• Title, Summary, Keyword: price prediction

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Price Determinant Factors of Artworks and Prediction Model Based on Machine Learning (작품 가격 추정을 위한 기계 학습 기법의 응용 및 가격 결정 요인 분석)

  • Jang, Dongryul;Park, Minjae
    • Journal of the Korean Society for Quality Management
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    • v.47 no.4
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    • pp.687-700
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    • 2019
  • Purpose: The purpose of this study is to investigate the interaction effects between price determinants of artworks. We expand the methodology in art market by applying machine learning techniques to estimate the price of artworks and compare linear regression and machine learning in terms of prediction accuracy. Methods: Moderated regression analysis was performed to verify the interaction effects of artistic characteristics on price. The moderating effects were studied by confirming the significance level of the interaction terms of the derived regression equation. In order to derive price estimation model, we use multiple linear regression analysis, which is a parametric statistical technique, and k-nearest neighbor (kNN) regression, which is a nonparametric statistical technique in machine learning methods. Results: Mostly, the influences of the price determinants of art are different according to the auction types and the artist 's reputation. However, the auction type did not control the influence of the genre of the work on the price. As a result of the analysis, the kNN regression was superior to the linear regression analysis based on the prediction accuracy. Conclusion: It provides a theoretical basis for the complexity that exists between pricing determinant factors of artworks. In addition, the nonparametric models and machine learning techniques as well as existing parameter models are implemented to estimate the artworks' price.

A Novel Parameter Initialization Technique for the Stock Price Movement Prediction Model

  • Nguyen-Thi, Thu;Yoon, Seokhoon
    • International journal of advanced smart convergence
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    • v.8 no.2
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    • pp.132-139
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    • 2019
  • We address the problem about forecasting the direction of stock price movement in the Korea market. Recently, the deep neural network is popularly applied in this area of research. In deep neural network systems, proper parameter initialization reduces training time and improves the performance of the model. Therefore, in our study, we propose a novel parameter initialization technique and apply this technique for the stock price movement prediction model. Specifically, we design a framework which consists of two models: a base model and a main prediction model. The base model constructed with LSTM is trained by using the large data which is generated by a large amount of the stock data to achieve optimal parameters. The main prediction model with the same architecture as the base model uses the optimal parameter initialization. Thus, the main prediction model is trained by only using the data of the given stock. Moreover, the stock price movements can be affected by other related information in the stock market. For this reason, we conducted our research with two types of inputs. The first type is the stock features, and the second type is a combination of the stock features and the Korea Composite Stock Price Index (KOSPI) features. Empirical results conducted on the top five stocks in the KOSPI list in terms of market capitalization indicate that our approaches achieve better predictive accuracy and F1-score comparing to other baseline models.

Stock Prediction Model based on Bidirectional LSTM Recurrent Neural Network (양방향 LSTM 순환신경망 기반 주가예측모델)

  • Joo, Il-Taeck;Choi, Seung-Ho
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.11 no.2
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    • pp.204-208
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    • 2018
  • In this paper, we proposed and evaluated the time series deep learning prediction model for learning fluctuation pattern of stock price. Recurrent neural networks, which can store previous information in the hidden layer, are suitable for the stock price prediction model, which is time series data. In order to maintain the long - term dependency by solving the gradient vanish problem in the recurrent neural network, we use LSTM with small memory inside the recurrent neural network. Furthermore, we proposed the stock price prediction model using bidirectional LSTM recurrent neural network in which the hidden layer is added in the reverse direction of the data flow for solving the limitation of the tendency of learning only based on the immediately preceding pattern of the recurrent neural network. In this experiment, we used the Tensorflow to learn the proposed stock price prediction model with stock price and trading volume input. In order to evaluate the performance of the stock price prediction, the mean square root error between the real stock price and the predicted stock price was obtained. As a result, the stock price prediction model using bidirectional LSTM recurrent neural network has improved prediction accuracy compared with unidirectional LSTM recurrent neural network.

Development of a Continuous Prediction System of Stock Price Based on HTM Network (HTM 기반의 주식가격 연속 예측 시스템 개발)

  • Seo, Dae-Ho;Bae, Sun-Gap;Kim, Sung-Jin;Kang, Hyun-Syug;Bae, Jong-Min
    • Journal of Korea Multimedia Society
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    • v.14 no.9
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    • pp.1152-1164
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    • 2011
  • Stock price is stream data to change continuously. The characteristics of these data, stock trends according to flow of time intervals may differ. therefore, stock price should be continuously prediction when the price is updated. In this paper, we propose the new prediction system that continuously predicts the stock price according to the predefined time intervals for the selected stock item using HTM model. We first present a preprocessor which normalizes the stock data and passes its result to the stream sensor. We next present a stream sensor which efficiently processes the continuous input. In addition, we devise a storage node which stores the prediction results for each level and passes it to next upper level and present the HTM network for prediction using these nodes. We show experimented our system using the actual stock price and shows its performance.

