The use of machine learning techniques to predict the next-day stock direction is established. To make prediction models more robust, a common approach is to combine historical time series and news sentiment analysis. Most of the trading simulations performed in this field rely on trend following strategies, which are aimed at identifying and following an ongoing price trend that is likely to persist in the next days. Conversely, a more limited effort has been devoted to applying machine learning techniques to predict trend reversal, i.e., changes in price directions. This paper investigates the relevance of news information and time series descriptors derived from technical analysis to predict trend reversal in the next days. It compares the performance of various classification models trained on (i) technical indicators, which indicate short-term overbought or oversold conditions, (ii) news sentiment descriptors, which express the opinion of the financial community, (iii) the historical time series, to highlight recurrences in price trends, and (iv) a combination of the above. The results achieved on an 11-year dataset related to the stocks of the U.S. S&P 500 index show that the strategies combining the historical values of news sentiment and stock price indicators averagely perform better than all the other tested combinations. Hence, news information is worth considering by trend reversal strategies.