Exploiting Consensus Mutual Fund Conviction Stock Picks

Does combining the wisdom of multiple stock-picking models via ensemble methods, as done in forecasting landfall of hurricanes, improve investment portfolio performance? In their September 2018 paper entitled “Ensemble Active Management”, Alexey Panchekha, Robert Tull and Matthew Bell test the application of ensemble methods to active portfolio management, looking for consensus or near-consensus among multiple, independent stock picking sources. Ensemble diversification tends to neutralize biases among individual sources when: (1) sources are independent; (2) sources employ different approaches; and, (3) most sources achieve at least 50% individual accuracies. As sources, they use the holdings and weights of 37 actively managed U.S. equity large-capitalization mutual funds, focusing on high-conviction stock selections (those with large mismatches with respect to market capitalization). Specifically, every two weeks they:

• Reform 30,000 randomly generated clusters of 10 mutual funds.
• For each cluster, reform a long-only Ensemble Active Management (EAM) portfolio consisting of the 50 stocks with the highest consensus overweights within the cluster.
• Calculate total returns for EAM portfolios, their respective clusters and the S&P 500 Index.

They debit performance of each EAM portfolio by the average contemporaneous expense ratio of the 37 mutual funds (average 0.94% across all years). To aggregate results, they calculate rolling 1-year and 3-year performances of EAM portfolios, mutual fund clusters and the index. Using daily estimated stock holdings and weights for the 37 mutual funds and associated stock prices as available during July 2007 through December 2017, they find that:

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