This paper attempts to apply Markov chain model to forecast the behaviour of Tata motors share indexed in Bombay stock exchange. Bombay stock exchange is a trading platform of shares in India. The stock market is attractive platform for investment, it is considered that both foreign and local investors will seize the opportunity and invest in the stock market. An understanding of the stock market trend in terms of predicting price movements is important for investment decisions. Markov chain model has been widely applied in predicting stock market trend. In many applications, it has been applied in predicting stock index for a group of stock but little has been done for a single stock. The overall objective of this study therefore, is to apply Markov chain to model and forecast trend. The study was conducted through 2 years historical data of Tata motors shares daily closing price. Secondary quantitative data on the daily closing share prices was obtained from Bombay stock exchange website over a period covering 1st January 2019 to 31st January 2022 forming a 765 days trading data panel. A Markov chain model has been determined based on probability transition matrix and initial state vector. MATLAB software is used to determine initial state vector, transition matrix and trend values using moving average. In the long run, irrespective of the current state of share price, the model predicted that the Tata motors share prices would increase, decrease and remains constant with a probability of 0.5288, 0.4699 and 0.0013, respectively.

Markov chain, transition matrix, initial state vector, MATLAB, moving average.

Received: September 22, 2022; Revised: November 9, 2022; Accepted: November 15, 2022; Published: November 29, 2022

How to cite this article: Kriti Verma and Ashish Kumar Jha, Forecasting stock trend of Tata Motors shares using Markov chain, Advances in Probability, Stochastic Processes and Applied Statistics 1(1) (2022), 37-44. DOI: 10.17654/PAS2022004

This Open Access Article is Licensed under Creative Commons Attribution 4.0 International License

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