The interaction between stock price process and market news has been widely analyzed by investors on different markets. Previous works, however, focus either on market news purely as exogenous factors that tend to lead price process or on the analysis of how past stock price process can affect future stock returns. To take a step forward, we quantitatively integrate information from both market news and stock prices in order to improve the accuracy of prediction on stock future price return in an intra-day trading context. In this paper, we present the design and architecture of our approach for market information fusion. By means of multiple kernel learning, the hidden information behind the two sources is effectively extracted, and more importantly, seamlessly integrated rather than simply combined by a single kernel approach. Experiments on comprehensive comparisons between our approach and three baseline methods (which use only one type of information, or naively combine the two sources) have been conducted on the intra-day tick-by-tick data of the Hong Kong Stock Exchange and market news archives of the same period. It has been shown that for both cross-validation and independent testing, our approach is able to achieve the best results.