Searching for and evaluating outsourced chief investment officers

This study analyzes the impact of the outsourced chief investment officer (OCIO) index and OCIO search consultant on asset owners' selection of OCIOs. We adopt an agent-based model with a reinforcement learning method by defining the OCIO search problem as a multi-armed bandit problem. Our model highlights the significance of the information regarding potential managers provided by the OCIO index and an OCIO search consultant. Our simulation results indicate that the more managers in the market, the harder it is for asset owners to identify the optimal OCIO. If asset owners place greater emphasis on their preferences when evaluating OCIOs, those challenges might be eased. Our results suggest that OCIO search consultants and the OCIO index can support asset owners' decision-making.