• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.552-560
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• 2021

With the recent development of the art distribution system, interest in art investment is increasing rather than seeing art as an object of aesthetic utility. Unlike stocks and bonds, the price of artworks has a heterogeneous characteristic that is determined by reflecting both objective and subjective factors, so the uncertainty in price prediction is high. In this study, we used LSTM Recurrent Neural Network deep learning model to predict the auction winning price by inputting the artist, physical and sales charateristics of the Korean artist. According to the result, the RMSE value, which explains the difference between the predicted and actual price by model, was 0.064. Painter Lee Dae Won had the highest predictive power, and Lee Joong Seop had the lowest. The results suggest the art market becomes more active as investment goods and demand for auction winning price increases.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.45-55
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• 2022

The size of the cryptocurrency market is growing. For example, market capitalization of bitcoin exceeded 500 trillion won. Accordingly, many studies have been conducted to predict the price of cryptocurrency, and most of them have similar methodology of predicting stock prices. However, unlike stock price predictions, machine learning become best model in cryptocurrency price predictions, conceptually cryptocurrency has no passive income from ownership, and statistically, cryptocurrency has at least three times higher liquidity than stocks. Thats why we argue that a methodology different from stock price prediction should be applied to cryptocurrency price prediction studies. We propose Reverse Walk-forward Validation (RWFV), which modifies Walk-forward Validation (WFV). Unlike WFV, RWFV measures accuracy for Validation by pinning the Validation dataset directly in front of the Test dataset in time series, and gradually increasing the size of the Training dataset in front of it in time series. Train data were cut according to the size of the Train dataset with the highest accuracy among all measured Validation accuracy, and then combined with Validation data to measure the accuracy of the Test data. Logistic regression analysis and Support Vector Machine (SVM) were used as the analysis model, and various algorithms and parameters such as L1, L2, rbf, and poly were applied for the reliability of our proposed RWFV. As a result, it was confirmed that all analysis models showed improved accuracy compared to existing studies, and on average, the accuracy increased by 1.23%p. This is a significant improvement in accuracy, given that most of the accuracy of cryptocurrency price prediction remains between 50% and 60% through previous studies.

• KIISE Transactions on Computing Practices
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• v.23 no.11
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• pp.625-631
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• 2017

The stock price prediction for stock markets remains an unsolved problem. Although there have been various overtures and studies to predict the price of stocks scientifically, it is impossible to predict the future precisely. However, stock price predictions have been a subject of interest in a variety of related fields such as economics, mathematics, physics, and computer science. In this paper, we will study fluctuation patterns of stock prices and predict future trends using the Deep learning. Therefore, this study presents the three deep learning models using Tensorflow, an open source framework in which each learning model accepts different input features. We expand the previous study that used simple price data. We measured the performance of three predictive models increasing the number of priced-based input features. Through this experiment, we measured the performance change of the predictive model depending on the price-based input features. Finally, we compared and analyzed the experiment result to evaluate the impact of the price-based input features in stock price prediction.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.7-10
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• 2022

본 연구는 국내 주식의 intraday 가격변화를 딥러닝 모형들로 예측하고 그 예측모형을 이용한 매매전략 딥러닝 모형을 제안한다. 주식의 intraday 가격변화에 따라서, 고빈도 매매, 주문집행문제 (order execution problem), 자동화 매매 등과 같은 intraday 주식 트레이딩의 수익률이 달라지기 때문에, 주식의 intraday 가격변화 예측은 주식 투자에 있어서 중요하다. 해외 시장에 대해서는 인공지능 등을 이용한 intraday 가격변화 예측 연구가 활발히 이루어졌지만, 국내의 경우 관련한 연구가 드물어 그 효용성이 명확히 드러나지 않았었다. 그에 따라서, KOSPI 50의 구성 종목에 대하여 정준의(canonical) 딥러닝 모형들을 적용하여 예측 성능을 비교한다. 또한, 그 예측모형들을 활용하여 간소화된 주문집행문제에서의 매매전략 딥러닝 모형을 제안한다. 그리고, 제안한 매매전략 딥러닝 모형을 KOSPI 50의 구성 종목에 대하여 실험하여, 제안한 방법론이 유효함을 밝힌다. 제시된 모형을 실제 주식 매매에 직접 적용하여 수익성을 개선을 기대할 수 있고, 사람이 직접 거래할지라도 효과적인 보조 지표가 될 수 있기에 본 논문은 실용적 의미를 지닌다.

• Journal of KIISE:Computing Practices and Letters
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• v.12 no.5
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• pp.275-285
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• 2006

This paper proposes an efficient market mechanism-based resource transaction model for grid computing. This model predicts the next resource demand of users and suggests reasonable resource price for both of customers and resource providers. This model increases resource transactions between customers and resource providers and reduces the average of transaction response times from resource providers. For prediction accuracy improvement of resource demands and suggestion of reasonable resource price, this model introduces a statistics-based prediction model and a price decision model of microeconomics. For performance evaluating, this paper measures resource demand prediction accuracy rate of users, response time of resource transaction, the number of resource transactions, and resource utilization. With 87.45% of reliable prediction accuracy, this model works on the less 72.39% of response time than existing resource transaction models in a grid computing environment. The number of transactions and the resource utilization increase up to 162.56% and up to 230%, respectively.

