Title page for etd-0725114-101833


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URN etd-0725114-101833
Author Shin-shou Chen
Author's Email Address No Public.
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Department Computer Science and Engineering
Year 2013
Semester 2
Degree Master
Type of Document
Language English
Title A Study of Multi-objective Genetic Models for Stock Selection
Date of Defense 2014-07-28
Page Count 76
Keyword
  • genetic algorithms
  • stock selection
  • asset allocation
  • feature selection
  • multi-objective optimization
  • Abstract Stock selection has long been recognized as a challenging and important task in finance. Recent advances in machine learning and data mining are leading to significant opportunities to solve these problems more effectively. In this study, we enrich our work for stock selection using single-objective genetic algorithms (SOGA) and extend it to multi-objective GA (MOGA) models. We first employ the SOGA for optimization of model parameters and feature selection for input variables to the model, and then devise a stock scoring mechanism to rank and select stocks for forming a portfolio. With each chromosome representing a feasible portfolio, the adopted MOGA models thus decide good portfolios by considering their return and risk. We also improve upon the MOGA models using financial knowledge to help selection of beneficial portfolios. Furthermore, we present an investigation for asset allocation in various industrial sectors using our proposed models. Based on the promising results, we expect this MOGA methodology to advance the current state of research in soft computing for real-world stock selection applications.
    Advisory Committee
  • Chang-Shing Lee - chair
  • Ming-Chao Chiang - co-chair
  • Chien-Feng Huang - co-chair
  • Tzung-Pei Hong - advisor
  • Files
  • etd-0725114-101833.pdf
  • Indicate in-campus at 2 year and off-campus access at 2 year.
    Date of Submission 2014-08-25

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