• Journal of Korea Port Economic Association
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• v.34 no.4
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• pp.181-190
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• 2018

The goal of this study is empirically to investigate the asymmetric relationship between two variables using the dry cargo freight rates and raw material price data from January 2012 to May 2018. First, we estimate the asymmetry of macroeconomic indicators of commodity prices by using a two - step threshold cointegration test. Second, the asymmetric relation test of the trade balance of existing commodity price changes is tested by bypassing to the high frequency dry cargo freight rate index. As a result of the estimation, in contrast to the existing linear analysis, each boundary value for the lower limit and the upper limit has different asymmetry. This implies that the period of fluctuation of the sudden residual that causes irregular rate of return fluctuations does not establish a long term equilibrium relationship between the raw material price and the dry cargo freight rate. Therefore, in order to consider the sudden price change in the analysis, it is necessary to include the band of inaction that controls the irregular volatility, which is consistent with the asymmetry hypothesis.

• Journal of Korea Multimedia Society
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• v.24 no.3
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• pp.448-459
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• 2021

In this paper, we propose a method of predicting the next-day stock price fluctuations of 10 KOSDAQ-listed companies in 5G, autonomous driving, and electricity sectors by training SVM, XGBoost, and LightGBM models from macroeconomic·financial market indicators, technical indicators, social interest indicators, and daily positive indices extracted from online news. In the three experiments to find out the usefulness of social interest indicators and daily positive indices, the average accuracy improved when each indicator and index was added to the models. In addition, when feature selection was performed to analyze the superiority of the extracted features, the average importance ranking of the social interest indicator and daily positive index was 5.45 and 1.08, respectively, it showed higher importance than the macroeconomic financial market indicators and technical indicators. With the results of these experiments, we confirmed the effectiveness of the social interest indicators as alternative data and the daily positive index for predicting stock price fluctuation.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.3
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• pp.33-42
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• 2022

Oil prices have become more volatile as a result of global economic contraction and control measures. Before and during the COVID-19 crisis, this study examines the relationship between oil price swings and daily stock returns in the power sector. The impact is investigated using a panel Vector Autoregressive (VAR) model. Granger causality tests are used to see if oil prices are effective in predicting returns. The dynamic impact of supply shocks is studied using Impulse Response Functions (IRFs). From January 2011 to May 2021, the study used daily data from all listed power sector enterprises on the Pakistan stock exchange. To investigate the differences in reactions between the Pre-COVID and COVID eras, the sample was separated into two groups. Oil shocks are inversely associated with daily firm stock returns. The conclusions are further supported by the lack of impact of stock prices on oil prices. The relationship, however, deteriorates during the COVID pandemic. We could not uncover any evidence of a significant relationship. In developing countries that rely on oil imports, the study sheds light on the utility of oil price shocks in daily stock return predictions.

• 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 Korea Port Economic Association
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• v.36 no.2
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• pp.109-124
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• 2020

Using panel data on freight rates and ship prices in the dry freighter market from January 2015 to December 2019, this study investigates the characteristics of shipping industry fluctuations. The analysis aims at two aspects of academic contribution. First, this study analyzes the relationship between shipping indicators and ship price based on separate dry-bulk ships, while the previous research considered the overall shipping index and weighted average ship prices. Second, the VAR model for the causality test is extended to a heterogeneous mixed panel model capable of limiting coefficients. There is a peak estimated by removing the cross-correlation problem, which is mainly raised in panel data analysis, using bootstrap estimation and solving the problem of information loss due to differences in non-stationary data. An empirical investigation of the causal relationship between economic fluctuations and ship price shows that the effect on the ship price from the freight is significant at the 1% level. This implies that there is a one-way relationship with demand in the shipping industry rather than a bilateral relationship.

• Journal of the Economic Geographical Society of Korea
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• v.25 no.1
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• pp.147-170
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• 2022

This article analyzes the spillover effects by dividing the weekly rate of return on apartment prices in 70 si-gun-gu (local area) in the Capital Region into three periods: the entire period (April 2008~August 2021); the period before the price surge (April 2008~October 2018); and the period of price surge (November, 2018~August 2021), based on a consideration of the cycle of fluctuations in apartment sales prices and the timing of the current government's policy interventions. The results obtained from this analysis are summarized as follows. First, the analysis of the spillover effects is similar to or different from the results of existing work depending on the period. The analysis of the spillover effects on the entire period and the period before the price surge shows that the 'Gangnam' effect exists in the apartment market in the Capital Region. On the other hand, the analysis of the spillover effects on the period of price surge reveals different results than before. The spillover effect index calculated through the analysis of the rolling sample decreases during the decline in the cycle of apartment sales prices, while the opposite trend is shown during the upward period. Looking at the timing between the peak of the spillover effect index and policy interventions, it appears that the government's policy interventions took place after the peak of the spillover effect index in 2017, before the peak in 2018 and 2019, and around or after the peak after 2020.

