In this paper， a mixed-frequency data driven financial distress prediction problem is considered. The exponential Almon weighting scheme of mixed-data sampling is introduced in the context of a Logistic regression， which allows for an individual weighting of high-frequent increments， hence distinguishing the importance of different macroeconomic period to explain the change of a listed companies’ financial status. In applying the suggested model to the period from 2007 to 2017， a total data set is used that includes 4 macroeconomic indicators of mixed annual， quarterly and monthly frequency and 15 potentially significant financial indicators of annual frequency for 350 Chinese listed manufacturing companies. The results show that （1） the model correctly classifies more financially distressed companies than classic Logistic model， in particular for forecasting horizons of 2-3 years. （2） The macroeconomic indicators observed at different frequency show their importance for both low frequency （annually） and high frequency （quarterly） financial distress prediction， while quarterly GDP growth rate and monthly inflation rate have more significant influence， both statistically and economically， on quarterly financial distress risk. All findings indicate that this paper provides a suitable approach incorporating mixed-frequency data for prediction of financial distress for listed companies in China.