• Journal of the Korean Home Economics Association
• /
• v.41 no.11
• /
• pp.151-169
• /
• 2003

This study is to investigate the important factor for household private education expenditure. Especially, this study analyzed the influence of financial management characteristics. For this, the income level is classified by comparative poverty and analyzed the influence power The data for this study was "the Korean Labor Panel" conducted by Korea Labor Institute in 2000. The result showed the demographic factors by the income level and financial characteristics have big difference. Also, income level affects private education expenditure. For lower income level, demographic factors affect more than financial factors. This result explained the private education expenditure as luxurious goods. For middle income level, financial factors affect more than demographic factors. This explained the private education expenditure as choice goods. For upper income level, the private education expenditure was explained as investment goods.

• Journal of Digital Convergence
• /
• v.14 no.7
• /
• pp.21-27
• /
• 2016

Big Data is gathering all the attention from every business community. Pervasive use of machine-to-machine (M2M) applications and mobile devices bring an explosion of data. By analyzing this data, the private and public sectors can benefit in the areas of cost reduction and productivity. The Korean government is actively pursuing Big Data initiatives to promote its usage. This paper aims to select industries which fit for the development of Big Data with a verification of the experts. The analytic hierarchy process (AHP) is applied to systematically derive the opinion of more than 50 professionals. Medical / welfare, transportation / warehousing, information and communications / information security, energy, the financial sector have been identified as promising industries. The results can be utilized in developing Big Data best practices thus contributing industrial development.

• Information Systems Review
• /
• v.22 no.1
• /
• pp.59-72
• /
• 2020

Artificial Intelligence is establishing itself as a familiar tool from an intractable concept. In this trend, financial sector is also looking to improve the problem of existing system which includes Fraud Detection System (FDS). It is being difficult to detect sophisticated cyber financial fraud using original rule-based FDS. This is because diversification of payment environment and increasing number of electronic financial transactions has been emerged. In order to overcome present FDS, this paper suggests 3 types of artificial intelligence models, Generative Adversarial Network (GAN), Deep Neural Network (DNN), and Convolutional Neural Network (CNN). GAN proves how data imbalance problem can be developed while DNN and CNN show how abnormal financial trading patterns can be precisely detected. In conclusion, among the experiments on this paper, WGAN has the highest improvement effects on data imbalance problem. DNN model reflects more effects on fraud classification comparatively.

• Advances in nano research
• /
• v.14 no.1
• /
• pp.67-80
• /
• 2023

Economic investigation is one of the main issues regarding the design and production of small-scale structures. This paper concerns the creation, implementation, and economic aspects of the cross-section profile of small-scale structures regarding the dynamic response of the free and forced vibration behavior of spinning nanoscale beams based on big data analysis. According to the financial analysis, the three practical non-uniform functions of cross-sections are compared to the uniform beam in the same weight and the equal material used. The previous studies reported that the uniform beams are more stable and contain a better frequency response based on the mechanical analysis. Still, concerning the economic investigation, which means the considered structures should have equal length and have the same weight in the aspect of material used, the conclusion can be different from the mechanical aspect. Consequently, in the current paper, the dynamic response along with computer technology as well as the big data analysis of the free and forced vibration of the nanobeam regarding the economic shape of the cross-section is scrutinized.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.32 no.1
• /
• pp.141-154
• /
• 2022

In the data economy era, various policies are being implemented to create an active data distribution environment. In South Korea, the formation of a big data distribution platform and data trading began with the launch of the Financial Data Exchange under public data governance. In the case of major advanced countries in the data field, they have built a data distribution environment based on the data broker industry for decades and have strengthened national data competitiveness through added values generated from the industry. However, behind the active data distribution through data brokers, there are numerous information security issues, which have resulted in various privacy issues and national security threats. These problems can occur sufficiently in the process of domestic financial data exchange. In our study, we analyzed various information security issues of data trading caused by data brokers and derived information security requirements to be considered when trading data. We verified whether information security requirements are well reflected in the information security policy for each transaction stage of the domestic financial data exchange. Based on the verification, measurements to strengthen information security for financial data exchange are presented in our paper.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.12 no.3
• /
• pp.499-506
• /
• 2017

Consideration of influential factors through division of capital market sector and interest rate sector to find and resolve the problems in current housing market and leasing market will become an important index to prepare measures for stabilization of housing sales market and housing lease market. Furthermore, a guideline will be provide you with preliminary data using Big Data to prepare for sudden price fluctuation because expected economic crisis, stock market situation, and uncertain future financial crisis can be predicted which may help anticipate real estate price index such as housing sales price index and housing lease price index.

