• 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.

• KIEAE Journal
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• v.10 no.4
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• pp.131-136
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• 2010

Because energy consumption of the construction part is very high, there is a growing need to introduce environment-friendly buildings. Therefore Green Building Rating System is enacted in Korea. Though environment-friendly factors such as green area affect the apartment housing price, there's no saying whether Green Building Rating System directly affect the apartment housing price. The purpose of this paper is to estimate the impact of Green Building Rating System on an apartment housing price. The analysis result demonstrated that Green Building Rating System don't affect the apartment housing price. This result means that there is a problem with the effectiveness of Green Building Rating System. The government ought to institute incentive program to ctivate the market of environment-friendly building.

• Korea Trade Review
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• v.46 no.4
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• pp.219-234
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• 2021

Armington elasticity has been a methodology for analyzing how much imports could increase in response to importing price cuts, assuming the possibility of incomplete substitution of domestic and imported products. This study calculates Armington elasticity values in Korean fisheries sector and presents an analysis method for classifying items based on price and preference differences. The model is modified reflecting the characteristics of the fisheries market along with the typical OLS, PAM, and ECM models. The result's implication is that products with a high import growth rate do not necessarily show a high Armington value, but it could be seen that price is not the only factor facilitating fisheries imports increase. Considering the items of which demand increases due to importing price cuts have an indiscriminate demand between domestic and imported products, the results could be interpreted that the Korean fisheries importing market has been easily affected by the changes in import prices. Fisheries grouping by price and preference demonstrates that explanatory variables other than price should be considered when estimating import demand.

• Journal of the Korea Society of Computer and Information
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• v.28 no.2
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• pp.201-207
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• 2023

In this paper, we propose an optimal model for mid to long-term price prediction of agricultural products using LGBM, MLP, LSTM, and GRU to compare and analyze the three strategies of the Multi-Step Time Series. The proposed model is designed to find the optimal combination between the models by selecting methods from various angles. Prior agricultural product price prediction studies have mainly adopted traditional econometric models such as ARIMA and LSTM-type models. In contrast, agricultural product price prediction studies related to Multi-Step Time Series were minimal. In this study, the experiment was conducted by dividing it into two periods according to the degree of volatility of agricultural product prices. As a result of the mid-to-long-term price prediction of three strategies, namely direct, hybrid, and multiple outputs, the hybrid approach showed relatively superior performance. This study academically and practically contributes to mid-to-long term daily price prediction by proposing an effective alternative.

• Journal of applied mathematics & informatics
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• v.39 no.1_2
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• pp.239-249
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• 2021

This study attempts to predict the price of gold futures, a real financial product, using ARIMA and LSTM. The wavelet analysis was applied to the data to predict the price of gold futures through LSTM and ARIMA. As results, it is confirmed that the prediction performance of the existing model of predict was improved. the case of predict of price of gold futures, we confirmed that the use of a deep learning model that is not affected by the non-stationary series data is suitable and the possibility of improving the accuracy of prediction through wavelet analysis.

• International conference on construction engineering and project management
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• 2007.03a
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• pp.106-118
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• 2007

Little attention has been focussed on a precise definition and evaluation mechanism for project management risk specifically related to contractors. When bidding, contractors traditionally price risks using unsystematic approaches. The high business failure rate our industry records may indicate that the current unsystematic mechanisms contractors use for building up contingencies may be inadequate. The reluctance of some contractors to include a price for risk in their tenders when bidding for work competitively may also not be a useful approach. Here, instead, we first define the meaning of contractor contingency, and then we develop a facile quantitative technique that contractors can use to estimate a price for project risk. This model will help contractors analyse their exposure to project risks; and also help them express the risk in monetary terms for management action. When bidding for work, they can decide how to allocate contingencies strategically in a way that balances risk and reward.

