Investment portfolio selection using a combination of improved BCO BP neural network prediction and genetic algorithms

Portfolio selection has always been a very popular topic in the financial community. Investors and fund managers are constantly on the search for better techniques to accurately identify potentially profitable stocks. In recent years, most of them have learned to leverage on the technological advanc...

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Main Author: Ong, Desmond Kok Chuan.
Other Authors: Yow Kin Choong
Format: Final Year Project
Language:English
Published: 2009
Subjects:
Online Access:http://hdl.handle.net/10356/19291
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-192912023-03-03T20:29:16Z Investment portfolio selection using a combination of improved BCO BP neural network prediction and genetic algorithms Ong, Desmond Kok Chuan. Yow Kin Choong School of Computer Engineering Centre for Computational Intelligence DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Portfolio selection has always been a very popular topic in the financial community. Investors and fund managers are constantly on the search for better techniques to accurately identify potentially profitable stocks. In recent years, most of them have learned to leverage on the technological advancement in computers to crunch huge volumes of data and try to predict the market as well as improve their portfolio management strategies. This report presents a portfolio selection system that selects stocks from an index and predicts their stock price in 7 days time. The stocks’ returns are reduced by 0.5% which is the estimated transaction cost. After which, the stocks are passed to a genetic algorithm (GA) to construct their suggested capital allocation. There are two types of investors considered, the risk-seeking investor and the risk-averse investor. The genetic algorithm is set to maximise the Sharpe ratio of the stocks with respect to the USD London Interbank Offered Rate (LIBOR) as the risk free asset for the risk-seeking investor. As for the risk-averse investor, the GA is set to minimise the variance also known as the risk of the portfolio. The prediction module is a Feedforward Backpropagation Multi-layer Perceptron neural network powered by an improved bacteria chemotaxis optimisation (IBCO) method. The module uses 10 technical indicators to predict the price difference of the stock price curve. The experiment was conducted with historical stock data from the Dow Jones Industrial Average (DJIA) index. Bachelor of Engineering (Computer Science) 2009-11-16T09:13:58Z 2009-11-16T09:13:58Z 2009 2009 Final Year Project (FYP) http://hdl.handle.net/10356/19291 en Nanyang Technological University 97 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
Ong, Desmond Kok Chuan.
Investment portfolio selection using a combination of improved BCO BP neural network prediction and genetic algorithms
description Portfolio selection has always been a very popular topic in the financial community. Investors and fund managers are constantly on the search for better techniques to accurately identify potentially profitable stocks. In recent years, most of them have learned to leverage on the technological advancement in computers to crunch huge volumes of data and try to predict the market as well as improve their portfolio management strategies. This report presents a portfolio selection system that selects stocks from an index and predicts their stock price in 7 days time. The stocks’ returns are reduced by 0.5% which is the estimated transaction cost. After which, the stocks are passed to a genetic algorithm (GA) to construct their suggested capital allocation. There are two types of investors considered, the risk-seeking investor and the risk-averse investor. The genetic algorithm is set to maximise the Sharpe ratio of the stocks with respect to the USD London Interbank Offered Rate (LIBOR) as the risk free asset for the risk-seeking investor. As for the risk-averse investor, the GA is set to minimise the variance also known as the risk of the portfolio. The prediction module is a Feedforward Backpropagation Multi-layer Perceptron neural network powered by an improved bacteria chemotaxis optimisation (IBCO) method. The module uses 10 technical indicators to predict the price difference of the stock price curve. The experiment was conducted with historical stock data from the Dow Jones Industrial Average (DJIA) index.
author2 Yow Kin Choong
author_facet Yow Kin Choong
Ong, Desmond Kok Chuan.
format Final Year Project
author Ong, Desmond Kok Chuan.
author_sort Ong, Desmond Kok Chuan.
title Investment portfolio selection using a combination of improved BCO BP neural network prediction and genetic algorithms
title_short Investment portfolio selection using a combination of improved BCO BP neural network prediction and genetic algorithms
title_full Investment portfolio selection using a combination of improved BCO BP neural network prediction and genetic algorithms
title_fullStr Investment portfolio selection using a combination of improved BCO BP neural network prediction and genetic algorithms
title_full_unstemmed Investment portfolio selection using a combination of improved BCO BP neural network prediction and genetic algorithms
title_sort investment portfolio selection using a combination of improved bco bp neural network prediction and genetic algorithms
publishDate 2009
url http://hdl.handle.net/10356/19291
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