• KDI Journal of Economic Policy
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• v.45 no.4
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• pp.57-77
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• 2023

The Chonsei component holds the highest level of weight (5.4%) in the composition of the Korean consumer price index (CPI). The variations in Chonsei prices are directly reflected in the CPI as a representation of cost swings. The Chonsei refers to a deposit that accumulates the costs related to housing services and is mostly affected by variations in rental rates. Nevertheless, it is important to note that Chonsei prices are also susceptible to fluctuations in interest rates, regardless of the rent prices. Therefore, if Chonsei were directly and one-to-one indexed to the CPI, they could include changes other than residential service prices. After analyzing the time series data of the Chonsei index and rent index inside the CPI, it becomes apparent that the Chonsei index displays an average annual growth rate of 2.3%, whilst the rent index reveals a growth rate of 0.9%. The observed disparity in growth rates indicates a divergence in trends between the two indices. It is posited that the Chonsei index, when capitalized, has had a more rapid increase compared to the rental index, owing to the gradual drop in interest rates. To effectively reflect fluctuations in the housing service costs, proxies for the Chonsei index were utilized in the construction of a consumer price index. The findings of our study suggest that, overall, the newly developed CPI demonstrates a comparatively lower rate of inflation when compared to the official CPI. Furthermore, the inclusion of imputed rents for owner-occupied housing in CPI amplifies this effect.

• Journal of the Korea Institute of Building Construction
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• v.24 no.2
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• pp.273-284
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• 2024

This study investigates the impact of international oil price fluctuations on overseas construction orders secured by domestic and foreign companies. The analysis employs statistical data spanning the past 20 years, encompassing international oil prices, overseas construction orders from domestic firms, and new overseas construction orders from the top 250 global construction companies. The correlation between these variables is assessed using correlation coefficients(R), determination coefficients(R²), and p-values. The results indicate a strong positive correlation between international oil prices and overseas construction orders. The correlation coefficient between domestic overseas construction orders and oil prices is found to be 0.8 or higher, signifying a significant influence. Similarly, a high correlation coefficient of 0.76 is observed between oil prices and new orders from leading global construction companies. Further analysis reveals a particularly strong correlation between oil prices and overseas construction orders in Asia and the Middle East, potentially due to the prevalence of oil-related projects in these regions. Additionally, a high correlation is observed between oil prices and orders for industrial facilities compared to architectural projects. This suggests an increase in plant construction volumes driven by fluctuations in oil prices. Based on these findings, the study proposes an entry strategy for navigating oil price volatility and maintaining competitiveness in the overseas construction market. Key recommendations include diversifying project locations and supplier bases; utilizing hedging techniques for exchange rate risk management, adapting to local infrastructure and market conditions, establishing local partnerships and securing skilled local labor, implementing technological innovations and digitization at construction sites to enhance productivity and cost reduction The insights gained from this study, coupled with the proposed overseas expansion strategies, offer valuable guidance for mitigating risks in the global construction market and fostering resilience in response to international oil price fluctuations. This approach is expected to strengthen the competitiveness of domestic and foreign construction firms seeking success in the international arena.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.9
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• pp.367-374
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• 2021

Stock price prediction is a subject of research in various fields such as economy, statistics, computer engineering, etc. In recent years, researches on predicting the movement of stock prices by learning artificial intelligence models from various indicators such as basic indicators and technical indicators have become active. This study proposes a deep learning model that predicts the ups and downs of KOSPI from overseas indices such as S&P500, past KOSPI indices, and trading trends by KOSPI investors. The proposed model extracts a latent variable using a stacked auto-encoder to predict stock price fluctuations, and predicts the fluctuation of the closing price compared to the market price of the day by learning an LSTM suitable for learning time series data from the extracted latent variable to decide to buy or sell based on the value. As a result of comparing the returns and prediction accuracy of the proposed model and the comparative models, the proposed model showed better performance than the comparative models.

• Environmental and Resource Economics Review
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• v.30 no.2
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• pp.325-345
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• 2021

In this paper, we empirically evaluate the potential performance of energy conversion policy and analyze its effects on power generation sector. We first examine the degree of substitutability between energy inputs by measuring the price elasticities of energy demands and then estimate the changes in CO₂ generation when the proportions of nuclear power plants and renewable power generation are increased. The shadow prices of nuclear power and renewable energy are calculated to compare the potential costs of power generation between the two energy sources. We analyze the impacts of the expansion of nuclear power plants and renewable power generation on power supply price. Nuclear and renewable energy were measured to be complementary to each other. The expansion of nuclear power plants has been more effective in reducing CO₂ emissions than increasing renewable power generation. In most years over 2002 to 2016, the impact of nuclear power expansion on the power supply price was generally higher than that of renewable power generation, with relatively large range of fluctuations.