Olokoyo, F., Taslim, G., Nwankwo, A., & Adedoyin, B. A. (2023). Optimizing Stock Portfolio using Multi-Objectives Mathematical model. Preprints. https://doi.org/10.20944/preprints202304.0399.v1
Chicago/Turabian Style
Olokoyo, F., Alexius Nwankwo and Bunmi-Alo Adedoyin. 2023 "Optimizing Stock Portfolio using Multi-Objectives Mathematical model" Preprints. https://doi.org/10.20944/preprints202304.0399.v1
Abstract
The method propose in this study for stock market multi-objective mathematical model was linear programming using simplex algorithms to optimize the portfolio in the Nigerian Stock Market. The study selected five banks from the list of operators in the market and data were gathered from the banks to have individual market performances over a period of 5years. The data collected contains: Geometric Mean of Monthly Capital Gain Yields and Annual Capital Gain Yields from which Dividend Yields (%) were deducted to arrive Actual rate of return for the banks and the expected rate of return. Furthermore, the risk of semi-absolute deviation below the expected return is reduced. The data was analysed using Python programming because of some clauses in the data gathered. At first, the data assumes integer (-or +) and random in nature. As such, Python programming is one of the software suitable for such solution since the barrier for additivity is broken. Based on the analysis, the study therefore conclude that the potential investor(s) should invest in 13 units of investment x(GTbank Plc), 3 units of First Bank Plc`s Investment (y), 450 units of Zenith bank`s investment (z), and 8 units of Wema bank`s investment (m), and no units of investment of (n) Access bank. These investment quantities will result in the Optimal profit of p= 12797.902 billion naira.
Keywords
Multi-objective Model; Annual Rate of returns; Expected Rate of returns; Dividend Yields; Capital gain
Subject
Business, Economics and Management, Finance
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.