Abstract
Volatility and randomness of stock prices makes them risky investments and
investors need a lot of information to make capital gains on their investments.
With the objective to cover the research gap of having limited application of
Markov chains to share prices traded at PSX and to aid investors in their
decision making, this paper assuming share volatility as a stochastic process
with Markovian property. Hence, this study attempts to propose a first order,
time homogenous Markov chain model for trend prediction of HBL closing
share prices on and between 24-9-2007 and 20-02-2015 i.e.1723 trading
days. The paper also has attempted to evaluate the accuracy of the prediction
of Markov chain model. To this effect, the study has derived a transition
probability matrix with three states i.e. (Decrease, Unchanged, and Increase)
as well as determine the state probabilities for the HBL share prices followed
by comparison to the actual share prices to evaluate the prediction accuracy
of the first order Markov chain model. Furthermore, an attempt has also
been made to estimate the long run steady state behavior of the share prices
of the HBL and the expected return time for the stock. The methodology is
applied to the daily share prices using MS excel and R.
Qurat-ul-Ain Sultan, Kaneez Fatima, Jameel Ahmed. (2019) Application of Markov Chain to Model and Predict Share Price Movements: A Study of HBL Share Price Movements in Pakistan’s Stock Market, Balochistan Review, Volume XL, Issue 1.
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