Mutual fund tournaments: a network DEA model using interim rankings to forecast risk-taking and fund inflows

Investors are attracted to well-performing funds, with the top-performing receiving a disproportionately high share of money inflows. This behaviour, combined with incentives like status and monetary rewards, drives the tournament effect, whereby fund managers adjust their portfolio risk to either catch up with competitors or lock their position. Data Envelopment Analysis (DEA) is useful for studying this complex behaviour because it does not assume a predetermined relationship between variables. In this study, we use the Network DEA approach to assess and forecast how efficiently mutual funds compete in this tournament. We propose and test a model with three stages: reacting to mid-year rankings, improving year-end rankings, and receiving inflows in the subsequent quarter. This study is the first to use DEA to examine dynamic behaviour in mutual fund tournaments. Our findings show that managers who improve their year-end ranks compared to their mid-year ranks are more likely to attract inflows efficiently. However, changes in portfolio beta, concentration, and equity exposure are not directly linked to the rewards at the end of the tournament. Our results remain consistent across different time frames and variable specifications.