The Influence of Markov Chain and Properties of Principal Component Solutions in The Analysis of Share Price Movements for Stock Market

Abstract

The stock market performance and operation has been widely recognized as a significantly viable investment field in financial markets. Therefore, this paper studied stochastic analysis of Markov chain and PCA in the closing share price data of Access and Fidelity banks (2016-2022) through Nigeria Stock Exchange. The share prices were transformed into 3-steps transition probability matrix solution to cover this number of years. The striking focus of this is on two merged banks where their transition probability matrix was considered.  The future share prices changes were known. The criteria of obtaining four share prices which formed from the two merged banks 2x2 matrices were given and analytical solution of principal component were considered for future stock price changes. From the solution matrix of two merged banks showed that the has the best probability of price increasing in the near future: 12%, best probability of reducing in future by 22% and best probability of no-change in the near future 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