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An introduction to mutual fund separation theorem

Mutual fund separation theorem refers to a subset of portfolio theory. It is a proven theorem that states any investor’s ideal portfolio can assemble by holding the particular mutual fund in opposite ratios. This fact is bound by the condition that the numbers of mutual funds are lesser than the numbers of individual assets in the portfolio. By mutual fund separation theorem homework help, our objective is the quantified yardstick portfolio of the assets that are available.

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Advantages of the theorem

Mutual fund separation theorem or simply mutual fund theorem is a well-organized tool for financial analysis and it has been recognized with the following disadvantages:

  • The applicable state of affairs can be thought of being true from an empirical perspective and then one can jump into the inferences that are needed for the functioning of assets, and conclude if they are true or false.
  • It would be easier for any investor to purchase smaller numbers of mutual funds than the number of individual assets based on certain criteria that need to be met.

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Criticism to the theorem

Mutual fund theorem makes use of the mean-variance framework to analyze a portfolio. It also lays down some verification that shows how diversification helps in reduction of risk factors involved. The theorem has often been subjected to criticisms.

The mutual fund separation theorem homework help provides a clearer picture of the same. Basically, they cannot be applied to markets that lack in limpidity, and also there are absence in transaction costs.

More about the theorem

Hyperbolic Absolute Risk Aversion, which is abbreviated as HARA, is a topic that students will frequently come across during course study. When opting for mutual fund separation theorem assignment help, you will get to know about it properly.

It is a methodology that makes the use of logarithmic function, power utility function and also exponential utility function. With this approach, we can calculate risk analysis with mean-variance analysis.

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