• The Journal of Asian Finance, Economics and Business
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• v.7 no.9
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• pp.169-178
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• 2020

In this paper, we provide an overview of financial development in Vietnam. Particularly, a new approach of this study is to measure financial development through improvements in depth, efficiency and access of the banking system and stock market. Further, the study examines the factors significantly affecting financial development in Vietnam. The data are collected in Vietnam, an emerging country with a limited financial development. We employ the Autoregressive Distributed Lag (ARDL) approach, which generates a high reliability and suits data characteristics of emerging countries like Vietnam. We observe that Vietnam's banking system plays a key role in supplying credits to the economy while the nascent stock market at a limited size shows its potential for a considerable growth in the future. We also find the influential determinants of financial development in Vietnam including real estate market (RE), economic growth (EG), consumer price index (CPI), and global financial crisis (GFC). These findings are essential for Vietnamese authorities in providing practical solutions in order to build a sustainable and synchronous financial development. They are also first empirical evidence relating to an overview of financial development in an emerging country, so they are not only valuable to Vietnam but also crucial to other emerging economies.

• Korean Journal of Construction Engineering and Management
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• v.11 no.5
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• pp.32-40
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• 2010

Recent periodical boom and burst of house price have made mortgage lending issues become the main public interest in Korean real estate market. However, because mortgage-lending issues had not been discussed until then, housing market forecasting associated with mortgage lending has been difficult while using an empirical approach. Thus, comprehensive and systematic approach is required as well as validity of mortgage lending policies should be evaluated. In this regard, this research conducts a sensitivity analysis to validate the proposed policies and estimates the effects of current policies on LTV and DTI ratios with a comparison of another policies scenario. A causal loop and sensitivity analysis using system dynamics confirmed that LTV and DTI regulation is strong clout to housing market. However, to prevent transfer of potential mortgage borrowers to nonmonetary institutions, regulations in loans of nonmonetary institutions should be practiced in accompaniment with regulations of primary lending agencies.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.123-138
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• 2017

Since the stock market is driven by the expectation of traders, studies have been conducted to predict stock price movements through analysis of various sources of text data. In order to predict stock price movements, research has been conducted not only on the relationship between text data and fluctuations in stock prices, but also on the trading stocks based on news articles and social media responses. Studies that predict the movements of stock prices have also applied classification algorithms with constructing term-document matrix in the same way as other text mining approaches. Because the document contains a lot of words, it is better to select words that contribute more for building a term-document matrix. Based on the frequency of words, words that show too little frequency or importance are removed. It also selects words according to their contribution by measuring the degree to which a word contributes to correctly classifying a document. The basic idea of constructing a term-document matrix was to collect all the documents to be analyzed and to select and use the words that have an influence on the classification. In this study, we analyze the documents for each individual item and select the words that are irrelevant for all categories as neutral words. We extract the words around the selected neutral word and use it to generate the term-document matrix. The neutral word itself starts with the idea that the stock movement is less related to the existence of the neutral words, and that the surrounding words of the neutral word are more likely to affect the stock price movements. And apply it to the algorithm that classifies the stock price fluctuations with the generated term-document matrix. In this study, we firstly removed stop words and selected neutral words for each stock. And we used a method to exclude words that are included in news articles for other stocks among the selected words. Through the online news portal, we collected four months of news articles on the top 10 market cap stocks. We split the news articles into 3 month news data as training data and apply the remaining one month news articles to the model to predict the stock price movements of the next day. We used SVM, Boosting and Random Forest for building models and predicting the movements of stock prices. The stock market opened for four months (2016/02/01 ~ 2016/05/31) for a total of 80 days, using the initial 60 days as a training set and the remaining 20 days as a test set. The proposed word - based algorithm in this study showed better classification performance than the word selection method based on sparsity. This study predicted stock price volatility by collecting and analyzing news articles of the top 10 stocks in market cap. We used the term - document matrix based classification model to estimate the stock price fluctuations and compared the performance of the existing sparse - based word extraction method and the suggested method of removing words from the term - document matrix. The suggested method differs from the word extraction method in that it uses not only the news articles for the corresponding stock but also other news items to determine the words to extract. In other words, it removed not only the words that appeared in all the increase and decrease but also the words that appeared common in the news for other stocks. When the prediction accuracy was compared, the suggested method showed higher accuracy. The limitation of this study is that the stock price prediction was set up to classify the rise and fall, and the experiment was conducted only for the top ten stocks. The 10 stocks used in the experiment do not represent the entire stock market. In addition, it is difficult to show the investment performance because stock price fluctuation and profit rate may be different. Therefore, it is necessary to study the research using more stocks and the yield prediction through trading simulation.

• Journal of Advanced Navigation Technology
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• v.24 no.3
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• pp.173-183
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• 2020

This research focused on such a passenger sample that used to fly between Incheon int'l airport(ICN) and Dubai int'l airport(DXB) using either a national carrier or gulf carriers because the route between ICN and DXB is one of the international air routes with the toughest competition under the global pressure of open air transport market. Based on the results from the empirical research, this paper proposed a competitive advantage which a national carrier must have to cope with global competition under the open sky policies and implications for sustainable strategies of them. National carrier passengers perceived product benefits had a significant positive effect on price reliability in spite of lower price competitiveness. Following the empirical analysis results, it was proven a national carrier should try to improve product benefit sought by passengers to maintain sustainable competitive advantage in the market against foreign airlines.

