Dissertation/Thesis Abstract

Application of Improved Feature Selection Algorithm in SVM Based Market Trend Prediction Model
by Li, Qi, M.S., Portland State University, 2018, 115; 10979352
Abstract (Summary)

In this study, a Prediction Accuracy Based Hill Climbing Feature Selection Algorithm (AHCFS) is created and compared with an Error Rate Based Sequential Feature Selection Algorithm (ERFS) which is an existing Matlab algorithm. The goal of the study is to create a new piece of an algorithm that has potential to outperform the existing Matlab sequential feature selection algorithm in predicting the movement of S&P 500 (

GSPC) prices under certain circumstances. The twoalgorithms are tested based on historical data of

GSPC, and SupportVector Machine (SVM) is employed by both as the classifier. A prediction without feature selection algorithm implemented is carried out and used as a baseline for comparison between the two algorithms. The prediction horizon set in this study for both algorithms varies from one to 60 days. The study results show that AHCFS reaches higher prediction accuracy than ERFS in the majority of the cases.

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Indexing (document details)
Advisor: Li, Fu
Commitee: Morris, James, Song, Xiaoyu
School: Portland State University
Department: Electrical and Computer Engineering
School Location: United States -- Oregon
Source: MAI 58/04M(E), Masters Abstracts International
Subjects: Finance, Computer science
Keywords: Algorithm, Feature selection, Marketing trend prediction, S&P 500
Publication Number: 10979352
ISBN: 978-0-438-93466-5
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