Nowadays, with the fast development of social media, the instant financial news can be quickly spread over the Internet and consequently results in the strong vibration of stock market within a short period of time. To capture the effect of financial news, various classification algorithms are proposed in financial data mining domain. Unfortunately, it has been shown that the positive news might not necessarily drive up the stock price. It is believed that there must exist some underlying events, called anchor events in this paper, which drive the stock market up or down. Therefore, this paper proposed the classification should be performed not only based on the textual features of financial news, but also the semantic distance between the testing set of news and the set of news of anchor events. Accordingly, such semantic distance is measured as deviated expectation calculated based on the distance between two set of extracted topics. The proposed method involves two steps, i.e., calculating whether the testing news is consistent with anchor events or not, and calculating how far the testing news is from the anchor ev ents. We then evaluate our method as well as the state-of-the-art classification algorithms on some real data sets. The promising experimental results have demonstrated that the proposed method is superior to the state-of-the-art classification algorithms in terms of classification accuracy.