Portofolio Management with Markowitz Model to Determine Optimal Investment Values

https://doi.org/10.46336/ijmsc.v2i3.120

Authors

  • Daniel Victorio Rudolfo Purba
  • Emir Shiddiq Fasya Department of Mathematics, Faculty of Mathematics and Natural Sciences, National University of the Republic of Indonesia, Bandung, Indonesia

Keywords:

Mathematics, Markowitz Model, Stock, Optimal Investment, Portofolio

Abstract

Investment is a commitment to a number of funds or other resources made at this time with the aim of obtaining a number of benefits in the future. Investments can be channeled through several instruments, such as deposits, gold, property, stocks, and many more. With the various types of investments that exist today, the difficulty of finding an investment cannot be an obstacle. In investment, the appropriate allocation of funds can be an important factor in obtaining profits. By using the correct method, the risk factors that may occur can be minimized as well as possible. The method that can be used in determining funds in investment activities is using the Markowitz Model. Then, the  author  initiates  a method  for  optimizing  assets  using  the  Markowitz  method.  In  this  research,  used  data  from  10  stocks  in Indonesia PANI, CLEO, DSSA, UNIC, ADRO, CITA, CAKK, TPIA, MYOR, ANTM. Then, the stocks will be arranged optimally portfolio. The optimal investment weight obtained for stocks from January 1, 2019 to December 31, 2023 using the Markowitz model, each share weighting namely 7,6% PANI, 18,05% CLEO, 8,48% DSSA, 16,902% UNIC, 12,471% ADRO, 10,496% CITA, 7,246% CAKK, 12,946% TPIA, 5,877% MYOR, and 0,464% ANTM company stocks and provide a portfolio ratio of 5.694581 which can also be interpreted that the optimal return ratio profit is 5.694581 times greater than the possible loss or portfolio variance.

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Published

2024-08-02

How to Cite

Purba, D. V. R., & Fasya, E. S. (2024). Portofolio Management with Markowitz Model to Determine Optimal Investment Values. International Journal of Mathematics, Statistics, and Computing, 2(3), 102–108. https://doi.org/10.46336/ijmsc.v2i3.120