• Journal of the Korean Data and Information Science Society
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• v.28 no.3
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• pp.617-624
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• 2017

The production of cabbage, which is mainly cultivated in open field, varies greatly depending on weather conditions, and the price fluctuation is largely due to the presence of a substitute crop. Previous studies predicted the production of cabbage using actual weather data, but in this study, we predicted the wholesale price using unstructured agricultural meteorological data on the web. From January 2009 to October 2016, we collected documents including the cabbage on the portal site, and extracted keywords related to weather in the collected documents. We compared the forecast wholesale prices of simple models and unstructured agricultural weather models at the time of shipment. The simple model is AR model using only wholesale price, and the unstructured agricultural weather model is AR model using unstructured agricultural weather data additionally. As a result, the performance of unstructured agricultural weather model was has been found to be more accurate prediction ability.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.30 no.1
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• pp.25-32
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• 2007

Automobile manufacturing companies should provide the products and service that can satisfy consumers who usually want various kinds of automobiles. This paper studied the relations among several demographic characteristics(gender, age, occupation, income) of consumers and automobile's attributes(engine performance, engine displacement, price, maintenance expenses, color, etc.) and preferred automobile(kind, size, company, price). For the relation research we established a model and related hypotheses and used a questionnaire survey, where 350 subjects were questioned. After the analysis, many statistically significant results are obtained : consumer's gender has relations with the preference for engine performance, engine displacement, price, color, and design; age has relations with the preference for engine displacement, maintenance expenses, free checking during A/S period, etc., occupation has relations with the preference for engine performance, engine displacement, price, maintenance expenses, fuel efficiency, etc. : income has relations with the preference for engine performance, engine displacement, price, maintenance expenses, mileage, reputation of car manufacturing companies, etc.; price and fuel efficiency has relations with the preference for car manufacturing companies. These results suggest the consumer-oriented sales of automobiles and are expected to be helpful for the effective strategy development of automobile marketing.

• Journal of Distribution Science
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• v.21 no.5
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• pp.101-111
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• 2023

Purpose: The purpose of this paper is to examine the effect of the VKOSPI index on short-term stock returns after a large-scale stock price shock of individual stocks of firms in the distribution industry in Korea. Research design, data, and methodology: This study investigates the effect of the change of the VKOSPI index or investor mood on abnormal returns after the event date from January 2004 to July 2022. The significance of the abnormal return, which is obtained by subtracting the rate of return estimated by the market model from the rate of actual return on each trading day after the event date, is determined based on T-test and multifactor regression analysis. Results: In Korea's distribution industry, the simultaneous occurrence of a bad investor mood and a large stock price decline, leads to stock price reversals. Conversely, the simultaneous occurrence of a good investor mood and a large-scale stock price rise leads to stock price drifts. We found that the VKOSPI index has strong explanatory power for these reversals and drifts even after considering both company-specific and event-specific factors. Conclusions: In Korea's distribution industry-related stock market, investors show an asymmetrical behavioral characteristic of overreacting to negative moods and underreacting to positive moods.

• The Journal of Society for e-Business Studies
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• v.27 no.1
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• pp.63-79
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• 2022

Recently, various studies have been conducted on stock price prediction using machine learning and deep learning techniques. Among these studies, the latest studies have attempted to predict stock prices using limit order books, which contain buy and sell order information of stocks. However, most of the studies using limit order books consider only the trend of limit order books over the most recent period of a specified length, and few studies consider both the medium and short term trends of limit order books. Therefore, in this paper, we propose a deep learning-based prediction model that predicts stock price more accurately by considering both the medium and short term trends of limit order books. Moreover, the proposed model considers news headlines during the same period to reflect the qualitative status of the company in the stock price prediction. The proposed model extracts the features of changes in limit order books with CNNs and the features of news headlines using Word2vec, and combines these information to predict whether a particular company's stock will rise or fall the next day. We conducted experiments to predict the daily stock price fluctuations of five stocks (Amazon, Apple, Facebook, Google, Tesla) with the proposed model using the real NASDAQ limit order book data and news headline data, and the proposed model improved the accuracy by up to 17.66%p and the average by 14.47%p on average. In addition, we conducted a simulated investment with the proposed model and earned a minimum of $492.46 and a maximum of $2,840.93 depending on the stock for 21 business days.