Stock trading and prediction using neural networks
This paper investigates the method of predicting stock price trends using rule-based neural network which was initially proposed by Seng-cho Timothy Chou, Chau-chen Yang, Chi-huang Chen and Feipei Lai in their paper “A Rule-based Neural Stock Trading Decision Support System” [27]. Artificial neur...
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| Format: | Final Year Project |
| Language: | English |
| Published: |
2010
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10356/40600 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | This paper investigates the method of predicting stock price trends using rule-based
neural network which was initially proposed by Seng-cho Timothy Chou, Chau-chen
Yang, Chi-huang Chen and Feipei Lai in their paper “A Rule-based Neural Stock
Trading Decision Support System” [27]. Artificial neural network (ANN) has one
input layer, one hidden layer and one output layer for supervised learning and
prediction. The neurogenetic model is trained by input features, which are derived
from a number of technical indicators being used by financial experts. After this, a
new set of test data will be put into the model for prediction. The genetic algorithm
(GA) optimizes the NN’s weights in the mean time. The output from the neural
network will be used to make trading decision based on the trading rule and threshold
value determined. By testing the proposed method with 18 companies in NYSE and
NASDAQ for 10 years from 1999 to 2009, an encouraging result has been showed. |
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