• Journal of the Economic Geographical Society of Korea
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• v.20 no.3
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• pp.405-418
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• 2017

SHIFT is public rental housing policy introduced by Seoul Metropolitan in 2007, which works as Chonsei(korean unique deposit rental system). This paper examines the effect of SHIFT on Chonsei prices of neighborhood apartments. To estimate the change in prices of Chonsei after the provision of SHIFT, I collect data on Chonsei prices of apartments within a 5km radius from the SHIFT housings. Summary of main results are following. Chonsei prices of the apartments within a 2-3km radius decreased by 4.4% after the provision of SHIFT housings. In contrast, when it comes to apartments within a 1-2km radius, I can't find the stochastic relationship between the provision of SHIFT hosing and price changes. This results can be explained by "Offset effects" caused by real estate development. Provision of SHIFT can sequentially induce nearby area's development, which plays a factor in the effect of price increases. And this offset effects varies in each apartment complex depending on demand for Chonsei and supply of the SHIFT.

• The Journal of the Korea Contents Association
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• v.9 no.3
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• pp.175-184
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• 2009

Korea shared 2.4% of world culture contents market in 2006 and ranked 9th. Therefore Korean government classified culture contents industry as a new growth-driving industries and started fixing the total contents policy promote system, exterminating illegal copying, protect copyright,improving contents creativity power, and supporting foreign market pioneering. Thus the importance of culture contents industry is increasing day by day. This study analyze the amount changes about production-inducing effect, value-added-inducing effects, employ-inducing effect, supply shortage effect and sectoral price effect, using inter-industry analysis according to time series. Especially, the sectoral price effect of culture contents industry increased by time pass. Thus, the influence of price changes in the culture contents industry to the other industries are increasing.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.1
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• pp.105-111
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• 2023

Recently, research on prediction algorithms using deep learning has been actively conducted. In addition, algorithmic trading (auto-trading) based on predictive power of artificial intelligence is also becoming one of the main investment methods in stock trading field, building its own history. Since the possibility of human error is blocked at source and traded mechanically according to the conditions, it is likely to be more profitable than humans in the long run. In particular, for the virtual currency market at least for now, unlike stocks, it is not possible to evaluate the intrinsic value of each cryptocurrencies. So it is far effective to approach them with technical analysis and cryptocurrency market might be the field that the performance of algorithmic trading can be maximized. Currently, the most commonly used artificial intelligence method for financial time series data analysis and forecasting is Long short-term memory(LSTM). However, even t4he LSTM also has deficiencies which constrain its widespread use. Therefore, many improvements are needed in the design of forecasting and investment algorithms in order to increase its utilization in actual investment situations. Meanwhile, Prophet, an artificial intelligence algorithm developed by Facebook (META) in 2017, is used to predict stock and cryptocurrency prices with high prediction accuracy. In particular, it is evaluated that Prophet predicts the price of virtual currencies better than that of stocks. In this study, we aim to show Prophet's virtual currency price prediction accuracy is higher than existing deep learning-based time series prediction method. In addition, we execute mock investment with Prophet predicted value. Evaluating the final value at the end of the investment, most of tested coins exceeded the initial investment recording a positive profit. In future research, we continue to test other coins to determine whether there is a significant difference in the predictive power by coin and therefore can establish investment strategies.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.81-94
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• 2022

Real estate and artworks were considered challenging investment targets for individual investors because of their relatively high average transaction price despite their long investment history. Recently, the so-called fractional investment, generally known as investing in a share of the ownership right for real-life assets, etc., and most investors perceive that they actually own a piece (fraction) of the ownership right through their investments, is gaining popularity. Founded in 2016, Musicow started the first service that allows users to invest in copyright fees related to music distribution. Using the LSTM algorithm, one of the deep learning algorithms, this research predict the price of right to participate in copyright fees traded in Musicow. In addition to variables related to claims such as transfer price, transaction volume of claims, and copyright fees, comprehensive indicators indicating the market conditions for music copyright fees participation, exchange rates reflecting economic conditions, KTB interest rates, and Korea Composite Stock Index were also used as variables. As a result, it was confirmed that the LSTM algorithm accurately predicts the transaction price even in the case of fractional investment which has a relatively low transaction volume.

• Culinary science and hospitality research
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• v.6 no.3
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• pp.167-192
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• 2000

The purpose of this study is to research consumer's action style. and then to make a marketing strategies. The strategic plan to consumer's action style on the fast-food industry were as follows; First, the group that coefficient of utilization is so frequent and an age is young are requested concentrative or discriminative marketing as the price discrimination and market segementation, the price value of the products value has to manage effectively, Second, establishment of corporate image and improvement of products image are requested. The result of this study, fast-food industry will be needed marketing activities that are discriminative strategies, positioning strategies, education training, and customer management. And system construction that is equivalent to customer needs, and the continuous coustomer studies are requested.

• Proceedings of the KIEE Conference
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• 2006.11a
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• pp.43-45
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• 2006

Because Cost Based Pool(CBP) has any locational signals for electricity price, there are any locational incentives for construction of new power plant high efficient. in case of Korean electricity power market, this incentives are very important to reduce loss and congestion. This Paper represent the effect of MLF(Marginal Loss Factor) as locational price signal in short period. we investigate mathematically loss reduced effect of MLF and prove to reduce transmission loss using 3bus test system.