• International Area Studies Review
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• v.15 no.1
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• pp.87-106
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• 2011

Using an event study, this paper investigates stock price reactions on Korean listed firms' convertible bond (CB) issue announcements over the sample period of January 2000 to November 2007. This study finds that on the Korean Security market, the CB issue announcements are associated with an increase in shareholder wealth on the announcement date. An information leakage by insider traders is also observable at preannouncement dates. Unlike the prior studies that indicate a prevailing negative effect on the announcements, this paper shows that domestic CB issue announcements as well as offshore ones yield a positive impact on the stock prices. This presents that in terms of stock price reactions to the CB issue announcements, the two CB issue markets show the positively same effects on shareholder wealth for the post-2000 period. For its drivers, this paper suggests that on the Korean market, firm size have negative relationship with the increase in the wealth incurred by the announcements. By contrast, an issue to maturity, a growth opportunity, and a relative issue size make a positive impact on it.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.257-264
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• 2003

In the context of a dynamic trading environment, the ultimate goal of the financial forecasting system is to optimize a specific trading objective. This paper proposes a two-phase (extraction and filtering) stock trading system that aims at maximizing the rates of returns. Extraction of stocks is performed by searching specific time-series patterns described by a combination of values of technical indicators. In the filtering phase, several rules are applied to the extracted sets of stocks to select stocks to be actually traded. The filtering rules are automatically induced from past data. From a large database of daily stock prices, the values of technical indicators are calculated. They are used to make the extraction patterns, and the distributions of the discretization intervals of the values are calculated for both positive and negative data sets. We assumed that the values in the intervals of distinctive distribution may contribute to the prediction of future trend of stocks, so the rules for filtering stocks are automatically induced from the data in those intervals. We show the rates of returns when using our trading system outperform the market average. These results mean rule induction method using distributional differences is useful.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.65 no.4
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• pp.298-302
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• 2016

Currently, logistics are in small quantities and in diverse forms, and the amounts are continuously increasing. Railway logistics however are losing their market share every year mainly due to low operation speed and loading time, which means the trucks are covering the most of the freights. In order to solve these situations, this paper proposed the high speed freight train as working multi-modality with other modes to make effective transshipment. The high speed freight train has maximum operation speed of 300km/h and electric power to run centralized power supply. There are large dual door system, bogie system covering fluctuating load of 15[ton], automatic loading device, ULD(unit load device) bed and ULD locking system in this freight rolling stock. We calculated the performance of powering and braking capacity for this train and proposed how many vehicles are composed of train set. The results in this paper can help to make a decision to define the technical specification of High-speed freight train for the efficiency of rail freight service.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.4
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• pp.299-306
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• 2022

Entrepreneurs with behavioral finance biases are more likely to make irrational or financially detrimental decisions. Understanding financial behavior biases can assist in making sound financial decisions. Behavioral finance is a new topic that can assist researchers in better understanding investor behavior and preferences while purchasing and selling stocks. Using measures such as independent t-tests and average Likert five-point scale scores, this study seeks to determine how entrepreneurs make investment decisions and whether gender makes a difference. The study is empirical, and data from 1000 entrepreneurs were collected through convenience sampling. The study's main findings show that there are numerous factors to consider while investing in stocks, including family planning, children's education, investment security, and recurring income. Both men and women attempt to invest in many asset classes, but certain investments are extremely risky, while others are low risk. As a result, investors should assess risk based on their age and experience rather than their gender; this indicates that an investment in venture capital has nothing to do with gender but everything to do with the investor's age.

