Low order modeling of seemingly random systems with application to stock market securities /
Even simple observation of stock price graphs can reveal dominant patterns. In our work, we will refer to such re-occurring, dominant patterns as "coherent structures", a term borrowed from the theory of turbulence in fluid dynamics. Stock price performance exhibits coherent structures, wh...
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| Format: | Thesis Book |
| Language: | English |
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[Place of publication not identified] :
[publisher not identified] ;
2002.
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| Summary: | Even simple observation of stock price graphs can reveal dominant patterns. In our work, we will refer to such re-occurring, dominant patterns as "coherent structures", a term borrowed from the theory of turbulence in fluid dynamics. Stock price performance exhibits coherent structures, which by definition make it non-random, although a price-versus-time graph might seem totally chaotic to the naked eye. In this work focus is on a phenomenological study of stock market behavior - study via its coherent structures. These coherent/periodic structures, if properly identified, can in turn be used to achieve low-order modeling (LOM) of a stock's behavior to the point that a seemingly random price graph can be modeled with only a handful of "coherent", well-behaved modes. Two approaches are examined towards the extraction of these coherent structures. First is identification of dominant frequencies via Fourier transform, followed by the extraction and modeling of these components. Secondly, Proper Orthogonal Decomposition (POD) is carried out on the data to identify the dominant modes. The work presents low-order modeling of typical technology stocks and forecasting techniques based on the low-order models. |
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| Item Description: | Vita. "Major subject: Aerospace Engineering". |
| Physical Description: | viii, 75 leaves : illustrations ; 28 cm. Issued also on microfiche from Lange Micrographics. |
| Bibliography: | Includes bibliographical references (leaf 74). |