• Journal of Korea Port Economic Association
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• v.37 no.2
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• pp.1-17
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• 2021

Private investment projects have the characteristic of generating profits for a long period of 30 to 40 years, and fluctuations in profits and costs occur over time, so the interaction of variables over time rather than statistical models or discounted cash flows If the system dynamics technique, which enables simulation of the system, is used, it is considered that meaningful simulation results can be derived for internal and external variables. In other words, by establishing a financial feasibility comparison/verification model based on system dynamics for private investment projects in the port sector that have not been attempted before, we compare the differences with the existing cash flow discount method, macroeconomic factors, operating period, social discount rate We will conduct a differentiated study that has not been tried before by simulating how the interrelationships of such variables affect the change in financial performance.

• Journal of Convergence for Information Technology
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• v.11 no.1
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• pp.28-33
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• 2021

Since the stock price is a measure of the future value of the company, when analyzing the stock price, the company's growth potential, such as sales and profits, is considered and invested in stocks. In order to set the criteria for selecting stocks, institutional investors look at current industry trends and macroeconomic indicators, first select relevant fields that can grow, then select related companies, analyze them, set a target price, then buy, and sell when the target price is reached. Stock trading is carried out in the same way. However, general individual investors do not have any knowledge of investment, and invest in items recommended by experts or acquaintances without analysis of financial statements or growth potential of the company, which is lower in terms of return than institutional investors and foreign investors. Therefore, in this study, we propose a research method to select undervalued stocks by analyzing ROE, an indicator that considers the growth potential of a company, such as sales and profits, and predict the stock price flow of the selected stock through deep learning algorithms. This study is conducted to help with investment.

• The Korean Journal of Applied Statistics
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• v.37 no.2
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• pp.255-263
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• 2024

GDP represents the total market value of goods and services produced by all economic entities, including households, businesses, and governments in a country, during a specific time period. It is a representative economic indicator that helps identify the size of a country's economy and influences government policies, so various studies are being conducted on it. This paper presents a GDP growth rate forecasting model based on a dynamic factor model using key macroeconomic indicators of G20 countries. The extracted factors are combined with various regression analysis methodologies to compare results. Additionally, traditional time series forecasting methods such as the ARIMA model and forecasting using common components are also evaluated. Considering the significant volatility of indicators following the COVID-19 pandemic, the forecast period is divided into pre-COVID and post-COVID periods. The findings reveal that the dynamic factor model, incorporating ridge regression and lasso regression, demonstrates the best performance both before and after COVID.

• Korean Journal of Construction Engineering and Management
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• v.19 no.2
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• pp.3-14
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• 2018

The system to appraise the execution capabilities of construction companies had been began as the Construction Contract Restriction System in 1958, was changed as the Construction Subcontract Restriction System in 1961, and finally has been operated as the Appraisal and Public Announcement of Execution Capacity (APAEC) from 1996. The APAEC system has been utilized as a firm and unique tool for assessing the execution capacities of construction companies despite many problems and continuous system changes. In spite of numerous studies to improve the APAEC system, however, efforts to analyze the system from the empirical point of view were insufficient. In this study, we analyze the status of APAEC system through analyzing correlations among assessment results of the APAEC, earned values of construction works, construction management performance indexes, and macroeconomic indexes for the past 10 years from 2007 to 2016. As a result of the analysis, it was found that Appraisal Value of Execution Capacity (AVEC) is excessively inflated in engineering and landscaping areas compared to Earned Value of Construction Work (EVCW) and the correlations between the AVECs and EVECs are not high in the areas of engineering, industrial equipment, and landscaping. In addition, technical appraisal values are excessively inflated in engineering and landscaping areas and correlations between AVEC and its components are high in the areas of engineering & building, industrial equipment, and large companies, but low in the areas of engineering, building, landscaping, and small and medium companies. Finally, the concentration of the AVEC is intensifying more and more and the concentration deteriorates construction management performance indexes and macroeconomic indexes. If we continuously improve the APAEC system based on the implications derived in this study, the APAEC system will be able to maintain it's position of a firm and unique means to access the execution capacities of construction companies.

• Journal of the Korea Society of Computer and Information
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• v.21 no.6
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• pp.9-19
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• 2016

In finance literature, stock liquidity showing how stocks can be cashed out in the market has received rich attentions from both academicians and practitioners. The reasons are plenty. First, it is known that stock liquidity affects significantly asset pricing. Second, macroeconomic announcements influence liquidity in the stock market. Therefore, stock liquidity itself affects investors' decision and managers' decision as well. Though there exist a great deal of literature about stock liquidity in finance literature, it is quite clear that there are no studies attempting to investigate the stock liquidity issue as one of decision making problems. In finance literature, most of stock liquidity studies had dealt with limited views such as how much it influences stock price, which variables are associated with describing the stock liquidity significantly, etc. However, this paper posits that stock liquidity issue may become a serious decision-making problem, and then be handled by using data mining techniques to estimate its future extent with statistical validity. In this sense, we collected financial data set from a number of manufacturing companies listed in KRX (Korea Exchange) during the period of 2010 to 2013. The reason why we selected dataset from 2010 was to avoid the after-shocks of financial crisis that occurred in 2008. We used Fn-GuidPro system to gather total 5,700 financial data set. Stock liquidity measure was computed by the procedures proposed by Amihud (2002) which is known to show best metrics for showing relationship with daily return. We applied five data mining techniques (or classifiers) such as Bayesian network, support vector machine (SVM), decision tree, neural network, and ensemble method. Bayesian networks include GBN (General Bayesian Network), NBN (Naive BN), TAN (Tree Augmented NBN). Decision tree uses CART and C4.5. Regression result was used as a benchmarking performance. Ensemble method uses two types-integration of two classifiers, and three classifiers. Ensemble method is based on voting for the sake of integrating classifiers. Among the single classifiers, CART showed best performance with 48.2%, compared with 37.18% by regression. Among the ensemble methods, the result from integrating TAN, CART, and SVM was best with 49.25%. Through the additional analysis in individual industries, those relatively stabilized industries like electronic appliances, wholesale & retailing, woods, leather-bags-shoes showed better performance over 50%.

