• Title/Summary/Keyword: Stock Price Forecasting

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Stock Forecasting Using Prophet vs. LSTM Model Applying Time-Series Prediction

  • Alshara, Mohammed Ali
    • International Journal of Computer Science & Network Security
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    • v.22 no.2
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    • pp.185-192
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    • 2022
  • Forecasting and time series modelling plays a vital role in the data analysis process. Time Series is widely used in analytics & data science. Forecasting stock prices is a popular and important topic in financial and academic studies. A stock market is an unregulated place for forecasting due to the absence of essential rules for estimating or predicting a stock price in the stock market. Therefore, predicting stock prices is a time-series problem and challenging. Machine learning has many methods and applications instrumental in implementing stock price forecasting, such as technical analysis, fundamental analysis, time series analysis, statistical analysis. This paper will discuss implementing the stock price, forecasting, and research using prophet and LSTM models. This process and task are very complex and involve uncertainty. Although the stock price never is predicted due to its ambiguous field, this paper aims to apply the concept of forecasting and data analysis to predict stocks.

Two-Stage Forecasting Using Change-Point Detection and Artificial Neural Networks for Stock Price Index (주가지수예측에서의 변환시점을 반영한 이단계 신경망 예측모형)

  • Oh, Kyong-Joo;Kim, Kyoung-Jae;Han, In-Goo
    • Asia pacific journal of information systems
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    • v.11 no.4
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    • pp.99-111
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    • 2001
  • The prediction of stock price index is a very difficult problem because of the complexity of stock market data. It has been studied by a number of researchers since they strongly affect other economic and financial parameters. The movement of stock price index has a series of change points due to the strategies of institutional investors. This study presents a two-stage forecasting model of stock price index using change-point detection and artificial neural networks. The basic concept of this proposed model is to obtain intervals divided by change points, to identify them as change-point groups, and to use them in stock price index forecasting. First, the proposed model tries to detect successive change points in stock price index. Then, the model forecasts the change-point group with the backpropagation neural network(BPN). Finally, the model forecasts the output with BPN. This study then examines the predictability of the integrated neural network model for stock price index forecasting using change-point detection.

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Neural Network Forecasting Using Data Mining Classifiers Based on Structural Change: Application to Stock Price Index

  • Oh, Kyong-Joo;Han, Ingoo
    • Communications for Statistical Applications and Methods
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    • v.8 no.2
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    • pp.543-556
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    • 2001
  • This study suggests integrated neural network modes for he stock price index forecasting using change-point detection. The basic concept of this proposed model is to obtain significant intervals occurred by change points, identify them as change-point groups, and reflect them in stock price index forecasting. The model is composed of three phases. The first phase is to detect successive structural changes in stock price index dataset. The second phase is to forecast change-point group with various data mining classifiers. The final phase is to forecast the stock price index with backpropagation neural networks. The proposed model is applied to the stock price index forecasting. This study then examines the predictability of integrated neural network models and compares the performance of data mining classifiers.

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Two-Stage forecasting Using Change-Point Detection and Artificial Neural Networks for Stock Price Index

  • Oh, Kyong-Joo;Kim, Kyoung-Jae;Ingoo Han
    • Proceedings of the Korea Inteligent Information System Society Conference
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    • 2000.11a
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    • pp.427-436
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    • 2000
  • The prediction of stock price index is a very difficult problem because of the complexity of the stock market data it data. It has been studied by a number of researchers since they strong1y affect other economic and financial parameters. The movement of stock price index has a series of change points due to the strategies of institutional investors. This study presents a two-stage forecasting model of stock price index using change-point detection and artificial neural networks. The basic concept of this proposed model is to obtain Intervals divided by change points, to identify them as change-point groups, and to use them in stock price index forecasting. First, the proposed model tries to detect successive change points in stock price index. Then, the model forecasts the change-point group with the backpropagation neural network (BPN). Fina1ly, the model forecasts the output with BPN. This study then examines the predictability of the integrated neural network model for stock price index forecasting using change-point detection.

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A Development for Short-term Stock Forecasting on Learning Agent System using Decision Tree Algorithm (의사결정 트리를 이용한 학습 에이전트 단기주가예측 시스템 개발)

  • 서장훈;장현수
    • Journal of the Korea Safety Management & Science
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    • v.6 no.2
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    • pp.211-229
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    • 2004
  • The basis of cyber trading has been sufficiently developed with innovative advancement of Internet Technology and the tendency of stock market investment has changed from long-term investment, which estimates the value of enterprises, to short-term investment, which focuses on getting short-term stock trading margin. Hence, this research shows a Short-term Stock Price Forecasting System on Learning Agent System using DTA(Decision Tree Algorithm) ; it collects real-time information of interest and favorite issues using Agent Technology through the Internet, and forms a decision tree, and creates a Rule-Base Database. Through this procedure the Short-term Stock Price Forecasting System provides customers with the prediction of the fluctuation of stock prices for each issue in near future and a point of sales and purchases. A Human being has the limitation of analytic ability and so through taking a look into and analyzing the fluctuation of stock prices, the Agent enables man to trace out the external factors of fluctuation of stock market on real-time. Therefore, we can check out the ups and downs of several issues at the same time and figure out the relationship and interrelation among many issues using the Agent. The SPFA (Stock Price Forecasting System) has such basic four phases as Data Collection, Data Processing, Learning, and Forecasting and Feedback.

