• Journal of the Korea Convergence Society
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• v.10 no.1
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• pp.7-12
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• 2019

The application of neural networks to stock forecasting has received a great deal of attention because no assumption about a suitable mathematical model has to be made prior to forecasting and they are capable of extracting useful information from data, which is required to describe nonlinear input-output relations of stock forecasting. The paper builds neural network models to forecast daily KOrea composite Stock Price Index (KOSPI), and their performance is demonstrated. MAPEs of NN1 model show 0.427 and 0.627 in its learning and test, respectively. Based on the predicted KOSPI price, the paper proposes an alpha trading for trades in Exchange Traded Funds (ETFs) that fluctuate with the KOSPI200. The alpha trading is tested with data from 125 trade days, and its trade return of 7.16 ~ 15.29 % suggests that the proposed alpha trading is effective.

• Korean Journal of Construction Engineering and Management
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• v.20 no.5
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• pp.3-11
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• 2019

This study is designed to examine the stock price of construction firms which are affected by the deregulation of new apartment sales price. As empirical methodology, it uses the traditional event study analysis to test the influence of the deregulation of new apartment sales price and the regression analysis to test which variables are related. The results of this study are summarized as follows : First, the cumulative abnormal return of stock is positive when government announced the deregulation of new apartment sales price. The cumulative abnormal return of stock for 21 trading day before -10 to +10 day is 25.51% which is significant different from zero at 1 percent level. This result suggests that the deregulation of new apartment sales price conveys good information to stock market that the firms performance will be good in the future. Second, in the regression analysis this study shows that the cumulative abnormal return of stock is related to firm's profit margin ratio.

• 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.

• Journal of the Korean Operations Research and Management Science Society
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• v.35 no.1
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• pp.83-95
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• 2010

This paper considers a firm that operates make-to-stock and make-to-order facilities in successive stages. The make-to-stock facility produces components which are consumed by the external market demand as well as the internal make-to-order operation. The make-to-order facility processes customer orders with the option of acceptance or rejection. In this paper, we address the problem of coordinating how to allocate the capacity of the make-to-stock facility to internal and external demands and how to control incoming customer orders at the make-to-order facility so as to maximize the firm's profit subject to the system costs. To deal with this issue, we formulate the problem as a Markov decision process and characterize the structure of the optimal inventory allocation and customer order control. In a numerical experiment, we compare the performance of the optimal policy to the heuristic with static inventory allocation and admission control under different operating conditions of the system.

• Knowledge Management Research
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• v.11 no.5
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• pp.1-23
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• 2010

In order to provide a better understanding about the phenomenon of KM assimilation, this study attempts to conceptually develop and empirically compare two different models: (1) the first model, which considers the KM process as the flow of knowledge that plays an intervening role between knowledge stocks (i.e., knowledge worker, technical knowledge infrastructure, external knowledge linkage, knowledge strategy, and internal knowledge climate) and the level of KM assimilation; and (2) the second model is a simple direct effect formulation without any distinction between knowledge stock and flow. These two models were then tested and compared using the responses of 187 Korean organizations that had already implemented enterprise-wide KM systems. The findings indicate that the two models are useful in explaining successful KM assimilation. However, the first causal model with the distinction between knowledge stock and flow assesses the effectiveness of KM more accurately than the second model without the distinction. Interestingly, the KM process was shown to be the most critical factor for the proliferation of KM activities across an organization. The findings of this study are expected to serve not only as early groundwork for researchers hoping to understand KM and its effective assimilation in organizations, but should also provide practitioners with guidelines as to how they can enhance their KM assimilation level so as to improve their organizational performance.

• KDI Journal of Economic Policy
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• v.33 no.4
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• pp.27-48
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• 2011

This paper investigates the extent of global and regional integration in East Asia using stock price index as a measure of economic performance. We employ a structural VAR model to separate the underlying shocks into "global", "regional" and "country-specific" shocks. The estimation results show that country-specific shocks still play a dominant role in East Asia although their role appears to have declined over time, especially after the 1997 financial crisis. Global and regional shocks are responsible for small but increasing shares of stock price fluctuations in all countries. The results indicate that the stock markets in East Asia remain dissimilar and are subject to asymmetric shocks in comparison to European countries.

