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Résumé : ABSTRACT We study the short‐term predictability of price limit hits. This limit on the trading price is a policy measure imposed with the intention of stabilizing the markets and has been in place for several decades in the Chinese stock markets. We employ feature engineering on past return data and train machine learning models for each individual stock. The results show that a mildly complex model based on ensembling and downsampling the historical information of the majority class (“non‐hit” samples) can substantially improve the forecast performance of a naive guess of 50% to about 66% in terms of balanced classification accuracy between true positives and true negatives. We also find that price limit hits of older stocks and of stocks belonging to the tertiary sector are more predictable. We interpret this result with the argument that certain stocks with a longer history are more susceptible to speculative behavior, thus increasing the probability and predictability of such price limit hits.