Anticipating Top Mutual Fund Stock Picks

Can researchers train machine learning models to mimic top mutual fund managers? In his August 2024 paper entitled “Machine Learning from the Best: Predicting the Holdings of Top Mutual Funds”, Jean-Paul van Brakel seeks to anticipate and exploit the stock picking of top-performing U.S. equity mutual fund managers by:

• Using a large set of holdings-based measures and component firm/stock factor exposures and accounting ratios to rank stocks on the likelihood that top managers will pick them. He considers five models: three machine learning models (decision tree, random forest and gradient boosting), a linear (logit regression) model and simple selection based on recent holdings. For machine learning models, he each quarter uses quarterly data from four years ago to one year ago for training and quarterly data from last year for generating holdings likelihoods for next quarter.
• Calculating average absolute Shapley value for each firm/stock characteristic to assess its importance in the decision process.
• Assessing economic value of predictions for each model by each quarter creating six groups of stocks, first sorting stocks into large and small and then sorting each size sort into thirds based on likelihood that top fund managers will pick the stocks. He then each quarter reforms a probable-minus-improbable (PMI) factor portfolio that is long large and small stocks with the highest likelihoods and short those with the lowest.

He applies a 6-factor (market, size, book-to-market, profitability, investment, momentum) model based on daily calculations to compare alphas across funds, features and the PMI factor portfolio. Using quarterly/daily data for U.S. broad equity mutual fund holdings/returns and associated individual firm/stock data starting December 1998 and ending December 2022 and December 2023, respectively, he finds that: (more…)