Price prediction has become a major task due to the explosive increase in the number of investors. The price prediction task has various types such as shares, stocks, foreign exchange instruments, and cryptocurrency. The literature includes several models for price prediction that can be classified based on the utilized methods into three main classes, namely, deep learning, machine learning, and statistical. In this context, we proposed several models’ architectures for price prediction. Among them, we proposed a hybrid one that incorporates long short-term memory (LSTM) and Convolution neural network (CNN) architectures, we called it CNN-LSTM. The proposed CNN-LSTM model makes use of the characteristics of the convolution layers for extracting useful features embedded in the time series data and the ability of LSTM architecture to learn long-term dependencies. The proposed architectures are thoroughly evaluated and compared against state-of-the-art methods on three different types of financial product datasets for stocks, foreign exchange instruments, and cryptocurrency. The obtained results show that the proposed CNN-LSTM has the best performance on average for the utilized evaluation metrics. Moreover, the proposed deep learning models were dominant in comparison to the state-of-the-art methods, machine learning models, and statistical models.