SpaeSoft Managerial risk assessment services

1. SpaeSoftManagerial risk assessment services

2. Software 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.

3. The methodological basis • 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.

4. Target systems in risk assessment • 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.

5. Multiperiod firm planning • 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

6. Multiperiod firm planning cont. The anatomy of the scenario P/Lt=profit and loss statement for year t BSt=balance sheet CFt=cash flow statement FRt=financial ratios

7. Multiperiod firm planning cont. • 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:

8. Multiperiod firm planning cont. • 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.

9. M random drawings of the uncertain pt from the underlying distributions and the resulting scenarios: Multiperiod firm planning cont.

10. Multiperiod firm planning cont. A suitable financial criterion, for example, a critical minimum level on the return on investment – ROIc (or EBIT, Cash Flows, discounted Net Profit, etc) - may be used as a criterion for accepting/rejecting the strategy:

11. Multiperiod firm planning cont. • 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.

12. Multiperiod firm planning: a summary • 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

13. Risk and project appraisal • 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).

14. Risk and project appraisal • The probability of the project falling below the critical level NPVc is obtained by Monte Carlo simulation.

15. Monte Carlo Analysis • 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.

16. The computational platform of SpaeSoft: The Genetic Hybrid Algorithm (GHA) • 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

17. The computer environment • 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.

18. References • Doukhan P, Massart P, Rio E (1994): The functional central limit theorem for strongly mixing processes. Annales de l’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. • Östermark, R. and K. Söderlund (1999): A multiperiod firm model for strategic decision support, Kybernetes28:5, pp. 538-556. • Östermark, R., H. Skrifvars, and T. Westerlund (2000): A nonlinear mixed integer multiperiod firm model, International Journal of Production Economics67, pp. 183-199. • Östermark, R. (2003): A multipurpose parallel Genetic Hybrid Algorithm for nonlinear nonconvex programming problems. European Journal of Operational Research,152, 195-214. • Östermark, R., J. Aaltonen, H. Saxén, and K. Söderlund (2004): Nonlinear modelling of the Finnish banking and finance branch index, European Journal of Finance10, pp. 277-289. • Östermark, 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.