• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.412-414
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• 2017

It is important to predict price of agricultural products accurately to government, local government, bodies in charge of agriculture. Production and shipping of agricultural products are affected by weather condition significantly. In this research, prediction model of a Chinese cabbage which is highly sensitive to weather condition is proposed using deep learning technique. After performance of proposed model and a model of previous research is compared, superiority of proposed model is proved.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.18 no.33
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• pp.173-183
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• 1995

The Objective of the study was to determine the usefulness of human resource accounting(HRA) information in assisting financial analysis in their investment decisions. The objective achieved by an investigation through which the reporting of HRA, combined with demographic factors that are independent or interactive, affects the decisions of financial analysts regarding the estimation of the market price of a hypothetical company's common stocks. Two kinds of research were conducted to increase the reliability of the study at the same time. Two or three sets of financial statement were prepared. Each consists of balance sheet and income statement. The actual financial statement was modified to exclude personal bias and opinion.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.28 no.4
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• pp.147-153
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• 2020

This study analyzed the outlook for aviation demand for the recovery of the aviation industry, focusing on airlines facing difficulties in management due to the Covid-19 crisis. Although the timing of the recovery in aviation demand is uncertain at the moment, this study is based on prior research related to Covid-19 and forecasts by aviation specialists, and analyzed by SWOT technique to a group of aviation experts to derive and suggest implications for the prospects of aviation demand. Looking at the implications based on the analysis results, first, customer trust to prevent infection should be considered a top priority for recovering aviation demand. Second, promote reasonable air price policy. Finally, it seeks to try various research and analysis techniques to predict long-term aviation demand to overcome Covid-19.

• The Journal of Industrial Distribution & Business
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• v.9 no.7
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• pp.53-59
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• 2018

Purpose - This study examines the effect of control-ownership wedge on stock crash risk. In Korea, controlling shareholders have exclusive control rights compared to their cash flow rights. With increasing disparity, controlling shareholders abuse their power and extract private benefits at the expense of the minority shareholders. Managers who are controlling shareholders of the companies tend not to disclose critical information that would prevent them from pursuing private interests. They accumulate negative information in the firm. When the accumulated bad news crosses a tipping point, it will be suddenly released to the market at once, resulting in an abrupt decline in stock prices. We predict that stock price crash likelihood due to information opaqueness increases as the wedge increases. Research design, data, and methodology - 831 KOSPI-listed firm-year observations are from KisValue database from 2005 to 2011. Control-ownership wedge is measured as the ratio (UCO -UCF)/UCO where UCF(UCO) is the ultimate cash-flow(control) rights of the largest controlling shareholder. Dependent variable CRASH is a dummy variable that equals one if the firm has at least 1 crash week during a year, and zero otherwise. Logistic regression is used to examine the relationship between control-ownership wedge and stock price crash risk. Results - Using a sample of KOSPI-listed firms in KisValue database for the period 2005-2011, we find that stock price crash risk increases as the disparity increases. Specifically, we find that the coefficient of WEDGE is significantly positive, supporting our prediction. The result implies that as controlling shareholders' ownership increases, controlling shareholders tend to withhold bad news. Conclusions - Our results show that agency problems arising from the divergence between control rights and cash flow rights increase the opaqueness of accounting information. Eventually, the accumulated bad news is released all at once, leading to stock price crashes. It could be seen that companies with high control-ownership wedge are likely to experience future stock price crashes. Our study is related to a broader literature that examined the effect of the control-ownership wedge on stock markets. Our findings suggest that the disparity is a meaningful predictor for future stock price crash risk. The results are expected to provide useful implications for firms, regulators, and investors.

• Journal of Korean Society of Forest Science
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• v.99 no.4
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• pp.479-486
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• 2010

We analyzed the relationships between the daily yield and quality of pine-mushroom to predict the annual production pattern and unit price of the mushroom with the records of pine-mushroom trade at Yeongdeok forestry cooperative's market for nine years (2000~2008). Although there were some exceptions due to extreme drought or extraordinary temperature, the production ratio of high quality (first and second grade) was more than 50% in early stage and decreased, while that of low quality (pileus opened and defected ones) showed increasing pattern after the production reached in peak. The ratio of high quality and that of low quality were reversed 1~9 days before the mushroom production reached the acme of daily yield, which allowed us to predict that the mushroom production would be decreased when the ratio of low quality overcomes that of high quality. The ratio of high quality preceded about 3~4 days prior to that of daily yield, and the mushroom yield showed significant correlations with the ratio of high quality mushroom prior to 3~4 days of the day with the coefficient larger than 0.5 (r=0.51 for 3 days and r=0.54 for 4 days). Thus, we concluded that the analysis of grade distribution of pine-mushroom at the market may provide a significant clue to predict production pattern of the mushroom. In addition, the price of high quality pine-mushroom showed clear negative correlations with the yield. Thus, the analysis may take a good role for the trading of pine-mushroom with providing information for predicting the price of pine-mushroom.