• The Korean Journal of Applied Statistics
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• v.34 no.5
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• pp.745-760
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• 2021

Prediction of a stock price has been a subject of interest for a long time in financial markets, and thus, many studies have been conducted in various directions. As the efficient market hypothesis introduced in the 1970s acquired supports, it came to be the majority opinion that it was impossible to predict stock prices. However, recent advances in predictive models have led to new attempts to predict the future prices. Here, we summarize past studies on the price prediction by evaluation measures, and predict the direction of stock prices of Samsung Electronics, LG Chem, and NAVER by applying various machine learning models. In addition to widely used technical indicator variables, accounting indicators such as Price Earning Ratio and Price Book-value Ratio and outputs of the hidden Markov Model are used as predictors. From the results of our analysis, we conclude that no models show significantly better accuracy and it is not possible to predict the direction of stock prices with models used. Considering that the models with extra predictors show relatively high test accuracy, we may expect the possibility of a meaningful improvement in prediction accuracy if proper variables that reflect the opinions and sentiments of investors would be utilized.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.1198-1199
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• 2023

부동산 시장은 다양한 요인에 의해 가격이 결정되며 거시경제 변수뿐 만 아니라 뉴스 기사, SNS 등 다양한 비정형 데이터의 영향을 받는다. 특히 뉴스 기사는 국민들이 느끼는 경제 심리를 반영하고 있어 부동산 가격에 영향을 크게 미치는 변수라고 판단된다. 본 연구에서는 뉴스 기사의 세분화된 감정 분석을 통해 전통적인 분석 방법보다 더 의미 있는 결과를 얻을 수 있는 부동산 가격 예측 모델을 생성하였으며 뉴스 기사로부터 심리 지수를 산출하기 위해 생성 AI 를 활용하였다. 제안하는 매매가격지수 예측 모델을 통해 부동산 시장과 뉴스 기사와의 관계성에 대해 파악할 수 있으며, 사회/경제적 동향을 반영한 부동산 가격 변동을 예측할 수 있을 것으로 보인다.

• Korean Journal of Construction Engineering and Management
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• v.21 no.6
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• pp.38-45
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• 2020

This study presents a systematic procedure for developing a short-term prediction deep learning model of rebar price using bidirectional LSTM, Random Search, data combination, Dropout. In general, users intuitively determine these values, making it time-consuming and repetitive attempts to explore results with good predictive performance, and the results found by these attempts cannot be guaranteed to be excellent. With the proposed approach presented in this study, the average accuracy of short-term price forecasts is approximately 98.32%. In addition, this approach could be used as basic data to produce good predictive results in a study that predicts prices with time series data based on statistics, including building materials other than rebars.

• Proceedings of the Korean Society for Quality Management Conference
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• 2006.04a
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• pp.506-512
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• 2006

다양한 아파트 특성들을 이용하여 아파트 가격을 추정하고 예측하는 연구 또한 많이 존재하고 있는 실정이다. 그렇지만 이러한 연구들 대부분이 회귀모형에 지나치게 의존하고 있는 실정이다 그러나 회귀모형은 단점보다 장점이 많은 모형이다. 본 연구는 회귀모형을 부정하기보다는 새로운 모형을 도입하여, 회귀모형의 문제점들을 극복하고 회귀모형과 상호보완적인 모형을 도입할 필요성에 의해서 본 연구를 수행한 것이다. 다양한 아파트 특성들에 대하여 신경망모형을 이용하여 아파트 가격을 예측하고, 기존의 회귀모형과 비교하는 것이 본 연구의 주목적이다 또한 회귀모형과 신경망모형의 상호 보완적인 측면을 규명하는 것은 본 연구의 부차적인 목적이 된다 아파트 특성들은 주변에서 쉽게 이용 가능한 데이터를 위주로 하였다. 2004년 6월 기준으로 서울시 송파구와 도봉구의 아파트 매매가격들과 12개의 아파트 특성들을 수집하였다. 아파트 매매가격들 (즉, 매매 하한가, 일반 거래가, 매매 상한가) 을 새로운 측정방법을 이용하여 하나의 매매가격으로 추정하였으며, 대표성을 가지도록 하였다. 신경망모형을 도입하여 아파트 특성들을 이용하여 아파트 가격을 정밀하고 유효하게 예측하고, 기존의 회귀모형들과 비교하는 것은 아파트 가격에 대한 연구 분야에 큰 의미가 있다 하겠다. 그리고 주택에 관한 기존의 연구와 신규 연구에 신경망모형이 활용될 수 있으리라 판단된다.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10a
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• pp.508-510
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• 2003

주식 가격의 결정은 시장 내 수요와 공급에 의해서 결정되며, 가격 변동은 일정한 패턴으로 움직인다고 가정한다. 이러한 패턴을 찾아내어 주식가격의 변동을 예측하는 분석 방법을 기술적 분석이라 한다. 기술적 분석에서는 수요.공급의 변화에 의해 추세가 변동되고, 모든 형태의 주가모형은 반복하려는 경향을 보인다고 가정한다. 이러한 가정하에 본 논문에서는 한국주가지수 200의 과거지수와 거래량을 분석하고, 일정한 패턴을 이용하여 미래의 지수를 예측하는 방법을 연구하였다.