• The Journal of Information Systems
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• v.32 no.1
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• pp.179-199
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• 2023

Purpose This study builds a prediction model to find stocks that can reach intrinsic value among KOSPI and KOSDAQ-listed companies to improve the stability and profitability of the stock investment. And investment simulations are conducted to verify whether stock investment performance is improved by comparing the prediction model, random stock selection, and the market indexes. Design/methodology/approach Value investment theory and machine learning techniques are applied to build the model. Various experiments find conditions such as the algorithm with the best predictive performance, learning period, and intrinsic value-reaching period. This study selects stocks through the prediction model learned with inventive variables, does not limit the holding period after buying to reach the intrinsic value of the stocks, and targets all KOSPI and KOSDAQ companies. The stock and financial data are collected for 21 years (2001-2021). Findings As a result of the experiment, using the random forest technique, the prediction model's performance was the best with one year of learning period and within one year of the intrinsic value reaching period. As a result of the investment simulation, the cumulative return of the prediction model was up to 1.68 times higher than the random stock selection and 17 times higher than the KOSPI index. The usefulness of the prediction model was confirmed in that the number of intrinsic values reaching the predicted stock was up to 70% higher than the random selection.

• Korea Trade Review
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• v.48 no.4
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• pp.209-232
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• 2023

This study aims to estimate the export performance factors of Korean Steel Products to the EU and the expected changes in exports according to the CBAM(Carbon Border Adjustment Mechanism). the factors influencing the export performance of Korean Steel Products to the EU were analyzed using a Gravity Model, and the expected export amount in the case of a Carbon Tax was calculated assuming that the CBAM would be implemented in 2026, As a result, it was empirically analyzed that economic growth, population growth, exchange rate and manufacturing production index of each EU country have a positive effect on exports in Korea, and it was analyzed that the effects of the single market and system due to the EU's economic community were also helpful in increasing exports but the Carbon Tax is imposed in 2026, reducing Korea's steel exports by about -3.6% to -5.7%

• The Korean Journal of Applied Statistics
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• v.28 no.3
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• pp.429-442
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• 2015

Volatility forecasting in financial markets is an important issue because it is directly related to the profit of return. The volatility is generally modeled as time-varying conditional heteroskedasticity. A generalized autoregressive conditional heteroskedastic (GARCH) model is often used for modeling; however, it is not suitable to reflect structural changes (such as a financial crisis or debt crisis) into the volatility. As a remedy, we introduce the Markov regime switching GARCH (MRS-GARCH) model. For the empirical example, we analyze and forecast the volatility of the daily Korea Composite Stock Price Index (KOSPI) data from January 4, 2000 to October 30, 2014. The result shows that the regime of low volatility persists with a leverage effect. We also observe that the performance of MRS-GARCH is superior to other GARCH models for in-sample fitting; in addition, it is also superior to other models for long-term forecasting in out-of-sample fitting. The MRS-GARCH model can be a good alternative to GARCH-type models because it can reflect financial market structural changes into modeling and volatility forecasting.

• The Korean Journal of Applied Statistics
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• v.23 no.4
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• pp.651-668
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• 2010

Various estimators of two risk measures of a specific financial portfolio, Value-at-Risk and Expected Shortfall, are compared for each case of 1-day and 10-day horizons. We use the Korea Composite Stock Price Index data of 20-year period including the year 2008 of the global financial crisis. Indexes of five foreign stock markets are also used for the empirical comparison study. The estimator considering both the heavy tail of loss distribution and the conditional heteroscedasticity of time series is of main concern, while other standard and new estimators are considered too. We investigate which estimator is best for the Korean stock market and which one shows the best overall performance.

• The Korean Journal of Applied Statistics
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• v.25 no.5
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• pp.757-773
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• 2012

Traditional value at risk(S-VaR) has a difficulity in predicting the future risk of financial asset prices since S-VaR is a backward looking measure based on the historical data of the underlying asset prices. In order to resolve the deficiency of S-VaR, an economic value at risk(E-VaR) using the risk neutral probability distributions is suggested since E-VaR is a forward looking measure based on the option price data. In this study E-VaR is estimated by assuming the generalized gamma distribution(GGD) as risk neutral density function which is implied in the option. The estimated E-VaR with GGD was compared with E-VaR estimates under the Black-Scholes model, two-lognormal mixture distribution, generalized extreme value distribution and S-VaR estimates under the normal distribution and GARCH(1, 1) model, respectively. The option market data of the KOSPI 200 index are used in order to compare the performances of the above VaR estimates. The results of the empirical analysis show that GGD seems to have a tendency to estimate VaR conservatively; however, GGD is superior to other models in the overall sense.

