Stock Market Prediction using Ensemble of Deep Neural Networks

2020 ◽  
Author(s):  
Lu Sin Chong ◽  
Kian Ming Lim ◽  
Chin Poo Lee
Keyword(s):  
Neural Networks ◽  
Stock Market ◽  
Deep Neural Networks ◽  
Stock Market Prediction

Stock Market Prediction using Deep Neural Networks

2019 ◽  
Vol 7 (4) ◽  
pp. 24-28
Author(s):  
Rohit Kumar ◽  
Rohit Gajbhiye ◽  
Isha Nikhar ◽  
Dyotak Thengdi ◽  
Sofia Pillai
Keyword(s):  
Neural Networks ◽  
Stock Market ◽  
Deep Neural Networks ◽  
Stock Market Prediction

Stock Market Prediction through Artificial Intelligence, Machine Learning and Neural Networks

2021 ◽  
Author(s):  
Ambarish Shashank Gadgil ◽  
Aditya Fakirmohan Desity ◽  
Prasanna Hemant Asole ◽  
Harsh Shailesh Dandge ◽  
Spurti Shinde
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Stock Market ◽  
Stock Market Prediction

scholarly journals Review for Optimisation of Neural Networks With Genetic Algorithms and Design of Experiments in Stock Market Prediction

2019 ◽  
Vol 22 ◽  
pp. 15-21 ◽  
Author(s):  
Yunus Emre Midilli ◽  
Sergei Parshutin
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Exchange Rate ◽  
Literature Review ◽  
Stock Market ◽  
Design Of Experiments ◽  
Stock Market Prediction ◽  
Input Layer ◽  
And Performance ◽  
Exchange Rate Prediction

Neural networks are commonly used methods in stock market predictions. From the earlier studies in the literature, the requirement of optimising neural networks has been emphasised to increase the profitability, accuracy and performance of neural networks in exchange rate prediction. The paper proposes a literature review of two techniques to optimise neural networks in stock market predictions: genetic algorithms and design of experiments. These two methods have been discussed in three approaches to optimise the following aspects of neural networks: variables, input layer and hyper-parameters.

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