DEVELOPMENT OF EXPECTED MONETARY VALUE USING BINOMIAL STATE PRICE IN DETERMINING STOCK INVESTMENT DECISIONS

  • Giovanny Theotista Data Science Study Program, Faculty of Technology and Design, Universitas Bunda Mulia, Indonesia
  • Margareta Febe Accounting Study Program, Faculty of Social and Humaniora, Universitas Bunda Mulia, Indonesia
  • Yvone Marshelly Accounting Study Program, Faculty of Social and Humaniora, Universitas Bunda Mulia, Indonesia
DOI: https://doi.org/10.30598/barekengvol17iss3pp1703-1712
Keywords: Expected Monetary Value, Binomial State Price, Stock

Abstract

Stock investment is an investment opportunity. This stock investment carries relatively high risk and therefore requires additional analysis to minimize losses and maximize profits. Expected Monetary Value (EMV) is a simple modeling method for estimating the value of an investment that will provide the greatest future return. The expected monetary value (EMV) method involves multiplying the total value of each scenario by the probability of that scenario occurring. However this method has weaknesses in terms of how many cases occur what is the value of each case and what is the probability of each case occurring. Binomial State Price is a method commonly used to calculate stock options and real options but includes the step of modeling the value of an investment in many situations and opportunities that arise in the future. In this paper, our objective is to develop the EMV method with the binomial state pricing model to determine the investment that offers the most favorable payoff. In short, we can develop the expected monetary value (EMV) method and the binomial state pricing model. It was found that this model always recommends stocks which have high dividens.

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Published
2023-09-30
How to Cite
[1]
G. Theotista, M. Febe, and Y. Marshelly, “DEVELOPMENT OF EXPECTED MONETARY VALUE USING BINOMIAL STATE PRICE IN DETERMINING STOCK INVESTMENT DECISIONS”, BAREKENG: J. Math. & App., vol. 17, no. 3, pp. 1703-1712, Sep. 2023.
Issue
Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Section
Articles

Copyright (c) 2023 Giovanny Theotista, Margareta Febe, Yvone Marshelly

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