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Proceedings Paper

Adaptation of the projection-slice theorem for stock valuation estimation using random Markov fields
Author(s): Vahid R. Riasati
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Paper Abstract

The Projection-Slice Synthetic Discriminant function filter is utilized with Random Markov Fields, RMF to estimate trends that may be used as prediction for stock valuation through the representation of the market behavior as a hidden Markov Model, HMM. In this work, we utilize a set of progressive and contiguous time segments of a given stock, and treat the set as a two dimensional object that has been represented by its one-d projections. The abstract two-D object is thus an incarnation of N-temporal projections. The HMM is then utilized to generate N+1 projections that maximizes the two-dimensional correlation peak between the data and the HMM-generated stochastic processes. This application of the PSDF provides a method of stock valuation prediction via the market stochastic behavior utilized in the filter.

Paper Details

Date Published: 13 April 2009
PDF: 12 pages
Proc. SPIE 7344, Data Mining, Intrusion Detection, Information Security and Assurance, and Data Networks Security 2009, 73440E (13 April 2009); doi: 10.1117/12.820420
Show Author Affiliations
Vahid R. Riasati, Consultant (United States)


Published in SPIE Proceedings Vol. 7344:
Data Mining, Intrusion Detection, Information Security and Assurance, and Data Networks Security 2009
Belur V. Dasarathy, Editor(s)

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