Stock Making Investment Decisions Using the Capital Asset Pricing Model (CAPM) Analysis of the Business Index-27 on the Indonesian Stock Exchange

https://doi.org/10.46336/ijmsc.v2i3.119

Authors

  • Veronica Clasrissa Putrie
  • Himda Anataya Nurdyah Department of Mathematics, Faculty of Mathematics and Natural Sciences, National University of the Republic of Indonesia, Bandung, Indonesia

Keywords:

Capital Asset Princing Model (CPAM), Stock, Return, Business-27 Index

Abstract

The purpose of this study is to measure the ability of the Capital Asset Princing Model (CPAM) in analyzing investment decision making by predicting the risk and return that will be obtained by investors and helping investors in choosing efficient and inefficient stocks. CAPM is a measuring tool that can be used to determine the level of risk and return obtained and evaluate the rate of return on investment. The purposive sampling technique is used in selecting samples to be used in the study, namely companies listed on the Indonesia Stock Exchange and their shares are consistently included in the Bisnis-27 stock index. The stock criteria used in selecting efficient stocks, namely when individual return results exceed the expected return. The results of this study indicate that there are 8 stocks that meet the efficiency criteria, namely ADRP, AKRA, AMRT, BBNI, BMRI, INKP, JSMR, and PGAS with individual returns results exceed the expected returns. In investment decisions, stocks that are included in these efficient stocks are the priority stocks that investors should buy. Based on the analysis, there is a non-linear relationship between systematic risk and expected stock returns, making an important contribution to investment decision making in the Indonesian stock market.

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Published

2024-08-02

How to Cite

Putrie, V. C., & Anataya Nurdyah, H. (2024). Stock Making Investment Decisions Using the Capital Asset Pricing Model (CAPM) Analysis of the Business Index-27 on the Indonesian Stock Exchange. International Journal of Mathematics, Statistics, and Computing, 2(3), 95–101. https://doi.org/10.46336/ijmsc.v2i3.119