CNNpred: CNN-based stock market prediction using a diverse set of variables

Hoseinzade, Ehsan; Haratizadeh, Saman

doi:10.1016/j.eswa.2019.03.029

Cited by 420 publications

(206 citation statements)

References 49 publications

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“…CNN is an interesting technique for high-dimensional data, such as images and time series data. CNN has been widely applied for feature selection and prediction of price movements [15]. In the convolutional neural network, not all hidden neurons are connected to each other [16,17].…”

Section: Proposed Methodsmentioning

confidence: 99%

Foreign exchange prediction based on indices and commodities price using convolutional neural network

Rassetiadi

Suharjito

2020

IJEECS

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The level of accuracy in predicting is the key in conducting forex trading activities in gaining profits. Some predictions are made only by using historical currency data to be predicted, this makes predictions less accurate because they do not consider external influences. This study examines external factors that can influence the results of predictions, by looking for the relationship between the value of indices such as NTFSE and S & P 500 and the value of commodities such as gold and silver to the prediction process of EUR / USD. Prediction carried out using a deep learning algorithm with the Convolutional Neural Network method uses 2 1-dimensional convolution layers with ReL activation. The data used is the value of Open, High, Low and Close prices on forex, indices and commodities which are combined into one with the close forex value target for the next 5 days. Testing of EUR / USD test data only gets MSE results of 0.00081894. While the results of testing of the combined test data between EUR / USD, indices and commodities producing MSE vary between 0.00068717 to 0.0109606 where the best combination is a combination of FTSE 100 and Natural Gas values. So it can be concluded that other factors included in predicting have an influence on the results obtained.

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Section: Proposed Methodsmentioning

confidence: 99%

Foreign exchange prediction based on indices and commodities price using convolutional neural network

Rassetiadi

Suharjito

2020

IJEECS

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“…CNN based prediction of American stock index was proposed in [1]. Features from multiple sources like daily closing prices, technical indicators, economic data, world stock markets, US dollar exchange rate against other countries, commodities, future contrasts etc., are used to predict the moving trends of American stock indexes.…”

Section: Related Workmentioning

confidence: 99%

“…It extracted discriminative low-dimensional features from the data set by considering 324 dimensions, Support Vector Machine (SVM) is used for applying regression extracted features. Like [1], this approach was used to predict only the next day indexes and not used for individual stocks. Long Short Term Memory (LSTM) method based stock price prediction was proposed in [4].…”

Section: Guruprasad S H Chandramoulimentioning

confidence: 99%

CNN based Stock Market Prediction

Guruprasad¹,

Chandramouli²

2020

IJEAT

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Abstract: Indian Stock market is highly dynamic and especially after globalization stock market modeling has become even more complex due to influence of multiple parameters. In presence of multiple parameters, some parameters have increased influence than others in prediction of stock market trends. This influence of individual parameters and their joint influence over time is better modeled with Convolutional Neural Network Classifiers. This work models the dynamics of stock market in terms of Convolutional Neural Networks and multiple parametersimpacting the stock trend. The proposed solution is implemented for Indian stock market for stocks in different sectors to prove its prediction accuracy.

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“…CNN is originally used in image processing and has excellent performance. Ehsan Hoseinzade et al [19] used CNN to extract the correlation of multisource data to achieve stock market forecasting. Jinho Lee et al [20] used stock chart images as input to the model and used the Deep Q-Network and convolutional neural networks for global stock market forecasting.…”

Section: Related Workmentioning

confidence: 99%

LSTM with Wavelet Transform Based Data Preprocessing for Stock Price Prediction

Liang

Sun

et al. 2019

Mathematical Problems in Engineering

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For profit maximization, the model-based stock price prediction can give valuable guidance to the investors. However, due to the existence of the high noise in financial data, it is inevitable that the deep neural networks trained by the original data fail to accurately predict the stock price. To address the problem, the wavelet threshold-denoising method, which has been widely applied in signal denoising, is adopted to preprocess the training data. The data preprocessing with the soft/hard threshold method can obviously restrain noise, and a new multioptimal combination wavelet transform (MOCWT) method is proposed. In this method, a novel threshold-denoising function is presented to reduce the degree of distortion in signal reconstruction. The experimental results clearly showed that the proposed MOCWT outperforms the traditional methods in the term of prediction accuracy.

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CNNpred: CNN-based stock market prediction using a diverse set of variables

Cited by 420 publications

References 49 publications

Foreign exchange prediction based on indices and commodities price using convolutional neural network

Foreign exchange prediction based on indices and commodities price using convolutional neural network

CNN based Stock Market Prediction

LSTM with Wavelet Transform Based Data Preprocessing for Stock Price Prediction

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