• The Journal of Asian Finance, Economics and Business
• /
• v.8 no.2
• /
• pp.143-150
• /
• 2021

This study investigates and analyzes the difference in Indonesian banks' earnings quality in the pre-audit and post-audit period. This study also investigates the difference in audit quality done by public accounting firms. This study employs time series data taken from the unaudited and audited financial statements of banks listed on the Indonesia Stock Exchange in 2012-2016. Sample selection is made by using a purposive sampling method. The population of this study is 43 banks, and after checking the data for validity and reliability, the final sample size was 26 banks. Audit quality is operationalized with the size of the auditor. Earnings quality is proxied by accruals calculated using the Beaver and Engel (1996) model. The data analysis method used in this study is the paired-sample t-test and chow test. This study shows that there is no difference in earnings quality in the pre-audit and post-audit period. This study also reveals no difference in audit quality between the big four and non-big four auditors. These findings mean that independent auditors do not play a useful role in increasing the reliability of accounting information presented by management to stakeholders. Besides, this study's results do not verify the agency theory regarding auditors' role to minimize opportunistic management behavior in preparing financial statements.

• Journal of the Korea Convergence Society
• /
• v.12 no.12
• /
• pp.49-57
• /
• 2021

With the advent of the era of the fourth industry, more and more scientific techniques are being used to solve decision-making problems. In particular, big data analysis technology is developing as it becomes easier to collect numerical data. Therefore, in this study, in order to overcome the limitations of qualitatively analyzing investment trends, the association of various products was analyzed using associated analysis techniques. For the experiment, two experimental periods were divided based on the COVID-19 economic crisis, and sales information from individuals, institutions, and foreign investors was collected, and related analysis algorithms were implemented through r software. As a result of the experiment, institutions and foreigners recently invested in the KOSPI and KOSDAQ markets and bought futures and products such as ETF. Individuals purchased ETN and ETF products together, which is presumed to be the result of the recent great interest in sector investment. In addition, after COVID-19, all investors tended to be passive in investing in high-risk products of futures and options. This paper is thought to be a useful reference for product sales and product design in the financial field.

• Korean Journal of Mathematics
• /
• v.22 no.3
• /
• pp.529-552
• /
• 2014

As we know, some indices and data are strong influence to the price movement of some assets now, but not to another assets and in future. Thus we define some asset models for several time intervals; intraday, weekly, monthly, and yearly asset models. We define these asset models by using Brownian motion with volatility and Poisson process, and several deterministic functions(index function, twitter data function and big-jump simple function etc). In our asset models, these deterministic functions are the positive or negative levels of auxiliary indices, of analyzed data, and for imminent and extreme state(for example, financial shock or the highest popularity in the market). These functions determined by indices, twitter data and shocking news are a kind of one of speciality of our asset models. For reasonableness of our asset models, we introduce several real data, figurers and tables, and simulations. Perhaps from our asset models, for short-term or long-term investment, we can classify and reference many kinds of usual auxiliary indices, information and data.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2018.05a
• /
• pp.320-322
• /
• 2018

Data analysis is a process of generating useful information by evaluating real-world raw data for making better decisions in business development. In the freight transport logistics companies, the analysis of freight data is increasingly garnering considerable importance among the users for making better decisions regarding freight cost reductions. Consequently, in this study, we used R programming language to analyze the freight data that are collected from freight transport logistics company. Usually, the freight rate varies based on chosen day of the week. In here, we analyzed and visualized the results such as frequency of cost vs days, frequency of requested goods in ton vs days, frequency of order vs days, and frequency of order status vs days for the last one-year freight data. These analysis results are beneficial in the viewpoint of the users in ordering process.