• Journal of KIISE:Databases
• /
• v.35 no.6
• /
• pp.533-543
• /
• 2008

With technological advances for storage volume size of a semiconductor memory, USB storage is made as products to support a high capacity storage. Hereby, consumers discard pint-sized USB storages which they already had, or do not use them efficiently. To integrate and unify these pint-sized USB storages as one big USB storage, we proposed Sigma Hub. It can be grouping multiple USB storages, which have each different volume size of memory storage, as logical unity Storage through USB Hub. The proposed Sigma Hub includes Sigma Controller as a core management module to unify the multiple USB storages in transaction level layer. Sigma controller can efficiently control transaction packet in Sigma Hub through a USB Storage-Integration algorithms which ensure an integrity for data read and write processes. Consequently, Sigma Hub enables the use of USB storage that is logical unity.

• Journal of Intelligence and Information Systems
• /
• v.20 no.3
• /
• pp.93-108
• /
• 2014

To support business decision making, interests and efforts to analyze and use transaction data in different perspectives are increasing. Such efforts are not only limited to customer management or marketing, but also used for monitoring and detecting fraud transactions. Fraud transactions are evolving into various patterns by taking advantage of information technology. To reflect the evolution of fraud transactions, there are many efforts on fraud detection methods and advanced application systems in order to improve the accuracy and ease of fraud detection. As a case of fraud detection, this study aims to provide effective fraud detection methods for auction exception agricultural products in the largest Korean agricultural wholesale market. Auction exception products policy exists to complement auction-based trades in agricultural wholesale market. That is, most trades on agricultural products are performed by auction; however, specific products are assigned as auction exception products when total volumes of products are relatively small, the number of wholesalers is small, or there are difficulties for wholesalers to purchase the products. However, auction exception products policy makes several problems on fairness and transparency of transaction, which requires help of fraud detection. In this study, to generate fraud detection rules, real huge agricultural products trade transaction data from 2008 to 2010 in the market are analyzed, which increase more than 1 million transactions and 1 billion US dollar in transaction volume. Agricultural transaction data has unique characteristics such as frequent changes in supply volumes and turbulent time-dependent changes in price. Since this was the first trial to identify fraud transactions in this domain, there was no training data set for supervised learning. So, fraud detection rules are generated using outlier detection approach. We assume that outlier transactions have more possibility of fraud transactions than normal transactions. The outlier transactions are identified to compare daily average unit price, weekly average unit price, and quarterly average unit price of product items. Also quarterly averages unit price of product items of the specific wholesalers are used to identify outlier transactions. The reliability of generated fraud detection rules are confirmed by domain experts. To determine whether a transaction is fraudulent or not, normal distribution and normalized Z-value concept are applied. That is, a unit price of a transaction is transformed to Z-value to calculate the occurrence probability when we approximate the distribution of unit prices to normal distribution. The modified Z-value of the unit price in the transaction is used rather than using the original Z-value of it. The reason is that in the case of auction exception agricultural products, Z-values are influenced by outlier fraud transactions themselves because the number of wholesalers is small. The modified Z-values are called Self-Eliminated Z-scores because they are calculated excluding the unit price of the specific transaction which is subject to check whether it is fraud transaction or not. To show the usefulness of the proposed approach, a prototype of fraud transaction detection system is developed using Delphi. The system consists of five main menus and related submenus. First functionalities of the system is to import transaction databases. Next important functions are to set up fraud detection parameters. By changing fraud detection parameters, system users can control the number of potential fraud transactions. Execution functions provide fraud detection results which are found based on fraud detection parameters. The potential fraud transactions can be viewed on screen or exported as files. The study is an initial trial to identify fraud transactions in Auction Exception Agricultural Products. There are still many remained research topics of the issue. First, the scope of analysis data was limited due to the availability of data. It is necessary to include more data on transactions, wholesalers, and producers to detect fraud transactions more accurately. Next, we need to extend the scope of fraud transaction detection to fishery products. Also there are many possibilities to apply different data mining techniques for fraud detection. For example, time series approach is a potential technique to apply the problem. Even though outlier transactions are detected based on unit prices of transactions, however it is possible to derive fraud detection rules based on transaction volumes.

• The Journal of Fisheries Business Administration
• /
• v.48 no.4
• /
• pp.1-10
• /
• 2017

The study analyzed the current status of fisheries port market and presented the direction of developments. The fisheries port market has become increasingly widespread due to the aging of the facilities and the scale of the fish product trade, and the number of distribution workers has also increased. The problems of fisheries port market are as follows. First, the transaction structure was changed as the proportion of aquaculture products increased. Second, the trading structure has changed, but it has failed to keep pace with the changes in the production structure. Third, the volume and amount of fish traded in aquaculture products and fish stocks increased. As a result, the growth rate of the fisheries port market is decreasing and profitability is deteriorating. The development direction of the fisheries port market is as follows. First, it is necessary to standardize the fisheries port market facilities, according to the type of fish products. Second, it needs to diversify its trade targets such as processed fish products and imported fish products. Third, it is necessary to diversify the business of the fisheries port market in order to increase profitability.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.16 no.2
• /
• pp.1-8
• /
• 2016

A convergence of finance and information technology brought a remarkable development in Fin-Tech industry. On the other hand, currently existing laws seemed inappropriate to address the liability of financial institutions, Fin-tech enterprises and consumers in case of financial accidents due to its ambiguity. The minimum insurance obligation by financial institutions specified under the Electronic Financial Transaction Act 2006 is not keeping with current reality, considering transaction volume, frequency of incidents, and security investments. This paper aims to lay stress on the need of cyber liability insurance by understanding the domestic financial incidents and management, and the limit of existing insurance policy.

