Advisor

Richard Tymerski

Date of Award

Winter 3-14-2013

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, 110 pages)

Subjects

Stock exchanges -- Computer simulation, Financial engineering, Stock price forecasting, Machine learning -- Mathematical models, Support vector machines

DOI

10.15760/etd.2000

Abstract

In this work, we propose and investigate a series of methods to predict stock market movements. These methods use stock market technical and macroeconomic indicators as inputs into different machine learning classifiers. The objective is to survey existing domain knowledge, and combine multiple techniques into one method to predict daily market movements for stocks. Approaches using nearest neighbor classification, support vector machine classification, K-means classification, principal component analysis and genetic algorithms for feature reduction and redefining the classification rule were explored. Ten stocks, 9 companies and 1 index, were used to evaluate each iteration of the trading method. The classification rate, modified Sharpe ratio and profit gained over the test period is used to evaluate each strategy. The findings showed nearest neighbor classification using genetic algorithm input feature reduction produced the best results, achieving higher profits than buy-and-hold for a majority of the companies.

Description

The code used to run simulations is available for download and is located below in Additional Files.

Persistent Identifier

http://archives.pdx.edu/ds/psu/12733

System Requirements

WinRAR software for supplemental file

FinalCode.rar (3962 kB)
Final code

Share

COinS