Using Evolutionary Optimization to Support Artificial Neural Networks for Time-Divided Forecasting: Application to Korea Stock Price Index

  • Oh, Kyong Joo
    • Communications for Statistical Applications and Methods
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    • v.10 no.1
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    • pp.153-166
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    • 2003
  • This study presents the time-divided forecasting model to integrate evolutionary optimization algorithm and change point detection based on artificial neural networks (ANN) for the prediction of (Korea) stock price index. The genetic algorithm(GA) is introduced as an evolutionary optimization method in this study. The basic concept of the proposed model is to obtain intervals divided by change points, to identify them as optimal or near-optimal change point groups, and to use them in the forecasting of the stock price index. The proposed model consists of three phases. The first phase detects successive change points. The second phase detects the change-point groups with the GA. Finally, the third phase forecasts the output with ANN using the GA. This study examines the predictability of the proposed model for the prediction of stock price index.

A Study on the Optimal Trading Frequency Pattern and Forecasting Timing in Real Time Stock Trading Using Deep Learning: Focused on KOSDAQ (딥러닝을 활용한 실시간 주식거래에서의 매매 빈도 패턴과 예측 시점에 관한 연구: KOSDAQ 시장을 중심으로)

  • Song, Hyun-Jung;Lee, Suk-Jun
    • The Journal of Information Systems
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    • v.27 no.3
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    • pp.123-140
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    • 2018
  • Purpose The purpose of this study is to explore the optimal trading frequency which is useful for stock price prediction by using deep learning for charting image data. We also want to identify the appropriate time for accurate forecasting of stock price when performing pattern analysis. Design/methodology/approach In order to find the optimal trading frequency patterns and forecast timings, this study is performed as follows. First, stock price data is collected using OpenAPI provided by Daishin Securities, and candle chart images are created by data frequency and forecasting time. Second, the patterns are generated by the charting images and the learning is performed using the CNN. Finally, we find the optimal trading frequency patterns and forecasting timings. Findings According to the experiment results, this study confirmed that when the 10 minute frequency data is judged to be a decline pattern at previous 1 tick, the accuracy of predicting the market frequency pattern at which the market decreasing is 76%, which is determined by the optimal frequency pattern. In addition, we confirmed that forecasting of the sales frequency pattern at previous 1 tick shows higher accuracy than previous 2 tick and 3 tick.

System Dynamics Approach for the Forecasting KOSPI (시스템다이내믹스를 활용한 종합 주가지수 예측 모델 연구)

  • Cho, Kang-Rae;Jeong, Kwan-Yong
    • Korean System Dynamics Review
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    • v.8 no.2
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    • pp.175-190
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    • 2007
  • Stock market volatility largely depends on firms' value and growth opportunities. However, with the globalization of world economy, the effect of the synchronization in major countries is gaining its importance. Also, domestically, the business cycle and cash market of the country are additional factors needed to be considered. The main purpose of this research is to attest the application and usefulness of System Dynamics as a general stock market forecasting tool. Throughout this research, System Dynamics suggests a conceptual model for forecasting a KOSPI(Korea Composite Stock Price Index), taking the factors of the composite stock price indexes in traditional researches. In conclusion of this research, System Dynamics was proved to bean appropriate model for forecasting the volatility and direction of a stock market as a whole. With its timely adaptability, System Dynamic overcomes the limit of traditional statistic models.

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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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Daily Stock Price Forecasting Using Deep Neural Network Model (심층 신경회로망 모델을 이용한 일별 주가 예측)

  • Hwang, Heesoo
    • Journal of the Korea Convergence Society
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    • v.9 no.6
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    • pp.39-44
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    • 2018
  • The application of deep neural networks to finance has received a great deal of attention from researchers because no assumption about a suitable mathematical model has to be made prior to forecasting and they are capable of extracting useful information from large sets of data, which is required to describe nonlinear input-output relations of financial time series. The paper presents a new deep neural network model where single layered autoencoder and 4 layered neural network are serially coupled for stock price forecasting. The autoencoder extracts deep features, which are fed into multi-layer neural networks to predict the next day's stock closing prices. The proposed deep neural network is progressively learned layer by layer ahead of the final learning of the total network. The proposed model to predict daily close prices of KOrea composite Stock Price Index (KOSPI) is built, and its performance is demonstrated.