Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/17034
Title: Computational modeling of the financial market : cooperative coevolutionary algorithm for prediction and trading in stock market (amended version)
Authors: Zhang, Yuanye
Keywords: DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2009
Abstract: In many real-world applications, people often target to obtain an accurate output in order to fulfill his/her objectives perfectly. However, to realize such perfection, basically, an appropriate optimal algorithm is required for promoting the result to a certain standard level which is commonly known as difficult in exploration and complex for implementation. Base on many researches done by the computer scientists, Artificial Intelligence (AI) is proven to be the most effective approach among various optimal algorithms. On top of that, coevolutionary algorithm (CEA) is taking a role as one of the most promising Artificial Intelligence (AI) techniques which is aiming to accomplish tasks automatically under the rules of genetic algorithm (GA). Normally, coevolutionary algorithm (CEA) can be categorized as cooperative algorithm, competitive algorithm, and the combination of the former two: cooperative-competitive algorithm. In this project, the cooperative coevolutionary algorithm (CCEA) is proposed and recommended as the optimization tool for prediction and trading in stock market to achieve better system utilization and result. The performance is analyzed against other existing architectures, and the result is encouraging. In addition, experiments conducted on real-life stock data also showed the feasibility and functionality of such design.
URI: http://hdl.handle.net/10356/17034
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
Zhang Yuanye 09.pdf
  Restricted Access
851.18 kBAdobe PDFView/Open

Page view(s) 50

314
Updated on May 16, 2021

Download(s)

10
Updated on May 16, 2021

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.