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Measuring the Efficiency of State-Owned Stocks Using Capital Asset Pricing Model (CAPM) I Kadek Bellyoni Dwijaya; Muhammad Yunus Kasim; Sri Dewi Fitrianingsih
Jurnal Nusantara Aplikasi Manajemen Bisnis Vol 9 No 1 (2024): Jurnal Nusantara Aplikasi Manajemen Bisnis
Publisher : UNIVERSITAS NUSANTARA PGRI KEDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/nusamba.v9i1.21162

Abstract

Abstract Research aim : This study examines market efficiency using the Capital Asset Pricing Model (CAPM) method on BUMN stocks in Indonesia. Design/Methode/Approach : This model explains the relationship between risk and return in an efficient market. The analysis focuses on using stock beta calculations for 5 years. Research Finding : The research results are as follows: 1) the BUMN stock market in Indonesia is classified as efficient, namely 11 stocks where 5 companies have positive individual returns, namely ANTM, TINS, KRAS, BBNI, and PGAS. There are 6 companies that have negative returns, namely WSKT, ADHI, WIKA, PTPP, SMBR, and BBTN. 2) the analysis was carried out during the covid-19 pandemic period, the basic material sector is a collection of stocks that are classified as resilient during the pandemic showing positive and efficient returns. 3) overall, there is a high systematic risk of BUMN stocks in Indonesia, and actively responds to any changes that occur in market prices. Theoretical contribution/Originality : This contribution is expected to be a reference for investors and market participants. Practitionel/Policy implication : These results provide information for investors to choose efficient State-Owned Enterprises (BUMN) stocks in deciding to invest and still consider the nature and characteristics of each investor. Research limitation : Future research is expected to use other methods in analyzing the level of stock risk such as Arbitrage Pricing Theory (APT) or estimating future stock prices using the ARIMA / GarCH forecasting method and increasing the number of samples in the study or different market indices so that the expected results are more accurate in predicting future stock prices.