, 44.8% had occurrence of new problems. And 9.7% required post-operative care after unplanned operations. 4) The most common cause responsible for unplanned ICU readmission were respiratory problem(38.3%) and cardiovascular problem(14.3%). 5) About 40% of unplanned ICU readmission occurred within 3 days after ICU discharge. 6) Average length of stay of the readmitted patients to ICUs were much longer than that of non-readmitted patients. 7) Hospital mortality rate was much higher for unplanned ICU readmitted patients(23.6%) than for non-readmitted patients(1.5%) (P<0.001). Conclusions : This study showed that the unplanned ICU readmitted patients had poor outcomes(high morality and increased length of stay). In addition study results suggest that more attention should be paid to patients in ICU with poor respiratory function or elderly patients, and careful clinical decisions are required at discharged from ICU to general ward.

• Journal of the Mineralogical Society of Korea
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• v.26 no.3
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• pp.151-160
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• 2013

A column experiment was carried out to study the reaction of Cr(VI) with organic carbon. Chemical analysis for the effluent collected at different times after the reaction of Cr(VI) with organic carbon in compost and SEM observation for the solid samples remaining after the reaction were conducted. Cr(VI) supplied to the column was not detected in the effluent from column at initial stage, but the concentration of Cr(VI) increased abruptly and maintained the initial supplied concentration (20 mg/kg), indicating that Cr(VI) was effectively removed from the solution at the first state. In general, the concentrations of cations and anions with the exception of $PO_4$ increased and decreased again. Considering that most of these ions were not detected or showed very low concentration, these ions are considered to originate from the organic carbon in the column. SEM observation showed that Cr was coprecipitated with Fe on the surface of organic carbon with small amount of other metals such as Mn, No, and Co. This indicated that on the reduction condition on the organic carbon, Cr(VI) was reduced to $Cr(OH)_3$ and coprecipitated with $Fe(OH)_3$, and that Fe is very important in the precipitation of Cr. After the soluble Fe and Mn are not dissolved any more, $Cr(OH)_3$ is not precipitated. Different from other ions, the concentrations of $PO_4$ decreased and increased, which was thought to be the result of the release of $PO_4$ from organic carbon and sorption on the precipitates. After the maximum sorption on the precipitates and no further release of Fe, the concentration of $PO_4$ returns to its original value measured for the ones released from the organic carbon.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.237-262
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• 2022

There has been a growing interest in IPOs (Initial Public Offerings) due to the profitable returns that IPO stocks can offer to investors. However, IPOs can be speculative investments that may involve substantial risk as well because shares tend to be volatile, and the supply of IPO shares is often highly limited. Therefore, it is crucially important that IPO investors are well informed of the issuing firms and the market before deciding whether to invest or not. Unlike institutional investors, individual investors are at a disadvantage since there are few opportunities for individuals to obtain information on the IPOs. In this regard, the purpose of this study is to provide individual investors with the information they may consider when making an IPO investment decision. This study presents a model that uses machine learning and text analysis to predict whether an IPO stock price would move up or down after the first 5 trading days. Our sample includes 691 Korean IPOs from June 2009 to December 2020. The input variables for the prediction are three tone variables created from IPO prospectuses and quantitative variables that are either firm-specific, issue-specific, or market-specific. The three prospectus tone variables indicate the percentage of positive, neutral, and negative sentences in a prospectus, respectively. We considered only the sentences in the Risk Factors section of a prospectus for the tone analysis in this study. All sentences were classified into 'positive', 'neutral', and 'negative' via text analysis using TF-IDF (Term Frequency - Inverse Document Frequency). Measuring the tone of each sentence was conducted by machine learning instead of a lexicon-based approach due to the lack of sentiment dictionaries suitable for Korean text analysis in the context of finance. For this reason, the training set was created by randomly selecting 10% of the sentences from each prospectus, and the sentence classification task on the training set was performed after reading each sentence in person. Then, based on the training set, a Support Vector Machine model was utilized to predict the tone of sentences in the test set. Finally, the machine learning model calculated the percentages of positive, neutral, and negative sentences in each prospectus. To predict the price movement of an IPO stock, four different machine learning techniques were applied: Logistic Regression, Random Forest, Support Vector Machine, and Artificial Neural Network. According to the results, models that use quantitative variables using technical analysis and prospectus tone variables together show higher accuracy than models that use only quantitative variables. More specifically, the prediction accuracy was improved by 1.45% points in the Random Forest model, 4.34% points in the Artificial Neural Network model, and 5.07% points in the Support Vector Machine model. After testing the performance of these machine learning techniques, the Artificial Neural Network model using both quantitative variables and prospectus tone variables was the model with the highest prediction accuracy rate, which was 61.59%. The results indicate that the tone of a prospectus is a significant factor in predicting the price movement of an IPO stock. In addition, the McNemar test was used to verify the statistically significant difference between the models. The model using only quantitative variables and the model using both the quantitative variables and the prospectus tone variables were compared, and it was confirmed that the predictive performance improved significantly at a 1% significance level.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.67-83
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• 2018

The Korean film industry with significant increase every year exceeded the number of cumulative audiences of 200 million people in 2013 finally. However, starting from 2015 the Korean film industry entered a period of low growth and experienced a negative growth after all in 2016. To overcome such difficulty, stakeholders like production company, distribution company, multiplex have attempted to maximize the market returns using strategies of predicting change of market and of responding to such market change immediately. Since a film is classified as one of experiential products, it is not easy to predict a box office record and the initial number of audiences before the film is released. And also, the number of audiences fluctuates with a variety of factors after the film is released. So, the production company and distribution company try to be guaranteed the number of screens at the opining time of a newly released by multiplex chains. However, the multiplex chains tend to open the screening schedule during only a week and then determine the number of screening of the forthcoming week based on the box office record and the evaluation of audiences. Many previous researches have conducted to deal with the prediction of box office records of films. In the early stage, the researches attempted to identify factors affecting the box office record. And nowadays, many studies have tried to apply various analytic techniques to the factors identified previously in order to improve the accuracy of prediction and to explain the effect of each factor instead of identifying new factors affecting the box office record. However, most of previous researches have limitations in that they used the total number of audiences from the opening to the end as a target variable, and this makes it difficult to predict and respond to the demand of market which changes dynamically. Therefore, the purpose of this study is to predict the weekly number of audiences of a newly released film so that the stakeholder can flexibly and elastically respond to the change of the number of audiences in the film. To that end, we considered the factors used in the previous studies affecting box office and developed new factors not used in previous studies such as the order of opening of movies, dynamics of sales. Along with the comprehensive factors, we used the machine learning method such as Random Forest, Multi Layer Perception, Support Vector Machine, and Naive Bays, to predict the number of cumulative visitors from the first week after a film release to the third week. At the point of the first and the second week, we predicted the cumulative number of visitors of the forthcoming week for a released film. And at the point of the third week, we predict the total number of visitors of the film. In addition, we predicted the total number of cumulative visitors also at the point of the both first week and second week using the same factors. As a result, we found the accuracy of predicting the number of visitors at the forthcoming week was higher than that of predicting the total number of them in all of three weeks, and also the accuracy of the Random Forest was the highest among the machine learning methods we used. This study has implications in that this study 1) considered various factors comprehensively which affect the box office record and merely addressed by other previous researches such as the weekly rating of audiences after release, the weekly rank of the film after release, and the weekly sales share after release, and 2) tried to predict and respond to the demand of market which changes dynamically by suggesting models which predicts the weekly number of audiences of newly released films so that the stakeholders can flexibly and elastically respond to the change of the number of audiences in the film.