Advisor(s)

Bill C. Giessen (1932-)

Contributor(s)

Graham B. Jones, Max Diem, Kamran M. Dadkhah, Nicole M. Boyson

Date of Award

2009

Date Accepted

3-2009

Degree Grantor

Northeastern University

Degree Level

Ph.D.

Degree Name

Doctor of Philosophy

Department or Academic Unit

College of Arts and Sciences. Department of Chemistry and Chemical Biology.

Keywords

Chemical biology, Chemometrics, Pattern recognition

Subject Categories

Chemometrics, Stock index futures--Mathematical models, Foreign exchange futures--Mathematical models, Futures market--Mathematical models

Disciplines

Other Chemistry | Statistical Models

Abstract

Cycle-theory-based market analysis is the main focus of this thesis, in which I try to find systematic methods to recognize and utilize market patterns, especially cyclic ones, to obtain a correct understanding of market movements. The KNN algorithm, a pattern recognition method extensively used in chemometrics, has been employed to recognize similarities of current market movements and historical markets to permit market forecasts. Bayesian analysis, another pattern recognition method, has been used to infer longer-term market trends based on observable shorter-term market behaviors and to improve the real-time application of the KNN algorithm. An artificial neural network method, an example of a non-linear information processing system, has also been applied in this research to combine cycle-relative information to market behavior modeling. The promising overall results show that there exists a correlation between current and historical price movements and shows the possibilities of utilizing pattern recognition methods to obtain correct market forecasts. Also, a novel use of moving averages, especially suitable for an oscillating market, has been introduced and successfully applied in market prediction. In this thesis, the S&P 500 futures market has been chosen as the market to study.

Document Type

Dissertation

Rights Information

Copyright 2009

Rights Holder

Tao Yu


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