ANALISIS FAKTOR-FAKTOR YANG BERPENGARUH TERHADAP BETA SAHAM PERUSAHAAN (Studi Empiris Pada Perusahaan Yang Tercatat Dalam Indeks Kompas100 di Bursa Efek Indonesia 2007 – 2010)
| Main Authors: | PRAKOSA, Agung Budi, HARYANTO , A Mulyo |
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| Format: | Thesis NonPeerReviewed application/pdf |
| Terbitan: |
, 2012
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| Subjects: | |
| Online Access: |
http://eprints.undip.ac.id/35576/1/Skripsi_PRAKOSO.pdf http://eprints.undip.ac.id/35576/ |
Daftar Isi:
- This study investigated the influence of fundamental variables of the company's stock beta. Because when investing in securities specifically common stock, investors face various risks, one of them is systematic risk. The systematic risk is measured by beta and each firm has a beta different from that of other firms The purpose of this research is to analyse: factors (asset growth, total asset turnover, firm size, financial leverage, operating leverage) affecting the risk (measured by stock beta) for 4 years, from 2007 until 2010; how asset growth, total asset turnover, firm size, financial leverage, operating leverage affect the stock beta of firms (partial and simultaneous) that listed in Kompas100 index; and what factor which has a dominant influence to dependent variable (stock beta). This research covers 32 companies which consistently included in Kompas100 index of the period 2007 – 2010 and used a purposive sampling. Data was analysed using multiple regression to test the effect of independent variables on dependent variable. F-test and t-test was conducted to test the hypothesis with 5% level of significant. Theoretical concepts used in this study is the concept of risk that is systematic risk. The result of investigation showed that not all independent variables influence significantly on stock beta. Three independent variables namely firm size (-), financial leverage (+), and operating leverage (-) which influence significant on systematic risk (beta). Asset Growth and total asset turnover did not affect significantly the stock beta. Lastly, the evidence shows that the prediction power of regression model is 22,5 %.