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On the Effect of Capital Asset Pricing Model on Precious Metals and Crude Oil Investments
Control Science and Engineering
Volume 2, Issue 2, December 2018, Pages: 66-70
Received: Nov. 28, 2018; Accepted: Jan. 2, 2019; Published: Feb. 18, 2019
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Oluwasegun Micheal Ibrahim, Department of Mathematical Sciences, African Institute for Mathematical Sciences, Kigali, Rwanda; Department of Mathematical Sciences, Adekunle Ajasin University, Akungba-Akoko, Nigeria
Dare Jayeola, Department of Mathematical Sciences, Adekunle Ajasin University, Akungba-Akoko, Nigeria
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Capital asset pricing model (CAPM) is a useful technique in portfolio management theory (PMT), it is based on a class of risk; the systematic risk associated with the fluctuation of security price that cannot be diversified away. Beta (β) is the measure of the systematic risk, which has a positive correlation with the expected return. Consequently, the investors’ aim is to make an optimal choice that will lead to the minimization of risk and maximization of return. To achieve this aim, standard theoretical and computational procedures must be followed. One way of doing this is to construct and analyze models capable of effectively minimizing risk, and proffer suggestions that would improve the return on investment. This paper investigates the relationship between risk and expected returns for investing in Precious metals and crude oil for five consecutive years: 2012 to 2016, using the CAPM. Two striking results were obtained from this research as control mechanisms for potential investors. First, it is revealed that the higher the value of (risk), the higher the expected returns for investing in Precious metals and crude oil. Second, the lower the risk associated with the Precious metal and crude oil’s investment, the lower the expected returns.
Beta Coefficient, Risk, Returns, Asset Pricing, Portfolio Management
To cite this article
Oluwasegun Micheal Ibrahim, Dare Jayeola, On the Effect of Capital Asset Pricing Model on Precious Metals and Crude Oil Investments, Control Science and Engineering. Vol. 2, No. 2, 2018, pp. 66-70. doi: 10.11648/j.cse.20180202.11
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