  Predicting fund performance is beneficial to both investors and fund
managers, and yet is a challenging task. In this paper, we have tested whether
deep learning models can predict fund performance more accurately than
traditional statistical techniques. Fund performance is typically evaluated by
the Sharpe ratio, which represents the risk-adjusted performance to ensure
meaningful comparability across funds. We calculated the annualised Sharpe
ratios based on the monthly returns time series data for more than 600 open-end
mutual funds investing in listed large-cap equities in the United States. We
find that long short-term memory (LSTM) and gated recurrent units (GRUs) deep
learning methods, both trained with modern Bayesian optimization, provide
higher accuracy in forecasting funds' Sharpe ratios than traditional
statistical ones. An ensemble method, which combines forecasts from LSTM and
GRUs, achieves the best performance of all models. There is evidence to say
that deep learning and ensembling offer promising solutions in addressing the
challenge of fund performance forecasting.