• Journal of KIISE:Databases
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• v.33 no.6
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• pp.644-653
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• 2006

This paper analyzes the properties of user query for stock investment recommendation, and defines the 'following relation', which is a new relation between two queries. A following relation between two queries $Q_1,\;Q_2$ and a recommendation value X means 'If the recommendation value of a preceding Query $Q_1$ is X, then a following query $Q_2$ always has X as its recommendation value'. If there exists a following relation between $Q_1\;and\;Q_2$, the recommendation value of $Q_2$ is decided immediately by that of $Q_1$, therefore we can eliminate the running process for $Q_2$. We suggest two methods in this paper. The former method analyzes all the following relations among user queries and represents them as a graph. The latter searches the graph and decides the order of queries to be processed, in order to make the number of eliminated query-running process maximized. When we apply the suggested procedures that use the following relation, most of user queries do not need to be processed directly, hence the performance of running overall queries is greatly improved. We examined the superiority of the suggested methods through experiments using real stock market data. According to the results of our experiments, overall query processing time has reduced less than 10% with our proposed methods, compared to the traditional procedure.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.2
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• pp.81-90
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• 2013

Material flow control (MFC) is a kind of operational policy to control of the movement of raw materials, components, and products through the manufacturing lines. It is very important because it varies throughput, line cycle time, and work-in-process (WIP) under the same manufacturing environments. MFC can be largely categorized into three types such as Push, Pull, and Hybrid. In this paper, we set various manufacturing environments to compare five existing MFC mechanisms: Push, Pull, and Hybrid (CONWIP, Gated MaxWIP, Critical WIP Loops, etc). Three manufacturing environments, manufacturing policies (make to stock and make to order), demand (low, medium, high), and line balancing (balanced, unbalanced, and highly unbalanced) are considered. The MFCs are compared in the point of the five functional efficiencies and the proposed compounded efficiency. The simulation results shows that the Push is superior in the functional efficiency and GMWIP is superior in the compounded efficiency.

• The Korean Journal of Financial Management
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• v.20 no.1
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• pp.261-277
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• 2003

The Korean Stock Market Surveillance System is desinged to control the volatility of stocks by drawing investor's attention and suppressing disguised demand, when stocks run up so rapidly in short period of time. Yet the Surveillance System has not been under empirical examination about its role and evolved in line with the Price Limit System. This study looks at the security returns under surveillance designation for 1995 -2001 period. The results indicate that the volatility of stocks has not been affected after surveillance designation. The constraints against the disguised demand, however, seems to limit the security returns rather than volatilities. These findings raises a question about the role of The Korean Stock Market Surveillance System for the control of volatility. The Surveillance System needs to be examined thoroughly about its role, function, and its conditions. Otherwise, the shareholders with less information could be placed at a disadvantage. This paper suggests that the system should be amended in an effort to make the volatility of stocks under control.

• Asia pacific journal of information systems
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• v.32 no.4
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• pp.707-727
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• 2022

Industrial revolution 4.0 makes business competition more challenging and will impact the instability of the company's financial performance. Dynamic environmental conditions make it difficult for companies to make predictions in making decisions. Investing in information technology (IT) is one way for companies to maintain financial stability and competitive advantage in dynamic competition. Resource-Based Theory (RBT) explains that information technology (IT) is a resource that can create a competitive advantage for the company. This study aims to examine the moderating role of dynamic industrial environments and IT strategic emphasis on the relationship between a lag effect of IT investment and firm's financial performance volatility. Using the data of companies listed on the Indonesia Stock Exchange (IDX) for five years starting from 2013-2017, the method used to estimate the research model's parameters is the generalized method of moments (GMM) approach. The results show that the industrial environment and the emphasis on IT strategy have a role in moderating and strengthening the relationship between the time lag in IT investment in reducing the firm's financial performance volatility.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.367-370
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• 2015

Building deterioration reflects the degradation of basic building performance including structural performance, energy performance, durability, and safety, and it also includes perceived deterioration, which considers a user-based perspective. More than 50% of the existing buildings in Korea are over 15 years old and public buildings compose 2.5% of all buildings domestically. Therefore, there are several different problems, such as poor energy efficiency, structural performance, and safety. To address the challenges of increasing stock in deteriorated buildings, it is necessary to make decisions about reconstruction or renovation. In this study, we propose a new method to evaluate public building value with a contingent valuation method (CVM). By estimating willing-to-pay (WTP) from users of private buildings in similar situation with the public building, it is possible to compare market prices and calculate a correction factor to adjust the WTP data. Finally, we apply the correction factor to the WTP of a public building and estimate market price, willingness to pay (WTP). Finally, we apply the correction factor to willing to pay (WTP) of public building and estimate market price.