Abstract: 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.
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Keywords and phrases: hidden Markov model, share price, probability distribution, national stock exchange, transition probability.
Received: September 1, 2023; Revised: March 30, 2024; Accepted: April 13, 2024; Published: April 25, 2024
How to cite this article: Tirupathi Rao Padi, Sarode Rekha and Gulbadin Farooq Dar, Predictive modeling for share closing prices through hidden Markov models with a special reference to the national stock exchange, Advances and Applications in Statistics 91(6) (2024), 673-697. https://doi.org/10.17654/0972361724036
This Open Access Article is Licensed under Creative Commons Attribution 4.0 International License
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