Adaptation of the projection-slice theorem for stock valuation estimation using random Markov fields

Vahid R. Riasati

doi:10.1117/12.820420

13 April 2009 Adaptation of the projection-slice theorem for stock valuation estimation using random Markov fields

Vahid R. Riasati

Proceedings Volume 7344, Data Mining, Intrusion Detection, Information Security and Assurance, and Data Networks Security 2009; 73440E (2009) https://doi.org/10.1117/12.820420
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

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.

Citation Download Citation

Vahid R. Riasati "Adaptation of the projection-slice theorem for stock valuation estimation using random Markov fields", Proc. SPIE 7344, Data Mining, Intrusion Detection, Information Security and Assurance, and Data Networks Security 2009, 73440E (13 April 2009); https://doi.org/10.1117/12.820420

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available