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SpaeSoft Managerial risk assessment services

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SpaeSoftManagerial risk assessment services

SpaeSoft delivers high precision software for

- strategic firm planning and risk assessment,
- investment analysis,
- forecasting,
- classification and
- optimization problems in
- corporate expert systems,
- decision support systems,
- logistics,
- sales management and
- production scheduling systems.

- SpaeSoft applies techniques of Artificial Intelligence (AI) combined with classical optimization methodology
- Computationally demanding simulation tasks (e.g., lead-lag orders in forecasting systems, structural decisions concerning neural networks, etc) may be automatized using AI.

- Risk assessment of the firm as a multiperiod decision system
- Measuring the risk of individual investments
- Forecasting performance of financial instruments and portfolio optimization
- Classifying objects in nonoverlapping groups (e.g., quality control in industrial processes: working/failing products; banking: healthy/financially distressed firms)
-> Possibilities to significant cost reductions through, e.g.

improved production scheduling, management of logistics and explicit recognition of risk in investment decisions.

- The financial statements, firm decisions and parameters for a planning horizon form an intertwined unity, a scenario:

where

xt = the key decisions of the firm in period (year) t (sales,

production, R & D, investments, financing, etc),

pt = all relevant parameters controlling the operating environment

of the firm (prices, costs, interest rates, exhange rates, chocks,

economic regulation, legislation, etc),

F = the vector function generating all financial statements and financial ratios according to strict accounting logic,

H = planning horizon

The anatomy of the scenario

P/Lt=profit and loss statement for year t

BSt=balance sheet

CFt=cash flow statement

FRt=financial ratios

- Assume that management has specified a strategy using human reasoning, high performance optimization technology or a combination of both, for example over the period 2008-2012:

- We want to assess the risk surface pertaining to this strategy when the unknown parameters (pt) are stochastic.
- The probability distributions are assumed to be mutually independent, multivariate dependence is recognized within the distributions, i.e. within subsets of the parameters.
- The distributions are extracted by statistical methods combined with human reasoning.
- With pt stochastic, the scenarios as a whole will be stochastic as well:

- Risk assessment is carried out through Monte Carlo Analysis, a well-known and robust simulation technique.
- A large number (M) of drawings of the uncertain ptare made from its underlying univariate/multivariate distributions
- wages, sales prices, R & D, investment costs, raw material costs, interest rates, exchange rates, delivery times, manufacturing times, credit risk, machine failure, etc.

- The scenarios will be approximately normally distributed by the central limit theorem under fairly general conditions.
- The distribution of the simulated scenarios forms the estimated risk surface of the planned decisions.

M random drawings of the uncertain pt from the underlying distributions and the resulting scenarios:

Multiperiod firm planning cont.

A suitable financial criterion, for example, a critical minimum level on the return on investment â€“ ROIc (or EBIT, Cash Flows, discounted Net Profit, etc) - may be used as a criterion for accepting/rejecting the strategy:

- The quantitative risk profile can be obtained
- for any item or all items simultaneously in the financial statements
- over any planning horizon

- The method is connectable to the technological/economic processes of the firm
- R & D, investments, logistics, production processes, pricing decisions, marketing campaigns, etc.

- Input:
- The managerial strategy based on
- Human reasoning
- Multiperiod optimization

- Accounting logic:
- Financial statements,
- Uncertain parameters (prices, wages, interest rates, etc)

- Monte Carlo Analysis:
- Probability distributions
- (statistical/judgemental)
- Large-scale simulations

Output:

The risk profile of all key financial items

- In this case the scenario represents the investment project.
- xtcontains the profile of the planned investment, e.g.
xt(i) = the number of units of investment object i acquired at time point t.

- pt = {ct+, ct-, rt} contains all relevant parameters, e.g. ct+(i) & ct-(i) = cash inflows & outflows per unit of investment object i at time point t, {rt(j), rt(k), rt(m)} = {the interest rate, the cost of equity, the weighted cost of capital (WACC)} at time point t, etc.
- Fis the evaluation function for the investment, in the simplest case the net present value formula (NPV).

- The probability of the project falling below the critical level NPVc is obtained by Monte Carlo simulation.

- Some powerful results concerning the central limit theorem extend it to cases with finite variance and non-identical distributions as well as non-independent random numbers (Lindeberg [1922], Doukhan et al [1994]).
- The method is used extensively in engineering, statistics, numerical mathematics and computational finance (e.g., Boyle, P (1977): A Monte Carlo Approach, Journal of Financial Economics, Hull J,C (2000): Options, futures and other derivatives (4th ed.)).
- It is used scarcely in the Scandinavian accounting profession eventhough it has an obvious potential in risk assessment for financial planning and investment problems.

- Spaesoft has its own platform for mathematical analysis, GHA, written in strict ANSI C and Fortran 90
- Useful for High-Performance computing on massively parallel supercomputers as well as on individual work stations/main frame computers

- SpaeSoft modules can be integrated into the corporate software.
- Availability: UNIX, Alpha & LINUX mainframe computers and massively parallel IBM and Cray supercomputers.
- SpaeSoft provides consultancy to the customer in model building and data gathering within the organization.

- Doukhan P, Massart P, Rio E (1994): The functional central limit theorem for strongly mixing processes. Annales de lâ€™I.H.P., section B, tome 30, no 1, 63-82
- Lindeberg J.W (1922): Eine neue Herleitung des Exponentialgesetzes in der Wahrscheinlichkeitsrechnung. Mathematische Zeitschrift 15, 211-225.
- Ã–stermark, R. and K. SÃ¶derlund (1999): A multiperiod firm model for strategic decision support, Kybernetes28:5, pp. 538-556.
- Ã–stermark, R., H. Skrifvars, and T. Westerlund (2000): A nonlinear mixed integer multiperiod firm model, International Journal of Production Economics67, pp. 183-199.
- Ã–stermark, R. (2003): A multipurpose parallel Genetic Hybrid Algorithm for nonlinear nonconvex programming problems. European Journal of Operational Research,152, 195-214.
- Ã–stermark, R., J. Aaltonen, H. SaxÃ©n, and K. SÃ¶derlund (2004): Nonlinear modelling of the Finnish banking and finance branch index, European Journal of Finance10, pp. 277-289.
- Ã–stermark, R. (2007): A flexible platform for mixed-integer non-linear programming problems. Kybernetes. The International Journal of Systems and Cybernetics.36, No 5/6, pp. 652-670.