Portfolio theory for assessing investment programs

Portfolio Theory for the assessment of investment programs Theory about the optimal mix of securities, first quantified by Markowitz in 1952. The starting point is an amount available for investment purposes that the investor wants to invest in securities for a period. The decisive earnings figure for him is the assets available at the end of the period. The model is based on the assumptions

1) a perfect capital market,
2) any divisibility of all securities as well as
3) a risk-averse decision maker who follows the (p, a) principle. This means that the decider is aiming for a high expected return (p) and a low standard deviation (a).
The aim of the risk-averse investor is now to create a so-called risk-efficient portfolio. A portfolio is risk-efficient if it is not dominated by any other portfolio in terms of p and a, ie there is no other portfolio which has a higher return with the same risk or a low risk with the same return. When composing the efficient portfolios, it must be taken into account that there is a more or less strong balance between the risks of the individual projects included in the program (diversification). From the number of efficient portfolios, the investor then selects the optimal (maximum benefit) portfolio for him, depending on his subjective risk preferences.

If the portfolio optimization model is expanded to include a risk-free investment or debt option, each investor creates a portfolio of uncertain securities with an identical structure, which he combines with the risk-free investment or debt, depending on his degree of risk aversion. The composition of the risky securities portfolio is in particular independent of the initial assets and the degree of risk aversion of the investor. This statement, known as the separation theorem, forms the basis for the transition from the individual portfolio decision to the capital asset pricing model as an equilibrium model for pricing on the Capital market.
The approach of the portfolio theory can in principle also be transferred in the model to the planning of property, plant and equipment investment programs (investment program planning). Only investment projects are considered that are associated with a disbursement at time 0 and that are expected to make a payment in an uncertain amount at time 1. The selection of the projects should be made in such a way that the highest possible final assets are achieved with the available capital amount. If the investor is risk-averse and based on the (p, a) principle, it is also possible to determine a risk-efficient investment program for investments in tangible assets. It should be noted, however, that these are not arbitrarily divisible, so that the separation theorem does not apply in this case.

However, it should be noted critically that the portfolio theory is a model that only takes two points in time into account; however, investments in property, plant and equipment have an impact over several periods and make payments in each period that are uncertain at the time of planning. The expansion of the model to more than two points in time is not possible without further ado, because then the investments at later points in time, depending on the change in the level of information that has occurred up to then, would have to be taken into account.

Difficulties in the practical application also arise from the need for information (investment controlling, information basis of the), since only subjective estimates exist for the determination of expected values, variances and correlation coefficients and in addition to the expected values and variances of the future payments for n projects also n (n- 1) / 2 covariances are required.

Even if the model does not appear to be very suitable for direct practical application in assessing property, plant and equipment investment programs, it nevertheless makes it possible to clarify the essential relationships. It is shown that each partial model can only provide approximate solutions in the sense of the inevitable reduction in complexity.

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