Advisor

Richard Tymerski

Date of Award

Fall 12-10-2013

Document Type

Thesis

Degree Name

Master of Science (M.S.) in Electrical and Computer Engineering

Department

Electrical and Computer Engineering

Physical Description

1 online resource (ix, 94 pages)

Subjects

Support vector machines, Stock index futures -- Computer simulation, Machine learning -- Mathematical models

DOI

10.15760/etd.1495

Abstract

In this work, we investigate different techniques to predict the monthly trend direction of the S&P 500 market index. The techniques use a machine learning classifier with technical and macroeconomic indicators as input features. The Support Vector Machine (SVM) classifier was explored in-depth in order to optimize the performance using four different kernels; Linear, Radial Basis Function (RBF), Polynomial, and Quadratic. A result found was the performance of the classifier can be optimized by reducing the number of macroeconomic features needed by 30% using Sequential Feature Selection. Further performance enhancement was achieved by optimizing the RBF kernel and SVM parameters through gridsearch. This resulted in final classification accuracy rates of 62% using technical features alone with gridsearch and 60.4% using macroeconomic features alone using Rankfeatures

Persistent Identifier

http://archives.pdx.edu/ds/psu/10371

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