Stock Price Prediction on Daily Stock Data using Deep Neural Networks

Jain, Sneh L.; Gupta, Roopam; Moghe, Asmita A.

doi:10.1109/icacat.2018.8933791

Cited by 37 publications

(27 citation statements)

References 7 publications

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“…Experiments on Chinese stock market index CSI300 showed the superiority of MFNN to traditional machine learning models, statistical models, CNN, RNN, and LSTM in terms of the accuracy, profitability, and stability. In fact, a more commonly used hybrid model is the CNN-LSTM model [14][15][16][17][18][19][20]. For example, in [14], the authors found that the CNN-LSTM model is superior to LSTM and CNN in stock price movement prediction.…”

Section: Model Enhancementmentioning

confidence: 99%

“…In fact, a more commonly used hybrid model is the CNN-LSTM model [14][15][16][17][18][19][20]. For example, in [14], the authors found that the CNN-LSTM model is superior to LSTM and CNN in stock price movement prediction. In [17], Li et al added an attention mechanism to the CNN-LSTM model and further improved its scalability and prediction accuracy.…”

Section: Model Enhancementmentioning

confidence: 99%

“…CNN [8], RNN [10], and LSTM [11] were commonly used deep learning models in predicting the stock price movement. In addition, constructing hybrid models is a popular way to enhance the performance of model, such as SVM-ANN model [12], CNN-SVM model [13], and CNN-LSTM model [14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, we proposed a CNN-LSTM model for stock price movement forecast. Compared with other CNN-LSTM models [14][15][16][17][18][19][20], the main difference between them and our proposed hybrid model lies on the CNN-based feature extraction module. eir feature extraction modules mainly aimed at extracting features from one-dimensional or two-dimensional input variables, while ours was aimed at three-dimensional input tensor.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning for Price Movement Prediction Using Convolutional Neural Network and Long Short-Term Memory

Yang

Zhai

Tao

2020

Mathematical Problems in Engineering

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The prediction of stock price movement direction is significant in financial studies. In recent years, a number of deep learning models have gradually been applied for stock predictions. This paper presents a deep learning framework to predict price movement direction based on historical information in financial time series. The framework combines a convolutional neural network (CNN) for feature extraction and a long short-term memory (LSTM) network for prediction. We specifically use a three-dimensional CNN for data input in the framework, including the information on time series, technical indicators, and the correlation between stock indices. And in the three-dimensional input tensor, the technical indicators are converted into deterministic trend signals and the stock indices are ranked by Pearson product-moment correlation coefficient (PPMCC). When training, a fully connected network is used to drive the CNN to learn a feature vector, which acts as the input of concatenated LSTM. After both the CNN and the LSTM are trained well, they are finally used for prediction in the testing set. The experimental results demonstrate that the framework outperforms state-of-the-art models in predicting stock price movement direction.

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Section: Model Enhancementmentioning

confidence: 99%

Section: Model Enhancementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep Learning for Price Movement Prediction Using Convolutional Neural Network and Long Short-Term Memory

Yang

Zhai

Tao

2020

Mathematical Problems in Engineering

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“…In a similar approach, Jain et al compared three different models (CNN, LSTM and CNN-LSTM) in future prices forecasting task [Jain et al 2018]. All models were based on 1-D convolution and the input data used were composed of open, high, low and close daily prices of 5 previous trading days sequentially.…”

Section: Related Workmentioning

confidence: 99%

Stock Trading Classifier with Multichannel Convolutional Neural Network

Nascimento

Costa

Bianchi

2020

Anais Do Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2020)

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Stock market forecasting has been a quite popular challenge in machine learning research. Recently, studies have been using deep learning techniques, such as Convolutional Neural Networks (CNN), to perform regression on the prices or classification on trading signal as an operation indication. However, they did not reach a satisfactory financial result. In this work we aim to design a financially profitable stock market method by proposing a novel approach called Multichannel CNN Trading Classifier (MCNN-TC). The model was evaluated using data from the Brazilian stock market. The results indicate a satisfactory financial trading performance compared to the Buy and Hold strategy and good classification metrics.

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A Proposal of a Method to Determine the Appropriate Learning Period in Stock Price Prediction Using Machine Learning

Shirata,

Harada

2024

IEEJ Transactions Elec Engng

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In this study, we propose a method to determine the appropriate learning period for each stock and the prediction period by considering stock price fluctuations for stock price prediction using machine learning. Our proposed method uses historical volatility as an indicator of the turning point to determine the learning period based on the policy that the fluctuations in the period after the major turning point of stock price fluctuations and the fluctuations in the prediction period are likely to be similar and that the prediction accuracy can be improved by eliminating the period before the turning point from the learning period. We used Long Short‐Term Memory (LSTM), which has been used in many related studies on stock price prediction, as the machine learning model. Experiments showed that the accuracy of predictions by neural networks trained with the learning period determined by the proposed method was better than that of predictions by neural networks trained with the same learning period for all stocks. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

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Stock Price Prediction on Daily Stock Data using Deep Neural Networks

Cited by 37 publications

References 7 publications

Deep Learning for Price Movement Prediction Using Convolutional Neural Network and Long Short-Term Memory

Deep Learning for Price Movement Prediction Using Convolutional Neural Network and Long Short-Term Memory

Stock Trading Classifier with Multichannel Convolutional Neural Network

A Proposal of a Method to Determine the Appropriate Learning Period in Stock Price Prediction Using Machine Learning

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