Results 11 to 20 of about 246,578 (242)
S_I_LSTM: stock price prediction based on multiple data sources and sentiment analysis
Connection Science, 2022 Stocks price prediction is a current hot spot with great promise and challenges. Recently, there have been many stock price prediction methods. However, the prediction accuracy of these methods is still far from satisfactory.
Shengting Wu +3 more
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Trading Network Predicts Stock Price [PDF]
Scientific Reports, 2014 AbstractStock price prediction is an important and challenging problem for studying financial markets. Existing studies are mainly based on the time series of stock price or the operation performance of listed company. In this paper, we propose to predict stock price based on investors' trading behavior.
Xiao Qian Sun, Hua Shen, Cheng Xue
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A stock price prediction method based on deep learning technology
International Journal of Crowd Science, 2021 PurposeStock price prediction is a hot topic and traditional prediction methods are usually based on statistical and econometric models. However, these models are difficult to deal with nonstationary time series data.
Xuan Ji, Jiachen Wang, Zhijun Yan
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A Hybrid Stock Price Prediction Model Based on PRE and Deep Neural Network
Data, 2022 Stock prices are volatile due to different factors that are involved in the stock market, such as geopolitical tension, company earnings, and commodity prices, affecting stock price.
Srivinay +3 more
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Stock price prediction using principal components
PLOS ONE, 2020 The literature provides strong evidence that stock prices can be predicted from past price data. Principal component analysis (PCA) is a widely used mathematical technique for dimensionality reduction and analysis of data by identifying a small number of principal components to explain the variation found in a data set.
Mahsa Ghorbani, Edwin K. P. Chong
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Indonesian Stock Price Prediction using Deep Learning during COVID-19 Financial Crisis
International Journal of Business, Economics, and Social Development, 2022 This research paper aims to use the deep learning model Long Short-Term Memory (LSTM) for the stock prediction model under the financial crisis of COVID-19. The financial impact of the COVID-19 has brought many of the world's indexes down.
Dian Angga Prasetyo, Rofikoh Rokhim
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IEEE Access, 2020 The classical linear multi-factor stock selection model is widely used for long-term stock price trend prediction. However, the stock market is chaotic, complex, and dynamic, for which reasons the linear model assumption may be unreasonable, and it is ...
Xianghui Yuan +3 more
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Importance of Event Binary Features in Stock Price Prediction
Applied Sciences, 2020 In Korea, because of the high interest in stock investment, many researchers have attempted to predict stock prices using deep learning. Studies to predict stock prices have been continuously conducted.
Yoojeong Song, Jongwoo Lee
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Impact of chart image characteristics on stock price prediction with a convolutional neural network.
PLoS ONE, 2021 Stock price prediction has long been the subject of research because of the importance of accuracy of prediction and the difficulty in forecasting. Traditionally, forecasting has involved linear models such as AR and MR or nonlinear models such as ANNs ...
Guangxun Jin, Ohbyung Kwon
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Stock Market Forecasting Based on Spatiotemporal Deep Learning
Entropy, 2023 This study introduces the Spacetimeformer model, a novel approach for predicting stock prices, leveraging the Transformer architecture with a time–space mechanism to capture both spatial and temporal interactions among stocks. Traditional Long–Short Term
Yung-Chen Li +3 more
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