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.

1. L. , Giorno, C. , & Richardson, P. (1998), Stock market fluctuations and consumption behavior: some recent evidence (No. 208). OECD Publishing
2. Mojaddady, Nabi and Khadivi, Stock Market Prediction using Twin Gaussian Process Regression Modeling, Department of Computer Engineering,Amirkabir University of Technology, Tehran, Iran
3. Hayes, M. (2002), Statistical Digital Signal Processing and Modeling, Pg 144, Singapore: John Wiley & Sons (Asia) Pte. Ltd.
4. Hayes, M. (2002), Statistical Digital Signal Processing and Modeling,Pg 145, Singapore: John Wiley & Sons (Asia) Pte. Ltd.
5. Dutta, Bandopadhyay, and Sengupta, Prediction of Stock Performance in the Indian Stock Market Using Logistic Regression, International Journal of Business and Information, Volume 7, Number 1, June 2012
6. Tiong, Ngo, Lee, Forex Trading Prediction using Linear Regression Line, Artificial Neural Network and Dynamic Time Warping Algorithms, 4 th International Conference on Computing and Informatics, ICOCI, August 2013
7. Gharehchopogh, F. S. , &Khalifehlou, Z. A. (2012), A New Approach in Software Cost Estimation Using Regression Based Classifier, AWERProcedia Information Technology and Computer Science, Vol 2, pp. 252-256.
8. Draper, N. R. , Smith, H. , &Pownell, E. (1966),Applied regression analysis, Vol 3, New York: Wiley
9. Gharehchopogh, Bonab, Khaze, A Linear Regression Approach To Prediction Of Stock Market Trading Volume: A Case Study, International Journal of Managing Value and Supply Chains (IJMVSC) Vol. 4, No. 3, September 2013
10. Rencher, Alvin C. ; Christensen, William F. (2012), Chapter 10, Multivariate regression – Section 10. 1, Introduction, Methods of Multivariate Analysis, Wiley Series in Probability and Statistics 709 (3rd ed. ), John Wiley & Sons, p. 19,ISBN 9781118391679.
11. Hayes, M. (2002). Statistical Digital Signal Processing and Modeling,Pg 499, Singapore: John Wiley & Sons (Asia) Pte. Ltd.
12. To establish notation for future use, we'll use x (i) to denote the "input" variables Andrew Ng, CS229 Lecture notes,pp 2-22
13. C. Sun, Stock Market Returns Predictability: Does Volatility Matter?, QMSS Master Thesis, Columbia University, Feb 2008

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