The Influence of Markov Chain and Properties of Principal Component Solutions in the Analysis of Share Price Movements for Stock Market
DOI:
https://doi.org/10.38142/jebd.v1i4.131Keywords:
Stochastic Analysis, Markov Chain, Capitalization, Transition MatrixAbstract
Purpose:
Stock market performance and operation have been widely recognized as a viable investment field in financial markets. Therefore, this paper studied the stochastic analysis of the Markov chain and PCA in the closing share price data of Access and Fidelity banks (2016-2022) through the Nigeria Stock Exchange. The share prices were transformed into a 3-step transition probability matrix solution to cover this number of years.
Methodology:
This research uses the Markov chain method, which defines a stochastic process. Mathematically, a stochastic process can be defined as a collection of random variables ordered in time and determined at a series of time points that may be continuous or discrete.
Findings:
The criteria for obtaining four share prices formed from the two merged banks' 2x2 matrices were given, and analytical solutions of principal components were considered for future stock price changes.
Implication:
The solution matrix of the two merged banks showed that they have the best probability of price increasing shortly: 12%, the best probability of reducing in the future by 22%, and the best probability of no-change shortly by 21%, which is a tool for proper decision making in the day-to-day management of the bank; which shows it is profit making organization and are hopeful for future investment plans both short or long term respectively.
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Copyright (c) 2023 Dagogo Allen Wokoma BISHOP, Innocent Uchenna AMADI, George ISOBEYE
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Creative Commons Attribution-NonCommercial 4.0 International License.
