1. 1 SHAREX: A MULTIPERIOD PORTFOLIO MANAGEMENT MODEL

2. 2 Key Features Integrated system of: 1.stock price forecasting 2.portfolio optimization 3.inventory management 4.facilities for incorporating alternative techniques

3. 3 Key Features: the necessary financial relations included liquidity and debt, inventory, risk control transactions in discrete batch sizes fixed and variable transactions costs free specification of planning horizon forecasting and optimization combined extensive simulations for strategy specification real time management guaranteed feasibility

4. 4 Large Scale Portfolio Management

5. 5 Immediate research Topics: parametric search under different economic conditions mixture density forecast models for skewed markets multicomputer implementation of SHAREX connections to efficient MINLP-solvers Utilizing VMA and IMA (volume/price index moving averages) in turning point detection

6. 6 Background Research Östermark, R. (1990): Portfolio Efficiency of Capital Asset Pricing Models. Empirical Evidence on Thin Stock Markets. Åbo Akademi University, ISBN 951-649-703-9. Östermark, R. (1991): Vector forecasting and dynamic portfolio selection. European Journal of Operational Research 55, 46-56. Östermark, R & Aaltonen J (1992): Recursive Portfolio Management: Large-Scale Evidence from Two Scandinavian Stock Markets. Computer Science in Economics and Management 5, 81-103. Östermark, R (2000a): A Hybrid Genetic Fuzzy Neural Network Algorithm Designed for Classification Problems Involving Several Groups. Fuzzy Sets and Systems 114:2, pp. 311-324. Östermark, R. (2000b) A Flexible Genetic Hybrid Algorithm for Nonlinear Mixed-integer Programming Problems. Accepted in EvolutionaryOptimization.

7. 7 Research (cont) Östermark, R., Westerlund, T. & Skrifvars, H. (2000): A Nonlinear Mixed-Integer Multiperiod Firm Model. International Journal of Production Economics 67, p. 188-199. Östermark, R. (2001): “Genetic modelling of multivariate EGARCHX-processes. Evidence on the international asset return signal response mechanism”. Forthcoming in Computational Statistics & Data Analysis 38/1, 2001, pp. 1-124. Östermark, R. (2002a): “Automatic detection of parsimony in heteroskedastic time series processes. Empirical tests on global asset returns with parallel geno-mathematical programming”. Soft Computing 6/1, pp. 45-63. Östermark, R. (2002b): “A Multipurpose Parallel Genetic Hybrid Algorithm for Nonlinear Non-convex Programming Problems”. Forthcoming in The European Journal of Operational Research.