• Knowledge Management Research
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• v.22 no.2
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• pp.19-32
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• 2021

Bitcoin, a representative cryptocurrency, is receiving a lot of attention around the world, and the price of Bitcoin shows high volatility. High volatility is a risk factor for investors and causes social problems caused by reckless investment. Since the price of Bitcoin responds quickly to changes in the world environment, we propose to predict the price volatility of Bitcoin by utilizing news information that provides a variety of information in real-time. In other words, positive news stimulates investor sentiment and negative news weakens investor sentiment. Therefore, in this study, sentiment information of news and deep learning were applied to predict the change in Bitcoin yield. A single predictive model of logit, artificial neural network, SVM, and LSTM was built, and an integrated model was proposed as a method to improve predictive performance. As a result of comparing the performance of the prediction model built on the historical price information and the prediction model reflecting the sentiment information of the news, it was found that the integrated model based on the sentiment information of the news was the best. This study will be able to prevent reckless investment and provide useful information to investors to make wise investments through a predictive model.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.2
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• pp.59-76
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• 2023

Since the COVID-19 era, the rise in apartment prices has been unconventional. In this uncertain real estate market, price prediction research is very important. In this paper, a model is created to predict the actual transaction price of future apartments after building a vast data set of 870,000 from 2015 to 2020 through data collection and crawling on various real estate sites and collecting as many variables as possible. This study first solved the multicollinearity problem by removing and combining variables. After that, a total of five variable selection algorithms were used to extract meaningful independent variables, such as Forward Selection, Backward Elimination, Stepwise Selection, L1 Regulation, and Principal Component Analysis(PCA). In addition, a total of four machine learning and deep learning algorithms were used for deep neural network(DNN), XGBoost, CatBoost, and Linear Regression to learn the model after hyperparameter optimization and compare predictive power between models. In the additional experiment, the experiment was conducted while changing the number of nodes and layers of the DNN to find the most appropriate number of nodes and layers. In conclusion, as a model with the best performance, the actual transaction price of apartments in 2021 was predicted and compared with the actual data in 2021. Through this, I am confident that machine learning and deep learning will help investors make the right decisions when purchasing homes in various economic situations.

• Journal of Digital Convergence
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• v.10 no.9
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• pp.357-362
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• 2012

We have compared and predicted for non-linear time series data which are real data having different variences using GRCA(1) model and neural network method. In particular, using Korea Composite Stock Price Index rate, mean square errors of prediction are obtained in genaralized random coefficient autoregressive model and neural network method. Neural network method prove to be better in short-term forecasting, however GRCA(1) model perform well in long-term forecasting.

• Journal of the Korean Society for Railway
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• v.8 no.3
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• pp.260-266
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• 2005

The deterioration of a shunting locomotive was characterized for the lifetime assessment. The locomotive has been used for shunting works in steel making processes, and in this investigation, various types of technical evaluation methods for the locomotive parts were employed to assess the current deterioration status and to provide important clue for lifetime prediction. Unlike other rolling stocks in railway applications, the diesel shunting locomotive is composed of major components such as diesel engine, transmission, gear box, brake system, electronic devices, etc., which cover more than 70 percent of the total price of the locomotive. Therefore, in this paper, each part of major components in the diesel locomotive was analyzed in terms of the degree of deterioration. The lift-cycle-cost (LCC) analysis was performed based on the maintenance and repair history as compared with economical cost to provide the cost-effective prediction, i.e., to assess either repair for reuse or putting the locomotive out of service based on cost-effective calculation.

• Asia pacific journal of information systems
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• v.4 no.1
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• pp.3-31
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• 1994

Potential employers considering locations for production or service facilities typically equire detailed advance knowledge of the wages they will be expected to offer for workers in various occupational categories. The State of Missouri s Department of Labor and Industrial Relations is often contacted by organizations requesting such information. The current wage rate survey approach, initiated in 1988, allows the Department to predict an appropriate wage rate for a given occupation in certain counties, adjusted for changes in the Consumer Price Index (CPI). However, both Department employees and firms have indicated that improved prediction responsiveness and accuracy are desirable. A major deficiency of the current approach is its inability to predict wages for unsurveyed counties. This paper describes a knowledge-based system (KBS), currently in the prototype testing stage, that is expected to supplement the wage rate survey in the near future.