• Journal of Digital Convergence
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• v.11 no.2
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• pp.175-182
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• 2013

In this paper, the simulation model of house price is presented on the basis of pricing mechanism between the demand and the supply of apartments in seoul. The algorithm of house price simulation model for calculating the rate of price over time includes feedback control theory. The feedback control theory consists of stock variable, flow variable, auxiliary variable and constant variable. We suggest that the future price of apartment is simulated using mutual interaction variables which are demand, supply, price and parameters among them. In this paper we considers three items which include the behavior of apartment price index, the size of demand and supply, and the forecasting of the apartment price in the future economic scenarios. The proposed price simulation model could be used in public needs for developing a house price regulation policy using financial and non-financial aids. And the quantitative simulation model is to be applied in practice with more specific real data and Powersim Software modeling tool.

• The Journal of the Korea Contents Association
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• v.7 no.12
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• pp.123-130
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• 2007

The purpose of this paper is to capture risk profiles of smaller-sized Korean firms $vis-{\grave{a}}-vis$ larger-sized firms during the Asian financial crisis. For this purpose, risk profiles are provided by estimating expected default risks and by tracking how these have changed during this period with respect to their magnitude, volatility, and sensitivity measures. Methodology used in this study employs the Black-Scholes-Merton model for producing estimates of default risks. And the conventional trans-log function is utilized for obtaining sensitivity measures of the estimated default risks. According to empirical evidence obtained here, it is revealed that contractions of corporate loans associated with IMF austerity policy was the main factor responsible for the drastic change in the default risk profile of Korean firms after occurrence of the Asian financial crisis.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.353-358
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• 2001

Since the financial crisis broke out, liquidity has become the critical issue in housing construction industry. In order to secure liquidity, it is prerequisite to precisely forecast cash flow. However, construction companies have failed to come up with a systematic process to manage and forecast cash flow. Until now, companies have solely relied on the prediction of profits and losses, which is carried out as they review business feasibility. To obtain more accurate cash flow forecast model, practical pattern of payments should be taken into account. In this theory, basic model that analyzes practical housing payment collection pattern resulting from prepayments and arrears is described. This model is to complement conventional cash flow forecast scheme in the phase of business feasibility review. Analysis result on final losses in cash that occur as a result of prepayment and arrears is considered in this model. Additionally, in the estimation of construction cost in the phase of business feasibility review, real construction prices instead of official prices are applied to enhance accuracy of cash outflow forecast. The proportion of payment made by a bill and changes in payment date caused by rescheduling of a bill are also factored in to estimate cash outflow. This model would contribute to achieving accurate cash flow forecast that better reflect real situation and to enhancing efficiency in capital management by giving a clear picture with regard to the demand and supply timing of capital.

• Journal of Digital Convergence
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• v.9 no.2
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• pp.33-40
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• 2011

It is very difficult to predict what effects the introduction of IFRS(International and Financial Reporting Standars) has on the existing system. Only the cautious and careful adoption of new system will minimize the confusion. The purpose of this paper is to propose appropriate design and construction of IS on financial statement according to the introduction of IFRS(International Financial Reporting Standard) in order to minimize the confusion. In order to meet that purpose, suggestions on five areas in consolidation financial statement are proposed. Those five aspects are as follows: Area of SPE Consolidation, Area of internal transaction setoff, Area of unrealized gain or loss setoff, Area of eliminate the investment account against the stockholder's equity, Making Consolidation footnote and Consolidation CoA as other area.

• Journal of Digital Convergence
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• v.15 no.6
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• pp.133-143
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• 2017

Data is the most important asset in the financial sector. On average, 71 percent of financial institutions generate competitive advantage over data analysis. In particular, in the card industry, the card transaction data is widely used in the development of merchant information, economic fluctuations, and information services by analyzing patterns of consumer behavior and preference trends of all customers. However, creation of new value through fusion of data is insufficient. This study introduces the analysis and forecasting of consumption trends of credit card companies which convergently analyzed the social data and the sales data of the company's own. BC Card developed an algorithm for linking card and social data with trend profiling, and developed a visualization system for analysis contents. In order to verify the performance, BC card analyzed the trends related to 'Six Pocket' and conducted th pilot marketing campaign. As a result, they increased marketing multiplier by 40~100%. This study has implications for creating a methodology and case for analyzing the convergence of structured and unstructured data analysis that have been done separately in the past. This will provide useful implications for future trends not only in card industry but also in other industries.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.1
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• pp.23-30
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• 2019

As deep learning with the network-based algorithms evolve, artificial intelligence is rapidly growing around the world. Among them, finance is expected to be the field where artificial intelligence is most used, and many studies have been done recently. The existing financial strategy using deep-run is vulnerable to volatility because it focuses on stock price forecasts for a single stock. Therefore, this study proposes to construct ETF products constructed through portfolio methods by calculating the stocks constituting funds by using deep learning. We analyze the performance of the proposed model in the KOSPI 100 index. Experimental results showed that the proposed model showed improved results in terms of returns or volatility.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.193-196
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• 2010

