Degree Name

MS (Master of Science)

Program

Mathematical Sciences

Date of Award

12-2015

Committee Chair or Co-Chairs

Jeff Knisley

Committee Members

Anant Godbole, Michele Joyner

Abstract

Traditional approaches to predicting financial market dynamics tend to be linear and stationary, whereas financial time series data is increasingly nonlinear and non-stationary. Lately, advances in dynamical systems theory have enabled the extraction of complex dynamics from time series data. These developments include theory of time delay embedding and phase space reconstruction of dynamical systems from a scalar time series. In this thesis, a time delay embedding approach for predicting intraday stock or stock index movement is developed. The approach combines methods of nonlinear time series analysis with those of causality testing, theory of dynamical systems and machine learning (artificial neural networks). The approach is then applied to the Standard and Poors Index, and the results from our method are compared to traditional methods applied to the same data set.

Document Type

Thesis - Open Access

Copyright

Copyright by the authors.

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