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Modeling Stock Prices with Different...

Barnett, Jessica.

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Modeling Stock Prices with Differential Equations.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Modeling Stock Prices with Differential Equations./
Author:	Barnett, Jessica.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	155 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-12, Section: B.
Contained By:	Dissertations Abstracts International80-12B.
Subject:	Applied Mathematics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13856216
ISBN:	9781392149669

Modeling Stock Prices with Differential Equations.
Barnett, Jessica.

Modeling Stock Prices with Differential Equations. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 155 p.

Source: Dissertations Abstracts International, Volume: 80-12, Section: B.

Thesis (Ph.D.)--The University of Alabama at Birmingham, 2019.

This item must not be added to any third party search indexes.

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.

ISBN: 9781392149669Subjects--Topical Terms:

1669109
Applied Mathematics.

Modeling Stock Prices with Differential Equations.
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020 $a 9781392149669
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100 1 $a Barnett, Jessica. $3 3438128
245 1 0 $a Modeling Stock Prices with Differential Equations.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2019
300 $a 155 p.
500 $a Source: Dissertations Abstracts International, Volume: 80-12, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Knowles, Ian W.
502 $a Thesis (Ph.D.)--The University of Alabama at Birmingham, 2019.
506 $a This item must not be added to any third party search indexes.
506 $a This item must not be sold to any third party vendors.
520 $a 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.
590 $a School code: 0005.
650 4 $a Applied Mathematics. $3 1669109
690 $a 0364
710 2 $a The University of Alabama at Birmingham. $b Applied Mathematics. $3 3188554
773 0 $t Dissertations Abstracts International $g 80-12B.
790 $a 0005
791 $a Ph.D.
792 $a 2019
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13856216