• The Journal of Information Technology
• /
• v.6 no.1
• /
• pp.49-63
• /
• 2003

In recent years, the number of public e-Marketplaces have grown according to the leap-frogging advancement of network technology based on open communication architecture named Internet. However, the transaction volume through public e-Marketplaces have been an immaterial increase, and some well-known sites closed their businesses. This study proposes the public e-Marketplace as an e-Business system which has been recognized the most strategic application of business information technologies (Barrett & Konsynski, 1982; Cash & Konsynski, 1985), and suggests some future strategic and technological direction which supports a firm's transaction infrastructure under internet architecture. For this purpose, it reviews evolution of business information technologies.

• The Journal of Bigdata
• /
• v.4 no.2
• /
• pp.115-126
• /
• 2019

There have been many discussions on export indices in trade exports, but there is no definite trade export index which can be explained by objective indicators. Korea International Trade Association (KITA), Korea Trade-Investment Promotion Agency (KOTRA), etc., but we are currently in the process of thinking about ways to express the capabilities of exporting companies. In this study, we constructed the AI data sets by setting the activity indicators such as the size of the company and the credit score, the number of transaction customers, the number of transactions, the number of items, the transaction volume, and the transaction period as features, Lightgbm. Using the Graph Neural Network as an industrial cluster classification model, the export live index which expresses the exportable capacity among companies, items, and business groups was calculated. This includes the past activity of the company from the current calculating index Objectivity.

• The Journal of Industrial Distribution & Business
• /
• v.9 no.6
• /
• pp.49-55
• /
• 2018

Purpose - This paper examines the effect of related party transactions on crash firm-specific stock price crash risk. Ownership of a typical Korean conglomerate is concentrated in a single family. In those entities, management and board positions are often filled by family members. Therefore, a dominant shareholder can benefit from related party transactions. In Korea, firms have to report related party transactions in financial statement footnotes. However, those are not disclosed in detail. The more related party transactions are the greater information risk. Thus, companies with related party transactions are likely to experience stock price crashes. Research design, data, and methodology - 2,598 firm-year observations are used for the main analysis. Those samples are from TS2000 database from 2009 to 2013, and the database covers KOSPI-listed firms in Korea. The proxy for related party transactions (RTP) is calculated by dividing total transactions to the related-party by total sales. A dummy variable is used as a dependent variable (CRASH) in the regression model. Logistic regression is used to explain the relationship between related party transactions and crash risk. Then, the sample was separated into two groups; tunneling firms and propping firms. The relation between related party transactions and crash risk variances with features of the transaction were investigated. Results - Using a sample of KOSPI-listed firms in TS2000 database for the period of 2009-2013, I find that stock price crash risk increases as the trade volume of related-party transactions increases. Specifically, I find that the coefficient of RPT is significantly positive, supporting the prediction. In addition, this relationship is strong and robust in tunneling firms. Conclusions - The results report that firms with related party transactions are more likely to experience stock price crashes. The results mean that related party transactions increase the possibility of future stock price crashes by enlarging information asymmetry between controlling shareholders and minority shareholders. In case of tunneling, it could be seen that related party transactions are positively associated with stock crash risk. The result implies that the characteristic of the transaction influences crash risk. This study is related to a literature that investigates the effect of related party transactions on the stock market.

• Asia-Pacific Journal of Business
• /
• v.11 no.2
• /
• pp.133-143
• /
• 2020

Purpose - This study seeks to summarize the tax changes in stock trading and analyze K-OTC stock trading data in 2017 and 2018 to infer the effects of the application of capital gains taxes by individual investors. Design/methodology/approach - This study analyzes the case of the expansion of the 2018 capital gains tax exemption in the K-OTC market, which exempts capital gains tax on the proceeds from the sale of individual investors of certain stocks under the temporary special law. Findings - In the K-OTC market, the amount of transactions has expanded since the capital gains tax exemption in 2018, but the volume of transactions and transaction turnover have decreased. In particular, the result of lower transaction turnover after the expansion is contrary to expectations. To control the macroscopic effects of the stock market, further analyses the transactions of capital gains tax-exempt stocks and non-exempt stocks. The turnover rate of exemption stocks is higher than that of the non-exempt stocks. In the case of transaction turnover, the two results are not consistent. However, the latter result is more meaningful because the comparison of exempt and non-exempt reduces distortion by macro effects. Research implications or Originality - To mitigate the impact of capital gains taxes on stock market, government authorities need to consider the gradual expansion of the scope of taxation, the application of separate taxation in the introduction of capital gains, the reduction tax rate on transfer income of listed shares, and the reduction tax rate on long-term holdings.

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

• The KIPS Transactions:PartC
• /
• v.18C no.1
• /
• pp.31-44
• /
• 2011

The applicability of network-based computing depends on the availability of the underlying network bandwidth. Such a growing gap between the capacity of the backbone network and the end users' needs results in a serious bottleneck of the access network in between. As a result, ISP incurs disadvantages in their business. If this situation is known to ISP in advance, or if ISP is able to predict traffic volume end-to-end link high-load zone, ISP and end users would be able to decrease the gap for ISP service quality. In this paper, simulation tools, such as ACE, ADM, and Flow Analysis, were used to be able to perceive traffic volume prediction and end-to-end link high-load zone. In using these simulation tools, we were able to estimate sequential transaction in real-network for e-Commerce. We also imported virtual network environment estimated network data, and create background traffic. In a virtual network environment like this, we were able to find out simulation results for traffic volume prediction and end-to-end link high-load zone according to the increase in the number of users based on virtual network environment.