Influence of Interests in Geographical Indication on the Prediction of Price Change of Agricultural Product : Case of Apples (지리적 표시제에 대한 관심이 농산물 가격변화 예측에 미치는 영향 연구 : 사과를 사례로)

  • Choi, Hyo Shin;Sohn, So Young
    • Journal of Korean Institute of Industrial Engineers
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    • v.41 no.4
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    • pp.359-367
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    • 2015
  • Geographical Indication (GI) has been used with the expectation to influence customer buying behavior. In this research, we empirically investigate if such relationship exists using apple price changes in Korea along with web search traffic reflecting customers' interest in GI. The experimental results indicate that the apple price of the past, apple supply and web search traffic including GI name were significant on the prediction of price change of Chungju while web search traffic of regional name and that of product were significant for Cheongsong apples with GI. In Yeongcheon with no GI, the apple price of the past turns out to be significant only. The results indicated that interests in GI can help the price prediction but the regional name itself can play the same role, if the GI product is well known in association with the region.

Eco-System: REC Price Prediction Simulation in Cloud Computing Environment (Eco-System: 클라우드 컴퓨팅환경에서 REC 가격예측 시뮬레이션)

  • Cho, Kyucheol
    • Journal of the Korea Society for Simulation
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    • v.23 no.4
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    • pp.1-8
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    • 2014
  • Cloud computing helps big data processing to make various information using IT resources. The government has to start the RPS(Renewable Portfolio Standard) and induce the production of electricity using renewable energy equipment. And the government manages system to gather big data that is distributed geographically. The companies can purchase the REC(Renewable Energy Certificate) to other electricity generation companies to fill shortage among their duty from the system. Because of the RPS use voluntary competitive market in REC trade and the prices have the large variation, RPS is necessary to predict the equitable REC price using RPS big data. This paper proposed REC price prediction method base on fuzzy logic using the price trend and trading condition infra in REC market, that is modeled in cloud computing environment. Cloud computing helps to analyze correlation and variables that act on REC price within RPS big data and the analysis can be predict REC price by simulation. Fuzzy logic presents balanced REC average trading prices using the trading quantity and price. The model presents REC average trading price using the trading quantity and price and the method helps induce well-converged price in the long run in cloud computing environment.

Electricity Price Prediction Model Based on Simultaneous Perturbation Stochastic Approximation

  • Ko, Hee-Sang;Lee, Kwang-Y.;Kim, Ho-Chan
    • Journal of Electrical Engineering and Technology
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    • v.3 no.1
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    • pp.14-19
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    • 2008
  • The paper presents an intelligent time series model to predict uncertain electricity market price in the deregulated industry environment. Since the price of electricity in a deregulated market is very volatile, it is difficult to estimate an accurate market price using historically observed data. The parameter of an intelligent time series model is obtained based on the simultaneous perturbation stochastic approximation (SPSA). The SPSA is flexible to use in high dimensional systems. Since prediction models have their modeling error, an error compensator is developed as compensation. The SPSA based intelligent model is applied to predict the electricity market price in the Pennsylvania-New Jersey-Maryland (PJM) electricity market.

A Comparative Analysis of the Prediction Models for the Direction of Stock Price Using the Online Company Reviews (기업 리뷰 정보를 활용한 주가 방향 예측 모델 비교 분석)

  • Lim, Yongtaek;Lim, Heuiseok
    • Journal of the Korea Convergence Society
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    • v.11 no.8
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    • pp.165-171
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    • 2020
  • Most of the stock price prediction research using text mining uses news and SNS data. However, there is a weakness that it is difficult to get honest and vivid information about companies from them. This paper deals with the problem of the prediction for the direction of stock price by doing text mining the online company reviews of internal staff indicating employee satisfaction. The comparative analysis of the prediction models for the direction of stock price showed the prediction model, which adds internal employee reviews, has better performance than those that did not. This paper presents the convergence study using natural language processing in financial engineering. In the field of stock price prediction, This paper pursued a new methodology that used employee satisfaction. In practice, it is expected to provide useful information in the field of forecasting stock price direction.

A Prediction of Stock Price Through the Big-data Analysis (인터넷 뉴스 빅데이터를 활용한 기업 주가지수 예측)

  • Yu, Ji Don;Lee, Ik Sun
    • Journal of the Society of Korea Industrial and Systems Engineering
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    • v.41 no.3
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    • pp.154-161
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    • 2018
  • This study conducted to predict the stock market prices based on the assumption that internet news articles might have an impact and effect on the rise and fall of stock market prices. The internet news articles were tested to evaluate the accuracy by comparing predicted values of the actual stock index and the forecasting models of the companies. This paper collected stock news from the internet, and analyzed and identified the relationship with the stock price index. Since the internet news contents consist mainly of unstructured texts, this study used text mining technique and multiple regression analysis technique to analyze news articles. A company H as a representative automobile manufacturing company was selected, and prediction models for the stock price index of company H was presented. Thus two prediction models for forecasting the upturn and decline of H stock index is derived and presented. Among the two prediction models, the error value of the prediction model (1) is low, and so the prediction performance of the model (1) is relatively better than that of the prediction model (2). As the further research, if the contents of this study are supplemented by real artificial intelligent investment decision system and applied to real investment, more practical research results will be able to be developed.

A Smoothing Method for Stock Price Prediction with Hidden Markov Models

  • Lee, Soon-Ho;Oh, Chang-Hyuck
    • Journal of the Korean Data and Information Science Society
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    • v.18 no.4
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    • pp.945-953
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    • 2007
  • In this paper, we propose a smoothing and thus noise-reducing method of data sequences for stock price prediction with hidden Markov models, HMMs. The suggested method just uses simple moving average. A proper average size is obtained from forecasting experiments with stock prices of bank sector of Korean Exchange. Forecasting method with HMM and moving average smoothing is compared with a conventional method.

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