• Journal of the Korean Society of Safety
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• v.34 no.5
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• pp.139-143
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• 2019

In this study, we tried to derive the most severe scenario and its critical speed by 1-D collision simulation with a variable formation vehicle in order to prepare for the change of demand in Seoul Metropolitan Subway Line 3, which is operated by fixed arrangement. After establishing various collision scenario conditions, the friction coefficient between the wheel and the rail was evaluated as 0.3, which is considered to be severe. As a result of analysis according to all scenarios, the most severe scenario conditions were confirmed by comparing rubber shock absorber performance and vehicle collision deceleration. In addition, a typical wheel-rail friction coefficient was derived through accident cases, and the analysis was performed again and compared. Finally, the criterion of the critical speed in the condition of the friction coefficient of the normal wheel - rail condition was confirmed.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.37-51
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• 2015

Due to the development of internet technology and the rapid increase of internet data, various studies are actively conducted on how to use and analyze internet data for various purposes. In particular, in recent years, a number of studies have been performed on the applications of text mining techniques in order to overcome the limitations of the current application of structured data. Especially, there are various studies on sentimental analysis to score opinions based on the distribution of polarity such as positivity or negativity of vocabularies or sentences of the texts in documents. As a part of such studies, this study tries to predict ups and downs of stock prices of companies by performing sentimental analysis on news contexts of the particular companies in the Internet. A variety of news on companies is produced online by different economic agents, and it is diffused quickly and accessed easily in the Internet. So, based on inefficient market hypothesis, we can expect that news information of an individual company can be used to predict the fluctuations of stock prices of the company if we apply proper data analysis techniques. However, as the areas of corporate management activity are different, an analysis considering characteristics of each company is required in the analysis of text data based on machine-learning. In addition, since the news including positive or negative information on certain companies have various impacts on other companies or industry fields, an analysis for the prediction of the stock price of each company is necessary. Therefore, this study attempted to predict changes in the stock prices of the individual companies that applied a sentimental analysis of the online news data. Accordingly, this study chose top company in KOSPI 200 as the subjects of the analysis, and collected and analyzed online news data by each company produced for two years on a representative domestic search portal service, Naver. In addition, considering the differences in the meanings of vocabularies for each of the certain economic subjects, it aims to improve performance by building up a lexicon for each individual company and applying that to an analysis. As a result of the analysis, the accuracy of the prediction by each company are different, and the prediction accurate rate turned out to be 56% on average. Comparing the accuracy of the prediction of stock prices on industry sectors, 'energy/chemical', 'consumer goods for living' and 'consumer discretionary' showed a relatively higher accuracy of the prediction of stock prices than other industries, while it was found that the sectors such as 'information technology' and 'shipbuilding/transportation' industry had lower accuracy of prediction. The number of the representative companies in each industry collected was five each, so it is somewhat difficult to generalize, but it could be confirmed that there was a difference in the accuracy of the prediction of stock prices depending on industry sectors. In addition, at the individual company level, the companies such as 'Kangwon Land', 'KT & G' and 'SK Innovation' showed a relatively higher prediction accuracy as compared to other companies, while it showed that the companies such as 'Young Poong', 'LG', 'Samsung Life Insurance', and 'Doosan' had a low prediction accuracy of less than 50%. In this paper, we performed an analysis of the share price performance relative to the prediction of individual companies through the vocabulary of pre-built company to take advantage of the online news information. In this paper, we aim to improve performance of the stock prices prediction, applying online news information, through the stock price prediction of individual companies. Based on this, in the future, it will be possible to find ways to increase the stock price prediction accuracy by complementing the problem of unnecessary words that are added to the sentiment dictionary.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.1
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• pp.57-64
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• 2017

In order to reduce damages to major railroad components, which have the potential to cause interruptions to railroad services and safety accidents and to generate unnecessary maintenance costs, the development of rolling stock maintenance technology is switching from preventive maintenance based on the inspection period to predictive maintenance technology, led by advanced countries. Furthermore, to enhance trust in accordance with the speedup of system and reduce maintenances cost simultaneously, the demand for fault diagnosis and prognostic health management technology is increasing. The objective of this paper is to propose a highly reliable learning model using various machine learning algorithms that can be applied to critical rolling stock components. This paper presents a model for railway rolling stock component fault diagnosis and conducts a mechanical failure diagnosis of motor components by applying the machine learning technique in order to ensure efficient maintenance support along with a data preprocessing plan for component fault diagnosis. This paper first defines a failure diagnosis model for rolling stock components. Function-based algorithms ANFIS and SMO were used as machine learning techniques for generating the failure diagnosis model. Two tree-based algorithms, RadomForest and CART, were also employed. In order to evaluate the performance of the algorithms to be used for diagnosing failures in motors as a critical railroad component, an experiment was carried out on 2 data sets with different classes (includes 6 classes and 3 class levels). According to the results of the experiment, the random forest algorithm, a tree-based machine learning technique, showed the best performance.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.207-210
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• 1996

Learning methodologies such as neural networks or genetic algorithms usually require long training times. Case based reasoning, however, attains peak performance swiftly and is often appropriate for learning even with small data sets. Previous work has shown that an extended case reasoning methodology can yield superior performance in the task of predicting financial data series. This paper examines the impact of reasoning procedures on stock price prediction. The following characteristics are evaluated: size of input vector, multiplicity of neighboring states, and a scaling factor for growth. The concepts are illustrated in the context of predicting the price of an individual price.