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A weak point of risk management in Russian agro-industrial complex is that the demand for information does not commonly meet the sufficient supply. This feature is of dualistic nature. On one hand, risk management applications of the data available from regular accounting and statistics, if possible at all, often require either developing original approaches or even special dedicated research. On the other hand, as a risk manager has made a decision on data sets to use, a lack of algorithms and, a fortiori, software that would suit to these specific data becomes evident.

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The scope of the study presented in this paper is the above mentioned weakness, limited to the case of making decisions aimed at optimizing investors' risk in order to make a specific investment area (agro-industrial complex in our case) more attractive. The toolset we develop allows a risk manager to satisfy a substantial part of their information demand using readily available data and uniform algorithms.

where p e P, k e K f e F, Kp is a set of time periods that covers the lifetime of projectp; F is a set of cash flow components, which consists of 1 for revenues, 2 for material costs, 3 for managerial costs, 4 for labor costs and 5 for taxes; xpjk is a value offth component of the cash flow generated by projectp in the period k; P is a set of projects under consideration.

We presume gamma distribution of managerial costs, labor costs and taxes. Assuming this distribution law for the remaining two components of cash flows causes computational problems, as it often happens with gamma distributions having low asymmetry. The reason is large magnitude of / in probability density of gamma distribution B"

Within the frameworks of cash flow simulation we express risk in the probability of financial failure of a project, i.e. the probability of negative project NPV. Information on this probability is required by the project risk manager. Information demand due to the research aimed at political advice concerning investment attractiveness can be effectively satisfied by the explicit relation between governmental support and this probability. To satisfy this demand, we need to account for the probability of negative NPV in presence of such support with respect to its specific rule(s). In particular, if this probability is targeted, the simulation would serve to determine the amount of funds that, given the specific rule of support, provides exactly T04 cases of negative NPV of ten thousand, where is the targeted probability of negative NPV. e24fc04721