1. ¹is a CFA charterholder and a member of the portfolio construction team within the Quantitative Investment Strategies group at Goldman Sachs Asset Management, where he focuses on long/short equity portfolios.
2. ²is a vice president in the Quantitative Invesment Strategies group at Goldman Sachs Asset Management, where he develops portfolio construction strategies. He holds a PhD in Operations Research from Cornell University and was a professor in the Department of Mathematical Sciences at Carnegie Mellon University prior to joining GSAM. He is a co-author of the forthcoming book Optimization Methods in Finance.
3. ³is a managing director and Head of Implementation Strategies in the Quantitative Investment Strategies group at Goldman Sachs Asset Management (GSAM). Previously, he led a team that built and managed GSAM's proprietary global equity risk and return attribution platform, which includes modelling, portfolio analytics and data warehousing. Peter has been with Goldman Sachs since 1998 and, prior to joining the firm, was a member of the original team that built RiskMetrics at J.P. Morgan.

Correspondence: Peter J Zangari, Goldman Sachs Asset Management, 32 Old Slip, 23rd Floor, New York, NY, 10005, USA, E-mail: peter.zangari@gs.com

Received 31 May 2006.

Abstract

This paper provides a brief survey of some of the key issues in building a successful quantitative equity portfolio construction platform, integrating a data warehouse, a rebalancing engine, a back-testing engine as well as an attribution methodology. Optimisation models and software are central elements of such a platform. They serve as sophisticated tools for transferring the excess return ideas generated through research and testing into portfolios that best represent these ideas. In addition to the standard mean-variance optimisation models that are adjusted for transaction costs and taxes, advanced topics such as multi-period portfolio selection models and robust optimisation approaches are discussed.