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PurposePrior research has documented inconclusive and/or mixed empirical evidence on the timing performance of hybrid funds. Their performance inferences generally do not efficiently control for fixed-income exposure, conditioning information, and cross-correlations in fund returns. This study examines the stock and bond timing performances of hybrid funds while controlling and accounting for these important issues. It also discusses the inferential implications of using alternative bootstrap resampling approaches.Design/methodology/approachWe examine the stock and bond timing performances of hybrid funds using (un)conditional multi-factor benchmark models with robust estimation inferences. We also rely on the block bootstrap method to account for cross-correlations in fund returns and to separate the effects of luck or sampling variation from manager skill.FindingsWe find that the timing performance of portfolios of funds is neutral and sensitive to controlling for fixed-income exposures and choice of the timing measurement model. The block-bootstrap analyses of funds in the tails of the distributions of stock timing performances suggest that sampling variation explains the underperformance of extreme left tail funds and confirms the good and bad luck in the bond timing management of tail funds. We report inference changes based on whether the Kosowski et al. or the Fama and French bootstrap approach is used.Originality/valueThis study provides extensive and robust evidence on the stock and bond timing performances of hybrid funds and their sensitivity based on (un)conditional linear multi-factor benchmark models. It examines the timing performances in the extreme tails funds using the block bootstrap method to efficiently identify (un)skilled fund managers. It also highlights the sensitivity of inferences to the choice of testing methodology.