Stock trading using fuzzy neural networks
The algorithm trading has be an increasing important trading method in today’s financial market. With the development of technology, the performance of the algorithm trading becomes very satisfactory to investors. One important category of the algorithm trading family, the fuzzy neural netw...
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sg-ntu-dr.10356-462132023-07-07T16:38:39Z Stock trading using fuzzy neural networks Xiao, Xiao Wang Lipo School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems The algorithm trading has be an increasing important trading method in today’s financial market. With the development of technology, the performance of the algorithm trading becomes very satisfactory to investors. One important category of the algorithm trading family, the fuzzy neural network based trading system, has been studies in the project. Historical Stock Prices of HSI and IBM downloaded from Yahoo finance are utilized as the inputs to stock price forecasting system. Using the time-delayed prices difference approach, ANFIS in the Matlab was the network trained and to process historical stock prices to generate future prices. A simply yet effective trading strategy, “LeadLag” is exploited to make the trading decision. The original historical prices of HSI and the predicted price based on the stock prices forecasting system in the upstream are loaded into the system. As the final result, the trading system with forecasting ability ended up with the capital value 3.54 times as the original value, and the system with optimized parameter has a dramatic final capital of the 294 times of the original one. Compared with the Buy and Hold strategy end value of 1.36 times of the original capital and end value of 1.91 times for convention trading system without forecasting, the trading system demonstrate superior performance over these two traditional methods. In order to validate the optimized parameter in the trading system, another set of historical prices from the Dow Jones Industrial Average is used. The same trend in the return occurs. The trading system of optimized parameter has the final capital of 11.2 times of the initial value, compared with the buy and hold 2.35 times and conventional trading system 1.72 times. Bachelor of Engineering 2011-07-07T01:19:21Z 2011-07-07T01:19:21Z 2011 2011 Final Year Project (FYP) http://hdl.handle.net/10356/46213 en Nanyang Technological University 72 p. application/pdf |
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DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems Xiao, Xiao Stock trading using fuzzy neural networks |
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The algorithm trading has be an increasing important trading method in today’s financial
market. With the development of technology, the performance of the algorithm trading
becomes very satisfactory to investors.
One important category of the algorithm trading family, the fuzzy neural network based trading system, has been studies in the project. Historical Stock Prices of HSI and IBM downloaded from Yahoo finance are utilized as the inputs to stock price forecasting system. Using the time-delayed prices difference approach, ANFIS in the Matlab was the network trained and to process historical stock prices to generate future prices.
A simply yet effective trading strategy, “LeadLag” is exploited to make the trading decision. The original historical prices of HSI and the predicted price based on the stock prices forecasting system in the upstream are loaded into the system. As the final result, the trading system with forecasting ability ended up with the capital value 3.54 times as the original value, and the system with optimized parameter has a dramatic final capital of the 294 times of the original one. Compared with the Buy and Hold strategy end value
of 1.36 times of the original capital and end value of 1.91 times for convention trading system without forecasting, the trading system demonstrate superior performance over these two traditional methods.
In order to validate the optimized parameter in the trading system, another set of
historical prices from the Dow Jones Industrial Average is used. The same trend in the return occurs. The trading system of optimized parameter has the final capital of 11.2 times of the initial value, compared with the buy and hold 2.35 times and conventional trading system 1.72 times. |
author2 |
Wang Lipo |
author_facet |
Wang Lipo Xiao, Xiao |
format |
Final Year Project |
author |
Xiao, Xiao |
author_sort |
Xiao, Xiao |
title |
Stock trading using fuzzy neural networks |
title_short |
Stock trading using fuzzy neural networks |
title_full |
Stock trading using fuzzy neural networks |
title_fullStr |
Stock trading using fuzzy neural networks |
title_full_unstemmed |
Stock trading using fuzzy neural networks |
title_sort |
stock trading using fuzzy neural networks |
publishDate |
2011 |
url |
http://hdl.handle.net/10356/46213 |
_version_ |
1772827930709196800 |