Stock trading using computational intelligence

Computational Intelligence has been widely used in recent years in many areas, such as speech recognition, image analysis, adaptive control and time series prediction. This research attempts to explore the usefulness of neural network and support vector machine in financial market. Two popular stock...

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Main Author: Zhu, Ming.
Other Authors: Wang Lipo
Format: Final Year Project
Language:English
Published: 2010
Subjects:
Online Access:http://hdl.handle.net/10356/40173
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-401732023-07-07T17:08:14Z Stock trading using computational intelligence Zhu, Ming. Wang Lipo School of Electrical and Electronic Engineering DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Computational Intelligence has been widely used in recent years in many areas, such as speech recognition, image analysis, adaptive control and time series prediction. This research attempts to explore the usefulness of neural network and support vector machine in financial market. Two popular stock market indexes have been studied: Hong Kong Hang Seng Stock Index and Dow Jones Transportation Index. The performance of neural network and support vector machine are evaluated in two dimensions: error in forecasting and trading profits. Popular technical indicator, percentage price oscillator (PPO), has been selected as training input and output. Predictive models use previous 8 days PPO to forecast future 5 days PPO. Empirical results on Hong Kong Hang Seng Index show that multilayer perceptron optimized with GA (MLP-GA) trading system obtain 6.71 times of original capital from 1997-1-29 to 2007-3-8, totally 2500 trading days. While support vector regression optimized by genetic algorithms (SVR-GA) trading system generates 5.705 times of original capital during the same time horizon. In contrast, conventional non-predictive trading system only produces 2.064 times of starting equity. “Buy and Hold” strategy gives 1.605 times return to investors. A recent published fuzzy trading system provides 5.781 dollars as final equity for 1 dollar initial investment. Further evaluations of two intelligent trading systems have been made. A back test using the same parameters and same assumptions on Dow Jones Transportation Index have further proved the robustness of the proposed trading systems. MLP-GA trading system provides 4.87 times of initial capital and SVR-GA trading system obtains 5.168 as final equity. These two intelligent trading systems again outperform conventional trading system, which generate 2.805 dollars for 1 dollar investment. Bachelor of Engineering 2010-06-11T03:50:37Z 2010-06-11T03:50:37Z 2010 2010 Final Year Project (FYP) http://hdl.handle.net/10356/40173 en Nanyang Technological University 60 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
spellingShingle DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Zhu, Ming.
Stock trading using computational intelligence
description Computational Intelligence has been widely used in recent years in many areas, such as speech recognition, image analysis, adaptive control and time series prediction. This research attempts to explore the usefulness of neural network and support vector machine in financial market. Two popular stock market indexes have been studied: Hong Kong Hang Seng Stock Index and Dow Jones Transportation Index. The performance of neural network and support vector machine are evaluated in two dimensions: error in forecasting and trading profits. Popular technical indicator, percentage price oscillator (PPO), has been selected as training input and output. Predictive models use previous 8 days PPO to forecast future 5 days PPO. Empirical results on Hong Kong Hang Seng Index show that multilayer perceptron optimized with GA (MLP-GA) trading system obtain 6.71 times of original capital from 1997-1-29 to 2007-3-8, totally 2500 trading days. While support vector regression optimized by genetic algorithms (SVR-GA) trading system generates 5.705 times of original capital during the same time horizon. In contrast, conventional non-predictive trading system only produces 2.064 times of starting equity. “Buy and Hold” strategy gives 1.605 times return to investors. A recent published fuzzy trading system provides 5.781 dollars as final equity for 1 dollar initial investment. Further evaluations of two intelligent trading systems have been made. A back test using the same parameters and same assumptions on Dow Jones Transportation Index have further proved the robustness of the proposed trading systems. MLP-GA trading system provides 4.87 times of initial capital and SVR-GA trading system obtains 5.168 as final equity. These two intelligent trading systems again outperform conventional trading system, which generate 2.805 dollars for 1 dollar investment.
author2 Wang Lipo
author_facet Wang Lipo
Zhu, Ming.
format Final Year Project
author Zhu, Ming.
author_sort Zhu, Ming.
title Stock trading using computational intelligence
title_short Stock trading using computational intelligence
title_full Stock trading using computational intelligence
title_fullStr Stock trading using computational intelligence
title_full_unstemmed Stock trading using computational intelligence
title_sort stock trading using computational intelligence
publishDate 2010
url http://hdl.handle.net/10356/40173
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