.svg){kind=logo}
PREDICTIVE MODELING FOR SHARE CLOSING PRICES THROUGH HIDDEN MARKOV MODELS WITH A SPECIAL REFERENCE TO THE NATIONAL STOCK EXCHANGE
Keywords:
hidden Markov model, share price, probability distribution, national stock exchange, transition probabilityDOI:
https://doi.org/10.17654/0972361724036Abstract
Predicting share prices is crucial in financial markets for traders and portfolio managers. This study employs hidden Markov modeling, focusing on three key parameters: initial probability vector (IPV), transition probability matrix (TPM), and emission/observed probability matrix (EPM/OPM). TPM and EPM are derived by considering both hidden and emission states. Probability distributions are formulated for increment, remain same, and decrement states. The model’s behaviour is explored through statistical measures and Pearson’s coefficients. This model aids in estimating stock price movements, and long-term and short-term returns, and can be compared with the capital asset pricing model (CAPM). Numerical illustrations are used for clarity, and model goodness of fit is assessed with the chi-square test. Developing user-friendly digital interfaces can enhance traders’ understanding of Wipro’s stock market behaviour in the Indian context.
Received: September 1, 2023
Revised: March 30, 2024
Accepted: April 13, 2024
References
L. Bachelier, Theoriedela Speculation, Annales Scientgiques del, Ecole Normale Sperieure 3 (1990), 21-86.
S. N. Neftci, Naive trading rules in financial markets and Wiener-Kolmogorov prediction theory: a study of technical analysis, Journal of Business 64(4) (1991), 549-571.
F. Black and M. Scholes, The pricing of options and corporate liabilities, Journal of Political Economy 81(3) (1973), 637-654.
T. R. Padi and C. Gudala, Linear birth, death and migration processes for portfolio management modeling, IOSR Journal of Mathematics (IOSR-JM) 11 (2015), 29 36.
M. R. Hassan, B. Nath and M. Kirley, A fusion model of HMM, ANN and GA for stock market forecasting, Expert Systems with Applications 33(1) (2007), 171 180.
S. Rao and J. Hong, Analysis of Hidden Markov Models and Support Vector Machines in Financial Applications, University of California at Berkeley, 2010.
I. Adesokan, P. Ngare and A. Kilishi, Analyzing expected returns of a stock using the Markov chain model and the capital asset pricing model, Appl. Math. Sci. 11 (2017), 2777-2788.
Z. Su and B. Yi, Research on HMM-based efficient stock price prediction, Mobile Information Systems Volume 2022, Article ID 8124149.
G. F. Dar, T. R. Padi and S. Rekha, Stock price prediction using a Markov chain model: a study for TCS share values, Adv. Appl. Stat. 80 (2022), 83-101.
T. R. Padi, G. F. Dar and S. Rekha, Markov modelling of Indian stock market prices with reference to state bank of India, International Journal of Statistics and Reliability Engineering 9(3) (2022), 481-492.
G. F. Dar, T. R. Padi, S. Rekha and Q. F. Dar, Stochastic modelling for the analysis and forecasting of stock market trend using hidden Markov model, Asian Journal of Probability and Statistics 18(1) (2022), 43-56.
S. B. Kilic, Estimating probability of session returns for Istanbul Stock Exchange 100 index as Markov chain process, Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 22(1) (2013), 12-20.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Pushpa Publishing House, Prayagraj, India
This work is licensed under a Creative Commons Attribution 4.0 International License.
____________________________
Attribution: Credit Pushpa Publishing House as the original publisher, including title and author(s) if applicable.
No Derivatives: Modifying or creating derivative works not allowed without written permission.
Contact Pushpa Publishing House for more info or permissions.
Journal Impact Factor: 