• Journal of Electrical Engineering and Technology
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• v.9 no.4
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• pp.1154-1162
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• 2014

In the restructured electricity market, Performance-Based Regulation (PBR) regime has been introduced to the distribution network. To ensure the network stability, this regime is used along with quality regulations. Quality regulation impose new financial risks on distribution system operators (DSOs). The poor quality of the network will result in reduced revenues for DSOs. The mentioned financial risks depend on the quality indices of the system. Based on annual variation of these indices, the cost of quality regulation will also vary. In this paper with regard to reclosing fault in distribution network, we develop a risk-based method to assess the financial risks caused by quality regulation for DSOs. Furthermore, in order to take the stochastic behavior of the distribution network and quality indices variations into account, time-sequential Monte Carlo simulation method is used. Using the proposed risk method, the effect of taking reclosing time into account will be examined on system quality indicators and the cost of quality regulation in Swedish rural reliability test system (SRRTS). The results show that taking reclosing fault into consideration, affects the system quality indicators, particularly annual average interruption frequency index of the system (SAIFI). Moreover taking reclosing fault into consideration also affects the quality regulations cost. Therefore, considering reclosing time provides a more realistic viewpoint about the financial risks arising from quality regulation for DSOs.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2003.05a
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• pp.214-225
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• 2003

Now a day, the company must strengthen the contact-point of the customer who the company has and has to block the secession of the customer by providing services or goods on time. Under this market situation, the corporation extends the CRM for the customer management and strategic management, and set the CRM-strategies up for managing the customer relationship. For this, the present enterprise's level and the business-ability for the management of the customer relationship should be considered. Therefore, in this study, we will analyze the critical factors to set the CRM up as a strategy by studying the literature review. In the critical factors, the factors of enterprise level as well as the technical factor will be included. Secondly, as you know, the BSC is used to evaluate the corporation as a index. In this study the BSC model is changed and rearranged for the applied BSC model to measure the C3M level of companies. Thirdly, based on the model developed, the factors in the first step are classified by levels and weighted values are calculated by using AHP method. As a result, we will show the diagnostic model for check the operational performance of management, marketing and sales etc.

• Communications for Statistical Applications and Methods
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• v.31 no.2
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• pp.213-234
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• 2024

In recent decades, increasing research attention has been directed toward predicting the price of stocks in financial markets using deep learning methods. For instance, recurrent neural network (RNN) is known to be competitive for datasets with time-series data. Long short term memory (LSTM) further improves RNN by providing an alternative approach to the gradient loss problem. LSTM has its own advantage in predictive accuracy by retaining memory for a longer time. In this paper, we combine both supervised and unsupervised dimension reduction methods with LSTM to enhance the forecasting performance and refer to this as a dimension reduction based LSTM (DR-LSTM) approach. For a supervised dimension reduction method, we use methods such as sliced inverse regression (SIR), sparse SIR, and kernel SIR. Furthermore, principal component analysis (PCA), sparse PCA, and kernel PCA are used as unsupervised dimension reduction methods. Using datasets of real stock market index (S&P 500, STOXX Europe 600, and KOSPI), we present a comparative study on predictive accuracy between six DR-LSTM methods and time series modeling.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.1
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• pp.83-91
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• 2014

The passive UHF RFID tags for item-level tagging are now explosively used in the supply chain and retail applications as they have several advantages, the two most relevant are cost and a longer read range. However, the signal interference problem between RFID tags and smart devices in real world is expected according to the smart-phone and tablet market growth together. The performance of RFID tags can be significantly less. The popular examples are the read-success rates and read range reduction. Especially, KT Corp. recently emphasized the serious signal interference at 900 MHz of LTE and old RFID frequencies through their public demonstration. By popular demands, this paper suggests the interference tolerance measurement method between the passive UHF RFID tag and the transmitted signal from a smart device. In addition, we selected three passive UHF RFID tags(Inlay) available on the market and quantitatively evaluated read range reduction results by interference signals using the PCR(Performance Change Rates) index. As a result, the LTE system is about three times as effective as the WCDMA system in terms of interference effects, and the read range performance of two RFID tags about 60 % drop.

• International Area Studies Review
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• v.16 no.1
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• pp.29-52
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• 2012

This study investigates the relationship between corporate social responsibility and corporate governance among major US corporations belonging to S&P 500 index during 2000-2008. Specifically, it examines the ways in which firms engage their boards of directors in integrated strategy by establishing a public affairs(PA) or social responsibility(SR) committee at the board level and the effects of this practice on their corporate social performance(CSP). The empirical findings show that negative CSP is the major driver for establishing such a committee, that is, firms suffering from negative CSP as a result of experiencing undesirable social events tend to establish such a committee. On the other hand, such a committee helps the firm increase positive CSP once it is established. In other words, the purpose of establishing such board-level committees is to address problems associated with negative CSP, and once established, they can help enhance positive CSP by enabling the firm to integrate market, non-market, and social responsibility aspects in strategy formulation more effectively. This is evidence that Baron's integrated strategy framework can help firms achieve tangible outcomes.