정보화 시대를 거치면서 모든 산업분야에서 대량의 데이터가 생성되고 관리되고 있다. 최근에는 비즈니스 환경의 변화로 인하여 의사결정을 지원할 수 있는 고급 정보에 대한 필요성이 대두되었으며 IT 기술의 발전과 더불어 데이터마이닝에 대한 많은 연구가 활발히 이루어졌다. 데이터마이닝은 금융, 정부, 제조, 유통 등 다양한 분야에서 활용되고 있다. 한편 의료데이터는 다른 산업분야의 데이터와 구별되는 특징이 있는데, 데이터의 이질성과 복잡성, 부정확성과 오류가능성, 불완전성과 윤리 및 법적인 문제, 개인정보보호, 특징 선택의 제한, 모델의 투명성과 설명력에 대한 높은 요구도 등이 그것이다. 이와 같은 이유로 의료데이터에 대한 접근은 제한적일 수 밖에 없다. 그럼에도 병원 전산화를 통해 발생하는 의료데이터의 양은 기하급수적으로 증가하고 있으며, 임상정보를 포함하는 의료데이터는 데이터 자체로도 가치가 매우 크다. 이에 본 논문은 국내 제 3차 의료기관의 2년간 내원환자에 대한 진단데이터를 사용하여 데이터마이닝의 연관법칙을 이용, 상병간의 관계를 연구하고자 하였다. 이를 통해 잠재고객에게는 객관화된 의료지표를 제공하고, 의료기관은 예측 가능한 정보를 종합의료시스템에 활용하여 고객만족도를 높이는 효과를 볼 수 있을 것으로 사료된다.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• Journal of Korean Society of Disaster and Security
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• v.5 no.2
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• pp.81-87
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• 2012

Critical infrastructures (energy, information technology and communications, banking, transportation, public government services, etc.) are now more vital to modern society. Citizens, businesses and governments all rely on an array of interlinked physical and information infrastructures to satisfy their needs and perform their daily operations. At the same time, these infrastructures are becoming increasingly interdependent, such that failure of one of them can often propagate and result in domino effects. Recent dramatic episodes, from 9/11 to the Madrid train bombings, the April 2010 ash cloud the power cuts in Korea in 2011, and the cyber-attacks have highlighted the need for a comprehensive, internationally coordinated policy for the protection of critical infrastructures. For the purposes of this report, we define critical infrastructure as infrastructure whose failure would result in substantial damage to society and/or the economy.

• Journal of Intelligence and Information Systems
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• v.16 no.2
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• pp.19-32
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• 2010

Volatility plays a central role in both academic and practical applications, especially in pricing financial derivative products and trading volatility strategies. This study presents a novel mechanism based on generalized autoregressive conditional heteroskedasticity (GARCH) models that is able to enhance the performance of intelligent volatility trading systems by predicting Korean stock market volatility more accurately. In particular, we embedded the concept of the volatility asymmetry documented widely in the literature into our model. The newly developed Korean stock market volatility index of KOSPI 200, VKOSPI, is used as a volatility proxy. It is the price of a linear portfolio of the KOSPI 200 index options and measures the effect of the expectations of dealers and option traders on stock market volatility for 30 calendar days. The KOSPI 200 index options market started in 1997 and has become the most actively traded market in the world. Its trading volume is more than 10 million contracts a day and records the highest of all the stock index option markets. Therefore, analyzing the VKOSPI has great importance in understanding volatility inherent in option prices and can afford some trading ideas for futures and option dealers. Use of the VKOSPI as volatility proxy avoids statistical estimation problems associated with other measures of volatility since the VKOSPI is model-free expected volatility of market participants calculated directly from the transacted option prices. This study estimates the symmetric and asymmetric GARCH models for the KOSPI 200 index from January 2003 to December 2006 by the maximum likelihood procedure. Asymmetric GARCH models include GJR-GARCH model of Glosten, Jagannathan and Runke, exponential GARCH model of Nelson and power autoregressive conditional heteroskedasticity (ARCH) of Ding, Granger and Engle. Symmetric GARCH model indicates basic GARCH (1, 1). Tomorrow's forecasted value and change direction of stock market volatility are obtained by recursive GARCH specifications from January 2007 to December 2009 and are compared with the VKOSPI. Empirical results indicate that negative unanticipated returns increase volatility more than positive return shocks of equal magnitude decrease volatility, indicating the existence of volatility asymmetry in the Korean stock market. The point value and change direction of tomorrow VKOSPI are estimated and forecasted by GARCH models. Volatility trading system is developed using the forecasted change direction of the VKOSPI, that is, if tomorrow VKOSPI is expected to rise, a long straddle or strangle position is established. A short straddle or strangle position is taken if VKOSPI is expected to fall tomorrow. Total profit is calculated as the cumulative sum of the VKOSPI percentage change. If forecasted direction is correct, the absolute value of the VKOSPI percentage changes is added to trading profit. It is subtracted from the trading profit if forecasted direction is not correct. For the in-sample period, the power ARCH model best fits in a statistical metric, Mean Squared Prediction Error (MSPE), and the exponential GARCH model shows the highest Mean Correct Prediction (MCP). The power ARCH model best fits also for the out-of-sample period and provides the highest probability for the VKOSPI change direction tomorrow. Generally, the power ARCH model shows the best fit for the VKOSPI. All the GARCH models provide trading profits for volatility trading system and the exponential GARCH model shows the best performance, annual profit of 197.56%, during the in-sample period. The GARCH models present trading profits during the out-of-sample period except for the exponential GARCH model. During the out-of-sample period, the power ARCH model shows the largest annual trading profit of 38%. The volatility clustering and asymmetry found in this research are the reflection of volatility non-linearity. This further suggests that combining the asymmetric GARCH models and artificial neural networks can significantly enhance the performance of the suggested volatility trading system, since artificial neural networks have been shown to effectively model nonlinear relationships.