Abstract
Many questions about mutual fund trading require daily holdings, yet mutual funds are only required to report quarterly holdings. I model intraquarter trading and use the genetic algorithm to estimate the trade pattern that is most consistent with the fund's daily reported returns. I validate the model empirically on a sample of institutional trades from Ancerno and I confirm that the method more accurately predicts daily holdings when compared to existing naive assumptions. Further, my method is substantially more accurate in classifying a fund’s tendency to supply liquidity, and this increased precision has important implications for identifying superior performing funds. Specifically, a long-short strategy based on the model’s liquidity provision measures earns significant abnormal returns, while a similar strategy that relies on quarterly holdings does not exhibit any outperformance.
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