A decision support system for strategic asset allocation

• Published in: Decision Support Systems Vol. 51; no. 3; pp. 549 - 561
• Main Authors: Beraldi, P., Violi, A., De Simone, F.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.06.2011; Elsevier; Elsevier Sequoia S.A
• Subjects: Allocations; Applied sciences; Asset allocation; Computation; Computer simulation; Decision support systems; Decision theory. Utility theory; Exact sciences and technology; Markets; Mathematical models; Mathematical programming; Operational research and scientific management; Operational research. Management science; Optimization; Optimization algorithms; Policies; Portfolio theory; Risk management; Risk theory. Actuarial science; Stochastic models; Stochastic programming; Strategic asset allocation; Studies; Risk management; Strategic asset allocation; Stochastic programming; High performance; Decision support system; Financial market; Risk analysis; Modeling; Forecasting; Optimization; Uncertain system; World wide web; Internet; Portfolio management
• ISSN: 0167-9236; 1873-5797
• DOI: 10.1016/j.dss.2011.02.017

Strategic asset allocation is a crucial activity for any institutional or individual investor. Given a set of asset classes, the problem concerns the definition and management over time of the best asset mix to achieve favorable returns subject to various uncertainties, policy and legal constraints, and other requirements. Although a considerable attention has been placed by the scientific community to address this problem by proposing sophisticated optimization models, limited effort has been devoted to the design of integrated framework that can be systematically used by financial operators. The paper presents a decision support system which integrates simulation techniques for forecasting future uncertain market conditions and sophisticated optimization models based on the stochastic programming paradigm. The system has been designed to be accessed via web and takes advantages of the increased computational power offered by high performance computing platforms. Real-world instances have been used to assess the performance of the decision support system also in comparison with more traditional portfolio optimization strategies.