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The above methods of assessment are based on different methodological and mathematical approaches; therefore, they yield slightly different results, which can be used to complement each other and thus gain a more complex picture about the risk of the investment. Calculating break-even points and sensitivity assessment are simple variance calculation methods with differing frameworks or backgrounds. The basis of decision is NPV, as the break even points of the various factors do not come complete with clearly defined rules and cut-off points for accepting or rejecting an investment. This is especially true for the dynamic payback period, which discriminates against long-term investments. Conclusions about the risk of the investment can be drawn if expected values and critical values show high disparity; this can be used for highlighting the more sensitive risk factors. However, in this case, we do not have a distribution of probability for factors, nor the probability of the occurrence of the critical values, so the use of data is limited. It is worth starting risk assessment with these simple methods of analysis to gain more insight into the underlying processes. If historic simulation is applied, we gain data for analysis from past figures. The basis for this procedure is sufficient quantity and quality data, which might pose considerable difficulties. On one hand, in the case of Hungary, the recent economic changes break the continuity of the data; on the other hand, it is often almost impossible to collect the necessary minimum of 50-100 figures for a realistic simulation generating the distribution of NPV. Setting the optimal time framework is also crucial: if data reach far back in the past, their applicability for present risk assessment is questionable. If the framework spans too short a period, the representativeness of the figures may be doubted. A frequent criticism of the method questions the approach that using real data from the past implies that the events in the past are likely to repeat, and new patterns are not expected to emerge. On the other hand, an advantage of the method is that it can be considered a complex calculation of variance that enables the analyst to assess possible future scenarios without having to define each and every expectation and prospect for the future. A wide range of mathematical skills is not necessary; therefore, it is easy to use, and the results are readily applicable, and expand on the results gained from more simple calculations aimed at assessing the risk of investments. All the above methods make an attempt at grabbing and describing risk itself. However, they simply cannot consider that investors may have different approaches to risk, which has a crucial impact on the decision about the investment. Investors expect benefits and positive outcomes proportionate to the risk undertaken; as a result, they have different sets of priorities for future cash flow. Although this factor does not appear in any of the models, using multiple models for an analysis can provide a wider range of information to be considered for assessing the profitability of an investment.


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