Stock trading & prediction using deep learning neural networks
This report describes various deep neural network models based on technical analysis to predict stock prices. Three stocks – Walmart, HSBC and Petrobras are chosen to test Multilayer Perceptron, Deep Belief Network (DBN), Convolutional Neural Network (CNN) and Random Forests. A hybrid model comprisi...
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| Format: | Final Year Project |
| Language: | English |
| Published: |
2017
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| Online Access: | http://hdl.handle.net/10356/71631 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | This report describes various deep neural network models based on technical analysis to predict stock prices. Three stocks – Walmart, HSBC and Petrobras are chosen to test Multilayer Perceptron, Deep Belief Network (DBN), Convolutional Neural Network (CNN) and Random Forests. A hybrid model comprising of a DBN with a NN stacked on top is proposed in this report. 25% of the data is used for testing while the rest is used for training and validation of models. The models are tested for a wide range of parameters including activation functions, number of hidden layers, 5 day vs 1 day forecast, 20, 10 and 5 day prior input (batch size), epochs, learning rate and momentum. The prediction results for the model with the optimal set of parameters are compared against a published paper’s work on Multilayer Perceptron model that used the same input data sets. |
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