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Reseach Article

Stock Market Prediction using Digital Signal Processing Models

Published on September 2015 by Shashankiyer, Nisarg R. Kamdar, Bahar Soparkar
CAE Proceedings on International Conference on Communication Technology
Foundation of Computer Science USA
ICCT2015 - Number 6
September 2015
Authors: Shashankiyer, Nisarg R. Kamdar, Bahar Soparkar
70cae137-4a8f-4409-b243-9edfa1e8a17a

Shashankiyer, Nisarg R. Kamdar, Bahar Soparkar . Stock Market Prediction using Digital Signal Processing Models. CAE Proceedings on International Conference on Communication Technology. ICCT2015, 6 (September 2015), 35-39.

@article{
author = { Shashankiyer, Nisarg R. Kamdar, Bahar Soparkar },
title = { Stock Market Prediction using Digital Signal Processing Models },
journal = { CAE Proceedings on International Conference on Communication Technology },
issue_date = { September 2015 },
volume = { ICCT2015 },
number = { 6 },
month = { September },
year = { 2015 },
issn = 0975-8887,
pages = { 35-39 },
numpages = 5,
url = { /proceedings/icct2015/number6/22677-1582/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 CAE Proceedings on International Conference on Communication Technology
%A Shashankiyer
%A Nisarg R. Kamdar
%A Bahar Soparkar
%T Stock Market Prediction using Digital Signal Processing Models
%J CAE Proceedings on International Conference on Communication Technology
%@ 0975-8887
%V ICCT2015
%N 6
%P 35-39
%D 2015
%I International Journal of Computer Applications
Abstract

This paper aims to exploit the temporal correlation that exists between the various stock market variablesemploying concepts of adaptive filters and signal modelling in order to predict future trends and prices, using two statistical processes. Linear regression algorithm (Gradient Descent)has been used for real time prediction. The Finite Impulse Response (FIR) adaptive filter is an iterative process that minimizes the mean square error. An extrapolation of Prony's Normal Equation has been used to predict values using the least square estimation. This models cross-section regression, i. e. the relationship between variables at a particular point in time. The analysis has been performed on stocks listed on NSADAQ and the mean square error was compared. This study reveals that the DSP techniques are adequate for modeling thevariation in stock prices.

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Index Terms

Computer Science
Information Sciences

Keywords

Stock Market Prediction Dsp Statistical Signal Processing Regression Models Prony's Algorithm