| Chiang, Urban, Baldridge | (1996) | NAV p.s. | 1981–1986 101 US mutual funds | 5 years to predict year 6 | BPN vs regression models | MAPE | BPN model provided better predictions compared to regression models based on MAPE |
| Indro et al. | (1999) | 1-factor Jensen's alpha | 1993–1995 559 US equity funds | 3 years (1 year to predict 1 year) | MLP with GRG2 | ME, MAE, MAPE, MSE | MLP model outperformed other models based on multiple error measures |
| Lin et al. | (2007) | NAV p.s. | 3 single national equity funds of Taiwan, US and Japan |
| RBFNN | Error Index (EI) | RBFNN found effective |
| Wang and Huang | (2010) | Sharpe index | 3 historical periods 1995–2000 Mutual funds listed in the Taiwan Economic Journal | 72 months (1 year to predict 1 year; every two years) | FANNC vs BPN | RMSE | FANNC model outperformed the BPN in terms of RMSE, providing more accurate predictions |
| Yan et al. | (2010) | NAV p.s. | 1 equity Chinese investment fund |
| BPN |
| good prediction accuracy |
| Ray and Vina | (2011) | NAV p.s. | 1999–2004 10 funds from India | 60 months | BPN |
| BPN demonstrated strong performance in predicting fund values |
| Priyadarshini and Babu | (2012) | NAV p.s. | 2003–2009 1 fund | 84 months | BPN | MAE, MSE, RMSE, MAPE, MPE | Error measures indicate solid performance of the BNP model |
| Priyadarshini | (2015) | NAV p.s. | 2006–2012 1 fund | 72 months | MLP | MAE, MSE, MAPE, RMSE, MPE | Good predictive performance based on these error metrics |
| Narula, Jha, Panda | (2015) | NAV p.s. | 15-Oct-2012 till 2-Jan-2014; 200 Indian funds | 300 consecutive trading days | FLANN vs RBF vs MLP | MAPE | FLANN performed well according to MAPE |
| Anish and Majhi | (2016) | NAV p.s. |
|
| RBF and FLANN | MAPE, RMSE | both models performed well, with FLANN having a slight advantage in terms of MAPE and RMSE |
| Anish, B. Majhi, R. Majhi | (2018) | NAV p.s. |
|
| RBF-PSO in comparison to MLANN, FLANN and RBFNN | MAPE, RMSE | The RBF-PSO model was the most accurate according to MAPE and RMSE |
| Han et al. | (2018) | NAV p.s. | 31-Aug-2015 till 1-Jul-2016 2 funds | 210 days | GRNN | RMSE, RTIC, MAE, MAPE, CE | GRNN provides highly accurate predictions |
| Pan et al. | 2019 | NAV p.s. | 31-Aug-2015 till 1-Jul-2016 17 balanced open-end funds | 210 days | BPN vs GABPN vs multiple regression | RMSE, RTIC, MAE, MAPE, CE | BPN model showed superior performance |
| Das et al. | (2020) |
| SBI Magnum Equity and UTI Equity | 2010 | BPN, RBPNN, RRBFNN | MSE, RMSE, MAPE | RBPNN outperformed over the other two prediction methods |
| Rout, Koudjonou, Satapathy | (2020) | NAV p.s. (normalized) | 1998–2002 5 equity funds | 1065–1255 days (80% of days in training and 20% of days in testing) | FLANN | RMSE, MAPE | FLANN found effective |
| Li and Rossi | (2020) | Carhart (1997) 4-factor adjusted alpha | 1980–2018 2980 US equity funds | 10 years of training to predict 1 subsequent year alpha | BRT, lasso, elastic net, random forest, NN | MAE, MSE, RMSE | Especially BRT and random forest outperform traditional regression models in predicting fund performance |
| Kaniel et al. | (2023) | 4/5/6/8-factor Jensen's alpha | 1980–2019 3275 U.S. equity funds | last month or year data to predict the next month | FFN | MAE | FFN models provided accurate predictions |
| DeMiguel et al. | (2023) | 6-factor Jensen's alpha | 1980–2020 8767 US equity funds | 10 years of training to predict 1 year alpha | Gradient boosting: random forest, elastic net | MAE, MSE, RMSE | these advanced machine learning models performed well in prediction accuracy over long training periods. |
