Stock market value prediction using neural networks

By Mahdi Pakdaman Naeini, Hamid Reza Taremian, Homa Baradaran Hashemi. 2010

162 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Management Science and Operations Research

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Citations in the corpus

Forecasting financial time series using a low complexity recurrent neural network and evolutionary learning approach (2015, methodology): “The use of ANN to predict the behavior and tendencies of stocks has demonstrated itself to be a viable alternative to existing conventional techniques (Andrade de Oliveira and Nobre, 2011; Naeini et al., 2010; Song et al., 2007; Lee and Chen, 2007; Ma et al., 2010).”
Recurrent Neural Networks Approach to the Financial Forecast of Google Assets (2017, neutral): “Even taking into account only the NNS ones, we can applications belonging to the MLPs methods, see, e.g., [16], convolutional neural networks (CNNs), see, e.g.,[6], Elman neural networks, see, e.g., [10], etc.”
Fintech Model: The Random Neural Network with Genetic Algorithm (2018, methodology): “et al [18] use a feedforward multilayer perceptron and an Elman recurrent Network to predict a company’s stock value.”
Short term stock price prediction using deep learning (2017, background): “Hashemi with his colleagues, in their research work stated that MLPs perform superior to LSTMs at prediction stock prices [7].”
Applications of ANNs in Stock Market Prediction : A Survey (2011, unknown): “In future work ANNs can also be explore for other applications and comparative study with other models.”
A recurrent neural network approach in predicting daily stock prices an application to the Sri Lankan stock market (2017, methodology): “A study was conducted in 2010 using Multi-Layer Perceptron (MLP) and Elman neural network to predict the stock values of 1094 companies traded in the Tehran Stock Market (during 2000 to 2005) based on stock share value history and found that MLP is more accurate in stock value prediction while Elman”
Foreign Currency Exchange Rates Prediction Using CGP and Recurrent Neural Network (2014, methodology): “(Naeini et al., 2010) focused on the use of a feed forward multilayer Perceptron (MLP) to use the historic records regarding the stock share in order to predict a company’s stock value and compared it with Elman Recurrent Network and Regression Model.”
Data Mining in Banking and its Applications-a Review (2013, methodology): “Data mining techniques for prediction like neural networks and linear regression can be employed for prediction of prices for stocks (Naeini et al ., 2010).”
Deep Learning for Stock Market Prediction (2020, background): “It is true that the stock values are generally dynamic, non-parametric and non-linear; therefore they often cause weak performance of the statistical models and disability to predict the accurate values and movements [6, 7] Machine learning is the most powerful tool which includes different algorith”