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Advisory Committee Chair

Ian W Knowles

Document Type

Dissertation

Date of Award

2019

Degree Name by School

Doctor of Philosophy (PhD) College of Arts and Sciences

Abstract

Forecasting the stock market has long been subject to speculation, especially after the global financial crisis in 2008. In this thesis, we formulate a predictive model of the trend of a chosen stock that is derived from verifiable economic tendencies (absent appropriate economic laws) that interact through a system of nonlinear delay differential equations. The system is populated inversely from current economic data and solved numerically using MATLAB. As a result, we are able to use the forecasted trend to estimate a stock’s drift in order to also incorporate the randomness of the stock market. Lastly, we compare the predictions made by our model to an actual stock’s performance.

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