• Korean Journal of Construction Engineering and Management
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• v.20 no.1
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• pp.124-132
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• 2019

Notwithstanding the crucial contribution of international construction industry in the national economy, previous studies on international construction contracts had mainly focused either on trend investigation or market share analysis at a point of time. Fundamentally, the international construction industry is fragmented due to its project-based nature, is heterogeneous that has to involve different firms from diverse fields, and tends to be dynamic according to macroeconomic conditions. Therefore, the combination of static and dynamic analyses is necessary to understand its underlying structure. This study analyzes the market structure of international construction contracts using the data of 9,173 projects awarded by Korean construction firms from 2000 to 2017. Industry-level performance data is analyzed both in static (market concentration) and dynamic (market mobility and instability) methods, and detailed methodology is also provided. Consequently, the static analysis result shows that the competition among Korean construction firms has been more intensified, and the dynamic analysis result indicates that market positions of Korean construction firms are unstable and vulnerable in most of the regions and the sectors. The combination of static and dynamic indices is found to be helpful to understand the underlying aspects of market structure and can be utilized as an effective strategic reference in the highly competitive market.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.

• Journal of Digital Convergence
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• v.15 no.11
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• pp.113-120
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• 2017

The purpose of this study is to analyze how the relative proportion of retention between real estate for business and investment real estate among the real estate held by corporations has been changed after and before the Financial Crisis as well as whether there has been any difference between KOSPI and KOSDAQ listed companies in terms of their share of the real estate. The increasing pattern of real estate owned by KOSDAQ were similar to the KOSPI companies except for investment properties during the Financial Crisis. The proportion of real estate owned by KOSPI had been lower than that of KOSDAQ companies in both investment and business real estate before the Financial Crisis. However, during the period of the Financial Crisis, the proportion of real estate for business held by KOSPI firms was higher than that of KOSDAQ firms. Furthermore, the portion of investment of real estate owned by KOSPI has remained higher than that of KOSDAQ after the Financial Crisis period and the recent period. Based on the results of this analysis, how the relevance of the change of portion between real estate for business and investment real estate affects management performance will be figured out in the future studies.

• The Korean Journal of Applied Statistics
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• v.23 no.6
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• pp.997-1021
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• 2010

This paper compares the two most widely used seasonal adjustment methods: the X-12-ARIMA and TRAMO-SEATS procedures. The basic features of these methods are discussed and compared in both their theoretical and empirical aspects. In doing so, the X-13A-S program is used to reevaluate their applicability to Korean macroeconomic data by considering possible structural breaks in the series. The finding is that both methods provide very reliable and stable estimates of seasonal factors and seasonally adjusted data. As for the empirical comparisons, TRAMO-SEATS appears to outperform X-12-ARIMA, although the results are somewhat mixed depending on the comparison criteria used and on the series under analysis. In particular, the performance of TRAMO-SEATS turns out to compare more favorably when seasonal adjustment is carried out to each sub-samples (by taking possible structural breaks into account) than when the whole sample period is used. The result suggests that as the model-based TRAMO-SEATS has a considerable theoretical appeal, some features of TRAMO-SEATS should further be incorporated into X-12-ARIMA until a standard and integrated procedure is reached by combining the theoretical coherence of TRAMO-SEATS and the empirical usefulness of X-12-ARIMA.

• Environmental and Resource Economics Review
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• v.16 no.1
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• pp.129-169
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• 2007

The purpose of this study is to experiment and simulate the newly-updated Second Generation Model for Korea (SGM-Korea). With the updated model, we tried to simulate effect of carbon tax on $CO_2$ emissions and other macroeconomic variables for Korea. The baseline data are compared with projected profiles by various scenarios to evaluate its performance. Our contribution in this study is to having up-graded the model from its earlier version by building new hybrid input-output table based on 2000 input-output and energy balanced tables. According to our estimation, total $CO_2$ emission in Korea has already increased in 2000 to about 1.86 times the 1990 figure. The level of carbon tax required for the current level of $CO_2$ emission to be reduced to the 1995 or 2000 level seems to be too high for Korean economy to bear. It is possible to find a reasonable level of carbon tax, however, if it can combine it with improvement of energy efficiency at the rate of 0.5% to 1% per year. For Korea to meet its obligation to reduce $CO_2$ emissions, therefore, it is imperative for her to improve energy efficiency as well as to develop alternative energy source reducing its dependence on fossil fuel.