Abstract

Knowledge Management (“KM”), in institutional asset management, is defined as the explicit and systematic management of knowledge – and its associated processes of creation, organization, diffusion, use and exploitation – in pursuit of improving portfolio performance and managing risks. Asset owners exist at the nexus of unique knowledge and data flows from partners such as asset managers, banks, and consultants. Innovative asset owners can crowdsource unique data sets from partners and leverage InvestTech to dynamically manage asset allocations (and risk factors). This paper describes the importance of converting KM into a skill that supports alpha generation, and how most asset owners can replicate this process to develop bespoke approaches based on specific relationships, key risks, and objectives. The paper demonstrates how investors can commit to their investment beliefs using KM (or conversely, imply their investment beliefs from investment choices made) around three major areas: (i) investing style (formulaic or discretionary); (ii) optimization method (for setting the Strategic Asset Allocation, (“SAA”) and Tactical Asset Allocation (“TAA”)); and (iii) rebalancing process (mechanical or informed). The paper extends the analysis to show how effective KM can also help with risk-management, with a particular example of a new approach to risk-management, termed “Agent-Based Risk Management,” and also how KM may lead to different implementations for say a public pension plan versus a captive insurance company.

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